MODSIM2025 Proceedings

Author	Paper Title	AUTHORS	DOI	File Path	Stream Number	Stream	Sub-session number	Session Name	Author Initial
Kanij T	A comparative study on subspace detection techniques for hyperspectral image classification	Tasnim A, Sayeed A, Kanij T	https://doi.org/10.36334/modsim2025.D01.tasnim	https://mssanz.org.au/modsim2025/files/D01.tasnim.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	K
Sayeed A	A comparative study on subspace detection techniques for hyperspectral image classification	Tasnim A, Sayeed A, Kanij T	https://doi.org/10.36334/modsim2025.D01.tasnim	https://mssanz.org.au/modsim2025/files/D01.tasnim.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	S
Tasnim A	A comparative study on subspace detection techniques for hyperspectral image classification	Tasnim A, Sayeed A, Kanij T	https://doi.org/10.36334/modsim2025.D01.tasnim	https://mssanz.org.au/modsim2025/files/D01.tasnim.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	T
Cherampatta Mana S	A comparison of monthly runoff models for their performance under variable climate regimes within Australia	Cherampatta Mana S, Peterson TJ, Croke B, Iwanaga T, Purves T, Lade SJ	https://doi.org/10.36334/modsim2025.K04.cherampattamana	https://mssanz.org.au/modsim2025/files/K04.cherampattamana.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	C
Croke B	A comparison of monthly runoff models for their performance under variable climate regimes within Australia	Cherampatta Mana S, Peterson TJ, Croke B, Iwanaga T, Purves T, Lade SJ	https://doi.org/10.36334/modsim2025.K04.cherampattamana	https://mssanz.org.au/modsim2025/files/K04.cherampattamana.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	C
Iwanaga T	A comparison of monthly runoff models for their performance under variable climate regimes within Australia	Cherampatta Mana S, Peterson TJ, Croke B, Iwanaga T, Purves T, Lade SJ	https://doi.org/10.36334/modsim2025.K04.cherampattamana	https://mssanz.org.au/modsim2025/files/K04.cherampattamana.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	I
Lade SJ	A comparison of monthly runoff models for their performance under variable climate regimes within Australia	Cherampatta Mana S, Peterson TJ, Croke B, Iwanaga T, Purves T, Lade SJ	https://doi.org/10.36334/modsim2025.K04.cherampattamana	https://mssanz.org.au/modsim2025/files/K04.cherampattamana.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	L
Peterson TJ	A comparison of monthly runoff models for their performance under variable climate regimes within Australia	Cherampatta Mana S, Peterson TJ, Croke B, Iwanaga T, Purves T, Lade SJ	https://doi.org/10.36334/modsim2025.K04.cherampattamana	https://mssanz.org.au/modsim2025/files/K04.cherampattamana.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	P
Purves T	A comparison of monthly runoff models for their performance under variable climate regimes within Australia	Cherampatta Mana S, Peterson TJ, Croke B, Iwanaga T, Purves T, Lade SJ	https://doi.org/10.36334/modsim2025.K04.cherampattamana	https://mssanz.org.au/modsim2025/files/K04.cherampattamana.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	P
Evans JP	A CORDEX Flagship pilot study for simulating island climates in the Pacific (IC-Pac)	Evans JP, Menkes C	https://doi.org/10.36334/modsim2025.G04.evans	https://mssanz.org.au/modsim2025/files/G04.evans.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	E
Menkes C	A CORDEX Flagship pilot study for simulating island climates in the Pacific (IC-Pac)	Evans JP, Menkes C	https://doi.org/10.36334/modsim2025.G04.evans	https://mssanz.org.au/modsim2025/files/G04.evans.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	M
Hasan MF	A CUDA-optimised key frame extraction for biometric labelling in dairy cattle identification	Hasan MF, Morse-McNabb EM	https://doi.org/10.36334/modsim2025.M03.hasan	https://mssanz.org.au/modsim2025/files/M03.hasan.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	H
Morse-McNabb EM	A CUDA-optimised key frame extraction for biometric labelling in dairy cattle identification	Hasan MF, Morse-McNabb EM	https://doi.org/10.36334/modsim2025.M03.hasan	https://mssanz.org.au/modsim2025/files/M03.hasan.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	M
Bayba D	A foundational framework for generative simulation models: pathway to generative digital twins for supply chain (INVITED SPEAKER)	Chotaliya S, Fowler J, Pedrielli G, Bayba D, Saini P, Yu K	https://doi.org/10.36334/modsim2025.M02.chotaliya	https://mssanz.org.au/modsim2025/files/M02.chotaliya.pdf	M	Operations Research	M2	Simulation modelling and analysis	B
Chotaliya S	A foundational framework for generative simulation models: pathway to generative digital twins for supply chain (INVITED SPEAKER)	Chotaliya S, Fowler J, Pedrielli G, Bayba D, Saini P, Yu K	https://doi.org/10.36334/modsim2025.M02.chotaliya	https://mssanz.org.au/modsim2025/files/M02.chotaliya.pdf	M	Operations Research	M2	Simulation modelling and analysis	C
Fowler J (John)	A foundational framework for generative simulation models: pathway to generative digital twins for supply chain (INVITED SPEAKER)	Chotaliya S, Fowler J, Pedrielli G, Bayba D, Saini P, Yu K	https://doi.org/10.36334/modsim2025.M02.chotaliya	https://mssanz.org.au/modsim2025/files/M02.chotaliya.pdf	M	Operations Research	M2	Simulation modelling and analysis	F
Pedrielli G	A foundational framework for generative simulation models: pathway to generative digital twins for supply chain (INVITED SPEAKER)	Chotaliya S, Fowler J, Pedrielli G, Bayba D, Saini P, Yu K	https://doi.org/10.36334/modsim2025.M02.chotaliya	https://mssanz.org.au/modsim2025/files/M02.chotaliya.pdf	M	Operations Research	M2	Simulation modelling and analysis	P
Saini P	A foundational framework for generative simulation models: pathway to generative digital twins for supply chain (INVITED SPEAKER)	Chotaliya S, Fowler J, Pedrielli G, Bayba D, Saini P, Yu K	https://doi.org/10.36334/modsim2025.M02.chotaliya	https://mssanz.org.au/modsim2025/files/M02.chotaliya.pdf	M	Operations Research	M2	Simulation modelling and analysis	S
Yu K	A foundational framework for generative simulation models: pathway to generative digital twins for supply chain (INVITED SPEAKER)	Chotaliya S, Fowler J, Pedrielli G, Bayba D, Saini P, Yu K	https://doi.org/10.36334/modsim2025.M02.chotaliya	https://mssanz.org.au/modsim2025/files/M02.chotaliya.pdf	M	Operations Research	M2	Simulation modelling and analysis	Y
Zhou M	A hybrid decomposition and machine learning model for forecasting coastal chlorophyll-a and total nitrogen	Zhu X, Zhou M	https://doi.org/10.36334/modsim2025.K08.zhu	https://mssanz.org.au/modsim2025/files/K08.zhu.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Zhu X	A hybrid decomposition and machine learning model for forecasting coastal chlorophyll-a and total nitrogen	Zhu X, Zhou M	https://doi.org/10.36334/modsim2025.K08.zhu	https://mssanz.org.au/modsim2025/files/K08.zhu.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Kulasiri D	A hybrid feature selection framework: balancing information preservation and multicollinearity in biological datasets	Mohajer S, Kulasiri D, Samarasinghe S	https://doi.org/10.36334/modsim2025.F11.mohajer	https://mssanz.org.au/modsim2025/files/F11.mohajer.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	K
Mohajer S	A hybrid feature selection framework: balancing information preservation and multicollinearity in biological datasets	Mohajer S, Kulasiri D, Samarasinghe S	https://doi.org/10.36334/modsim2025.F11.mohajer	https://mssanz.org.au/modsim2025/files/F11.mohajer.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	M
Samarasinghe S	A hybrid feature selection framework: balancing information preservation and multicollinearity in biological datasets	Mohajer S, Kulasiri D, Samarasinghe S	https://doi.org/10.36334/modsim2025.F11.mohajer	https://mssanz.org.au/modsim2025/files/F11.mohajer.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	S
Cao Y (Yifan)	A hybrid machine learning–process-based model approach of simulating ammonia volatilization in rice paddies	Cao Y, Pan B, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.B08.cao	https://mssanz.org.au/modsim2025/files/B08.cao.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Chen D (Deli)	A hybrid machine learning–process-based model approach of simulating ammonia volatilization in rice paddies	Cao Y, Pan B, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.B08.cao	https://mssanz.org.au/modsim2025/files/B08.cao.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Lam SK (Shu Kee)	A hybrid machine learning–process-based model approach of simulating ammonia volatilization in rice paddies	Cao Y, Pan B, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.B08.cao	https://mssanz.org.au/modsim2025/files/B08.cao.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
Pan B (Baobao)	A hybrid machine learning–process-based model approach of simulating ammonia volatilization in rice paddies	Cao Y, Pan B, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.B08.cao	https://mssanz.org.au/modsim2025/files/B08.cao.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	P
Chandra R	A hybrid quantile deep learning ensemble framework catering to streamflow uncertainty and extreme events	Kapoor A, Marshall L, Pathiraja S, Chandra R	https://doi.org/10.36334/modsim2025.C07.kapoor	https://mssanz.org.au/modsim2025/files/C07.kapoor.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	C
Kapoor A	A hybrid quantile deep learning ensemble framework catering to streamflow uncertainty and extreme events	Kapoor A, Marshall L, Pathiraja S, Chandra R	https://doi.org/10.36334/modsim2025.C07.kapoor	https://mssanz.org.au/modsim2025/files/C07.kapoor.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	K
Marshall L	A hybrid quantile deep learning ensemble framework catering to streamflow uncertainty and extreme events	Kapoor A, Marshall L, Pathiraja S, Chandra R	https://doi.org/10.36334/modsim2025.C07.kapoor	https://mssanz.org.au/modsim2025/files/C07.kapoor.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	M
Pathiraja S	A hybrid quantile deep learning ensemble framework catering to streamflow uncertainty and extreme events	Kapoor A, Marshall L, Pathiraja S, Chandra R	https://doi.org/10.36334/modsim2025.C07.kapoor	https://mssanz.org.au/modsim2025/files/C07.kapoor.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	P
Chan F	A LASSO-based approach to wage spillover	Chan F, Harris M, Singh R, Yeo WE	https://doi.org/10.36334/modsim2025.D01.chanf	https://mssanz.org.au/modsim2025/files/D01.chanf.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	C
Harris M	A LASSO-based approach to wage spillover	Chan F, Harris M, Singh R, Yeo WE	https://doi.org/10.36334/modsim2025.D01.chanf	https://mssanz.org.au/modsim2025/files/D01.chanf.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	H
Singh R	A LASSO-based approach to wage spillover	Chan F, Harris M, Singh R, Yeo WE	https://doi.org/10.36334/modsim2025.D01.chanf	https://mssanz.org.au/modsim2025/files/D01.chanf.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	S
Yeo WE	A LASSO-based approach to wage spillover	Chan F, Harris M, Singh R, Yeo WE	https://doi.org/10.36334/modsim2025.D01.chanf	https://mssanz.org.au/modsim2025/files/D01.chanf.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	Y
Jiang Z (Ze)	A machine learning framework to enhance long-term drought forecast with spectrally transformed climate predictors	Zhang L, Jiang Z, Johnson B, Sharma A	https://doi.org/10.36334/modsim2025.J06.zhang	https://mssanz.org.au/modsim2025/files/J06.zhang.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	J
Johnson B	A machine learning framework to enhance long-term drought forecast with spectrally transformed climate predictors	Zhang L, Jiang Z, Johnson B, Sharma A	https://doi.org/10.36334/modsim2025.J06.zhang	https://mssanz.org.au/modsim2025/files/J06.zhang.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	J
Sharma A	A machine learning framework to enhance long-term drought forecast with spectrally transformed climate predictors	Zhang L, Jiang Z, Johnson B, Sharma A	https://doi.org/10.36334/modsim2025.J06.zhang	https://mssanz.org.au/modsim2025/files/J06.zhang.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	S
Zhang L (Liangjing)	A machine learning framework to enhance long-term drought forecast with spectrally transformed climate predictors	Zhang L, Jiang Z, Johnson B, Sharma A	https://doi.org/10.36334/modsim2025.J06.zhang	https://mssanz.org.au/modsim2025/files/J06.zhang.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	Z
Strauch M	A model workflow for land use planning, optimising water re-use and nutrient retention	Wittekind CIH, Strauch M, Witing F, Volk M	https://doi.org/10.36334/modsim2025.F07.wittekind	https://mssanz.org.au/modsim2025/files/F07.wittekind.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	S
Volk M	A model workflow for land use planning, optimising water re-use and nutrient retention	Wittekind CIH, Strauch M, Witing F, Volk M	https://doi.org/10.36334/modsim2025.F07.wittekind	https://mssanz.org.au/modsim2025/files/F07.wittekind.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	V
Witing F	A model workflow for land use planning, optimising water re-use and nutrient retention	Wittekind CIH, Strauch M, Witing F, Volk M	https://doi.org/10.36334/modsim2025.F07.wittekind	https://mssanz.org.au/modsim2025/files/F07.wittekind.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	W
Wittekind CIH	A model workflow for land use planning, optimising water re-use and nutrient retention	Wittekind CIH, Strauch M, Witing F, Volk M	https://doi.org/10.36334/modsim2025.F07.wittekind	https://mssanz.org.au/modsim2025/files/F07.wittekind.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	W
Ano K	A modified multilevel Monte Carlo method and its numerical performance	Inui H, Kurushima A, Ano K	https://doi.org/10.36334/modsim2025.A01.inui	https://mssanz.org.au/modsim2025/files/A01.inui.pdf	A	Applied and computational mathematics	A1	Applied Probability	A
Inui H	A modified multilevel Monte Carlo method and its numerical performance	Inui H, Kurushima A, Ano K	https://doi.org/10.36334/modsim2025.A01.inui	https://mssanz.org.au/modsim2025/files/A01.inui.pdf	A	Applied and computational mathematics	A1	Applied Probability	I
Kurushima A	A modified multilevel Monte Carlo method and its numerical performance	Inui H, Kurushima A, Ano K	https://doi.org/10.36334/modsim2025.A01.inui	https://mssanz.org.au/modsim2025/files/A01.inui.pdf	A	Applied and computational mathematics	A1	Applied Probability	K
Aryal S	A multi-index assessment of climate variability and agricultural drought in Afghanistan using station-observed historical data	Wahid S, Bhattarai U, Chen Y, Taylor P, Karim F, Penton DJ, Aryal S	https://doi.org/10.36334/modsim2025.K03.wahid	https://mssanz.org.au/modsim2025/files/K03.wahid.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	A
Bhattarai U	A multi-index assessment of climate variability and agricultural drought in Afghanistan using station-observed historical data	Wahid S, Bhattarai U, Chen Y, Taylor P, Karim F, Penton DJ, Aryal S	https://doi.org/10.36334/modsim2025.K03.wahid	https://mssanz.org.au/modsim2025/files/K03.wahid.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	B
Chen Y (Yun)	A multi-index assessment of climate variability and agricultural drought in Afghanistan using station-observed historical data	Wahid S, Bhattarai U, Chen Y, Taylor P, Karim F, Penton DJ, Aryal S	https://doi.org/10.36334/modsim2025.K03.wahid	https://mssanz.org.au/modsim2025/files/K03.wahid.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	C
Karim F	A multi-index assessment of climate variability and agricultural drought in Afghanistan using station-observed historical data	Wahid S, Bhattarai U, Chen Y, Taylor P, Karim F, Penton DJ, Aryal S	https://doi.org/10.36334/modsim2025.K03.wahid	https://mssanz.org.au/modsim2025/files/K03.wahid.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	K
Penton DJ	A multi-index assessment of climate variability and agricultural drought in Afghanistan using station-observed historical data	Wahid S, Bhattarai U, Chen Y, Taylor P, Karim F, Penton DJ, Aryal S	https://doi.org/10.36334/modsim2025.K03.wahid	https://mssanz.org.au/modsim2025/files/K03.wahid.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	P
Taylor P	A multi-index assessment of climate variability and agricultural drought in Afghanistan using station-observed historical data	Wahid S, Bhattarai U, Chen Y, Taylor P, Karim F, Penton DJ, Aryal S	https://doi.org/10.36334/modsim2025.K03.wahid	https://mssanz.org.au/modsim2025/files/K03.wahid.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	T
Wahid S	A multi-index assessment of climate variability and agricultural drought in Afghanistan using station-observed historical data	Wahid S, Bhattarai U, Chen Y, Taylor P, Karim F, Penton DJ, Aryal S	https://doi.org/10.36334/modsim2025.K03.wahid	https://mssanz.org.au/modsim2025/files/K03.wahid.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	W
Baum M	A multi-scale modelling framework for assessing environmental impacts of pumped hydro energy storage	de Groot A, Weber T, Egger F, Baum M	https://doi.org/10.36334/modsim2025.L02.degroot	https://mssanz.org.au/modsim2025/files/L02.degroot.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	B
de Groot A	A multi-scale modelling framework for assessing environmental impacts of pumped hydro energy storage	de Groot L, van der Linden M	https://doi.org/10.36334/modsim2025.L02.degroot	https://mssanz.org.au/modsim2025/files/L02.degroot.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	d
Egger F	A multi-scale modelling framework for assessing environmental impacts of pumped hydro energy storage	https://doi.org/10.36334/modsim.2025.L02.deGroot	https://doi.org/10.36334/modsim2025.L02.degroot	https://mssanz.org.au/modsim2025/files/L02.degroot.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	E
Weber T	A multi-scale modelling framework for assessing environmental impacts of pumped hydro energy storage	de Groot L, van der Linden M	https://doi.org/10.36334/modsim2025.L02.degroot	https://mssanz.org.au/modsim2025/files/L02.degroot.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	W
Juniper JA	A multisectoral bio-physical Stock-Flow-Consistent input-output macroeconomic modelling analysis for Australian transition to low-emission energy	Khandoker T, Juniper JA, Reedman LJ	https://doi.org/10.36334/modsim2025.E03.khandoker	https://mssanz.org.au/modsim2025/files/E03.khandoker.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	J
Khandoker T	A multisectoral bio-physical Stock-Flow-Consistent input-output macroeconomic modelling analysis for Australian transition to low-emission energy	Khandoker T, Juniper JA, Reedman LJ	https://doi.org/10.36334/modsim2025.E03.khandoker	https://mssanz.org.au/modsim2025/files/E03.khandoker.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	K
Reedman LJ	A multisectoral bio-physical Stock-Flow-Consistent input-output macroeconomic modelling analysis for Australian transition to low-emission energy	Khandoker T, Juniper JA, Reedman LJ	https://doi.org/10.36334/modsim2025.E03.khandoker	https://mssanz.org.au/modsim2025/files/E03.khandoker.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	R
Guo D (Danlu)	A new metric for better identifying and understanding rainfall-runoff events	Khoie MMM, Guo D, Wasko C	https://doi.org/10.36334/modsim2025.K07.khoie	https://mssanz.org.au/modsim2025/files/K07.khoie.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	G
Khoie MMM	A new metric for better identifying and understanding rainfall-runoff events	Khoie MMM, Guo D, Wasko C	https://doi.org/10.36334/modsim2025.K07.khoie	https://mssanz.org.au/modsim2025/files/K07.khoie.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	K
Wasko C	A new metric for better identifying and understanding rainfall-runoff events	Khoie MMM, Guo D, Wasko C	https://doi.org/10.36334/modsim2025.K07.khoie	https://mssanz.org.au/modsim2025/files/K07.khoie.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	W
Guan H	A new physics-guided deep learning model for improving plant transpiration estimation under water-limited conditions	Liu B, Guan H	https://doi.org/10.36334/modsim2025.J10.liu	https://mssanz.org.au/modsim2025/files/J10.liu.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	G
Liu B	A new physics-guided deep learning model for improving plant transpiration estimation under water-limited conditions	Liu B, Guan H	https://doi.org/10.36334/modsim2025.J10.liu	https://mssanz.org.au/modsim2025/files/J10.liu.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	L
Evans JP	A next-generation tool for bias-correcting RCM inputs to enhance regional climate projections	Kim Y, Evans JP	https://doi.org/10.36334/modsim2025.G04.kim	https://mssanz.org.au/modsim2025/files/G04.kim.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	E
Kim Y (Youngil)	A next-generation tool for bias-correcting RCM inputs to enhance regional climate projections	Kim Y, Evans JP	https://doi.org/10.36334/modsim2025.G04.kim	https://mssanz.org.au/modsim2025/files/G04.kim.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	K
Zhao C (Chenguang)	A novel deep learning model for post-processing of short- and medium-term daily precipitation forecasts	Zhao C	https://doi.org/10.36334/modsim2025.K06.zhao	https://mssanz.org.au/modsim2025/files/K06.zhao.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	Z
Beecham R	A novel framework for estimating probable maximum tropical cyclone with sparse records	Dutta K, Beecham R, Gan R	https://doi.org/10.36334/modsim2025.K07.dutta	https://mssanz.org.au/modsim2025/files/K07.dutta.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	B
Dutta K	A novel framework for estimating probable maximum tropical cyclone with sparse records	Dutta K, Beecham R, Gan R	https://doi.org/10.36334/modsim2025.K07.dutta	https://mssanz.org.au/modsim2025/files/K07.dutta.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	D
Gan R	A novel framework for estimating probable maximum tropical cyclone with sparse records	Dutta K, Beecham R, Gan R	https://doi.org/10.36334/modsim2025.K07.dutta	https://mssanz.org.au/modsim2025/files/K07.dutta.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	G
Bentley A	A paradigm shift in nitrogen management: In-silico insights into biological nitrification inhibition in farming systems	Garba I, Buss W, Wang E, O’Sullivan C, Gupta V, Bentley A, Verburg K	https://doi.org/10.36334/modsim2025.B03.garba	https://mssanz.org.au/modsim2025/files/B03.garba.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	B
Buss W	A paradigm shift in nitrogen management: In-silico insights into biological nitrification inhibition in farming systems	Garba I, Buss W, Wang E, O’Sullivan C, Gupta V, Bentley A, Verburg K	https://doi.org/10.36334/modsim2025.B03.garba	https://mssanz.org.au/modsim2025/files/B03.garba.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	B
Garba II	A paradigm shift in nitrogen management: In-silico insights into biological nitrification inhibition in farming systems	Garba I, Buss W, Wang E, O’Sullivan C, Gupta V, Bentley A, Verburg K	https://doi.org/10.36334/modsim2025.B03.garba	https://mssanz.org.au/modsim2025/files/B03.garba.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	G
Gupta V	A paradigm shift in nitrogen management: In-silico insights into biological nitrification inhibition in farming systems	Garba I, Buss W, Wang E, O’Sullivan C, Gupta V, Bentley A, Verburg K	https://doi.org/10.36334/modsim2025.B03.garba	https://mssanz.org.au/modsim2025/files/B03.garba.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	G
O’Sullivan C	A paradigm shift in nitrogen management: In-silico insights into biological nitrification inhibition in farming systems	Garba I, Buss W, Wang E, O’Sullivan C, Gupta V, Bentley A, Verburg K	https://doi.org/10.36334/modsim2025.B03.garba	https://mssanz.org.au/modsim2025/files/B03.garba.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	O
Verburg K	A paradigm shift in nitrogen management: In-silico insights into biological nitrification inhibition in farming systems	Garba I, Buss W, Wang E, O’Sullivan C, Gupta V, Bentley A, Verburg K	https://doi.org/10.36334/modsim2025.B03.garba	https://mssanz.org.au/modsim2025/files/B03.garba.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	V
Wang E (Enli)	A paradigm shift in nitrogen management: In-silico insights into biological nitrification inhibition in farming systems	Garba I, Buss W, Wang E, O’Sullivan C, Gupta V, Bentley A, Verburg K	https://doi.org/10.36334/modsim2025.B03.garba	https://mssanz.org.au/modsim2025/files/B03.garba.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	W
Dunstall M	A practical approach to electricity network hardening decisions	Dunstall M, Nelson T, Nolan T	https://doi.org/10.36334/modsim2025.M01.dunstall	https://mssanz.org.au/modsim2025/files/M01.dunstall.pdf	M	Operations Research	M1	OR methods and applications	D
Nelson T	A practical approach to electricity network hardening decisions	Dunstall M, Nelson T, Nolan T	https://doi.org/10.36334/modsim2025.M01.dunstall	https://mssanz.org.au/modsim2025/files/M01.dunstall.pdf	M	Operations Research	M1	OR methods and applications	N
Nolan T	A practical approach to electricity network hardening decisions	Dunstall M, Nelson T, Nolan T	https://doi.org/10.36334/modsim2025.M01.dunstall	https://mssanz.org.au/modsim2025/files/M01.dunstall.pdf	M	Operations Research	M1	OR methods and applications	N
Randall LJ	A preliminary erosion model for gullies with complex geometries	Randall LJ	https://doi.org/10.36334/modsim2025.L04.randall	https://mssanz.org.au/modsim2025/files/L04.randall.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	R
Amellina A	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	A
Chaudhary N	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	C
Crona B	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	C
Fetzer I	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	F
Gotangco Gonzales CK	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	G
Lade SJ	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	L
Marone D	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	M
Parlato G	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	P
Rocha J	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	R
Vu J	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	V
Wang Erlandsson L	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	W
Zoller H	A prototype Earth System Impact score for businesses and investors	Lade SJ, Amellina A, Chaudhary N, Crona B, Fetzer I, Gotangco Gonzales CK, Marone D, Parlato G, Rocha J, Vu J, Wang Erlandsson L, Zoller H	https://doi.org/10.36334/modsim2025.D02.lade	https://mssanz.org.au/modsim2025/files/D02.lade.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	Z
Jovanoski Z	A Random Forest analysis of variables influencing pyrocumulonimbus (pyroCb) occurrence over temperate southeast Australia	Ma W, Sharples JJ, Jovanoski Z	https://doi.org/10.36334/modsim2025.G08.ma	https://mssanz.org.au/modsim2025/files/G08.ma.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	J
Ma W	A Random Forest analysis of variables influencing pyrocumulonimbus (pyroCb) occurrence over temperate southeast Australia	Ma W, Sharples JJ, Jovanoski Z	https://doi.org/10.36334/modsim2025.G08.ma	https://mssanz.org.au/modsim2025/files/G08.ma.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	M
Sharples JJ	A Random Forest analysis of variables influencing pyrocumulonimbus (pyroCb) occurrence over temperate southeast Australia	Ma W, Sharples JJ, Jovanoski Z	https://doi.org/10.36334/modsim2025.G08.ma	https://mssanz.org.au/modsim2025/files/G08.ma.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Blackstock KL	A return to decision support: How can decision support tools be used at the science-policy interface?	Matthews KB, Wardell-Johnson DH, Miller DG, Tavana M, Whitaker J, Blackstock KL	https://doi.org/10.36334/modsim2025.F05.matthews	https://mssanz.org.au/modsim2025/files/F05.matthews.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	B
Matthews KB	A return to decision support: How can decision support tools be used at the science-policy interface?	Matthews KB, Wardell-Johnson DH, Miller DG, Tavana M, Whitaker J, Blackstock KL	https://doi.org/10.36334/modsim2025.F05.matthews	https://mssanz.org.au/modsim2025/files/F05.matthews.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	M
Miller DG	A return to decision support: How can decision support tools be used at the science-policy interface?	Matthews KB, Wardell-Johnson DH, Miller DG, Tavana M, Whitaker J, Blackstock KL	https://doi.org/10.36334/modsim2025.F05.matthews	https://mssanz.org.au/modsim2025/files/F05.matthews.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	M
Tavana M	A return to decision support: How can decision support tools be used at the science-policy interface?	Matthews KB, Wardell-Johnson DH, Miller DG, Tavana M, Whitaker J, Blackstock KL	https://doi.org/10.36334/modsim2025.F05.matthews	https://mssanz.org.au/modsim2025/files/F05.matthews.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	T
Wardell-Johnson DH	A return to decision support: How can decision support tools be used at the science-policy interface?	Matthews KB, Wardell-Johnson DH, Miller DG, Tavana M, Whitaker J, Blackstock KL	https://doi.org/10.36334/modsim2025.F05.matthews	https://mssanz.org.au/modsim2025/files/F05.matthews.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	W
Whitaker J	A return to decision support: How can decision support tools be used at the science-policy interface?	Matthews KB, Wardell-Johnson DH, Miller DG, Tavana M, Whitaker J, Blackstock KL	https://doi.org/10.36334/modsim2025.F05.matthews	https://mssanz.org.au/modsim2025/files/F05.matthews.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	W
Hai F	A sensitivity analysis of the Activated Sludge process in a single reactor configuration	Watt S, Nelson M, Hai F, Sidhu H	https://doi.org/10.36334/modsim2025.A06.watt	https://mssanz.org.au/modsim2025/files/A06.watt.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	H
Nelson M	A sensitivity analysis of the Activated Sludge process in a single reactor configuration	Watt S, Nelson M, Hai F, Sidhu H	https://doi.org/10.36334/modsim2025.A06.watt	https://mssanz.org.au/modsim2025/files/A06.watt.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	N
Sidhu H	A sensitivity analysis of the Activated Sludge process in a single reactor configuration	Watt S, Nelson M, Hai F, Sidhu H	https://doi.org/10.36334/modsim2025.A06.watt	https://mssanz.org.au/modsim2025/files/A06.watt.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	S
Watt S	A sensitivity analysis of the Activated Sludge process in a single reactor configuration	Watt S, Nelson M, Hai F, Sidhu H	https://doi.org/10.36334/modsim2025.A06.watt	https://mssanz.org.au/modsim2025/files/A06.watt.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	W
Brinsmead T	A simplified electricity system model for exploring least cost generation mixes (INVITED SPEAKER)	Graham P, Foster J, Mediwaththe C, Green D, Brinsmead T	https://doi.org/10.36334/modsim2025.E03.grahamp	https://mssanz.org.au/modsim2025/files/E03.grahamp.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	B
Foster J	A simplified electricity system model for exploring least cost generation mixes (INVITED SPEAKER)	Graham P, Foster J, Mediwaththe C, Green D, Brinsmead T	https://doi.org/10.36334/modsim2025.E03.grahamp	https://mssanz.org.au/modsim2025/files/E03.grahamp.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	F
Graham P	A simplified electricity system model for exploring least cost generation mixes (INVITED SPEAKER)	Graham P, Foster J, Mediwaththe C, Green D, Brinsmead T	https://doi.org/10.36334/modsim2025.E03.grahamp	https://mssanz.org.au/modsim2025/files/E03.grahamp.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	G
Green D	A simplified electricity system model for exploring least cost generation mixes (INVITED SPEAKER)	Graham P, Foster J, Mediwaththe C, Green D, Brinsmead T	https://doi.org/10.36334/modsim2025.E03.grahamp	https://mssanz.org.au/modsim2025/files/E03.grahamp.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	G
Mediwaththe C	A simplified electricity system model for exploring least cost generation mixes (INVITED SPEAKER)	Graham P, Foster J, Mediwaththe C, Green D, Brinsmead T	https://doi.org/10.36334/modsim2025.E03.grahamp	https://mssanz.org.au/modsim2025/files/E03.grahamp.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	M
Beller K	A spatio-stochastic SEIR model of the 1789 smallpox epidemic in Dharawal Country	Nitschke MC, Williams AN, Ingrey SD, Griffiths B, Beller K, Irish P, Saltre F, Bradshaw CJA	https://doi.org/10.36334/modsim2025.I05.nitschke	https://mssanz.org.au/modsim2025/files/I05.nitschke.pdf	I	Participatory decision-making, socioecological systems, and education	I5	Indigenous Modelling and Participatory Approaches	B
Bradshaw CJA	A spatio-stochastic SEIR model of the 1789 smallpox epidemic in Dharawal Country	Nitschke MC, Williams AN, Ingrey SD, Griffiths B, Beller K, Irish P, Saltre F, Bradshaw CJA	https://doi.org/10.36334/modsim2025.I05.nitschke	https://mssanz.org.au/modsim2025/files/I05.nitschke.pdf	I	Participatory decision-making, socioecological systems, and education	I5	Indigenous Modelling and Participatory Approaches	B
Griffiths B	A spatio-stochastic SEIR model of the 1789 smallpox epidemic in Dharawal Country	Nitschke MC, Williams AN, Ingrey SD, Griffiths B, Beller K, Irish P, Saltre F, Bradshaw CJA	https://doi.org/10.36334/modsim2025.I05.nitschke	https://mssanz.org.au/modsim2025/files/I05.nitschke.pdf	I	Participatory decision-making, socioecological systems, and education	I5	Indigenous Modelling and Participatory Approaches	G
Ingrey SD	A spatio-stochastic SEIR model of the 1789 smallpox epidemic in Dharawal Country	Nitschke MC, Williams AN, Ingrey SD, Griffiths B, Beller K, Irish P, Saltre F, Bradshaw CJA	https://doi.org/10.36334/modsim2025.I05.nitschke	https://mssanz.org.au/modsim2025/files/I05.nitschke.pdf	I	Participatory decision-making, socioecological systems, and education	I5	Indigenous Modelling and Participatory Approaches	I
Irish P	A spatio-stochastic SEIR model of the 1789 smallpox epidemic in Dharawal Country	Nitschke MC, Williams AN, Ingrey SD, Griffiths B, Beller K, Irish P, Saltre F, Bradshaw CJA	https://doi.org/10.36334/modsim2025.I05.nitschke	https://mssanz.org.au/modsim2025/files/I05.nitschke.pdf	I	Participatory decision-making, socioecological systems, and education	I5	Indigenous Modelling and Participatory Approaches	I
Nitschke MC	A spatio-stochastic SEIR model of the 1789 smallpox epidemic in Dharawal Country	Nitschke MC, Williams AN, Ingrey SD, Griffiths B, Beller K, Irish P, Saltre F, Bradshaw CJA	https://doi.org/10.36334/modsim2025.I05.nitschke	https://mssanz.org.au/modsim2025/files/I05.nitschke.pdf	I	Participatory decision-making, socioecological systems, and education	I5	Indigenous Modelling and Participatory Approaches	N
Saltre F	A spatio-stochastic SEIR model of the 1789 smallpox epidemic in Dharawal Country	Nitschke MC, Williams AN, Ingrey SD, Griffiths B, Beller K, Irish P, Saltre F, Bradshaw CJA	https://doi.org/10.36334/modsim2025.I05.nitschke	https://mssanz.org.au/modsim2025/files/I05.nitschke.pdf	I	Participatory decision-making, socioecological systems, and education	I5	Indigenous Modelling and Participatory Approaches	S
Williams AN	A spatio-stochastic SEIR model of the 1789 smallpox epidemic in Dharawal Country	Nitschke MC, Williams AN, Ingrey SD, Griffiths B, Beller K, Irish P, Saltre F, Bradshaw CJA	https://doi.org/10.36334/modsim2025.I05.nitschke	https://mssanz.org.au/modsim2025/files/I05.nitschke.pdf	I	Participatory decision-making, socioecological systems, and education	I5	Indigenous Modelling and Participatory Approaches	W
Bell ST	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	B
Cerecke C	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	C
Chuang OA	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	C
Cichota R	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	C
Jenkins H	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	J
Lin HT	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	L
Liu D	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	L
Tang J	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	T
Van Houtte C	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	V
Zhang J	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	Z
Zhu J	A state-variable based approach to model integration in a horticultural modelling platform	Bell ST, Zhu J, Cichota R, Jenkins H, Liu D, Chuang OA, Zhang J, Van Houtte C, Tang J, Cerecke C, Lin HT	https://doi.org/10.36334/modsim2025.F07.bell	https://mssanz.org.au/modsim2025/files/F07.bell.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	Z
Gallant H	A stochastic nonlinear model of influence in synchronisation dynamics	Gallant H, Roberts D, Kalloniatis A	https://doi.org/10.36334/modsim2025.I03.gallant	https://mssanz.org.au/modsim2025/files/I03.gallant.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	G
Kalloniatis A	A stochastic nonlinear model of influence in synchronisation dynamics	Gallant H, Roberts D, Kalloniatis A	https://doi.org/10.36334/modsim2025.I03.gallant	https://mssanz.org.au/modsim2025/files/I03.gallant.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	K
Roberts D	A stochastic nonlinear model of influence in synchronisation dynamics	Gallant H, Roberts D, Kalloniatis A	https://doi.org/10.36334/modsim2025.I03.gallant	https://mssanz.org.au/modsim2025/files/I03.gallant.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	R
Hashemi Madani FS	A structured framework for context-based selection of DMDU approaches in environmental water management	Hashemi Madani FS, Horne A, John A	https://doi.org/10.36334/modsim2025.J08.hashemimadani	https://mssanz.org.au/modsim2025/files/J08.hashemimadani.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	H
Horne A	A structured framework for context-based selection of DMDU approaches in environmental water management	Hashemi Madani FS, Horne A, John A	https://doi.org/10.36334/modsim2025.J08.hashemimadani	https://mssanz.org.au/modsim2025/files/J08.hashemimadani.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	H
John A	A structured framework for context-based selection of DMDU approaches in environmental water management	Hashemi Madani FS, Horne A, John A	https://doi.org/10.36334/modsim2025.J08.hashemimadani	https://mssanz.org.au/modsim2025/files/J08.hashemimadani.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	J
Duo KQ	A systematic review of quantitative models for African swine fever: Diversity across host and environmental drivers	Ligue-Sabio KDB, Duo KQ, Nazarthy Y, Sartorius B, Kanamori LF, Sucol Y, Lau CL	https://doi.org/10.36334/modsim2025.H04.liguesabio	https://mssanz.org.au/modsim2025/files/H04.liguesabio.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	D
Kanamori LF	A systematic review of quantitative models for African swine fever: Diversity across host and environmental drivers	Ligue-Sabio KDB, Duo KQ, Nazarthy Y, Sartorius B, Kanamori LF, Sucol Y, Lau CL	https://doi.org/10.36334/modsim2025.H04.liguesabio	https://mssanz.org.au/modsim2025/files/H04.liguesabio.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	K
Lau CL	A systematic review of quantitative models for African swine fever: Diversity across host and environmental drivers	Ligue-Sabio KDB, Duo KQ, Nazarthy Y, Sartorius B, Kanamori LF, Sucol Y, Lau CL	https://doi.org/10.36334/modsim2025.H04.liguesabio	https://mssanz.org.au/modsim2025/files/H04.liguesabio.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	L
Ligue-Sabio KDB	A systematic review of quantitative models for African swine fever: Diversity across host and environmental drivers	Ligue-Sabio KDB, Duo KQ, Nazarthy Y, Sartorius B, Kanamori LF, Sucol Y, Lau CL	https://doi.org/10.36334/modsim2025.H04.liguesabio	https://mssanz.org.au/modsim2025/files/H04.liguesabio.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	L
Nazarthy Y	A systematic review of quantitative models for African swine fever: Diversity across host and environmental drivers	Ligue-Sabio KDB, Duo KQ, Nazarthy Y, Sartorius B, Kanamori LF, Sucol Y, Lau CL	https://doi.org/10.36334/modsim2025.H04.liguesabio	https://mssanz.org.au/modsim2025/files/H04.liguesabio.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	N
Sartorius B	A systematic review of quantitative models for African swine fever: Diversity across host and environmental drivers	Ligue-Sabio KDB, Duo KQ, Nazarthy Y, Sartorius B, Kanamori LF, Sucol Y, Lau CL	https://doi.org/10.36334/modsim2025.H04.liguesabio	https://mssanz.org.au/modsim2025/files/H04.liguesabio.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	S
Sucol Y	A systematic review of quantitative models for African swine fever: Diversity across host and environmental drivers	Ligue-Sabio KDB, Duo KQ, Nazarthy Y, Sartorius B, Kanamori LF, Sucol Y, Lau CL	https://doi.org/10.36334/modsim2025.H04.liguesabio	https://mssanz.org.au/modsim2025/files/H04.liguesabio.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	S
Young L	A systems approach to quantifying experience in cross-functional collaboration to improve strategic thinking in the national intelligence community	Young L	https://doi.org/10.36334/modsim2025.M01.young	https://mssanz.org.au/modsim2025/files/M01.young.pdf	M	Operations Research	M1	OR methods and applications	Y
Khandoker T	A system-wide Energy Return on Investment (EROI) approach to Australia’s renewables transition	Lloyd‑Hurwitz B, Khandoker T, Reedman L	https://doi.org/10.36334/modsim2025.E03.lloydhurwitz	https://mssanz.org.au/modsim2025/files/E03.lloydhurwitz.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	K
Lloyd‑Hurwitz B	A system-wide Energy Return on Investment (EROI) approach to Australia’s renewables transition	Lloyd‑Hurwitz B, Khandoker T, Reedman L	https://doi.org/10.36334/modsim2025.E03.lloydhurwitz	https://mssanz.org.au/modsim2025/files/E03.lloydhurwitz.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	L
Reedman L	A system-wide Energy Return on Investment (EROI) approach to Australia’s renewables transition	Lloyd‑Hurwitz B, Khandoker T, Reedman L	https://doi.org/10.36334/modsim2025.E03.lloydhurwitz	https://mssanz.org.au/modsim2025/files/E03.lloydhurwitz.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	R
Nakagiri N	A three-patch predator-prey model: migration expands the survival region	Nakagiri N, Sakisaka Y, Tainaka K	https://doi.org/10.36334/modsim2025.F01.nakagiri	https://mssanz.org.au/modsim2025/files/F01.nakagiri.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	N
Sakisaka Y	A three-patch predator-prey model: migration expands the survival region	Nakagiri N, Sakisaka Y, Tainaka K	https://doi.org/10.36334/modsim2025.F01.nakagiri	https://mssanz.org.au/modsim2025/files/F01.nakagiri.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	S
Tainaka K	A three-patch predator-prey model: migration expands the survival region	Nakagiri N, Sakisaka Y, Tainaka K	https://doi.org/10.36334/modsim2025.F01.nakagiri	https://mssanz.org.au/modsim2025/files/F01.nakagiri.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	T
Awan UK	A time-series analysis of on-farm storage development in the Northern Murray-Darling Basin	Awan UK, Reinfelds I, Song E, Stuart M, Song J	https://doi.org/10.36334/modsim2025.F04.awan	https://mssanz.org.au/modsim2025/files/F04.awan.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	A
Reinfelds I	A time-series analysis of on-farm storage development in the Northern Murray-Darling Basin	Awan UK, Reinfelds I, Song E, Stuart M, Song J	https://doi.org/10.36334/modsim2025.F04.awan	https://mssanz.org.au/modsim2025/files/F04.awan.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	R
Song E	A time-series analysis of on-farm storage development in the Northern Murray-Darling Basin	Awan UK, Reinfelds I, Song E, Stuart M, Song J	https://doi.org/10.36334/modsim2025.F04.awan	https://mssanz.org.au/modsim2025/files/F04.awan.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	S
Song J (June)	A time-series analysis of on-farm storage development in the Northern Murray-Darling Basin	Awan UK, Reinfelds I, Song E, Stuart M, Song J	https://doi.org/10.36334/modsim2025.F04.awan	https://mssanz.org.au/modsim2025/files/F04.awan.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	S
Stuart M	A time-series analysis of on-farm storage development in the Northern Murray-Darling Basin	Awan UK, Reinfelds I, Song E, Stuart M, Song J	https://doi.org/10.36334/modsim2025.F04.awan	https://mssanz.org.au/modsim2025/files/F04.awan.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	S
Shi Y	A top-down changepoint detection framework based on on two-state Markov-switching model	Zhang N, Shi Y	https://doi.org/10.36334/modsim2025.D06.zhang	https://mssanz.org.au/modsim2025/files/D06.zhang.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	S
Zhang N (Ning)	A top-down changepoint detection framework based on on two-state Markov-switching model	Zhang N, Shi Y	https://doi.org/10.36334/modsim2025.D06.zhang	https://mssanz.org.au/modsim2025/files/D06.zhang.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	Z
Ogilvie JC	A variable-rate sampling framework for efficient aerodynamic data sampling in CFD applications	Whitehouse S, Ogilvie JC, Wharington J	https://doi.org/10.36334/modsim2025.A06.whitehouse	https://mssanz.org.au/modsim2025/files/A06.whitehouse.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	O
Wharington J	A variable-rate sampling framework for efficient aerodynamic data sampling in CFD applications	Whitehouse S, Ogilvie JC, Wharington J	https://doi.org/10.36334/modsim2025.A06.whitehouse	https://mssanz.org.au/modsim2025/files/A06.whitehouse.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	W
Whitehouse S	A variable-rate sampling framework for efficient aerodynamic data sampling in CFD applications	Whitehouse S, Ogilvie JC, Wharington J	https://doi.org/10.36334/modsim2025.A06.whitehouse	https://mssanz.org.au/modsim2025/files/A06.whitehouse.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	W
Kemp D (David)	A verification of the hydrological impact of 50 years of development in an urban catchment	Kemp D, Myers B, Pan L	https://doi.org/10.36334/modsim2025.G06.kemp	https://mssanz.org.au/modsim2025/files/G06.kemp.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	K
Myers B	A verification of the hydrological impact of 50 years of development in an urban catchment	Kemp D, Myers B, Pan L	https://doi.org/10.36334/modsim2025.G06.kemp	https://mssanz.org.au/modsim2025/files/G06.kemp.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	M
Pan L (Liping)	A verification of the hydrological impact of 50 years of development in an urban catchment	Kemp D, Myers B, Pan L	https://doi.org/10.36334/modsim2025.G06.kemp	https://mssanz.org.au/modsim2025/files/G06.kemp.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	P
Beecham R	Abrupt drought-to-flood transitions in Australia (INVITED SPEAKER)	Gan R, Dutta D, Beecham R, Sugiyanto M, Brown A	https://doi.org/10.36334/modsim2025.J02.gan	https://mssanz.org.au/modsim2025/files/J02.gan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Brown A (Andrew)	Abrupt drought-to-flood transitions in Australia (INVITED SPEAKER)	Gan R, Dutta D, Beecham R, Sugiyanto M, Brown A	https://doi.org/10.36334/modsim2025.J02.gan	https://mssanz.org.au/modsim2025/files/J02.gan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Dutta D	Abrupt drought-to-flood transitions in Australia (INVITED SPEAKER)	Gan R, Dutta D, Beecham R, Sugiyanto M, Brown A	https://doi.org/10.36334/modsim2025.J02.gan	https://mssanz.org.au/modsim2025/files/J02.gan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Gan R	Abrupt drought-to-flood transitions in Australia (INVITED SPEAKER)	Gan R, Dutta D, Beecham R, Sugiyanto M, Brown A	https://doi.org/10.36334/modsim2025.J02.gan	https://mssanz.org.au/modsim2025/files/J02.gan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	G
Sugiyanto M	Abrupt drought-to-flood transitions in Australia (INVITED SPEAKER)	Gan R, Dutta D, Beecham R, Sugiyanto M, Brown A	https://doi.org/10.36334/modsim2025.J02.gan	https://mssanz.org.au/modsim2025/files/J02.gan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	S
Davies G	Accommodating undulating tidal planes in tsunami models	Macaulay M, Davies G	https://doi.org/10.36334/modsim2025.G09.macaulay	https://mssanz.org.au/modsim2025/files/G09.macaulay.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	D
Macaulay M	Accommodating undulating tidal planes in tsunami models	Macaulay M, Davies G	https://doi.org/10.36334/modsim2025.G09.macaulay	https://mssanz.org.au/modsim2025/files/G09.macaulay.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Karunaratne S	Accounting for fire and grazing effects to improve soil organic modelling through emulator-based parameter recalibration	Ugbaje SU, Pagendam D, Karunaratne S	https://doi.org/10.36334/modsim2025.C06.ugbaje	https://mssanz.org.au/modsim2025/files/C06.ugbaje.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	K
Pagendam D	Accounting for fire and grazing effects to improve soil organic modelling through emulator-based parameter recalibration	Ugbaje SU, Pagendam D, Karunaratne S	https://doi.org/10.36334/modsim2025.C06.ugbaje	https://mssanz.org.au/modsim2025/files/C06.ugbaje.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	P
Ugbaje SU	Accounting for fire and grazing effects to improve soil organic modelling through emulator-based parameter recalibration	Ugbaje SU, Pagendam D, Karunaratne S	https://doi.org/10.36334/modsim2025.C06.ugbaje	https://mssanz.org.au/modsim2025/files/C06.ugbaje.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	U
Furková F	Advanced spatial modelling for analysing regional emissions: a spline approach to the environmental kuznets hypothesis	Furková F	https://doi.org/10.36334/modsim2025.F01.Furkova	https://mssanz.org.au/modsim2025/files/F01.Furkova.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	F
Wasko C	Advancing Australia’s flood guidance: Lessons learned and future directions	Wasko C	https://doi.org/10.36334/modsim2025.P05.wasko	https://mssanz.org.au/modsim2025/files/P05.wasko.pdf	P	Plenary	P5	Plenary	W
Yang X (Xihua)	Advancing erosion predictive capabilities through deep learning in southeast Australia	Zhu EQ, Yang X	https://doi.org/10.36334/modsim2025.G02.zhu	https://mssanz.org.au/modsim2025/files/G02.zhu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Y
Zhu EQ (Esther Qinggaozi)	Advancing erosion predictive capabilities through deep learning in southeast Australia	Zhu EQ, Yang X	https://doi.org/10.36334/modsim2025.G02.zhu	https://mssanz.org.au/modsim2025/files/G02.zhu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Z
Ali MT	Advancing water–energy–food–ecosystem nexus planning through digital tools: Applications from the Indus Basin	Hafeez M, Liaqat UW, Ali MT, Ikram I	https://doi.org/10.36334/modsim2025.J02.hafeez	https://mssanz.org.au/modsim2025/files/J02.hafeez.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	A
Hafeez M (Mohsin)	Advancing water–energy–food–ecosystem nexus planning through digital tools: Applications from the Indus Basin	Hafeez M, Liaqat UW, Ali MT, Ikram I	https://doi.org/10.36334/modsim2025.J02.hafeez	https://mssanz.org.au/modsim2025/files/J02.hafeez.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	H
Ikram I	Advancing water–energy–food–ecosystem nexus planning through digital tools: Applications from the Indus Basin	Hafeez M, Liaqat UW, Ali MT, Ikram I	https://doi.org/10.36334/modsim2025.J02.hafeez	https://mssanz.org.au/modsim2025/files/J02.hafeez.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	I
Liaqat UW	Advancing water–energy–food–ecosystem nexus planning through digital tools: Applications from the Indus Basin	Hafeez M, Liaqat UW, Ali MT, Ikram I	https://doi.org/10.36334/modsim2025.J02.hafeez	https://mssanz.org.au/modsim2025/files/J02.hafeez.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	L
Kajubi E	Ahead of the curve: Data-driven identification of hydrological state shifts	Kajubi E, Peterson TJ, Mondal A, Zahedi S	https://doi.org/10.36334/modsim2025.J01.kajubi	https://mssanz.org.au/modsim2025/files/J01.kajubi.pdf	J	Water resources	J1	Long term shifts in hydrological systems	K
Mondal A	Ahead of the curve: Data-driven identification of hydrological state shifts	Kajubi E, Peterson TJ, Mondal A, Zahedi S	https://doi.org/10.36334/modsim2025.J01.kajubi	https://mssanz.org.au/modsim2025/files/J01.kajubi.pdf	J	Water resources	J1	Long term shifts in hydrological systems	M
Peterson TJ	Ahead of the curve: Data-driven identification of hydrological state shifts	Kajubi E, Peterson TJ, Mondal A, Zahedi S	https://doi.org/10.36334/modsim2025.J01.kajubi	https://mssanz.org.au/modsim2025/files/J01.kajubi.pdf	J	Water resources	J1	Long term shifts in hydrological systems	P
Zahedi S	Ahead of the curve: Data-driven identification of hydrological state shifts	Kajubi E, Peterson TJ, Mondal A, Zahedi S	https://doi.org/10.36334/modsim2025.J01.kajubi	https://mssanz.org.au/modsim2025/files/J01.kajubi.pdf	J	Water resources	J1	Long term shifts in hydrological systems	Z
Bae S (Sejong)	AI and ML transform clinical trials using real-world evidence to improve design, analysis, outcomes	Bae S, Wang C, Le T, Singh KP, Bartolucci A	https://doi.org/10.36334/modsim2025.H01.bae	https://mssanz.org.au/modsim2025/files/H01.bae.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	B
Bartolucci A	AI and ML transform clinical trials using real-world evidence to improve design, analysis, outcomes	Bae S, Wang C, Le T, Singh KP, Bartolucci A	https://doi.org/10.36334/modsim2025.H01.bae	https://mssanz.org.au/modsim2025/files/H01.bae.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	B
Le T (Tuan)	AI and ML transform clinical trials using real-world evidence to improve design, analysis, outcomes	Bae S, Wang C, Le T, Singh KP, Bartolucci A	https://doi.org/10.36334/modsim2025.H01.bae	https://mssanz.org.au/modsim2025/files/H01.bae.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	L
Singh KP	AI and ML transform clinical trials using real-world evidence to improve design, analysis, outcomes	Bae S, Wang C, Le T, Singh KP, Bartolucci A	https://doi.org/10.36334/modsim2025.H01.bae	https://mssanz.org.au/modsim2025/files/H01.bae.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	S
Wang C (Chenguang)	AI and ML transform clinical trials using real-world evidence to improve design, analysis, outcomes	Bae S, Wang C, Le T, Singh KP, Bartolucci A	https://doi.org/10.36334/modsim2025.H01.bae	https://mssanz.org.au/modsim2025/files/H01.bae.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	W
Crase S	AI Large Language Models for control of uncrewed aerial systems	Crase S, Pywell S, Rosser K	https://doi.org/10.36334/modsim2025.C02.crase	https://mssanz.org.au/modsim2025/files/C02.crase.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	C
Pywell S	AI Large Language Models for control of uncrewed aerial systems	Crase S, Pywell S, Rosser K	https://doi.org/10.36334/modsim2025.C02.crase	https://mssanz.org.au/modsim2025/files/C02.crase.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	P
Rosser K	AI Large Language Models for control of uncrewed aerial systems	Crase S, Pywell S, Rosser K	https://doi.org/10.36334/modsim2025.C02.crase	https://mssanz.org.au/modsim2025/files/C02.crase.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	R
Ghasemi M	AI-driven detection of false data injection attacks in OLTC-controlled microgrids	Rao S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.rao	https://mssanz.org.au/modsim2025/files/E04.rao.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	G
Nazari A	AI-driven detection of false data injection attacks in OLTC-controlled microgrids	Rao S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.rao	https://mssanz.org.au/modsim2025/files/E04.rao.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	N
Rao S	AI-driven detection of false data injection attacks in OLTC-controlled microgrids	Rao S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.rao	https://mssanz.org.au/modsim2025/files/E04.rao.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	R
Shiri F	AI-driven detection of false data injection attacks in OLTC-controlled microgrids	Rao S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.rao	https://mssanz.org.au/modsim2025/files/E04.rao.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	S
Thiruvady D	AI-driven detection of false data injection attacks in OLTC-controlled microgrids	Rao S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.rao	https://mssanz.org.au/modsim2025/files/E04.rao.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	T
van der Meer R	AI-driven detection of false data injection attacks in OLTC-controlled microgrids	Rao S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.rao	https://mssanz.org.au/modsim2025/files/E04.rao.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	v
Halog A	AI-enhanced life cycle assessment of circular energy–waste systems in sustainable built environments	Wang S, Halog A	https://doi.org/10.36334/modsim2025.E02.wangs	https://mssanz.org.au/modsim2025/files/E02.wangs.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	H
Wang S	AI-enhanced life cycle assessment of circular energy–waste systems in sustainable built environments	Wang S, Halog A	https://doi.org/10.36334/modsim2025.E02.wangs	https://mssanz.org.au/modsim2025/files/E02.wangs.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	W
Hood C	Air quality forecasts made affordable	Vieno M, Reis S, Scheffler J, Hood C, Wang Y, Liska T, Nemitz E	https://doi.org/10.36334/modsim2025.G07.vieno	https://mssanz.org.au/modsim2025/files/G07.vieno.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	H
Liska T	Air quality forecasts made affordable	Vieno M, Reis S, Scheffler J, Hood C, Wang Y, Liska T, Nemitz E	https://doi.org/10.36334/modsim2025.G07.vieno	https://mssanz.org.au/modsim2025/files/G07.vieno.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	L
Nemitz E	Air quality forecasts made affordable	Vieno M, Reis S, Scheffler J, Hood C, Wang Y, Liska T, Nemitz E	https://doi.org/10.36334/modsim2025.G07.vieno	https://mssanz.org.au/modsim2025/files/G07.vieno.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	N
Reis S	Air quality forecasts made affordable	Vieno M, Reis S, Scheffler J, Hood C, Wang Y, Liska T, Nemitz E	https://doi.org/10.36334/modsim2025.G07.vieno	https://mssanz.org.au/modsim2025/files/G07.vieno.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	R
Scheffler J	Air quality forecasts made affordable	Vieno M, Reis S, Scheffler J, Hood C, Wang Y, Liska T, Nemitz E	https://doi.org/10.36334/modsim2025.G07.vieno	https://mssanz.org.au/modsim2025/files/G07.vieno.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	S
Vieno M	Air quality forecasts made affordable	Vieno M, Reis S, Scheffler J, Hood C, Wang Y, Liska T, Nemitz E	https://doi.org/10.36334/modsim2025.G07.vieno	https://mssanz.org.au/modsim2025/files/G07.vieno.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	V
Wang Y (Yuanlin)	Air quality forecasts made affordable	Vieno M, Reis S, Scheffler J, Hood C, Wang Y, Liska T, Nemitz E	https://doi.org/10.36334/modsim2025.G07.vieno	https://mssanz.org.au/modsim2025/files/G07.vieno.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	W
Beck R	Air quality modelling for Pacific Island countries: Insights from the Solomon Islands and Fiji	Liska T, Scheffler J, Hood C, Wang Y, Beck R, Tomlinson SJ, Carnell EJ, Nemitz E, Vieno M	https://doi.org/10.36334/modsim2025.F01.liska	https://mssanz.org.au/modsim2025/files/F01.liska.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	B
Carnell EJ	Air quality modelling for Pacific Island countries: Insights from the Solomon Islands and Fiji	Liska T, Scheffler J, Hood C, Wang Y, Beck R, Tomlinson SJ, Carnell EJ, Nemitz E, Vieno M	https://doi.org/10.36334/modsim2025.F01.liska	https://mssanz.org.au/modsim2025/files/F01.liska.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	C
Hood C	Air quality modelling for Pacific Island countries: Insights from the Solomon Islands and Fiji	Liska T, Scheffler J, Hood C, Wang Y, Beck R, Tomlinson SJ, Carnell EJ, Nemitz E, Vieno M	https://doi.org/10.36334/modsim2025.F01.liska	https://mssanz.org.au/modsim2025/files/F01.liska.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	H
Liska T	Air quality modelling for Pacific Island countries: Insights from the Solomon Islands and Fiji	Liska T, Scheffler J, Hood C, Wang Y, Beck R, Tomlinson SJ, Carnell EJ, Nemitz E, Vieno M	https://doi.org/10.36334/modsim2025.F01.liska	https://mssanz.org.au/modsim2025/files/F01.liska.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	L
Nemitz E	Air quality modelling for Pacific Island countries: Insights from the Solomon Islands and Fiji	Liska T, Scheffler J, Hood C, Wang Y, Beck R, Tomlinson SJ, Carnell EJ, Nemitz E, Vieno M	https://doi.org/10.36334/modsim2025.F01.liska	https://mssanz.org.au/modsim2025/files/F01.liska.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	N
Scheffler J	Air quality modelling for Pacific Island countries: Insights from the Solomon Islands and Fiji	Liska T, Scheffler J, Hood C, Wang Y, Beck R, Tomlinson SJ, Carnell EJ, Nemitz E, Vieno M	https://doi.org/10.36334/modsim2025.F01.liska	https://mssanz.org.au/modsim2025/files/F01.liska.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	S
Tomlinson SJ	Air quality modelling for Pacific Island countries: Insights from the Solomon Islands and Fiji	Liska T, Scheffler J, Hood C, Wang Y, Beck R, Tomlinson SJ, Carnell EJ, Nemitz E, Vieno M	https://doi.org/10.36334/modsim2025.F01.liska	https://mssanz.org.au/modsim2025/files/F01.liska.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	T
Vieno M	Air quality modelling for Pacific Island countries: Insights from the Solomon Islands and Fiji	Liska T, Scheffler J, Hood C, Wang Y, Beck R, Tomlinson SJ, Carnell EJ, Nemitz E, Vieno M	https://doi.org/10.36334/modsim2025.F01.liska	https://mssanz.org.au/modsim2025/files/F01.liska.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	V
Wang Y (Yuanlin)	Air quality modelling for Pacific Island countries: Insights from the Solomon Islands and Fiji	Liska T, Scheffler J, Hood C, Wang Y, Beck R, Tomlinson SJ, Carnell EJ, Nemitz E, Vieno M	https://doi.org/10.36334/modsim2025.F01.liska	https://mssanz.org.au/modsim2025/files/F01.liska.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	W
Armstrong J	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	A
Austin T	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	A
Bennett J	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	B
Gearhart S	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	G
Gwynne S	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	G
Koolhof IS	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	K
Lebbin P	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	L
Ozcakir OM	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	O
Pepper CA	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	P
Roberts S	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	R
Waite LL	An adaptable modelling framework for infection risk and control	Ozcakir OM, Koolhof IS, Waite LL, Austin T, Pepper CA, Roberts S, Lebbin P, Bennett J, Gearhart S, Gwynne S, Armstrong J	https://doi.org/10.36334/modsim2025.H02.ozcakir	https://mssanz.org.au/modsim2025/files/H02.ozcakir.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	W
Jung W	An agent-based model for the formation of common ground	Jung W, Mathew TJ, Zino L, Kashima Y, Ye M	https://doi.org/10.36334/modsim2025.I03.jung	https://mssanz.org.au/modsim2025/files/I03.jung.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	J
Kashima Y	An agent-based model for the formation of common ground	Jung W, Mathew TJ, Zino L, Kashima Y, Ye M	https://doi.org/10.36334/modsim2025.I03.jung	https://mssanz.org.au/modsim2025/files/I03.jung.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	K
Mathew TJ	An agent-based model for the formation of common ground	Jung W, Mathew TJ, Zino L, Kashima Y, Ye M	https://doi.org/10.36334/modsim2025.I03.jung	https://mssanz.org.au/modsim2025/files/I03.jung.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	M
Ye M	An agent-based model for the formation of common ground	Jung W, Mathew TJ, Zino L, Kashima Y, Ye M	https://doi.org/10.36334/modsim2025.I03.jung	https://mssanz.org.au/modsim2025/files/I03.jung.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	Y
Zino L	An agent-based model for the formation of common ground	Jung W, Mathew TJ, Zino L, Kashima Y, Ye M	https://doi.org/10.36334/modsim2025.I03.jung	https://mssanz.org.au/modsim2025/files/I03.jung.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	Z
Rogers AJ	An agent-based simulation model for defence workforce planning	Rogers A J, Turan H H	https://doi.org/10.36334/modsim2025.M02.rogers	https://mssanz.org.au/modsim2025/files/M02.rogers.pdf	M	Operations Research	M2	Simulation modelling and analysis	R
Turan HH	An agent-based simulation model for defence workforce planning	Rogers A J, Turan H H	https://doi.org/10.36334/modsim2025.M02.rogers	https://mssanz.org.au/modsim2025/files/M02.rogers.pdf	M	Operations Research	M2	Simulation modelling and analysis	T
Hayes MG	An economic balance sheet approach to quantify the risks affecting broadacre grain farmland value in Western Australia	Hayes MG	https://doi.org/10.36334/modsim2025.D06.hayes	https://mssanz.org.au/modsim2025/files/D06.hayes.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	H
Fernando V	An improved routing and scheduling approach for home-based chemotherapy and infusion services with a single nurse	Fernando V, Ozlen M, Taheri S	https://doi.org/10.36334/modsim2025.H03.fernando	https://mssanz.org.au/modsim2025/files/H03.fernando.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	F
Ozlen M	An improved routing and scheduling approach for home-based chemotherapy and infusion services with a single nurse	Fernando V, Ozlen M, Taheri S	https://doi.org/10.36334/modsim2025.H03.fernando	https://mssanz.org.au/modsim2025/files/H03.fernando.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	O
Taheri S	An improved routing and scheduling approach for home-based chemotherapy and infusion services with a single nurse	Fernando V, Ozlen M, Taheri S	https://doi.org/10.36334/modsim2025.H03.fernando	https://mssanz.org.au/modsim2025/files/H03.fernando.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	T
Bouzidi N	An integrated PMP-ABM model of adaptive enforcement and behavioral spillover	Bouzidi N, Klassert C, Pérez‑Blanco CD	https://doi.org/10.36334/modsim2025.F07.bouzidi	https://mssanz.org.au/modsim2025/files/F07.bouzidi.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	B
Klassert C	An integrated PMP-ABM model of adaptive enforcement and behavioral spillover	Bouzidi N, Klassert C, Pérez‑Blanco CD	https://doi.org/10.36334/modsim2025.F07.bouzidi	https://mssanz.org.au/modsim2025/files/F07.bouzidi.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	K
Pérez‑Blanco CD	An integrated PMP-ABM model of adaptive enforcement and behavioral spillover	Bouzidi N, Klassert C, Pérez‑Blanco CD	https://doi.org/10.36334/modsim2025.F07.bouzidi	https://mssanz.org.au/modsim2025/files/F07.bouzidi.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	P
Cu P	Analysing water security using NARCliM 2.0 climate projection	Dutta J, Vaze J, Kim S	https://doi.org/10.36334/modsim2025.J02.dutta	https://mssanz.org.au/modsim2025/files/J02.dutta.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	C
Dutta J	Analysing water security using NARCliM 2.0 climate projection	Dutta J, Vaze J, Kim S	https://doi.org/10.36334/modsim2025.J02.dutta	https://mssanz.org.au/modsim2025/files/J02.dutta.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Higgins P	Analysing water security using NARCliM 2.0 climate projection	https://doi.org/10.36334/modsim.2025.J02.Dutta	https://doi.org/10.36334/modsim2025.J02.dutta	https://mssanz.org.au/modsim2025/files/J02.dutta.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	H
Ji F	Analysing water security using NARCliM 2.0 climate projection	https://doi.org/10.36334/modsim.2025.J02.Dutta	https://doi.org/10.36334/modsim2025.J02.dutta	https://mssanz.org.au/modsim2025/files/J02.dutta.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	J
Loonat N	Analysing water security using NARCliM 2.0 climate projection	https://doi.org/10.36334/modsim.2025.J02.Dutta	https://doi.org/10.36334/modsim2025.J02.dutta	https://mssanz.org.au/modsim2025/files/J02.dutta.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	L
Podger GM	Analysing water security using NARCliM 2.0 climate projection	https://doi.org/10.36334/modsim.2025.J02.Dutta	https://doi.org/10.36334/modsim2025.J02.dutta	https://mssanz.org.au/modsim2025/files/J02.dutta.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Reyes R	Analysing water security using NARCliM 2.0 climate projection	https://doi.org/10.36334/modsim.2025.J02.Dutta	https://doi.org/10.36334/modsim2025.J02.dutta	https://mssanz.org.au/modsim2025/files/J02.dutta.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Trim A	Analysing water security using NARCliM 2.0 climate projection	Dutta J, Vaze J, Kim S	https://doi.org/10.36334/modsim2025.J02.dutta	https://mssanz.org.au/modsim2025/files/J02.dutta.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	T
Boland J	Analysis of changes in peak temperature impact on energy demand in South Australia	Ekanayake S, Boland J, Myers B, Hewa GA	https://doi.org/10.36334/modsim2025.E01.ekanayake	https://mssanz.org.au/modsim2025/files/E01.ekanayake.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	B
Ekanayake S	Analysis of changes in peak temperature impact on energy demand in South Australia	Ekanayake S, Boland J, Myers B, Hewa GA	https://doi.org/10.36334/modsim2025.E01.ekanayake	https://mssanz.org.au/modsim2025/files/E01.ekanayake.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	E
Hewa GA	Analysis of changes in peak temperature impact on energy demand in South Australia	Ekanayake S, Boland J, Myers B, Hewa GA	https://doi.org/10.36334/modsim2025.E01.ekanayake	https://mssanz.org.au/modsim2025/files/E01.ekanayake.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	H
Myers B	Analysis of changes in peak temperature impact on energy demand in South Australia	Ekanayake S, Boland J, Myers B, Hewa GA	https://doi.org/10.36334/modsim2025.E01.ekanayake	https://mssanz.org.au/modsim2025/files/E01.ekanayake.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	M
Loh X	Analysis of deviations in electricity demand forecasts	Loh X, Nguyen A, Slater R, Tran T	https://doi.org/10.36334/modsim2025.E04.loh	https://mssanz.org.au/modsim2025/files/E04.loh.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	L
Nguyen A	Analysis of deviations in electricity demand forecasts	Loh X, Nguyen A, Slater R, Tran T	https://doi.org/10.36334/modsim2025.E04.loh	https://mssanz.org.au/modsim2025/files/E04.loh.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	N
Slater R	Analysis of deviations in electricity demand forecasts	Loh X, Nguyen A, Slater R, Tran T	https://doi.org/10.36334/modsim2025.E04.loh	https://mssanz.org.au/modsim2025/files/E04.loh.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	S
Tran T	Analysis of deviations in electricity demand forecasts	Loh X, Nguyen A, Slater R, Tran T	https://doi.org/10.36334/modsim2025.E04.loh	https://mssanz.org.au/modsim2025/files/E04.loh.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	T
van der Merwe M	Analysis of sonar dipping regimes by analytical means	van der Merwe M, Beecham R, Dutta D	https://doi.org/10.36334/modsim2025.M01.vandermerwe	https://mssanz.org.au/modsim2025/files/M01.vandermerwe.pdf	M	Operations Research	M1	OR methods and applications	v
Almeida A	Application of hydro-economic modelling for water resource management in the Kamala River Basin, Nepal	Basnyat D, Almeida A, Lamsal G, Foran T, Wahid S, Penton D, Shrestha A	https://doi.org/10.36334/modsim2025.J02.basnyat	https://mssanz.org.au/modsim2025/files/J02.basnyat.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	A
Basnyat D	Application of hydro-economic modelling for water resource management in the Kamala River Basin, Nepal	Basnyat D, Almeida A, Lamsal G, Foran T, Wahid S, Penton D, Shrestha A	https://doi.org/10.36334/modsim2025.J02.basnyat	https://mssanz.org.au/modsim2025/files/J02.basnyat.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Foran T	Application of hydro-economic modelling for water resource management in the Kamala River Basin, Nepal	Basnyat D, Almeida A, Lamsal G, Foran T, Wahid S, Penton D, Shrestha A	https://doi.org/10.36334/modsim2025.J02.basnyat	https://mssanz.org.au/modsim2025/files/J02.basnyat.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	F
Lamsal G	Application of hydro-economic modelling for water resource management in the Kamala River Basin, Nepal	Basnyat D, Almeida A, Lamsal G, Foran T, Wahid S, Penton D, Shrestha A	https://doi.org/10.36334/modsim2025.J02.basnyat	https://mssanz.org.au/modsim2025/files/J02.basnyat.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	L
Penton D	Application of hydro-economic modelling for water resource management in the Kamala River Basin, Nepal	Basnyat D, Almeida A, Lamsal G, Foran T, Wahid S, Penton D, Shrestha A	https://doi.org/10.36334/modsim2025.J02.basnyat	https://mssanz.org.au/modsim2025/files/J02.basnyat.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Shrestha A	Application of hydro-economic modelling for water resource management in the Kamala River Basin, Nepal	Basnyat D, Almeida A, Lamsal G, Foran T, Wahid S, Penton D, Shrestha A	https://doi.org/10.36334/modsim2025.J02.basnyat	https://mssanz.org.au/modsim2025/files/J02.basnyat.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	S
Wahid S	Application of hydro-economic modelling for water resource management in the Kamala River Basin, Nepal	Basnyat D, Almeida A, Lamsal G, Foran T, Wahid S, Penton D, Shrestha A	https://doi.org/10.36334/modsim2025.J02.basnyat	https://mssanz.org.au/modsim2025/files/J02.basnyat.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	W
Chakori S	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	C
Douglas D	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	D
Fulton EA	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	F
Grigg N	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	G
Ligtermoet E	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	L
Munera‑Roldan C	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	M
Onyango E	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	O
Stirling E	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	S
Subramaniam R	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	S
Szetey K	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	S
Ward D	Applying an ethical lens for more responsible modelling practice	Szetey K, Ward D, Chakori S, Douglas D, Fulton EA, Grigg N, Ligtermoet E, Munera‑Roldan C, Onyango E, Stirling E, Subramaniam R	https://doi.org/10.36334/modsim2025.I04.szetey	https://mssanz.org.au/modsim2025/files/I04.szetey.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	W
Chapman SC	APSIM crop model innovations for breeding and deep learning	Chapman SC, Hammer GL	https://doi.org/10.36334/modsim2025.B01.chapman	https://mssanz.org.au/modsim2025/files/B01.chapman.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	C
Hammer GL	APSIM crop model innovations for breeding and deep learning	Chapman SC, Hammer GL	https://doi.org/10.36334/modsim2025.B01.chapman	https://mssanz.org.au/modsim2025/files/B01.chapman.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	H
Li C (Chenxia)	Aquifer elastic skeletal storage coefficient estimation via PS-InSAR, TICA, and Bayesian inference under precipitation variability	Li C, Yu J, Zhu L	https://doi.org/10.36334/modsim2025.G02.lic	https://mssanz.org.au/modsim2025/files/G02.lic.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	L
Yu J (Jie)	Aquifer elastic skeletal storage coefficient estimation via PS-InSAR, TICA, and Bayesian inference under precipitation variability	Li C, Yu J, Zhu L	https://doi.org/10.36334/modsim2025.G02.lic	https://mssanz.org.au/modsim2025/files/G02.lic.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Y
Zhu L (Lin)	Aquifer elastic skeletal storage coefficient estimation via PS-InSAR, TICA, and Bayesian inference under precipitation variability	Li C, Yu J, Zhu L	https://doi.org/10.36334/modsim2025.G02.lic	https://mssanz.org.au/modsim2025/files/G02.lic.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Z
Egger F	Are flow thresholds and observed hydrological data enough to evaluate the effect of environmental watering?	Egger F, Weber T, Sengupta A, Gibbs M	https://doi.org/10.36334/modsim2025.F04.egger	https://mssanz.org.au/modsim2025/files/F04.egger.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	E
Gibbs MS	Are flow thresholds and observed hydrological data enough to evaluate the effect of environmental watering?	Egger F, Weber T, Sengupta A, Gibbs M	https://doi.org/10.36334/modsim2025.F04.egger	https://mssanz.org.au/modsim2025/files/F04.egger.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	G
Sengupta A	Are flow thresholds and observed hydrological data enough to evaluate the effect of environmental watering?	Egger F, Weber T, Sengupta A, Gibbs M	https://doi.org/10.36334/modsim2025.F04.egger	https://mssanz.org.au/modsim2025/files/F04.egger.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	S
Weber T	Are flow thresholds and observed hydrological data enough to evaluate the effect of environmental watering?	Egger F, Weber T, Sengupta A, Gibbs M	https://doi.org/10.36334/modsim2025.F04.egger	https://mssanz.org.au/modsim2025/files/F04.egger.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	W
Baker R	ARES: Aerial Response and Engagement Simulator	Honhaga I, Do AV, Teffra E, Perry A, Baker R, Szabo C	https://doi.org/10.36334/modsim2025.M02.honhaga	https://mssanz.org.au/modsim2025/files/M02.honhaga.pdf	M	Operations Research	M2	Simulation modelling and analysis	B
Do AV	ARES: Aerial Response and Engagement Simulator	Honhaga I, Do AV, Teffra E, Perry A, Baker R, Szabo C	https://doi.org/10.36334/modsim2025.M02.honhaga	https://mssanz.org.au/modsim2025/files/M02.honhaga.pdf	M	Operations Research	M2	Simulation modelling and analysis	D
Honhaga I	ARES: Aerial Response and Engagement Simulator	Honhaga I, Do AV, Teffra E, Perry A, Baker R, Szabo C	https://doi.org/10.36334/modsim2025.M02.honhaga	https://mssanz.org.au/modsim2025/files/M02.honhaga.pdf	M	Operations Research	M2	Simulation modelling and analysis	H
Perry A	ARES: Aerial Response and Engagement Simulator	Honhaga I, Do AV, Teffra E, Perry A, Baker R, Szabo C	https://doi.org/10.36334/modsim2025.M02.honhaga	https://mssanz.org.au/modsim2025/files/M02.honhaga.pdf	M	Operations Research	M2	Simulation modelling and analysis	P
Szabo C	ARES: Aerial Response and Engagement Simulator	Honhaga I, Do AV, Teffra E, Perry A, Baker R, Szabo C	https://doi.org/10.36334/modsim2025.M02.honhaga	https://mssanz.org.au/modsim2025/files/M02.honhaga.pdf	M	Operations Research	M2	Simulation modelling and analysis	S
Teffra E	ARES: Aerial Response and Engagement Simulator	Honhaga I, Do AV, Teffra E, Perry A, Baker R, Szabo C	https://doi.org/10.36334/modsim2025.M02.honhaga	https://mssanz.org.au/modsim2025/files/M02.honhaga.pdf	M	Operations Research	M2	Simulation modelling and analysis	T
Hancock GR	Assessing soil erosion on post-mining and analogous natural landscapes with SIBERIA and SSSPAM models	Senanayake IP, Hancock GR	https://doi.org/10.36334/modsim2025.F08.senanayake	https://mssanz.org.au/modsim2025/files/F08.senanayake.pdf	F	Environment and ecology	F8	GIS and environmental modelling	H
Senanayake IP	Assessing soil erosion on post-mining and analogous natural landscapes with SIBERIA and SSSPAM models	Senanayake IP, Hancock GR	https://doi.org/10.36334/modsim2025.F08.senanayake	https://mssanz.org.au/modsim2025/files/F08.senanayake.pdf	F	Environment and ecology	F8	GIS and environmental modelling	S
Doody T	Assessing sun-induced fluorescence capability for detecting water stress responses in arid vegetation groundwater dependent ecosystems	Gao S, Doody T	https://doi.org/10.36334/modsim2025.J11.gao	https://mssanz.org.au/modsim2025/files/J11.gao.pdf	J	Water resources	J11	Ecohydrological Modelling	D
Gao S (Sicong)	Assessing sun-induced fluorescence capability for detecting water stress responses in arid vegetation groundwater dependent ecosystems	Gao S, Doody T	https://doi.org/10.36334/modsim2025.J11.gao	https://mssanz.org.au/modsim2025/files/J11.gao.pdf	J	Water resources	J11	Ecohydrological Modelling	G
Gou Y	Assessing the contribution of grasslands to Australian national carbon sequestration under a changing climate	Gou Y, Xu T, Xu Y	https://doi.org/10.36334/modsim2025.G02.gou	https://mssanz.org.au/modsim2025/files/G02.gou.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	G
Xu T (Tingbao)	Assessing the contribution of grasslands to Australian national carbon sequestration under a changing climate	Gou Y, Xu T, Xu Y	https://doi.org/10.36334/modsim2025.G02.gou	https://mssanz.org.au/modsim2025/files/G02.gou.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	X
Xu Y	Assessing the contribution of grasslands to Australian national carbon sequestration under a changing climate	Gou Y, Xu T, Xu Y	https://doi.org/10.36334/modsim2025.G02.gou	https://mssanz.org.au/modsim2025/files/G02.gou.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	X
He L (Lulu)	Assessing uncertainty and sensitivity in forest fire behaviour models	He L, Liu J, Zecchin AC, Maier HR, Reinke K	https://doi.org/10.36334/modsim2025.G08.he	https://mssanz.org.au/modsim2025/files/G08.he.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	H
Liu J (Jingwen)	Assessing uncertainty and sensitivity in forest fire behaviour models	He L, Liu J, Zecchin AC, Maier HR, Reinke K	https://doi.org/10.36334/modsim2025.G08.he	https://mssanz.org.au/modsim2025/files/G08.he.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	L
Maier HR	Assessing uncertainty and sensitivity in forest fire behaviour models	He L, Liu J, Zecchin AC, Maier HR, Reinke K	https://doi.org/10.36334/modsim2025.G08.he	https://mssanz.org.au/modsim2025/files/G08.he.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	M
Reinke K	Assessing uncertainty and sensitivity in forest fire behaviour models	He L, Liu J, Zecchin AC, Maier HR, Reinke K	https://doi.org/10.36334/modsim2025.G08.he	https://mssanz.org.au/modsim2025/files/G08.he.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	R
Zecchin AC	Assessing uncertainty and sensitivity in forest fire behaviour models	He L, Liu J, Zecchin AC, Maier HR, Reinke K	https://doi.org/10.36334/modsim2025.G08.he	https://mssanz.org.au/modsim2025/files/G08.he.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	Z
Montazeri M	Assessment of the impact of climate projection resolution on river system outcomes in the Murray-Darling Basin	Montazeri M, Robertson DE	https://doi.org/10.36334/modsim2025.J07.montazeri	https://mssanz.org.au/modsim2025/files/J07.montazeri.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	M
Robertson DE	Assessment of the impact of climate projection resolution on river system outcomes in the Murray-Darling Basin	Montazeri M, Robertson DE	https://doi.org/10.36334/modsim2025.J07.montazeri	https://mssanz.org.au/modsim2025/files/J07.montazeri.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	R
Bristow J	Assumptions tracking in system models: Case studies from digital twins in agriculture and horticulture	Zhang J, Jenkins H, Bristow J, van Houtte C, Lin HT	https://doi.org/10.36334/modsim2025.C03.zhang	https://mssanz.org.au/modsim2025/files/C03.zhang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	B
Jenkins H	Assumptions tracking in system models: Case studies from digital twins in agriculture and horticulture	Zhang J, Jenkins H, Bristow J, van Houtte C, Lin HT	https://doi.org/10.36334/modsim2025.C03.zhang	https://mssanz.org.au/modsim2025/files/C03.zhang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	J
Lin HT (Harris T)	Assumptions tracking in system models: Case studies from digital twins in agriculture and horticulture	Zhang J, Jenkins H, Bristow J, van Houtte C, Lin HT	https://doi.org/10.36334/modsim2025.C03.zhang	https://mssanz.org.au/modsim2025/files/C03.zhang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	L
van Houtte C	Assumptions tracking in system models: Case studies from digital twins in agriculture and horticulture	Zhang J, Jenkins H, Bristow J, van Houtte C, Lin HT	https://doi.org/10.36334/modsim2025.C03.zhang	https://mssanz.org.au/modsim2025/files/C03.zhang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	v
Zhang J (Jingjing)	Assumptions tracking in system models: Case studies from digital twins in agriculture and horticulture	Zhang J, Jenkins H, Bristow J, van Houtte C, Lin HT	https://doi.org/10.36334/modsim2025.C03.zhang	https://mssanz.org.au/modsim2025/files/C03.zhang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	Z
Chiew FHS	Attributing hydrological shifts in drought-affected catchments across the Murray-Darling Basin	Zheng H, Peña-Arancibia JL, Fu G, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.J01.zheng	https://mssanz.org.au/modsim2025/files/J01.zheng.pdf	J	Water resources	J1	Long term shifts in hydrological systems	C
Fu G (Guobin)	Attributing hydrological shifts in drought-affected catchments across the Murray-Darling Basin	Zheng H, Peña-Arancibia JL, Fu G, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.J01.zheng	https://mssanz.org.au/modsim2025/files/J01.zheng.pdf	J	Water resources	J1	Long term shifts in hydrological systems	F
Peña-Arancibia JL	Attributing hydrological shifts in drought-affected catchments across the Murray-Darling Basin	Zheng H, Peña-Arancibia JL, Fu G, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.J01.zheng	https://mssanz.org.au/modsim2025/files/J01.zheng.pdf	J	Water resources	J1	Long term shifts in hydrological systems	P
Post D	Attributing hydrological shifts in drought-affected catchments across the Murray-Darling Basin	Zheng H, Peña-Arancibia JL, Fu G, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.J01.zheng	https://mssanz.org.au/modsim2025/files/J01.zheng.pdf	J	Water resources	J1	Long term shifts in hydrological systems	P
Yu Y (Yingying)	Attributing hydrological shifts in drought-affected catchments across the Murray-Darling Basin	Zheng H, Peña-Arancibia JL, Fu G, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.J01.zheng	https://mssanz.org.au/modsim2025/files/J01.zheng.pdf	J	Water resources	J1	Long term shifts in hydrological systems	Y
Zheng H (Hongzing)	Attributing hydrological shifts in drought-affected catchments across the Murray-Darling Basin	Zheng H, Peña-Arancibia JL, Fu G, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.J01.zheng	https://mssanz.org.au/modsim2025/files/J01.zheng.pdf	J	Water resources	J1	Long term shifts in hydrological systems	Z
Riches V	Australian Groundwater Modelling Guidelines: past and future	Woods JA, Riches V	https://doi.org/10.36334/modsim2025.J05.woods	https://mssanz.org.au/modsim2025/files/J05.woods.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	R
Woods JA	Australian Groundwater Modelling Guidelines: past and future	Woods JA, Riches V	https://doi.org/10.36334/modsim2025.J05.woods	https://mssanz.org.au/modsim2025/files/J05.woods.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	W
Holgate CM	Australian meteorological drought: a review of process understanding, research gaps and model development needs	Holgate CM	https://doi.org/10.36334/modsim2025.J06.holgate	https://mssanz.org.au/modsim2025/files/J06.holgate.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	H
Al‑Naseri R	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	A
Carouge C	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	C
Harman I	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	H
Knauer J	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	K
Law R	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	L
Medlyn B	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	M
Norton A	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	N
Smith B	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	S
Srbinovsky J	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	S
Whyborn L	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	W
Williams L	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	W
Ziehn T	Australian trees for the Australian Earth System Model	Norton A, Law R, Ziehn T, Srbinovsky J, Harman I, Knauer J, Smith B, Williams L, Al‑Naseri R, Medlyn B, Whyborn L, Carouge C	https://doi.org/10.36334/modsim2025.F12.norton	https://mssanz.org.au/modsim2025/files/F12.norton.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	Z
Denman S	Automated detection of small vessels on waterways for actionable insights	Price A, McClymont A, Turner C, Roelfsema C, Denman S, Mengersen K, Vercelloni J	https://doi.org/10.36334/modsim2025.A04.price	https://mssanz.org.au/modsim2025/files/A04.price.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	D
McClymont A	Automated detection of small vessels on waterways for actionable insights	Price A, McClymont A, Turner C, Roelfsema C, Denman S, Mengersen K, Vercelloni J	https://doi.org/10.36334/modsim2025.A04.price	https://mssanz.org.au/modsim2025/files/A04.price.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	M
Mengersen K	Automated detection of small vessels on waterways for actionable insights	Price A, McClymont A, Turner C, Roelfsema C, Denman S, Mengersen K, Vercelloni J	https://doi.org/10.36334/modsim2025.A04.price	https://mssanz.org.au/modsim2025/files/A04.price.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	M
Price A	Automated detection of small vessels on waterways for actionable insights	Price A, McClymont A, Turner C, Roelfsema C, Denman S, Mengersen K, Vercelloni J	https://doi.org/10.36334/modsim2025.A04.price	https://mssanz.org.au/modsim2025/files/A04.price.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	P
Roelfsema C	Automated detection of small vessels on waterways for actionable insights	Price A, McClymont A, Turner C, Roelfsema C, Denman S, Mengersen K, Vercelloni J	https://doi.org/10.36334/modsim2025.A04.price	https://mssanz.org.au/modsim2025/files/A04.price.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	R
Turner C	Automated detection of small vessels on waterways for actionable insights	Price A, McClymont A, Turner C, Roelfsema C, Denman S, Mengersen K, Vercelloni J	https://doi.org/10.36334/modsim2025.A04.price	https://mssanz.org.au/modsim2025/files/A04.price.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	T
Vercelloni J	Automated detection of small vessels on waterways for actionable insights	Price A, McClymont A, Turner C, Roelfsema C, Denman S, Mengersen K, Vercelloni J	https://doi.org/10.36334/modsim2025.A04.price	https://mssanz.org.au/modsim2025/files/A04.price.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	V
Hou X (Xue)	Automating the extension of time series inputs for the Source GBCCL model	Hou X, Sheedy T	https://doi.org/10.36334/modsim2025.J02.hou	https://mssanz.org.au/modsim2025/files/J02.hou.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	H
Sheedy T	Automating the extension of time series inputs for the Source GBCCL model	Hou X, Sheedy T	https://doi.org/10.36334/modsim2025.J02.hou	https://mssanz.org.au/modsim2025/files/J02.hou.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	S
Bennett H	Autonomy integration for small satellite payloads: A roadmap	Bennett H, Katudia A, Lingard D, Wallace N, Ingram T, Sukkarieh S	https://doi.org/10.36334/modsim2025.C02.bennett	https://mssanz.org.au/modsim2025/files/C02.bennett.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	B
Ingram T	Autonomy integration for small satellite payloads: A roadmap	Bennett H, Katudia A, Lingard D, Wallace N, Ingram T, Sukkarieh S	https://doi.org/10.36334/modsim2025.C02.bennett	https://mssanz.org.au/modsim2025/files/C02.bennett.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	I
Katudia A	Autonomy integration for small satellite payloads: A roadmap	Bennett H, Katudia A, Lingard D, Wallace N, Ingram T, Sukkarieh S	https://doi.org/10.36334/modsim2025.C02.bennett	https://mssanz.org.au/modsim2025/files/C02.bennett.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	K
Lingard D	Autonomy integration for small satellite payloads: A roadmap	Bennett H, Katudia A, Lingard D, Wallace N, Ingram T, Sukkarieh S	https://doi.org/10.36334/modsim2025.C02.bennett	https://mssanz.org.au/modsim2025/files/C02.bennett.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	L
Sukkarieh S	Autonomy integration for small satellite payloads: A roadmap	Bennett H, Katudia A, Lingard D, Wallace N, Ingram T, Sukkarieh S	https://doi.org/10.36334/modsim2025.C02.bennett	https://mssanz.org.au/modsim2025/files/C02.bennett.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	S
Wallace N	Autonomy integration for small satellite payloads: A roadmap	Bennett H, Katudia A, Lingard D, Wallace N, Ingram T, Sukkarieh S	https://doi.org/10.36334/modsim2025.C02.bennett	https://mssanz.org.au/modsim2025/files/C02.bennett.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	W
Aihemaiti M	Bankable by design: Currency settlement, FX hedging and CHPS demand support for a Pilbara-Incheon Green-Hydrogen Pilot Corridor	Aihemaiti M, Halog A	https://doi.org/10.36334/modsim2025.E03.aihemaiti	https://mssanz.org.au/modsim2025/files/E03.aihemaiti.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	A
Halog A	Bankable by design: Currency settlement, FX hedging and CHPS demand support for a Pilbara-Incheon Green-Hydrogen Pilot Corridor	Aihemaiti M, Halog A	https://doi.org/10.36334/modsim2025.E03.aihemaiti	https://mssanz.org.au/modsim2025/files/E03.aihemaiti.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	H
Abel M	Bayesian inference for model construction in hydrology	Niven RK, Taghavi N, Cordier L, Mohammad-Djafari A, Abel M, Quade M	https://doi.org/10.36334/modsim2025.J10.niven	https://mssanz.org.au/modsim2025/files/J10.niven.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	A
Cordier L	Bayesian inference for model construction in hydrology	Niven RK, Taghavi N, Cordier L, Mohammad-Djafari A, Abel M, Quade M	https://doi.org/10.36334/modsim2025.J10.niven	https://mssanz.org.au/modsim2025/files/J10.niven.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	C
Mohammad-Djafari A	Bayesian inference for model construction in hydrology	Niven RK, Taghavi N, Cordier L, Mohammad-Djafari A, Abel M, Quade M	https://doi.org/10.36334/modsim2025.J10.niven	https://mssanz.org.au/modsim2025/files/J10.niven.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	M
Niven RK	Bayesian inference for model construction in hydrology	Niven RK, Taghavi N, Cordier L, Mohammad-Djafari A, Abel M, Quade M	https://doi.org/10.36334/modsim2025.J10.niven	https://mssanz.org.au/modsim2025/files/J10.niven.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	N
Quade M	Bayesian inference for model construction in hydrology	Niven RK, Taghavi N, Cordier L, Mohammad-Djafari A, Abel M, Quade M	https://doi.org/10.36334/modsim2025.J10.niven	https://mssanz.org.au/modsim2025/files/J10.niven.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	Q
Taghavi N	Bayesian inference for model construction in hydrology	Niven RK, Taghavi N, Cordier L, Mohammad-Djafari A, Abel M, Quade M	https://doi.org/10.36334/modsim2025.J10.niven	https://mssanz.org.au/modsim2025/files/J10.niven.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	T
Ahmad F	Benchmarking satellite-based evapotranspiration models with flux towers for agricultural water management	Liaqat UW, Hafeez M, Ahmad F	https://doi.org/10.36334/modsim2025.K03.liaqat	https://mssanz.org.au/modsim2025/files/K03.liaqat.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	A
Hafeez M (Mohsin)	Benchmarking satellite-based evapotranspiration models with flux towers for agricultural water management	Liaqat UW, Hafeez M, Ahmad F	https://doi.org/10.36334/modsim2025.K03.liaqat	https://mssanz.org.au/modsim2025/files/K03.liaqat.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	H
Liaqat UW	Benchmarking satellite-based evapotranspiration models with flux towers for agricultural water management	Liaqat UW, Hafeez M, Ahmad F	https://doi.org/10.36334/modsim2025.K03.liaqat	https://mssanz.org.au/modsim2025/files/K03.liaqat.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	L
Jiang Z (Ze)	Bending time and space to predict hydrological extremes at decadal timescales (STREAM J KEYNOTE)	Sharma A, Jiang Z, Zhang L, Thapa S	https://doi.org/10.36334/modsim2025.J10.sharma	https://mssanz.org.au/modsim2025/files/J10.sharma.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	J
Sharma A	Bending time and space to predict hydrological extremes at decadal timescales (STREAM J KEYNOTE)	Sharma A, Jiang Z, Zhang L, Thapa S	https://doi.org/10.36334/modsim2025.J10.sharma	https://mssanz.org.au/modsim2025/files/J10.sharma.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	S
Thapa S	Bending time and space to predict hydrological extremes at decadal timescales (STREAM J KEYNOTE)	Sharma A, Jiang Z, Zhang L, Thapa S	https://doi.org/10.36334/modsim2025.J10.sharma	https://mssanz.org.au/modsim2025/files/J10.sharma.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	T
Zhang L (Liangjing)	Bending time and space to predict hydrological extremes at decadal timescales (STREAM J KEYNOTE)	Sharma A, Jiang Z, Zhang L, Thapa S	https://doi.org/10.36334/modsim2025.J10.sharma	https://mssanz.org.au/modsim2025/files/J10.sharma.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	Z
Fowler K	Beyond correlation: Cause-effect relations in hydrometeorological systems	Yadav VK, Vishwakarma BD, Peel M, Ryu D, Fowler K	https://doi.org/10.36334/modsim2025.K02.yadav	https://mssanz.org.au/modsim2025/files/K02.yadav.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	F
Peel MC	Beyond correlation: Cause-effect relations in hydrometeorological systems	Yadav VK, Vishwakarma BD, Peel M, Ryu D, Fowler K	https://doi.org/10.36334/modsim2025.K02.yadav	https://mssanz.org.au/modsim2025/files/K02.yadav.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	P
Ryu D	Beyond correlation: Cause-effect relations in hydrometeorological systems	Yadav VK, Vishwakarma BD, Peel M, Ryu D, Fowler K	https://doi.org/10.36334/modsim2025.K02.yadav	https://mssanz.org.au/modsim2025/files/K02.yadav.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	R
Vishwakarma BD	Beyond correlation: Cause-effect relations in hydrometeorological systems	Yadav VK, Vishwakarma BD, Peel M, Ryu D, Fowler K	https://doi.org/10.36334/modsim2025.K02.yadav	https://mssanz.org.au/modsim2025/files/K02.yadav.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	V
Yadav VK	Beyond correlation: Cause-effect relations in hydrometeorological systems	Yadav VK, Vishwakarma BD, Peel M, Ryu D, Fowler K	https://doi.org/10.36334/modsim2025.K02.yadav	https://mssanz.org.au/modsim2025/files/K02.yadav.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	Y
Pembleton KG	Beyond the paddock: Reflecting on APSIM’s legacy and reimagining its future – A panel session	Pembleton KG	https://doi.org/10.36334/modsim2025.B01.pembleton	https://mssanz.org.au/modsim2025/files/B01.pembleton.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	P
Beck R	Biogenic and anthropogenic contributions to air quality extreme events in urban centres in Southeast Asia	Scheffler J, Vieno M, Beck R, Liska T, Wang Y, Tomlinson SJ, Carnell EJ, Nemitz E	https://doi.org/10.36334/modsim2025.G07.scheffler	https://mssanz.org.au/modsim2025/files/G07.scheffler.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	B
Carnell EJ	Biogenic and anthropogenic contributions to air quality extreme events in urban centres in Southeast Asia	Scheffler J, Vieno M, Beck R, Liska T, Wang Y, Tomlinson SJ, Carnell EJ, Nemitz E	https://doi.org/10.36334/modsim2025.G07.scheffler	https://mssanz.org.au/modsim2025/files/G07.scheffler.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	C
Liska T	Biogenic and anthropogenic contributions to air quality extreme events in urban centres in Southeast Asia	Scheffler J, Vieno M, Beck R, Liska T, Wang Y, Tomlinson SJ, Carnell EJ, Nemitz E	https://doi.org/10.36334/modsim2025.G07.scheffler	https://mssanz.org.au/modsim2025/files/G07.scheffler.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	L
Nemitz E	Biogenic and anthropogenic contributions to air quality extreme events in urban centres in Southeast Asia	Scheffler J, Vieno M, Beck R, Liska T, Wang Y, Tomlinson SJ, Carnell EJ, Nemitz E	https://doi.org/10.36334/modsim2025.G07.scheffler	https://mssanz.org.au/modsim2025/files/G07.scheffler.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	N
Scheffler J	Biogenic and anthropogenic contributions to air quality extreme events in urban centres in Southeast Asia	Scheffler J, Vieno M, Beck R, Liska T, Wang Y, Tomlinson SJ, Carnell EJ, Nemitz E	https://doi.org/10.36334/modsim2025.G07.scheffler	https://mssanz.org.au/modsim2025/files/G07.scheffler.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	S
Tomlinson SJ	Biogenic and anthropogenic contributions to air quality extreme events in urban centres in Southeast Asia	Scheffler J, Vieno M, Beck R, Liska T, Wang Y, Tomlinson SJ, Carnell EJ, Nemitz E	https://doi.org/10.36334/modsim2025.G07.scheffler	https://mssanz.org.au/modsim2025/files/G07.scheffler.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	T
Vieno M	Biogenic and anthropogenic contributions to air quality extreme events in urban centres in Southeast Asia	Scheffler J, Vieno M, Beck R, Liska T, Wang Y, Tomlinson SJ, Carnell EJ, Nemitz E	https://doi.org/10.36334/modsim2025.G07.scheffler	https://mssanz.org.au/modsim2025/files/G07.scheffler.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	V
Wang Y (Yuanlin)	Biogenic and anthropogenic contributions to air quality extreme events in urban centres in Southeast Asia	Scheffler J, Vieno M, Beck R, Liska T, Wang Y, Tomlinson SJ, Carnell EJ, Nemitz E	https://doi.org/10.36334/modsim2025.G07.scheffler	https://mssanz.org.au/modsim2025/files/G07.scheffler.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	W
Al‑Shammari D	Boundary line analysis for estimating attainable soil organic carbon across Australian grain regions and farm-level constraints	Hossen B, Filippi P, Hoskin N, Al‑Shammari D, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.hossen	https://mssanz.org.au/modsim2025/files/F13.hossen.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	A
Bishop TFA	Boundary line analysis for estimating attainable soil organic carbon across Australian grain regions and farm-level constraints	Hossen B, Filippi P, Hoskin N, Al‑Shammari D, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.hossen	https://mssanz.org.au/modsim2025/files/F13.hossen.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	B
Filippi P	Boundary line analysis for estimating attainable soil organic carbon across Australian grain regions and farm-level constraints	Hossen B, Filippi P, Hoskin N, Al‑Shammari D, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.hossen	https://mssanz.org.au/modsim2025/files/F13.hossen.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	F
Hoskin N	Boundary line analysis for estimating attainable soil organic carbon across Australian grain regions and farm-level constraints	Hossen B, Filippi P, Hoskin N, Al‑Shammari D, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.hossen	https://mssanz.org.au/modsim2025/files/F13.hossen.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	H
Hossen B	Boundary line analysis for estimating attainable soil organic carbon across Australian grain regions and farm-level constraints	Hossen B, Filippi P, Hoskin N, Al‑Shammari D, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.hossen	https://mssanz.org.au/modsim2025/files/F13.hossen.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	H
Karunaratne SB	Boundary line analysis for estimating attainable soil organic carbon across Australian grain regions and farm-level constraints	Hossen B, Filippi P, Hoskin N, Al‑Shammari D, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.hossen	https://mssanz.org.au/modsim2025/files/F13.hossen.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	K
Frost AJ	BRAIN: Building A national, hourly, kilometre-scale precipitation analysis product for Australia	Zhang Y, Wang QJ, Frost AJ, Pudashine J, Trewin B, Velasco-Forero C, Su CH, Villani V	https://doi.org/10.36334/modsim2025.K02.zhang	https://mssanz.org.au/modsim2025/files/K02.zhang.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	F
Pudashine J	BRAIN: Building A national, hourly, kilometre-scale precipitation analysis product for Australia	Zhang Y, Wang QJ, Frost AJ, Pudashine J, Trewin B, Velasco-Forero C, Su CH, Villani V	https://doi.org/10.36334/modsim2025.K02.zhang	https://mssanz.org.au/modsim2025/files/K02.zhang.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	P
Su CH	BRAIN: Building A national, hourly, kilometre-scale precipitation analysis product for Australia	Zhang Y, Wang QJ, Frost AJ, Pudashine J, Trewin B, Velasco-Forero C, Su CH, Villani V	https://doi.org/10.36334/modsim2025.K02.zhang	https://mssanz.org.au/modsim2025/files/K02.zhang.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	S
Trewin B	BRAIN: Building A national, hourly, kilometre-scale precipitation analysis product for Australia	Zhang Y, Wang QJ, Frost AJ, Pudashine J, Trewin B, Velasco-Forero C, Su CH, Villani V	https://doi.org/10.36334/modsim2025.K02.zhang	https://mssanz.org.au/modsim2025/files/K02.zhang.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	T
Velasco-Forero C	BRAIN: Building A national, hourly, kilometre-scale precipitation analysis product for Australia	Zhang Y, Wang QJ, Frost AJ, Pudashine J, Trewin B, Velasco-Forero C, Su CH, Villani V	https://doi.org/10.36334/modsim2025.K02.zhang	https://mssanz.org.au/modsim2025/files/K02.zhang.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	V
Villani V	BRAIN: Building A national, hourly, kilometre-scale precipitation analysis product for Australia	Zhang Y, Wang QJ, Frost AJ, Pudashine J, Trewin B, Velasco-Forero C, Su CH, Villani V	https://doi.org/10.36334/modsim2025.K02.zhang	https://mssanz.org.au/modsim2025/files/K02.zhang.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	V
Wang QJ	BRAIN: Building A national, hourly, kilometre-scale precipitation analysis product for Australia	Zhang Y, Wang QJ, Frost AJ, Pudashine J, Trewin B, Velasco-Forero C, Su CH, Villani V	https://doi.org/10.36334/modsim2025.K02.zhang	https://mssanz.org.au/modsim2025/files/K02.zhang.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	W
Zhang Y (Yuhang)	BRAIN: Building A national, hourly, kilometre-scale precipitation analysis product for Australia	Zhang Y, Wang QJ, Frost AJ, Pudashine J, Trewin B, Velasco-Forero C, Su CH, Villani V	https://doi.org/10.36334/modsim2025.K02.zhang	https://mssanz.org.au/modsim2025/files/K02.zhang.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	Z
Laffan S	Breaks and trends in MODIS time series across the semi-arid Paroo River catchment, Australia	Laffan S	https://doi.org/10.36334/modsim2025.F08.laffan	https://mssanz.org.au/modsim2025/files/F08.laffan.pdf	F	Environment and ecology	F8	GIS and environmental modelling	L
Ou J	Bridging process knowledge and deep learning for daily-scale maize phenology modeling under climate variability	Ou J	https://doi.org/10.36334/modsim2025.B08.ou	https://mssanz.org.au/modsim2025/files/B08.ou.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	O
Chakravarthy K	Building a water quality model for Receiving Environment Digital Twin (REDiT)	Lai C, Chakravarthy K, Jackson C	https://doi.org/10.36334/modsim2025.L04.lai	https://mssanz.org.au/modsim2025/files/L04.lai.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	C
Jackson C	Building a water quality model for Receiving Environment Digital Twin (REDiT)	Lai C, Chakravarthy K, Jackson C	https://doi.org/10.36334/modsim2025.L04.lai	https://mssanz.org.au/modsim2025/files/L04.lai.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	J
Lai C	Building a water quality model for Receiving Environment Digital Twin (REDiT)	Lai C, Chakravarthy K, Jackson C	https://doi.org/10.36334/modsim2025.L04.lai	https://mssanz.org.au/modsim2025/files/L04.lai.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	L
Brede M	Calibrating robust conceptual hydrological models for future extremes	Dykman K, Brede M	https://doi.org/10.36334/modsim2025.K04.dykman	https://mssanz.org.au/modsim2025/files/K04.dykman.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	B
Dykman K	Calibrating robust conceptual hydrological models for future extremes	Dykman K, Brede M	https://doi.org/10.36334/modsim2025.K04.dykman	https://mssanz.org.au/modsim2025/files/K04.dykman.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	D
Hall A (Andrew)	Calibration and evaluation of APSIM Next Generation for grapevine growth and water use in Australia	Solgi S, Petrie PR, Horta A, Knowling MJ, Hall A	https://doi.org/10.36334/modsim2025.B02.solgi	https://mssanz.org.au/modsim2025/files/B02.solgi.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Horta A	Calibration and evaluation of APSIM Next Generation for grapevine growth and water use in Australia	Solgi S, Petrie PR, Horta A, Knowling MJ, Hall A	https://doi.org/10.36334/modsim2025.B02.solgi	https://mssanz.org.au/modsim2025/files/B02.solgi.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Knowling MJ	Calibration and evaluation of APSIM Next Generation for grapevine growth and water use in Australia	Solgi S, Petrie PR, Horta A, Knowling MJ, Hall A	https://doi.org/10.36334/modsim2025.B02.solgi	https://mssanz.org.au/modsim2025/files/B02.solgi.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	K
Petrie PR	Calibration and evaluation of APSIM Next Generation for grapevine growth and water use in Australia	Solgi S, Petrie PR, Horta A, Knowling MJ, Hall A	https://doi.org/10.36334/modsim2025.B02.solgi	https://mssanz.org.au/modsim2025/files/B02.solgi.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	P
Solgi S	Calibration and evaluation of APSIM Next Generation for grapevine growth and water use in Australia	Solgi S, Petrie PR, Horta A, Knowling MJ, Hall A	https://doi.org/10.36334/modsim2025.B02.solgi	https://mssanz.org.au/modsim2025/files/B02.solgi.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	S
Heiss I	Calibration of APSIMx for key crops in Germany with public data	Heiss I, Katte AS, Koop S, Vogeler I	https://doi.org/10.36334/modsim2025.B02.heiss	https://mssanz.org.au/modsim2025/files/B02.heiss.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Katte AS	Calibration of APSIMx for key crops in Germany with public data	Heiss I, Katte AS, Koop S, Vogeler I	https://doi.org/10.36334/modsim2025.B02.heiss	https://mssanz.org.au/modsim2025/files/B02.heiss.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	K
Koop S	Calibration of APSIMx for key crops in Germany with public data	Heiss I, Katte AS, Koop S, Vogeler I	https://doi.org/10.36334/modsim2025.B02.heiss	https://mssanz.org.au/modsim2025/files/B02.heiss.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	K
Vogeler I	Calibration of APSIMx for key crops in Germany with public data	Heiss I, Katte AS, Koop S, Vogeler I	https://doi.org/10.36334/modsim2025.B02.heiss	https://mssanz.org.au/modsim2025/files/B02.heiss.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	V
Fowler K	Can conceptual rainfall-runoff models capture multi-annual storage dynamics?	Zhang Z, Fowler K, Peel M	https://doi.org/10.36334/modsim2025.K04.zhang	https://mssanz.org.au/modsim2025/files/K04.zhang.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	F
Peel MC	Can conceptual rainfall-runoff models capture multi-annual storage dynamics?	Zhang Z, Fowler K, Peel M	https://doi.org/10.36334/modsim2025.K04.zhang	https://mssanz.org.au/modsim2025/files/K04.zhang.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	P
Zhang Z (Ziqi)	Can conceptual rainfall-runoff models capture multi-annual storage dynamics?	Zhang Z, Fowler K, Peel M	https://doi.org/10.36334/modsim2025.K04.zhang	https://mssanz.org.au/modsim2025/files/K04.zhang.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	Z
Burns GZ	Can our conceptual hydrological models really ‘get’ evapotranspiration? Lessons from testing 15 equations	Burns GZ, Fowler KJA, Peel MC, Stephens CM	https://doi.org/10.36334/modsim2025.K04.burns	https://mssanz.org.au/modsim2025/files/K04.burns.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	B
Fowler KJA	Can our conceptual hydrological models really ‘get’ evapotranspiration? Lessons from testing 15 equations	Burns GZ, Fowler KJA, Peel MC, Stephens CM	https://doi.org/10.36334/modsim2025.K04.burns	https://mssanz.org.au/modsim2025/files/K04.burns.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	F
Peel MC	Can our conceptual hydrological models really ‘get’ evapotranspiration? Lessons from testing 15 equations	Burns GZ, Fowler KJA, Peel MC, Stephens CM	https://doi.org/10.36334/modsim2025.K04.burns	https://mssanz.org.au/modsim2025/files/K04.burns.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	P
Stephens CM	Can our conceptual hydrological models really ‘get’ evapotranspiration? Lessons from testing 15 equations	Burns GZ, Fowler KJA, Peel MC, Stephens CM	https://doi.org/10.36334/modsim2025.K04.burns	https://mssanz.org.au/modsim2025/files/K04.burns.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	S
Green H	Carbon dioxide removal via enhanced weathering: Modelling the effect of soil properties	Green H, Larsen P, Liu Y, Nelson P	https://doi.org/10.36334/modsim2025.F13.green	https://mssanz.org.au/modsim2025/files/F13.green.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	G
Larsen P	Carbon dioxide removal via enhanced weathering: Modelling the effect of soil properties	Green H, Larsen P, Liu Y, Nelson P	https://doi.org/10.36334/modsim2025.F13.green	https://mssanz.org.au/modsim2025/files/F13.green.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	L
Liu Y (Yang)	Carbon dioxide removal via enhanced weathering: Modelling the effect of soil properties	Green H, Larsen P, Liu Y, Nelson P	https://doi.org/10.36334/modsim2025.F13.green	https://mssanz.org.au/modsim2025/files/F13.green.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	L
Nelson P	Carbon dioxide removal via enhanced weathering: Modelling the effect of soil properties	Green H, Larsen P, Liu Y, Nelson P	https://doi.org/10.36334/modsim2025.F13.green	https://mssanz.org.au/modsim2025/files/F13.green.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	N
Boland J	Challenges in deriving representative rainfall-runoff model parameters for flood forecasting: Insights from Sixth Creek, South Australia	Wijekoon T, Hewa GA, Kemp D, Gamage SHPW, Boland J	https://doi.org/10.36334/modsim2025.G06.wijekoon	https://mssanz.org.au/modsim2025/files/G06.wijekoon.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	B
Gamage SHPW	Challenges in deriving representative rainfall-runoff model parameters for flood forecasting: Insights from Sixth Creek, South Australia	Wijekoon T, Hewa GA, Kemp D, Gamage SHPW, Boland J	https://doi.org/10.36334/modsim2025.G06.wijekoon	https://mssanz.org.au/modsim2025/files/G06.wijekoon.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	G
Hewa GA	Challenges in deriving representative rainfall-runoff model parameters for flood forecasting: Insights from Sixth Creek, South Australia	Wijekoon T, Hewa GA, Kemp D, Gamage SHPW, Boland J	https://doi.org/10.36334/modsim2025.G06.wijekoon	https://mssanz.org.au/modsim2025/files/G06.wijekoon.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	H
Kemp D (David)	Challenges in deriving representative rainfall-runoff model parameters for flood forecasting: Insights from Sixth Creek, South Australia	Wijekoon T, Hewa GA, Kemp D, Gamage SHPW, Boland J	https://doi.org/10.36334/modsim2025.G06.wijekoon	https://mssanz.org.au/modsim2025/files/G06.wijekoon.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	K
Wijekoon T	Challenges in deriving representative rainfall-runoff model parameters for flood forecasting: Insights from Sixth Creek, South Australia	Wijekoon T, Hewa GA, Kemp D, Gamage SHPW, Boland J	https://doi.org/10.36334/modsim2025.G06.wijekoon	https://mssanz.org.au/modsim2025/files/G06.wijekoon.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	W
Campos Teixeira P	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	C
Crosbie RS	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	C
Devanand A	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	D
Doble R	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	D
Fu G (Guobin)	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	F
Gibbs MS	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	G
Gonzalez D	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	G
Gunner W	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	G
Pickett T	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	P
Post D	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	P
Ticehurst C	Changes in groundwater recharge under 2030 and 2050 climates in the Murray-Darling Basin	Crosbie R, Doble R, Fu G, Campos Teixeira P, Pickett T, Devanand A, Ticehurst C, Gibbs M, Gunner W, Gonzalez D, Post D	https://doi.org/10.36334/modsim2025.F03.crosbie	https://mssanz.org.au/modsim2025/files/F03.crosbie.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	T
Baig F	Changes in water availability and dryness under global climate change	Faiz MA, Baig F, Zhang Y	https://doi.org/10.36334/modsim2025.K03.faiz	https://mssanz.org.au/modsim2025/files/K03.faiz.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	B
Faiz MA	Changes in water availability and dryness under global climate change	Faiz MA, Baig F, Zhang Y	https://doi.org/10.36334/modsim2025.K03.faiz	https://mssanz.org.au/modsim2025/files/K03.faiz.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	F
Zhang Y (Yongqiang)	Changes in water availability and dryness under global climate change	Faiz MA, Baig F, Zhang Y	https://doi.org/10.36334/modsim2025.K03.faiz	https://mssanz.org.au/modsim2025/files/K03.faiz.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	Z
Bende‑Michl U	Changes to wet and dry spell characteristics in Australian catchments	Thomas S, Wasko C, Guo D, Bende‑Michl U, Peel M	https://doi.org/10.36334/modsim2025.K07.thomas	https://mssanz.org.au/modsim2025/files/K07.thomas.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	B
Guo D (Danlu)	Changes to wet and dry spell characteristics in Australian catchments	Thomas S, Wasko C, Guo D, Bende‑Michl U, Peel M	https://doi.org/10.36334/modsim2025.K07.thomas	https://mssanz.org.au/modsim2025/files/K07.thomas.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	G
Peel MC	Changes to wet and dry spell characteristics in Australian catchments	Thomas S, Wasko C, Guo D, Bende‑Michl U, Peel M	https://doi.org/10.36334/modsim2025.K07.thomas	https://mssanz.org.au/modsim2025/files/K07.thomas.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	P
Thomas S	Changes to wet and dry spell characteristics in Australian catchments	Thomas S, Wasko C, Guo D, Bende‑Michl U, Peel M	https://doi.org/10.36334/modsim2025.K07.thomas	https://mssanz.org.au/modsim2025/files/K07.thomas.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	T
Wasko C	Changes to wet and dry spell characteristics in Australian catchments	Thomas S, Wasko C, Guo D, Bende‑Michl U, Peel M	https://doi.org/10.36334/modsim2025.K07.thomas	https://mssanz.org.au/modsim2025/files/K07.thomas.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	W
Dillon S	Characterisation of new cultivars in national variety trials using genomic prediction and crop growth model	Zheng B, Hu P, Whish J, Lilley J, Wang E, Dillon S, Helliwell C	https://doi.org/10.36334/modsim2025.B06.zheng	https://mssanz.org.au/modsim2025/files/B06.zheng.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	D
Helliwell C	Characterisation of new cultivars in national variety trials using genomic prediction and crop growth model	Zheng B, Hu P, Whish J, Lilley J, Wang E, Dillon S, Helliwell C	https://doi.org/10.36334/modsim2025.B06.zheng	https://mssanz.org.au/modsim2025/files/B06.zheng.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	H
Hu P (Pengcheng)	Characterisation of new cultivars in national variety trials using genomic prediction and crop growth model	Zheng B, Hu P, Whish J, Lilley J, Wang E, Dillon S, Helliwell C	https://doi.org/10.36334/modsim2025.B06.zheng	https://mssanz.org.au/modsim2025/files/B06.zheng.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	H
Lilley J	Characterisation of new cultivars in national variety trials using genomic prediction and crop growth model	Zheng B, Hu P, Whish J, Lilley J, Wang E, Dillon S, Helliwell C	https://doi.org/10.36334/modsim2025.B06.zheng	https://mssanz.org.au/modsim2025/files/B06.zheng.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	L
Wang E (Enli)	Characterisation of new cultivars in national variety trials using genomic prediction and crop growth model	Zheng B, Hu P, Whish J, Lilley J, Wang E, Dillon S, Helliwell C	https://doi.org/10.36334/modsim2025.B06.zheng	https://mssanz.org.au/modsim2025/files/B06.zheng.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	W
Whish J	Characterisation of new cultivars in national variety trials using genomic prediction and crop growth model	Zheng B, Hu P, Whish J, Lilley J, Wang E, Dillon S, Helliwell C	https://doi.org/10.36334/modsim2025.B06.zheng	https://mssanz.org.au/modsim2025/files/B06.zheng.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	W
Zheng B (Bangyou)	Characterisation of new cultivars in national variety trials using genomic prediction and crop growth model	Zheng B, Hu P, Whish J, Lilley J, Wang E, Dillon S, Helliwell C	https://doi.org/10.36334/modsim2025.B06.zheng	https://mssanz.org.au/modsim2025/files/B06.zheng.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	Z
Marshall L	Characterising multi-source uncertainties in satellite-based surrogate river discharge models	Yoon HN, Marshall L, Sharma A	https://doi.org/10.36334/modsim2025.J10.yoon	https://mssanz.org.au/modsim2025/files/J10.yoon.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	M
Sharma A	Characterising multi-source uncertainties in satellite-based surrogate river discharge models	Yoon HN, Marshall L, Sharma A	https://doi.org/10.36334/modsim2025.J10.yoon	https://mssanz.org.au/modsim2025/files/J10.yoon.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	S
Yoon HN	Characterising multi-source uncertainties in satellite-based surrogate river discharge models	Yoon HN, Marshall L, Sharma A	https://doi.org/10.36334/modsim2025.J10.yoon	https://mssanz.org.au/modsim2025/files/J10.yoon.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	Y
Han K (Kwanghee)	Characterizing transboundary dam operations using only remote sensing data: insights from the Korea border	Han K, Kim S, Mehrotra R, Sharma A	https://doi.org/10.36334/modsim2025.J10.han	https://mssanz.org.au/modsim2025/files/J10.han.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	H
Kim S (Seokhyeon)	Characterizing transboundary dam operations using only remote sensing data: insights from the Korea border	Han K, Kim S, Mehrotra R, Sharma A	https://doi.org/10.36334/modsim2025.J10.han	https://mssanz.org.au/modsim2025/files/J10.han.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	K
Mehrotra R	Characterizing transboundary dam operations using only remote sensing data: insights from the Korea border	Han K, Kim S, Mehrotra R, Sharma A	https://doi.org/10.36334/modsim2025.J10.han	https://mssanz.org.au/modsim2025/files/J10.han.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	M
Sharma A	Characterizing transboundary dam operations using only remote sensing data: insights from the Korea border	Han K, Kim S, Mehrotra R, Sharma A	https://doi.org/10.36334/modsim2025.J10.han	https://mssanz.org.au/modsim2025/files/J10.han.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	S
Boss M	Characterizing yield through wheat's perception of time: Phenomics, enviromics and genomics-based predictions	Roth L, Herrera J M, Levy Hӓner L, Pellet D, Fossati D, Boss M, Chen X, Nousi P, Volpi M	https://doi.org/10.36334/modsim2025.F11.roth	https://mssanz.org.au/modsim2025/files/F11.roth.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	B
Chen X (Xiaoran)	Characterizing yield through wheat's perception of time: Phenomics, enviromics and genomics-based predictions	Roth L, Herrera J M, Levy Hӓner L, Pellet D, Fossati D, Boss M, Chen X, Nousi P, Volpi M	https://doi.org/10.36334/modsim2025.F11.roth	https://mssanz.org.au/modsim2025/files/F11.roth.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	C
Fossati D	Characterizing yield through wheat's perception of time: Phenomics, enviromics and genomics-based predictions	Roth L, Herrera J M, Levy Hӓner L, Pellet D, Fossati D, Boss M, Chen X, Nousi P, Volpi M	https://doi.org/10.36334/modsim2025.F11.roth	https://mssanz.org.au/modsim2025/files/F11.roth.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	F
Herrera JM	Characterizing yield through wheat's perception of time: Phenomics, enviromics and genomics-based predictions	Roth L, Herrera J M, Levy Hӓner L, Pellet D, Fossati D, Boss M, Chen X, Nousi P, Volpi M	https://doi.org/10.36334/modsim2025.F11.roth	https://mssanz.org.au/modsim2025/files/F11.roth.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	H
Levy Hӓner L	Characterizing yield through wheat's perception of time: Phenomics, enviromics and genomics-based predictions	Roth L, Herrera J M, Levy Hӓner L, Pellet D, Fossati D, Boss M, Chen X, Nousi P, Volpi M	https://doi.org/10.36334/modsim2025.F11.roth	https://mssanz.org.au/modsim2025/files/F11.roth.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	L
Nousi P	Characterizing yield through wheat's perception of time: Phenomics, enviromics and genomics-based predictions	Roth L, Herrera J M, Levy Hӓner L, Pellet D, Fossati D, Boss M, Chen X, Nousi P, Volpi M	https://doi.org/10.36334/modsim2025.F11.roth	https://mssanz.org.au/modsim2025/files/F11.roth.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	N
Pellet D	Characterizing yield through wheat's perception of time: Phenomics, enviromics and genomics-based predictions	Roth L, Herrera J M, Levy Hӓner L, Pellet D, Fossati D, Boss M, Chen X, Nousi P, Volpi M	https://doi.org/10.36334/modsim2025.F11.roth	https://mssanz.org.au/modsim2025/files/F11.roth.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	P
Roth L	Characterizing yield through wheat's perception of time: Phenomics, enviromics and genomics-based predictions	Roth L, Herrera J M, Levy Hӓner L, Pellet D, Fossati D, Boss M, Chen X, Nousi P, Volpi M	https://doi.org/10.36334/modsim2025.F11.roth	https://mssanz.org.au/modsim2025/files/F11.roth.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	R
Volpi M	Characterizing yield through wheat's perception of time: Phenomics, enviromics and genomics-based predictions	Roth L, Herrera J M, Levy Hӓner L, Pellet D, Fossati D, Boss M, Chen X, Nousi P, Volpi M	https://doi.org/10.36334/modsim2025.F11.roth	https://mssanz.org.au/modsim2025/files/F11.roth.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	V
Hobday A	Charting the future: A horizon scan for ecological forecasting research in Oceania	Hobday A	https://doi.org/10.36334/modsim2025.F09.hobday	https://mssanz.org.au/modsim2025/files/F09.hobday.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	H
Hashemi Madani FS	Climate and land-use change impacts on flow and non-flow drivers of ecological outcomes	Hashemi Madani FS, Horne A, John A	https://doi.org/10.36334/modsim2025.J02.hashemimadani	https://mssanz.org.au/modsim2025/files/J02.hashemimadani.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	H
Horne A	Climate and land-use change impacts on flow and non-flow drivers of ecological outcomes	Hashemi Madani FS, Horne A, John A	https://doi.org/10.36334/modsim2025.J02.hashemimadani	https://mssanz.org.au/modsim2025/files/J02.hashemimadani.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	H
John A	Climate and land-use change impacts on flow and non-flow drivers of ecological outcomes	Hashemi Madani FS, Horne A, John A	https://doi.org/10.36334/modsim2025.J02.hashemimadani	https://mssanz.org.au/modsim2025/files/J02.hashemimadani.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	J
McCosker R	Climate change and gully erosion in Queensland - a preliminary investigation	Roberts M E, McCosker R, Rice E	https://doi.org/10.36334/modsim2025.L04.roberts	https://mssanz.org.au/modsim2025/files/L04.roberts.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	M
Rice E	Climate change and gully erosion in Queensland - a preliminary investigation	Roberts M E, McCosker R, Rice E	https://doi.org/10.36334/modsim2025.L04.roberts	https://mssanz.org.au/modsim2025/files/L04.roberts.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	R
Roberts ME	Climate change and gully erosion in Queensland - a preliminary investigation	Roberts M E, McCosker R, Rice E	https://doi.org/10.36334/modsim2025.L04.roberts	https://mssanz.org.au/modsim2025/files/L04.roberts.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	R
Pamminger L	Climate change appears to alter the proportion of rainfall that becomes streamflow in Australia	Pamminger L, Peterson T, Peel M	https://doi.org/10.36334/modsim2025.K04.pamminger	https://mssanz.org.au/modsim2025/files/K04.pamminger.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	P
Peel MC	Climate change appears to alter the proportion of rainfall that becomes streamflow in Australia	Pamminger L, Peterson T, Peel M	https://doi.org/10.36334/modsim2025.K04.pamminger	https://mssanz.org.au/modsim2025/files/K04.pamminger.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	P
Peterson T	Climate change appears to alter the proportion of rainfall that becomes streamflow in Australia	Pamminger L, Peterson T, Peel M	https://doi.org/10.36334/modsim2025.K04.pamminger	https://mssanz.org.au/modsim2025/files/K04.pamminger.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	P
Huang Z	Climate change dominates recent increase in streamflow in the Yellow River Basin	Huang Z, Li C, Zhang Y	https://doi.org/10.36334/modsim2025.K08.huang	https://mssanz.org.au/modsim2025/files/K08.huang.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	H
Li C (Congcong)	Climate change dominates recent increase in streamflow in the Yellow River Basin	Huang Z, Li C, Zhang Y	https://doi.org/10.36334/modsim2025.K08.huang	https://mssanz.org.au/modsim2025/files/K08.huang.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	L
Zhang Y (Yongqiang)	Climate change dominates recent increase in streamflow in the Yellow River Basin	Huang Z, Li C, Zhang Y	https://doi.org/10.36334/modsim2025.K08.huang	https://mssanz.org.au/modsim2025/files/K08.huang.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Gao Y (Yang)	Climate change drove the decline in Yangtze Estuary net primary production over the past two decades	Wang M, Gao Y	https://doi.org/10.36334/modsim2025.K08.wang	https://mssanz.org.au/modsim2025/files/K08.wang.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	G
Wang M	Climate change drove the decline in Yangtze Estuary net primary production over the past two decades	Wang M, Gao Y	https://doi.org/10.36334/modsim2025.K08.wang	https://mssanz.org.au/modsim2025/files/K08.wang.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	W
Chian G	Climate change rainfall data algorithm for continuous modelling of stormwater	Stancu M, Chian G	https://doi.org/10.36334/modsim2025.G03.stancu	https://mssanz.org.au/modsim2025/files/G03.stancu.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	C
Stancu M	Climate change rainfall data algorithm for continuous modelling of stormwater	Stancu M, Chian G	https://doi.org/10.36334/modsim2025.G03.stancu	https://mssanz.org.au/modsim2025/files/G03.stancu.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	S
Hall A (Andrew)	Climate change will accelerate seasonal fire weather severity by 8–24% in Southeastern Australia	Roba N, Zeleke K, Hall A, Haregeweyn N	https://doi.org/10.36334/modsim2025.G08.roba	https://mssanz.org.au/modsim2025/files/G08.roba.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	H
Haregeweyn N	Climate change will accelerate seasonal fire weather severity by 8–24% in Southeastern Australia	Roba N, Zeleke K, Hall A, Haregeweyn N	https://doi.org/10.36334/modsim2025.G08.roba	https://mssanz.org.au/modsim2025/files/G08.roba.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	H
Roba N	Climate change will accelerate seasonal fire weather severity by 8–24% in Southeastern Australia	Roba N, Zeleke K, Hall A, Haregeweyn N	https://doi.org/10.36334/modsim2025.G08.roba	https://mssanz.org.au/modsim2025/files/G08.roba.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	R
Zeleke K	Climate change will accelerate seasonal fire weather severity by 8–24% in Southeastern Australia	Roba N, Zeleke K, Hall A, Haregeweyn N	https://doi.org/10.36334/modsim2025.G08.roba	https://mssanz.org.au/modsim2025/files/G08.roba.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	Z
Cracknell N	Climate data revisions and their impact on hydrological modelling	Cracknell N, Reyes R, Simpson J, Dutta D	https://doi.org/10.36334/modsim2025.J02.cracknell	https://mssanz.org.au/modsim2025/files/J02.cracknell.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	C
Dutta D	Climate data revisions and their impact on hydrological modelling	Cracknell N, Reyes R, Simpson J, Dutta D	https://doi.org/10.36334/modsim2025.J02.cracknell	https://mssanz.org.au/modsim2025/files/J02.cracknell.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Reyes R	Climate data revisions and their impact on hydrological modelling	Cracknell N, Reyes R, Simpson J, Dutta D	https://doi.org/10.36334/modsim2025.J02.cracknell	https://mssanz.org.au/modsim2025/files/J02.cracknell.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Simpson J	Climate data revisions and their impact on hydrological modelling	Cracknell N, Reyes R, Simpson J, Dutta D	https://doi.org/10.36334/modsim2025.J02.cracknell	https://mssanz.org.au/modsim2025/files/J02.cracknell.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	S
Culley S (Sam)	Climate risk assessment using multiple conceptual system models: A framework and application to water demand	Leigh R, Knowling MJ, Westra S, McInerney D, Culley S	https://doi.org/10.36334/modsim2025.B08.leigh	https://mssanz.org.au/modsim2025/files/B08.leigh.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Knowling MJ	Climate risk assessment using multiple conceptual system models: A framework and application to water demand	Leigh R, Knowling MJ, Westra S, McInerney D, Culley S	https://doi.org/10.36334/modsim2025.B08.leigh	https://mssanz.org.au/modsim2025/files/B08.leigh.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	K
Leigh R	Climate risk assessment using multiple conceptual system models: A framework and application to water demand	Leigh R, Knowling MJ, Westra S, McInerney D, Culley S	https://doi.org/10.36334/modsim2025.B08.leigh	https://mssanz.org.au/modsim2025/files/B08.leigh.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
McInerney D	Climate risk assessment using multiple conceptual system models: A framework and application to water demand	Leigh R, Knowling MJ, Westra S, McInerney D, Culley S	https://doi.org/10.36334/modsim2025.B08.leigh	https://mssanz.org.au/modsim2025/files/B08.leigh.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	M
Westra S	Climate risk assessment using multiple conceptual system models: A framework and application to water demand	Leigh R, Knowling MJ, Westra S, McInerney D, Culley S	https://doi.org/10.36334/modsim2025.B08.leigh	https://mssanz.org.au/modsim2025/files/B08.leigh.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	W
Bai Y	Climate-driven rice expansion in northeast China: A geospatial analysis	Bai Y, Xu T, Strong C	https://doi.org/10.36334/modsim2025.B05.bai	https://mssanz.org.au/modsim2025/files/B05.bai.pdf	B	Biological systems	B5	Agricultural Systems	B
Strong C	Climate-driven rice expansion in northeast China: A geospatial analysis	Bai Y, Xu T, Strong C	https://doi.org/10.36334/modsim2025.B05.bai	https://mssanz.org.au/modsim2025/files/B05.bai.pdf	B	Biological systems	B5	Agricultural Systems	S
Xu T (Tingbao)	Climate-driven rice expansion in northeast China: A geospatial analysis	Bai Y, Xu T, Strong C	https://doi.org/10.36334/modsim2025.B05.bai	https://mssanz.org.au/modsim2025/files/B05.bai.pdf	B	Biological systems	B5	Agricultural Systems	X
Ahmad MD	Climate-resilient and adaptive water management for resilient farming and improved productivity in Pakistan	Ahmad MD, Cuddy SM, Watto MA, Yu Y	https://doi.org/10.36334/modsim2025.F04.ahmad	https://mssanz.org.au/modsim2025/files/F04.ahmad.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	A
Cuddy SM	Climate-resilient and adaptive water management for resilient farming and improved productivity in Pakistan	Ahmad MUD, Cuddy SM, Watto MA, Yu Y	https://doi.org/10.36334/modsim2025.F04.ahmad	https://mssanz.org.au/modsim2025/files/F04.ahmad.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	C
Watto MA	Climate-resilient and adaptive water management for resilient farming and improved productivity in Pakistan	Ahmad MUD, Cuddy SM, Watto MA, Yu Y	https://doi.org/10.36334/modsim2025.F04.ahmad	https://mssanz.org.au/modsim2025/files/F04.ahmad.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	W
Yu Y	Climate-resilient and adaptive water management for resilient farming and improved productivity in Pakistan	Ahmad MUD, Cuddy SM, Watto MA, Yu Y	https://doi.org/10.36334/modsim2025.F04.ahmad	https://mssanz.org.au/modsim2025/files/F04.ahmad.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	Y
Beard S	Clustering irregular cytokine time series using Gaussian Process Mixture Models	Beard S	https://doi.org/10.36334/modsim2025.H02.beard	https://mssanz.org.au/modsim2025/files/H02.beard.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	B
Breda A	Combining catchment hydro-sedimentological models with wetland eco-geomorphological models to assess mangrove resilience to climate and land use changes in the Pacific Islands	Jorquera E, Rodríguez JF, Saco PM, Quijano Baron JP, Breda A, Sandi S	https://doi.org/10.36334/modsim2025.J11.jorquera	https://mssanz.org.au/modsim2025/files/J11.jorquera.pdf	J	Water resources	J11	Ecohydrological Modelling	B
Jorquera E	Combining catchment hydro-sedimentological models with wetland eco-geomorphological models to assess mangrove resilience to climate and land use changes in the Pacific Islands	Jorquera E, Rodríguez JF, Saco PM, Quijano Baron JP, Breda A, Sandi S	https://doi.org/10.36334/modsim2025.J11.jorquera	https://mssanz.org.au/modsim2025/files/J11.jorquera.pdf	J	Water resources	J11	Ecohydrological Modelling	J
Quijano Baron JP	Combining catchment hydro-sedimentological models with wetland eco-geomorphological models to assess mangrove resilience to climate and land use changes in the Pacific Islands	Jorquera E, Rodríguez JF, Saco PM, Quijano Baron JP, Breda A, Sandi S	https://doi.org/10.36334/modsim2025.J11.jorquera	https://mssanz.org.au/modsim2025/files/J11.jorquera.pdf	J	Water resources	J11	Ecohydrological Modelling	Q
Rodríguez JF	Combining catchment hydro-sedimentological models with wetland eco-geomorphological models to assess mangrove resilience to climate and land use changes in the Pacific Islands	Jorquera E, Rodríguez JF, Saco PM, Quijano Baron JP, Breda A, Sandi S	https://doi.org/10.36334/modsim2025.J11.jorquera	https://mssanz.org.au/modsim2025/files/J11.jorquera.pdf	J	Water resources	J11	Ecohydrological Modelling	R
Saco PM	Combining catchment hydro-sedimentological models with wetland eco-geomorphological models to assess mangrove resilience to climate and land use changes in the Pacific Islands	Jorquera E, Rodríguez JF, Saco PM, Quijano Baron JP, Breda A, Sandi S	https://doi.org/10.36334/modsim2025.J11.jorquera	https://mssanz.org.au/modsim2025/files/J11.jorquera.pdf	J	Water resources	J11	Ecohydrological Modelling	S
Sandi S	Combining catchment hydro-sedimentological models with wetland eco-geomorphological models to assess mangrove resilience to climate and land use changes in the Pacific Islands	Jorquera E, Rodríguez JF, Saco PM, Quijano Baron JP, Breda A, Sandi S	https://doi.org/10.36334/modsim2025.J11.jorquera	https://mssanz.org.au/modsim2025/files/J11.jorquera.pdf	J	Water resources	J11	Ecohydrological Modelling	S
Brooks S	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	B
Doble R	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	D
Dunlop M	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	D
Gao S (Sicong)	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	G
Gibbs MS	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	G
Melbourne-Thomas J	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	M
Mullis H	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	M
O’Sullivan J	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	O
Pritchard J	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	P
Round V	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	R
Sengupta A	Communicating climate information for adaptation through familiar frames	Round V, Brooks S, Doble R, Dunlop M, Gao S, Gibbs M, Melbourne‑Thomas J, Mullis H, O’Sullivan J, Pritchard J, Sengupta A	https://doi.org/10.36334/modsim2025.F03.round	https://mssanz.org.au/modsim2025/files/F03.round.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	S
Bishop T	Comparing machine learning and process-based models for predicting wheat flowering timing	Ledvinka HD, Richetti J, Bishop T, Filippi P	https://doi.org/10.36334/modsim2025.F11.ledvinka	https://mssanz.org.au/modsim2025/files/F11.ledvinka.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	B
Filippi P	Comparing machine learning and process-based models for predicting wheat flowering timing	Ledvinka HD, Richetti J, Bishop T, Filippi P	https://doi.org/10.36334/modsim2025.F11.ledvinka	https://mssanz.org.au/modsim2025/files/F11.ledvinka.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	F
Ledvinka HD	Comparing machine learning and process-based models for predicting wheat flowering timing	Ledvinka HD, Richetti J, Bishop T, Filippi P	https://doi.org/10.36334/modsim2025.F11.ledvinka	https://mssanz.org.au/modsim2025/files/F11.ledvinka.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	L
Richetti J	Comparing machine learning and process-based models for predicting wheat flowering timing	Ledvinka HD, Richetti J, Bishop T, Filippi P	https://doi.org/10.36334/modsim2025.F11.ledvinka	https://mssanz.org.au/modsim2025/files/F11.ledvinka.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	R
Grey V	Comparing methods for assessing suspended sediment and turbidity trends in rivers: A Melbourne example	Lintern A, Grey V, Kemp A	https://doi.org/10.36334/modsim2025.L04.lintern	https://mssanz.org.au/modsim2025/files/L04.lintern.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	G
Kemp A	Comparing methods for assessing suspended sediment and turbidity trends in rivers: A Melbourne example	Lintern A, Grey V, Kemp A	https://doi.org/10.36334/modsim2025.L04.lintern	https://mssanz.org.au/modsim2025/files/L04.lintern.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	K
Lintern A	Comparing methods for assessing suspended sediment and turbidity trends in rivers: A Melbourne example	Lintern A, Grey V, Kemp A	https://doi.org/10.36334/modsim2025.L04.lintern	https://mssanz.org.au/modsim2025/files/L04.lintern.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	L
Boland J	Comparing methods to reconstruct river cross sections from LiDAR DEMs for improved flood plain modelling	Karunanayake C, Hewa GA, Peters S, Kemp D, Boland J, Walpita Gamage S, Myers B	https://doi.org/10.36334/modsim2025.G06.karunanayake	https://mssanz.org.au/modsim2025/files/G06.karunanayake.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	B
Hewa GA	Comparing methods to reconstruct river cross sections from LiDAR DEMs for improved flood plain modelling	Karunanayake C, Hewa GA, Peters S, Kemp D, Boland J, Walpita Gamage S, Myers B	https://doi.org/10.36334/modsim2025.G06.karunanayake	https://mssanz.org.au/modsim2025/files/G06.karunanayake.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	H
Karunanayake C	Comparing methods to reconstruct river cross sections from LiDAR DEMs for improved flood plain modelling	Karunanayake C, Hewa GA, Peters S, Kemp D, Boland J, Walpita Gamage S, Myers B	https://doi.org/10.36334/modsim2025.G06.karunanayake	https://mssanz.org.au/modsim2025/files/G06.karunanayake.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	K
Kemp D (David)	Comparing methods to reconstruct river cross sections from LiDAR DEMs for improved flood plain modelling	Karunanayake C, Hewa GA, Peters S, Kemp D, Boland J, Walpita Gamage S, Myers B	https://doi.org/10.36334/modsim2025.G06.karunanayake	https://mssanz.org.au/modsim2025/files/G06.karunanayake.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	K
Myers B	Comparing methods to reconstruct river cross sections from LiDAR DEMs for improved flood plain modelling	Karunanayake C, Hewa GA, Peters S, Kemp D, Boland J, Walpita Gamage S, Myers B	https://doi.org/10.36334/modsim2025.G06.karunanayake	https://mssanz.org.au/modsim2025/files/G06.karunanayake.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	M
Peters S	Comparing methods to reconstruct river cross sections from LiDAR DEMs for improved flood plain modelling	Karunanayake C, Hewa GA, Peters S, Kemp D, Boland J, Walpita Gamage S, Myers B	https://doi.org/10.36334/modsim2025.G06.karunanayake	https://mssanz.org.au/modsim2025/files/G06.karunanayake.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	P
Walpita Gamage S	Comparing methods to reconstruct river cross sections from LiDAR DEMs for improved flood plain modelling	Karunanayake C, Hewa GA, Peters S, Kemp D, Boland J, Walpita Gamage S, Myers B	https://doi.org/10.36334/modsim2025.G06.karunanayake	https://mssanz.org.au/modsim2025/files/G06.karunanayake.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	W
Booker D	Comparing the performances of a physically based hydrologic model and machine learning model in the Ashburton catchment, New Zealand	Yang J, Miura Y, Sawada Y, He Q, Rajanayaka C, Booker D, Oki T, Liu P, Croke B	https://doi.org/10.36334/modsim2025.J09.yang	https://mssanz.org.au/modsim2025/files/J09.yang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	B
Croke B	Comparing the performances of a physically based hydrologic model and machine learning model in the Ashburton catchment, New Zealand	Yang J, Miura Y, Sawada Y, He Q, Rajanayaka C, Booker D, Oki T, Liu P, Croke B	https://doi.org/10.36334/modsim2025.J09.yang	https://mssanz.org.au/modsim2025/files/J09.yang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	C
He Q	Comparing the performances of a physically based hydrologic model and machine learning model in the Ashburton catchment, New Zealand	Yang J, Miura Y, Sawada Y, He Q, Rajanayaka C, Booker D, Oki T, Liu P, Croke B	https://doi.org/10.36334/modsim2025.J09.yang	https://mssanz.org.au/modsim2025/files/J09.yang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	H
Liu P (Pan)	Comparing the performances of a physically based hydrologic model and machine learning model in the Ashburton catchment, New Zealand	Yang J, Miura Y, Sawada Y, He Q, Rajanayaka C, Booker D, Oki T, Liu P, Croke B	https://doi.org/10.36334/modsim2025.J09.yang	https://mssanz.org.au/modsim2025/files/J09.yang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	L
Miura Y	Comparing the performances of a physically based hydrologic model and machine learning model in the Ashburton catchment, New Zealand	Yang J, Miura Y, Sawada Y, He Q, Rajanayaka C, Booker D, Oki T, Liu P, Croke B	https://doi.org/10.36334/modsim2025.J09.yang	https://mssanz.org.au/modsim2025/files/J09.yang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	M
Oki T	Comparing the performances of a physically based hydrologic model and machine learning model in the Ashburton catchment, New Zealand	Yang J, Miura Y, Sawada Y, He Q, Rajanayaka C, Booker D, Oki T, Liu P, Croke B	https://doi.org/10.36334/modsim2025.J09.yang	https://mssanz.org.au/modsim2025/files/J09.yang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	O
Rajanayaka C	Comparing the performances of a physically based hydrologic model and machine learning model in the Ashburton catchment, New Zealand	Yang J, Miura Y, Sawada Y, He Q, Rajanayaka C, Booker D, Oki T, Liu P, Croke B	https://doi.org/10.36334/modsim2025.J09.yang	https://mssanz.org.au/modsim2025/files/J09.yang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	R
Sawada Y	Comparing the performances of a physically based hydrologic model and machine learning model in the Ashburton catchment, New Zealand	Yang J, Miura Y, Sawada Y, He Q, Rajanayaka C, Booker D, Oki T, Liu P, Croke B	https://doi.org/10.36334/modsim2025.J09.yang	https://mssanz.org.au/modsim2025/files/J09.yang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	S
Yang J (Jing)	Comparing the performances of a physically based hydrologic model and machine learning model in the Ashburton catchment, New Zealand	Yang J, Miura Y, Sawada Y, He Q, Rajanayaka C, Booker D, Oki T, Liu P, Croke B	https://doi.org/10.36334/modsim2025.J09.yang	https://mssanz.org.au/modsim2025/files/J09.yang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	Y
Dhumale L	Complexity of river water quality parameters using a nonlinear dynamic approach	Dhumale L, Maheshwari B, Walker G	https://doi.org/10.36334/modsim2025.L01.dhumale	https://mssanz.org.au/modsim2025/files/L01.dhumale.pdf	L	Water quality	L1	Catchment Water Quality Modelling	D
Maheshwari B	Complexity of river water quality parameters using a nonlinear dynamic approach	Dhumale L, Maheshwari B, Walker G	https://doi.org/10.36334/modsim2025.L01.dhumale	https://mssanz.org.au/modsim2025/files/L01.dhumale.pdf	L	Water quality	L1	Catchment Water Quality Modelling	M
Walker G	Complexity of river water quality parameters using a nonlinear dynamic approach	Dhumale L, Maheshwari B, Walker G	https://doi.org/10.36334/modsim2025.L01.dhumale	https://mssanz.org.au/modsim2025/files/L01.dhumale.pdf	L	Water quality	L1	Catchment Water Quality Modelling	W
Bradhurst RA	Computational modelling of black swan bioterror events	Bradhurst RA	https://doi.org/10.36334/modsim2025.H05.bradhurst	https://mssanz.org.au/modsim2025/files/H05.bradhurst.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	B
Rojas AM	Conceptualisation of simulation platform for assessing residential construction code impacts	Rojas A M, Wang C‑H, Wright A	https://doi.org/10.36334/modsim2025.E01.rojas	https://mssanz.org.au/modsim2025/files/E01.rojas.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	R
Wang C‑H (Chi-Hsiang)	Conceptualisation of simulation platform for assessing residential construction code impacts	Rojas A M, Wang C‑H, Wright A	https://doi.org/10.36334/modsim2025.E01.rojas	https://mssanz.org.au/modsim2025/files/E01.rojas.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	W
Wright A	Conceptualisation of simulation platform for assessing residential construction code impacts	Rojas A M, Wang C‑H, Wright A	https://doi.org/10.36334/modsim2025.E01.rojas	https://mssanz.org.au/modsim2025/files/E01.rojas.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	W
Athukorala R	Confronting data leakage in environmental model optimisation	Vervoort RW, Kapoor A, Chandra R, Athukorala R	https://doi.org/10.36334/modsim2025.C07.vervoort	https://mssanz.org.au/modsim2025/files/C07.vervoort.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	A
Chandra R	Confronting data leakage in environmental model optimisation	Vervoort RW, Kapoor A, Chandra R, Athukorala R	https://doi.org/10.36334/modsim2025.C07.vervoort	https://mssanz.org.au/modsim2025/files/C07.vervoort.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	C
Kapoor A	Confronting data leakage in environmental model optimisation	Vervoort RW, Kapoor A, Chandra R, Athukorala R	https://doi.org/10.36334/modsim2025.C07.vervoort	https://mssanz.org.au/modsim2025/files/C07.vervoort.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	K
Vervoort RW	Confronting data leakage in environmental model optimisation	Vervoort RW, Kapoor A, Chandra R, Athukorala R	https://doi.org/10.36334/modsim2025.C07.vervoort	https://mssanz.org.au/modsim2025/files/C07.vervoort.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	V
Beveridge C	Connecting a prior-knowledge network with APSIM: Towards breeding by design	Mitsanis C, Fortuna N, Hammer G, Van Oosteroom E, Beveridge C	https://doi.org/10.36334/modsim2025.B02.mitsanis	https://mssanz.org.au/modsim2025/files/B02.mitsanis.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	B
Fortuna N	Connecting a prior-knowledge network with APSIM: Towards breeding by design	Mitsanis C, Fortuna N, Hammer G, Van Oosteroom E, Beveridge C	https://doi.org/10.36334/modsim2025.B02.mitsanis	https://mssanz.org.au/modsim2025/files/B02.mitsanis.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	F
Hammer G	Connecting a prior-knowledge network with APSIM: Towards breeding by design	Mitsanis C, Fortuna N, Hammer G, Van Oosteroom E, Beveridge C	https://doi.org/10.36334/modsim2025.B02.mitsanis	https://mssanz.org.au/modsim2025/files/B02.mitsanis.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Mitsanis C	Connecting a prior-knowledge network with APSIM: Towards breeding by design	Mitsanis C, Fortuna N, Hammer G, Van Oosteroom E, Beveridge C	https://doi.org/10.36334/modsim2025.B02.mitsanis	https://mssanz.org.au/modsim2025/files/B02.mitsanis.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	M
Van Oosteroom E	Connecting a prior-knowledge network with APSIM: Towards breeding by design	Mitsanis C, Fortuna N, Hammer G, Van Oosteroom E, Beveridge C	https://doi.org/10.36334/modsim2025.B02.mitsanis	https://mssanz.org.au/modsim2025/files/B02.mitsanis.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	V
Cuddy SM	Consolidating Flow-MER modelling tools and metadata for national environmental reporting	Rezvani M, Cuddy S M, Hou X	https://doi.org/10.36334/modsim2025.C05.rezvani	https://mssanz.org.au/modsim2025/files/C05.rezvani.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	C
Hou X (Xinyu)	Consolidating Flow-MER modelling tools and metadata for national environmental reporting	Rezvani M, Cuddy S M, Hou X	https://doi.org/10.36334/modsim2025.C05.rezvani	https://mssanz.org.au/modsim2025/files/C05.rezvani.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	H
Rezvani M	Consolidating Flow-MER modelling tools and metadata for national environmental reporting	Rezvani M, Cuddy S M, Hou X	https://doi.org/10.36334/modsim2025.C05.rezvani	https://mssanz.org.au/modsim2025/files/C05.rezvani.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	R
Kim S (Seokhyeon)	Constrained negentropy optimisation (CoNE-opt): leveraging independent component analysis for the fusion of satellite-derived data products	Shah S, Liu Y, Kim S, Sharma A	https://doi.org/10.36334/modsim2025.J10.shah	https://mssanz.org.au/modsim2025/files/J10.shah.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	K
Liu Y (Yi)	Constrained negentropy optimisation (CoNE-opt): leveraging independent component analysis for the fusion of satellite-derived data products	Shah S, Liu Y, Kim S, Sharma A	https://doi.org/10.36334/modsim2025.J10.shah	https://mssanz.org.au/modsim2025/files/J10.shah.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	L
Shah S	Constrained negentropy optimisation (CoNE-opt): leveraging independent component analysis for the fusion of satellite-derived data products	Shah S, Liu Y, Kim S, Sharma A	https://doi.org/10.36334/modsim2025.J10.shah	https://mssanz.org.au/modsim2025/files/J10.shah.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	S
Sharma A	Constrained negentropy optimisation (CoNE-opt): leveraging independent component analysis for the fusion of satellite-derived data products	Shah S, Liu Y, Kim S, Sharma A	https://doi.org/10.36334/modsim2025.J10.shah	https://mssanz.org.au/modsim2025/files/J10.shah.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	S
Galbadrakh A	Construction of an automatic driver mdoelling system based on fuzzy inference from vehicle trajectory data	Li L, Galbadrakh A, Namekawa M	https://doi.org/10.36334/modsim2025.C04.li	https://mssanz.org.au/modsim2025/files/C04.li.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	G
Li L	Construction of an automatic driver mdoelling system based on fuzzy inference from vehicle trajectory data	Li L, Galbadrakh A, Namekawa M	https://doi.org/10.36334/modsim2025.C04.li	https://mssanz.org.au/modsim2025/files/C04.li.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	L
Namekawa M	Construction of an automatic driver mdoelling system based on fuzzy inference from vehicle trajectory data	Li L, Galbadrakh A, Namekawa M	https://doi.org/10.36334/modsim2025.C04.li	https://mssanz.org.au/modsim2025/files/C04.li.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	N
Borchers‑Arriagada N	Continental-scale machine learning classification of fire smoke pollution events for health research in Australia, 2001-2020	Gopi K, Shao J, Morgan G, Yuen C, Borchers‑Arriagada N, van Buskirk J, Jegasothy E, Hanigan I	https://doi.org/10.36334/modsim2025.G07.gopi	https://mssanz.org.au/modsim2025/files/G07.gopi.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	B
Gopi K	Continental-scale machine learning classification of fire smoke pollution events for health research in Australia, 2001-2020	Gopi K, Shao J, Morgan G, Yuen C, Borchers‑Arriagada N, van Buskirk J, Jegasothy E, Hanigan I	https://doi.org/10.36334/modsim2025.G07.gopi	https://mssanz.org.au/modsim2025/files/G07.gopi.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	G
Hanigan I	Continental-scale machine learning classification of fire smoke pollution events for health research in Australia, 2001-2020	Gopi K, Shao J, Morgan G, Yuen C, Borchers‑Arriagada N, van Buskirk J, Jegasothy E, Hanigan I	https://doi.org/10.36334/modsim2025.G07.gopi	https://mssanz.org.au/modsim2025/files/G07.gopi.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	H
Jegasothy E	Continental-scale machine learning classification of fire smoke pollution events for health research in Australia, 2001-2020	Gopi K, Shao J, Morgan G, Yuen C, Borchers‑Arriagada N, van Buskirk J, Jegasothy E, Hanigan I	https://doi.org/10.36334/modsim2025.G07.gopi	https://mssanz.org.au/modsim2025/files/G07.gopi.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	J
Morgan G	Continental-scale machine learning classification of fire smoke pollution events for health research in Australia, 2001-2020	Gopi K, Shao J, Morgan G, Yuen C, Borchers‑Arriagada N, van Buskirk J, Jegasothy E, Hanigan I	https://doi.org/10.36334/modsim2025.G07.gopi	https://mssanz.org.au/modsim2025/files/G07.gopi.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	M
Shao J	Continental-scale machine learning classification of fire smoke pollution events for health research in Australia, 2001-2020	Gopi K, Shao J, Morgan G, Yuen C, Borchers‑Arriagada N, van Buskirk J, Jegasothy E, Hanigan I	https://doi.org/10.36334/modsim2025.G07.gopi	https://mssanz.org.au/modsim2025/files/G07.gopi.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	S
van Buskirk J	Continental-scale machine learning classification of fire smoke pollution events for health research in Australia, 2001-2020	Gopi K, Shao J, Morgan G, Yuen C, Borchers‑Arriagada N, van Buskirk J, Jegasothy E, Hanigan I	https://doi.org/10.36334/modsim2025.G07.gopi	https://mssanz.org.au/modsim2025/files/G07.gopi.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	v
Yuen C	Continental-scale machine learning classification of fire smoke pollution events for health research in Australia, 2001-2020	Gopi K, Shao J, Morgan G, Yuen C, Borchers‑Arriagada N, van Buskirk J, Jegasothy E, Hanigan I	https://doi.org/10.36334/modsim2025.G07.gopi	https://mssanz.org.au/modsim2025/files/G07.gopi.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	Y
Guan H	Continuous water potential monitoring for soil-plant continuum hydraulic parameterisation and simulation	Guan H	https://doi.org/10.36334/modsim2025.J03.guan	https://mssanz.org.au/modsim2025/files/J03.guan.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	G
Biggs J	Correlating rainfall and simulated pasture growth for developing sustainable grazing insurance products in northern Australia	Ly H, Thorburn P, Biggs J, Martin B, Everingham Y	https://doi.org/10.36334/modsim2025.B05.ly	https://mssanz.org.au/modsim2025/files/B05.ly.pdf	B	Biological systems	B5	Agricultural Systems	B
Everingham Y	Correlating rainfall and simulated pasture growth for developing sustainable grazing insurance products in northern Australia	Ly H, Thorburn P, Biggs J, Martin B, Everingham Y	https://doi.org/10.36334/modsim2025.B05.ly	https://mssanz.org.au/modsim2025/files/B05.ly.pdf	B	Biological systems	B5	Agricultural Systems	E
Ly H	Correlating rainfall and simulated pasture growth for developing sustainable grazing insurance products in northern Australia	Ly H, Thorburn P, Biggs J, Martin B, Everingham Y	https://doi.org/10.36334/modsim2025.B05.ly	https://mssanz.org.au/modsim2025/files/B05.ly.pdf	B	Biological systems	B5	Agricultural Systems	L
Martin B	Correlating rainfall and simulated pasture growth for developing sustainable grazing insurance products in northern Australia	Ly H, Thorburn P, Biggs J, Martin B, Everingham Y	https://doi.org/10.36334/modsim2025.B05.ly	https://mssanz.org.au/modsim2025/files/B05.ly.pdf	B	Biological systems	B5	Agricultural Systems	M
Thorburn P	Correlating rainfall and simulated pasture growth for developing sustainable grazing insurance products in northern Australia	Ly H, Thorburn P, Biggs J, Martin B, Everingham Y	https://doi.org/10.36334/modsim2025.B05.ly	https://mssanz.org.au/modsim2025/files/B05.ly.pdf	B	Biological systems	B5	Agricultural Systems	T
Li X (Xiaojuan)	Correlations of land surface temperature with climatic and oceanic factors in China (1952–2022)	Zhang Y, Li X, Yin L	https://doi.org/10.36334/modsim2025.G02.zhang	https://mssanz.org.au/modsim2025/files/G02.zhang.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	L
Yin L	Correlations of land surface temperature with climatic and oceanic factors in China (1952–2022)	Zhang Y, Li X, Yin L	https://doi.org/10.36334/modsim2025.G02.zhang	https://mssanz.org.au/modsim2025/files/G02.zhang.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Y
Zhang Y (Yunfei)	Correlations of land surface temperature with climatic and oceanic factors in China (1952–2022)	Zhang Y, Li X, Yin L	https://doi.org/10.36334/modsim2025.G02.zhang	https://mssanz.org.au/modsim2025/files/G02.zhang.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Z
Arief V	Cost-effective agronomic practices could unlock Australian wheat yield potential (INVITED SPEAKER)	Li S, Zheng B, Hu P, Fernandez J, Lei Y, James C, Chen Q, Arief V, Chapman S	https://doi.org/10.36334/modsim2025.B08.li	https://mssanz.org.au/modsim2025/files/B08.li.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	A
Chapman S	Cost-effective agronomic practices could unlock Australian wheat yield potential (INVITED SPEAKER)	Li S, Zheng B, Hu P, Fernandez J, Lei Y, James C, Chen Q, Arief V, Chapman S	https://doi.org/10.36334/modsim2025.B08.li	https://mssanz.org.au/modsim2025/files/B08.li.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Chen Q (Qiaomin)	Cost-effective agronomic practices could unlock Australian wheat yield potential (INVITED SPEAKER)	Li S, Zheng B, Hu P, Fernandez J, Lei Y, James C, Chen Q, Arief V, Chapman S	https://doi.org/10.36334/modsim2025.B08.li	https://mssanz.org.au/modsim2025/files/B08.li.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Fernandez J	Cost-effective agronomic practices could unlock Australian wheat yield potential (INVITED SPEAKER)	Li S, Zheng B, Hu P, Fernandez J, Lei Y, James C, Chen Q, Arief V, Chapman S	https://doi.org/10.36334/modsim2025.B08.li	https://mssanz.org.au/modsim2025/files/B08.li.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	F
Hu P (Pengcheng)	Cost-effective agronomic practices could unlock Australian wheat yield potential (INVITED SPEAKER)	Li S, Zheng B, Hu P, Fernandez J, Lei Y, James C, Chen Q, Arief V, Chapman S	https://doi.org/10.36334/modsim2025.B08.li	https://mssanz.org.au/modsim2025/files/B08.li.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	H
James C	Cost-effective agronomic practices could unlock Australian wheat yield potential (INVITED SPEAKER)	Li S, Zheng B, Hu P, Fernandez J, Lei Y, James C, Chen Q, Arief V, Chapman S	https://doi.org/10.36334/modsim2025.B08.li	https://mssanz.org.au/modsim2025/files/B08.li.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	J
Lei Y (Yeming)	Cost-effective agronomic practices could unlock Australian wheat yield potential (INVITED SPEAKER)	Li S, Zheng B, Hu P, Fernandez J, Lei Y, James C, Chen Q, Arief V, Chapman S	https://doi.org/10.36334/modsim2025.B08.li	https://mssanz.org.au/modsim2025/files/B08.li.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
Li S (Sivi)	Cost-effective agronomic practices could unlock Australian wheat yield potential (INVITED SPEAKER)	Li S, Zheng B, Hu P, Fernandez J, Lei Y, James C, Chen Q, Arief V, Chapman S	https://doi.org/10.36334/modsim2025.B08.li	https://mssanz.org.au/modsim2025/files/B08.li.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
Zheng B (Bangyou)	Cost-effective agronomic practices could unlock Australian wheat yield potential (INVITED SPEAKER)	Li S, Zheng B, Hu P, Fernandez J, Lei Y, James C, Chen Q, Arief V, Chapman S	https://doi.org/10.36334/modsim2025.B08.li	https://mssanz.org.au/modsim2025/files/B08.li.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	Z
Chen N (Nuo)	Cost-effectiveness of lung cancer screening strategies in China: Markov model and discrete event simulation comparison	Lyu J, Chen N	https://doi.org/10.36334/modsim2025.H05.lyu	https://mssanz.org.au/modsim2025/files/H05.lyu.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	C
Lyu J (Juntao)	Cost-effectiveness of lung cancer screening strategies in China: Markov model and discrete event simulation comparison	Lyu J, Chen N	https://doi.org/10.36334/modsim2025.H05.lyu	https://mssanz.org.au/modsim2025/files/H05.lyu.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	L
Hipsey MR	Coupling MODFLOW and GLM-AED for integrated simulation of lake–aquifer interactions	Vergara‑Sáez C, Hipsey MR	https://doi.org/10.36334/modsim2025.L02.vergarasaez	https://mssanz.org.au/modsim2025/files/L02.vergarasaez.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	H
Vergara‑Sáez C	Coupling MODFLOW and GLM-AED for integrated simulation of lake–aquifer interactions	Vergara‑Sáez C, Hipsey MR	https://doi.org/10.36334/modsim2025.L02.vergarasaez	https://mssanz.org.au/modsim2025/files/L02.vergarasaez.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	V
Schaerf TM	Data-driven models of collective motion derived directly from force maps	Schaerf TM	https://doi.org/10.36334/modsim2025.B09.schaerf	https://mssanz.org.au/modsim2025/files/B09.schaerf.pdf	B	Biological systems	B9	Advances in agent-based modelling and their statistical challenges in biological, ecological and agricultural systems	S
Alikhani M	Data-driven rolling forecasting for wind and solar generation: Evaluating statistical models across historical data lengths	Alikhani M	https://doi.org/10.36334/modsim2025.E04.alikhani	https://mssanz.org.au/modsim2025/files/E04.alikhani.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	A
Bishop TFA	Depth-resolved soil organic carbon estimation: a scalable approach	van Kretschmar M, Filippi P, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.vankretschmar	https://mssanz.org.au/modsim2025/files/F13.vankretschmar.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	B
Filippi P	Depth-resolved soil organic carbon estimation: a scalable approach	van Kretschmar M, Filippi P, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.vankretschmar	https://mssanz.org.au/modsim2025/files/F13.vankretschmar.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	F
Karunaratne SB	Depth-resolved soil organic carbon estimation: a scalable approach	van Kretschmar M, Filippi P, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.vankretschmar	https://mssanz.org.au/modsim2025/files/F13.vankretschmar.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	K
van Kretschmar M	Depth-resolved soil organic carbon estimation: a scalable approach	van Kretschmar M, Filippi P, Karunaratne SB, Bishop TFA	https://doi.org/10.36334/modsim2025.F13.vankretschmar	https://mssanz.org.au/modsim2025/files/F13.vankretschmar.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	v
Abbass HA	Descriptive and predictive modelling for professional darts	Aly A, Abbass HA	https://doi.org/10.36334/modsim2025.M01.aly	https://mssanz.org.au/modsim2025/files/M01.aly.pdf	M	Operations Research	M1	OR methods and applications	A
Aly A	Descriptive and predictive modelling for professional darts	Aly A, Abbass HA	https://doi.org/10.36334/modsim2025.M01.aly	https://mssanz.org.au/modsim2025/files/M01.aly.pdf	M	Operations Research	M1	OR methods and applications	A
Balaburov E	Design validation and verification for non-intrusive flight test instrumentation (NIFTI) using Model-Based Systems Engineering (MBSE)	Pattarakunnan K, Bruce S, Balaburov E, Mancarella S	https://doi.org/10.36334/modsim2025.C01.pattarakunnan	https://mssanz.org.au/modsim2025/files/C01.pattarakunnan.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	B
Bruce S	Design validation and verification for non-intrusive flight test instrumentation (NIFTI) using Model-Based Systems Engineering (MBSE)	Pattarakunnan K, Bruce S, Balaburov E, Mancarella S	https://doi.org/10.36334/modsim2025.C01.pattarakunnan	https://mssanz.org.au/modsim2025/files/C01.pattarakunnan.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	B
Mancarella S	Design validation and verification for non-intrusive flight test instrumentation (NIFTI) using Model-Based Systems Engineering (MBSE)	Pattarakunnan K, Bruce S, Balaburov E, Mancarella S	https://doi.org/10.36334/modsim2025.C01.pattarakunnan	https://mssanz.org.au/modsim2025/files/C01.pattarakunnan.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	M
Pattarakunnan K	Design validation and verification for non-intrusive flight test instrumentation (NIFTI) using Model-Based Systems Engineering (MBSE)	Pattarakunnan K, Bruce S, Balaburov E, Mancarella S	https://doi.org/10.36334/modsim2025.C01.pattarakunnan	https://mssanz.org.au/modsim2025/files/C01.pattarakunnan.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	P
Campbell R	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	C
Crawford J	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	C
Flick L	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	F
Fraser G	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	F
Gerber C	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	G
Hawkins SG	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	H
Hughes CE	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	H
Kohlmann F	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	K
Leslie A	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	L
Noble W	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	N
Shu Y	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	S
Stobaus T	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	S
Suckow A	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	S
Theile M	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	T
Waltenberg K	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	W
Welti N	Designing for reality: A bottom-up approach to data interoperability	Welti N, Hawkins SG, Waltenberg K, Hughes CE, Shu Y, Campbell R, Gerber C, Crawford J, Suckow A, Leslie A, Fraser G, Stobaus T, Kohlmann F, Noble W, Theile M, Flick L	https://doi.org/10.36334/modsim2025.C05.welti	https://mssanz.org.au/modsim2025/files/C05.welti.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	W
Knowles D	Designing personalized levodopa regimens using minimal inputs and hybrid simulation modelling	Knowles D	https://doi.org/10.36334/modsim2025.H02.knowles	https://mssanz.org.au/modsim2025/files/H02.knowles.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	K
Corry PG (Paul)	DesRail: a new discrete-event-simulation library for high-performance rail network simulations	Corry PG	https://doi.org/10.36334/modsim2025.M02.corry	https://mssanz.org.au/modsim2025/files/M02.corry.pdf	M	Operations Research	M2	Simulation modelling and analysis	C
Jiang J (Jipng)	Detection and driving factor analysis of hypoxia in river estuarine zones by entropy methods	Pang T, Xiong Y, Zhang X, Wang H, Jiang J	https://doi.org/10.36334/modsim2025.L01.pang	https://mssanz.org.au/modsim2025/files/L01.pang.pdf	L	Water quality	L1	Catchment Water Quality Modelling	J
Pang T (Tianrui)	Detection and driving factor analysis of hypoxia in river estuarine zones by entropy methods	Pang T, Xiong Y, Zhang X, Wang H, Jiang J	https://doi.org/10.36334/modsim2025.L01.pang	https://mssanz.org.au/modsim2025/files/L01.pang.pdf	L	Water quality	L1	Catchment Water Quality Modelling	P
Wang H (Hongjie)	Detection and driving factor analysis of hypoxia in river estuarine zones by entropy methods	Pang T, Xiong Y, Zhang X, Wang H, Jiang J	https://doi.org/10.36334/modsim2025.L01.pang	https://mssanz.org.au/modsim2025/files/L01.pang.pdf	L	Water quality	L1	Catchment Water Quality Modelling	W
Xiong Y (Ye)	Detection and driving factor analysis of hypoxia in river estuarine zones by entropy methods	Pang T, Xiong Y, Zhang X, Wang H, Jiang J	https://doi.org/10.36334/modsim2025.L01.pang	https://mssanz.org.au/modsim2025/files/L01.pang.pdf	L	Water quality	L1	Catchment Water Quality Modelling	X
Zhang X (Xiaoyu)	Detection and driving factor analysis of hypoxia in river estuarine zones by entropy methods	Pang T, Xiong Y, Zhang X, Wang H, Jiang J	https://doi.org/10.36334/modsim2025.L01.pang	https://mssanz.org.au/modsim2025/files/L01.pang.pdf	L	Water quality	L1	Catchment Water Quality Modelling	Z
Cu P	Developing a Source model of the Upper Macquarie and Upper Coxs river systems	Podger GM, Cu P, Trim A, Dutta D, Loonat N, Tang Y, Reyes R, Fonseka C	https://doi.org/10.36334/modsim2025.J02.podger	https://mssanz.org.au/modsim2025/files/J02.podger.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	C
Dutta D	Developing a Source model of the Upper Macquarie and Upper Coxs river systems	Podger GM, Cu P, Trim A, Dutta D, Loonat N, Tang Y, Reyes R, Fonseka C	https://doi.org/10.36334/modsim2025.J02.podger	https://mssanz.org.au/modsim2025/files/J02.podger.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Fonseka C	Developing a Source model of the Upper Macquarie and Upper Coxs river systems	Podger GM, Cu P, Trim A, Dutta D, Loonat N, Tang Y, Reyes R, Fonseka C	https://doi.org/10.36334/modsim2025.J02.podger	https://mssanz.org.au/modsim2025/files/J02.podger.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	F
Loonat N	Developing a Source model of the Upper Macquarie and Upper Coxs river systems	Podger GM, Cu P, Trim A, Dutta D, Loonat N, Tang Y, Reyes R, Fonseka C	https://doi.org/10.36334/modsim2025.J02.podger	https://mssanz.org.au/modsim2025/files/J02.podger.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	L
Podger GM	Developing a Source model of the Upper Macquarie and Upper Coxs river systems	Podger GM, Cu P, Trim A, Dutta D, Loonat N, Tang Y, Reyes R, Fonseka C	https://doi.org/10.36334/modsim2025.J02.podger	https://mssanz.org.au/modsim2025/files/J02.podger.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Reyes R	Developing a Source model of the Upper Macquarie and Upper Coxs river systems	Podger GM, Cu P, Trim A, Dutta D, Loonat N, Tang Y, Reyes R, Fonseka C	https://doi.org/10.36334/modsim2025.J02.podger	https://mssanz.org.au/modsim2025/files/J02.podger.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Tang Y	Developing a Source model of the Upper Macquarie and Upper Coxs river systems	Podger GM, Cu P, Trim A, Dutta D, Loonat N, Tang Y, Reyes R, Fonseka C	https://doi.org/10.36334/modsim2025.J02.podger	https://mssanz.org.au/modsim2025/files/J02.podger.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	T
Trim A	Developing a Source model of the Upper Macquarie and Upper Coxs river systems	Podger GM, Cu P, Trim A, Dutta D, Loonat N, Tang Y, Reyes R, Fonseka C	https://doi.org/10.36334/modsim2025.J02.podger	https://mssanz.org.au/modsim2025/files/J02.podger.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	T
Cetin L	Developing a water quality modelling toolkit: Load estimates in Port Phillip and Western Port catchments	Kemp A, Cetin L, Grey V	https://doi.org/10.36334/modsim2025.L01.kemp	https://mssanz.org.au/modsim2025/files/L01.kemp.pdf	L	Water quality	L1	Catchment Water Quality Modelling	C
Grey V	Developing a water quality modelling toolkit: Load estimates in Port Phillip and Western Port catchments	Kemp A, Cetin L, Grey V	https://doi.org/10.36334/modsim2025.L01.kemp	https://mssanz.org.au/modsim2025/files/L01.kemp.pdf	L	Water quality	L1	Catchment Water Quality Modelling	G
Kemp A	Developing a water quality modelling toolkit: Load estimates in Port Phillip and Western Port catchments	Kemp A, Cetin L, Grey V	https://doi.org/10.36334/modsim2025.L01.kemp	https://mssanz.org.au/modsim2025/files/L01.kemp.pdf	L	Water quality	L1	Catchment Water Quality Modelling	K
Bell L	Developing frost and heat stress damage functions to quantify yield loss for canola and wheat with APSIM	Hu P, He D, Zheng B, Whish J, Kirkegaard J, Bell L, Lilley J	https://doi.org/10.36334/modsim2025.B02.hu	https://mssanz.org.au/modsim2025/files/B02.hu.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	B
He D	Developing frost and heat stress damage functions to quantify yield loss for canola and wheat with APSIM	Hu P, He D, Zheng B, Whish J, Kirkegaard J, Bell L, Lilley J	https://doi.org/10.36334/modsim2025.B02.hu	https://mssanz.org.au/modsim2025/files/B02.hu.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Hu P (Pengcheng)	Developing frost and heat stress damage functions to quantify yield loss for canola and wheat with APSIM	Hu P, He D, Zheng B, Whish J, Kirkegaard J, Bell L, Lilley J	https://doi.org/10.36334/modsim2025.B02.hu	https://mssanz.org.au/modsim2025/files/B02.hu.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Kirkegaard J	Developing frost and heat stress damage functions to quantify yield loss for canola and wheat with APSIM	Hu P, He D, Zheng B, Whish J, Kirkegaard J, Bell L, Lilley J	https://doi.org/10.36334/modsim2025.B02.hu	https://mssanz.org.au/modsim2025/files/B02.hu.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	K
Lilley J	Developing frost and heat stress damage functions to quantify yield loss for canola and wheat with APSIM	Hu P, He D, Zheng B, Whish J, Kirkegaard J, Bell L, Lilley J	https://doi.org/10.36334/modsim2025.B02.hu	https://mssanz.org.au/modsim2025/files/B02.hu.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	L
Whish J	Developing frost and heat stress damage functions to quantify yield loss for canola and wheat with APSIM	Hu P, He D, Zheng B, Whish J, Kirkegaard J, Bell L, Lilley J	https://doi.org/10.36334/modsim2025.B02.hu	https://mssanz.org.au/modsim2025/files/B02.hu.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	W
Zheng B (Bangyou)	Developing frost and heat stress damage functions to quantify yield loss for canola and wheat with APSIM	Hu P, He D, Zheng B, Whish J, Kirkegaard J, Bell L, Lilley J	https://doi.org/10.36334/modsim2025.B02.hu	https://mssanz.org.au/modsim2025/files/B02.hu.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	Z
Prasain S	Developing of integrated model to evaluate the sustainability of climate smart and drought resilience agricultural adaptation technologies concerning water and food security in Australia	Prasain S	https://doi.org/10.36334/modsim2025.B07.prasain	https://mssanz.org.au/modsim2025/files/B07.prasain.pdf	B	Biological systems	B7	Optimising farming systems to improve climate resilience and sustainability under water-constraint environments	P
Hughes JD	Development and evaluation of "climate-robust" rainfall runoff model parameter libraries	Hughes JD, Kim SSH	https://doi.org/10.36334/modsim2025.J03.hughes	https://mssanz.org.au/modsim2025/files/J03.hughes.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	H
Kim SSH	Development and evaluation of "climate-robust" rainfall runoff model parameter libraries	Hughes JD, Kim SSH	https://doi.org/10.36334/modsim2025.J03.hughes	https://mssanz.org.au/modsim2025/files/J03.hughes.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	K
Antille DL	Development and evaluation of a Taro Model within the APSIM plant modelling framework	Beletse YG, Prasad VD, Sachan H, Krishna D, Crimp SJ, Antille DL	https://doi.org/10.36334/modsim2025.B02.beletse	https://mssanz.org.au/modsim2025/files/B02.beletse.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	A
Beletse YG	Development and evaluation of a Taro Model within the APSIM plant modelling framework	Beletse YG, Prasad VD, Sachan H, Krishna D, Crimp SJ, Antille DL	https://doi.org/10.36334/modsim2025.B02.beletse	https://mssanz.org.au/modsim2025/files/B02.beletse.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	B
Crimp SJ	Development and evaluation of a Taro Model within the APSIM plant modelling framework	Beletse YG, Prasad VD, Sachan H, Krishna D, Crimp SJ, Antille DL	https://doi.org/10.36334/modsim2025.B02.beletse	https://mssanz.org.au/modsim2025/files/B02.beletse.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	C
Krishna D	Development and evaluation of a Taro Model within the APSIM plant modelling framework	Beletse YG, Prasad VD, Sachan H, Krishna D, Crimp SJ, Antille DL	https://doi.org/10.36334/modsim2025.B02.beletse	https://mssanz.org.au/modsim2025/files/B02.beletse.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	K
Prasad VD	Development and evaluation of a Taro Model within the APSIM plant modelling framework	Beletse YG, Prasad VD, Sachan H, Krishna D, Crimp SJ, Antille DL	https://doi.org/10.36334/modsim2025.B02.beletse	https://mssanz.org.au/modsim2025/files/B02.beletse.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	P
Sachan H	Development and evaluation of a Taro Model within the APSIM plant modelling framework	Beletse YG, Prasad VD, Sachan H, Krishna D, Crimp SJ, Antille DL	https://doi.org/10.36334/modsim2025.B02.beletse	https://mssanz.org.au/modsim2025/files/B02.beletse.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	S
Alzubaidi S	Development of an agent-based model for movement and foraging of sheep flock	Welch M, Alzubaidi S, Schaerf T	https://doi.org/10.36334/modsim2025.B09.welch	https://mssanz.org.au/modsim2025/files/B09.welch.pdf	B	Biological systems	B9	Advances in agent-based modelling and their statistical challenges in biological, ecological and agricultural systems	A
Schaerf T	Development of an agent-based model for movement and foraging of sheep flock	Welch M, Alzubaidi S, Schaerf T	https://doi.org/10.36334/modsim2025.B09.welch	https://mssanz.org.au/modsim2025/files/B09.welch.pdf	B	Biological systems	B9	Advances in agent-based modelling and their statistical challenges in biological, ecological and agricultural systems	S
Welch M	Development of an agent-based model for movement and foraging of sheep flock	Welch M, Alzubaidi S, Schaerf T	https://doi.org/10.36334/modsim2025.B09.welch	https://mssanz.org.au/modsim2025/files/B09.welch.pdf	B	Biological systems	B9	Advances in agent-based modelling and their statistical challenges in biological, ecological and agricultural systems	W
Dykman J	Development of an Australia-wide extreme storms database for hydrologic risk assessments	Dykman J, Kalloniatis A, Gallant H	https://doi.org/10.36334/modsim2025.J06.dykman	https://mssanz.org.au/modsim2025/files/J06.dykman.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	D
Gallant H	Development of an Australia-wide extreme storms database for hydrologic risk assessments	Dykman J, Kalloniatis A, Gallant H	https://doi.org/10.36334/modsim2025.J06.dykman	https://mssanz.org.au/modsim2025/files/J06.dykman.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	G
Kalloniatis A	Development of an Australia-wide extreme storms database for hydrologic risk assessments	Dykman J, Kalloniatis A, Gallant H	https://doi.org/10.36334/modsim2025.J06.dykman	https://mssanz.org.au/modsim2025/files/J06.dykman.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	K
Angus SD	DIMESim: an agent-based model of the psychological dynamics of collective action	Thomas EF, Ye M, Mathew TJ, Angus SD, Louis W	https://doi.org/10.36334/modsim2025.I03.thomas	https://mssanz.org.au/modsim2025/files/I03.thomas.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	A
Louis W	DIMESim: an agent-based model of the psychological dynamics of collective action	Thomas EF, Ye M, Mathew TJ, Angus SD, Louis W	https://doi.org/10.36334/modsim2025.I03.thomas	https://mssanz.org.au/modsim2025/files/I03.thomas.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	L
Mathew TJ	DIMESim: an agent-based model of the psychological dynamics of collective action	Thomas EF, Ye M, Mathew TJ, Angus SD, Louis W	https://doi.org/10.36334/modsim2025.I03.thomas	https://mssanz.org.au/modsim2025/files/I03.thomas.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	M
Thomas EF	DIMESim: an agent-based model of the psychological dynamics of collective action	Thomas EF, Ye M, Mathew TJ, Angus SD, Louis W	https://doi.org/10.36334/modsim2025.I03.thomas	https://mssanz.org.au/modsim2025/files/I03.thomas.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	T
Ye M	DIMESim: an agent-based model of the psychological dynamics of collective action	Thomas EF, Ye M, Mathew TJ, Angus SD, Louis W	https://doi.org/10.36334/modsim2025.I03.thomas	https://mssanz.org.au/modsim2025/files/I03.thomas.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	Y
Aberathne I	Discovery of MRI biomarker for early Alzheimer's disease integrating regional atrophy and ventricular expansion	Aberathne I, Kulasiri D, Samarasinghe S, Zhang J	https://doi.org/10.36334/modsim2025.H01.aberathne	https://mssanz.org.au/modsim2025/files/H01.aberathne.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	A
Kulasiri D	Discovery of MRI biomarker for early Alzheimer's disease integrating regional atrophy and ventricular expansion	Aberathne I, Kulasiri D, Samarasinghe S, Zhang J	https://doi.org/10.36334/modsim2025.H01.aberathne	https://mssanz.org.au/modsim2025/files/H01.aberathne.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	K
Samarasinghe S	Discovery of MRI biomarker for early Alzheimer's disease integrating regional atrophy and ventricular expansion	Aberathne I, Kulasiri D, Samarasinghe S, Zhang J	https://doi.org/10.36334/modsim2025.H01.aberathne	https://mssanz.org.au/modsim2025/files/H01.aberathne.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	S
Zhang J	Discovery of MRI biomarker for early Alzheimer's disease integrating regional atrophy and ventricular expansion	Aberathne I, Kulasiri D, Samarasinghe S, Zhang J	https://doi.org/10.36334/modsim2025.H01.aberathne	https://mssanz.org.au/modsim2025/files/H01.aberathne.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	Z
Baker P	Do androids dream of workflow provenance? An intelligent interface to the Provena metadata system	Baker P, Shewan T, Yu J, Leighton B, Petridis R	https://doi.org/10.36334/modsim2025.F10.baker	https://mssanz.org.au/modsim2025/files/F10.baker.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	B
Leighton B	Do androids dream of workflow provenance? An intelligent interface to the Provena metadata system	Baker P, Shewan T, Yu J, Leighton B, Petridis R	https://doi.org/10.36334/modsim2025.F10.baker	https://mssanz.org.au/modsim2025/files/F10.baker.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	L
Petridis R	Do androids dream of workflow provenance? An intelligent interface to the Provena metadata system	Baker P, Shewan T, Yu J, Leighton B, Petridis R	https://doi.org/10.36334/modsim2025.F10.baker	https://mssanz.org.au/modsim2025/files/F10.baker.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	P
Shewan T	Do androids dream of workflow provenance? An intelligent interface to the Provena metadata system	Baker P, Shewan T, Yu J, Leighton B, Petridis R	https://doi.org/10.36334/modsim2025.F10.baker	https://mssanz.org.au/modsim2025/files/F10.baker.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	S
Yu J	Do androids dream of workflow provenance? An intelligent interface to the Provena metadata system	Baker P, Shewan T, Yu J, Leighton B, Petridis R	https://doi.org/10.36334/modsim2025.F10.baker	https://mssanz.org.au/modsim2025/files/F10.baker.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	Y
Chan F	Do offer contracts, platform yield in deal structure matter? Analyses of U.S. securities-based crowdfunding markets	Chua P, Chan F, Singh R	https://doi.org/10.36334/modsim2025.D05.chua	https://mssanz.org.au/modsim2025/files/D05.chua.pdf	D	Economics and finance	D5	Decision-making under uncertainty in economics and finance	C
Chua P	Do offer contracts, platform yield in deal structure matter? Analyses of U.S. securities-based crowdfunding markets	Chua P, Chan F, Singh R	https://doi.org/10.36334/modsim2025.D05.chua	https://mssanz.org.au/modsim2025/files/D05.chua.pdf	D	Economics and finance	D5	Decision-making under uncertainty in economics and finance	C
Singh R	Do offer contracts, platform yield in deal structure matter? Analyses of U.S. securities-based crowdfunding markets	Chua P, Chan F, Singh R	https://doi.org/10.36334/modsim2025.D05.chua	https://mssanz.org.au/modsim2025/files/D05.chua.pdf	D	Economics and finance	D5	Decision-making under uncertainty in economics and finance	S
Chang C-L	Do sustainability metrics matter? ESG effects on green bond premiums	Cheng W, Chang CL	https://doi.org/10.36334/modsim2025.D02.cheng	https://mssanz.org.au/modsim2025/files/D02.cheng.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	C
Cheng W-W	Do sustainability metrics matter? ESG effects on green bond premiums	Cheng W, Chang CL	https://doi.org/10.36334/modsim2025.D02.cheng	https://mssanz.org.au/modsim2025/files/D02.cheng.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	C
Leighton B	Does a water policy paper plus the Self-Thinking Data Manifest (STDM) equal an educational experience?	Penton DJ, Leighton B	https://doi.org/10.36334/modsim2025.F10.penton	https://mssanz.org.au/modsim2025/files/F10.penton.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	L
Penton DJ	Does a water policy paper plus the Self-Thinking Data Manifest (STDM) equal an educational experience?	Penton DJ, Leighton B	https://doi.org/10.36334/modsim2025.F10.penton	https://mssanz.org.au/modsim2025/files/F10.penton.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	P
Yap G	Does overtourism and climate risk nexus exist? A cross-country examination	Yap G	https://doi.org/10.36334/modsim2025.D02.yap	https://mssanz.org.au/modsim2025/files/D02.yap.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	Y
McCosker R	Drift analysis of a SLAM-based handheld laser scanner in gully environments	Rice E, McCosker R	https://doi.org/10.36334/modsim2025.L04.rice	https://mssanz.org.au/modsim2025/files/L04.rice.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	M
Rice E	Drift analysis of a SLAM-based handheld laser scanner in gully environments	Rice E, McCosker R	https://doi.org/10.36334/modsim2025.L04.rice	https://mssanz.org.au/modsim2025/files/L04.rice.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	R
Batelaan O	Drywells for the future: Managing urban stormwater in a changing climate	Pan L, Guan H, Myers B, Sapdhare H, Batelaan O	https://doi.org/10.36334/modsim2025.G06.pan	https://mssanz.org.au/modsim2025/files/G06.pan.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	B
Guan H	Drywells for the future: Managing urban stormwater in a changing climate	Pan L, Guan H, Myers B, Sapdhare H, Batelaan O	https://doi.org/10.36334/modsim2025.G06.pan	https://mssanz.org.au/modsim2025/files/G06.pan.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	G
Myers B	Drywells for the future: Managing urban stormwater in a changing climate	Pan L, Guan H, Myers B, Sapdhare H, Batelaan O	https://doi.org/10.36334/modsim2025.G06.pan	https://mssanz.org.au/modsim2025/files/G06.pan.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	M
Pan L (Liping)	Drywells for the future: Managing urban stormwater in a changing climate	Pan L, Guan H, Myers B, Sapdhare H, Batelaan O	https://doi.org/10.36334/modsim2025.G06.pan	https://mssanz.org.au/modsim2025/files/G06.pan.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	P
Sapdhare H	Drywells for the future: Managing urban stormwater in a changing climate	Pan L, Guan H, Myers B, Sapdhare H, Batelaan O	https://doi.org/10.36334/modsim2025.G06.pan	https://mssanz.org.au/modsim2025/files/G06.pan.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	S
Chen D (Dianyu)	Dynamic light extinction coefficient: A key to improve evapotranspiration partitioning for orchards in diverse climates	Chen D	https://doi.org/10.36334/modsim2025.B08.chend	https://mssanz.org.au/modsim2025/files/B08.chend.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Bai L	Dynamic short-term solar forecasting using historical data and machine learning under varying weather conditions	Bai L	https://doi.org/10.36334/modsim2025.E04.bai	https://mssanz.org.au/modsim2025/files/E04.bai.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	B
Mudaliar R	Dynamics and stability of milling behaviour under perturbation	Smith KCM, Schaerf T, Welch M, Mudaliar R	https://doi.org/10.36334/modsim2025.M02.smith	https://mssanz.org.au/modsim2025/files/M02.smith.pdf	M	Operations Research	M2	Simulation modelling and analysis	M
Schaerf T	Dynamics and stability of milling behaviour under perturbation	Smith KCM, Schaerf T, Welch M, Mudaliar R	https://doi.org/10.36334/modsim2025.M02.smith	https://mssanz.org.au/modsim2025/files/M02.smith.pdf	M	Operations Research	M2	Simulation modelling and analysis	S
Smith KCM	Dynamics and stability of milling behaviour under perturbation	Smith KCM, Schaerf T, Welch M, Mudaliar R	https://doi.org/10.36334/modsim2025.M02.smith	https://mssanz.org.au/modsim2025/files/M02.smith.pdf	M	Operations Research	M2	Simulation modelling and analysis	S
Welch M	Dynamics and stability of milling behaviour under perturbation	Smith KCM, Schaerf T, Welch M, Mudaliar R	https://doi.org/10.36334/modsim2025.M02.smith	https://mssanz.org.au/modsim2025/files/M02.smith.pdf	M	Operations Research	M2	Simulation modelling and analysis	W
Aeman H	Early forecasting of crop irrigation for sustainable water use with satellite data and machine learning	Aeman H, Hafeez M, Liaqat UW, Munir S	https://doi.org/10.36334/modsim2025.F11.aeman	https://mssanz.org.au/modsim2025/files/F11.aeman.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	A
Hafeez M (Mohsin)	Early forecasting of crop irrigation for sustainable water use with satellite data and machine learning	Aeman H, Hafeez M, Liaqat UW, Munir S	https://doi.org/10.36334/modsim2025.F11.aeman	https://mssanz.org.au/modsim2025/files/F11.aeman.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	H
Liaqat UW	Early forecasting of crop irrigation for sustainable water use with satellite data and machine learning	Aeman H, Hafeez M, Liaqat UW, Munir S	https://doi.org/10.36334/modsim2025.F11.aeman	https://mssanz.org.au/modsim2025/files/F11.aeman.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	L
Munir S	Early forecasting of crop irrigation for sustainable water use with satellite data and machine learning	Aeman H, Hafeez M, Liaqat UW, Munir S	https://doi.org/10.36334/modsim2025.F11.aeman	https://mssanz.org.au/modsim2025/files/F11.aeman.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	M
Bae S	Early mortality risk prediction in acutely injured service members using machine learning algorithms	Le TD, Cook A, Bae S, Gurney JM, Singh KP	https://doi.org/10.36334/modsim2025.H01.le	https://mssanz.org.au/modsim2025/files/H01.le.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	B
Cook A	Early mortality risk prediction in acutely injured service members using machine learning algorithms	Le TD, Cook A, Bae S, Gurney JM, Singh KP	https://doi.org/10.36334/modsim2025.H01.le	https://mssanz.org.au/modsim2025/files/H01.le.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	C
Gurney JM	Early mortality risk prediction in acutely injured service members using machine learning algorithms	Le TD, Cook A, Bae S, Gurney JM, Singh KP	https://doi.org/10.36334/modsim2025.H01.le	https://mssanz.org.au/modsim2025/files/H01.le.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	G
Le TD (Tuan)	Early mortality risk prediction in acutely injured service members using machine learning algorithms	Le TD, Cook A, Bae S, Gurney JM, Singh KP	https://doi.org/10.36334/modsim2025.H01.le	https://mssanz.org.au/modsim2025/files/H01.le.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	L
Singh KP	Early mortality risk prediction in acutely injured service members using machine learning algorithms	Le TD, Cook A, Bae S, Gurney JM, Singh KP	https://doi.org/10.36334/modsim2025.H01.le	https://mssanz.org.au/modsim2025/files/H01.le.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	S
Drielsma MJ	Ecological community modelling: climate-informed adaptation strategies for southeastern Australia’s biodiversity	Thonell JL, Groenewegen RJ, McNellie MJ, Sramek MH, Drielsma MJ, Love J, Manion G	https://doi.org/10.36334/modsim2025.F03.thonell	https://mssanz.org.au/modsim2025/files/F03.thonell.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	D
Groenewegen RJ	Ecological community modelling: climate-informed adaptation strategies for southeastern Australia’s biodiversity	Thonell JL, Groenewegen RJ, McNellie MJ, Sramek MH, Drielsma MJ, Love J, Manion G	https://doi.org/10.36334/modsim2025.F03.thonell	https://mssanz.org.au/modsim2025/files/F03.thonell.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	G
Love J	Ecological community modelling: climate-informed adaptation strategies for southeastern Australia’s biodiversity	Thonell JL, Groenewegen RJ, McNellie MJ, Sramek MH, Drielsma MJ, Love J, Manion G	https://doi.org/10.36334/modsim2025.F03.thonell	https://mssanz.org.au/modsim2025/files/F03.thonell.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	L
Manion G	Ecological community modelling: climate-informed adaptation strategies for southeastern Australia’s biodiversity	Thonell JL, Groenewegen RJ, McNellie MJ, Sramek MH, Drielsma MJ, Love J, Manion G	https://doi.org/10.36334/modsim2025.F03.thonell	https://mssanz.org.au/modsim2025/files/F03.thonell.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	M
McNellie MJ	Ecological community modelling: climate-informed adaptation strategies for southeastern Australia’s biodiversity	Thonell JL, Groenewegen RJ, McNellie MJ, Sramek MH, Drielsma MJ, Love J, Manion G	https://doi.org/10.36334/modsim2025.F03.thonell	https://mssanz.org.au/modsim2025/files/F03.thonell.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	M
Sramek MH	Ecological community modelling: climate-informed adaptation strategies for southeastern Australia’s biodiversity	Thonell JL, Groenewegen RJ, McNellie MJ, Sramek MH, Drielsma MJ, Love J, Manion G	https://doi.org/10.36334/modsim2025.F03.thonell	https://mssanz.org.au/modsim2025/files/F03.thonell.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	S
Thonell JL	Ecological community modelling: climate-informed adaptation strategies for southeastern Australia’s biodiversity	Thonell JL, Groenewegen RJ, McNellie MJ, Sramek MH, Drielsma MJ, Love J, Manion G	https://doi.org/10.36334/modsim2025.F03.thonell	https://mssanz.org.au/modsim2025/files/F03.thonell.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	T
Ellsworth D	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	E
Fleischer K	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	F
Goll D	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	G
Jiang M (Mingkai)	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	J
Knauer J	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	K
Lyu H (He)	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	L
Medlyn B	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	M
Olin S	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	O
Schufft K	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	S
Smith B	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	S
Wang B (Bin)	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	W
Wårlind D	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	W
Yang X (Xiaojuan)	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	Y
Yu L (Liu)	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	Y
Zaehle S	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	Z
Zhang H (Haicheng)	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	Z
Zhang X (Xueqian)	Ecological forecasting to support experimental design: the EucFACE experiment as a case study	Medlyn B, Jiang M, Wang B, Lyu H, Zhang X, Wårlind D, Knauer J, Fleischer K, Goll D, Olin S, Yang X, Yu L, Zaehle S, Zhang H, Schufft K, Ellsworth D, Smith B	https://doi.org/10.36334/modsim2025.F09.medlyn	https://mssanz.org.au/modsim2025/files/F09.medlyn.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	Z
Craig AT	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	C
Devine G	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	D
Furuya‑Kanamori L	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	F
Hickson RI	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	H
Johnson BJ	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	J
Kiani B	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	K
Lau CL	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	L
Moore K	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	M
Mordecai EA	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	M
Sartorius B	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	S
Skinner EB	Ecological suitability of Japanese encephalitis virus in Australia: a modelling analysis of vector-host transmission dynamics to potential spillover in humans	Skinner EB, Sartorius B, Furuya‑Kanamori L, Craig AT, Kiani B, Johnson BJ, Moore K, Hickson RI, Mordecai EA, Devine G, Lau CL	https://doi.org/10.36334/modsim2025.H04.skinner	https://mssanz.org.au/modsim2025/files/H04.skinner.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	S
Gao S (Sicong)	Ecological thresholds of concern in the Murray-Darling Basin	Stratford D, Gao S, Thomas H, Linke S, Merrin L, Pritchard J, King D	https://doi.org/10.36334/modsim2025.F03.stratford	https://mssanz.org.au/modsim2025/files/F03.stratford.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	G
King D	Ecological thresholds of concern in the Murray-Darling Basin	Stratford D, Gao S, Thomas H, Linke S, Merrin L, Pritchard J, King D	https://doi.org/10.36334/modsim2025.F03.stratford	https://mssanz.org.au/modsim2025/files/F03.stratford.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	K
Linke S	Ecological thresholds of concern in the Murray-Darling Basin	Stratford D, Gao S, Thomas H, Linke S, Merrin L, Pritchard J, King D	https://doi.org/10.36334/modsim2025.F03.stratford	https://mssanz.org.au/modsim2025/files/F03.stratford.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	L
Merrin L	Ecological thresholds of concern in the Murray-Darling Basin	Stratford D, Gao S, Thomas H, Linke S, Merrin L, Pritchard J, King D	https://doi.org/10.36334/modsim2025.F03.stratford	https://mssanz.org.au/modsim2025/files/F03.stratford.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	M
Pritchard J	Ecological thresholds of concern in the Murray-Darling Basin	Stratford D, Gao S, Thomas H, Linke S, Merrin L, Pritchard J, King D	https://doi.org/10.36334/modsim2025.F03.stratford	https://mssanz.org.au/modsim2025/files/F03.stratford.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	P
Stratford D	Ecological thresholds of concern in the Murray-Darling Basin	Stratford D, Gao S, Thomas H, Linke S, Merrin L, Pritchard J, King D	https://doi.org/10.36334/modsim2025.F03.stratford	https://mssanz.org.au/modsim2025/files/F03.stratford.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	S
Thomas H	Ecological thresholds of concern in the Murray-Darling Basin	Stratford D, Gao S, Thomas H, Linke S, Merrin L, Pritchard J, King D	https://doi.org/10.36334/modsim2025.F03.stratford	https://mssanz.org.au/modsim2025/files/F03.stratford.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	T
Sharma V	Economic modelling of rooftop photovoltaic and battery storage for affordable low-emission energy transitions	Sharma V	https://doi.org/10.36334/modsim2025.E03.sharma	https://mssanz.org.au/modsim2025/files/E03.sharma.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	S
Andrys J	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	A
Barnes K	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	B
Choudhury D	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	C
di Virgilio G	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	d
Ji F (Fei)	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	J
Kala J	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	K
Lam SH (Sean)	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	L
Li Y (Yue)	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	L
Lingala J	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	L
Marinelli M	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	M
Mughal MO	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	M
Oliver J	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	O
Riley ML	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	R
Rocha C	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	R
Tam E (Eugene)	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	T
Tran K (Kim)	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	T
Wamahiu K	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	W
White S	Efficient high-resolution climate simulations for Western Australia: optimising domain design and resource allocation	Kala J, Andrys J, Lam SH, Mughal MO, Lingala J, Wamahiu K, Barnes K, Tran K, Oliver J, di Virgilio G, White S, Ji F, Tam E, Choudhury D, Li Y, Rocha C, Riley ML, Marinelli M	https://doi.org/10.36334/modsim2025.G03.kala	https://mssanz.org.au/modsim2025/files/G03.kala.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	W
Adams MP	Efficient sampling from a multivariate normal distribution subject to linear equality and inequality constraints	Adams MP, Monsalve-Bravo GM, Dowdell LG, Sisson SA, Drovandi C	https://doi.org/10.36334/modsim2025.A01.adams	https://mssanz.org.au/modsim2025/files/A01.adams.pdf	A	Applied and computational mathematics	A1	Applied Probability	A
Dowdell LG	Efficient sampling from a multivariate normal distribution subject to linear equality and inequality constraints	Adams MP, Monsalve-Bravo GM, Dowdell LG, Sisson SA, Drovandi C	https://doi.org/10.36334/modsim2025.A01.adams	https://mssanz.org.au/modsim2025/files/A01.adams.pdf	A	Applied and computational mathematics	A1	Applied Probability	D
Drovandi C	Efficient sampling from a multivariate normal distribution subject to linear equality and inequality constraints	Adams MP, Monsalve-Bravo GM, Dowdell LG, Sisson SA, Drovandi C	https://doi.org/10.36334/modsim2025.A01.adams	https://mssanz.org.au/modsim2025/files/A01.adams.pdf	A	Applied and computational mathematics	A1	Applied Probability	D
Monsalve-Bravo GM	Efficient sampling from a multivariate normal distribution subject to linear equality and inequality constraints	Adams MP, Monsalve-Bravo GM, Dowdell LG, Sisson SA, Drovandi C	https://doi.org/10.36334/modsim2025.A01.adams	https://mssanz.org.au/modsim2025/files/A01.adams.pdf	A	Applied and computational mathematics	A1	Applied Probability	M
Sisson SA	Efficient sampling from a multivariate normal distribution subject to linear equality and inequality constraints	Adams MP, Monsalve-Bravo GM, Dowdell LG, Sisson SA, Drovandi C	https://doi.org/10.36334/modsim2025.A01.adams	https://mssanz.org.au/modsim2025/files/A01.adams.pdf	A	Applied and computational mathematics	A1	Applied Probability	S
Ashman P	Electrification of cement clinker production: A case study on variable renewable energy integration	Leow CY, Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Coventry J, Pye J	https://doi.org/10.36334/modsim2025.E02.leow	https://mssanz.org.au/modsim2025/files/E02.leow.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	A
Benz T	Electrification of cement clinker production: A case study on variable renewable energy integration	Leow CY, Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Coventry J, Pye J	https://doi.org/10.36334/modsim2025.E02.leow	https://mssanz.org.au/modsim2025/files/E02.leow.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	B
Coventry J	Electrification of cement clinker production: A case study on variable renewable energy integration	Leow CY, Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Coventry J, Pye J	https://doi.org/10.36334/modsim2025.E02.leow	https://mssanz.org.au/modsim2025/files/E02.leow.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	C
Fontalvo A	Electrification of cement clinker production: A case study on variable renewable energy integration	Leow CY, Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Coventry J, Pye J	https://doi.org/10.36334/modsim2025.E02.leow	https://mssanz.org.au/modsim2025/files/E02.leow.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	F
Leow CY	Electrification of cement clinker production: A case study on variable renewable energy integration	Leow CY, Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Coventry J, Pye J	https://doi.org/10.36334/modsim2025.E02.leow	https://mssanz.org.au/modsim2025/files/E02.leow.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	L
Pye J	Electrification of cement clinker production: A case study on variable renewable energy integration	Leow CY, Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Coventry J, Pye J	https://doi.org/10.36334/modsim2025.E02.leow	https://mssanz.org.au/modsim2025/files/E02.leow.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	P
Saw W	Electrification of cement clinker production: A case study on variable renewable energy integration	Leow CY, Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Coventry J, Pye J	https://doi.org/10.36334/modsim2025.E02.leow	https://mssanz.org.au/modsim2025/files/E02.leow.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	S
Wang Y (Ye)	Electrification of cement clinker production: A case study on variable renewable energy integration	Leow CY, Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Coventry J, Pye J	https://doi.org/10.36334/modsim2025.E02.leow	https://mssanz.org.au/modsim2025/files/E02.leow.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	W
Hall A (Andy)	Embedding public health in environmental stewardship: lessons from participatory landscape modelling in central New South Wales, Australia	Onyango E, Szetey K, Hall A	https://doi.org/10.36334/modsim2025.F05.onyango	https://mssanz.org.au/modsim2025/files/F05.onyango.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	H
Onyango E	Embedding public health in environmental stewardship: lessons from participatory landscape modelling in central New South Wales, Australia	Onyango E, Szetey K, Hall A	https://doi.org/10.36334/modsim2025.F05.onyango	https://mssanz.org.au/modsim2025/files/F05.onyango.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	O
Szetey K	Embedding public health in environmental stewardship: lessons from participatory landscape modelling in central New South Wales, Australia	Onyango E, Szetey K, Hall A	https://doi.org/10.36334/modsim2025.F05.onyango	https://mssanz.org.au/modsim2025/files/F05.onyango.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	S
Dandy GC	Embracing scenario thinking in water resources management under deep uncertainty	Wu W, Eamen L, Dandy GC, Maier HR, Razavi S, Kwakkel J, Huang J, Kuczera G	https://doi.org/10.36334/modsim2025.J08.wu	https://mssanz.org.au/modsim2025/files/J08.wu.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	D
Eamen L	Embracing scenario thinking in water resources management under deep uncertainty	Wu W, Eamen L, Dandy GC, Maier HR, Razavi S, Kwakkel J, Huang J, Kuczera G	https://doi.org/10.36334/modsim2025.J08.wu	https://mssanz.org.au/modsim2025/files/J08.wu.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	E
Huang J (Jiajia)	Embracing scenario thinking in water resources management under deep uncertainty	Wu W, Eamen L, Dandy GC, Maier HR, Razavi S, Kwakkel J, Huang J, Kuczera G	https://doi.org/10.36334/modsim2025.J08.wu	https://mssanz.org.au/modsim2025/files/J08.wu.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	H
Kuczera G	Embracing scenario thinking in water resources management under deep uncertainty	Wu W, Eamen L, Dandy GC, Maier HR, Razavi S, Kwakkel J, Huang J, Kuczera G	https://doi.org/10.36334/modsim2025.J08.wu	https://mssanz.org.au/modsim2025/files/J08.wu.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	K
Kwakkel J	Embracing scenario thinking in water resources management under deep uncertainty	Wu W, Eamen L, Dandy GC, Maier HR, Razavi S, Kwakkel J, Huang J, Kuczera G	https://doi.org/10.36334/modsim2025.J08.wu	https://mssanz.org.au/modsim2025/files/J08.wu.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	K
Maier HR	Embracing scenario thinking in water resources management under deep uncertainty	Wu W, Eamen L, Dandy GC, Maier HR, Razavi S, Kwakkel J, Huang J, Kuczera G	https://doi.org/10.36334/modsim2025.J08.wu	https://mssanz.org.au/modsim2025/files/J08.wu.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	M
Razavi S	Embracing scenario thinking in water resources management under deep uncertainty	Wu W, Eamen L, Dandy GC, Maier HR, Razavi S, Kwakkel J, Huang J, Kuczera G	https://doi.org/10.36334/modsim2025.J08.wu	https://mssanz.org.au/modsim2025/files/J08.wu.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	R
Wu W (Wenyan)	Embracing scenario thinking in water resources management under deep uncertainty	Wu W, Eamen L, Dandy GC, Maier HR, Razavi S, Kwakkel J, Huang J, Kuczera G	https://doi.org/10.36334/modsim2025.J08.wu	https://mssanz.org.au/modsim2025/files/J08.wu.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	W
Bonu MSK	Employing image processing and anomaly detection for faulty solar cells	Bonu MSK, Ghahremani A, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.E04.bonu	https://mssanz.org.au/modsim2025/files/E04.bonu.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	B
Ghahremani A	Employing image processing and anomaly detection for faulty solar cells	Bonu MSK, Ghahremani A, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.E04.bonu	https://mssanz.org.au/modsim2025/files/E04.bonu.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	G
Ghasemi M	Employing image processing and anomaly detection for faulty solar cells	Bonu MSK, Ghahremani A, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.E04.bonu	https://mssanz.org.au/modsim2025/files/E04.bonu.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	G
Nazari A	Employing image processing and anomaly detection for faulty solar cells	Bonu MSK, Ghahremani A, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.E04.bonu	https://mssanz.org.au/modsim2025/files/E04.bonu.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	N
Thiruvady D	Employing image processing and anomaly detection for faulty solar cells	Bonu MSK, Ghahremani A, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.E04.bonu	https://mssanz.org.au/modsim2025/files/E04.bonu.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	T
van der Meer R	Employing image processing and anomaly detection for faulty solar cells	Bonu MSK, Ghahremani A, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.E04.bonu	https://mssanz.org.au/modsim2025/files/E04.bonu.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	v
Cheng L	Enhanced model parameter transferability to ungauged catchment through regionally constrained calibration	Wang X, Zheng H, Shao Q, Cheng L, Liu P, Zhang L	https://doi.org/10.36334/modsim2025.J10.wang	https://mssanz.org.au/modsim2025/files/J10.wang.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	C
Liu P	Enhanced model parameter transferability to ungauged catchment through regionally constrained calibration	Wang X, Zheng H, Shao Q, Cheng L, Liu P, Zhang L	https://doi.org/10.36334/modsim2025.J10.wang	https://mssanz.org.au/modsim2025/files/J10.wang.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	L
Shao Q	Enhanced model parameter transferability to ungauged catchment through regionally constrained calibration	Wang X, Zheng H, Shao Q, Cheng L, Liu P, Zhang L	https://doi.org/10.36334/modsim2025.J10.wang	https://mssanz.org.au/modsim2025/files/J10.wang.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	S
Wang X	Enhanced model parameter transferability to ungauged catchment through regionally constrained calibration	Wang X, Zheng H, Shao Q, Cheng L, Liu P, Zhang L	https://doi.org/10.36334/modsim2025.J10.wang	https://mssanz.org.au/modsim2025/files/J10.wang.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	W
Zhang L (Liangjing)	Enhanced model parameter transferability to ungauged catchment through regionally constrained calibration	Wang X, Zheng H, Shao Q, Cheng L, Liu P, Zhang L	https://doi.org/10.36334/modsim2025.J10.wang	https://mssanz.org.au/modsim2025/files/J10.wang.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	Z
Zheng H (Hongzing)	Enhanced model parameter transferability to ungauged catchment through regionally constrained calibration	Wang X, Zheng H, Shao Q, Cheng L, Liu P, Zhang L	https://doi.org/10.36334/modsim2025.J10.wang	https://mssanz.org.au/modsim2025/files/J10.wang.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	Z
Curtis M	Enhanced tools necessary for managing a high distributed solar photovoltaic electricity system	Panicciari E, Curtis M, Grantham A	https://doi.org/10.36334/modsim2025.E01.panicciari	https://mssanz.org.au/modsim2025/files/E01.panicciari.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	C
Grantham A	Enhanced tools necessary for managing a high distributed solar photovoltaic electricity system	Panicciari E, Curtis M, Grantham A	https://doi.org/10.36334/modsim2025.E01.panicciari	https://mssanz.org.au/modsim2025/files/E01.panicciari.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	G
Panicciari E	Enhanced tools necessary for managing a high distributed solar photovoltaic electricity system	Panicciari E, Curtis M, Grantham A	https://doi.org/10.36334/modsim2025.E01.panicciari	https://mssanz.org.au/modsim2025/files/E01.panicciari.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	P
Brown A (Alice)	Enhancing environmental water management in Source: decoupling spell identification from ordering at environmental flow nodes	Sapkota M, Brown A, Roberts T, Davidson A, Khan A, Tarquinio F	https://doi.org/10.36334/modsim2025.J02.sapkota	https://mssanz.org.au/modsim2025/files/J02.sapkota.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Davidson A	Enhancing environmental water management in Source: decoupling spell identification from ordering at environmental flow nodes	Sapkota M, Brown A, Roberts T, Davidson A, Khan A, Tarquinio F	https://doi.org/10.36334/modsim2025.J02.sapkota	https://mssanz.org.au/modsim2025/files/J02.sapkota.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Khan A	Enhancing environmental water management in Source: decoupling spell identification from ordering at environmental flow nodes	Sapkota M, Brown A, Roberts T, Davidson A, Khan A, Tarquinio F	https://doi.org/10.36334/modsim2025.J02.sapkota	https://mssanz.org.au/modsim2025/files/J02.sapkota.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	K
Roberts T	Enhancing environmental water management in Source: decoupling spell identification from ordering at environmental flow nodes	Sapkota M, Brown A, Roberts T, Davidson A, Khan A, Tarquinio F	https://doi.org/10.36334/modsim2025.J02.sapkota	https://mssanz.org.au/modsim2025/files/J02.sapkota.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Sapkota M	Enhancing environmental water management in Source: decoupling spell identification from ordering at environmental flow nodes	Sapkota M, Brown A, Roberts T, Davidson A, Khan A, Tarquinio F	https://doi.org/10.36334/modsim2025.J02.sapkota	https://mssanz.org.au/modsim2025/files/J02.sapkota.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	S
Tarquinio F	Enhancing environmental water management in Source: decoupling spell identification from ordering at environmental flow nodes	Sapkota M, Brown A, Roberts T, Davidson A, Khan A, Tarquinio F	https://doi.org/10.36334/modsim2025.J02.sapkota	https://mssanz.org.au/modsim2025/files/J02.sapkota.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	T
Chen C	Enhancing near-surface soil water and temperature simulation through fine-scale field observations and mechanistic modelling	Luo T, Verburg K, Micin S, Sheng W, Chen C, Rich S, Rebetzke G, Fletcher A	https://doi.org/10.36334/modsim2025.B03.luo	https://mssanz.org.au/modsim2025/files/B03.luo.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	C
Fletcher A	Enhancing near-surface soil water and temperature simulation through fine-scale field observations and mechanistic modelling	Luo T, Verburg K, Micin S, Sheng W, Chen C, Rich S, Rebetzke G, Fletcher A	https://doi.org/10.36334/modsim2025.B03.luo	https://mssanz.org.au/modsim2025/files/B03.luo.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	F
Luo T	Enhancing near-surface soil water and temperature simulation through fine-scale field observations and mechanistic modelling	Luo T, Verburg K, Micin S, Sheng W, Chen C, Rich S, Rebetzke G, Fletcher A	https://doi.org/10.36334/modsim2025.B03.luo	https://mssanz.org.au/modsim2025/files/B03.luo.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	L
Micin S	Enhancing near-surface soil water and temperature simulation through fine-scale field observations and mechanistic modelling	Luo T, Verburg K, Micin S, Sheng W, Chen C, Rich S, Rebetzke G, Fletcher A	https://doi.org/10.36334/modsim2025.B03.luo	https://mssanz.org.au/modsim2025/files/B03.luo.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	M
Rebetzke G	Enhancing near-surface soil water and temperature simulation through fine-scale field observations and mechanistic modelling	Luo T, Verburg K, Micin S, Sheng W, Chen C, Rich S, Rebetzke G, Fletcher A	https://doi.org/10.36334/modsim2025.B03.luo	https://mssanz.org.au/modsim2025/files/B03.luo.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	R
Rich S	Enhancing near-surface soil water and temperature simulation through fine-scale field observations and mechanistic modelling	Luo T, Verburg K, Micin S, Sheng W, Chen C, Rich S, Rebetzke G, Fletcher A	https://doi.org/10.36334/modsim2025.B03.luo	https://mssanz.org.au/modsim2025/files/B03.luo.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	R
Sheng W	Enhancing near-surface soil water and temperature simulation through fine-scale field observations and mechanistic modelling	Luo T, Verburg K, Micin S, Sheng W, Chen C, Rich S, Rebetzke G, Fletcher A	https://doi.org/10.36334/modsim2025.B03.luo	https://mssanz.org.au/modsim2025/files/B03.luo.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	S
Verburg K	Enhancing near-surface soil water and temperature simulation through fine-scale field observations and mechanistic modelling	Luo T, Verburg K, Micin S, Sheng W, Chen C, Rich S, Rebetzke G, Fletcher A	https://doi.org/10.36334/modsim2025.B03.luo	https://mssanz.org.au/modsim2025/files/B03.luo.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	V
Abellan E	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	A
Canvin J	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	C
Esler R	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	E
Johnson R	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	J
Kelly A	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	K
McIntyre K	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	M
Mentiplay D	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	M
Owen B	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	O
Trotta B	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	T
Whelan J	Enhancing NWP post-processing with IMPROVER	Owen B, Abellan E, Canvin J, Esler R, Johnson R, Kelly A, McIntyre K, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owen	https://mssanz.org.au/modsim2025/files/K02.owen.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	W
Wu L (Lingyun)	Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on Probabilistic Double Machine Learning method	Zhan S, Ye A, Wu L, Zhao C	https://doi.org/10.36334/modsim2025.K08.zhan	https://mssanz.org.au/modsim2025/files/K08.zhan.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	W
Ye A (Aizhong)	Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on Probabilistic Double Machine Learning method	Zhan S, Ye A, Wu L, Zhao C	https://doi.org/10.36334/modsim2025.K08.zhan	https://mssanz.org.au/modsim2025/files/K08.zhan.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Y
Zhan S (Shengsheng)	Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on Probabilistic Double Machine Learning method	Zhan S, Ye A, Wu L, Zhao C	https://doi.org/10.36334/modsim2025.K08.zhan	https://mssanz.org.au/modsim2025/files/K08.zhan.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Zhao C (Chenguang)	Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on Probabilistic Double Machine Learning method	Zhan S, Ye A, Wu L, Zhao C	https://doi.org/10.36334/modsim2025.K08.zhan	https://mssanz.org.au/modsim2025/files/K08.zhan.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Fu C (Chenhao)	Estimating evapotranspiration from deciduous broadleaf forests by considering the effect of foliar litter	Fu C	https://doi.org/10.36334/modsim2025.J11.fu	https://mssanz.org.au/modsim2025/files/J11.fu.pdf	J	Water resources	J11	Ecohydrological Modelling	F
Chen D (Deli)	Estimating fractions of N₂O emissions from nitrification and denitrification using data assimilation	Pan B, Huang Y, Xia L, Luo Y, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.A04.pan	https://mssanz.org.au/modsim2025/files/A04.pan.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	C
Huang Y (Yuanyuan)	Estimating fractions of N₂O emissions from nitrification and denitrification using data assimilation	Pan B, Huang Y, Xia L, Luo Y, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.A04.pan	https://mssanz.org.au/modsim2025/files/A04.pan.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	H
Lam SK (Shu Kee)	Estimating fractions of N₂O emissions from nitrification and denitrification using data assimilation	Pan B, Huang Y, Xia L, Luo Y, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.A04.pan	https://mssanz.org.au/modsim2025/files/A04.pan.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	L
Luo Y (Yiqi)	Estimating fractions of N₂O emissions from nitrification and denitrification using data assimilation	Pan B, Huang Y, Xia L, Luo Y, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.A04.pan	https://mssanz.org.au/modsim2025/files/A04.pan.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	L
Pan B (Baobao)	Estimating fractions of N₂O emissions from nitrification and denitrification using data assimilation	Pan B, Huang Y, Xia L, Luo Y, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.A04.pan	https://mssanz.org.au/modsim2025/files/A04.pan.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	P
Xia L (Longlong)	Estimating fractions of N₂O emissions from nitrification and denitrification using data assimilation	Pan B, Huang Y, Xia L, Luo Y, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.A04.pan	https://mssanz.org.au/modsim2025/files/A04.pan.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	X
Canadell J	Estimating land-to-coast ocean fluxes of dissolved organic carbon using satellite observations and catchment models (INVITED SPEAKER)	Unnithan SLK, Cherukuru N, Lehmann E, Ingleton T, Canadell J	https://doi.org/10.36334/modsim2025.L01.unnithan	https://mssanz.org.au/modsim2025/files/L01.unnithan.pdf	L	Water quality	L1	Catchment Water Quality Modelling	C
Cherukuru N	Estimating land-to-coast ocean fluxes of dissolved organic carbon using satellite observations and catchment models (INVITED SPEAKER)	Unnithan SLK, Cherukuru N, Lehmann E, Ingleton T, Canadell J	https://doi.org/10.36334/modsim2025.L01.unnithan	https://mssanz.org.au/modsim2025/files/L01.unnithan.pdf	L	Water quality	L1	Catchment Water Quality Modelling	C
Ingleton T	Estimating land-to-coast ocean fluxes of dissolved organic carbon using satellite observations and catchment models (INVITED SPEAKER)	Unnithan SLK, Cherukuru N, Lehmann E, Ingleton T, Canadell J	https://doi.org/10.36334/modsim2025.L01.unnithan	https://mssanz.org.au/modsim2025/files/L01.unnithan.pdf	L	Water quality	L1	Catchment Water Quality Modelling	I
Lehmann E	Estimating land-to-coast ocean fluxes of dissolved organic carbon using satellite observations and catchment models (INVITED SPEAKER)	Unnithan SLK, Cherukuru N, Lehmann E, Ingleton T, Canadell J	https://doi.org/10.36334/modsim2025.L01.unnithan	https://mssanz.org.au/modsim2025/files/L01.unnithan.pdf	L	Water quality	L1	Catchment Water Quality Modelling	L
Unnithan SLK	Estimating land-to-coast ocean fluxes of dissolved organic carbon using satellite observations and catchment models (INVITED SPEAKER)	Unnithan SLK, Cherukuru N, Lehmann E, Ingleton T, Canadell J	https://doi.org/10.36334/modsim2025.L01.unnithan	https://mssanz.org.au/modsim2025/files/L01.unnithan.pdf	L	Water quality	L1	Catchment Water Quality Modelling	U
Chan F	Estimating sparse variance-covariance using shrinkage methods: applications to error components models	Chan F, Chariag R	https://doi.org/10.36334/modsim2025.D01.chanf2	https://mssanz.org.au/modsim2025/files/D01.chanf2.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	C
Chariag R	Estimating sparse variance-covariance using shrinkage methods: applications to error components models	Chan F, Chariag R	https://doi.org/10.36334/modsim2025.D01.chanf2	https://mssanz.org.au/modsim2025/files/D01.chanf2.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	C
Gibbs MS	Estimating streamflow discharge using the ‘wisdom of the crowd’	Petheram C, Gibbs M, Hughes J, Seo L	https://doi.org/10.36334/modsim2025.J03.petheram	https://mssanz.org.au/modsim2025/files/J03.petheram.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	G
Hughes J	Estimating streamflow discharge using the ‘wisdom of the crowd’	Petheram C, Gibbs M, Hughes J, Seo L	https://doi.org/10.36334/modsim2025.J03.petheram	https://mssanz.org.au/modsim2025/files/J03.petheram.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	H
Petheram C	Estimating streamflow discharge using the ‘wisdom of the crowd’	Petheram C, Gibbs M, Hughes J, Seo L	https://doi.org/10.36334/modsim2025.J03.petheram	https://mssanz.org.au/modsim2025/files/J03.petheram.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	P
Seo L	Estimating streamflow discharge using the ‘wisdom of the crowd’	Petheram C, Gibbs M, Hughes J, Seo L	https://doi.org/10.36334/modsim2025.J03.petheram	https://mssanz.org.au/modsim2025/files/J03.petheram.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	S
Guo X	Estimating surface water fractions for enhanced flood monitoring	Guo X, Wang QJ, Western A, Ryu D, Sharples W	https://doi.org/10.36334/modsim2025.G09.guo	https://mssanz.org.au/modsim2025/files/G09.guo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	G
Ryu D	Estimating surface water fractions for enhanced flood monitoring	Guo X, Wang QJ, Western A, Ryu D, Sharples W	https://doi.org/10.36334/modsim2025.G09.guo	https://mssanz.org.au/modsim2025/files/G09.guo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	R
Sharples W	Estimating surface water fractions for enhanced flood monitoring	Guo X, Wang QJ, Western A, Ryu D, Sharples W	https://doi.org/10.36334/modsim2025.G09.guo	https://mssanz.org.au/modsim2025/files/G09.guo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	S
Wang QJ	Estimating surface water fractions for enhanced flood monitoring	Guo X, Wang QJ, Western A, Ryu D, Sharples W	https://doi.org/10.36334/modsim2025.G09.guo	https://mssanz.org.au/modsim2025/files/G09.guo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	W
Western A	Estimating surface water fractions for enhanced flood monitoring	Guo X, Wang QJ, Western A, Ryu D, Sharples W	https://doi.org/10.36334/modsim2025.G09.guo	https://mssanz.org.au/modsim2025/files/G09.guo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	W
Hou W	Estimation of land evapotranspiration over China based on Fengyun satellite	Hou W, Zhang Y	https://doi.org/10.36334/modsim2025.J10.hou	https://mssanz.org.au/modsim2025/files/J10.hou.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	H
Zhang Y (Yongqiang)	Estimation of land evapotranspiration over China based on Fengyun satellite	Hou W, Zhang Y	https://doi.org/10.36334/modsim2025.J10.hou	https://mssanz.org.au/modsim2025/files/J10.hou.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	Z
Jiang Z (Ze)	Evaluating stochastic weather generators for simulating hydrological extremes	Johnson F, Jiang Z, Stephens C	https://doi.org/10.36334/modsim2025.J05.johnson	https://mssanz.org.au/modsim2025/files/J05.johnson.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	J
Johnson F	Evaluating stochastic weather generators for simulating hydrological extremes	Johnson F, Jiang Z, Stephens C	https://doi.org/10.36334/modsim2025.J05.johnson	https://mssanz.org.au/modsim2025/files/J05.johnson.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	J
Stephens C	Evaluating stochastic weather generators for simulating hydrological extremes	Johnson F, Jiang Z, Stephens C	https://doi.org/10.36334/modsim2025.J05.johnson	https://mssanz.org.au/modsim2025/files/J05.johnson.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	S
Brentnall S	Evaluating strategies to improve patient and staff outcomes in a distributed paediatric ICU: A simulation study at the Children’s Hospital at Westmead	Currie DJ, Brentnall S, Festa M	https://doi.org/10.36334/modsim2025.H02.currie	https://mssanz.org.au/modsim2025/files/H02.currie.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	B
Currie DJ	Evaluating strategies to improve patient and staff outcomes in a distributed paediatric ICU: A simulation study at the Children’s Hospital at Westmead	Currie DJ, Brentnall S, Festa M	https://doi.org/10.36334/modsim2025.H02.currie	https://mssanz.org.au/modsim2025/files/H02.currie.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	C
Festa M	Evaluating strategies to improve patient and staff outcomes in a distributed paediatric ICU: A simulation study at the Children’s Hospital at Westmead	Currie DJ, Brentnall S, Festa M	https://doi.org/10.36334/modsim2025.H02.currie	https://mssanz.org.au/modsim2025/files/H02.currie.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	F
Cuddy SM	Evaluating the ecological outcomes of environmental watering: the role of the counterfactual	Cuddy SM	https://doi.org/10.36334/modsim2025.F04.cuddy	https://mssanz.org.au/modsim2025/files/F04.cuddy.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	C
Robertson‑Wall MG	Evaluating whether the A-VIX improves volatility forecasts in the Australian equity market	Robertson‑Wall M G	https://doi.org/10.36334/modsim2025.D06.robertsonwall	https://mssanz.org.au/modsim2025/files/D06.robertsonwall.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	R
Han SC	Evaluating WRF-Hydro model reliability for water resource assessment in a semi-arid catchment of Australia	Wickramarachchi M, Khaki M, Yeo IY, Han SC	https://doi.org/10.36334/modsim2025.J10.wickramarachchi	https://mssanz.org.au/modsim2025/files/J10.wickramarachchi.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	H
Khaki M	Evaluating WRF-Hydro model reliability for water resource assessment in a semi-arid catchment of Australia	Wickramarachchi M, Khaki M, Yeo IY, Han SC	https://doi.org/10.36334/modsim2025.J10.wickramarachchi	https://mssanz.org.au/modsim2025/files/J10.wickramarachchi.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	K
Wickramarachchi M	Evaluating WRF-Hydro model reliability for water resource assessment in a semi-arid catchment of Australia	Wickramarachchi M, Khaki M, Yeo IY, Han SC	https://doi.org/10.36334/modsim2025.J10.wickramarachchi	https://mssanz.org.au/modsim2025/files/J10.wickramarachchi.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	W
Yeo IY	Evaluating WRF-Hydro model reliability for water resource assessment in a semi-arid catchment of Australia	Wickramarachchi M, Khaki M, Yeo IY, Han SC	https://doi.org/10.36334/modsim2025.J10.wickramarachchi	https://mssanz.org.au/modsim2025/files/J10.wickramarachchi.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	Y
Alam MMT	Evaluation of empirical and radiative transfer modelling for estimating biochemistry using remote sensing data	Simic Milas A, Alam MMT	https://doi.org/10.36334/modsim2025.F11.simicmilas	https://mssanz.org.au/modsim2025/files/F11.simicmilas.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	A
Simic Milas A	Evaluation of empirical and radiative transfer modelling for estimating biochemistry using remote sensing data	Simic Milas A, Alam MMT	https://doi.org/10.36334/modsim2025.F11.simicmilas	https://mssanz.org.au/modsim2025/files/F11.simicmilas.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	S
Yang X	Event-based erosion modelling - A case study of ex-tropical cyclone Alfred	Yang X, Zhu EQ	https://doi.org/10.36334/modsim2025.G09.yang	https://mssanz.org.au/modsim2025/files/G09.yang.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	Y
Zhu EQ (Esther Qinggaozi)	Event-based erosion modelling - A case study of ex-tropical cyclone Alfred	Yang X, Zhu EQ	https://doi.org/10.36334/modsim2025.G09.yang	https://mssanz.org.au/modsim2025/files/G09.yang.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	Z
Sun Z	Exploring geographical modal and its implication to support geographical integration	Sun Z	https://doi.org/10.36334/modsim2025.C04.sun	https://mssanz.org.au/modsim2025/files/C04.sun.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	S
Xu Z (Zhenwu)	Extended global evapotranspiration and gross primary production dataset for 1981–2024	Xu Z, Zhang Y	https://doi.org/10.36334/modsim2025.K03.xu	https://mssanz.org.au/modsim2025/files/K03.xu.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	X
Zhang Y (Yongqiang)	Extended global evapotranspiration and gross primary production dataset for 1981–2024	Xu Z, Zhang Y	https://doi.org/10.36334/modsim2025.K03.xu	https://mssanz.org.au/modsim2025/files/K03.xu.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	Z
Elliott AH	Extended uncertainty analysis for the simple catchment load model SPARROW and implications for water management	Elliott AH, Semadeni-Davies AF	https://doi.org/10.36334/modsim2025.L01.elliott	https://mssanz.org.au/modsim2025/files/L01.elliott.pdf	L	Water quality	L1	Catchment Water Quality Modelling	E
Semadeni-Davies AF	Extended uncertainty analysis for the simple catchment load model SPARROW and implications for water management	Elliott AH, Semadeni-Davies AF	https://doi.org/10.36334/modsim2025.L01.elliott	https://mssanz.org.au/modsim2025/files/L01.elliott.pdf	L	Water quality	L1	Catchment Water Quality Modelling	S
Harley MD	Extreme coastal wave events: assessing global cumulative energy trends and wind forcing resolution impacts	Villalba IB, Harley MD, Sharma A	https://doi.org/10.36334/modsim2025.G04.villalba	https://mssanz.org.au/modsim2025/files/G04.villalba.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	H
Sharma A	Extreme coastal wave events: assessing global cumulative energy trends and wind forcing resolution impacts	Villalba IB, Harley MD, Sharma A	https://doi.org/10.36334/modsim2025.G04.villalba	https://mssanz.org.au/modsim2025/files/G04.villalba.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	S
Villalba IB	Extreme coastal wave events: assessing global cumulative energy trends and wind forcing resolution impacts	Villalba IB, Harley MD, Sharma A	https://doi.org/10.36334/modsim2025.G04.villalba	https://mssanz.org.au/modsim2025/files/G04.villalba.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	V
May RJ	Facilitating techno-economic evaluation of green hydrogen production pathways through accessible optimisation	May RJ, Thomson ME	https://doi.org/10.36334/modsim2025.E02.may	https://mssanz.org.au/modsim2025/files/E02.may.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	M
Thomson ME	Facilitating techno-economic evaluation of green hydrogen production pathways through accessible optimisation	May RJ, Thomson ME	https://doi.org/10.36334/modsim2025.E02.may	https://mssanz.org.au/modsim2025/files/E02.may.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	T
Dzator J	Facilities for addressing disaster challenges and promoting economic development in Africa	Dzator M, Simelane P, Dzator J	https://doi.org/10.36334/modsim2025.G03.dzator	https://mssanz.org.au/modsim2025/files/G03.dzator.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	D
Dzator M	Facilities for addressing disaster challenges and promoting economic development in Africa	Dzator M, Simelane P, Dzator J	https://doi.org/10.36334/modsim2025.G03.dzator	https://mssanz.org.au/modsim2025/files/G03.dzator.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	D
Simelane P	Facilities for addressing disaster challenges and promoting economic development in Africa	Dzator M, Simelane P, Dzator J	https://doi.org/10.36334/modsim2025.G03.dzator	https://mssanz.org.au/modsim2025/files/G03.dzator.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	S
Singh R	Factor models in housing research: Addressing methodlogical gaps for enhanced policy decision-making	Singh R	https://doi.org/10.36334/modsim2025.D05.singh	https://mssanz.org.au/modsim2025/files/D05.singh.pdf	D	Economics and finance	D5	Decision-making under uncertainty in economics and finance	S
Quaine PF	Farm dam and licenced diversions under climate change	Quaine PF	https://doi.org/10.36334/modsim2025.J02.quaine	https://mssanz.org.au/modsim2025/files/J02.quaine.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	Q
Boston T	Fine-tuning remote sensing foundation models for improved live fuel moisture content estimation	Boston T, Yebra M	https://doi.org/10.36334/modsim2025.F08.boston	https://mssanz.org.au/modsim2025/files/F08.boston.pdf	F	Environment and ecology	F8	GIS and environmental modelling	B
Yebra M	Fine-tuning remote sensing foundation models for improved live fuel moisture content estimation	Boston T, Yebra M	https://doi.org/10.36334/modsim2025.F08.boston	https://mssanz.org.au/modsim2025/files/F08.boston.pdf	F	Environment and ecology	F8	GIS and environmental modelling	Y
Brennan E	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Cressall B	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	C
de Kleermaeker S	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	d
Dixon S	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Fuller J	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	F
Gijsbers P	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	G
Peralta L	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Price N	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Rahman J (Joel)	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Urich C	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	U
Vorreiter D	FIRM: cloud-native integration framework for basin-scale hydrological modelling in the Murray-Darling Basin	Gijsbers P, de Kleermaeker S, Urich C, Rahman J, Fuller J, Brennan E, Peralta L, Vorreiter D, Cressall B, Price N, Dixon S	https://doi.org/10.36334/modsim2025.J02.gijsbers	https://mssanz.org.au/modsim2025/files/J02.gijsbers.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	V
Carrara E	Flash flood nowcasting based on deep learning and radar rainfall estimates	Hou J, Sharples W, Pudashine J, Thran M, Velasco‑Forero C, Fox‑Hughes P, Carrara E	https://doi.org/10.36334/modsim2025.C07.hou	https://mssanz.org.au/modsim2025/files/C07.hou.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	C
Fox‑Hughes P	Flash flood nowcasting based on deep learning and radar rainfall estimates	Hou J, Sharples W, Pudashine J, Thran M, Velasco‑Forero C, Fox‑Hughes P, Carrara E	https://doi.org/10.36334/modsim2025.C07.hou	https://mssanz.org.au/modsim2025/files/C07.hou.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	F
Hou J	Flash flood nowcasting based on deep learning and radar rainfall estimates	Hou J, Sharples W, Pudashine J, Thran M, Velasco‑Forero C, Fox‑Hughes P, Carrara E	https://doi.org/10.36334/modsim2025.C07.hou	https://mssanz.org.au/modsim2025/files/C07.hou.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	H
Pudashine J	Flash flood nowcasting based on deep learning and radar rainfall estimates	Hou J, Sharples W, Pudashine J, Thran M, Velasco‑Forero C, Fox‑Hughes P, Carrara E	https://doi.org/10.36334/modsim2025.C07.hou	https://mssanz.org.au/modsim2025/files/C07.hou.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	P
Sharples W	Flash flood nowcasting based on deep learning and radar rainfall estimates	Hou J, Sharples W, Pudashine J, Thran M, Velasco‑Forero C, Fox‑Hughes P, Carrara E	https://doi.org/10.36334/modsim2025.C07.hou	https://mssanz.org.au/modsim2025/files/C07.hou.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	S
Thran M	Flash flood nowcasting based on deep learning and radar rainfall estimates	Hou J, Sharples W, Pudashine J, Thran M, Velasco‑Forero C, Fox‑Hughes P, Carrara E	https://doi.org/10.36334/modsim2025.C07.hou	https://mssanz.org.au/modsim2025/files/C07.hou.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	T
Velasco‑Forero C	Flash flood nowcasting based on deep learning and radar rainfall estimates	Hou J, Sharples W, Pudashine J, Thran M, Velasco‑Forero C, Fox‑Hughes P, Carrara E	https://doi.org/10.36334/modsim2025.C07.hou	https://mssanz.org.au/modsim2025/files/C07.hou.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	V
Chiew FHS	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	C
Devanand A	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	D
Gibbs MS	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	G
Karim F	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	K
Khan Z (Zaved)	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	K
Mateo C	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Post D	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	P
Robertson DE	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	R
Teng J	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	T
Zheng H (Hongzing)	Floodplain inundation modelling using CaMa-Flood across the Murray-Darling Basin	Khan Z, Chiew F, Zheng H, Teng J, Devanand A, Post D, Robertson DE, Gibbs M, Karim F, Mateo C	https://doi.org/10.36334/modsim2025.G09.khan	https://mssanz.org.au/modsim2025/files/G09.khan.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	Z
Blakely T	Forecasting all risk factors for all countries	Wilson T, Dhungel B, Blakely T	https://doi.org/10.36334/modsim2025.H02.wilson	https://mssanz.org.au/modsim2025/files/H02.wilson.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	B
Dhungel B	Forecasting all risk factors for all countries	Wilson T, Dhungel B, Blakely T	https://doi.org/10.36334/modsim2025.H02.wilson	https://mssanz.org.au/modsim2025/files/H02.wilson.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	D
Wilson T	Forecasting all risk factors for all countries	Wilson T, Dhungel B, Blakely T	https://doi.org/10.36334/modsim2025.H02.wilson	https://mssanz.org.au/modsim2025/files/H02.wilson.pdf	H	Health and biosecurity	H2	Data science and simulation modelling methods in health	W
Arisian S	Forecasting Australian pulse export trade flows and uncovering key trade determinants using machine learning	Perera DN, Arisian S, Du Y, Jayatilleke S, Nguyen S	https://doi.org/10.36334/modsim2025.D04.perera	https://mssanz.org.au/modsim2025/files/D04.perera.pdf	D	Economics and finance	D4	International Trade, Supply Chains, and Global Markets	A
Du Y	Forecasting Australian pulse export trade flows and uncovering key trade determinants using machine learning	Perera DN, Arisian S, Du Y, Jayatilleke S, Nguyen S	https://doi.org/10.36334/modsim2025.D04.perera	https://mssanz.org.au/modsim2025/files/D04.perera.pdf	D	Economics and finance	D4	International Trade, Supply Chains, and Global Markets	D
Jayatilleke S	Forecasting Australian pulse export trade flows and uncovering key trade determinants using machine learning	Perera DN, Arisian S, Du Y, Jayatilleke S, Nguyen S	https://doi.org/10.36334/modsim2025.D04.perera	https://mssanz.org.au/modsim2025/files/D04.perera.pdf	D	Economics and finance	D4	International Trade, Supply Chains, and Global Markets	J
Nguyen S	Forecasting Australian pulse export trade flows and uncovering key trade determinants using machine learning	Perera DN, Arisian S, Du Y, Jayatilleke S, Nguyen S	https://doi.org/10.36334/modsim2025.D04.perera	https://mssanz.org.au/modsim2025/files/D04.perera.pdf	D	Economics and finance	D4	International Trade, Supply Chains, and Global Markets	N
Perera DN	Forecasting Australian pulse export trade flows and uncovering key trade determinants using machine learning	Perera DN, Arisian S, Du Y, Jayatilleke S, Nguyen S	https://doi.org/10.36334/modsim2025.D04.perera	https://mssanz.org.au/modsim2025/files/D04.perera.pdf	D	Economics and finance	D4	International Trade, Supply Chains, and Global Markets	P
Boland J	Forecasting diffuse solar radiation	Boland J	https://doi.org/10.36334/modsim2025.E01.boland	https://mssanz.org.au/modsim2025/files/E01.boland.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	B
Carey CC	Forecasting with FLARE – predicting Lake Alexandrina water quality (INVITED SPEAKER)	Sims C, Olsson F, Thomas RQ, Hipsey MR, Delany AD, Rumbelow A, Carey CC	https://doi.org/10.36334/modsim2025.L02.sims	https://mssanz.org.au/modsim2025/files/L02.sims.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	C
Delany AD	Forecasting with FLARE – predicting Lake Alexandrina water quality (INVITED SPEAKER)	Sims C, Olsson F, Thomas RQ, Hipsey MR, Delany AD, Rumbelow A, Carey CC	https://doi.org/10.36334/modsim2025.L02.sims	https://mssanz.org.au/modsim2025/files/L02.sims.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	D
Hipsey MR	Forecasting with FLARE – predicting Lake Alexandrina water quality (INVITED SPEAKER)	Sims C, Olsson F, Thomas RQ, Hipsey MR, Delany AD, Rumbelow A, Carey CC	https://doi.org/10.36334/modsim2025.L02.sims	https://mssanz.org.au/modsim2025/files/L02.sims.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	H
Olsson F	Forecasting with FLARE – predicting Lake Alexandrina water quality (INVITED SPEAKER)	Sims C, Olsson F, Thomas RQ, Hipsey MR, Delany AD, Rumbelow A, Carey CC	https://doi.org/10.36334/modsim2025.L02.sims	https://mssanz.org.au/modsim2025/files/L02.sims.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	O
Rumbelow A	Forecasting with FLARE – predicting Lake Alexandrina water quality (INVITED SPEAKER)	Sims C, Olsson F, Thomas RQ, Hipsey MR, Delany AD, Rumbelow A, Carey CC	https://doi.org/10.36334/modsim2025.L02.sims	https://mssanz.org.au/modsim2025/files/L02.sims.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	R
Sims C	Forecasting with FLARE – predicting Lake Alexandrina water quality (INVITED SPEAKER)	Sims C, Olsson F, Thomas RQ, Hipsey MR, Delany AD, Rumbelow A, Carey CC	https://doi.org/10.36334/modsim2025.L02.sims	https://mssanz.org.au/modsim2025/files/L02.sims.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	S
Thomas RQ	Forecasting with FLARE – predicting Lake Alexandrina water quality (INVITED SPEAKER)	Sims C, Olsson F, Thomas RQ, Hipsey MR, Delany AD, Rumbelow A, Carey CC	https://doi.org/10.36334/modsim2025.L02.sims	https://mssanz.org.au/modsim2025/files/L02.sims.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	T
Cohen RCZ	Framework for deriving future hazard layers from future climate data for physical climate risk assessments	Cohen RCZ, Lee G, Garg N, Prakash M, Trinh K	https://doi.org/10.36334/modsim2025.G03.cohen	https://mssanz.org.au/modsim2025/files/G03.cohen.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	C
Garg N	Framework for deriving future hazard layers from future climate data for physical climate risk assessments	Cohen RCZ, Lee G, Garg N, Prakash M, Trinh K	https://doi.org/10.36334/modsim2025.G03.cohen	https://mssanz.org.au/modsim2025/files/G03.cohen.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	G
Lee G	Framework for deriving future hazard layers from future climate data for physical climate risk assessments	Cohen RCZ, Lee G, Garg N, Prakash M, Trinh K	https://doi.org/10.36334/modsim2025.G03.cohen	https://mssanz.org.au/modsim2025/files/G03.cohen.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	L
Prakash M	Framework for deriving future hazard layers from future climate data for physical climate risk assessments	Cohen RCZ, Lee G, Garg N, Prakash M, Trinh K	https://doi.org/10.36334/modsim2025.G03.cohen	https://mssanz.org.au/modsim2025/files/G03.cohen.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	P
Trinh K	Framework for deriving future hazard layers from future climate data for physical climate risk assessments	Cohen RCZ, Lee G, Garg N, Prakash M, Trinh K	https://doi.org/10.36334/modsim2025.G03.cohen	https://mssanz.org.au/modsim2025/files/G03.cohen.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	T
Babcock R	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	B
Copcutt M	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	C
Cyriac A	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	C
D’Adamo N	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	D
Greenwood J	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	G
Melbourne-Thomas J	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	M
Sheppard M	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	S
Slawinski D	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	S
Sun C	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	S
Taylor J	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	T
Thomson D	From climate refugium to heatwave epicentre: Will the 2025 Ningaloo bleaching become a new normal?	Sun C, Cyriac A, Copcutt M, Greenwood J, Melbourne‑Thomas J, Thomson D, Sheppard M, Babcock R, Slawinski D, D’Adamo N, Taylor J	https://doi.org/10.36334/modsim2025.F09.sun	https://mssanz.org.au/modsim2025/files/F09.sun.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	T
Aly E	From competing to complementary: Digital engineering ecosystems as Net Zero Enablers	Aly E, Elsawah S	https://doi.org/10.36334/modsim2025.F05.aly	https://mssanz.org.au/modsim2025/files/F05.aly.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	A
Elsawah S	From competing to complementary: Digital engineering ecosystems as Net Zero Enablers	Aly E, Elsawah S	https://doi.org/10.36334/modsim2025.F05.aly	https://mssanz.org.au/modsim2025/files/F05.aly.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	E
Greenville A	From data gaps to climate projections: A framework for forecasting species population abundance	Gunadasa V, Greenville A, Wardle GM	https://doi.org/10.36334/modsim2025.F09.gunadasa	https://mssanz.org.au/modsim2025/files/F09.gunadasa.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	G
Gunadasa V	From data gaps to climate projections: A framework for forecasting species population abundance	Gunadasa V, Greenville A, Wardle GM	https://doi.org/10.36334/modsim2025.F09.gunadasa	https://mssanz.org.au/modsim2025/files/F09.gunadasa.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	G
Wardle GM	From data gaps to climate projections: A framework for forecasting species population abundance	Gunadasa V, Greenville A, Wardle GM	https://doi.org/10.36334/modsim2025.F09.gunadasa	https://mssanz.org.au/modsim2025/files/F09.gunadasa.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	W
Durand RB	From disaster to debt: Exploring the link between natural catastrophes and syndicated loans	Murad SMW, Durand RB, Zheng C	https://doi.org/10.36334/modsim2025.D06.murad	https://mssanz.org.au/modsim2025/files/D06.murad.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	D
Murad SMW	From disaster to debt: Exploring the link between natural catastrophes and syndicated loans	Murad SMW, Durand RB, Zheng C	https://doi.org/10.36334/modsim2025.D06.murad	https://mssanz.org.au/modsim2025/files/D06.murad.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	M
Zheng C	From disaster to debt: Exploring the link between natural catastrophes and syndicated loans	Murad SMW, Durand RB, Zheng C	https://doi.org/10.36334/modsim2025.D06.murad	https://mssanz.org.au/modsim2025/files/D06.murad.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	Z
Bennett W	From monitoring to modelling soil erosion and sediment transport in gullied tropical savanna catchments (INVITED SPEAKER)	Wynants M, Doriean N, Spencer J, Bennett W, Boeckx P, Brooks A	https://doi.org/10.36334/modsim2025.L04.wynants	https://mssanz.org.au/modsim2025/files/L04.wynants.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	B
Boeckx P	From monitoring to modelling soil erosion and sediment transport in gullied tropical savanna catchments (INVITED SPEAKER)	Wynants M, Doriean N, Spencer J, Bennett W, Boeckx P, Brooks A	https://doi.org/10.36334/modsim2025.L04.wynants	https://mssanz.org.au/modsim2025/files/L04.wynants.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	B
Brooks A	From monitoring to modelling soil erosion and sediment transport in gullied tropical savanna catchments (INVITED SPEAKER)	Wynants M, Doriean N, Spencer J, Bennett W, Boeckx P, Brooks A	https://doi.org/10.36334/modsim2025.L04.wynants	https://mssanz.org.au/modsim2025/files/L04.wynants.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	B
Doriean N	From monitoring to modelling soil erosion and sediment transport in gullied tropical savanna catchments (INVITED SPEAKER)	Wynants M, Doriean N, Spencer J, Bennett W, Boeckx P, Brooks A	https://doi.org/10.36334/modsim2025.L04.wynants	https://mssanz.org.au/modsim2025/files/L04.wynants.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	D
Spencer J	From monitoring to modelling soil erosion and sediment transport in gullied tropical savanna catchments (INVITED SPEAKER)	Wynants M, Doriean N, Spencer J, Bennett W, Boeckx P, Brooks A	https://doi.org/10.36334/modsim2025.L04.wynants	https://mssanz.org.au/modsim2025/files/L04.wynants.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	S
Wynants M	From monitoring to modelling soil erosion and sediment transport in gullied tropical savanna catchments (INVITED SPEAKER)	Wynants M, Doriean N, Spencer J, Bennett W, Boeckx P, Brooks A	https://doi.org/10.36334/modsim2025.L04.wynants	https://mssanz.org.au/modsim2025/files/L04.wynants.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	W
Akhter F	From snowmelt to fields: Synthesizing water supply, use, and management gaps across Kabul River Basin	Akhter F, Awan UK, Borgemeister C, Tischbein B	https://doi.org/10.36334/modsim2025.K01.akhter	https://mssanz.org.au/modsim2025/files/K01.akhter.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	A
Awan UK	From snowmelt to fields: Synthesizing water supply, use, and management gaps across Kabul River Basin	Akhter F, Awan UK, Borgemeister C, Tischbein B	https://doi.org/10.36334/modsim2025.K01.akhter	https://mssanz.org.au/modsim2025/files/K01.akhter.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	A
Borgemeister C	From snowmelt to fields: Synthesizing water supply, use, and management gaps across Kabul River Basin	Akhter F, Awan UK, Borgemeister C, Tischbein B	https://doi.org/10.36334/modsim2025.K01.akhter	https://mssanz.org.au/modsim2025/files/K01.akhter.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	B
Tischbein B	From snowmelt to fields: Synthesizing water supply, use, and management gaps across Kabul River Basin	Akhter F, Awan UK, Borgemeister C, Tischbein B	https://doi.org/10.36334/modsim2025.K01.akhter	https://mssanz.org.au/modsim2025/files/K01.akhter.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	T
Reeves M T	Fuel moisture lag effects in fire spread calculations	Reeves M T, Swedosh W	https://doi.org/10.36334/modsim2025.G08.reeves	https://mssanz.org.au/modsim2025/files/G08.reeves.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	R
Swedosh W	Fuel moisture lag effects in fire spread calculations	Reeves M T, Swedosh W	https://doi.org/10.36334/modsim2025.G08.reeves	https://mssanz.org.au/modsim2025/files/G08.reeves.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Entwistle H	Full information problem with random horizon	Entwistle H, Sofronov G	https://doi.org/10.36334/modsim2025.A01.entwistle	https://mssanz.org.au/modsim2025/files/A01.entwistle.pdf	A	Applied and computational mathematics	A1	Applied Probability	E
Sofronov G	Full information problem with random horizon	Entwistle H, Sofronov G	https://doi.org/10.36334/modsim2025.A01.entwistle	https://mssanz.org.au/modsim2025/files/A01.entwistle.pdf	A	Applied and computational mathematics	A1	Applied Probability	S
Kapugama Geeganage D	FUSE: Forecasting with UNSPSC-Enhanced granularity	Wang E, Kapugama Geeganage D, Kemp O	https://doi.org/10.36334/modsim2025.D01.wang	https://mssanz.org.au/modsim2025/files/D01.wang.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	K
Kemp O	FUSE: Forecasting with UNSPSC-Enhanced granularity	Wang E, Kapugama Geeganage D, Kemp O	https://doi.org/10.36334/modsim2025.D01.wang	https://mssanz.org.au/modsim2025/files/D01.wang.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	K
Wang E (Erli)	FUSE: Forecasting with UNSPSC-Enhanced granularity	Wang E, Kapugama Geeganage D, Kemp O	https://doi.org/10.36334/modsim2025.D01.wang	https://mssanz.org.au/modsim2025/files/D01.wang.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	W
Liu S	Gap-filling terrestrial water storage in severe Amazon droughts	Liu S, McVicar T, Liu Y	https://doi.org/10.36334/modsim2025.K07.liu	https://mssanz.org.au/modsim2025/files/K07.liu.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	L
Liu Y (Yi)	Gap-filling terrestrial water storage in severe Amazon droughts	Liu S, McVicar T, Liu Y	https://doi.org/10.36334/modsim2025.K07.liu	https://mssanz.org.au/modsim2025/files/K07.liu.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	L
McVicar T	Gap-filling terrestrial water storage in severe Amazon droughts	Liu S, McVicar T, Liu Y	https://doi.org/10.36334/modsim2025.K07.liu	https://mssanz.org.au/modsim2025/files/K07.liu.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	M
Dunne R	Gender wap gap analysis using machine learning methods	Dunne D, Saft M, Peel M	https://doi.org/10.36334/modsim2025.D01.dunne	https://mssanz.org.au/modsim2025/files/D01.dunne.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	D
Chan F	Generating initial values using artificial neural network	Chan F	https://doi.org/10.36334/modsim2025.D01.chan	https://mssanz.org.au/modsim2025/files/D01.chan.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	C
Fakhimi M	Generative AI applications in system dynamics modelling	Fakhimi M, Hosseini SH	https://doi.org/10.36334/modsim2025.I02.fakhimi	https://mssanz.org.au/modsim2025/files/I02.fakhimi.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	F
Hosseini SH	Generative AI applications in system dynamics modelling	Fakhimi M, Hosseini SH	https://doi.org/10.36334/modsim2025.I02.fakhimi	https://mssanz.org.au/modsim2025/files/I02.fakhimi.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	H
Baker P	Generative AI enhancing understanding of vast integrated environmental assessments	Leighton B, Lee A, Baker P	https://doi.org/10.36334/modsim2025.F10.leighton	https://mssanz.org.au/modsim2025/files/F10.leighton.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	B
Lee A	Generative AI enhancing understanding of vast integrated environmental assessments	Leighton B, Lee A, Baker P	https://doi.org/10.36334/modsim2025.F10.leighton	https://mssanz.org.au/modsim2025/files/F10.leighton.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	L
Leighton B	Generative AI enhancing understanding of vast integrated environmental assessments	Leighton B, Lee A, Baker P	https://doi.org/10.36334/modsim2025.F10.leighton	https://mssanz.org.au/modsim2025/files/F10.leighton.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	L
Akbarzadeh Khorshidi H	Genomic risk stratification in optimising intermediate risk prostate cancer management in Australia: A discrete event simulation	Lyu J, Chen G, Corcoran N, Akbarzadeh Khorshidi H	https://doi.org/10.36334/modsim2025.H03.lyu	https://mssanz.org.au/modsim2025/files/H03.lyu.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	A
Chen G (Gang)	Genomic risk stratification in optimising intermediate risk prostate cancer management in Australia: A discrete event simulation	Lyu J, Chen G, Corcoran N, Akbarzadeh Khorshidi H	https://doi.org/10.36334/modsim2025.H03.lyu	https://mssanz.org.au/modsim2025/files/H03.lyu.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	C
Corcoran N	Genomic risk stratification in optimising intermediate risk prostate cancer management in Australia: A discrete event simulation	Lyu J, Chen G, Corcoran N, Akbarzadeh Khorshidi H	https://doi.org/10.36334/modsim2025.H03.lyu	https://mssanz.org.au/modsim2025/files/H03.lyu.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	C
Lyu J (Juntao)	Genomic risk stratification in optimising intermediate risk prostate cancer management in Australia: A discrete event simulation	Lyu J, Chen G, Corcoran N, Akbarzadeh Khorshidi H	https://doi.org/10.36334/modsim2025.H03.lyu	https://mssanz.org.au/modsim2025/files/H03.lyu.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	L
Chen M (Min)	Geo-analysis model interoperability in the AI era: Challenges, solutions and future directions	Zhang F, Chen M	https://doi.org/10.36334/modsim2025.C04.zhang	https://mssanz.org.au/modsim2025/files/C04.zhang.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	C
Zhang F (Fengyuan)	Geo-analysis model interoperability in the AI era: Challenges, solutions and future directions	Zhang F, Chen M	https://doi.org/10.36334/modsim2025.C04.zhang	https://mssanz.org.au/modsim2025/files/C04.zhang.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	Z
Cadavid Restrepo A	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	C
Graves P	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	G
Lau C	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	L
Mayfield HJ	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	M
McLure A	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	M
McPherson B	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	M
Rigby L	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	R
Sartorius B	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	S
Thomsen R	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	T
Viali S	Geostatistical modelling of Lymphatic Filariasis in Samoa using integrated human and mosquito surveillance data	McLure A, Mayfield HJ, Cadavid Restrepo A, Lau C, McPherson B, Rigby L, Graves P, Thomsen R, Viali S, Sartorius B	https://doi.org/10.36334/modsim2025.H04.mclure	https://mssanz.org.au/modsim2025/files/H04.mclure.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	V
Ma Y (Yizhe)	Global ecosystems reveal divergent evapotranspiration response patterns to drought stress	Ma Y, Zhang Y	https://doi.org/10.36334/modsim2025.K03.ma	https://mssanz.org.au/modsim2025/files/K03.ma.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	M
Zhang Y (Yongqiang)	Global ecosystems reveal divergent evapotranspiration response patterns to drought stress	Ma Y, Zhang Y	https://doi.org/10.36334/modsim2025.K03.ma	https://mssanz.org.au/modsim2025/files/K03.ma.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	Z
Jia J	Global lake primary productivity and carbon source-sink processes and their impact mechanisms	Jia J	https://doi.org/10.36334/modsim2025.K08.jia	https://mssanz.org.au/modsim2025/files/K08.jia.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	J
Harrison MT	Global sensitivity analysis and parameter optimisation of APSIM Next Generation	Liu K, Harrison MT	https://doi.org/10.36334/modsim2025.B07.liu	https://mssanz.org.au/modsim2025/files/B07.liu.pdf	B	Biological systems	B7	Optimising farming systems to improve climate resilience and sustainability under water-constraint environments	H
Liu K	Global sensitivity analysis and parameter optimisation of APSIM Next Generation	Liu K, Harrison MT	https://doi.org/10.36334/modsim2025.B07.liu	https://mssanz.org.au/modsim2025/files/B07.liu.pdf	B	Biological systems	B7	Optimising farming systems to improve climate resilience and sustainability under water-constraint environments	L
Dougherty B	GrowPF – A new model for tracking growth and body composition in ruminants (STREAM B KEYNOTE)	Dougherty B, Western A, Seed A	https://doi.org/10.36334/modsim2025.B04.dougherty	https://mssanz.org.au/modsim2025/files/B04.dougherty.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	D
Seed A	GrowPF – A new model for tracking growth and body composition in ruminants (STREAM B KEYNOTE)	Dougherty B, Western A, Seed A	https://doi.org/10.36334/modsim2025.B04.dougherty	https://mssanz.org.au/modsim2025/files/B04.dougherty.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	S
Western A	GrowPF – A new model for tracking growth and body composition in ruminants (STREAM B KEYNOTE)	Dougherty B, Western A, Seed A	https://doi.org/10.36334/modsim2025.B04.dougherty	https://mssanz.org.au/modsim2025/files/B04.dougherty.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	W
Adarsh S	Harnessing quantum variational circuits for enhanced streamflow forecasting in the Greater Pamba River Basin, India	Nair GR, Adarsh S	https://doi.org/10.36334/modsim2025.J06.arathynair	https://mssanz.org.au/modsim2025/files/J06.arathynair.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	A
Nair GR	Harnessing quantum variational circuits for enhanced streamflow forecasting in the Greater Pamba River Basin, India	Nair GR, Adarsh S	https://doi.org/10.36334/modsim2025.J06.arathynair	https://mssanz.org.au/modsim2025/files/J06.arathynair.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	N
Coleman RA	Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both world	Grey V, Fletcher TD, Smith-Miles K, Hatt BE, Coleman RA	https://doi.org/10.36334/modsim2025.J05.grey	https://mssanz.org.au/modsim2025/files/J05.grey.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	C
Fletcher TD	Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both world	Grey V, Fletcher TD, Smith-Miles K, Hatt BE, Coleman RA	https://doi.org/10.36334/modsim2025.J05.grey	https://mssanz.org.au/modsim2025/files/J05.grey.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	F
Grey V	Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both world	Grey V, Fletcher TD, Smith-Miles K, Hatt BE, Coleman RA	https://doi.org/10.36334/modsim2025.J05.grey	https://mssanz.org.au/modsim2025/files/J05.grey.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	G
Hatt BE	Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both world	Grey V, Fletcher TD, Smith-Miles K, Hatt BE, Coleman RA	https://doi.org/10.36334/modsim2025.J05.grey	https://mssanz.org.au/modsim2025/files/J05.grey.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	H
Smith-Miles K	Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both world	Grey V, Fletcher TD, Smith-Miles K, Hatt BE, Coleman RA	https://doi.org/10.36334/modsim2025.J05.grey	https://mssanz.org.au/modsim2025/files/J05.grey.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	S
Chapman SC	Hierarchical ODE models for heading date estimation in wheat	Fernandez JA, Wang Z, Chapman SC	https://doi.org/10.36334/modsim2025.B06.fernandez	https://mssanz.org.au/modsim2025/files/B06.fernandez.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	C
Fernandez JA	Hierarchical ODE models for heading date estimation in wheat	Fernandez JA, Wang Z, Chapman SC	https://doi.org/10.36334/modsim2025.B06.fernandez	https://mssanz.org.au/modsim2025/files/B06.fernandez.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	F
Wang Z (Zijian)	Hierarchical ODE models for heading date estimation in wheat	Fernandez JA, Wang Z, Chapman SC	https://doi.org/10.36334/modsim2025.B06.fernandez	https://mssanz.org.au/modsim2025/files/B06.fernandez.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	W
Box P	High resolution mapping of buffelgrass using dense time series of imagery and open source data	Box P, Lieper I, Nano C, Cobban D, Brim‑Box J	https://doi.org/10.36334/modsim2025.F01.box	https://mssanz.org.au/modsim2025/files/F01.box.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	B
Brim‑Box J	High resolution mapping of buffelgrass using dense time series of imagery and open source data	Box P, Lieper I, Nano C, Cobban D, Brim‑Box J	https://doi.org/10.36334/modsim2025.F01.box	https://mssanz.org.au/modsim2025/files/F01.box.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	B
Cobban D	High resolution mapping of buffelgrass using dense time series of imagery and open source data	Box P, Lieper I, Nano C, Cobban D, Brim‑Box J	https://doi.org/10.36334/modsim2025.F01.box	https://mssanz.org.au/modsim2025/files/F01.box.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	C
Lieper I	High resolution mapping of buffelgrass using dense time series of imagery and open source data	Box P, Lieper I, Nano C, Cobban D, Brim‑Box J	https://doi.org/10.36334/modsim2025.F01.box	https://mssanz.org.au/modsim2025/files/F01.box.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	L
Nano C	High resolution mapping of buffelgrass using dense time series of imagery and open source data	Box P, Lieper I, Nano C, Cobban D, Brim‑Box J	https://doi.org/10.36334/modsim2025.F01.box	https://mssanz.org.au/modsim2025/files/F01.box.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	N
Clark S	High-fidelity emulation of a groundwater simulator using a deep LSTM neural network architecture	Pagendam D, Siade A, Clark S, Dabrowski J, Li Z, Janardhanan S	https://doi.org/10.36334/modsim2025.C06.pagendam	https://mssanz.org.au/modsim2025/files/C06.pagendam.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	C
Dabrowski J	High-fidelity emulation of a groundwater simulator using a deep LSTM neural network architecture	Pagendam D, Siade A, Clark S, Dabrowski J, Li Z, Janardhanan S	https://doi.org/10.36334/modsim2025.C06.pagendam	https://mssanz.org.au/modsim2025/files/C06.pagendam.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	D
Janardhanan S	High-fidelity emulation of a groundwater simulator using a deep LSTM neural network architecture	Pagendam D, Siade A, Clark S, Dabrowski J, Li Z, Janardhanan S	https://doi.org/10.36334/modsim2025.C06.pagendam	https://mssanz.org.au/modsim2025/files/C06.pagendam.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	J
Li Z (Zhibin)	High-fidelity emulation of a groundwater simulator using a deep LSTM neural network architecture	Pagendam D, Siade A, Clark S, Dabrowski J, Li Z, Janardhanan S	https://doi.org/10.36334/modsim2025.C06.pagendam	https://mssanz.org.au/modsim2025/files/C06.pagendam.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	L
Pagendam D	High-fidelity emulation of a groundwater simulator using a deep LSTM neural network architecture	Pagendam D, Siade A, Clark S, Dabrowski J, Li Z, Janardhanan S	https://doi.org/10.36334/modsim2025.C06.pagendam	https://mssanz.org.au/modsim2025/files/C06.pagendam.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	P
Siade A	High-fidelity emulation of a groundwater simulator using a deep LSTM neural network architecture	Pagendam D, Siade A, Clark S, Dabrowski J, Li Z, Janardhanan S	https://doi.org/10.36334/modsim2025.C06.pagendam	https://mssanz.org.au/modsim2025/files/C06.pagendam.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	S
Condie S	High-resolution climate projections reveal future bleaching risks in the Great Barrier Reef	Cyriac A, Sun C, Taylor J, Matear R, Condie S, Reddy PJ, Liu T	https://doi.org/10.36334/modsim2025.F09.cyriac	https://mssanz.org.au/modsim2025/files/F09.cyriac.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	C
Cyriac A	High-resolution climate projections reveal future bleaching risks in the Great Barrier Reef	Cyriac A, Sun C, Taylor J, Matear R, Condie S, Reddy PJ, Liu T	https://doi.org/10.36334/modsim2025.F09.cyriac	https://mssanz.org.au/modsim2025/files/F09.cyriac.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	C
Liu T	High-resolution climate projections reveal future bleaching risks in the Great Barrier Reef	Cyriac A, Sun C, Taylor J, Matear R, Condie S, Reddy PJ, Liu T	https://doi.org/10.36334/modsim2025.F09.cyriac	https://mssanz.org.au/modsim2025/files/F09.cyriac.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	L
Matear R	High-resolution climate projections reveal future bleaching risks in the Great Barrier Reef	Cyriac A, Sun C, Taylor J, Matear R, Condie S, Reddy PJ, Liu T	https://doi.org/10.36334/modsim2025.F09.cyriac	https://mssanz.org.au/modsim2025/files/F09.cyriac.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	M
Reddy PJ	High-resolution climate projections reveal future bleaching risks in the Great Barrier Reef	Cyriac A, Sun C, Taylor J, Matear R, Condie S, Reddy PJ, Liu T	https://doi.org/10.36334/modsim2025.F09.cyriac	https://mssanz.org.au/modsim2025/files/F09.cyriac.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	R
Sun C	High-resolution climate projections reveal future bleaching risks in the Great Barrier Reef	Cyriac A, Sun C, Taylor J, Matear R, Condie S, Reddy PJ, Liu T	https://doi.org/10.36334/modsim2025.F09.cyriac	https://mssanz.org.au/modsim2025/files/F09.cyriac.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	S
Taylor J	High-resolution climate projections reveal future bleaching risks in the Great Barrier Reef	Cyriac A, Sun C, Taylor J, Matear R, Condie S, Reddy PJ, Liu T	https://doi.org/10.36334/modsim2025.F09.cyriac	https://mssanz.org.au/modsim2025/files/F09.cyriac.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	T
Sowden M	High-resolution landscape fire monitoring: satellite-guided reconstruction of ground-level air pollutants at ten minute and 500 m scale	Sowden M	https://doi.org/10.36334/modsim2025.G07.sowden	https://mssanz.org.au/modsim2025/files/G07.sowden.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	S
Ayaz NU	High-resolution near-surface soil moisture mapping for precision irrigation using a UAV	Ayaz NU, Walker JP, Wu X, Hills J, Ye N, Brakhasi F, Popstefanija I	https://doi.org/10.36334/modsim2025.J09.ayaz	https://mssanz.org.au/modsim2025/files/J09.ayaz.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	A
Brakhasi F	High-resolution near-surface soil moisture mapping for precision irrigation using a UAV	Ayaz NU, Walker JP, Wu X, Hills J, Ye N, Brakhasi F, Popstefanija I	https://doi.org/10.36334/modsim2025.J09.ayaz	https://mssanz.org.au/modsim2025/files/J09.ayaz.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	B
Hills J	High-resolution near-surface soil moisture mapping for precision irrigation using a UAV	Ayaz NU, Walker JP, Wu X, Hills J, Ye N, Brakhasi F, Popstefanija I	https://doi.org/10.36334/modsim2025.J09.ayaz	https://mssanz.org.au/modsim2025/files/J09.ayaz.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	H
Popstefanija I	High-resolution near-surface soil moisture mapping for precision irrigation using a UAV	Ayaz NU, Walker JP, Wu X, Hills J, Ye N, Brakhasi F, Popstefanija I	https://doi.org/10.36334/modsim2025.J09.ayaz	https://mssanz.org.au/modsim2025/files/J09.ayaz.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	P
Walker JP	High-resolution near-surface soil moisture mapping for precision irrigation using a UAV	Ayaz NU, Walker JP, Wu X, Hills J, Ye N, Brakhasi F, Popstefanija I	https://doi.org/10.36334/modsim2025.J09.ayaz	https://mssanz.org.au/modsim2025/files/J09.ayaz.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	W
Wu X	High-resolution near-surface soil moisture mapping for precision irrigation using a UAV	Ayaz NU, Walker JP, Wu X, Hills J, Ye N, Brakhasi F, Popstefanija I	https://doi.org/10.36334/modsim2025.J09.ayaz	https://mssanz.org.au/modsim2025/files/J09.ayaz.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	W
Ye N	High-resolution near-surface soil moisture mapping for precision irrigation using a UAV	Ayaz NU, Walker JP, Wu X, Hills J, Ye N, Brakhasi F, Popstefanija I	https://doi.org/10.36334/modsim2025.J09.ayaz	https://mssanz.org.au/modsim2025/files/J09.ayaz.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	Y
Bolger M	Holistic naval ship design in CSIRO Workspace	Rose D, Hield P, Bolger M, Hetherton L, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.rose	https://mssanz.org.au/modsim2025/files/C01.rose.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	B
Hetherton L	Holistic naval ship design in CSIRO Workspace	Rose D, Hield P, Bolger M, Hetherton L, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.rose	https://mssanz.org.au/modsim2025/files/C01.rose.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	H
Hield P	Holistic naval ship design in CSIRO Workspace	Rose D, Hield P, Bolger M, Hetherton L, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.rose	https://mssanz.org.au/modsim2025/files/C01.rose.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	H
Rose D	Holistic naval ship design in CSIRO Workspace	Rose D, Hield P, Bolger M, Hetherton L, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.rose	https://mssanz.org.au/modsim2025/files/C01.rose.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	R
Thomas D (David)	Holistic naval ship design in CSIRO Workspace	Rose D, Hield P, Bolger M, Hetherton L, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.rose	https://mssanz.org.au/modsim2025/files/C01.rose.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	T
Watkins D	Holistic naval ship design in CSIRO Workspace	Rose D, Hield P, Bolger M, Hetherton L, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.rose	https://mssanz.org.au/modsim2025/files/C01.rose.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	W
Biggs J	How and for how long should you “spin-up” soil C sub-pools in an APSIM simulation?	Pasley H, Biggs J, Huth N, Verburg K	https://doi.org/10.36334/modsim2025.B03.pasley	https://mssanz.org.au/modsim2025/files/B03.pasley.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	B
Huth N	How and for how long should you “spin-up” soil C sub-pools in an APSIM simulation?	Pasley H, Biggs J, Huth N, Verburg K	https://doi.org/10.36334/modsim2025.B03.pasley	https://mssanz.org.au/modsim2025/files/B03.pasley.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	H
Pasley H	How and for how long should you “spin-up” soil C sub-pools in an APSIM simulation?	Pasley H, Biggs J, Huth N, Verburg K	https://doi.org/10.36334/modsim2025.B03.pasley	https://mssanz.org.au/modsim2025/files/B03.pasley.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	P
Verburg K	How and for how long should you “spin-up” soil C sub-pools in an APSIM simulation?	Pasley H, Biggs J, Huth N, Verburg K	https://doi.org/10.36334/modsim2025.B03.pasley	https://mssanz.org.au/modsim2025/files/B03.pasley.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	V
Boon C	How does bathymetric resolution and mesh design influence estuarine flushing time estimates?	Moravej M, Branson P, Hipsey M, Boon C, Raj David D, Singh A, Gibbes B	https://doi.org/10.36334/modsim2025.L02.moravej	https://mssanz.org.au/modsim2025/files/L02.moravej.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	B
Branson P	How does bathymetric resolution and mesh design influence estuarine flushing time estimates?	Moravej M, Branson P, Hipsey M, Boon C, Raj David D, Singh A, Gibbes B	https://doi.org/10.36334/modsim2025.L02.moravej	https://mssanz.org.au/modsim2025/files/L02.moravej.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	B
Gibbes B	How does bathymetric resolution and mesh design influence estuarine flushing time estimates?	Moravej M, Branson P, Hipsey M, Boon C, Raj David D, Singh A, Gibbes B	https://doi.org/10.36334/modsim2025.L02.moravej	https://mssanz.org.au/modsim2025/files/L02.moravej.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	G
Hipsey M	How does bathymetric resolution and mesh design influence estuarine flushing time estimates?	Moravej M, Branson P, Hipsey M, Boon C, Raj David D, Singh A, Gibbes B	https://doi.org/10.36334/modsim2025.L02.moravej	https://mssanz.org.au/modsim2025/files/L02.moravej.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	H
Moravej M	How does bathymetric resolution and mesh design influence estuarine flushing time estimates?	Moravej M, Branson P, Hipsey M, Boon C, Raj David D, Singh A, Gibbes B	https://doi.org/10.36334/modsim2025.L02.moravej	https://mssanz.org.au/modsim2025/files/L02.moravej.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	M
Raj David D	How does bathymetric resolution and mesh design influence estuarine flushing time estimates?	Moravej M, Branson P, Hipsey M, Boon C, Raj David D, Singh A, Gibbes B	https://doi.org/10.36334/modsim2025.L02.moravej	https://mssanz.org.au/modsim2025/files/L02.moravej.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	R
Singh A	How does bathymetric resolution and mesh design influence estuarine flushing time estimates?	Moravej M, Branson P, Hipsey M, Boon C, Raj David D, Singh A, Gibbes B	https://doi.org/10.36334/modsim2025.L02.moravej	https://mssanz.org.au/modsim2025/files/L02.moravej.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	S
Fowler K	How fast can a MARRMoT run? Improving runtimes for the Modular Assessment of Rainfall-Runoff Toolbox (MARRMoT)	Fowler K, Mandy D, Zhang Z, Zhou J, Lerat J, Kavetski D, Knoben W	https://doi.org/10.36334/modsim2025.A02.fowler	https://mssanz.org.au/modsim2025/files/A02.fowler.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	F
Kavetski D	How fast can a MARRMoT run? Improving runtimes for the Modular Assessment of Rainfall-Runoff Toolbox (MARRMoT)	Fowler K, Mandy D, Zhang Z, Zhou J, Lerat J, Kavetski D, Knoben W	https://doi.org/10.36334/modsim2025.A02.fowler	https://mssanz.org.au/modsim2025/files/A02.fowler.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	K
Knoben W	How fast can a MARRMoT run? Improving runtimes for the Modular Assessment of Rainfall-Runoff Toolbox (MARRMoT)	Fowler K, Mandy D, Zhang Z, Zhou J, Lerat J, Kavetski D, Knoben W	https://doi.org/10.36334/modsim2025.A02.fowler	https://mssanz.org.au/modsim2025/files/A02.fowler.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	K
Lerat J	How fast can a MARRMoT run? Improving runtimes for the Modular Assessment of Rainfall-Runoff Toolbox (MARRMoT)	Fowler K, Mandy D, Zhang Z, Zhou J, Lerat J, Kavetski D, Knoben W	https://doi.org/10.36334/modsim2025.A02.fowler	https://mssanz.org.au/modsim2025/files/A02.fowler.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	L
Mandy D	How fast can a MARRMoT run? Improving runtimes for the Modular Assessment of Rainfall-Runoff Toolbox (MARRMoT)	Fowler K, Mandy D, Zhang Z, Zhou J, Lerat J, Kavetski D, Knoben W	https://doi.org/10.36334/modsim2025.A02.fowler	https://mssanz.org.au/modsim2025/files/A02.fowler.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	M
Zhang Z (Ziqi)	How fast can a MARRMoT run? Improving runtimes for the Modular Assessment of Rainfall-Runoff Toolbox (MARRMoT)	Fowler K, Mandy D, Zhang Z, Zhou J, Lerat J, Kavetski D, Knoben W	https://doi.org/10.36334/modsim2025.A02.fowler	https://mssanz.org.au/modsim2025/files/A02.fowler.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	Z
Zhou J	How fast can a MARRMoT run? Improving runtimes for the Modular Assessment of Rainfall-Runoff Toolbox (MARRMoT)	Fowler K, Mandy D, Zhang Z, Zhou J, Lerat J, Kavetski D, Knoben W	https://doi.org/10.36334/modsim2025.A02.fowler	https://mssanz.org.au/modsim2025/files/A02.fowler.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	Z
Batelaan O	How indices contribute to global water scarcity estimation uncertainty	Gray C, Maier H, Westra S, Leonard M, Batelaan O	https://doi.org/10.36334/modsim2025.J08.gray	https://mssanz.org.au/modsim2025/files/J08.gray.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	B
Gray C	How indices contribute to global water scarcity estimation uncertainty	Gray C, Maier H, Westra S, Leonard M, Batelaan O	https://doi.org/10.36334/modsim2025.J08.gray	https://mssanz.org.au/modsim2025/files/J08.gray.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	G
Leonard M (Michael)	How indices contribute to global water scarcity estimation uncertainty	Gray C, Maier H, Westra S, Leonard M, Batelaan O	https://doi.org/10.36334/modsim2025.J08.gray	https://mssanz.org.au/modsim2025/files/J08.gray.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	L
Maier HR	How indices contribute to global water scarcity estimation uncertainty	Gray C, Maier H, Westra S, Leonard M, Batelaan O	https://doi.org/10.36334/modsim2025.J08.gray	https://mssanz.org.au/modsim2025/files/J08.gray.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	M
Westra S	How indices contribute to global water scarcity estimation uncertainty	Gray C, Maier H, Westra S, Leonard M, Batelaan O	https://doi.org/10.36334/modsim2025.J08.gray	https://mssanz.org.au/modsim2025/files/J08.gray.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	W
Bai Q	How much data is enough? Evaluating training length in physics-informed LSTM for soil moisture	Jiang C, Hardie M, Bai Q	https://doi.org/10.36334/modsim2025.C07.jiang	https://mssanz.org.au/modsim2025/files/C07.jiang.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	B
Hardie M	How much data is enough? Evaluating training length in physics-informed LSTM for soil moisture	Jiang C, Hardie M, Bai Q	https://doi.org/10.36334/modsim2025.C07.jiang	https://mssanz.org.au/modsim2025/files/C07.jiang.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	H
Jiang C	How much data is enough? Evaluating training length in physics-informed LSTM for soil moisture	Jiang C, Hardie M, Bai Q	https://doi.org/10.36334/modsim2025.C07.jiang	https://mssanz.org.au/modsim2025/files/C07.jiang.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	J
Gaydon DS	How well does APSIM NG simulate wheat yields with gridded data?	Richetti J, Lawes R A, Whan A, Gaydon D S, Thorburn P	https://doi.org/10.36334/modsim2025.B02.richetti	https://mssanz.org.au/modsim2025/files/B02.richetti.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	G
Lawes RA	How well does APSIM NG simulate wheat yields with gridded data?	Richetti J, Lawes R A, Whan A, Gaydon D S, Thorburn P	https://doi.org/10.36334/modsim2025.B02.richetti	https://mssanz.org.au/modsim2025/files/B02.richetti.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	L
Richetti J	How well does APSIM NG simulate wheat yields with gridded data?	Richetti J, Lawes R A, Whan A, Gaydon D S, Thorburn P	https://doi.org/10.36334/modsim2025.B02.richetti	https://mssanz.org.au/modsim2025/files/B02.richetti.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	R
Thorburn P	How well does APSIM NG simulate wheat yields with gridded data?	Richetti J, Lawes R A, Whan A, Gaydon D S, Thorburn P	https://doi.org/10.36334/modsim2025.B02.richetti	https://mssanz.org.au/modsim2025/files/B02.richetti.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	T
Whan A	How well does APSIM NG simulate wheat yields with gridded data?	Richetti J, Lawes R A, Whan A, Gaydon D S, Thorburn P	https://doi.org/10.36334/modsim2025.B02.richetti	https://mssanz.org.au/modsim2025/files/B02.richetti.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	W
Chimprang N	Hybrid forecasting of crude oil prices using WT‑QPSO‑ANFIS	Tansuchat R, Chimprang N	https://doi.org/10.36334/modsim2025.D01.tansuchat	https://mssanz.org.au/modsim2025/files/D01.tansuchat.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	C
Tansuchat R	Hybrid forecasting of crude oil prices using WT‑QPSO‑ANFIS	Tansuchat R, Chimprang N	https://doi.org/10.36334/modsim2025.D01.tansuchat	https://mssanz.org.au/modsim2025/files/D01.tansuchat.pdf	D	Economics and finance	D1	Machine Learning Applications in Economics and Finance	T
Hughes J	Hydrodynamic modelling of floods and developing an emulator for long term inundation predictions	Karim F, Hughes J, Kim S, Ticehurst C, Marvanek S, Petheram C	https://doi.org/10.36334/modsim2025.G09.karim	https://mssanz.org.au/modsim2025/files/G09.karim.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	H
Karim F	Hydrodynamic modelling of floods and developing an emulator for long term inundation predictions	Karim F, Hughes J, Kim S, Ticehurst C, Marvanek S, Petheram C	https://doi.org/10.36334/modsim2025.G09.karim	https://mssanz.org.au/modsim2025/files/G09.karim.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	K
Kim SSH	Hydrodynamic modelling of floods and developing an emulator for long term inundation predictions	Karim F, Hughes J, Kim S, Ticehurst C, Marvanek S, Petheram C	https://doi.org/10.36334/modsim2025.G09.karim	https://mssanz.org.au/modsim2025/files/G09.karim.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	K
Marvanek S	Hydrodynamic modelling of floods and developing an emulator for long term inundation predictions	Karim F, Hughes J, Kim S, Ticehurst C, Marvanek S, Petheram C	https://doi.org/10.36334/modsim2025.G09.karim	https://mssanz.org.au/modsim2025/files/G09.karim.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Petheram C	Hydrodynamic modelling of floods and developing an emulator for long term inundation predictions	Karim F, Hughes J, Kim S, Ticehurst C, Marvanek S, Petheram C	https://doi.org/10.36334/modsim2025.G09.karim	https://mssanz.org.au/modsim2025/files/G09.karim.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	P
Ticehurst C	Hydrodynamic modelling of floods and developing an emulator for long term inundation predictions	Karim F, Hughes J, Kim S, Ticehurst C, Marvanek S, Petheram C	https://doi.org/10.36334/modsim2025.G09.karim	https://mssanz.org.au/modsim2025/files/G09.karim.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	T
Cinque K	Hydrodynamic modelling of water quality impacts of petrol-powered botas on a drinking water reservoir	Cinque K, Costello S	https://doi.org/10.36334/modsim2025.L04.cinque	https://mssanz.org.au/modsim2025/files/L04.cinque.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	C
Costello S	Hydrodynamic modelling of water quality impacts of petrol-powered botas on a drinking water reservoir	Cinque K, Costello S	https://doi.org/10.36334/modsim2025.L04.cinque	https://mssanz.org.au/modsim2025/files/L04.cinque.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	C
Horne A	Hydro-economic modelling of climate change impacts on water allocations and markets	John A, Horne A, Hughes N	https://doi.org/10.36334/modsim2025.J02.john	https://mssanz.org.au/modsim2025/files/J02.john.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	H
Hughes N	Hydro-economic modelling of climate change impacts on water allocations and markets	John A, Horne A, Hughes N	https://doi.org/10.36334/modsim2025.J02.john	https://mssanz.org.au/modsim2025/files/J02.john.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	H
John A	Hydro-economic modelling of climate change impacts on water allocations and markets	John A, Horne A, Hughes N	https://doi.org/10.36334/modsim2025.J02.john	https://mssanz.org.au/modsim2025/files/J02.john.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	J
Cook FJ	Hydrologic response unit: The missing link in hydrology	Cook FJ, Weber T, Filipovic V	https://doi.org/10.36334/modsim2025.L04.cook	https://mssanz.org.au/modsim2025/files/L04.cook.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	C
Filipovic V	Hydrologic response unit: The missing link in hydrology	Cook FJ, Weber T, Filipovic V	https://doi.org/10.36334/modsim2025.L04.cook	https://mssanz.org.au/modsim2025/files/L04.cook.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	F
Weber T	Hydrologic response unit: The missing link in hydrology	Cook FJ, Weber T, Filipovic V	https://doi.org/10.36334/modsim2025.L04.cook	https://mssanz.org.au/modsim2025/files/L04.cook.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	W
Hammad M	Hydrological modelling under uncertainty: Can two-state residuals improve error representation?	Hammad M, Mehrotra R, Sharma A	https://doi.org/10.36334/modsim2025.K06.hammad	https://mssanz.org.au/modsim2025/files/K06.hammad.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	H
Mehrotra R	Hydrological modelling under uncertainty: Can two-state residuals improve error representation?	Hammad M, Mehrotra R, Sharma A	https://doi.org/10.36334/modsim2025.K06.hammad	https://mssanz.org.au/modsim2025/files/K06.hammad.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	M
Sharma A	Hydrological modelling under uncertainty: Can two-state residuals improve error representation?	Hammad M, Mehrotra R, Sharma A	https://doi.org/10.36334/modsim2025.K06.hammad	https://mssanz.org.au/modsim2025/files/K06.hammad.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	S
Aramini D	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	A
Beecham R	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	B
Dutta D	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	D
Fowler K	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	F
French N	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	F
Gan R	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	G
Gardiya Weligamage H	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	G
Higgins P	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	H
Reagan‑Beasley D	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	R
Saft M	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	S
Simons M	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	S
Tang Y	Hydrological non-stationarity in the northern Murray-Darling Basin	Gardiya Weligamage H, Fowler K, Saft M, Beecham R, French N, Dutta D, Higgins P, Gan R, Simons M, Tang Y, Reagan‑Beasley D, Aramini D	https://doi.org/10.36334/modsim2025.J01.gardiyaweligamage	https://mssanz.org.au/modsim2025/files/J01.gardiyaweligamage.pdf	J	Water resources	J1	Long term shifts in hydrological systems	T
Kanij T	Hyperspectral image analysis for environmental monitoring: advances of the last decade	Tasnim A, Sayeed A, Kanij T	https://doi.org/10.36334/modsim2025.J03.tasnim	https://mssanz.org.au/modsim2025/files/J03.tasnim.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	K
Sayeed A	Hyperspectral image analysis for environmental monitoring: advances of the last decade	Tasnim A, Sayeed A, Kanij T	https://doi.org/10.36334/modsim2025.J03.tasnim	https://mssanz.org.au/modsim2025/files/J03.tasnim.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	S
Tasnim A	Hyperspectral image analysis for environmental monitoring: advances of the last decade	Tasnim A, Sayeed A, Kanij T	https://doi.org/10.36334/modsim2025.J03.tasnim	https://mssanz.org.au/modsim2025/files/J03.tasnim.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	T
Knowling MJ	Identifying ‘macro’ drivers of cropping system performance	Luo L, Knowling MJ	https://doi.org/10.36334/modsim2025.B05.luo	https://mssanz.org.au/modsim2025/files/B05.luo.pdf	B	Biological systems	B5	Agricultural Systems	K
Luo L	Identifying ‘macro’ drivers of cropping system performance	Luo L, Knowling MJ	https://doi.org/10.36334/modsim2025.B05.luo	https://mssanz.org.au/modsim2025/files/B05.luo.pdf	B	Biological systems	B5	Agricultural Systems	L
Box P	Identifying and mapping groundwater dependent ecosystems from phenological behaviour in time series of satellite imagery in the Northern Territory	Job M, Box P	https://doi.org/10.36334/modsim2025.F08.job	https://mssanz.org.au/modsim2025/files/F08.job.pdf	F	Environment and ecology	F8	GIS and environmental modelling	B
Job M	Identifying and mapping groundwater dependent ecosystems from phenological behaviour in time series of satellite imagery in the Northern Territory	Job M, Box P	https://doi.org/10.36334/modsim2025.F08.job	https://mssanz.org.au/modsim2025/files/F08.job.pdf	F	Environment and ecology	F8	GIS and environmental modelling	J
Guillaume JHA	Identifying intervention options that promote coral reef resilience under climate uncertainty	Sanches VH, Iwanaga T, Ribeiro de Almeida P, Mathews SA, Guillaume JHA, Lade SJ	https://doi.org/10.36334/modsim2025.F05.hiratasanches	https://mssanz.org.au/modsim2025/files/F05.hiratasanches.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	G
Iwanaga T	Identifying intervention options that promote coral reef resilience under climate uncertainty	Sanches VH, Iwanaga T, Ribeiro de Almeida P, Mathews SA, Guillaume JHA, Lade SJ	https://doi.org/10.36334/modsim2025.F05.hiratasanches	https://mssanz.org.au/modsim2025/files/F05.hiratasanches.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	I
Lade SJ	Identifying intervention options that promote coral reef resilience under climate uncertainty	Sanches VH, Iwanaga T, Ribeiro de Almeida P, Mathews SA, Guillaume JHA, Lade SJ	https://doi.org/10.36334/modsim2025.F05.hiratasanches	https://mssanz.org.au/modsim2025/files/F05.hiratasanches.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	L
Mathews SA	Identifying intervention options that promote coral reef resilience under climate uncertainty	Sanches VH, Iwanaga T, Ribeiro de Almeida P, Mathews SA, Guillaume JHA, Lade SJ	https://doi.org/10.36334/modsim2025.F05.hiratasanches	https://mssanz.org.au/modsim2025/files/F05.hiratasanches.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	M
Ribeiro de Almeida P	Identifying intervention options that promote coral reef resilience under climate uncertainty	Sanches VH, Iwanaga T, Ribeiro de Almeida P, Mathews SA, Guillaume JHA, Lade SJ	https://doi.org/10.36334/modsim2025.F05.hiratasanches	https://mssanz.org.au/modsim2025/files/F05.hiratasanches.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	R
Sanches VH	Identifying intervention options that promote coral reef resilience under climate uncertainty	Sanches VH, Iwanaga T, Ribeiro de Almeida P, Mathews SA, Guillaume JHA, Lade SJ	https://doi.org/10.36334/modsim2025.F05.hiratasanches	https://mssanz.org.au/modsim2025/files/F05.hiratasanches.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	S
Hutchinson MF	Impact of topographic data resolution on spatial interpolation of hourly rainfall splines for flood inundation modelling (INVITED SPEAKER)	Nguyen C, Vaze J, Mateo CMR, Teng J, Hutchinson MF	https://doi.org/10.36334/modsim2025.G09.nguyen	https://mssanz.org.au/modsim2025/files/G09.nguyen.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	H
Mateo CMR	Impact of topographic data resolution on spatial interpolation of hourly rainfall splines for flood inundation modelling (INVITED SPEAKER)	Nguyen C, Vaze J, Mateo CMR, Teng J, Hutchinson MF	https://doi.org/10.36334/modsim2025.G09.nguyen	https://mssanz.org.au/modsim2025/files/G09.nguyen.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Nguyen C	Impact of topographic data resolution on spatial interpolation of hourly rainfall splines for flood inundation modelling (INVITED SPEAKER)	Nguyen C, Vaze J, Mateo CMR, Teng J, Hutchinson MF	https://doi.org/10.36334/modsim2025.G09.nguyen	https://mssanz.org.au/modsim2025/files/G09.nguyen.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	N
Teng J	Impact of topographic data resolution on spatial interpolation of hourly rainfall splines for flood inundation modelling (INVITED SPEAKER)	Nguyen C, Vaze J, Mateo CMR, Teng J, Hutchinson MF	https://doi.org/10.36334/modsim2025.G09.nguyen	https://mssanz.org.au/modsim2025/files/G09.nguyen.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	T
Vaze J	Impact of topographic data resolution on spatial interpolation of hourly rainfall splines for flood inundation modelling (INVITED SPEAKER)	Nguyen C, Vaze J, Mateo CMR, Teng J, Hutchinson MF	https://doi.org/10.36334/modsim2025.G09.nguyen	https://mssanz.org.au/modsim2025/files/G09.nguyen.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	V
Pepler A	Impactful wet/windy extremes in Australia (INVITED SPEAKER)	Pepler A	https://doi.org/10.36334/modsim2025.K07.pepler	https://mssanz.org.au/modsim2025/files/K07.pepler.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	P
Liu D (Dedi)	Impacts of inter-basin water diversion projects on feedback loops of water supply-hydropower generation-environment conservation nexus	Wang J, Liu D	https://doi.org/10.36334/modsim2025.A02.wang	https://mssanz.org.au/modsim2025/files/A02.wang.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	L
Wang J	Impacts of inter-basin water diversion projects on feedback loops of water supply-hydropower generation-environment conservation nexus	Wang J, Liu D	https://doi.org/10.36334/modsim2025.A02.wang	https://mssanz.org.au/modsim2025/files/A02.wang.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	W
Brown A (Andrew)	Implementing dead storage representation in the Barwon-Darling Source model to improve low flow and cease to flow behaviour	Regan-Beasley D, Podger S, Teh S, Brown A	https://doi.org/10.36334/modsim2025.J02.reganbeasley	https://mssanz.org.au/modsim2025/files/J02.reganbeasley.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Podger S	Implementing dead storage representation in the Barwon-Darling Source model to improve low flow and cease to flow behaviour	Regan-Beasley D, Podger S, Teh S, Brown A	https://doi.org/10.36334/modsim2025.J02.reganbeasley	https://mssanz.org.au/modsim2025/files/J02.reganbeasley.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Regan-Beasley D	Implementing dead storage representation in the Barwon-Darling Source model to improve low flow and cease to flow behaviour	Regan-Beasley D, Podger S, Teh S, Brown A	https://doi.org/10.36334/modsim2025.J02.reganbeasley	https://mssanz.org.au/modsim2025/files/J02.reganbeasley.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Teh S	Implementing dead storage representation in the Barwon-Darling Source model to improve low flow and cease to flow behaviour	Regan-Beasley D, Podger S, Teh S, Brown A	https://doi.org/10.36334/modsim2025.J02.reganbeasley	https://mssanz.org.au/modsim2025/files/J02.reganbeasley.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	T
Akhtar K	Importance of scientific rigour in validating machine learning models: The Google global flood model example	Razavi S, Li K, Maier HR, Hrachowitz M, Nabavi E, Harvey N, Akhtar K, Unduche F	https://doi.org/10.36334/modsim2025.C07.razavi	https://mssanz.org.au/modsim2025/files/C07.razavi.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	A
Harvey N	Importance of scientific rigour in validating machine learning models: The Google global flood model example	Razavi S, Li K, Maier HR, Hrachowitz M, Nabavi E, Harvey N, Akhtar K, Unduche F	https://doi.org/10.36334/modsim2025.C07.razavi	https://mssanz.org.au/modsim2025/files/C07.razavi.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	H
Hrachowitz M	Importance of scientific rigour in validating machine learning models: The Google global flood model example	Razavi S, Li K, Maier HR, Hrachowitz M, Nabavi E, Harvey N, Akhtar K, Unduche F	https://doi.org/10.36334/modsim2025.C07.razavi	https://mssanz.org.au/modsim2025/files/C07.razavi.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	H
Li K	Importance of scientific rigour in validating machine learning models: The Google global flood model example	Razavi S, Li K, Maier HR, Hrachowitz M, Nabavi E, Harvey N, Akhtar K, Unduche F	https://doi.org/10.36334/modsim2025.C07.razavi	https://mssanz.org.au/modsim2025/files/C07.razavi.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	L
Maier HR	Importance of scientific rigour in validating machine learning models: The Google global flood model example	Razavi S, Li K, Maier HR, Hrachowitz M, Nabavi E, Harvey N, Akhtar K, Unduche F	https://doi.org/10.36334/modsim2025.C07.razavi	https://mssanz.org.au/modsim2025/files/C07.razavi.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	M
Nabavi E	Importance of scientific rigour in validating machine learning models: The Google global flood model example	Razavi S, Li K, Maier HR, Hrachowitz M, Nabavi E, Harvey N, Akhtar K, Unduche F	https://doi.org/10.36334/modsim2025.C07.razavi	https://mssanz.org.au/modsim2025/files/C07.razavi.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	N
Razavi S	Importance of scientific rigour in validating machine learning models: The Google global flood model example	Razavi S, Li K, Maier HR, Hrachowitz M, Nabavi E, Harvey N, Akhtar K, Unduche F	https://doi.org/10.36334/modsim2025.C07.razavi	https://mssanz.org.au/modsim2025/files/C07.razavi.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	R
Unduche F	Importance of scientific rigour in validating machine learning models: The Google global flood model example	Razavi S, Li K, Maier HR, Hrachowitz M, Nabavi E, Harvey N, Akhtar K, Unduche F	https://doi.org/10.36334/modsim2025.C07.razavi	https://mssanz.org.au/modsim2025/files/C07.razavi.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	U
Darr S	Improved hillslope erosion prediction for the Great Barrier Reef Catchments	Yu B, Rahman J, Darr S, Fentie B	https://doi.org/10.36334/modsim2025.L04.yu	https://mssanz.org.au/modsim2025/files/L04.yu.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	D
Fentie B	Improved hillslope erosion prediction for the Great Barrier Reef Catchments	Yu B, Rahman J, Darr S, Fentie B	https://doi.org/10.36334/modsim2025.L04.yu	https://mssanz.org.au/modsim2025/files/L04.yu.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	F
Rahman J (Joel)	Improved hillslope erosion prediction for the Great Barrier Reef Catchments	Yu B, Rahman J, Darr S, Fentie B	https://doi.org/10.36334/modsim2025.L04.yu	https://mssanz.org.au/modsim2025/files/L04.yu.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	R
Yu B	Improved hillslope erosion prediction for the Great Barrier Reef Catchments	Yu B, Rahman J, Darr S, Fentie B	https://doi.org/10.36334/modsim2025.L04.yu	https://mssanz.org.au/modsim2025/files/L04.yu.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	Y
Fowler J (John)	Improving adoption of quantum vector annealing in supply chains	Rogers D, Fowler J	https://doi.org/10.36334/modsim2025.M02.rogersd	https://mssanz.org.au/modsim2025/files/M02.rogersd.pdf	M	Operations Research	M2	Simulation modelling and analysis	F
Rogers D	Improving adoption of quantum vector annealing in supply chains	Rogers D, Fowler J	https://doi.org/10.36334/modsim2025.M02.rogersd	https://mssanz.org.au/modsim2025/files/M02.rogersd.pdf	M	Operations Research	M2	Simulation modelling and analysis	R
Chen D (Deli)	Improving APSIM modules for enhanced-efficiency fertilizers to simulate N2O emissions in vegetable systems	Liu D, Pan B, Lam SK, Chen D	https://doi.org/10.36334/modsim2025.B03.liu	https://mssanz.org.au/modsim2025/files/B03.liu.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	C
Lam SK (Shu Kee)	Improving APSIM modules for enhanced-efficiency fertilizers to simulate N2O emissions in vegetable systems	Liu D, Pan B, Lam SK, Chen D	https://doi.org/10.36334/modsim2025.B03.liu	https://mssanz.org.au/modsim2025/files/B03.liu.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	L
Liu D (Deyao)	Improving APSIM modules for enhanced-efficiency fertilizers to simulate N2O emissions in vegetable systems	Liu D, Pan B, Lam SK, Chen D	https://doi.org/10.36334/modsim2025.B03.liu	https://mssanz.org.au/modsim2025/files/B03.liu.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	L
Pan B (Baobao)	Improving APSIM modules for enhanced-efficiency fertilizers to simulate N2O emissions in vegetable systems	Liu D, Pan B, Lam SK, Chen D	https://doi.org/10.36334/modsim2025.B03.liu	https://mssanz.org.au/modsim2025/files/B03.liu.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	P
Croke B	Improving the discrete form of the Nash cascade	Croke B, McCulloch G	https://doi.org/10.36334/modsim2025.A02.croke	https://mssanz.org.au/modsim2025/files/A02.croke.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	C
McCulloch G	Improving the discrete form of the Nash cascade	Croke B, McCulloch G	https://doi.org/10.36334/modsim2025.A02.croke	https://mssanz.org.au/modsim2025/files/A02.croke.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	M
Chen D (Dawei)	Improving the understanding and prediction of low extreme dissolved oxygen (DO) events in Victorian rivers	Yan P, Pietsch H, Chen D, Guo D	https://doi.org/10.36334/modsim2025.L01.yan	https://mssanz.org.au/modsim2025/files/L01.yan.pdf	L	Water quality	L1	Catchment Water Quality Modelling	C
Guo D (Danlu)	Improving the understanding and prediction of low extreme dissolved oxygen (DO) events in Victorian rivers	Yan P, Pietsch H, Chen D, Guo D	https://doi.org/10.36334/modsim2025.L01.yan	https://mssanz.org.au/modsim2025/files/L01.yan.pdf	L	Water quality	L1	Catchment Water Quality Modelling	G
Pietsch H	Improving the understanding and prediction of low extreme dissolved oxygen (DO) events in Victorian rivers	Yan P, Pietsch H, Chen D, Guo D	https://doi.org/10.36334/modsim2025.L01.yan	https://mssanz.org.au/modsim2025/files/L01.yan.pdf	L	Water quality	L1	Catchment Water Quality Modelling	P
Yan P (Peiran)	Improving the understanding and prediction of low extreme dissolved oxygen (DO) events in Victorian rivers	Yan P, Pietsch H, Chen D, Guo D	https://doi.org/10.36334/modsim2025.L01.yan	https://mssanz.org.au/modsim2025/files/L01.yan.pdf	L	Water quality	L1	Catchment Water Quality Modelling	Y
Ghobakhlou A	Impute-Net: A deep learning approach to missing observations in satellite imagery	Pahlevi AR, Ghobakhlou A, Whalley J	https://doi.org/10.36334/modsim2025.F10.pahlevi	https://mssanz.org.au/modsim2025/files/F10.pahlevi.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	G
Pahlevi AR	Impute-Net: A deep learning approach to missing observations in satellite imagery	Pahlevi AR, Ghobakhlou A, Whalley J	https://doi.org/10.36334/modsim2025.F10.pahlevi	https://mssanz.org.au/modsim2025/files/F10.pahlevi.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	P
Whalley J	Impute-Net: A deep learning approach to missing observations in satellite imagery	Pahlevi AR, Ghobakhlou A, Whalley J	https://doi.org/10.36334/modsim2025.F10.pahlevi	https://mssanz.org.au/modsim2025/files/F10.pahlevi.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	W
Mateo C	Including flooded vegetation in Earth-observation flood maps to assist in the assessment of hydrological models	Ticehurst CJ, Vaze J, Montazeri M, Mateo C, Nguyen C	https://doi.org/10.36334/modsim2025.G09.ticehurst	https://mssanz.org.au/modsim2025/files/G09.ticehurst.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Montazeri M	Including flooded vegetation in Earth-observation flood maps to assist in the assessment of hydrological models	Ticehurst CJ, Vaze J, Montazeri M, Mateo C, Nguyen C	https://doi.org/10.36334/modsim2025.G09.ticehurst	https://mssanz.org.au/modsim2025/files/G09.ticehurst.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Nguyen C	Including flooded vegetation in Earth-observation flood maps to assist in the assessment of hydrological models	Ticehurst CJ, Vaze J, Montazeri M, Mateo C, Nguyen C	https://doi.org/10.36334/modsim2025.G09.ticehurst	https://mssanz.org.au/modsim2025/files/G09.ticehurst.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	N
Ticehurst CJ	Including flooded vegetation in Earth-observation flood maps to assist in the assessment of hydrological models	Ticehurst CJ, Vaze J, Montazeri M, Mateo C, Nguyen C	https://doi.org/10.36334/modsim2025.G09.ticehurst	https://mssanz.org.au/modsim2025/files/G09.ticehurst.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	T
Vaze J	Including flooded vegetation in Earth-observation flood maps to assist in the assessment of hydrological models	Ticehurst CJ, Vaze J, Montazeri M, Mateo C, Nguyen C	https://doi.org/10.36334/modsim2025.G09.ticehurst	https://mssanz.org.au/modsim2025/files/G09.ticehurst.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	V
Campos Teixeira P	Incorporating land use change adjustments into basin-scale groundwater recharge estimates in the Murray-Darling Basin	Crosbie R, Campos Teixeira P	https://doi.org/10.36334/modsim2025.J03.crosbie	https://mssanz.org.au/modsim2025/files/J03.crosbie.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	C
Crosbie RS	Incorporating land use change adjustments into basin-scale groundwater recharge estimates in the Murray-Darling Basin	Crosbie R, Campos Teixeira P	https://doi.org/10.36334/modsim2025.J03.crosbie	https://mssanz.org.au/modsim2025/files/J03.crosbie.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	C
Dodd M	Incorporating treading damage on wet soils in APSIM	Dodd B, Foster J, Graham P	https://doi.org/10.36334/modsim2025.B03.dodd	https://mssanz.org.au/modsim2025/files/B03.dodd.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	D
Snow V	Incorporating treading damage on wet soils in APSIM	Dodd B, Foster J, Graham P	https://doi.org/10.36334/modsim2025.B03.dodd	https://mssanz.org.au/modsim2025/files/B03.dodd.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	S
Triadis D	Incorporating treading damage on wet soils in APSIM	Dodd B, Foster J, Graham P	https://doi.org/10.36334/modsim2025.B03.dodd	https://mssanz.org.au/modsim2025/files/B03.dodd.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	T
McRae RHD	Indirect effects of climate change on forests and their fires – pitfalls for modellers	McRae RHD	https://doi.org/10.36334/modsim2025.G08.mcrae	https://mssanz.org.au/modsim2025/files/G08.mcrae.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	M
Hayde P	Industry application of multispecies modelling to track chloramine decay in a water network	Obeid R, Hayde P	https://doi.org/10.36334/modsim2025.L03.obeid	https://mssanz.org.au/modsim2025/files/L03.obeid.pdf	L	Water quality	L3	Modelling water quality treatment and management	H
Obeid R	Industry application of multispecies modelling to track chloramine decay in a water network	Obeid R, Hayde P	https://doi.org/10.36334/modsim2025.L03.obeid	https://mssanz.org.au/modsim2025/files/L03.obeid.pdf	L	Water quality	L3	Modelling water quality treatment and management	O
Cao Y (Yuan)	Informed performance assessment of water resources systems under uncertainty by considering trade-offs between performance and risk appetite (NVITED SPEAKER)	Cao Y, Wu W, Maier HR, Gibbs MS	https://doi.org/10.36334/modsim2025.J08.cao	https://mssanz.org.au/modsim2025/files/J08.cao.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	C
Gibbs MS	Informed performance assessment of water resources systems under uncertainty by considering trade-offs between performance and risk appetite (NVITED SPEAKER)	Cao Y, Wu W, Maier HR, Gibbs MS	https://doi.org/10.36334/modsim2025.J08.cao	https://mssanz.org.au/modsim2025/files/J08.cao.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	G
Maier HR	Informed performance assessment of water resources systems under uncertainty by considering trade-offs between performance and risk appetite (NVITED SPEAKER)	Cao Y, Wu W, Maier HR, Gibbs MS	https://doi.org/10.36334/modsim2025.J08.cao	https://mssanz.org.au/modsim2025/files/J08.cao.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	M
Wu W (Wenyan)	Informed performance assessment of water resources systems under uncertainty by considering trade-offs between performance and risk appetite (NVITED SPEAKER)	Cao Y, Wu W, Maier HR, Gibbs MS	https://doi.org/10.36334/modsim2025.J08.cao	https://mssanz.org.au/modsim2025/files/J08.cao.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	W
Bolz P	Informing risk and opportunity management in coal mine pit lakes via modelling experiments	Bulovic N, McIntyre N, Chrystal R, Bolz P	https://doi.org/10.36334/modsim2025.L02.bulovic	https://mssanz.org.au/modsim2025/files/L02.bulovic.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	B
Bulovic N	Informing risk and opportunity management in coal mine pit lakes via modelling experiments	Bulovic N, McIntyre N, Chrystal R, Bolz P	https://doi.org/10.36334/modsim2025.L02.bulovic	https://mssanz.org.au/modsim2025/files/L02.bulovic.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	B
Chrystal R	Informing risk and opportunity management in coal mine pit lakes via modelling experiments	Bulovic N, McIntyre N, Chrystal R, Bolz P	https://doi.org/10.36334/modsim2025.L02.bulovic	https://mssanz.org.au/modsim2025/files/L02.bulovic.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	C
McIntyre N	Informing risk and opportunity management in coal mine pit lakes via modelling experiments	Bulovic N, McIntyre N, Chrystal R, Bolz P	https://doi.org/10.36334/modsim2025.L02.bulovic	https://mssanz.org.au/modsim2025/files/L02.bulovic.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	M
Armstrong M	Integrated assessment and modelling for water management: lessons from transdisciplinary research	Hamilton SH, Merritt W, Jakeman AJ, Elsawah S, Pollino C, Horne A, Armstrong M, Maier HR	https://doi.org/10.36334/modsim2025.J05.hamilton	https://mssanz.org.au/modsim2025/files/J05.hamilton.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	A
Elsawah S	Integrated assessment and modelling for water management: lessons from transdisciplinary research	Hamilton SH, Merritt W, Jakeman AJ, Elsawah S, Pollino C, Horne A, Armstrong M, Maier HR	https://doi.org/10.36334/modsim2025.J05.hamilton	https://mssanz.org.au/modsim2025/files/J05.hamilton.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	E
Hamilton SH	Integrated assessment and modelling for water management: lessons from transdisciplinary research	Hamilton SH, Merritt W, Jakeman AJ, Elsawah S, Pollino C, Horne A, Armstrong M, Maier HR	https://doi.org/10.36334/modsim2025.J05.hamilton	https://mssanz.org.au/modsim2025/files/J05.hamilton.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	H
Horne A	Integrated assessment and modelling for water management: lessons from transdisciplinary research	Hamilton SH, Merritt W, Jakeman AJ, Elsawah S, Pollino C, Horne A, Armstrong M, Maier HR	https://doi.org/10.36334/modsim2025.J05.hamilton	https://mssanz.org.au/modsim2025/files/J05.hamilton.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	H
Jakeman AJ	Integrated assessment and modelling for water management: lessons from transdisciplinary research	Hamilton SH, Merritt W, Jakeman AJ, Elsawah S, Pollino C, Horne A, Armstrong M, Maier HR	https://doi.org/10.36334/modsim2025.J05.hamilton	https://mssanz.org.au/modsim2025/files/J05.hamilton.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	J
Maier HR	Integrated assessment and modelling for water management: lessons from transdisciplinary research	Hamilton SH, Merritt W, Jakeman AJ, Elsawah S, Pollino C, Horne A, Armstrong M, Maier HR	https://doi.org/10.36334/modsim2025.J05.hamilton	https://mssanz.org.au/modsim2025/files/J05.hamilton.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	M
Merritt W	Integrated assessment and modelling for water management: lessons from transdisciplinary research	Hamilton SH, Merritt W, Jakeman AJ, Elsawah S, Pollino C, Horne A, Armstrong M, Maier HR	https://doi.org/10.36334/modsim2025.J05.hamilton	https://mssanz.org.au/modsim2025/files/J05.hamilton.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	M
Pollino C	Integrated assessment and modelling for water management: lessons from transdisciplinary research	Hamilton SH, Merritt W, Jakeman AJ, Elsawah S, Pollino C, Horne A, Armstrong M, Maier HR	https://doi.org/10.36334/modsim2025.J05.hamilton	https://mssanz.org.au/modsim2025/files/J05.hamilton.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	P
Haslum P	Integrated task and motion planning for keel block optimisation at Captain Cook Graving Dock	Jones W, Kurniawati H, Haslum P, Manchester I	https://doi.org/10.36334/modsim2025.M01.jones	https://mssanz.org.au/modsim2025/files/M01.jones.pdf	M	Operations Research	M1	OR methods and applications	H
Jones W	Integrated task and motion planning for keel block optimisation at Captain Cook Graving Dock	Jones W, Kurniawati H, Haslum P, Manchester I	https://doi.org/10.36334/modsim2025.M01.jones	https://mssanz.org.au/modsim2025/files/M01.jones.pdf	M	Operations Research	M1	OR methods and applications	J
Kurniawati H	Integrated task and motion planning for keel block optimisation at Captain Cook Graving Dock	Jones W, Kurniawati H, Haslum P, Manchester I	https://doi.org/10.36334/modsim2025.M01.jones	https://mssanz.org.au/modsim2025/files/M01.jones.pdf	M	Operations Research	M1	OR methods and applications	K
Manchester I	Integrated task and motion planning for keel block optimisation at Captain Cook Graving Dock	Jones W, Kurniawati H, Haslum P, Manchester I	https://doi.org/10.36334/modsim2025.M01.jones	https://mssanz.org.au/modsim2025/files/M01.jones.pdf	M	Operations Research	M1	OR methods and applications	M
Chapman SC	Integrating crop modelling and machine learning for non-destructive estimation of crop traits	Chen Q, Hu P, Zheng B, Chapman SC	https://doi.org/10.36334/modsim2025.B08.chenq	https://mssanz.org.au/modsim2025/files/B08.chenq.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Chen Q (Qiaomin)	Integrating crop modelling and machine learning for non-destructive estimation of crop traits	Chen Q, Hu P, Zheng B, Chapman SC	https://doi.org/10.36334/modsim2025.B08.chenq	https://mssanz.org.au/modsim2025/files/B08.chenq.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Hu P (Pengcheng)	Integrating crop modelling and machine learning for non-destructive estimation of crop traits	Chen Q, Hu P, Zheng B, Chapman SC	https://doi.org/10.36334/modsim2025.B08.chenq	https://mssanz.org.au/modsim2025/files/B08.chenq.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	H
Zheng B (Bangyou)	Integrating crop modelling and machine learning for non-destructive estimation of crop traits	Chen Q, Hu P, Zheng B, Chapman SC	https://doi.org/10.36334/modsim2025.B08.chenq	https://mssanz.org.au/modsim2025/files/B08.chenq.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	Z
Barker L	Integrating Indigenous Knowledge and Indigenous Data Sovereignty for sustainable water management	Sedran‑Price C, Dixon A, Connolly M, Barker R, Barker L, Van Ogtrop F, Hooper F	https://doi.org/10.36334/modsim2025.J07.sedranprice	https://mssanz.org.au/modsim2025/files/J07.sedranprice.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	B
Barker R	Integrating Indigenous Knowledge and Indigenous Data Sovereignty for sustainable water management	Sedran‑Price C, Dixon A, Connolly M, Barker R, Barker L, Van Ogtrop F, Hooper F	https://doi.org/10.36334/modsim2025.J07.sedranprice	https://mssanz.org.au/modsim2025/files/J07.sedranprice.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	B
Connolly M	Integrating Indigenous Knowledge and Indigenous Data Sovereignty for sustainable water management	Sedran‑Price C, Dixon A, Connolly M, Barker R, Barker L, Van Ogtrop F, Hooper F	https://doi.org/10.36334/modsim2025.J07.sedranprice	https://mssanz.org.au/modsim2025/files/J07.sedranprice.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	C
Dixon A	Integrating Indigenous Knowledge and Indigenous Data Sovereignty for sustainable water management	Sedran‑Price C, Dixon A, Connolly M, Barker R, Barker L, Van Ogtrop F, Hooper F	https://doi.org/10.36334/modsim2025.J07.sedranprice	https://mssanz.org.au/modsim2025/files/J07.sedranprice.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	D
Hooper F	Integrating Indigenous Knowledge and Indigenous Data Sovereignty for sustainable water management	Sedran‑Price C, Dixon A, Connolly M, Barker R, Barker L, Van Ogtrop F, Hooper F	https://doi.org/10.36334/modsim2025.J07.sedranprice	https://mssanz.org.au/modsim2025/files/J07.sedranprice.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	H
Sedran‑Price C	Integrating Indigenous Knowledge and Indigenous Data Sovereignty for sustainable water management	Sedran‑Price C, Dixon A, Connolly M, Barker R, Barker L, Van Ogtrop F, Hooper F	https://doi.org/10.36334/modsim2025.J07.sedranprice	https://mssanz.org.au/modsim2025/files/J07.sedranprice.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	S
Van Ogtrop F	Integrating Indigenous Knowledge and Indigenous Data Sovereignty for sustainable water management	Sedran‑Price C, Dixon A, Connolly M, Barker R, Barker L, Van Ogtrop F, Hooper F	https://doi.org/10.36334/modsim2025.J07.sedranprice	https://mssanz.org.au/modsim2025/files/J07.sedranprice.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	V
Biddulph B	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	B
Bloomfield M	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	B
Brown H (Hamish)	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	B
Celestina C	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	C
Harris F	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Hu P (Pengcheng)	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Hunt J	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Huth N	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Hyles J	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Lilley J	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	L
Porker K	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	P
Trevaskis B	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	T
Wang E (Enli)	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	W
Whish J	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	W
Zhao Z (Zhigan)	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	Z
Zheng B (Bangyou)	Integrating molecular insights into phenology modelling enhances flowering time predictions (INVITED SPEAKER)	Wang E, Brown H, Zheng B, Zhao Z, Huth N, Hunt J, Hyles J, Bloomfield M, Celestina C, Porker K, Harris F, Biddulph B, Trevaskis B, Hu P, Lilley J, Whish J	https://doi.org/10.36334/modsim2025.B02.wang	https://mssanz.org.au/modsim2025/files/B02.wang.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	Z
Gong H (Huili)	Integrating time-series InSAR and ensemble learning model for land subsidence susceptibility mapping	Li S, Zhu L, Gong H, Li X, Zhang N, Meng X	https://doi.org/10.36334/modsim2025.G02.li	https://mssanz.org.au/modsim2025/files/G02.li.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	G
Li S (Shuai)	Integrating time-series InSAR and ensemble learning model for land subsidence susceptibility mapping	Li S, Zhu L, Gong H, Li X, Zhang N, Meng X	https://doi.org/10.36334/modsim2025.G02.li	https://mssanz.org.au/modsim2025/files/G02.li.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	L
Li X (Xiaojuan)	Integrating time-series InSAR and ensemble learning model for land subsidence susceptibility mapping	Li S, Zhu L, Gong H, Li X, Zhang N, Meng X	https://doi.org/10.36334/modsim2025.G02.li	https://mssanz.org.au/modsim2025/files/G02.li.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	L
Meng X (Xiangyu)	Integrating time-series InSAR and ensemble learning model for land subsidence susceptibility mapping	Li S, Zhu L, Gong H, Li X, Zhang N, Meng X	https://doi.org/10.36334/modsim2025.G02.li	https://mssanz.org.au/modsim2025/files/G02.li.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	M
Zhang N (Ning)	Integrating time-series InSAR and ensemble learning model for land subsidence susceptibility mapping	Li S, Zhu L, Gong H, Li X, Zhang N, Meng X	https://doi.org/10.36334/modsim2025.G02.li	https://mssanz.org.au/modsim2025/files/G02.li.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Z
Zhu L (Lin)	Integrating time-series InSAR and ensemble learning model for land subsidence susceptibility mapping	Li S, Zhu L, Gong H, Li X, Zhang N, Meng X	https://doi.org/10.36334/modsim2025.G02.li	https://mssanz.org.au/modsim2025/files/G02.li.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Z
Cao Y (Yuan)	Integration of operational and infrastructure decisions into long-term planning and management of water resources systems under deep uncertainty	Huang J, Wu W, Maier HR, Hughes J, Wang QJ, Cao Y	https://doi.org/10.36334/modsim2025.J08.huang	https://mssanz.org.au/modsim2025/files/J08.huang.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	C
Huang J (Jiajia)	Integration of operational and infrastructure decisions into long-term planning and management of water resources systems under deep uncertainty	Huang J, Wu W, Maier HR, Hughes J, Wang QJ, Cao Y	https://doi.org/10.36334/modsim2025.J08.huang	https://mssanz.org.au/modsim2025/files/J08.huang.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	H
Hughes J	Integration of operational and infrastructure decisions into long-term planning and management of water resources systems under deep uncertainty	Huang J, Wu W, Maier HR, Hughes J, Wang QJ, Cao Y	https://doi.org/10.36334/modsim2025.J08.huang	https://mssanz.org.au/modsim2025/files/J08.huang.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	H
Maier HR	Integration of operational and infrastructure decisions into long-term planning and management of water resources systems under deep uncertainty	Huang J, Wu W, Maier HR, Hughes J, Wang QJ, Cao Y	https://doi.org/10.36334/modsim2025.J08.huang	https://mssanz.org.au/modsim2025/files/J08.huang.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	M
Wang QJ	Integration of operational and infrastructure decisions into long-term planning and management of water resources systems under deep uncertainty	Huang J, Wu W, Maier HR, Hughes J, Wang QJ, Cao Y	https://doi.org/10.36334/modsim2025.J08.huang	https://mssanz.org.au/modsim2025/files/J08.huang.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	W
Wu W (Wenyan)	Integration of operational and infrastructure decisions into long-term planning and management of water resources systems under deep uncertainty	Huang J, Wu W, Maier HR, Hughes J, Wang QJ, Cao Y	https://doi.org/10.36334/modsim2025.J08.huang	https://mssanz.org.au/modsim2025/files/J08.huang.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	W
Ashman P	Integration of variable-rate industrial processes with variable renewable energy: A case study in H2-DRI	Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Rahbari A, Convetry J, Pye J	https://doi.org/10.36334/modsim2025.E02.fontalvo	https://mssanz.org.au/modsim2025/files/E02.fontalvo.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	A
Benz T	Integration of variable-rate industrial processes with variable renewable energy: A case study in H2-DRI	Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Rahbari A, Convetry J, Pye J	https://doi.org/10.36334/modsim2025.E02.fontalvo	https://mssanz.org.au/modsim2025/files/E02.fontalvo.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	B
Convetry J	Integration of variable-rate industrial processes with variable renewable energy: A case study in H2-DRI	Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Rahbari A, Convetry J, Pye J	https://doi.org/10.36334/modsim2025.E02.fontalvo	https://mssanz.org.au/modsim2025/files/E02.fontalvo.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	C
Fontalvo A	Integration of variable-rate industrial processes with variable renewable energy: A case study in H2-DRI	Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Rahbari A, Convetry J, Pye J	https://doi.org/10.36334/modsim2025.E02.fontalvo	https://mssanz.org.au/modsim2025/files/E02.fontalvo.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	F
Pye J	Integration of variable-rate industrial processes with variable renewable energy: A case study in H2-DRI	Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Rahbari A, Convetry J, Pye J	https://doi.org/10.36334/modsim2025.E02.fontalvo	https://mssanz.org.au/modsim2025/files/E02.fontalvo.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	P
Rahbari A	Integration of variable-rate industrial processes with variable renewable energy: A case study in H2-DRI	Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Rahbari A, Convetry J, Pye J	https://doi.org/10.36334/modsim2025.E02.fontalvo	https://mssanz.org.au/modsim2025/files/E02.fontalvo.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	R
Saw W	Integration of variable-rate industrial processes with variable renewable energy: A case study in H2-DRI	Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Rahbari A, Convetry J, Pye J	https://doi.org/10.36334/modsim2025.E02.fontalvo	https://mssanz.org.au/modsim2025/files/E02.fontalvo.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	S
Wang Y (Ye)	Integration of variable-rate industrial processes with variable renewable energy: A case study in H2-DRI	Fontalvo A, Wang Y, Benz T, Saw W, Ashman P, Rahbari A, Convetry J, Pye J	https://doi.org/10.36334/modsim2025.E02.fontalvo	https://mssanz.org.au/modsim2025/files/E02.fontalvo.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	W
Di Virgilio G	Intensification of short-duration extreme precipitation over Greater Sydney: Observations and climate projections	Khadke L, Evans JP, Kim Y, Di Virgilio G	https://doi.org/10.36334/modsim2025.G04.khadke	https://mssanz.org.au/modsim2025/files/G04.khadke.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	D
Evans JP	Intensification of short-duration extreme precipitation over Greater Sydney: Observations and climate projections	Khadke L, Evans JP, Kim Y, Di Virgilio G	https://doi.org/10.36334/modsim2025.G04.khadke	https://mssanz.org.au/modsim2025/files/G04.khadke.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	E
Khadke L	Intensification of short-duration extreme precipitation over Greater Sydney: Observations and climate projections	Khadke L, Evans JP, Kim Y, Di Virgilio G	https://doi.org/10.36334/modsim2025.G04.khadke	https://mssanz.org.au/modsim2025/files/G04.khadke.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	K
Kim Y (Youngil)	Intensification of short-duration extreme precipitation over Greater Sydney: Observations and climate projections	Khadke L, Evans JP, Kim Y, Di Virgilio G	https://doi.org/10.36334/modsim2025.G04.khadke	https://mssanz.org.au/modsim2025/files/G04.khadke.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	K
Chen Y (Yaning)	Intensified water-ecological risks in the arid regions of Central Asia under global warming	Chen Y, Li Z, Wang C, Wang X	https://doi.org/10.36334/modsim2025.K03.chen	https://mssanz.org.au/modsim2025/files/K03.chen.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	C
Li Z (Zhi)	Intensified water-ecological risks in the arid regions of Central Asia under global warming	Chen Y, Li Z, Wang C, Wang X	https://doi.org/10.36334/modsim2025.K03.chen	https://mssanz.org.au/modsim2025/files/K03.chen.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	L
Wang C (Chuan)	Intensified water-ecological risks in the arid regions of Central Asia under global warming	Chen Y, Li Z, Wang C, Wang X	https://doi.org/10.36334/modsim2025.K03.chen	https://mssanz.org.au/modsim2025/files/K03.chen.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	W
Wang X (Xuechun)	Intensified water-ecological risks in the arid regions of Central Asia under global warming	Chen Y, Li Z, Wang C, Wang X	https://doi.org/10.36334/modsim2025.K03.chen	https://mssanz.org.au/modsim2025/files/K03.chen.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	W
Tu Z	Interactive effects of vegetation responses to elevated atmospheric CO₂ on drought conditions in Asia	Tu Z	https://doi.org/10.36334/modsim2025.J06.tu	https://mssanz.org.au/modsim2025/files/J06.tu.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	T
Chopin J (Joshua)	Interpretable deep learning for energy forecasting and outlier detection	Chopin J	https://doi.org/10.36334/modsim2025.E04.chopin	https://mssanz.org.au/modsim2025/files/E04.chopin.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	C
Iwanaga T	Interpretable sensitivity analysis of a coral ecosystem model: An assessment of CoralBlox with Shapley Effects	Ribeiro de Almeida P, Tan D, Matthews S, Iwanaga T	https://doi.org/10.36334/modsim2025.F05.ribeirodealmeida	https://mssanz.org.au/modsim2025/files/F05.ribeirodealmeida.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	I
Matthews S	Interpretable sensitivity analysis of a coral ecosystem model: An assessment of CoralBlox with Shapley Effects	Ribeiro de Almeida P, Tan D, Matthews S, Iwanaga T	https://doi.org/10.36334/modsim2025.F05.ribeirodealmeida	https://mssanz.org.au/modsim2025/files/F05.ribeirodealmeida.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	M
Ribeiro de Almeida P	Interpretable sensitivity analysis of a coral ecosystem model: An assessment of CoralBlox with Shapley Effects	Ribeiro de Almeida P, Tan D, Matthews S, Iwanaga T	https://doi.org/10.36334/modsim2025.F05.ribeirodealmeida	https://mssanz.org.au/modsim2025/files/F05.ribeirodealmeida.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	R
Tan D (Daniel)	Interpretable sensitivity analysis of a coral ecosystem model: An assessment of CoralBlox with Shapley Effects	Ribeiro de Almeida P, Tan D, Matthews S, Iwanaga T	https://doi.org/10.36334/modsim2025.F05.ribeirodealmeida	https://mssanz.org.au/modsim2025/files/F05.ribeirodealmeida.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	T
Bridgart R	Introducing a fast and scalable method for floodplain inundation modelling	Teng J, Yang A, Penton DJ, Ticehurst C, Marvanek S, Vaze J, Mateo C, Bridgart R	https://doi.org/10.36334/modsim2025.G09.teng	https://mssanz.org.au/modsim2025/files/G09.teng.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	B
Marvanek S	Introducing a fast and scalable method for floodplain inundation modelling	Teng J, Yang A, Penton DJ, Ticehurst C, Marvanek S, Vaze J, Mateo C, Bridgart R	https://doi.org/10.36334/modsim2025.G09.teng	https://mssanz.org.au/modsim2025/files/G09.teng.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Mateo C	Introducing a fast and scalable method for floodplain inundation modelling	Teng J, Yang A, Penton DJ, Ticehurst C, Marvanek S, Vaze J, Mateo C, Bridgart R	https://doi.org/10.36334/modsim2025.G09.teng	https://mssanz.org.au/modsim2025/files/G09.teng.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Penton DJ	Introducing a fast and scalable method for floodplain inundation modelling	Teng J, Yang A, Penton DJ, Ticehurst C, Marvanek S, Vaze J, Mateo C, Bridgart R	https://doi.org/10.36334/modsim2025.G09.teng	https://mssanz.org.au/modsim2025/files/G09.teng.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	P
Teng J	Introducing a fast and scalable method for floodplain inundation modelling	Teng J, Yang A, Penton DJ, Ticehurst C, Marvanek S, Vaze J, Mateo C, Bridgart R	https://doi.org/10.36334/modsim2025.G09.teng	https://mssanz.org.au/modsim2025/files/G09.teng.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	T
Ticehurst C	Introducing a fast and scalable method for floodplain inundation modelling	Teng J, Yang A, Penton DJ, Ticehurst C, Marvanek S, Vaze J, Mateo C, Bridgart R	https://doi.org/10.36334/modsim2025.G09.teng	https://mssanz.org.au/modsim2025/files/G09.teng.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	T
Vaze J	Introducing a fast and scalable method for floodplain inundation modelling	Teng J, Yang A, Penton DJ, Ticehurst C, Marvanek S, Vaze J, Mateo C, Bridgart R	https://doi.org/10.36334/modsim2025.G09.teng	https://mssanz.org.au/modsim2025/files/G09.teng.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	V
Yang A	Introducing a fast and scalable method for floodplain inundation modelling	Teng J, Yang A, Penton DJ, Ticehurst C, Marvanek S, Vaze J, Mateo C, Bridgart R	https://doi.org/10.36334/modsim2025.G09.teng	https://mssanz.org.au/modsim2025/files/G09.teng.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	Y
Bradhurst R	Investigating the influence of disease spillover between feral and domestic pigs in ASF outbreaks using AADIS	Oberin M, Stevenson M, Brookes V, Firestone S, Bradhurst R	https://doi.org/10.36334/modsim2025.M02.oberin	https://mssanz.org.au/modsim2025/files/M02.oberin.pdf	M	Operations Research	M2	Simulation modelling and analysis	B
Brookes V	Investigating the influence of disease spillover between feral and domestic pigs in ASF outbreaks using AADIS	Oberin M, Stevenson M, Brookes V, Firestone S, Bradhurst R	https://doi.org/10.36334/modsim2025.M02.oberin	https://mssanz.org.au/modsim2025/files/M02.oberin.pdf	M	Operations Research	M2	Simulation modelling and analysis	B
Firestone S	Investigating the influence of disease spillover between feral and domestic pigs in ASF outbreaks using AADIS	Oberin M, Stevenson M, Brookes V, Firestone S, Bradhurst R	https://doi.org/10.36334/modsim2025.M02.oberin	https://mssanz.org.au/modsim2025/files/M02.oberin.pdf	M	Operations Research	M2	Simulation modelling and analysis	F
Oberin M	Investigating the influence of disease spillover between feral and domestic pigs in ASF outbreaks using AADIS	Oberin M, Stevenson M, Brookes V, Firestone S, Bradhurst R	https://doi.org/10.36334/modsim2025.M02.oberin	https://mssanz.org.au/modsim2025/files/M02.oberin.pdf	M	Operations Research	M2	Simulation modelling and analysis	O
Stevenson M	Investigating the influence of disease spillover between feral and domestic pigs in ASF outbreaks using AADIS	Oberin M, Stevenson M, Brookes V, Firestone S, Bradhurst R	https://doi.org/10.36334/modsim2025.M02.oberin	https://mssanz.org.au/modsim2025/files/M02.oberin.pdf	M	Operations Research	M2	Simulation modelling and analysis	S
Haque KMS	Irrigated rice yield response to projected climate change in northwest Bangladesh	Hossain MB, Zeleke K, Liu DL, Haque KMS, Wang B	https://doi.org/10.36334/modsim2025.B08.hossain	https://mssanz.org.au/modsim2025/files/B08.hossain.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	H
Hossain MB	Irrigated rice yield response to projected climate change in northwest Bangladesh	Hossain MB, Zeleke K, Liu DL, Haque KMS, Wang B	https://doi.org/10.36334/modsim2025.B08.hossain	https://mssanz.org.au/modsim2025/files/B08.hossain.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	H
Liu DL (De Li)	Irrigated rice yield response to projected climate change in northwest Bangladesh	Hossain MB, Zeleke K, Liu DL, Haque KMS, Wang B	https://doi.org/10.36334/modsim2025.B08.hossain	https://mssanz.org.au/modsim2025/files/B08.hossain.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
Wang B (Bin)	Irrigated rice yield response to projected climate change in northwest Bangladesh	Hossain MB, Zeleke K, Liu DL, Haque KMS, Wang B	https://doi.org/10.36334/modsim2025.B08.hossain	https://mssanz.org.au/modsim2025/files/B08.hossain.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	W
Zeleke K	Irrigated rice yield response to projected climate change in northwest Bangladesh	Hossain MB, Zeleke K, Liu DL, Haque KMS, Wang B	https://doi.org/10.36334/modsim2025.B08.hossain	https://mssanz.org.au/modsim2025/files/B08.hossain.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	Z
Weber T	Is our approach to rainfall-runoff modelling future climate ready?	Weber T	https://doi.org/10.36334/modsim2025.J07.weber	https://mssanz.org.au/modsim2025/files/J07.weber.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	W
Magsarjav S	Is traditional natural language processing ‘dead’?	Magsarjav S	https://doi.org/10.36334/modsim2025.A03.magsarjav	https://mssanz.org.au/modsim2025/files/A03.magsarjav.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	M
Ghasemi M (Mohammad)	Joint optimisation of app data caching and computation offloading in edge computing	Raina S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.M03.raina	https://mssanz.org.au/modsim2025/files/M03.raina.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	G
Nazari A	Joint optimisation of app data caching and computation offloading in edge computing	Raina S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.M03.raina	https://mssanz.org.au/modsim2025/files/M03.raina.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	N
Raina S	Joint optimisation of app data caching and computation offloading in edge computing	Raina S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.M03.raina	https://mssanz.org.au/modsim2025/files/M03.raina.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	R
Thiruvady D	Joint optimisation of app data caching and computation offloading in edge computing	Raina S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.M03.raina	https://mssanz.org.au/modsim2025/files/M03.raina.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	T
van der Meer R	Joint optimisation of app data caching and computation offloading in edge computing	Raina S, Nazari A, Ghasemi M, Thiruvady D, Van Der Meer R	https://doi.org/10.36334/modsim2025.M03.raina	https://mssanz.org.au/modsim2025/files/M03.raina.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	v
Bai WLH	Journey costing based scheduling of uncrewed aerial vehicles with energy constraints and wind effects	Sherman GD, Bai WLH, Campbell BC	https://doi.org/10.36334/modsim2025.C02.sherman	https://mssanz.org.au/modsim2025/files/C02.sherman.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	B
Campbell BC (Benjamin C)	Journey costing based scheduling of uncrewed aerial vehicles with energy constraints and wind effects	Sherman GD, Bai WLH, Campbell BC	https://doi.org/10.36334/modsim2025.C02.sherman	https://mssanz.org.au/modsim2025/files/C02.sherman.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	C
Sherman GD	Journey costing based scheduling of uncrewed aerial vehicles with energy constraints and wind effects	Sherman GD, Bai WLH, Campbell BC	https://doi.org/10.36334/modsim2025.C02.sherman	https://mssanz.org.au/modsim2025/files/C02.sherman.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	S
Allingham D	Knowledge integration and model evaluation for cross-sectoral agricultural climate change modelling	Allingham D, Lawson J, Ellis B, Kelley J, Young R	https://doi.org/10.36334/modsim2025.B08.allingham	https://mssanz.org.au/modsim2025/files/B08.allingham.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	A
Ellis B	Knowledge integration and model evaluation for cross-sectoral agricultural climate change modelling	Allingham D, Lawson J, Ellis B, Kelley J, Young R	https://doi.org/10.36334/modsim2025.B08.allingham	https://mssanz.org.au/modsim2025/files/B08.allingham.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	E
Kelley J	Knowledge integration and model evaluation for cross-sectoral agricultural climate change modelling	Allingham D, Lawson J, Ellis B, Kelley J, Young R	https://doi.org/10.36334/modsim2025.B08.allingham	https://mssanz.org.au/modsim2025/files/B08.allingham.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	K
Lawson J	Knowledge integration and model evaluation for cross-sectoral agricultural climate change modelling	Allingham D, Lawson J, Ellis B, Kelley J, Young R	https://doi.org/10.36334/modsim2025.B08.allingham	https://mssanz.org.au/modsim2025/files/B08.allingham.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
Young R	Knowledge integration and model evaluation for cross-sectoral agricultural climate change modelling	Allingham D, Lawson J, Ellis B, Kelley J, Young R	https://doi.org/10.36334/modsim2025.B08.allingham	https://mssanz.org.au/modsim2025/files/B08.allingham.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	Y
Jolma A	Lähde: information system for maintaining up-to-date information on forest resources in Finland	Jolma A	https://doi.org/10.36334/modsim2025.F08.jolma	https://mssanz.org.au/modsim2025/files/F08.jolma.pdf	F	Environment and ecology	F8	GIS and environmental modelling	J
Chen B (Beibei)	Land subsidence and rebound response to groundwater recovery in the Beijing Plain: A new hydrological perspective	Meng D, Chen B, Gong H, Zhang S, Ma R, Zhou C, Lei K	https://doi.org/10.36334/modsim2025.G02.meng	https://mssanz.org.au/modsim2025/files/G02.meng.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	C
Gong H (Huili)	Land subsidence and rebound response to groundwater recovery in the Beijing Plain: A new hydrological perspective	Meng D, Chen B, Gong H, Zhang S, Ma R, Zhou C, Lei K	https://doi.org/10.36334/modsim2025.G02.meng	https://mssanz.org.au/modsim2025/files/G02.meng.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	G
Lei K (Kunchao)	Land subsidence and rebound response to groundwater recovery in the Beijing Plain: A new hydrological perspective	Meng D, Chen B, Gong H, Zhang S, Ma R, Zhou C, Lei K	https://doi.org/10.36334/modsim2025.G02.meng	https://mssanz.org.au/modsim2025/files/G02.meng.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	L
Ma R (Rui)	Land subsidence and rebound response to groundwater recovery in the Beijing Plain: A new hydrological perspective	Meng D, Chen B, Gong H, Zhang S, Ma R, Zhou C, Lei K	https://doi.org/10.36334/modsim2025.G02.meng	https://mssanz.org.au/modsim2025/files/G02.meng.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	M
Meng D	Land subsidence and rebound response to groundwater recovery in the Beijing Plain: A new hydrological perspective	Meng D, Chen B, Gong H, Zhang S, Ma R, Zhou C, Lei K	https://doi.org/10.36334/modsim2025.G02.meng	https://mssanz.org.au/modsim2025/files/G02.meng.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	M
Zhang S	Land subsidence and rebound response to groundwater recovery in the Beijing Plain: A new hydrological perspective	Meng D, Chen B, Gong H, Zhang S, Ma R, Zhou C, Lei K	https://doi.org/10.36334/modsim2025.G02.meng	https://mssanz.org.au/modsim2025/files/G02.meng.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Z
Zhou C (Chaofan)	Land subsidence and rebound response to groundwater recovery in the Beijing Plain: A new hydrological perspective	Meng D, Chen B, Gong H, Zhang S, Ma R, Zhou C, Lei K	https://doi.org/10.36334/modsim2025.G02.meng	https://mssanz.org.au/modsim2025/files/G02.meng.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Z
Li C (Congcong)	Land use and land cover change impact on the actual evapotranspiration in China	Li C, Zhang Y	https://doi.org/10.36334/modsim2025.K03.li	https://mssanz.org.au/modsim2025/files/K03.li.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	L
Zhang Y (Yongqiang)	Land use and land cover change impact on the actual evapotranspiration in China	Li C, Zhang Y	https://doi.org/10.36334/modsim2025.K03.li	https://mssanz.org.au/modsim2025/files/K03.li.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	Z
Bennett J	Learning from model failures: challenges in hydrological observations and modelling	Johnson F, Bennett J, Fowler K, Thyer M, Wasko C, Saco P, Lerat J	https://doi.org/10.36334/modsim2025.K05.johnson	https://mssanz.org.au/modsim2025/files/K05.johnson.pdf	K	Hydroclimate	K5	When models fail: lessons learned from false starts, wrong turns and dead ends	B
Fowler K	Learning from model failures: challenges in hydrological observations and modelling	Johnson F, Bennett J, Fowler K, Thyer M, Wasko C, Saco P, Lerat J	https://doi.org/10.36334/modsim2025.K05.johnson	https://mssanz.org.au/modsim2025/files/K05.johnson.pdf	K	Hydroclimate	K5	When models fail: lessons learned from false starts, wrong turns and dead ends	F
Johnson F	Learning from model failures: challenges in hydrological observations and modelling	Johnson F, Bennett J, Fowler K, Thyer M, Wasko C, Saco P, Lerat J	https://doi.org/10.36334/modsim2025.K05.johnson	https://mssanz.org.au/modsim2025/files/K05.johnson.pdf	K	Hydroclimate	K5	When models fail: lessons learned from false starts, wrong turns and dead ends	J
Lerat J	Learning from model failures: challenges in hydrological observations and modelling	Johnson F, Bennett J, Fowler K, Thyer M, Wasko C, Saco P, Lerat J	https://doi.org/10.36334/modsim2025.K05.johnson	https://mssanz.org.au/modsim2025/files/K05.johnson.pdf	K	Hydroclimate	K5	When models fail: lessons learned from false starts, wrong turns and dead ends	L
Saco P	Learning from model failures: challenges in hydrological observations and modelling	Johnson F, Bennett J, Fowler K, Thyer M, Wasko C, Saco P, Lerat J	https://doi.org/10.36334/modsim2025.K05.johnson	https://mssanz.org.au/modsim2025/files/K05.johnson.pdf	K	Hydroclimate	K5	When models fail: lessons learned from false starts, wrong turns and dead ends	S
Thyer M	Learning from model failures: challenges in hydrological observations and modelling	Johnson F, Bennett J, Fowler K, Thyer M, Wasko C, Saco P, Lerat J	https://doi.org/10.36334/modsim2025.K05.johnson	https://mssanz.org.au/modsim2025/files/K05.johnson.pdf	K	Hydroclimate	K5	When models fail: lessons learned from false starts, wrong turns and dead ends	T
Wasko C	Learning from model failures: challenges in hydrological observations and modelling	Johnson F, Bennett J, Fowler K, Thyer M, Wasko C, Saco P, Lerat J	https://doi.org/10.36334/modsim2025.K05.johnson	https://mssanz.org.au/modsim2025/files/K05.johnson.pdf	K	Hydroclimate	K5	When models fail: lessons learned from false starts, wrong turns and dead ends	W
Graham P	Least-cost modelling of electricity systems with a high share of weather dependent renewable generation	Graham P	https://doi.org/10.36334/modsim2025.E03.graham	https://mssanz.org.au/modsim2025/files/E03.graham.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	G
McCullough DP	Levelling up: Modelling flood inundation changes in the South Australian River Murray	McCullough DP	https://doi.org/10.36334/modsim2025.K08.mccullough	https://mssanz.org.au/modsim2025/files/K08.mccullough.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	M
Battaglia M	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	B
Bruce J	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	B
Eccleston R	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	E
Hammond P	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	H
Hortle R	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	H
Johnson L	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	J
Linkston L	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	L
Moallemi EA	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	M
Mueller S	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	M
Nadeem H	Leveraging participatory system dynamics to coordinate regional net zero transitions in Australia	Hammond P, Nadeem H, Mueller S, Bruce J, Battaglia M, Hortle R, Linkston L, Johnson L, Eccleston R, Moallemi EA	https://doi.org/10.36334/modsim2025.I02.hammond	https://mssanz.org.au/modsim2025/files/I02.hammond.pdf	I	Participatory decision-making, socioecological systems, and education	I2	Advancing System Dynamics Modelling: Integrating Emerging Technologies and Participatory Approaches for Enhanced Decision Support in Social Systems	N
Herckenrath D	Leveraging petrophysical logs in data-scarce groundwater environments	Herckenrath D, Shanmugam S, Rojas R, Phillipson K	https://doi.org/10.36334/modsim2025.K01.herckenrath	https://mssanz.org.au/modsim2025/files/K01.herckenrath.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	H
Phillipson K	Leveraging petrophysical logs in data-scarce groundwater environments	Herckenrath D, Shanmugam S, Rojas R, Phillipson K	https://doi.org/10.36334/modsim2025.K01.herckenrath	https://mssanz.org.au/modsim2025/files/K01.herckenrath.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	P
Rojas R	Leveraging petrophysical logs in data-scarce groundwater environments	Herckenrath D, Shanmugam S, Rojas R, Phillipson K	https://doi.org/10.36334/modsim2025.K01.herckenrath	https://mssanz.org.au/modsim2025/files/K01.herckenrath.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	R
Shanmugam S	Leveraging petrophysical logs in data-scarce groundwater environments	Herckenrath D, Shanmugam S, Rojas R, Phillipson K	https://doi.org/10.36334/modsim2025.K01.herckenrath	https://mssanz.org.au/modsim2025/files/K01.herckenrath.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	S
Armstrong M	Limitations and best practice for bottom-up climate risk assessments	Culley S, Armstrong M, John A, Horne A, Mudaliar C, Ren L, Westra S, Maier HR, Bennett B	https://doi.org/10.36334/modsim2025.J05.culley	https://mssanz.org.au/modsim2025/files/J05.culley.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	A
Bennett B	Limitations and best practice for bottom-up climate risk assessments	Culley S, Armstrong M, John A, Horne A, Mudaliar C, Ren L, Westra S, Maier HR, Bennett B	https://doi.org/10.36334/modsim2025.J05.culley	https://mssanz.org.au/modsim2025/files/J05.culley.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	B
Culley S (Sam)	Limitations and best practice for bottom-up climate risk assessments	Culley S, Armstrong M, John A, Horne A, Mudaliar C, Ren L, Westra S, Maier HR, Bennett B	https://doi.org/10.36334/modsim2025.J05.culley	https://mssanz.org.au/modsim2025/files/J05.culley.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	C
Horne A	Limitations and best practice for bottom-up climate risk assessments	Culley S, Armstrong M, John A, Horne A, Mudaliar C, Ren L, Westra S, Maier HR, Bennett B	https://doi.org/10.36334/modsim2025.J05.culley	https://mssanz.org.au/modsim2025/files/J05.culley.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	H
John A	Limitations and best practice for bottom-up climate risk assessments	Culley S, Armstrong M, John A, Horne A, Mudaliar C, Ren L, Westra S, Maier HR, Bennett B	https://doi.org/10.36334/modsim2025.J05.culley	https://mssanz.org.au/modsim2025/files/J05.culley.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	J
Maier HR	Limitations and best practice for bottom-up climate risk assessments	Culley S, Armstrong M, John A, Horne A, Mudaliar C, Ren L, Westra S, Maier HR, Bennett B	https://doi.org/10.36334/modsim2025.J05.culley	https://mssanz.org.au/modsim2025/files/J05.culley.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	M
Mudaliar C	Limitations and best practice for bottom-up climate risk assessments	Culley S, Armstrong M, John A, Horne A, Mudaliar C, Ren L, Westra S, Maier HR, Bennett B	https://doi.org/10.36334/modsim2025.J05.culley	https://mssanz.org.au/modsim2025/files/J05.culley.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	M
Ren L	Limitations and best practice for bottom-up climate risk assessments	Culley S, Armstrong M, John A, Horne A, Mudaliar C, Ren L, Westra S, Maier HR, Bennett B	https://doi.org/10.36334/modsim2025.J05.culley	https://mssanz.org.au/modsim2025/files/J05.culley.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	R
Westra S	Limitations and best practice for bottom-up climate risk assessments	Culley S, Armstrong M, John A, Horne A, Mudaliar C, Ren L, Westra S, Maier HR, Bennett B	https://doi.org/10.36334/modsim2025.J05.culley	https://mssanz.org.au/modsim2025/files/J05.culley.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	W
de Haan FJ	LLM-assisted initial problem conceptualization in decision-making under deep uncertainty	Pei Z, Lipovetzky N, de Haan FJ, Moallemi EA, Rojas‑Arevalo AM	https://doi.org/10.36334/modsim2025.F10.pei	https://mssanz.org.au/modsim2025/files/F10.pei.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	d
Lipovetzky N	LLM-assisted initial problem conceptualization in decision-making under deep uncertainty	Pei Z, Lipovetzky N, de Haan FJ, Moallemi EA, Rojas‑Arevalo AM	https://doi.org/10.36334/modsim2025.F10.pei	https://mssanz.org.au/modsim2025/files/F10.pei.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	L
Moallemi EA	LLM-assisted initial problem conceptualization in decision-making under deep uncertainty	Pei Z, Lipovetzky N, de Haan FJ, Moallemi EA, Rojas‑Arevalo AM	https://doi.org/10.36334/modsim2025.F10.pei	https://mssanz.org.au/modsim2025/files/F10.pei.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	M
Pei Z	LLM-assisted initial problem conceptualization in decision-making under deep uncertainty	Pei Z, Lipovetzky N, de Haan FJ, Moallemi EA, Rojas‑Arevalo AM	https://doi.org/10.36334/modsim2025.F10.pei	https://mssanz.org.au/modsim2025/files/F10.pei.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	P
Rojas‑Arevalo AM	LLM-assisted initial problem conceptualization in decision-making under deep uncertainty	Pei Z, Lipovetzky N, de Haan FJ, Moallemi EA, Rojas‑Arevalo AM	https://doi.org/10.36334/modsim2025.F10.pei	https://mssanz.org.au/modsim2025/files/F10.pei.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	R
Podsosonnaya M	Logistic regression model for early detection and prediction of algal blooms in estuarine environments	Podsosonnaya M, Schreider M, Schreider S	https://doi.org/10.36334/modsim2025.J11.podsosonnaya	https://mssanz.org.au/modsim2025/files/J11.podsosonnaya.pdf	J	Water resources	J11	Ecohydrological Modelling	P
Schreider M	Logistic regression model for early detection and prediction of algal blooms in estuarine environments	Podsosonnaya M, Schreider M, Schreider S	https://doi.org/10.36334/modsim2025.J11.podsosonnaya	https://mssanz.org.au/modsim2025/files/J11.podsosonnaya.pdf	J	Water resources	J11	Ecohydrological Modelling	S
Schreider S	Logistic regression model for early detection and prediction of algal blooms in estuarine environments	Podsosonnaya M, Schreider M, Schreider S	https://doi.org/10.36334/modsim2025.J11.podsosonnaya	https://mssanz.org.au/modsim2025/files/J11.podsosonnaya.pdf	J	Water resources	J11	Ecohydrological Modelling	S
Boland JW	Long Short-Term Memory and statistical time series analysis forecast models for renewable energy and prices	Cirocco LR, Chiera B, Chopin J, Boland JW	https://doi.org/10.36334/modsim2025.E04.cirocco	https://mssanz.org.au/modsim2025/files/E04.cirocco.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	B
Chiera B	Long Short-Term Memory and statistical time series analysis forecast models for renewable energy and prices	Cirocco LR, Chiera B, Chopin J, Boland JW	https://doi.org/10.36334/modsim2025.E04.cirocco	https://mssanz.org.au/modsim2025/files/E04.cirocco.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	C
Chopin J (Joshua)	Long Short-Term Memory and statistical time series analysis forecast models for renewable energy and prices	Cirocco LR, Chiera B, Chopin J, Boland JW	https://doi.org/10.36334/modsim2025.E04.cirocco	https://mssanz.org.au/modsim2025/files/E04.cirocco.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	C
Cirocco LR	Long Short-Term Memory and statistical time series analysis forecast models for renewable energy and prices	Cirocco LR, Chiera B, Chopin J, Boland JW	https://doi.org/10.36334/modsim2025.E04.cirocco	https://mssanz.org.au/modsim2025/files/E04.cirocco.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	C
Guan H	Low flow prediction from land surface temperature	Yang W, Guan H	https://doi.org/10.36334/modsim2025.K04.yangw	https://mssanz.org.au/modsim2025/files/K04.yangw.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	G
Yang W	Low flow prediction from land surface temperature	Yang W, Guan H	https://doi.org/10.36334/modsim2025.K04.yangw	https://mssanz.org.au/modsim2025/files/K04.yangw.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	Y
Simpson AR	LSTM-based policy model for minimising pump operational costs in water distribution systems	Zheng L, Wu W, Simpson AR, Wang Y	https://doi.org/10.36334/modsim2025.J04.zheng	https://mssanz.org.au/modsim2025/files/J04.zheng.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	S
Wang Y (Ye)	LSTM-based policy model for minimising pump operational costs in water distribution systems	Zheng L, Wu W, Simpson AR, Wang Y	https://doi.org/10.36334/modsim2025.J04.zheng	https://mssanz.org.au/modsim2025/files/J04.zheng.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	W
Wu W (Wenyan)	LSTM-based policy model for minimising pump operational costs in water distribution systems	Zheng L, Wu W, Simpson AR, Wang Y	https://doi.org/10.36334/modsim2025.J04.zheng	https://mssanz.org.au/modsim2025/files/J04.zheng.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	W
Zheng L	LSTM-based policy model for minimising pump operational costs in water distribution systems	Zheng L, Wu W, Simpson AR, Wang Y	https://doi.org/10.36334/modsim2025.J04.zheng	https://mssanz.org.au/modsim2025/files/J04.zheng.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	Z
Aeman H	Machine learning approach to irrigation demand forecasting in the Indus Basin using Google Earth Engine	Hafeez M, Aeman H, Liaqat UW	https://doi.org/10.36334/modsim2025.B06.hafeez	https://mssanz.org.au/modsim2025/files/B06.hafeez.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	A
Hafeez M (Mohsin)	Machine learning approach to irrigation demand forecasting in the Indus Basin using Google Earth Engine	Hafeez M, Aeman H, Liaqat UW	https://doi.org/10.36334/modsim2025.B06.hafeez	https://mssanz.org.au/modsim2025/files/B06.hafeez.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	H
Liaqat UW	Machine learning approach to irrigation demand forecasting in the Indus Basin using Google Earth Engine	Hafeez M, Aeman H, Liaqat UW	https://doi.org/10.36334/modsim2025.B06.hafeez	https://mssanz.org.au/modsim2025/files/B06.hafeez.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	L
Baumgartner J	Machine learning for locust population dynamics and outbreak prediction	Baumgartner J, Spessa A, Camac J	https://doi.org/10.36334/modsim2025.F11.baumgartner	https://mssanz.org.au/modsim2025/files/F11.baumgartner.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	B
Camac J	Machine learning for locust population dynamics and outbreak prediction	Baumgartner J, Spessa A, Camac J	https://doi.org/10.36334/modsim2025.F11.baumgartner	https://mssanz.org.au/modsim2025/files/F11.baumgartner.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	C
Spessa A	Machine learning for locust population dynamics and outbreak prediction	Baumgartner J, Spessa A, Camac J	https://doi.org/10.36334/modsim2025.F11.baumgartner	https://mssanz.org.au/modsim2025/files/F11.baumgartner.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	S
Humphries M	Making sense of heart rate variability: Identifying meaningful metrics for stress and pain prediction	Lim J, Humphries M, Tuke J, Sanchez R, Klyne D	https://doi.org/10.36334/modsim2025.A03.lim	https://mssanz.org.au/modsim2025/files/A03.lim.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	H
Klyne D	Making sense of heart rate variability: Identifying meaningful metrics for stress and pain prediction	Lim J, Humphries M, Tuke J, Sanchez R, Klyne D	https://doi.org/10.36334/modsim2025.A03.lim	https://mssanz.org.au/modsim2025/files/A03.lim.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	K
Lim J	Making sense of heart rate variability: Identifying meaningful metrics for stress and pain prediction	Lim J, Humphries M, Tuke J, Sanchez R, Klyne D	https://doi.org/10.36334/modsim2025.A03.lim	https://mssanz.org.au/modsim2025/files/A03.lim.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	L
Sanchez R	Making sense of heart rate variability: Identifying meaningful metrics for stress and pain prediction	Lim J, Humphries M, Tuke J, Sanchez R, Klyne D	https://doi.org/10.36334/modsim2025.A03.lim	https://mssanz.org.au/modsim2025/files/A03.lim.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	S
Tuke J	Making sense of heart rate variability: Identifying meaningful metrics for stress and pain prediction	Lim J, Humphries M, Tuke J, Sanchez R, Klyne D	https://doi.org/10.36334/modsim2025.A03.lim	https://mssanz.org.au/modsim2025/files/A03.lim.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	T
Shu Y (Yucheng)	MapMate: A Framework Bridging Natural Language Interaction and Map Design Through Large Language Models	Tang Z, Shu Y, Yue S, Wen Y	https://doi.org/10.36334/modsim2025.C04.tang	https://mssanz.org.au/modsim2025/files/C04.tang.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	S
Tang Z (Zihao)	MapMate: A Framework Bridging Natural Language Interaction and Map Design Through Large Language Models	Tang Z, Shu Y, Yue S, Wen Y	https://doi.org/10.36334/modsim2025.C04.tang	https://mssanz.org.au/modsim2025/files/C04.tang.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	T
Wen Y	MapMate: A Framework Bridging Natural Language Interaction and Map Design Through Large Language Models	Tang Z, Shu Y, Yue S, Wen Y	https://doi.org/10.36334/modsim2025.C04.tang	https://mssanz.org.au/modsim2025/files/C04.tang.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	W
Yue S	MapMate: A Framework Bridging Natural Language Interaction and Map Design Through Large Language Models	Tang Z, Shu Y, Yue S, Wen Y	https://doi.org/10.36334/modsim2025.C04.tang	https://mssanz.org.au/modsim2025/files/C04.tang.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	Y
Laffan S	Mapping and modelling bushfire exposure at the wildland–urban interface in Australia	Mehnaz N, Sutherland D, Sharples J, Laffan S	https://doi.org/10.36334/modsim2025.G08.mehnaz	https://mssanz.org.au/modsim2025/files/G08.mehnaz.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	L
Mehnaz N	Mapping and modelling bushfire exposure at the wildland–urban interface in Australia	Mehnaz N, Sutherland D, Sharples J, Laffan S	https://doi.org/10.36334/modsim2025.G08.mehnaz	https://mssanz.org.au/modsim2025/files/G08.mehnaz.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	M
Sharples JJ	Mapping and modelling bushfire exposure at the wildland–urban interface in Australia	Mehnaz N, Sutherland D, Sharples J, Laffan S	https://doi.org/10.36334/modsim2025.G08.mehnaz	https://mssanz.org.au/modsim2025/files/G08.mehnaz.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Sutherland D	Mapping and modelling bushfire exposure at the wildland–urban interface in Australia	Mehnaz N, Sutherland D, Sharples J, Laffan S	https://doi.org/10.36334/modsim2025.G08.mehnaz	https://mssanz.org.au/modsim2025/files/G08.mehnaz.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Bottini G	Mapping crop area and fertilizer application rates globally for 173 crops	Tang FHM, Nguyen TH, Bottini G, Conchedda G, Casse L, Obli‑Laryea G, Tubiello FN, Maggi F	https://doi.org/10.36334/modsim2025.F11.tang	https://mssanz.org.au/modsim2025/files/F11.tang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	B
Casse L	Mapping crop area and fertilizer application rates globally for 173 crops	Tang FHM, Nguyen TH, Bottini G, Conchedda G, Casse L, Obli‑Laryea G, Tubiello FN, Maggi F	https://doi.org/10.36334/modsim2025.F11.tang	https://mssanz.org.au/modsim2025/files/F11.tang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	C
Conchedda G	Mapping crop area and fertilizer application rates globally for 173 crops	Tang FHM, Nguyen TH, Bottini G, Conchedda G, Casse L, Obli‑Laryea G, Tubiello FN, Maggi F	https://doi.org/10.36334/modsim2025.F11.tang	https://mssanz.org.au/modsim2025/files/F11.tang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	C
Maggi F	Mapping crop area and fertilizer application rates globally for 173 crops	Tang FHM, Nguyen TH, Bottini G, Conchedda G, Casse L, Obli‑Laryea G, Tubiello FN, Maggi F	https://doi.org/10.36334/modsim2025.F11.tang	https://mssanz.org.au/modsim2025/files/F11.tang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	M
Nguyen TH	Mapping crop area and fertilizer application rates globally for 173 crops	Tang FHM, Nguyen TH, Bottini G, Conchedda G, Casse L, Obli‑Laryea G, Tubiello FN, Maggi F	https://doi.org/10.36334/modsim2025.F11.tang	https://mssanz.org.au/modsim2025/files/F11.tang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	N
Obli‑Laryea G	Mapping crop area and fertilizer application rates globally for 173 crops	Tang FHM, Nguyen TH, Bottini G, Conchedda G, Casse L, Obli‑Laryea G, Tubiello FN, Maggi F	https://doi.org/10.36334/modsim2025.F11.tang	https://mssanz.org.au/modsim2025/files/F11.tang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	O
Tang FHM	Mapping crop area and fertilizer application rates globally for 173 crops	Tang FHM, Nguyen TH, Bottini G, Conchedda G, Casse L, Obli‑Laryea G, Tubiello FN, Maggi F	https://doi.org/10.36334/modsim2025.F11.tang	https://mssanz.org.au/modsim2025/files/F11.tang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	T
Tubiello FN	Mapping crop area and fertilizer application rates globally for 173 crops	Tang FHM, Nguyen TH, Bottini G, Conchedda G, Casse L, Obli‑Laryea G, Tubiello FN, Maggi F	https://doi.org/10.36334/modsim2025.F11.tang	https://mssanz.org.au/modsim2025/files/F11.tang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	T
Guan H	Mapping groundwater dependent ecosystems and their degradation using selected hydroclimatic windows	Guan H, Li H	https://doi.org/10.36334/modsim2025.J10.guan	https://mssanz.org.au/modsim2025/files/J10.guan.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	G
Li H (Huixiang)	Mapping groundwater dependent ecosystems and their degradation using selected hydroclimatic windows	Guan H, Li H	https://doi.org/10.36334/modsim2025.J10.guan	https://mssanz.org.au/modsim2025/files/J10.guan.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	L
Mokany K	Mapping plant phenological traits at fine resolution continentally by combining process-based modelling and remote sensing	Yang J, Mokany K	https://doi.org/10.36334/modsim2025.F12.yang	https://mssanz.org.au/modsim2025/files/F12.yang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	M
Yang J	Mapping plant phenological traits at fine resolution continentally by combining process-based modelling and remote sensing	Yang J, Mokany K	https://doi.org/10.36334/modsim2025.F12.yang	https://mssanz.org.au/modsim2025/files/F12.yang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	Y
Cao Y (Yijing)	Marginal yield recessions can achieve agricultural water savings	Yang X, Zhang Y, Wang E, Zhang X, Li C, Xu Z, Tang Z, Cao Y	https://doi.org/10.36334/modsim2025.K03.yang	https://mssanz.org.au/modsim2025/files/K03.yang.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	C
Li C (Congcong)	Marginal yield recessions can achieve agricultural water savings	Yang X, Zhang Y, Wang E, Zhang X, Li C, Xu Z, Tang Z, Cao Y	https://doi.org/10.36334/modsim2025.K03.yang	https://mssanz.org.au/modsim2025/files/K03.yang.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	L
Tang Z (Zixuan)	Marginal yield recessions can achieve agricultural water savings	Yang X, Zhang Y, Wang E, Zhang X, Li C, Xu Z, Tang Z, Cao Y	https://doi.org/10.36334/modsim2025.K03.yang	https://mssanz.org.au/modsim2025/files/K03.yang.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	T
Wang E (Enli)	Marginal yield recessions can achieve agricultural water savings	Yang X, Zhang Y, Wang E, Zhang X, Li C, Xu Z, Tang Z, Cao Y	https://doi.org/10.36334/modsim2025.K03.yang	https://mssanz.org.au/modsim2025/files/K03.yang.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	W
Xu Z (Zhenwu)	Marginal yield recessions can achieve agricultural water savings	Yang X, Zhang Y, Wang E, Zhang X, Li C, Xu Z, Tang Z, Cao Y	https://doi.org/10.36334/modsim2025.K03.yang	https://mssanz.org.au/modsim2025/files/K03.yang.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	X
Yang X (Xuening)	Marginal yield recessions can achieve agricultural water savings	Yang X, Zhang Y, Wang E, Zhang X, Li C, Xu Z, Tang Z, Cao Y	https://doi.org/10.36334/modsim2025.K03.yang	https://mssanz.org.au/modsim2025/files/K03.yang.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	Y
Zhang X (Xuanze)	Marginal yield recessions can achieve agricultural water savings	Yang X, Zhang Y, Wang E, Zhang X, Li C, Xu Z, Tang Z, Cao Y	https://doi.org/10.36334/modsim2025.K03.yang	https://mssanz.org.au/modsim2025/files/K03.yang.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	Z
Zhang Y (Yongqiang)	Marginal yield recessions can achieve agricultural water savings	Yang X, Zhang Y, Wang E, Zhang X, Li C, Xu Z, Tang Z, Cao Y	https://doi.org/10.36334/modsim2025.K03.yang	https://mssanz.org.au/modsim2025/files/K03.yang.pdf	K	Hydroclimate	K3	Advancing Drought Monitoring, Prediction, And Resilience For Sustainable Development	Z
Spillias S	Marine ecosystem modelling with generative AI (STREAM F KEYNOTE)	Spillias S	https://doi.org/10.36334/modsim2025.F10.spillias	https://mssanz.org.au/modsim2025/files/F10.spillias.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	S
Casse L	Mechanistic modelling of socil organic-inorganic carbon dynamics	Casse L, Minasny B, Field DJ, McBratney A, Maggi F	https://doi.org/10.36334/modsim2025.F13.casse	https://mssanz.org.au/modsim2025/files/F13.casse.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	C
Field DJ	Mechanistic modelling of socil organic-inorganic carbon dynamics	Casse L, Minasny B, Field DJ, McBratney A, Maggi F	https://doi.org/10.36334/modsim2025.F13.casse	https://mssanz.org.au/modsim2025/files/F13.casse.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	F
Maggi F	Mechanistic modelling of socil organic-inorganic carbon dynamics	Casse L, Minasny B, Field DJ, McBratney A, Maggi F	https://doi.org/10.36334/modsim2025.F13.casse	https://mssanz.org.au/modsim2025/files/F13.casse.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	M
McBratney A	Mechanistic modelling of socil organic-inorganic carbon dynamics	Casse L, Minasny B, Field DJ, McBratney A, Maggi F	https://doi.org/10.36334/modsim2025.F13.casse	https://mssanz.org.au/modsim2025/files/F13.casse.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	M
Minasny B	Mechanistic modelling of socil organic-inorganic carbon dynamics	Casse L, Minasny B, Field DJ, McBratney A, Maggi F	https://doi.org/10.36334/modsim2025.F13.casse	https://mssanz.org.au/modsim2025/files/F13.casse.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	M
Ashbolt SC	Melbourne’s water supply modelling information system: Progress update and next steps	Kularathna MDUP, Vu K, Ashbolt SC	https://doi.org/10.36334/modsim2025.J04.kularathna	https://mssanz.org.au/modsim2025/files/J04.kularathna.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	A
Kularathna MDUP	Melbourne’s water supply modelling information system: Progress update and next steps	Kularathna MDUP, Vu K, Ashbolt SC	https://doi.org/10.36334/modsim2025.J04.kularathna	https://mssanz.org.au/modsim2025/files/J04.kularathna.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	K
Vu K	Melbourne’s water supply modelling information system: Progress update and next steps	Kularathna MDUP, Vu K, Ashbolt SC	https://doi.org/10.36334/modsim2025.J04.kularathna	https://mssanz.org.au/modsim2025/files/J04.kularathna.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	V
Barnes MA	Meteorological drought termination: processes and trends	Holgate CM, Barnes MA, Jin C, Parker TJ	https://doi.org/10.36334/modsim2025.K07.holgate	https://mssanz.org.au/modsim2025/files/K07.holgate.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	B
Holgate CM	Meteorological drought termination: processes and trends	Holgate CM, Barnes MA, Jin C, Parker TJ	https://doi.org/10.36334/modsim2025.K07.holgate	https://mssanz.org.au/modsim2025/files/K07.holgate.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	H
Jin C	Meteorological drought termination: processes and trends	Holgate CM, Barnes MA, Jin C, Parker TJ	https://doi.org/10.36334/modsim2025.K07.holgate	https://mssanz.org.au/modsim2025/files/K07.holgate.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	J
Parker TJ	Meteorological drought termination: processes and trends	Holgate CM, Barnes MA, Jin C, Parker TJ	https://doi.org/10.36334/modsim2025.K07.holgate	https://mssanz.org.au/modsim2025/files/K07.holgate.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	P
Dennis G	Minimising ergonomic impact in automated workspaces through reinforcement learning	Garcia‑Flores R, Dennis G, Smith D, Kandanaarachchi S, Rahman A, Harrison S	https://doi.org/10.36334/modsim2025.M03.garciaflores	https://mssanz.org.au/modsim2025/files/M03.garciaflores.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	D
Garcia‑Flores R	Minimising ergonomic impact in automated workspaces through reinforcement learning	Garcia‑Flores R, Dennis G, Smith D, Kandanaarachchi S, Rahman A, Harrison S	https://doi.org/10.36334/modsim2025.M03.garciaflores	https://mssanz.org.au/modsim2025/files/M03.garciaflores.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	G
Harrison S	Minimising ergonomic impact in automated workspaces through reinforcement learning	Garcia‑Flores R, Dennis G, Smith D, Kandanaarachchi S, Rahman A, Harrison S	https://doi.org/10.36334/modsim2025.M03.garciaflores	https://mssanz.org.au/modsim2025/files/M03.garciaflores.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	H
Kandanaarachchi S	Minimising ergonomic impact in automated workspaces through reinforcement learning	Garcia‑Flores R, Dennis G, Smith D, Kandanaarachchi S, Rahman A, Harrison S	https://doi.org/10.36334/modsim2025.M03.garciaflores	https://mssanz.org.au/modsim2025/files/M03.garciaflores.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	K
Rahman A	Minimising ergonomic impact in automated workspaces through reinforcement learning	Garcia‑Flores R, Dennis G, Smith D, Kandanaarachchi S, Rahman A, Harrison S	https://doi.org/10.36334/modsim2025.M03.garciaflores	https://mssanz.org.au/modsim2025/files/M03.garciaflores.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	R
Smith D	Minimising ergonomic impact in automated workspaces through reinforcement learning	Garcia‑Flores R, Dennis G, Smith D, Kandanaarachchi S, Rahman A, Harrison S	https://doi.org/10.36334/modsim2025.M03.garciaflores	https://mssanz.org.au/modsim2025/files/M03.garciaflores.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	S
Bennett JC	Modelling buddy - making large language models competent assistants in environmental modelling	Perraud JM, Freebairn AC, Bennett JC, Robertson DE	https://doi.org/10.36334/modsim2025.F10.perraud	https://mssanz.org.au/modsim2025/files/F10.perraud.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	B
Freebairn AC	Modelling buddy - making large language models competent assistants in environmental modelling	Perraud JM, Freebairn AC, Bennett JC, Robertson DE	https://doi.org/10.36334/modsim2025.F10.perraud	https://mssanz.org.au/modsim2025/files/F10.perraud.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	F
Perraud JM	Modelling buddy - making large language models competent assistants in environmental modelling	Perraud JM, Freebairn AC, Bennett JC, Robertson DE	https://doi.org/10.36334/modsim2025.F10.perraud	https://mssanz.org.au/modsim2025/files/F10.perraud.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	P
Robertson DE	Modelling buddy - making large language models competent assistants in environmental modelling	Perraud JM, Freebairn AC, Bennett JC, Robertson DE	https://doi.org/10.36334/modsim2025.F10.perraud	https://mssanz.org.au/modsim2025/files/F10.perraud.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	R
Eccles R	Modelling changes in extreme rainfall for Australia	Wasko C, Jayaweera L, Nathan R, Eccles R, Syktus J	https://doi.org/10.36334/modsim2025.K07.wasko	https://mssanz.org.au/modsim2025/files/K07.wasko.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	E
Jayaweera L	Modelling changes in extreme rainfall for Australia	Wasko C, Jayaweera L, Nathan R, Eccles R, Syktus J	https://doi.org/10.36334/modsim2025.K07.wasko	https://mssanz.org.au/modsim2025/files/K07.wasko.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	J
Nathan R	Modelling changes in extreme rainfall for Australia	Wasko C, Jayaweera L, Nathan R, Eccles R, Syktus J	https://doi.org/10.36334/modsim2025.K07.wasko	https://mssanz.org.au/modsim2025/files/K07.wasko.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	N
Syktus J	Modelling changes in extreme rainfall for Australia	Wasko C, Jayaweera L, Nathan R, Eccles R, Syktus J	https://doi.org/10.36334/modsim2025.K07.wasko	https://mssanz.org.au/modsim2025/files/K07.wasko.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	S
Wasko C	Modelling changes in extreme rainfall for Australia	Wasko C, Jayaweera L, Nathan R, Eccles R, Syktus J	https://doi.org/10.36334/modsim2025.K07.wasko	https://mssanz.org.au/modsim2025/files/K07.wasko.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	W
Hoang GB	Modelling complex systems to analyse social media for belief revision	Hoang GB	https://doi.org/10.36334/modsim2025.A03.hoang	https://mssanz.org.au/modsim2025/files/A03.hoang.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	H
Chamberlain T	Modelling dissolved inorganic nitrogen losses from the Queensland sugarcane industry	Chamberlain T, Shepherd N, Noory H, Donaldson S	https://doi.org/10.36334/modsim2025.L04.chamberlain	https://mssanz.org.au/modsim2025/files/L04.chamberlain.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	C
Donaldson S	Modelling dissolved inorganic nitrogen losses from the Queensland sugarcane industry	Chamberlain T, Shepherd N, Noory H, Donaldson S	https://doi.org/10.36334/modsim2025.L04.chamberlain	https://mssanz.org.au/modsim2025/files/L04.chamberlain.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	D
Noory H	Modelling dissolved inorganic nitrogen losses from the Queensland sugarcane industry	Chamberlain T, Shepherd N, Noory H, Donaldson S	https://doi.org/10.36334/modsim2025.L04.chamberlain	https://mssanz.org.au/modsim2025/files/L04.chamberlain.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	N
Shepherd N	Modelling dissolved inorganic nitrogen losses from the Queensland sugarcane industry	Chamberlain T, Shepherd N, Noory H, Donaldson S	https://doi.org/10.36334/modsim2025.L04.chamberlain	https://mssanz.org.au/modsim2025/files/L04.chamberlain.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	S
Beletse Y	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	B
Cossani CM	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	C
Deery D	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	D
Dreccer F	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	D
He D (Di)	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Hu P (Pengcheng)	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Leske B	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	L
Lilley J	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	L
Nguyen H (Ha)	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	N
Richetti J	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	R
Sadras V	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	S
Whish J	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	W
Zheng B (Bangyou)	Modelling frost and heat impact on yields: challenges and perspectives	Richetti J, Sadras V, He D, Leske B, Hu P, Beletse Y, Cossani C M, Nguyen H, Zheng B, Deery D, Dreccer F, Whish J, Lilley J	https://doi.org/10.36334/modsim2025.B02.richettij	https://mssanz.org.au/modsim2025/files/B02.richettij.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	Z
Halog A	Modelling green hydrogen for a fairer, cleaner Australia: The role of digital twins and circular economy	Kark‑Levin Y, Halog A	https://doi.org/10.36334/modsim2025.E03.karklevin	https://mssanz.org.au/modsim2025/files/E03.karklevin.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	H
Kark‑Levin Y	Modelling green hydrogen for a fairer, cleaner Australia: The role of digital twins and circular economy	Kark‑Levin Y, Halog A	https://doi.org/10.36334/modsim2025.E03.karklevin	https://mssanz.org.au/modsim2025/files/E03.karklevin.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	K
Bell LW	Modelling greenhouse gas emissions in mixed crop–livestock farming systems	Jayasinghe P, Bell LW, Mayberry D, Sevenster M, Thorburn PJ	https://doi.org/10.36334/modsim2025.B05.jayasinghe	https://mssanz.org.au/modsim2025/files/B05.jayasinghe.pdf	B	Biological systems	B5	Agricultural Systems	B
Jayasinghe P	Modelling greenhouse gas emissions in mixed crop–livestock farming systems	Jayasinghe P, Bell LW, Mayberry D, Sevenster M, Thorburn PJ	https://doi.org/10.36334/modsim2025.B05.jayasinghe	https://mssanz.org.au/modsim2025/files/B05.jayasinghe.pdf	B	Biological systems	B5	Agricultural Systems	J
Mayberry D	Modelling greenhouse gas emissions in mixed crop–livestock farming systems	Jayasinghe P, Bell LW, Mayberry D, Sevenster M, Thorburn PJ	https://doi.org/10.36334/modsim2025.B05.jayasinghe	https://mssanz.org.au/modsim2025/files/B05.jayasinghe.pdf	B	Biological systems	B5	Agricultural Systems	M
Sevenster M	Modelling greenhouse gas emissions in mixed crop–livestock farming systems	Jayasinghe P, Bell LW, Mayberry D, Sevenster M, Thorburn PJ	https://doi.org/10.36334/modsim2025.B05.jayasinghe	https://mssanz.org.au/modsim2025/files/B05.jayasinghe.pdf	B	Biological systems	B5	Agricultural Systems	S
Thorburn PJ	Modelling greenhouse gas emissions in mixed crop–livestock farming systems	Jayasinghe P, Bell LW, Mayberry D, Sevenster M, Thorburn PJ	https://doi.org/10.36334/modsim2025.B05.jayasinghe	https://mssanz.org.au/modsim2025/files/B05.jayasinghe.pdf	B	Biological systems	B5	Agricultural Systems	T
Das Bhowmik R	Modelling heteroscedasticity and detecting shifts in rating curves for reliable streamflow records	Kona SV, Das Bhowmik R, Peel M, Western A	https://doi.org/10.36334/modsim2025.J02.kona	https://mssanz.org.au/modsim2025/files/J02.kona.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Kona SV	Modelling heteroscedasticity and detecting shifts in rating curves for reliable streamflow records	Kona SV, Das Bhowmik R, Peel M, Western A	https://doi.org/10.36334/modsim2025.J02.kona	https://mssanz.org.au/modsim2025/files/J02.kona.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	K
Peel MC	Modelling heteroscedasticity and detecting shifts in rating curves for reliable streamflow records	Kona SV, Das Bhowmik R, Peel M, Western A	https://doi.org/10.36334/modsim2025.J02.kona	https://mssanz.org.au/modsim2025/files/J02.kona.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Western A	Modelling heteroscedasticity and detecting shifts in rating curves for reliable streamflow records	Kona SV, Das Bhowmik R, Peel M, Western A	https://doi.org/10.36334/modsim2025.J02.kona	https://mssanz.org.au/modsim2025/files/J02.kona.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	W
Raveendran N	Modelling insurance losses from Australian bushfires	Sofronova K, Raveendran N	https://doi.org/10.36334/modsim2025.A04.sofronova	https://mssanz.org.au/modsim2025/files/A04.sofronova.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	R
Sofronova K	Modelling insurance losses from Australian bushfires	Sofronova K, Raveendran N	https://doi.org/10.36334/modsim2025.A04.sofronova	https://mssanz.org.au/modsim2025/files/A04.sofronova.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	S
Chenu K	Modelling limiting transpiration under high evaporative demand to improve wheat productivity in dry environments	Chenu K, Collins B	https://doi.org/10.36334/modsim2025.B02.chenu	https://mssanz.org.au/modsim2025/files/B02.chenu.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	C
Collins B	Modelling limiting transpiration under high evaporative demand to improve wheat productivity in dry environments	Chenu K, Collins B	https://doi.org/10.36334/modsim2025.B02.chenu	https://mssanz.org.au/modsim2025/files/B02.chenu.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	C
Liedloff A	Modelling livestock systems: bottom-up, top-down, or a meeting in the middle	Liedloff A, Snow V	https://doi.org/10.36334/modsim2025.B01.liedloff	https://mssanz.org.au/modsim2025/files/B01.liedloff.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	L
Snow V	Modelling livestock systems: bottom-up, top-down, or a meeting in the middle	Liedloff A, Snow V	https://doi.org/10.36334/modsim2025.B01.liedloff	https://mssanz.org.au/modsim2025/files/B01.liedloff.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	S
Kev D	Modelling long distance gravity flow channel design and alignment	Seo L, Petheram C, Kev D, Read A	https://doi.org/10.36334/modsim2025.J09.seo	https://mssanz.org.au/modsim2025/files/J09.seo.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	K
Petheram C	Modelling long distance gravity flow channel design and alignment	Seo L, Petheram C, Kev D, Read A	https://doi.org/10.36334/modsim2025.J09.seo	https://mssanz.org.au/modsim2025/files/J09.seo.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	P
Read A	Modelling long distance gravity flow channel design and alignment	Seo L, Petheram C, Kev D, Read A	https://doi.org/10.36334/modsim2025.J09.seo	https://mssanz.org.au/modsim2025/files/J09.seo.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	R
Seo L	Modelling long distance gravity flow channel design and alignment	Seo L, Petheram C, Kev D, Read A	https://doi.org/10.36334/modsim2025.J09.seo	https://mssanz.org.au/modsim2025/files/J09.seo.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	S
Davies P	Modelling mercury transport in legacy mining catchments in Victoria, Australia	Kyaw Kyaw A, Lintern A, Tang FHM, Schneider L, Lawrence S, Davies P	https://doi.org/10.36334/modsim2025.L01.kyaw	https://mssanz.org.au/modsim2025/files/L01.kyaw.pdf	L	Water quality	L1	Catchment Water Quality Modelling	D
Kyaw Kyaw A	Modelling mercury transport in legacy mining catchments in Victoria, Australia	Kyaw Kyaw A, Lintern A, Tang FHM, Schneider L, Lawrence S, Davies P	https://doi.org/10.36334/modsim2025.L01.kyaw	https://mssanz.org.au/modsim2025/files/L01.kyaw.pdf	L	Water quality	L1	Catchment Water Quality Modelling	K
Lawrence S	Modelling mercury transport in legacy mining catchments in Victoria, Australia	Kyaw Kyaw A, Lintern A, Tang FHM, Schneider L, Lawrence S, Davies P	https://doi.org/10.36334/modsim2025.L01.kyaw	https://mssanz.org.au/modsim2025/files/L01.kyaw.pdf	L	Water quality	L1	Catchment Water Quality Modelling	L
Lintern A	Modelling mercury transport in legacy mining catchments in Victoria, Australia	Kyaw Kyaw A, Lintern A, Tang FHM, Schneider L, Lawrence S, Davies P	https://doi.org/10.36334/modsim2025.L01.kyaw	https://mssanz.org.au/modsim2025/files/L01.kyaw.pdf	L	Water quality	L1	Catchment Water Quality Modelling	L
Schneider L	Modelling mercury transport in legacy mining catchments in Victoria, Australia	Kyaw Kyaw A, Lintern A, Tang FHM, Schneider L, Lawrence S, Davies P	https://doi.org/10.36334/modsim2025.L01.kyaw	https://mssanz.org.au/modsim2025/files/L01.kyaw.pdf	L	Water quality	L1	Catchment Water Quality Modelling	S
Tang FHM	Modelling mercury transport in legacy mining catchments in Victoria, Australia	Kyaw Kyaw A, Lintern A, Tang FHM, Schneider L, Lawrence S, Davies P	https://doi.org/10.36334/modsim2025.L01.kyaw	https://mssanz.org.au/modsim2025/files/L01.kyaw.pdf	L	Water quality	L1	Catchment Water Quality Modelling	T
Grace W	Modelling microgrids	Grace W	https://doi.org/10.36334/modsim2025.E05.grace	https://mssanz.org.au/modsim2025/files/E05.grace.pdf	E	Energy, integrated infrastructure and urban planning	E5	The Role of Microgrids in Building Resilient, Low-Carbon Communities	G
Awad J	Modelling monochloramine decay and the impact of zinc in a simulated drinking water distribution system	Kulkarni V, Awad J, van Leeuwen JA	https://doi.org/10.36334/modsim2025.L03.kulkarni	https://mssanz.org.au/modsim2025/files/L03.kulkarni.pdf	L	Water quality	L3	Modelling water quality treatment and management	A
Kulkarni V	Modelling monochloramine decay and the impact of zinc in a simulated drinking water distribution system	Kulkarni V, Awad J, van Leeuwen JA	https://doi.org/10.36334/modsim2025.L03.kulkarni	https://mssanz.org.au/modsim2025/files/L03.kulkarni.pdf	L	Water quality	L3	Modelling water quality treatment and management	K
van Leeuwen JA	Modelling monochloramine decay and the impact of zinc in a simulated drinking water distribution system	Kulkarni V, Awad J, van Leeuwen JA	https://doi.org/10.36334/modsim2025.L03.kulkarni	https://mssanz.org.au/modsim2025/files/L03.kulkarni.pdf	L	Water quality	L3	Modelling water quality treatment and management	v
Brennan E	Modelling of environmental water holders’ behaviour: A risk-based approach	Lee J, Brennan E, Korn A	https://doi.org/10.36334/modsim2025.J08.lee	https://mssanz.org.au/modsim2025/files/J08.lee.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	B
Korn A	Modelling of environmental water holders’ behaviour: A risk-based approach	Lee J, Brennan E, Korn A	https://doi.org/10.36334/modsim2025.J08.lee	https://mssanz.org.au/modsim2025/files/J08.lee.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	K
Lee J	Modelling of environmental water holders’ behaviour: A risk-based approach	Lee J, Brennan E, Korn A	https://doi.org/10.36334/modsim2025.J08.lee	https://mssanz.org.au/modsim2025/files/J08.lee.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	L
Hu X	Modelling of soil nitrogen dynamics in a smart-irrigated maize field in Newry, Victoria	Hu X, Tang FHM, Walker JP, Pasut C, Lintern A	https://doi.org/10.36334/modsim2025.B03.hu	https://mssanz.org.au/modsim2025/files/B03.hu.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	H
Lintern A	Modelling of soil nitrogen dynamics in a smart-irrigated maize field in Newry, Victoria	Hu X, Tang FHM, Walker JP, Pasut C, Lintern A	https://doi.org/10.36334/modsim2025.B03.hu	https://mssanz.org.au/modsim2025/files/B03.hu.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	L
Pasut C	Modelling of soil nitrogen dynamics in a smart-irrigated maize field in Newry, Victoria	Hu X, Tang FHM, Walker JP, Pasut C, Lintern A	https://doi.org/10.36334/modsim2025.B03.hu	https://mssanz.org.au/modsim2025/files/B03.hu.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	P
Tang FHM	Modelling of soil nitrogen dynamics in a smart-irrigated maize field in Newry, Victoria	Hu X, Tang FHM, Walker JP, Pasut C, Lintern A	https://doi.org/10.36334/modsim2025.B03.hu	https://mssanz.org.au/modsim2025/files/B03.hu.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	T
Walker JP	Modelling of soil nitrogen dynamics in a smart-irrigated maize field in Newry, Victoria	Hu X, Tang FHM, Walker JP, Pasut C, Lintern A	https://doi.org/10.36334/modsim2025.B03.hu	https://mssanz.org.au/modsim2025/files/B03.hu.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	W
Ahammed F	Modelling of stormwater drainage systems using RCP 8.5 climate change scenario	Ahammed F	https://doi.org/10.36334/modsim2025.J08.ahammed	https://mssanz.org.au/modsim2025/files/J08.ahammed.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	A
Brown H (Hamish)	Modelling orchard systems in APSIM	Brown H, Khaembah E, Yang X, Holzworth D, Huth N, Cichota R, Sharp J, Reeves S	https://doi.org/10.36334/modsim2025.B02.brown	https://mssanz.org.au/modsim2025/files/B02.brown.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	B
Cichota R	Modelling orchard systems in APSIM	Brown H, Khaembah E, Yang X, Holzworth D, Huth N, Cichota R, Sharp J, Reeves S	https://doi.org/10.36334/modsim2025.B02.brown	https://mssanz.org.au/modsim2025/files/B02.brown.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	C
Holzworth D	Modelling orchard systems in APSIM	Brown H, Khaembah E, Yang X, Holzworth D, Huth N, Cichota R, Sharp J, Reeves S	https://doi.org/10.36334/modsim2025.B02.brown	https://mssanz.org.au/modsim2025/files/B02.brown.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Huth N	Modelling orchard systems in APSIM	Brown H, Khaembah E, Yang X, Holzworth D, Huth N, Cichota R, Sharp J, Reeves S	https://doi.org/10.36334/modsim2025.B02.brown	https://mssanz.org.au/modsim2025/files/B02.brown.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Khaembah E	Modelling orchard systems in APSIM	Brown H, Khaembah E, Yang X, Holzworth D, Huth N, Cichota R, Sharp J, Reeves S	https://doi.org/10.36334/modsim2025.B02.brown	https://mssanz.org.au/modsim2025/files/B02.brown.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	K
Reeves S	Modelling orchard systems in APSIM	Brown H, Khaembah E, Yang X, Holzworth D, Huth N, Cichota R, Sharp J, Reeves S	https://doi.org/10.36334/modsim2025.B02.brown	https://mssanz.org.au/modsim2025/files/B02.brown.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	R
Sharp J	Modelling orchard systems in APSIM	Brown H, Khaembah E, Yang X, Holzworth D, Huth N, Cichota R, Sharp J, Reeves S	https://doi.org/10.36334/modsim2025.B02.brown	https://mssanz.org.au/modsim2025/files/B02.brown.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	S
Yang X	Modelling orchard systems in APSIM	Brown H, Khaembah E, Yang X, Holzworth D, Huth N, Cichota R, Sharp J, Reeves S	https://doi.org/10.36334/modsim2025.B02.brown	https://mssanz.org.au/modsim2025/files/B02.brown.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	Y
Hock K	Modelling performance of uncrewed autonomous system swarms against directed energy weapon systems	Hock K, Weinberg GV	https://doi.org/10.36334/modsim2025.M02.hock	https://mssanz.org.au/modsim2025/files/M02.hock.pdf	M	Operations Research	M2	Simulation modelling and analysis	H
Weinberg GV	Modelling performance of uncrewed autonomous system swarms against directed energy weapon systems	Hock K, Weinberg GV	https://doi.org/10.36334/modsim2025.M02.hock	https://mssanz.org.au/modsim2025/files/M02.hock.pdf	M	Operations Research	M2	Simulation modelling and analysis	W
Armour B	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	A
Asanopoulos C	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	A
Brown G	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	B
Crawford D	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	C
De Antoni Migliorati M	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	D
Farrell M	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	F
Garrard M	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	G
Hoyle F	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	H
Karunaratne S	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	K
Lauerwald R	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	L
Li M	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	L
Massad RS	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	M
McCaskill M	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	M
Moreton R	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	M
O’Keeffe T	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	O
Pasut C	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	P
Polain K	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	P
Reeves S	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	R
Schapel A	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	S
Wilson B	Modelling soil organic carbon change: the role of climate, soil, land-management, and carbon input in Australian farming systems	Pasut C, Li M, Armour B, Asanopoulos C, Brown G, Crawford D, De Antoni Migliorati M, Lauerwald R, Garrard M, Massad RS, Hoyle F, McCaskill M, Moreton R, O’Keeffe T, Polain K, Schapel A, Reeves S, Wilson B, Farrell M, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.pasut	https://mssanz.org.au/modsim2025/files/F13.pasut.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	W
Kean J	Modelling spread of invasive organisms as part of a New Zealand Biosecurity Risk Evaluation Framework	Sharp J, Turner R, Kean J, Phillips C	https://doi.org/10.36334/modsim2025.F05.sharp	https://mssanz.org.au/modsim2025/files/F05.sharp.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	K
Phillips C	Modelling spread of invasive organisms as part of a New Zealand Biosecurity Risk Evaluation Framework	Sharp J, Turner R, Kean J, Phillips C	https://doi.org/10.36334/modsim2025.F05.sharp	https://mssanz.org.au/modsim2025/files/F05.sharp.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	P
Sharp J	Modelling spread of invasive organisms as part of a New Zealand Biosecurity Risk Evaluation Framework	Sharp J, Turner R, Kean J, Phillips C	https://doi.org/10.36334/modsim2025.F05.sharp	https://mssanz.org.au/modsim2025/files/F05.sharp.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	S
Turner R	Modelling spread of invasive organisms as part of a New Zealand Biosecurity Risk Evaluation Framework	Sharp J, Turner R, Kean J, Phillips C	https://doi.org/10.36334/modsim2025.F05.sharp	https://mssanz.org.au/modsim2025/files/F05.sharp.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	T
Davidson EC	Modelling the closed loop between a personalised recommender system and users' opinion dynamics	Davidson EC, Ye M	https://doi.org/10.36334/modsim2025.I03.davidson	https://mssanz.org.au/modsim2025/files/I03.davidson.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	D
Ye M	Modelling the closed loop between a personalised recommender system and users' opinion dynamics	Davidson EC, Ye M	https://doi.org/10.36334/modsim2025.I03.davidson	https://mssanz.org.au/modsim2025/files/I03.davidson.pdf	I	Participatory decision-making, socioecological systems, and education	I3	Agent-based and data-driven modelling of complex social systems in public information environments	Y
Anese J	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	A
Batelaan O	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	B
Fallowfield H	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	F
Graetz D	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	G
Leonard M (Michael)	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	L
Maier HR	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	M
Mullins B	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	M
Reeve P	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	R
Wallis I	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	W
Walton K	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	W
Watt E	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	W
Westra S	Modelling the cost and viability of brackish groundwater desalination for agriculture in the Murray–Darling Basin	Reeve P, Anese J, Mullins B, Wallis I, Batelaan O, Fallowfield H, Maier H, Westra S, Walton K, Watt E, Graetz D, Leonard M	https://doi.org/10.36334/modsim2025.K01.reeve	https://mssanz.org.au/modsim2025/files/K01.reeve.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	W
Nguyen D (Duc)	Modelling the effects of vegetation on mitigation of gully erosion using Computational Fluid Dynamics (CFD)	Nguyen D, Wakes S, Roberts ME	https://doi.org/10.36334/modsim2025.L04.nguyen	https://mssanz.org.au/modsim2025/files/L04.nguyen.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	N
Roberts ME	Modelling the effects of vegetation on mitigation of gully erosion using Computational Fluid Dynamics (CFD)	Nguyen D, Wakes S, Roberts ME	https://doi.org/10.36334/modsim2025.L04.nguyen	https://mssanz.org.au/modsim2025/files/L04.nguyen.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	R
Wakes S	Modelling the effects of vegetation on mitigation of gully erosion using Computational Fluid Dynamics (CFD)	Nguyen D, Wakes S, Roberts ME	https://doi.org/10.36334/modsim2025.L04.nguyen	https://mssanz.org.au/modsim2025/files/L04.nguyen.pdf	L	Water quality	L4	Modelling Erosion and Pollutant Dynamics	W
Carnie-Bronca S	Modelling the evolution of topics in social media conversations	Carnie-Bronca S	https://doi.org/10.36334/modsim2025.A03.carniebronca	https://mssanz.org.au/modsim2025/files/A03.carniebronca.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	C
Beverly C	Modelling the frequency and extent of waterlogging: a case study	Beverly C, Weeks A, McCaskill M, Harrison M	https://doi.org/10.36334/modsim2025.J10.beverly	https://mssanz.org.au/modsim2025/files/J10.beverly.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	B
Harrison M	Modelling the frequency and extent of waterlogging: a case study	Beverly C, Weeks A, McCaskill M, Harrison M	https://doi.org/10.36334/modsim2025.J10.beverly	https://mssanz.org.au/modsim2025/files/J10.beverly.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	H
McCaskill M	Modelling the frequency and extent of waterlogging: a case study	Beverly C, Weeks A, McCaskill M, Harrison M	https://doi.org/10.36334/modsim2025.J10.beverly	https://mssanz.org.au/modsim2025/files/J10.beverly.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	M
Weeks A	Modelling the frequency and extent of waterlogging: a case study	Beverly C, Weeks A, McCaskill M, Harrison M	https://doi.org/10.36334/modsim2025.J10.beverly	https://mssanz.org.au/modsim2025/files/J10.beverly.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	W
Aflah WN	Modelling the hydrological impacts of leaky weirs on catchment rehydration using SWAT+ Modelstorage	Aflah WN, van Ogtrop F, Vervoort RW	https://doi.org/10.36334/modsim2025.J02.aflah	https://mssanz.org.au/modsim2025/files/J02.aflah.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	A
van Ogtrop F	Modelling the hydrological impacts of leaky weirs on catchment rehydration using SWAT+ Modelstorage	Aflah WN, van Ogtrop F, Vervoort RW	https://doi.org/10.36334/modsim2025.J02.aflah	https://mssanz.org.au/modsim2025/files/J02.aflah.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	v
Vervoort RW	Modelling the hydrological impacts of leaky weirs on catchment rehydration using SWAT+ Modelstorage	Aflah WN, van Ogtrop F, Vervoort RW	https://doi.org/10.36334/modsim2025.J02.aflah	https://mssanz.org.au/modsim2025/files/J02.aflah.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	V
Gordon S	Modelling the impact of novel Rht genes and biodegradable mulching on wheat early growth and yield	Zhen X, He D, Rebetzke GJ, Gordon S, Wang E	https://doi.org/10.36334/modsim2025.B08.zhen	https://mssanz.org.au/modsim2025/files/B08.zhen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	G
He D	Modelling the impact of novel Rht genes and biodegradable mulching on wheat early growth and yield	Zhen X, He D, Rebetzke GJ, Gordon S, Wang E	https://doi.org/10.36334/modsim2025.B08.zhen	https://mssanz.org.au/modsim2025/files/B08.zhen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	H
Rebetzke GJ	Modelling the impact of novel Rht genes and biodegradable mulching on wheat early growth and yield	Zhen X, He D, Rebetzke GJ, Gordon S, Wang E	https://doi.org/10.36334/modsim2025.B08.zhen	https://mssanz.org.au/modsim2025/files/B08.zhen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	R
Wang E (Enli)	Modelling the impact of novel Rht genes and biodegradable mulching on wheat early growth and yield	Zhen X, He D, Rebetzke GJ, Gordon S, Wang E	https://doi.org/10.36334/modsim2025.B08.zhen	https://mssanz.org.au/modsim2025/files/B08.zhen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	W
Zhen X (Xioaxing)	Modelling the impact of novel Rht genes and biodegradable mulching on wheat early growth and yield	Zhen X, He D, Rebetzke GJ, Gordon S, Wang E	https://doi.org/10.36334/modsim2025.B08.zhen	https://mssanz.org.au/modsim2025/files/B08.zhen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	Z
Pindor B	Modelling the intensity of Victorian bushfire as observed by Himawari-9 (STREAM G KEYNOTE)	Pindor B	https://doi.org/10.36334/modsim2025.G08.pindor	https://mssanz.org.au/modsim2025/files/G08.pindor.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	P
Dougherty J	Modelling the whole farm effects of livestock selection: opportunities and limitations	Keogh T, Dougherty J, Liedloff A, Thomas D, Schmoelzl S	https://doi.org/10.36334/modsim2025.B04.keogh	https://mssanz.org.au/modsim2025/files/B04.keogh.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	D
Keogh T	Modelling the whole farm effects of livestock selection: opportunities and limitations	Keogh T, Dougherty J, Liedloff A, Thomas D, Schmoelzl S	https://doi.org/10.36334/modsim2025.B04.keogh	https://mssanz.org.au/modsim2025/files/B04.keogh.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	K
Liedloff A	Modelling the whole farm effects of livestock selection: opportunities and limitations	Keogh T, Dougherty J, Liedloff A, Thomas D, Schmoelzl S	https://doi.org/10.36334/modsim2025.B04.keogh	https://mssanz.org.au/modsim2025/files/B04.keogh.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	L
Schmoelzl S	Modelling the whole farm effects of livestock selection: opportunities and limitations	Keogh T, Dougherty J, Liedloff A, Thomas D, Schmoelzl S	https://doi.org/10.36334/modsim2025.B04.keogh	https://mssanz.org.au/modsim2025/files/B04.keogh.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	S
Thomas D (Dean)	Modelling the whole farm effects of livestock selection: opportunities and limitations	Keogh T, Dougherty J, Liedloff A, Thomas D, Schmoelzl S	https://doi.org/10.36334/modsim2025.B04.keogh	https://mssanz.org.au/modsim2025/files/B04.keogh.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	T
Klewicki J	Modelling thermal stratification in river waterholes using GLM	Rabiei A, Western A, Philip J, Klewicki J	https://doi.org/10.36334/modsim2025.L02.rabiei	https://mssanz.org.au/modsim2025/files/L02.rabiei.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	K
Philip J	Modelling thermal stratification in river waterholes using GLM	Rabiei A, Western A, Philip J, Klewicki J	https://doi.org/10.36334/modsim2025.L02.rabiei	https://mssanz.org.au/modsim2025/files/L02.rabiei.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	P
Rabiei A	Modelling thermal stratification in river waterholes using GLM	Rabiei A, Western A, Philip J, Klewicki J	https://doi.org/10.36334/modsim2025.L02.rabiei	https://mssanz.org.au/modsim2025/files/L02.rabiei.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	R
Western A	Modelling thermal stratification in river waterholes using GLM	Rabiei A, Western A, Philip J, Klewicki J	https://doi.org/10.36334/modsim2025.L02.rabiei	https://mssanz.org.au/modsim2025/files/L02.rabiei.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	W
Simmons C	Modelling tomorrow: Bridging science, society and innovation for a resilient South Australia	Simmons C	https://doi.org/10.36334/modsim2025.P01.simmons	https://mssanz.org.au/modsim2025/files/P01.simmons.pdf	P	Plenary	P1	Plenary	S
Grazian C	Modelling upstream water quality from downstream observations in the Muttama catchment	Khang VS, Grazian C, Vervoort RW	https://doi.org/10.36334/modsim2025.L01.khang	https://mssanz.org.au/modsim2025/files/L01.khang.pdf	L	Water quality	L1	Catchment Water Quality Modelling	G
Khang VS	Modelling upstream water quality from downstream observations in the Muttama catchment	Khang VS, Grazian C, Vervoort RW	https://doi.org/10.36334/modsim2025.L01.khang	https://mssanz.org.au/modsim2025/files/L01.khang.pdf	L	Water quality	L1	Catchment Water Quality Modelling	K
Vervoort RW	Modelling upstream water quality from downstream observations in the Muttama catchment	Khang VS, Grazian C, Vervoort RW	https://doi.org/10.36334/modsim2025.L01.khang	https://mssanz.org.au/modsim2025/files/L01.khang.pdf	L	Water quality	L1	Catchment Water Quality Modelling	V
Grace W	Modelling urban emissions – a Perth case study	Grace W	https://doi.org/10.36334/modsim2025.E01.grace	https://mssanz.org.au/modsim2025/files/E01.grace.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	G
Evans JP	Modelling urban heat islands: The role of high-resolution urban morphology	Imran HM, Mughal MO, Kala J, Evans J, Stuart A	https://doi.org/10.36334/modsim2025.G06.imran	https://mssanz.org.au/modsim2025/files/G06.imran.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	E
Imran HM	Modelling urban heat islands: The role of high-resolution urban morphology	Imran HM, Mughal MO, Kala J, Evans J, Stuart A	https://doi.org/10.36334/modsim2025.G06.imran	https://mssanz.org.au/modsim2025/files/G06.imran.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	I
Kala J	Modelling urban heat islands: The role of high-resolution urban morphology	Imran HM, Mughal MO, Kala J, Evans J, Stuart A	https://doi.org/10.36334/modsim2025.G06.imran	https://mssanz.org.au/modsim2025/files/G06.imran.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	K
Mughal MO	Modelling urban heat islands: The role of high-resolution urban morphology	Imran HM, Mughal MO, Kala J, Evans J, Stuart A	https://doi.org/10.36334/modsim2025.G06.imran	https://mssanz.org.au/modsim2025/files/G06.imran.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	M
Stuart A	Modelling urban heat islands: The role of high-resolution urban morphology	Imran HM, Mughal MO, Kala J, Evans J, Stuart A	https://doi.org/10.36334/modsim2025.G06.imran	https://mssanz.org.au/modsim2025/files/G06.imran.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	S
Breda A	Modelling wetland vulnerability to sea-level rise	Saco PM, Rodríguez JF, Breda A, Sandi E, Sandi S	https://doi.org/10.36334/modsim2025.J11.saco	https://mssanz.org.au/modsim2025/files/J11.saco.pdf	J	Water resources	J11	Ecohydrological Modelling	B
Rodríguez JF	Modelling wetland vulnerability to sea-level rise	Saco PM, Rodríguez JF, Breda A, Sandi E, Sandi S	https://doi.org/10.36334/modsim2025.J11.saco	https://mssanz.org.au/modsim2025/files/J11.saco.pdf	J	Water resources	J11	Ecohydrological Modelling	R
Saco PM	Modelling wetland vulnerability to sea-level rise	Saco PM, Rodríguez JF, Breda A, Sandi E, Sandi S	https://doi.org/10.36334/modsim2025.J11.saco	https://mssanz.org.au/modsim2025/files/J11.saco.pdf	J	Water resources	J11	Ecohydrological Modelling	S
Sandi E	Modelling wetland vulnerability to sea-level rise	Saco PM, Rodríguez JF, Breda A, Sandi E, Sandi S	https://doi.org/10.36334/modsim2025.J11.saco	https://mssanz.org.au/modsim2025/files/J11.saco.pdf	J	Water resources	J11	Ecohydrological Modelling	S
Sandi S	Modelling wetland vulnerability to sea-level rise	Saco PM, Rodríguez JF, Breda A, Sandi E, Sandi S	https://doi.org/10.36334/modsim2025.J11.saco	https://mssanz.org.au/modsim2025/files/J11.saco.pdf	J	Water resources	J11	Ecohydrological Modelling	S
Ellis R	Modernising reach-scale stream bank erosion estimation in Australia	Ellis R, Marsh N, Waters D	https://doi.org/10.36334/modsim2025.L01.ellis	https://mssanz.org.au/modsim2025/files/L01.ellis.pdf	L	Water quality	L1	Catchment Water Quality Modelling	E
Marsh N	Modernising reach-scale stream bank erosion estimation in Australia	Ellis R, Marsh N, Waters D	https://doi.org/10.36334/modsim2025.L01.ellis	https://mssanz.org.au/modsim2025/files/L01.ellis.pdf	L	Water quality	L1	Catchment Water Quality Modelling	M
Waters D	Modernising reach-scale stream bank erosion estimation in Australia	Ellis R, Marsh N, Waters D	https://doi.org/10.36334/modsim2025.L01.ellis	https://mssanz.org.au/modsim2025/files/L01.ellis.pdf	L	Water quality	L1	Catchment Water Quality Modelling	W
Evans JP	Modes of pyrocumulonimbus development in southeast Australia	Wilson CS, Sharples JJ, Evans JP	https://doi.org/10.36334/modsim2025.G08.wilson	https://mssanz.org.au/modsim2025/files/G08.wilson.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	E
Sharples JJ	Modes of pyrocumulonimbus development in southeast Australia	Wilson CS, Sharples JJ, Evans JP	https://doi.org/10.36334/modsim2025.G08.wilson	https://mssanz.org.au/modsim2025/files/G08.wilson.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Wilson CS	Modes of pyrocumulonimbus development in southeast Australia	Wilson CS, Sharples JJ, Evans JP	https://doi.org/10.36334/modsim2025.G08.wilson	https://mssanz.org.au/modsim2025/files/G08.wilson.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	W
Knowling MJ	More profits in good years, less risk in bad: Physics-informed reinforcement learning (RL) to inform crop selection	Wang W, Knowling MJ	https://doi.org/10.36334/modsim2025.B06.wang	https://mssanz.org.au/modsim2025/files/B06.wang.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	K
Wang W	More profits in good years, less risk in bad: Physics-informed reinforcement learning (RL) to inform crop selection	Wang W, Knowling MJ	https://doi.org/10.36334/modsim2025.B06.wang	https://mssanz.org.au/modsim2025/files/B06.wang.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	W
Beesley B	Mothership and multi-tender routing: an optimisation framework for coral deployment under bathymetric constraints	Lippmann RB, Iwanaga T, Helmstedt KJ, Ribeiro de Almeida P, Beesley B, Corry P	https://doi.org/10.36334/modsim2025.M01.lippmann	https://mssanz.org.au/modsim2025/files/M01.lippmann.pdf	M	Operations Research	M1	OR methods and applications	B
Corry PG (Paul)	Mothership and multi-tender routing: an optimisation framework for coral deployment under bathymetric constraints	Lippmann RB, Iwanaga T, Helmstedt KJ, Ribeiro de Almeida P, Beesley B, Corry P	https://doi.org/10.36334/modsim2025.M01.lippmann	https://mssanz.org.au/modsim2025/files/M01.lippmann.pdf	M	Operations Research	M1	OR methods and applications	C
Helmstedt KJ	Mothership and multi-tender routing: an optimisation framework for coral deployment under bathymetric constraints	Lippmann RB, Iwanaga T, Helmstedt KJ, Ribeiro de Almeida P, Beesley B, Corry P	https://doi.org/10.36334/modsim2025.M01.lippmann	https://mssanz.org.au/modsim2025/files/M01.lippmann.pdf	M	Operations Research	M1	OR methods and applications	H
Iwanaga T	Mothership and multi-tender routing: an optimisation framework for coral deployment under bathymetric constraints	Lippmann RB, Iwanaga T, Helmstedt KJ, Ribeiro de Almeida P, Beesley B, Corry P	https://doi.org/10.36334/modsim2025.M01.lippmann	https://mssanz.org.au/modsim2025/files/M01.lippmann.pdf	M	Operations Research	M1	OR methods and applications	I
Lippmann RB	Mothership and multi-tender routing: an optimisation framework for coral deployment under bathymetric constraints	Lippmann RB, Iwanaga T, Helmstedt KJ, Ribeiro de Almeida P, Beesley B, Corry P	https://doi.org/10.36334/modsim2025.M01.lippmann	https://mssanz.org.au/modsim2025/files/M01.lippmann.pdf	M	Operations Research	M1	OR methods and applications	L
Ribeiro de Almeida P	Mothership and multi-tender routing: an optimisation framework for coral deployment under bathymetric constraints	Lippmann RB, Iwanaga T, Helmstedt KJ, Ribeiro de Almeida P, Beesley B, Corry P	https://doi.org/10.36334/modsim2025.M01.lippmann	https://mssanz.org.au/modsim2025/files/M01.lippmann.pdf	M	Operations Research	M1	OR methods and applications	R
Bennett J	Multi lead time parameter inference for post-processing precipitation forecasts	Shrestha D, Robertson D, Bennett J, Schepen A	https://doi.org/10.36334/modsim2025.K02.shrestha	https://mssanz.org.au/modsim2025/files/K02.shrestha.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	B
Robertson D	Multi lead time parameter inference for post-processing precipitation forecasts	Shrestha D, Robertson D, Bennett J, Schepen A	https://doi.org/10.36334/modsim2025.K02.shrestha	https://mssanz.org.au/modsim2025/files/K02.shrestha.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	R
Schepen A	Multi lead time parameter inference for post-processing precipitation forecasts	Shrestha D, Robertson D, Bennett J, Schepen A	https://doi.org/10.36334/modsim2025.K02.shrestha	https://mssanz.org.au/modsim2025/files/K02.shrestha.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	S
Shrestha D	Multi lead time parameter inference for post-processing precipitation forecasts	Shrestha D, Robertson D, Bennett J, Schepen A	https://doi.org/10.36334/modsim2025.K02.shrestha	https://mssanz.org.au/modsim2025/files/K02.shrestha.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	S
Akbar D	Multi-crop modelling framework for evaluating irrigation strategies and sowing window optimisation across Australian production environments	Sarker J, Akbar D, Rolfe J	https://doi.org/10.36334/modsim2025.B05.sarker	https://mssanz.org.au/modsim2025/files/B05.sarker.pdf	B	Biological systems	B5	Agricultural Systems	A
Rolfe J	Multi-crop modelling framework for evaluating irrigation strategies and sowing window optimisation across Australian production environments	Sarker J, Akbar D, Rolfe J	https://doi.org/10.36334/modsim2025.B05.sarker	https://mssanz.org.au/modsim2025/files/B05.sarker.pdf	B	Biological systems	B5	Agricultural Systems	R
Sarker J	Multi-crop modelling framework for evaluating irrigation strategies and sowing window optimisation across Australian production environments	Sarker J, Akbar D, Rolfe J	https://doi.org/10.36334/modsim2025.B05.sarker	https://mssanz.org.au/modsim2025/files/B05.sarker.pdf	B	Biological systems	B5	Agricultural Systems	S
Coutts A	Multi-objective and multi-stage optimisation for force structure design	Nguyen MT, Widjaja I, Coutts A	https://doi.org/10.36334/modsim2025.M01.nguyen	https://mssanz.org.au/modsim2025/files/M01.nguyen.pdf	M	Operations Research	M1	OR methods and applications	C
Nguyen MT	Multi-objective and multi-stage optimisation for force structure design	Nguyen MT, Widjaja I, Coutts A	https://doi.org/10.36334/modsim2025.M01.nguyen	https://mssanz.org.au/modsim2025/files/M01.nguyen.pdf	M	Operations Research	M1	OR methods and applications	N
Widjaja I	Multi-objective and multi-stage optimisation for force structure design	Nguyen MT, Widjaja I, Coutts A	https://doi.org/10.36334/modsim2025.M01.nguyen	https://mssanz.org.au/modsim2025/files/M01.nguyen.pdf	M	Operations Research	M1	OR methods and applications	W
Evans JP	Multiscale analysis of terrain–lightning relationships using machine learning	Rahmani S, Evans J, Sharples J	https://doi.org/10.36334/modsim2025.G08.rahmani	https://mssanz.org.au/modsim2025/files/G08.rahmani.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	E
Rahmani S	Multiscale analysis of terrain–lightning relationships using machine learning	Rahmani S, Evans J, Sharples J	https://doi.org/10.36334/modsim2025.G08.rahmani	https://mssanz.org.au/modsim2025/files/G08.rahmani.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	R
Sharples JJ	Multiscale analysis of terrain–lightning relationships using machine learning	Rahmani S, Evans J, Sharples J	https://doi.org/10.36334/modsim2025.G08.rahmani	https://mssanz.org.au/modsim2025/files/G08.rahmani.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Lintern A	MUSIC:'s Bioretention Node under climate change: What changes do we need?	Wake J, Lintern A, Winfrey B, Zamyadi A, Tang FHM	https://doi.org/10.36334/modsim2025.L03.wake	https://mssanz.org.au/modsim2025/files/L03.wake.pdf	L	Water quality	L3	Modelling water quality treatment and management	L
Tang FHM	MUSIC:'s Bioretention Node under climate change: What changes do we need?	Wake J, Lintern A, Winfrey B, Zamyadi A, Tang FHM	https://doi.org/10.36334/modsim2025.L03.wake	https://mssanz.org.au/modsim2025/files/L03.wake.pdf	L	Water quality	L3	Modelling water quality treatment and management	T
Wake J	MUSIC:'s Bioretention Node under climate change: What changes do we need?	Wake J, Lintern A, Winfrey B, Zamyadi A, Tang FHM	https://doi.org/10.36334/modsim2025.L03.wake	https://mssanz.org.au/modsim2025/files/L03.wake.pdf	L	Water quality	L3	Modelling water quality treatment and management	W
Winfrey B	MUSIC:'s Bioretention Node under climate change: What changes do we need?	Wake J, Lintern A, Winfrey B, Zamyadi A, Tang FHM	https://doi.org/10.36334/modsim2025.L03.wake	https://mssanz.org.au/modsim2025/files/L03.wake.pdf	L	Water quality	L3	Modelling water quality treatment and management	W
Zamyadi A	MUSIC:'s Bioretention Node under climate change: What changes do we need?	Wake J, Lintern A, Winfrey B, Zamyadi A, Tang FHM	https://doi.org/10.36334/modsim2025.L03.wake	https://mssanz.org.au/modsim2025/files/L03.wake.pdf	L	Water quality	L3	Modelling water quality treatment and management	Z
Crosbie RS	Muti-variable calibration to improve robustness of landscape models under change	Gibbs M, Wang B, Crosbie R, Montazeri M, Vaze J, Yang A	https://doi.org/10.36334/modsim2025.J07.gibbs	https://mssanz.org.au/modsim2025/files/J07.gibbs.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	C
Gibbs MS	Muti-variable calibration to improve robustness of landscape models under change	Gibbs M, Wang B, Crosbie R, Montazeri M, Vaze J, Yang A	https://doi.org/10.36334/modsim2025.J07.gibbs	https://mssanz.org.au/modsim2025/files/J07.gibbs.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	G
Montazeri M	Muti-variable calibration to improve robustness of landscape models under change	Gibbs M, Wang B, Crosbie R, Montazeri M, Vaze J, Yang A	https://doi.org/10.36334/modsim2025.J07.gibbs	https://mssanz.org.au/modsim2025/files/J07.gibbs.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	M
Vaze J	Muti-variable calibration to improve robustness of landscape models under change	Gibbs M, Wang B, Crosbie R, Montazeri M, Vaze J, Yang A	https://doi.org/10.36334/modsim2025.J07.gibbs	https://mssanz.org.au/modsim2025/files/J07.gibbs.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	V
Wang B (Bill)	Muti-variable calibration to improve robustness of landscape models under change	Gibbs M, Wang B, Crosbie R, Montazeri M, Vaze J, Yang A	https://doi.org/10.36334/modsim2025.J07.gibbs	https://mssanz.org.au/modsim2025/files/J07.gibbs.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	W
Yang A	Muti-variable calibration to improve robustness of landscape models under change	Gibbs M, Wang B, Crosbie R, Montazeri M, Vaze J, Yang A	https://doi.org/10.36334/modsim2025.J07.gibbs	https://mssanz.org.au/modsim2025/files/J07.gibbs.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	Y
Ching M	Naomi and Murrumbidgee water balance tool – an integrated approach for river system manaement	Ching M, Inamdar P, Nicholls D, Rahman J, Smith T	https://doi.org/10.36334/modsim2025.J02.ching	https://mssanz.org.au/modsim2025/files/J02.ching.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	C
Inamdar P	Naomi and Murrumbidgee water balance tool – an integrated approach for river system manaement	Ching M, Inamdar P, Nicholls D, Rahman J, Smith T	https://doi.org/10.36334/modsim2025.J02.ching	https://mssanz.org.au/modsim2025/files/J02.ching.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	I
Nicholls D	Naomi and Murrumbidgee water balance tool – an integrated approach for river system manaement	Ching M, Inamdar P, Nicholls D, Rahman J, Smith T	https://doi.org/10.36334/modsim2025.J02.ching	https://mssanz.org.au/modsim2025/files/J02.ching.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	N
Rahman J (Joel)	Naomi and Murrumbidgee water balance tool – an integrated approach for river system manaement	Ching M, Inamdar P, Nicholls D, Rahman J, Smith T	https://doi.org/10.36334/modsim2025.J02.ching	https://mssanz.org.au/modsim2025/files/J02.ching.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Smith T	Naomi and Murrumbidgee water balance tool – an integrated approach for river system manaement	Ching M, Inamdar P, Nicholls D, Rahman J, Smith T	https://doi.org/10.36334/modsim2025.J02.ching	https://mssanz.org.au/modsim2025/files/J02.ching.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	S
Evans D	National-scale monitoring of irrigated agricultural water use using remote sensing evapotranspiration and a hydrological framework	Liu N, Smith GS, Evans D, McVicar TR, Peña‑Arancibia JL, Tetreault‑Campbell S, Pascoe S	https://doi.org/10.36334/modsim2025.J05.liu	https://mssanz.org.au/modsim2025/files/J05.liu.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	E
Liu N	National-scale monitoring of irrigated agricultural water use using remote sensing evapotranspiration and a hydrological framework	Liu N, Smith GS, Evans D, McVicar TR, Peña‑Arancibia JL, Tetreault‑Campbell S, Pascoe S	https://doi.org/10.36334/modsim2025.J05.liu	https://mssanz.org.au/modsim2025/files/J05.liu.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	L
McVicar TR	National-scale monitoring of irrigated agricultural water use using remote sensing evapotranspiration and a hydrological framework	Liu N, Smith GS, Evans D, McVicar TR, Peña‑Arancibia JL, Tetreault‑Campbell S, Pascoe S	https://doi.org/10.36334/modsim2025.J05.liu	https://mssanz.org.au/modsim2025/files/J05.liu.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	M
Pascoe S	National-scale monitoring of irrigated agricultural water use using remote sensing evapotranspiration and a hydrological framework	Liu N, Smith GS, Evans D, McVicar TR, Peña‑Arancibia JL, Tetreault‑Campbell S, Pascoe S	https://doi.org/10.36334/modsim2025.J05.liu	https://mssanz.org.au/modsim2025/files/J05.liu.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	P
Peña‑Arancibia JL	National-scale monitoring of irrigated agricultural water use using remote sensing evapotranspiration and a hydrological framework	Liu N, Smith GS, Evans D, McVicar TR, Peña‑Arancibia JL, Tetreault‑Campbell S, Pascoe S	https://doi.org/10.36334/modsim2025.J05.liu	https://mssanz.org.au/modsim2025/files/J05.liu.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	P
Smith GS	National-scale monitoring of irrigated agricultural water use using remote sensing evapotranspiration and a hydrological framework	Liu N, Smith GS, Evans D, McVicar TR, Peña‑Arancibia JL, Tetreault‑Campbell S, Pascoe S	https://doi.org/10.36334/modsim2025.J05.liu	https://mssanz.org.au/modsim2025/files/J05.liu.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	S
Tetreault‑Campbell S	National-scale monitoring of irrigated agricultural water use using remote sensing evapotranspiration and a hydrological framework	Liu N, Smith GS, Evans D, McVicar TR, Peña‑Arancibia JL, Tetreault‑Campbell S, Pascoe S	https://doi.org/10.36334/modsim2025.J05.liu	https://mssanz.org.au/modsim2025/files/J05.liu.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	T
Culley S (Sam)	Natural hazards ahead: Navigating uncertainty with stakeholder-led scenarios	Miller A, Culley S, Radford D, van Delden H, Zecchin A, Vanhout R, Donohue T, Whiting T, Maier HR	https://doi.org/10.36334/modsim2025.F05.miller	https://mssanz.org.au/modsim2025/files/F05.miller.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	C
Donohue T	Natural hazards ahead: Navigating uncertainty with stakeholder-led scenarios	Miller A, Culley S, Radford D, van Delden H, Zecchin A, Vanhout R, Donohue T, Whiting T, Maier HR	https://doi.org/10.36334/modsim2025.F05.miller	https://mssanz.org.au/modsim2025/files/F05.miller.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	D
Maier HR	Natural hazards ahead: Navigating uncertainty with stakeholder-led scenarios	Miller A, Culley S, Radford D, van Delden H, Zecchin A, Vanhout R, Donohue T, Whiting T, Maier HR	https://doi.org/10.36334/modsim2025.F05.miller	https://mssanz.org.au/modsim2025/files/F05.miller.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	M
Miller A	Natural hazards ahead: Navigating uncertainty with stakeholder-led scenarios	Miller A, Culley S, Radford D, van Delden H, Zecchin A, Vanhout R, Donohue T, Whiting T, Maier HR	https://doi.org/10.36334/modsim2025.F05.miller	https://mssanz.org.au/modsim2025/files/F05.miller.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	M
Radford DAG	Natural hazards ahead: Navigating uncertainty with stakeholder-led scenarios	Miller A, Culley S, Radford D, van Delden H, Zecchin A, Vanhout R, Donohue T, Whiting T, Maier HR	https://doi.org/10.36334/modsim2025.F05.miller	https://mssanz.org.au/modsim2025/files/F05.miller.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	R
van Delden H	Natural hazards ahead: Navigating uncertainty with stakeholder-led scenarios	Miller A, Culley S, Radford D, van Delden H, Zecchin A, Vanhout R, Donohue T, Whiting T, Maier HR	https://doi.org/10.36334/modsim2025.F05.miller	https://mssanz.org.au/modsim2025/files/F05.miller.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	v
Vanhout R	Natural hazards ahead: Navigating uncertainty with stakeholder-led scenarios	Miller A, Culley S, Radford D, van Delden H, Zecchin A, Vanhout R, Donohue T, Whiting T, Maier HR	https://doi.org/10.36334/modsim2025.F05.miller	https://mssanz.org.au/modsim2025/files/F05.miller.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	V
Whiting T	Natural hazards ahead: Navigating uncertainty with stakeholder-led scenarios	Miller A, Culley S, Radford D, van Delden H, Zecchin A, Vanhout R, Donohue T, Whiting T, Maier HR	https://doi.org/10.36334/modsim2025.F05.miller	https://mssanz.org.au/modsim2025/files/F05.miller.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	W
Zecchin A	Natural hazards ahead: Navigating uncertainty with stakeholder-led scenarios	Miller A, Culley S, Radford D, van Delden H, Zecchin A, Vanhout R, Donohue T, Whiting T, Maier HR	https://doi.org/10.36334/modsim2025.F05.miller	https://mssanz.org.au/modsim2025/files/F05.miller.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	Z
Bennett B	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	B
Croke B	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	C
Gupta H	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	G
Kavetski D	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	K
Leonard M (Michael)	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	L
Maier HR	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	M
McInerney D	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	M
Shanafield M	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	S
Thyer M	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	T
Westra S	Neglecting hydrological errors can severely impact predictions of water resource system performance	Thyer M, McInerney D, Kavetski D, Westra S, Maier H, Shanafield M, Croke B, Gupta H, Bennett B, Leonard M	https://doi.org/10.36334/modsim2025.J08.thyer	https://mssanz.org.au/modsim2025/files/J08.thyer.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	W
Lang B	Network analysis of Australian Internet traffic dynamics	Lang B, Roughan M, Nguyen H	https://doi.org/10.36334/modsim2025.A03.lang	https://mssanz.org.au/modsim2025/files/A03.lang.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	L
Nguyen H	Network analysis of Australian Internet traffic dynamics	Lang B, Roughan M, Nguyen H	https://doi.org/10.36334/modsim2025.A03.lang	https://mssanz.org.au/modsim2025/files/A03.lang.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	N
Roughan M	Network analysis of Australian Internet traffic dynamics	Lang B, Roughan M, Nguyen H	https://doi.org/10.36334/modsim2025.A03.lang	https://mssanz.org.au/modsim2025/files/A03.lang.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	R
He D	New approaches for simulating germination and emergence in APSIM	He D, Rebetzke GJ, Luo T, Zhen X, Wang E	https://doi.org/10.36334/modsim2025.B02.he	https://mssanz.org.au/modsim2025/files/B02.he.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	H
Luo T	New approaches for simulating germination and emergence in APSIM	He D, Rebetzke GJ, Luo T, Zhen X, Wang E	https://doi.org/10.36334/modsim2025.B02.he	https://mssanz.org.au/modsim2025/files/B02.he.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	L
Rebetzke GJ	New approaches for simulating germination and emergence in APSIM	He D, Rebetzke GJ, Luo T, Zhen X, Wang E	https://doi.org/10.36334/modsim2025.B02.he	https://mssanz.org.au/modsim2025/files/B02.he.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	R
Wang E (Enli)	New approaches for simulating germination and emergence in APSIM	He D, Rebetzke GJ, Luo T, Zhen X, Wang E	https://doi.org/10.36334/modsim2025.B02.he	https://mssanz.org.au/modsim2025/files/B02.he.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	W
Zhen X (Xiaoxing)	New approaches for simulating germination and emergence in APSIM	He D, Rebetzke GJ, Luo T, Zhen X, Wang E	https://doi.org/10.36334/modsim2025.B02.he	https://mssanz.org.au/modsim2025/files/B02.he.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	Z
Hayde P	New hydraulic model for integrated water management	Obeid R, Wang XJ, Hayde P	https://doi.org/10.36334/modsim2025.A02.obeid	https://mssanz.org.au/modsim2025/files/A02.obeid.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	H
Obeid R	New hydraulic model for integrated water management	Obeid R, Wang XJ, Hayde P	https://doi.org/10.36334/modsim2025.A02.obeid	https://mssanz.org.au/modsim2025/files/A02.obeid.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	O
Wang XJ	New hydraulic model for integrated water management	Obeid R, Wang XJ, Hayde P	https://doi.org/10.36334/modsim2025.A02.obeid	https://mssanz.org.au/modsim2025/files/A02.obeid.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	W
L’Ecuyer P	New random number generation tools for parallel environments (STREAM A KEYNOTE)	L’Ecuyer P	https://doi.org/10.36334/modsim2025.A01.lecuyer	https://mssanz.org.au/modsim2025/files/A01.lecuyer.pdf	A	Applied and computational mathematics	A1	Applied Probability	L
Graham P	Next-generation electric vehicle forecasting: long-term sales projections in the Australian context	Mediwaththe C, Graham P, Green D	https://doi.org/10.36334/modsim2025.E03.mediwaththe	https://mssanz.org.au/modsim2025/files/E03.mediwaththe.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	G
Green D	Next-generation electric vehicle forecasting: long-term sales projections in the Australian context	Mediwaththe C, Graham P, Green D	https://doi.org/10.36334/modsim2025.E03.mediwaththe	https://mssanz.org.au/modsim2025/files/E03.mediwaththe.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	G
Mediwaththe C	Next-generation electric vehicle forecasting: long-term sales projections in the Australian context	Mediwaththe C, Graham P, Green D	https://doi.org/10.36334/modsim2025.E03.mediwaththe	https://mssanz.org.au/modsim2025/files/E03.mediwaththe.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	M
Chen D (Deli)	Nitrogen loss in sugarcane: uncertainty and sensitivity analysis of APSIM predictions	Kolar P, Pan B, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.B03.kolar	https://mssanz.org.au/modsim2025/files/B03.kolar.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	C
Kolar P	Nitrogen loss in sugarcane: uncertainty and sensitivity analysis of APSIM predictions	Kolar P, Pan B, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.B03.kolar	https://mssanz.org.au/modsim2025/files/B03.kolar.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	K
Lam SK (Shu Kee)	Nitrogen loss in sugarcane: uncertainty and sensitivity analysis of APSIM predictions	Kolar P, Pan B, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.B03.kolar	https://mssanz.org.au/modsim2025/files/B03.kolar.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	L
Pan B (Baobao)	Nitrogen loss in sugarcane: uncertainty and sensitivity analysis of APSIM predictions	Kolar P, Pan B, Chen D, Lam SK	https://doi.org/10.36334/modsim2025.B03.kolar	https://mssanz.org.au/modsim2025/files/B03.kolar.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	P
Lerat J	Non-linear reservoir modelling without tears using the QuaSoARe approach	Lerat J	https://doi.org/10.36334/modsim2025.A02.lerat	https://mssanz.org.au/modsim2025/files/A02.lerat.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	L
Barron O	Novel assessment of trajectories of change in Murray-Darling Basin riverine tree ecosystems	Doody T, Barron O, Emelyanova I	https://doi.org/10.36334/modsim2025.F04.doody	https://mssanz.org.au/modsim2025/files/F04.doody.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	B
Doody T	Novel assessment of trajectories of change in Murray-Darling Basin riverine tree ecosystems	Doody T, Barron O, Emelyanova I	https://doi.org/10.36334/modsim2025.F04.doody	https://mssanz.org.au/modsim2025/files/F04.doody.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	D
Emelyanova I	Novel assessment of trajectories of change in Murray-Darling Basin riverine tree ecosystems	Doody T, Barron O, Emelyanova I	https://doi.org/10.36334/modsim2025.F04.doody	https://mssanz.org.au/modsim2025/files/F04.doody.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	E
Almeida Lima V	Nutrient leaching and water recycling potential for vegetable production in Western Australia’s sandy soils	Almeida Lima V, Glicerio JM, Miranda JH, Lantzke N	https://doi.org/10.36334/modsim2025.L03.almeidalima	https://mssanz.org.au/modsim2025/files/L03.almeidalima.pdf	L	Water quality	L3	Modelling water quality treatment and management	A
Glicerio JM	Nutrient leaching and water recycling potential for vegetable production in Western Australia’s sandy soils	Almeida Lima V, Glicerio JM, Miranda JH, Lantzke N	https://doi.org/10.36334/modsim2025.L03.almeidalima	https://mssanz.org.au/modsim2025/files/L03.almeidalima.pdf	L	Water quality	L3	Modelling water quality treatment and management	G
Lantzke N	Nutrient leaching and water recycling potential for vegetable production in Western Australia’s sandy soils	Almeida Lima V, Glicerio JM, Miranda JH, Lantzke N	https://doi.org/10.36334/modsim2025.L03.almeidalima	https://mssanz.org.au/modsim2025/files/L03.almeidalima.pdf	L	Water quality	L3	Modelling water quality treatment and management	L
Miranda JH	Nutrient leaching and water recycling potential for vegetable production in Western Australia’s sandy soils	Almeida Lima V, Glicerio JM, Miranda JH, Lantzke N	https://doi.org/10.36334/modsim2025.L03.almeidalima	https://mssanz.org.au/modsim2025/files/L03.almeidalima.pdf	L	Water quality	L3	Modelling water quality treatment and management	M
McGinty N	Objective evidence for the future	McGinty N	https://doi.org/10.36334/modsim2025.P03.mcginty	https://mssanz.org.au/modsim2025/files/P03.mcginty.pdf	P	Plenary	P3	Plenary	M
McRae RHD	Observations of vorticity-driven lateral spread in a wildfire	McRae RHD	https://doi.org/10.36334/modsim2025.G08.mcraer	https://mssanz.org.au/modsim2025/files/G08.mcraer.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	M
Jeanneau A	Old tools, new landscapes: Application of multi-objective evolutionary algorithms to achieve bushfire risk reduction objectives	Radford DAG, Maier HR, Zecchin AC, van Delden H, Jeanneau A	https://doi.org/10.36334/modsim2025.G07.radford	https://mssanz.org.au/modsim2025/files/G07.radford.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	J
Maier HR	Old tools, new landscapes: Application of multi-objective evolutionary algorithms to achieve bushfire risk reduction objectives	Radford DAG, Maier HR, Zecchin AC, van Delden H, Jeanneau A	https://doi.org/10.36334/modsim2025.G07.radford	https://mssanz.org.au/modsim2025/files/G07.radford.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	M
Radford DAG	Old tools, new landscapes: Application of multi-objective evolutionary algorithms to achieve bushfire risk reduction objectives	Radford DAG, Maier HR, Zecchin AC, van Delden H, Jeanneau A	https://doi.org/10.36334/modsim2025.G07.radford	https://mssanz.org.au/modsim2025/files/G07.radford.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	R
van Delden H	Old tools, new landscapes: Application of multi-objective evolutionary algorithms to achieve bushfire risk reduction objectives	Radford DAG, Maier HR, Zecchin AC, van Delden H, Jeanneau A	https://doi.org/10.36334/modsim2025.G07.radford	https://mssanz.org.au/modsim2025/files/G07.radford.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	v
Zecchin AC	Old tools, new landscapes: Application of multi-objective evolutionary algorithms to achieve bushfire risk reduction objectives	Radford DAG, Maier HR, Zecchin AC, van Delden H, Jeanneau A	https://doi.org/10.36334/modsim2025.G07.radford	https://mssanz.org.au/modsim2025/files/G07.radford.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	Z
Akbarzadeh Khorshidi H	On causal loop diagrams and directed acyclic graphs: Insights in health systems and genomics	Akbarzadeh Khorshidi H, Franchini F, Chen G	https://doi.org/10.36334/modsim2025.H03.akbarzadehkhorshidi	https://mssanz.org.au/modsim2025/files/H03.akbarzadehkhorshidi.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	A
Chen G (Gang)	On causal loop diagrams and directed acyclic graphs: Insights in health systems and genomics	Akbarzadeh Khorshidi H, Franchini F, Chen G	https://doi.org/10.36334/modsim2025.H03.akbarzadehkhorshidi	https://mssanz.org.au/modsim2025/files/H03.akbarzadehkhorshidi.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	C
Franchini F	On causal loop diagrams and directed acyclic graphs: Insights in health systems and genomics	Akbarzadeh Khorshidi H, Franchini F, Chen G	https://doi.org/10.36334/modsim2025.H03.akbarzadehkhorshidi	https://mssanz.org.au/modsim2025/files/H03.akbarzadehkhorshidi.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	F
Kim DJ	Operational satellite-based disaster monitoring on the cloud: Case studies from Asia (INVITED SPEAKER)	Kim DJ, Lee S, Song J	https://doi.org/10.36334/modsim2025.G02.kim	https://mssanz.org.au/modsim2025/files/G02.kim.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	K
Lee S	Operational satellite-based disaster monitoring on the cloud: Case studies from Asia (INVITED SPEAKER)	Kim DJ, Lee S, Song J	https://doi.org/10.36334/modsim2025.G02.kim	https://mssanz.org.au/modsim2025/files/G02.kim.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	L
Song J (Juyoung)	Operational satellite-based disaster monitoring on the cloud: Case studies from Asia (INVITED SPEAKER)	Kim DJ, Lee S, Song J	https://doi.org/10.36334/modsim2025.G02.kim	https://mssanz.org.au/modsim2025/files/G02.kim.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	S
Aghaeimeybodi M	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	A
Beath A	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	B
Beck F	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	B
Coventry J	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	C
Hosseini T	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	H
Iyer S	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	I
Li C (Chengzhe)	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	L
Mojiri A	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	M
Pye J	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	P
Wang S	Optimisation of energy supply to industry hub with multi-criteria selection and techno-economic assessment	Wang S, Li C, Mojiri A, Iyer S, Aghaeimeybodi M, Pye J, Beath A, Beck F, Hosseini T, Coventry J	https://doi.org/10.36334/modsim2025.E02.wang	https://mssanz.org.au/modsim2025/files/E02.wang.pdf	E	Energy, integrated infrastructure and urban planning	E2	Decarbonisation of industrial processes	W
Hayward J	Optimisation of renewable electricity supply for brackish groundwater desalination in Western Australia	Wu H, West S, Hayward J	https://doi.org/10.36334/modsim2025.E03.wu	https://mssanz.org.au/modsim2025/files/E03.wu.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	H
West S	Optimisation of renewable electricity supply for brackish groundwater desalination in Western Australia	Wu H, West S, Hayward J	https://doi.org/10.36334/modsim2025.E03.wu	https://mssanz.org.au/modsim2025/files/E03.wu.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	W
Wu H	Optimisation of renewable electricity supply for brackish groundwater desalination in Western Australia	Wu H, West S, Hayward J	https://doi.org/10.36334/modsim2025.E03.wu	https://mssanz.org.au/modsim2025/files/E03.wu.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	W
Mennecke L	Optimising port–hinterland container transportation for resilience and sustainability	Mennecke L, Pannek J, Papini M	https://doi.org/10.36334/modsim2025.M01.mennecke	https://mssanz.org.au/modsim2025/files/M01.mennecke.pdf	M	Operations Research	M1	OR methods and applications	M
Pannek J	Optimising port–hinterland container transportation for resilience and sustainability	Mennecke L, Pannek J, Papini M	https://doi.org/10.36334/modsim2025.M01.mennecke	https://mssanz.org.au/modsim2025/files/M01.mennecke.pdf	M	Operations Research	M1	OR methods and applications	P
Papini M	Optimising port–hinterland container transportation for resilience and sustainability	Mennecke L, Pannek J, Papini M	https://doi.org/10.36334/modsim2025.M01.mennecke	https://mssanz.org.au/modsim2025/files/M01.mennecke.pdf	M	Operations Research	M1	OR methods and applications	P
Shepherd NE	Optimising sugarcane fertiliser rates for modelling water quality in the Wet Tropics	Shepherd NE	https://doi.org/10.36334/modsim2025.L01.shepherd	https://mssanz.org.au/modsim2025/files/L01.shepherd.pdf	L	Water quality	L1	Catchment Water Quality Modelling	S
Fan Z	Optimizing sustainable water resource utilization of inter-basin water transfer under climate change	Fan Z, Fu X, Li Y, Zhao X	https://doi.org/10.36334/modsim2025.J08.fan	https://mssanz.org.au/modsim2025/files/J08.fan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	F
Fu X	Optimizing sustainable water resource utilization of inter-basin water transfer under climate change	Fan Z, Fu X, Li Y, Zhao X	https://doi.org/10.36334/modsim2025.J08.fan	https://mssanz.org.au/modsim2025/files/J08.fan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	F
Li Y (Yu)	Optimizing sustainable water resource utilization of inter-basin water transfer under climate change	Fan Z, Fu X, Li Y, Zhao X	https://doi.org/10.36334/modsim2025.J08.fan	https://mssanz.org.au/modsim2025/files/J08.fan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	L
Zhao X (Xiaodan)	Optimizing sustainable water resource utilization of inter-basin water transfer under climate change	Fan Z, Fu X, Li Y, Zhao X	https://doi.org/10.36334/modsim2025.J08.fan	https://mssanz.org.au/modsim2025/files/J08.fan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	Z
Jayasinghe P	Parameterisation of APSIM AgPasture for tropical pasture species; Brachiaria Mulato II and Gatton Panic	Jayasinghe P, Pembleton KG	https://doi.org/10.36334/modsim2025.B02.jayasinghe	https://mssanz.org.au/modsim2025/files/B02.jayasinghe.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	J
Pembleton KG	Parameterisation of APSIM AgPasture for tropical pasture species; Brachiaria Mulato II and Gatton Panic	Jayasinghe P, Pembleton KG	https://doi.org/10.36334/modsim2025.B02.jayasinghe	https://mssanz.org.au/modsim2025/files/B02.jayasinghe.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	P
Strauch M	ParetoPick-R; an interactive tool for turning multi-objective optimisation results into actionable solutions	Wittekind CIH, Strauch M, Witing F, Volk M	https://doi.org/10.36334/modsim2025.F05.wittekind	https://mssanz.org.au/modsim2025/files/F05.wittekind.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	S
Volk M	ParetoPick-R; an interactive tool for turning multi-objective optimisation results into actionable solutions	Wittekind CIH, Strauch M, Witing F, Volk M	https://doi.org/10.36334/modsim2025.F05.wittekind	https://mssanz.org.au/modsim2025/files/F05.wittekind.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	V
Witing F	ParetoPick-R; an interactive tool for turning multi-objective optimisation results into actionable solutions	Wittekind CIH, Strauch M, Witing F, Volk M	https://doi.org/10.36334/modsim2025.F05.wittekind	https://mssanz.org.au/modsim2025/files/F05.wittekind.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	W
Wittekind CIH	ParetoPick-R; an interactive tool for turning multi-objective optimisation results into actionable solutions	Wittekind CIH, Strauch M, Witing F, Volk M	https://doi.org/10.36334/modsim2025.F05.wittekind	https://mssanz.org.au/modsim2025/files/F05.wittekind.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	W
Karunaratne S	Partitioning soil organic carbon for Australian croplands	Yang J, Paust C, Ugbaje S, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.yang	https://mssanz.org.au/modsim2025/files/F13.yang.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	K
Paust C	Partitioning soil organic carbon for Australian croplands	Yang J, Paust C, Ugbaje S, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.yang	https://mssanz.org.au/modsim2025/files/F13.yang.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	P
Ugbaje S	Partitioning soil organic carbon for Australian croplands	Yang J, Paust C, Ugbaje S, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.yang	https://mssanz.org.au/modsim2025/files/F13.yang.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	U
Yang J	Partitioning soil organic carbon for Australian croplands	Yang J, Paust C, Ugbaje S, Karunaratne S	https://doi.org/10.36334/modsim2025.F13.yang	https://mssanz.org.au/modsim2025/files/F13.yang.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	Y
van der Merwe M	Passive sonar sensor placement for undersea surveillance	Weinberg GV, van der Merwe M	https://doi.org/10.36334/modsim2025.M01.weinberg	https://mssanz.org.au/modsim2025/files/M01.weinberg.pdf	M	Operations Research	M1	OR methods and applications	v
Weinberg GV	Passive sonar sensor placement for undersea surveillance	Weinberg GV, van der Merwe M	https://doi.org/10.36334/modsim2025.M01.weinberg	https://mssanz.org.au/modsim2025/files/M01.weinberg.pdf	M	Operations Research	M1	OR methods and applications	W
Chen F	Pathway of soil improvement through straw return to promote yield increase cannot be ignored	Chen F	https://doi.org/10.36334/modsim2025.B08.chenf	https://mssanz.org.au/modsim2025/files/B08.chenf.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Rodriguez D	Pathways towards NetZero emissions in grain cropping farms	Tan Z, Rodriguez D	https://doi.org/10.36334/modsim2025.B05.tan	https://mssanz.org.au/modsim2025/files/B05.tan.pdf	B	Biological systems	B5	Agricultural Systems	R
Tan Z	Pathways towards NetZero emissions in grain cropping farms	Tan Z, Rodriguez D	https://doi.org/10.36334/modsim2025.B05.tan	https://mssanz.org.au/modsim2025/files/B05.tan.pdf	B	Biological systems	B5	Agricultural Systems	T
La D	Patient-level modelling of long-term cardiovascular and mortality outcomes in chronic kidney disease	Tran‑Duy A, Srikhom S, La D, Nelson C, Manski‑Nankervis JA	https://doi.org/10.36334/modsim2025.H05.tranduy	https://mssanz.org.au/modsim2025/files/H05.tranduy.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	L
Manski‑Nankervis JA	Patient-level modelling of long-term cardiovascular and mortality outcomes in chronic kidney disease	Tran‑Duy A, Srikhom S, La D, Nelson C, Manski‑Nankervis JA	https://doi.org/10.36334/modsim2025.H05.tranduy	https://mssanz.org.au/modsim2025/files/H05.tranduy.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	M
Nelson C	Patient-level modelling of long-term cardiovascular and mortality outcomes in chronic kidney disease	Tran‑Duy A, Srikhom S, La D, Nelson C, Manski‑Nankervis JA	https://doi.org/10.36334/modsim2025.H05.tranduy	https://mssanz.org.au/modsim2025/files/H05.tranduy.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	N
Srikhom S	Patient-level modelling of long-term cardiovascular and mortality outcomes in chronic kidney disease	Tran‑Duy A, Srikhom S, La D, Nelson C, Manski‑Nankervis JA	https://doi.org/10.36334/modsim2025.H05.tranduy	https://mssanz.org.au/modsim2025/files/H05.tranduy.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	S
Tran‑Duy A	Patient-level modelling of long-term cardiovascular and mortality outcomes in chronic kidney disease	Tran‑Duy A, Srikhom S, La D, Nelson C, Manski‑Nankervis JA	https://doi.org/10.36334/modsim2025.H05.tranduy	https://mssanz.org.au/modsim2025/files/H05.tranduy.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	T
Knowling MJ	PEST++SQP: a new, non-intrusive, open-source package for high-dimensional non-linear optimisation under uncertainty	Macasieb RQ, Knowling MJ, White JT	https://doi.org/10.36334/modsim2025.A04.macasieb	https://mssanz.org.au/modsim2025/files/A04.macasieb.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	K
Macasieb RQ	PEST++SQP: a new, non-intrusive, open-source package for high-dimensional non-linear optimisation under uncertainty	Macasieb RQ, Knowling MJ, White JT	https://doi.org/10.36334/modsim2025.A04.macasieb	https://mssanz.org.au/modsim2025/files/A04.macasieb.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	M
White JT	PEST++SQP: a new, non-intrusive, open-source package for high-dimensional non-linear optimisation under uncertainty	Macasieb RQ, Knowling MJ, White JT	https://doi.org/10.36334/modsim2025.A04.macasieb	https://mssanz.org.au/modsim2025/files/A04.macasieb.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	W
Calderon‑Morales E	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	C
Choat B	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	C
Holzworth D	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	H
Inbar A	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	I
Knauer J	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	K
Medlyn B	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	M
Nichols E	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	N
Pendall E	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	P
Peters J	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	P
Stephens C	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	S
Williams L	Physically-based simulation of plant hydraulic transport and drought stress in Australian vegetation	Stephens C, Medlyn B, Holzworth D, Knauer J, Inbar A, Williams L, Choat B, Peters J, Calderon‑Morales E, Nichols E, Pendall E	https://doi.org/10.36334/modsim2025.F09.stephens	https://mssanz.org.au/modsim2025/files/F09.stephens.pdf	F	Environment and ecology	F9	Ecological Forecasting: Advancing ecological theory and management in Oceania with near-term forecasts	W
Feng P	Physics-guided deep learning for crop phenology simulation	Feng P	https://doi.org/10.36334/modsim2025.B08.feng	https://mssanz.org.au/modsim2025/files/B08.feng.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	F
Acosta CM	Physics-informed graph neural networks for operational flood modelling	Acosta CM, Herath HMVV, Saha A, Rasnayaka S, Marshall L	https://doi.org/10.36334/modsim2025.J10.acosta	https://mssanz.org.au/modsim2025/files/J10.acosta.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	A
Herath HMVV	Physics-informed graph neural networks for operational flood modelling	Acosta CM, Herath HMVV, Saha A, Rasnayaka S, Marshall L	https://doi.org/10.36334/modsim2025.J10.acosta	https://mssanz.org.au/modsim2025/files/J10.acosta.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	H
Marshall L	Physics-informed graph neural networks for operational flood modelling	Acosta CM, Herath HMVV, Saha A, Rasnayaka S, Marshall L	https://doi.org/10.36334/modsim2025.J10.acosta	https://mssanz.org.au/modsim2025/files/J10.acosta.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	M
Rasnayaka S	Physics-informed graph neural networks for operational flood modelling	Acosta CM, Herath HMVV, Saha A, Rasnayaka S, Marshall L	https://doi.org/10.36334/modsim2025.J10.acosta	https://mssanz.org.au/modsim2025/files/J10.acosta.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	R
Saha A	Physics-informed graph neural networks for operational flood modelling	Acosta CM, Herath HMVV, Saha A, Rasnayaka S, Marshall L	https://doi.org/10.36334/modsim2025.J10.acosta	https://mssanz.org.au/modsim2025/files/J10.acosta.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	S
Chryss A	PipeTwin: A scalable framework for Digital Twin deployment in mineral suspension pipelines	Xie P, Zheng E, Nguyen D, Chryss A, Hetherton L	https://doi.org/10.36334/modsim2025.C03.xie	https://mssanz.org.au/modsim2025/files/C03.xie.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	C
Hetherton L	PipeTwin: A scalable framework for Digital Twin deployment in mineral suspension pipelines	Xie P, Zheng E, Nguyen D, Chryss A, Hetherton L	https://doi.org/10.36334/modsim2025.C03.xie	https://mssanz.org.au/modsim2025/files/C03.xie.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	H
Nguyen D	PipeTwin: A scalable framework for Digital Twin deployment in mineral suspension pipelines	Xie P, Zheng E, Nguyen D, Chryss A, Hetherton L	https://doi.org/10.36334/modsim2025.C03.xie	https://mssanz.org.au/modsim2025/files/C03.xie.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	N
Xie P	PipeTwin: A scalable framework for Digital Twin deployment in mineral suspension pipelines	Xie P, Zheng E, Nguyen D, Chryss A, Hetherton L	https://doi.org/10.36334/modsim2025.C03.xie	https://mssanz.org.au/modsim2025/files/C03.xie.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	X
Zheng E	PipeTwin: A scalable framework for Digital Twin deployment in mineral suspension pipelines	Xie P, Zheng E, Nguyen D, Chryss A, Hetherton L	https://doi.org/10.36334/modsim2025.C03.xie	https://mssanz.org.au/modsim2025/files/C03.xie.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	Z
Zhai X	Predicting flood event class using a novel class membership function and hydrological modeling	Zhang Y, Zhang Y, Zhai X	https://doi.org/10.36334/modsim2025.K08.zhangy	https://mssanz.org.au/modsim2025/files/K08.zhangy.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Zhang Y (Yongqiang)	Predicting flood event class using a novel class membership function and hydrological modeling	Zhang Y, Zhang Y, Zhai X	https://doi.org/10.36334/modsim2025.K08.zhangy	https://mssanz.org.au/modsim2025/files/K08.zhangy.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Zhang Y (Yongyong)	Predicting flood event class using a novel class membership function and hydrological modeling	Zhang Y, Zhang Y, Zhai X	https://doi.org/10.36334/modsim2025.K08.zhangy	https://mssanz.org.au/modsim2025/files/K08.zhangy.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Huang Q	Predicting global runoff and hydrological drought in 1991-2024 using the HBV-PML model	Zhang Y, Wei H, Xu Z, Huang Q	https://doi.org/10.36334/modsim2025.K08.zhang	https://mssanz.org.au/modsim2025/files/K08.zhang.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	H
Wei H	Predicting global runoff and hydrological drought in 1991-2024 using the HBV-PML model	Zhang Y, Wei H, Xu Z, Huang Q	https://doi.org/10.36334/modsim2025.K08.zhang	https://mssanz.org.au/modsim2025/files/K08.zhang.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	W
Xu Z (Zhenwu)	Predicting global runoff and hydrological drought in 1991-2024 using the HBV-PML model	Zhang Y, Wei H, Xu Z, Huang Q	https://doi.org/10.36334/modsim2025.K08.zhang	https://mssanz.org.au/modsim2025/files/K08.zhang.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	X
Zhang Y (Yongqiang)	Predicting global runoff and hydrological drought in 1991-2024 using the HBV-PML model	Zhang Y, Wei H, Xu Z, Huang Q	https://doi.org/10.36334/modsim2025.K08.zhang	https://mssanz.org.au/modsim2025/files/K08.zhang.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Escribà‑Gelonch M	Predicting lettuce biomass under six climate zones using machine learning in a digital twin system	Liang S, Parvin MI, Long NVD, Escribà‑Gelonch M, Hessel V	https://doi.org/10.36334/modsim2025.C03.liang	https://mssanz.org.au/modsim2025/files/C03.liang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	E
Hessel V	Predicting lettuce biomass under six climate zones using machine learning in a digital twin system	Liang S, Parvin MI, Long NVD, Escribà‑Gelonch M, Hessel V	https://doi.org/10.36334/modsim2025.C03.liang	https://mssanz.org.au/modsim2025/files/C03.liang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	H
Liang S	Predicting lettuce biomass under six climate zones using machine learning in a digital twin system	Liang S, Parvin MI, Long NVD, Escribà‑Gelonch M, Hessel V	https://doi.org/10.36334/modsim2025.C03.liang	https://mssanz.org.au/modsim2025/files/C03.liang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	L
Long NVD	Predicting lettuce biomass under six climate zones using machine learning in a digital twin system	Liang S, Parvin MI, Long NVD, Escribà‑Gelonch M, Hessel V	https://doi.org/10.36334/modsim2025.C03.liang	https://mssanz.org.au/modsim2025/files/C03.liang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	L
Parvin MI	Predicting lettuce biomass under six climate zones using machine learning in a digital twin system	Liang S, Parvin MI, Long NVD, Escribà‑Gelonch M, Hessel V	https://doi.org/10.36334/modsim2025.C03.liang	https://mssanz.org.au/modsim2025/files/C03.liang.pdf	C	Computer science and engineering	C3	Digital twins and mixed reality	P
Lachireddy K	Predictive modelling of Ross River virus in NSW	Sumithra S, McLure A, Lal A, Lachireddy K	https://doi.org/10.36334/modsim2025.H04.sumithra	https://mssanz.org.au/modsim2025/files/H04.sumithra.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	L
Lal A	Predictive modelling of Ross River virus in NSW	Sumithra S, McLure A, Lal A, Lachireddy K	https://doi.org/10.36334/modsim2025.H04.sumithra	https://mssanz.org.au/modsim2025/files/H04.sumithra.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	L
McLure A	Predictive modelling of Ross River virus in NSW	Sumithra S, McLure A, Lal A, Lachireddy K	https://doi.org/10.36334/modsim2025.H04.sumithra	https://mssanz.org.au/modsim2025/files/H04.sumithra.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	M
Sumithra S	Predictive modelling of Ross River virus in NSW	Sumithra S, McLure A, Lal A, Lachireddy K	https://doi.org/10.36334/modsim2025.H04.sumithra	https://mssanz.org.au/modsim2025/files/H04.sumithra.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	S
Mitchard A	Predictive power: Unlocking grid stability through solar forecasting	Mitchard A	https://doi.org/10.36334/modsim2025.E01.mitchard	https://mssanz.org.au/modsim2025/files/E01.mitchard.pdf	E	Energy, integrated infrastructure and urban planning	E1	Tools for the Energy Transition	M
Brown H (Hamish)	Prioritising investment in diverse horticulture cropping systems for water quality outcomes using APSIM Next Gen	Reeves S H, Brown H, Oag D	https://doi.org/10.36334/modsim2025.B05.reeves	https://mssanz.org.au/modsim2025/files/B05.reeves.pdf	B	Biological systems	B5	Agricultural Systems	B
Oag D	Prioritising investment in diverse horticulture cropping systems for water quality outcomes using APSIM Next Gen	Reeves S H, Brown H, Oag D	https://doi.org/10.36334/modsim2025.B05.reeves	https://mssanz.org.au/modsim2025/files/B05.reeves.pdf	B	Biological systems	B5	Agricultural Systems	O
Reeves SH	Prioritising investment in diverse horticulture cropping systems for water quality outcomes using APSIM Next Gen	Reeves S H, Brown H, Oag D	https://doi.org/10.36334/modsim2025.B05.reeves	https://mssanz.org.au/modsim2025/files/B05.reeves.pdf	B	Biological systems	B5	Agricultural Systems	R
Marshall L	Probabilistic thinking in environmental modelling and leadership	Marshall L	https://doi.org/10.36334/modsim2025.P04.marshall	https://mssanz.org.au/modsim2025/files/P04.marshall.pdf	P	Plenary	P4	Plenary	M
Athukorala R	Process-informed stepwise multi-variable SWAT calibration framework	Nervi E, Athukorala R, van Ogtrop F, Vervoort RW	https://doi.org/10.36334/modsim2025.J10.nervi	https://mssanz.org.au/modsim2025/files/J10.nervi.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	A
Nervi E	Process-informed stepwise multi-variable SWAT calibration framework	Nervi E, Athukorala R, van Ogtrop F, Vervoort RW	https://doi.org/10.36334/modsim2025.J10.nervi	https://mssanz.org.au/modsim2025/files/J10.nervi.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	N
van Ogtrop F	Process-informed stepwise multi-variable SWAT calibration framework	Nervi E, Athukorala R, van Ogtrop F, Vervoort RW	https://doi.org/10.36334/modsim2025.J10.nervi	https://mssanz.org.au/modsim2025/files/J10.nervi.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	v
Vervoort RW	Process-informed stepwise multi-variable SWAT calibration framework	Nervi E, Athukorala R, van Ogtrop F, Vervoort RW	https://doi.org/10.36334/modsim2025.J10.nervi	https://mssanz.org.au/modsim2025/files/J10.nervi.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	V
Medlyn B	Projected changes in biomass carbon under future climate scenarios in semi-arid southeastern Australia	Wang B, Smith B, Waters C, Medlyn B	https://doi.org/10.36334/modsim2025.F12.wang	https://mssanz.org.au/modsim2025/files/F12.wang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	M
Smith B	Projected changes in biomass carbon under future climate scenarios in semi-arid southeastern Australia	Wang B, Smith B, Waters C, Medlyn B	https://doi.org/10.36334/modsim2025.F12.wang	https://mssanz.org.au/modsim2025/files/F12.wang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	S
Wang B (Bin)	Projected changes in biomass carbon under future climate scenarios in semi-arid southeastern Australia	Wang B, Smith B, Waters C, Medlyn B	https://doi.org/10.36334/modsim2025.F12.wang	https://mssanz.org.au/modsim2025/files/F12.wang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	W
Waters C	Projected changes in biomass carbon under future climate scenarios in semi-arid southeastern Australia	Wang B, Smith B, Waters C, Medlyn B	https://doi.org/10.36334/modsim2025.F12.wang	https://mssanz.org.au/modsim2025/files/F12.wang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	W
Ma Y (Yuxin)	Projecting future changes in soil organic carbon across New South Wales using machine learning and climate projections	Ma Y, Yang X	https://doi.org/10.36334/modsim2025.F13.ma	https://mssanz.org.au/modsim2025/files/F13.ma.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	M
Yang X	Projecting future changes in soil organic carbon across New South Wales using machine learning and climate projections	Ma Y, Yang X	https://doi.org/10.36334/modsim2025.F13.ma	https://mssanz.org.au/modsim2025/files/F13.ma.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	Y
Bayer P	Projections of global groundwater temperatures due to climate change	Irvine DJ, Benz SA, Rau GC, Bayer P, Menberg K, Blum P, Jamieson RC, Griebler C, Kurylyk BL	https://doi.org/10.36334/modsim2025.G04.irvine	https://mssanz.org.au/modsim2025/files/G04.irvine.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	B
Benz SA	Projections of global groundwater temperatures due to climate change	Irvine DJ, Benz SA, Rau GC, Bayer P, Menberg K, Blum P, Jamieson RC, Griebler C, Kurylyk BL	https://doi.org/10.36334/modsim2025.G04.irvine	https://mssanz.org.au/modsim2025/files/G04.irvine.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	B
Blum P	Projections of global groundwater temperatures due to climate change	Irvine DJ, Benz SA, Rau GC, Bayer P, Menberg K, Blum P, Jamieson RC, Griebler C, Kurylyk BL	https://doi.org/10.36334/modsim2025.G04.irvine	https://mssanz.org.au/modsim2025/files/G04.irvine.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	B
Griebler C	Projections of global groundwater temperatures due to climate change	Irvine DJ, Benz SA, Rau GC, Bayer P, Menberg K, Blum P, Jamieson RC, Griebler C, Kurylyk BL	https://doi.org/10.36334/modsim2025.G04.irvine	https://mssanz.org.au/modsim2025/files/G04.irvine.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	G
Irvine DJ	Projections of global groundwater temperatures due to climate change	Irvine DJ, Benz SA, Rau GC, Bayer P, Menberg K, Blum P, Jamieson RC, Griebler C, Kurylyk BL	https://doi.org/10.36334/modsim2025.G04.irvine	https://mssanz.org.au/modsim2025/files/G04.irvine.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	I
Jamieson RC	Projections of global groundwater temperatures due to climate change	Irvine DJ, Benz SA, Rau GC, Bayer P, Menberg K, Blum P, Jamieson RC, Griebler C, Kurylyk BL	https://doi.org/10.36334/modsim2025.G04.irvine	https://mssanz.org.au/modsim2025/files/G04.irvine.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	J
Kurylyk BL	Projections of global groundwater temperatures due to climate change	Irvine DJ, Benz SA, Rau GC, Bayer P, Menberg K, Blum P, Jamieson RC, Griebler C, Kurylyk BL	https://doi.org/10.36334/modsim2025.G04.irvine	https://mssanz.org.au/modsim2025/files/G04.irvine.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	K
Menberg K	Projections of global groundwater temperatures due to climate change	Irvine DJ, Benz SA, Rau GC, Bayer P, Menberg K, Blum P, Jamieson RC, Griebler C, Kurylyk BL	https://doi.org/10.36334/modsim2025.G04.irvine	https://mssanz.org.au/modsim2025/files/G04.irvine.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	M
Rau GC	Projections of global groundwater temperatures due to climate change	Irvine DJ, Benz SA, Rau GC, Bayer P, Menberg K, Blum P, Jamieson RC, Griebler C, Kurylyk BL	https://doi.org/10.36334/modsim2025.G04.irvine	https://mssanz.org.au/modsim2025/files/G04.irvine.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	R
Baasch J	Protecting Australia's investment in the National Hydrological Modelling Platform	Baasch J, Green T, Roberts T, Someth P, Tirupathi N	https://doi.org/10.36334/modsim2025.J02.baasch	https://mssanz.org.au/modsim2025/files/J02.baasch.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Green T	Protecting Australia's investment in the National Hydrological Modelling Platform	Baasch J, Green T, Roberts T, Someth P, Tirupathi N	https://doi.org/10.36334/modsim2025.J02.baasch	https://mssanz.org.au/modsim2025/files/J02.baasch.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	G
Roberts T	Protecting Australia's investment in the National Hydrological Modelling Platform	Baasch J, Green T, Roberts T, Someth P, Tirupathi N	https://doi.org/10.36334/modsim2025.J02.baasch	https://mssanz.org.au/modsim2025/files/J02.baasch.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Someth P	Protecting Australia's investment in the National Hydrological Modelling Platform	Baasch J, Green T, Roberts T, Someth P, Tirupathi N	https://doi.org/10.36334/modsim2025.J02.baasch	https://mssanz.org.au/modsim2025/files/J02.baasch.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	S
Tirupathi N	Protecting Australia's investment in the National Hydrological Modelling Platform	Baasch J, Green T, Roberts T, Someth P, Tirupathi N	https://doi.org/10.36334/modsim2025.J02.baasch	https://mssanz.org.au/modsim2025/files/J02.baasch.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	T
Chen M (Min)	PyGeoModel: A python package for integrating intelligent geographic model services into Jupyter's computing environment	Ma P, Chen M	https://doi.org/10.36334/modsim2025.C04.ma	https://mssanz.org.au/modsim2025/files/C04.ma.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	C
Ma P	PyGeoModel: A python package for integrating intelligent geographic model services into Jupyter's computing environment	Ma P, Chen M	https://doi.org/10.36334/modsim2025.C04.ma	https://mssanz.org.au/modsim2025/files/C04.ma.pdf	C	Computer science and engineering	C4	Open and intelligent modeling and simulation	M
Cox I	Qualitative network modelling to assess climate-sensitive vibrio and antimicrobial resistance in Tasmanian oyster supply chain	Subramaniam RC, Cox I, Onyango E	https://doi.org/10.36334/modsim2025.H04.subramaniam	https://mssanz.org.au/modsim2025/files/H04.subramaniam.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	C
Onyango E	Qualitative network modelling to assess climate-sensitive vibrio and antimicrobial resistance in Tasmanian oyster supply chain	Subramaniam RC, Cox I, Onyango E	https://doi.org/10.36334/modsim2025.H04.subramaniam	https://mssanz.org.au/modsim2025/files/H04.subramaniam.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	O
Subramaniam RC	Qualitative network modelling to assess climate-sensitive vibrio and antimicrobial resistance in Tasmanian oyster supply chain	Subramaniam RC, Cox I, Onyango E	https://doi.org/10.36334/modsim2025.H04.subramaniam	https://mssanz.org.au/modsim2025/files/H04.subramaniam.pdf	H	Health and biosecurity	H4	Modelling the impact of environmental pressures on vector-borne and zoonotic disease burdens	S
Ren Y (Yi)	Quantifying annotator reliability in moral classification of texts	Ren Y	https://doi.org/10.36334/modsim2025.A03.ren	https://mssanz.org.au/modsim2025/files/A03.ren.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	R
Fisher R	Quantifying parameter sensitivity in Ecopath with Ecosim Ecotracer models using a new Python wrapper	Tan D, Koppel D, Iwanaga T, Fisher R, Haller‑Bull V, Matthews S	https://doi.org/10.36334/modsim2025.B06.tan	https://mssanz.org.au/modsim2025/files/B06.tan.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	F
Haller‑Bull V	Quantifying parameter sensitivity in Ecopath with Ecosim Ecotracer models using a new Python wrapper	Tan D, Koppel D, Iwanaga T, Fisher R, Haller‑Bull V, Matthews S	https://doi.org/10.36334/modsim2025.B06.tan	https://mssanz.org.au/modsim2025/files/B06.tan.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	H
Iwanaga T	Quantifying parameter sensitivity in Ecopath with Ecosim Ecotracer models using a new Python wrapper	Tan D, Koppel D, Iwanaga T, Fisher R, Haller‑Bull V, Matthews S	https://doi.org/10.36334/modsim2025.B06.tan	https://mssanz.org.au/modsim2025/files/B06.tan.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	I
Koppel D	Quantifying parameter sensitivity in Ecopath with Ecosim Ecotracer models using a new Python wrapper	Tan D, Koppel D, Iwanaga T, Fisher R, Haller‑Bull V, Matthews S	https://doi.org/10.36334/modsim2025.B06.tan	https://mssanz.org.au/modsim2025/files/B06.tan.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	K
Matthews S	Quantifying parameter sensitivity in Ecopath with Ecosim Ecotracer models using a new Python wrapper	Tan D, Koppel D, Iwanaga T, Fisher R, Haller‑Bull V, Matthews S	https://doi.org/10.36334/modsim2025.B06.tan	https://mssanz.org.au/modsim2025/files/B06.tan.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	M
Tan D (Daniel)	Quantifying parameter sensitivity in Ecopath with Ecosim Ecotracer models using a new Python wrapper	Tan D, Koppel D, Iwanaga T, Fisher R, Haller‑Bull V, Matthews S	https://doi.org/10.36334/modsim2025.B06.tan	https://mssanz.org.au/modsim2025/files/B06.tan.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	T
Peel MC	Quantifying the global impact of atmospheric rivers on the recurrence of flood events	Pradhan S, Wasko C, Peel MC	https://doi.org/10.36334/modsim2025.K07.pradhan	https://mssanz.org.au/modsim2025/files/K07.pradhan.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	P
Pradhan S	Quantifying the global impact of atmospheric rivers on the recurrence of flood events	Pradhan S, Wasko C, Peel MC	https://doi.org/10.36334/modsim2025.K07.pradhan	https://mssanz.org.au/modsim2025/files/K07.pradhan.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	P
Wasko C	Quantifying the global impact of atmospheric rivers on the recurrence of flood events	Pradhan S, Wasko C, Peel MC	https://doi.org/10.36334/modsim2025.K07.pradhan	https://mssanz.org.au/modsim2025/files/K07.pradhan.pdf	K	Hydroclimate	K7	Understanding hydrological extremes: trends, drivers and impacts	W
Chen Y (Yuqing)	Quantifying vegetation and soil organic carbon and their drivers in dryland ecosystems of Northwest, China	Chen Y, Xi H, Zhu M, Wang B, Cheng W, Hao Y, Qu L, Wang Z	https://doi.org/10.36334/modsim2025.B08.chen	https://mssanz.org.au/modsim2025/files/B08.chen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Cheng W (Wenju)	Quantifying vegetation and soil organic carbon and their drivers in dryland ecosystems of Northwest, China	Chen Y, Xi H, Zhu M, Wang B, Cheng W, Hao Y, Qu L, Wang Z	https://doi.org/10.36334/modsim2025.B08.chen	https://mssanz.org.au/modsim2025/files/B08.chen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Hao Y (Yulian)	Quantifying vegetation and soil organic carbon and their drivers in dryland ecosystems of Northwest, China	Chen Y, Xi H, Zhu M, Wang B, Cheng W, Hao Y, Qu L, Wang Z	https://doi.org/10.36334/modsim2025.B08.chen	https://mssanz.org.au/modsim2025/files/B08.chen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	H
Qu L (Linbo)	Quantifying vegetation and soil organic carbon and their drivers in dryland ecosystems of Northwest, China	Chen Y, Xi H, Zhu M, Wang B, Cheng W, Hao Y, Qu L, Wang Z	https://doi.org/10.36334/modsim2025.B08.chen	https://mssanz.org.au/modsim2025/files/B08.chen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	Q
Wang B (Bin)	Quantifying vegetation and soil organic carbon and their drivers in dryland ecosystems of Northwest, China	Chen Y, Xi H, Zhu M, Wang B, Cheng W, Hao Y, Qu L, Wang Z	https://doi.org/10.36334/modsim2025.B08.chen	https://mssanz.org.au/modsim2025/files/B08.chen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	W
Wang Z (Zhehui)	Quantifying vegetation and soil organic carbon and their drivers in dryland ecosystems of Northwest, China	Chen Y, Xi H, Zhu M, Wang B, Cheng W, Hao Y, Qu L, Wang Z	https://doi.org/10.36334/modsim2025.B08.chen	https://mssanz.org.au/modsim2025/files/B08.chen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	W
Xi H (Haiyang)	Quantifying vegetation and soil organic carbon and their drivers in dryland ecosystems of Northwest, China	Chen Y, Xi H, Zhu M, Wang B, Cheng W, Hao Y, Qu L, Wang Z	https://doi.org/10.36334/modsim2025.B08.chen	https://mssanz.org.au/modsim2025/files/B08.chen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	X
Zhu M (Meng)	Quantifying vegetation and soil organic carbon and their drivers in dryland ecosystems of Northwest, China	Chen Y, Xi H, Zhu M, Wang B, Cheng W, Hao Y, Qu L, Wang Z	https://doi.org/10.36334/modsim2025.B08.chen	https://mssanz.org.au/modsim2025/files/B08.chen.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	Z
Austin K	Quantitative evaluation of the influence of rainfall runoff model parameter uncertainty	Jordan P, Scorah M, Thu KS, Finger M, Cheng X, Szabo K, Nathan R, Austin K	https://doi.org/10.36334/modsim2025.J08.jordan	https://mssanz.org.au/modsim2025/files/J08.jordan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	A
Cheng X (Xiang)	Quantitative evaluation of the influence of rainfall runoff model parameter uncertainty	Jordan P, Scorah M, Thu KS, Finger M, Cheng X, Szabo K, Nathan R, Austin K	https://doi.org/10.36334/modsim2025.J08.jordan	https://mssanz.org.au/modsim2025/files/J08.jordan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	C
Finger M	Quantitative evaluation of the influence of rainfall runoff model parameter uncertainty	Jordan P, Scorah M, Thu KS, Finger M, Cheng X, Szabo K, Nathan R, Austin K	https://doi.org/10.36334/modsim2025.J08.jordan	https://mssanz.org.au/modsim2025/files/J08.jordan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	F
Jordan P	Quantitative evaluation of the influence of rainfall runoff model parameter uncertainty	Jordan P, Scorah M, Thu KS, Finger M, Cheng X, Szabo K, Nathan R, Austin K	https://doi.org/10.36334/modsim2025.J08.jordan	https://mssanz.org.au/modsim2025/files/J08.jordan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	J
Nathan R	Quantitative evaluation of the influence of rainfall runoff model parameter uncertainty	Jordan P, Scorah M, Thu KS, Finger M, Cheng X, Szabo K, Nathan R, Austin K	https://doi.org/10.36334/modsim2025.J08.jordan	https://mssanz.org.au/modsim2025/files/J08.jordan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	N
Scorah M	Quantitative evaluation of the influence of rainfall runoff model parameter uncertainty	Jordan P, Scorah M, Thu KS, Finger M, Cheng X, Szabo K, Nathan R, Austin K	https://doi.org/10.36334/modsim2025.J08.jordan	https://mssanz.org.au/modsim2025/files/J08.jordan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	S
Szabo K	Quantitative evaluation of the influence of rainfall runoff model parameter uncertainty	Jordan P, Scorah M, Thu KS, Finger M, Cheng X, Szabo K, Nathan R, Austin K	https://doi.org/10.36334/modsim2025.J08.jordan	https://mssanz.org.au/modsim2025/files/J08.jordan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	S
Thu KS	Quantitative evaluation of the influence of rainfall runoff model parameter uncertainty	Jordan P, Scorah M, Thu KS, Finger M, Cheng X, Szabo K, Nathan R, Austin K	https://doi.org/10.36334/modsim2025.J08.jordan	https://mssanz.org.au/modsim2025/files/J08.jordan.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	T
Arora M	Rainfall-based estimation of fertiliser nitrogen inputs and their influence on stream nitrogen in Victorian catchments	Babatunde O, Arora M, Nara SNV, Guo D, Cartwright I, Western AW	https://doi.org/10.36334/modsim2025.L01.babatunde	https://mssanz.org.au/modsim2025/files/L01.babatunde.pdf	L	Water quality	L1	Catchment Water Quality Modelling	A
Babatunde O	Rainfall-based estimation of fertiliser nitrogen inputs and their influence on stream nitrogen in Victorian catchments	Babatunde O, Arora M, Nara SNV, Guo D, Cartwright I, Western AW	https://doi.org/10.36334/modsim2025.L01.babatunde	https://mssanz.org.au/modsim2025/files/L01.babatunde.pdf	L	Water quality	L1	Catchment Water Quality Modelling	B
Cartwright I	Rainfall-based estimation of fertiliser nitrogen inputs and their influence on stream nitrogen in Victorian catchments	Babatunde O, Arora M, Nara SNV, Guo D, Cartwright I, Western AW	https://doi.org/10.36334/modsim2025.L01.babatunde	https://mssanz.org.au/modsim2025/files/L01.babatunde.pdf	L	Water quality	L1	Catchment Water Quality Modelling	C
Guo D (Danlu)	Rainfall-based estimation of fertiliser nitrogen inputs and their influence on stream nitrogen in Victorian catchments	Babatunde O, Arora M, Nara SNV, Guo D, Cartwright I, Western AW	https://doi.org/10.36334/modsim2025.L01.babatunde	https://mssanz.org.au/modsim2025/files/L01.babatunde.pdf	L	Water quality	L1	Catchment Water Quality Modelling	G
Nara SNV	Rainfall-based estimation of fertiliser nitrogen inputs and their influence on stream nitrogen in Victorian catchments	Babatunde O, Arora M, Nara SNV, Guo D, Cartwright I, Western AW	https://doi.org/10.36334/modsim2025.L01.babatunde	https://mssanz.org.au/modsim2025/files/L01.babatunde.pdf	L	Water quality	L1	Catchment Water Quality Modelling	N
Western AW	Rainfall-based estimation of fertiliser nitrogen inputs and their influence on stream nitrogen in Victorian catchments	Babatunde O, Arora M, Nara SNV, Guo D, Cartwright I, Western AW	https://doi.org/10.36334/modsim2025.L01.babatunde	https://mssanz.org.au/modsim2025/files/L01.babatunde.pdf	L	Water quality	L1	Catchment Water Quality Modelling	W
Brooks S	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	B
Davies M	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	D
Doble R	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble K, Crosbie R, McCallum J	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	D
Doody T	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	D
Dunlop M	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble K, Crosbie R, McCallum J	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	D
Dupuy M	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	D
Gao S (Sicong)	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	G
Gibbs MS	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	G
Hopkins M	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	H
King A	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	K
Melbourne-Thomas J	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	M
O'Sullivan J	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	O
Pritchard J	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	P
Round V	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	R
Sengupta A	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble K, Crosbie R, McCallum J	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	S
Stratford D	Ramsar wetland climate adaptation: lessons learnt from a participatory process	Doble R, Sengupta A, Dunlop M, Melbourne-Thomas J, Round V, Hopkins M, Pritchard J, Davies M, Brooks S, Gibbs M, Dupuy M, Gao S, O'Sullivan J, Stratford D, King A, Doody T	https://doi.org/10.36334/modsim2025.K01.doble	https://mssanz.org.au/modsim2025/files/K01.doble.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	S
Robertson DE	Rapid assessment of environmental flow targets using Bayesian logistic regression	Schepen A, Robertson DE	https://doi.org/10.36334/modsim2025.J08.schepen	https://mssanz.org.au/modsim2025/files/J08.schepen.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	R
Schepen A	Rapid assessment of environmental flow targets using Bayesian logistic regression	Schepen A, Robertson DE	https://doi.org/10.36334/modsim2025.J08.schepen	https://mssanz.org.au/modsim2025/files/J08.schepen.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	S
Bakharia A	Ready or not, here AI comes: Meaningful AI integration needs collaboration of universities and industry	Egger F, O’Brien K, Bakharia A, Miller L, Cuthbert S, Francey M	https://doi.org/10.36334/modsim2025.I04.egger	https://mssanz.org.au/modsim2025/files/I04.egger.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	B
Cuthbert S	Ready or not, here AI comes: Meaningful AI integration needs collaboration of universities and industry	Egger F, O’Brien K, Bakharia A, Miller L, Cuthbert S, Francey M	https://doi.org/10.36334/modsim2025.I04.egger	https://mssanz.org.au/modsim2025/files/I04.egger.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	C
Egger F	Ready or not, here AI comes: Meaningful AI integration needs collaboration of universities and industry	Egger F, O’Brien K, Bakharia A, Miller L, Cuthbert S, Francey M	https://doi.org/10.36334/modsim2025.I04.egger	https://mssanz.org.au/modsim2025/files/I04.egger.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	E
Francey M	Ready or not, here AI comes: Meaningful AI integration needs collaboration of universities and industry	Egger F, O’Brien K, Bakharia A, Miller L, Cuthbert S, Francey M	https://doi.org/10.36334/modsim2025.I04.egger	https://mssanz.org.au/modsim2025/files/I04.egger.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	F
Miller L	Ready or not, here AI comes: Meaningful AI integration needs collaboration of universities and industry	Egger F, O’Brien K, Bakharia A, Miller L, Cuthbert S, Francey M	https://doi.org/10.36334/modsim2025.I04.egger	https://mssanz.org.au/modsim2025/files/I04.egger.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	M
O’Brien KR	Ready or not, here AI comes: Meaningful AI integration needs collaboration of universities and industry	Egger F, O’Brien K, Bakharia A, Miller L, Cuthbert S, Francey M	https://doi.org/10.36334/modsim2025.I04.egger	https://mssanz.org.au/modsim2025/files/I04.egger.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	O
Allen DE	Realised volatility estimation shortcuts: An empirical analysis	Allen DE, Chang C, Ng KH, Peiris S	https://doi.org/10.36334/modsim2025.D02.allen	https://mssanz.org.au/modsim2025/files/D02.allen.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	A
Chang C-L	Realised volatility estimation shortcuts: An empirical analysis	Allen DE, Chang C, Ng KH, Peiris S	https://doi.org/10.36334/modsim2025.D02.allen	https://mssanz.org.au/modsim2025/files/D02.allen.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	C
Ng KH	Realised volatility estimation shortcuts: An empirical analysis	Allen DE, Chang C, Ng KH, Peiris S	https://doi.org/10.36334/modsim2025.D02.allen	https://mssanz.org.au/modsim2025/files/D02.allen.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	N
Peiris S	Realised volatility estimation shortcuts: An empirical analysis	Allen DE, Chang C, Ng KH, Peiris S	https://doi.org/10.36334/modsim2025.D02.allen	https://mssanz.org.au/modsim2025/files/D02.allen.pdf	D	Economics and finance	D2	Modelling Risk Management, ESG, Climate Finance, Policy Evaluation, and Tourism Economics	P
Burns MJ	Real-time hydrological-hydraulic hybrid modelling to inform smart management of urban flow regimes	Zhang L, Burns MJ, Russell KL, Xu WD, Fletcher TD	https://doi.org/10.36334/modsim2025.J09.zhang	https://mssanz.org.au/modsim2025/files/J09.zhang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	B
Fletcher TD	Real-time hydrological-hydraulic hybrid modelling to inform smart management of urban flow regimes	Zhang L, Burns MJ, Russell KL, Xu WD, Fletcher TD	https://doi.org/10.36334/modsim2025.J09.zhang	https://mssanz.org.au/modsim2025/files/J09.zhang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	F
Russell KL	Real-time hydrological-hydraulic hybrid modelling to inform smart management of urban flow regimes	Zhang L, Burns MJ, Russell KL, Xu WD, Fletcher TD	https://doi.org/10.36334/modsim2025.J09.zhang	https://mssanz.org.au/modsim2025/files/J09.zhang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	R
Xu WD	Real-time hydrological-hydraulic hybrid modelling to inform smart management of urban flow regimes	Zhang L, Burns MJ, Russell KL, Xu WD, Fletcher TD	https://doi.org/10.36334/modsim2025.J09.zhang	https://mssanz.org.au/modsim2025/files/J09.zhang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	X
Zhang L (Lin)	Real-time hydrological-hydraulic hybrid modelling to inform smart management of urban flow regimes	Zhang L, Burns MJ, Russell KL, Xu WD, Fletcher TD	https://doi.org/10.36334/modsim2025.J09.zhang	https://mssanz.org.au/modsim2025/files/J09.zhang.pdf	J	Water resources	J9	Challenges, new concepts and new methods for water resources management	Z
Edmunds P	Real-time water quality forecasting in rivers using satellite data and dynamic models (INVITED SPEAKER)	Whitehead PG, Edmunds P	https://doi.org/10.36334/modsim2025.L02.whitehead	https://mssanz.org.au/modsim2025/files/L02.whitehead.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	E
Whitehead PG	Real-time water quality forecasting in rivers using satellite data and dynamic models (INVITED SPEAKER)	Whitehead PG, Edmunds P	https://doi.org/10.36334/modsim2025.L02.whitehead	https://mssanz.org.au/modsim2025/files/L02.whitehead.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	W
Holzworth D	Redesigning the APSIM test and deployment system to bring benefits to developers	Paroz A, Rich J, Holzworth D, Huth N	https://doi.org/10.36334/modsim2025.B04.paroz	https://mssanz.org.au/modsim2025/files/B04.paroz.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	H
Huth N	Redesigning the APSIM test and deployment system to bring benefits to developers	Paroz A, Rich J, Holzworth D, Huth N	https://doi.org/10.36334/modsim2025.B04.paroz	https://mssanz.org.au/modsim2025/files/B04.paroz.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	H
Paroz A	Redesigning the APSIM test and deployment system to bring benefits to developers	Paroz A, Rich J, Holzworth D, Huth N	https://doi.org/10.36334/modsim2025.B04.paroz	https://mssanz.org.au/modsim2025/files/B04.paroz.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	P
Rich J	Redesigning the APSIM test and deployment system to bring benefits to developers	Paroz A, Rich J, Holzworth D, Huth N	https://doi.org/10.36334/modsim2025.B04.paroz	https://mssanz.org.au/modsim2025/files/B04.paroz.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	R
Grier B	Reef depth as a robust reef characteristic for intervention planning	Grier B, Iwanaga T	https://doi.org/10.36334/modsim2025.F05.grier	https://mssanz.org.au/modsim2025/files/F05.grier.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	G
Iwanaga T	Reef depth as a robust reef characteristic for intervention planning	Grier B, Iwanaga T	https://doi.org/10.36334/modsim2025.F05.grier	https://mssanz.org.au/modsim2025/files/F05.grier.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	I
Hore-Lacy W	Refining the Value-Ag framework for innovation impact evaluation in smallholder agrifood systems	Monjardino M, Hore-Lacy W	https://doi.org/10.36334/modsim2025.B06.monjardino	https://mssanz.org.au/modsim2025/files/B06.monjardino.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	H
Monjardino M	Refining the Value-Ag framework for innovation impact evaluation in smallholder agrifood systems	Monjardino M, Hore-Lacy W	https://doi.org/10.36334/modsim2025.B06.monjardino	https://mssanz.org.au/modsim2025/files/B06.monjardino.pdf	B	Biological systems	B6	Integrating data and system knowledge to support decision-making: Advances, applications and lessons learned	M
Pepler A	Regional climate model projections of Australia's future weather systems	Pepler A	https://doi.org/10.36334/modsim2025.G04.pepler	https://mssanz.org.au/modsim2025/files/G04.pepler.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	P
Doody T	Reliability, Resilience and Vulnerability (RRV) framework as a tool to assess vegetation water stress	Wimalasuriya R, Wardle G, Doody T, Gao S, Vervoort W	https://doi.org/10.36334/modsim2025.J11.wimalasuriya	https://mssanz.org.au/modsim2025/files/J11.wimalasuriya.pdf	J	Water resources	J11	Ecohydrological Modelling	D
Gao S (Sicong)	Reliability, Resilience and Vulnerability (RRV) framework as a tool to assess vegetation water stress	Wimalasuriya R, Wardle G, Doody T, Gao S, Vervoort W	https://doi.org/10.36334/modsim2025.J11.wimalasuriya	https://mssanz.org.au/modsim2025/files/J11.wimalasuriya.pdf	J	Water resources	J11	Ecohydrological Modelling	G
Vervoort RW	Reliability, Resilience and Vulnerability (RRV) framework as a tool to assess vegetation water stress	Wimalasuriya R, Wardle G, Doody T, Gao S, Vervoort W	https://doi.org/10.36334/modsim2025.J11.wimalasuriya	https://mssanz.org.au/modsim2025/files/J11.wimalasuriya.pdf	J	Water resources	J11	Ecohydrological Modelling	V
Wardle G	Reliability, Resilience and Vulnerability (RRV) framework as a tool to assess vegetation water stress	Wimalasuriya R, Wardle G, Doody T, Gao S, Vervoort W	https://doi.org/10.36334/modsim2025.J11.wimalasuriya	https://mssanz.org.au/modsim2025/files/J11.wimalasuriya.pdf	J	Water resources	J11	Ecohydrological Modelling	W
Wimalasuriya R	Reliability, Resilience and Vulnerability (RRV) framework as a tool to assess vegetation water stress	Wimalasuriya R, Wardle G, Doody T, Gao S, Vervoort W	https://doi.org/10.36334/modsim2025.J11.wimalasuriya	https://mssanz.org.au/modsim2025/files/J11.wimalasuriya.pdf	J	Water resources	J11	Ecohydrological Modelling	W
Amir QM	Remote and retrospective calibration of soil moisture probes	Amir QM, Bishop TFA, Van Ogtrop FF	https://doi.org/10.36334/modsim2025.J03.amir	https://mssanz.org.au/modsim2025/files/J03.amir.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	A
Bishop TFA	Remote and retrospective calibration of soil moisture probes	Amir QM, Bishop TFA, Van Ogtrop FF	https://doi.org/10.36334/modsim2025.J03.amir	https://mssanz.org.au/modsim2025/files/J03.amir.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	B
Van Ogtrop FF	Remote and retrospective calibration of soil moisture probes	Amir QM, Bishop TFA, Van Ogtrop FF	https://doi.org/10.36334/modsim2025.J03.amir	https://mssanz.org.au/modsim2025/files/J03.amir.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	V
Hilton JE	Resolving radiation in physics-based wildfire spread modelling	Swedosh W, Sharples JJ, Sutherland D, Hilton JE	https://doi.org/10.36334/modsim2025.G08.swedosh	https://mssanz.org.au/modsim2025/files/G08.swedosh.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	H
Sharples JJ	Resolving radiation in physics-based wildfire spread modelling	Swedosh W, Sharples JJ, Sutherland D, Hilton JE	https://doi.org/10.36334/modsim2025.G08.swedosh	https://mssanz.org.au/modsim2025/files/G08.swedosh.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Sutherland D	Resolving radiation in physics-based wildfire spread modelling	Swedosh W, Sharples JJ, Sutherland D, Hilton JE	https://doi.org/10.36334/modsim2025.G08.swedosh	https://mssanz.org.au/modsim2025/files/G08.swedosh.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Swedosh W	Resolving radiation in physics-based wildfire spread modelling	Swedosh W, Sharples JJ, Sutherland D, Hilton JE	https://doi.org/10.36334/modsim2025.G08.swedosh	https://mssanz.org.au/modsim2025/files/G08.swedosh.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Escudero SG	Resolving the hydrodynamics of a coastal wetland in Moreton Bay	Kurucz K, Escudero SG, Hipsey MR	https://doi.org/10.36334/modsim2025.J11.kurucz	https://mssanz.org.au/modsim2025/files/J11.kurucz.pdf	J	Water resources	J11	Ecohydrological Modelling	E
Hipsey MR	Resolving the hydrodynamics of a coastal wetland in Moreton Bay	Kurucz K, Escudero SG, Hipsey MR	https://doi.org/10.36334/modsim2025.J11.kurucz	https://mssanz.org.au/modsim2025/files/J11.kurucz.pdf	J	Water resources	J11	Ecohydrological Modelling	H
Kurucz K	Resolving the hydrodynamics of a coastal wetland in Moreton Bay	Kurucz K, Escudero SG, Hipsey MR	https://doi.org/10.36334/modsim2025.J11.kurucz	https://mssanz.org.au/modsim2025/files/J11.kurucz.pdf	J	Water resources	J11	Ecohydrological Modelling	K
Pannipitiya D	Responding to the challenges and complexities of urban water management in the Sydney Metro – SSTOM	Pannipitiya N, Pannipitiya D	https://doi.org/10.36334/modsim2025.L03.pannipitiya	https://mssanz.org.au/modsim2025/files/L03.pannipitiya.pdf	L	Water quality	L3	Modelling water quality treatment and management	P
Pannipitiya N	Responding to the challenges and complexities of urban water management in the Sydney Metro – SSTOM	Pannipitiya N, Pannipitiya D	https://doi.org/10.36334/modsim2025.L03.pannipitiya	https://mssanz.org.au/modsim2025/files/L03.pannipitiya.pdf	L	Water quality	L3	Modelling water quality treatment and management	P
Crosland P	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	C
Ferdousi S	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	F
Hickie IB	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	H
Ho N	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	H
Hosseini SH	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	H
Huntley S	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	H
Lam D	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	L
Loblay V	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	L
Munasinghe S	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	M
Nguyen KH	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	N
Occhipinti JA	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	O
Piper S	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	P
Skinner A	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	S
Song YJC	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	S
Tran K (Kristen)	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	T
Vacher C	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	V
Waterhouse‑Bushnell Z	Rethinking the scale-up of digital technologies for youth mental health: insights from participatory systems modelling in Western Sydney	Hosseini SH, Crosland P, Ho N, Skinner A, Vacher C, Tran K, Nguyen KH, Piper S, Huntley S, Song YJC, Loblay V, Ferdousi S, Munasinghe S, Waterhouse‑Bushnell Z, Lam D, Hickie IB, Occhipinti JA	https://doi.org/10.36334/modsim2025.H03.hosseini	https://mssanz.org.au/modsim2025/files/H03.hosseini.pdf	H	Health and biosecurity	H3	Evidence-Based and Optimal Decision-Making in Health Services Research through Simulation and Modelling	W
Bishop T	Revealing seasonal plasticity of whole-plant hydraulic properties using sap-flow and stem water-potential monitoring	Zhang Y, Collyer B, Filippi P, Bishop T	https://doi.org/10.36334/modsim2025.F12.zhang	https://mssanz.org.au/modsim2025/files/F12.zhang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	B
Collyer B	Revealing seasonal plasticity of whole-plant hydraulic properties using sap-flow and stem water-potential monitoring	Zhang Y, Collyer B, Filippi P, Bishop T	https://doi.org/10.36334/modsim2025.F12.zhang	https://mssanz.org.au/modsim2025/files/F12.zhang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	C
Filippi P	Revealing seasonal plasticity of whole-plant hydraulic properties using sap-flow and stem water-potential monitoring	Zhang Y, Collyer B, Filippi P, Bishop T	https://doi.org/10.36334/modsim2025.F12.zhang	https://mssanz.org.au/modsim2025/files/F12.zhang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	F
Zhang Z	Revealing seasonal plasticity of whole-plant hydraulic properties using sap-flow and stem water-potential monitoring	Zhang Y, Collyer B, Filippi P, Bishop T	https://doi.org/10.36334/modsim2025.F12.zhang	https://mssanz.org.au/modsim2025/files/F12.zhang.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	Z
Ng TLS	Risk mitigation and human judgment in environmental watering under forecast uncertainty	Ng TLS, Robertson DE	https://doi.org/10.36334/modsim2025.J08.ng	https://mssanz.org.au/modsim2025/files/J08.ng.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	N
Robertson DE	Risk mitigation and human judgment in environmental watering under forecast uncertainty	Ng TLS, Robertson DE	https://doi.org/10.36334/modsim2025.J08.ng	https://mssanz.org.au/modsim2025/files/J08.ng.pdf	J	Water resources	J8	Decision-Making Under Uncertainty in Water Resources Management	R
Snow V	Robust models don’t grow on equations (or data) alone. The challenges of process-based modelling of pastoral systems	Snow V	https://doi.org/10.36334/modsim2025.P02.snow	https://mssanz.org.au/modsim2025/files/P02.snow.pdf	P	Plenary	P2	Plenary	S
Batelaan O	Root-zone 'periscope': Modelling plant-available water storage dynamics across scales from above ground observations	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.J11.zhang	https://mssanz.org.au/modsim2025/files/J11.zhang.pdf	J	Water resources	J11	Ecohydrological Modelling	B
Guan H	Root-zone 'periscope': Modelling plant-available water storage dynamics across scales from above ground observations	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.J11.zhang	https://mssanz.org.au/modsim2025/files/J11.zhang.pdf	J	Water resources	J11	Ecohydrological Modelling	G
Singha K	Root-zone 'periscope': Modelling plant-available water storage dynamics across scales from above ground observations	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.J11.zhang	https://mssanz.org.au/modsim2025/files/J11.zhang.pdf	J	Water resources	J11	Ecohydrological Modelling	S
Veneklaas E	Root-zone 'periscope': Modelling plant-available water storage dynamics across scales from above ground observations	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.J11.zhang	https://mssanz.org.au/modsim2025/files/J11.zhang.pdf	J	Water resources	J11	Ecohydrological Modelling	V
Zhang Z	Root-zone 'periscope': Modelling plant-available water storage dynamics across scales from above ground observations	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.J11.zhang	https://mssanz.org.au/modsim2025/files/J11.zhang.pdf	J	Water resources	J11	Ecohydrological Modelling	Z
Azeem M	Rules of thumb or models: Exploring diversity in growers’ nitrogen fertiliser decisions	Azeem M, Brown B, Burton M, Bell L, Llewellyn R	https://doi.org/10.36334/modsim2025.D05.azeem	https://mssanz.org.au/modsim2025/files/D05.azeem.pdf	D	Economics and finance	D5	Decision-making under uncertainty in economics and finance	A
Bell L	Rules of thumb or models: Exploring diversity in growers’ nitrogen fertiliser decisions	Azeem M, Brown B, Burton M, Bell L, Llewellyn R	https://doi.org/10.36334/modsim2025.D05.azeem	https://mssanz.org.au/modsim2025/files/D05.azeem.pdf	D	Economics and finance	D5	Decision-making under uncertainty in economics and finance	B
Brown B	Rules of thumb or models: Exploring diversity in growers’ nitrogen fertiliser decisions	Azeem M, Brown B, Burton M, Bell L, Llewellyn R	https://doi.org/10.36334/modsim2025.D05.azeem	https://mssanz.org.au/modsim2025/files/D05.azeem.pdf	D	Economics and finance	D5	Decision-making under uncertainty in economics and finance	B
Burton M	Rules of thumb or models: Exploring diversity in growers’ nitrogen fertiliser decisions	Azeem M, Brown B, Burton M, Bell L, Llewellyn R	https://doi.org/10.36334/modsim2025.D05.azeem	https://mssanz.org.au/modsim2025/files/D05.azeem.pdf	D	Economics and finance	D5	Decision-making under uncertainty in economics and finance	B
Llewellyn R	Rules of thumb or models: Exploring diversity in growers’ nitrogen fertiliser decisions	Azeem M, Brown B, Burton M, Bell L, Llewellyn R	https://doi.org/10.36334/modsim2025.D05.azeem	https://mssanz.org.au/modsim2025/files/D05.azeem.pdf	D	Economics and finance	D5	Decision-making under uncertainty in economics and finance	L
Baek JB	Safety management of hazardous chemicals in semiconductor wafer cleaning equipment focus of subcontractor practices	Lee JS, Baek JB, Park KS, Lee MJ	https://doi.org/10.36334/modsim2025.H01.lee	https://mssanz.org.au/modsim2025/files/H01.lee.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	B
Lee JS	Safety management of hazardous chemicals in semiconductor wafer cleaning equipment focus of subcontractor practices	Lee JS, Baek JB, Park KS, Lee MJ	https://doi.org/10.36334/modsim2025.H01.lee	https://mssanz.org.au/modsim2025/files/H01.lee.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	L
Lee MJ	Safety management of hazardous chemicals in semiconductor wafer cleaning equipment focus of subcontractor practices	Lee JS, Baek JB, Park KS, Lee MJ	https://doi.org/10.36334/modsim2025.H01.lee	https://mssanz.org.au/modsim2025/files/H01.lee.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	L
Park KS	Safety management of hazardous chemicals in semiconductor wafer cleaning equipment focus of subcontractor practices	Lee JS, Baek JB, Park KS, Lee MJ	https://doi.org/10.36334/modsim2025.H01.lee	https://mssanz.org.au/modsim2025/files/H01.lee.pdf	H	Health and biosecurity	H1	Use of Artificial Intelligence and Machine Learning in the Design and Modeling of Disease, Cancer, and Safety Data	P
Botha H	Satellite-based assessment of marine phytoplankton productivity responses to climate and anthropogenic pressures in Asia-Pacific waters	Xu Y, Xu T, Doran B, Botha H	https://doi.org/10.36334/modsim2025.G02.xu	https://mssanz.org.au/modsim2025/files/G02.xu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	B
Doran B	Satellite-based assessment of marine phytoplankton productivity responses to climate and anthropogenic pressures in Asia-Pacific waters	Xu Y, Xu T, Doran B, Botha H	https://doi.org/10.36334/modsim2025.G02.xu	https://mssanz.org.au/modsim2025/files/G02.xu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	D
Xu T (Tingbao)	Satellite-based assessment of marine phytoplankton productivity responses to climate and anthropogenic pressures in Asia-Pacific waters	Xu Y, Xu T, Doran B, Botha H	https://doi.org/10.36334/modsim2025.G02.xu	https://mssanz.org.au/modsim2025/files/G02.xu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	X
Xu Y	Satellite-based assessment of marine phytoplankton productivity responses to climate and anthropogenic pressures in Asia-Pacific waters	Xu Y, Xu T, Doran B, Botha H	https://doi.org/10.36334/modsim2025.G02.xu	https://mssanz.org.au/modsim2025/files/G02.xu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	X
Ahmad F	Satellite-based water budgeting and seasonal groundwater stress assessment in Indus Basin using flux towers	Liaqat UW, Hafeez M, Bodla HU, Ahmad F	https://doi.org/10.36334/modsim2025.J10.liaqat	https://mssanz.org.au/modsim2025/files/J10.liaqat.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	A
Bodla HU	Satellite-based water budgeting and seasonal groundwater stress assessment in Indus Basin using flux towers	Liaqat UW, Hafeez M, Bodla HU, Ahmad F	https://doi.org/10.36334/modsim2025.J10.liaqat	https://mssanz.org.au/modsim2025/files/J10.liaqat.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	B
Hafeez M (Mohsin)	Satellite-based water budgeting and seasonal groundwater stress assessment in Indus Basin using flux towers	Liaqat UW, Hafeez M, Bodla HU, Ahmad F	https://doi.org/10.36334/modsim2025.J10.liaqat	https://mssanz.org.au/modsim2025/files/J10.liaqat.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	H
Liaqat UW	Satellite-based water budgeting and seasonal groundwater stress assessment in Indus Basin using flux towers	Liaqat UW, Hafeez M, Bodla HU, Ahmad F	https://doi.org/10.36334/modsim2025.J10.liaqat	https://mssanz.org.au/modsim2025/files/J10.liaqat.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	L
Kavetski D	Scale-invariant models for extreme rainfall: Application to Australian sites	You L, Kavetski D, Lambert M	https://doi.org/10.36334/modsim2025.J06.you	https://mssanz.org.au/modsim2025/files/J06.you.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	K
Lambert M	Scale-invariant models for extreme rainfall: Application to Australian sites	You L, Kavetski D, Lambert M	https://doi.org/10.36334/modsim2025.J06.you	https://mssanz.org.au/modsim2025/files/J06.you.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	L
You L	Scale-invariant models for extreme rainfall: Application to Australian sites	You L, Kavetski D, Lambert M	https://doi.org/10.36334/modsim2025.J06.you	https://mssanz.org.au/modsim2025/files/J06.you.pdf	J	Water resources	J6	Hydroclimate extremes forecasting: from droughts to floods	Y
Driver P	Scenario simulation modelling for future states of Australian socioecological landscapes	Szetey K, Driver P, Luxton S, Prober SM, Richards AE	https://doi.org/10.36334/modsim2025.F05.szetey	https://mssanz.org.au/modsim2025/files/F05.szetey.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	D
Luxton S	Scenario simulation modelling for future states of Australian socioecological landscapes	Szetey K, Driver P, Luxton S, Prober SM, Richards AE	https://doi.org/10.36334/modsim2025.F05.szetey	https://mssanz.org.au/modsim2025/files/F05.szetey.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	L
Prober SM	Scenario simulation modelling for future states of Australian socioecological landscapes	Szetey K, Driver P, Luxton S, Prober SM, Richards AE	https://doi.org/10.36334/modsim2025.F05.szetey	https://mssanz.org.au/modsim2025/files/F05.szetey.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	P
Richards AE	Scenario simulation modelling for future states of Australian socioecological landscapes	Szetey K, Driver P, Luxton S, Prober SM, Richards AE	https://doi.org/10.36334/modsim2025.F05.szetey	https://mssanz.org.au/modsim2025/files/F05.szetey.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	R
Szetey K	Scenario simulation modelling for future states of Australian socioecological landscapes	Szetey K, Driver P, Luxton S, Prober SM, Richards AE	https://doi.org/10.36334/modsim2025.F05.szetey	https://mssanz.org.au/modsim2025/files/F05.szetey.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	S
Campos P	Scenarios in stories: Communicating flood inundation modelling in the Cooper Creek using interactive StoryMaps	Mateo CMR, Vaze J, Holland K, Marvanek S, Zheng H, Wang B, London A, Campos P, Huddlestone-Holmes C	https://doi.org/10.36334/modsim2025.G09.mateo	https://mssanz.org.au/modsim2025/files/G09.mateo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	C
Holland K	Scenarios in stories: Communicating flood inundation modelling in the Cooper Creek using interactive StoryMaps	Mateo CMR, Vaze J, Holland K, Marvanek S, Zheng H, Wang B, London A, Campos P, Huddlestone-Holmes C	https://doi.org/10.36334/modsim2025.G09.mateo	https://mssanz.org.au/modsim2025/files/G09.mateo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	H
Huddlestone-Holmes C	Scenarios in stories: Communicating flood inundation modelling in the Cooper Creek using interactive StoryMaps	Mateo CMR, Vaze J, Holland K, Marvanek S, Zheng H, Wang B, London A, Campos P, Huddlestone-Holmes C	https://doi.org/10.36334/modsim2025.G09.mateo	https://mssanz.org.au/modsim2025/files/G09.mateo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	H
London A	Scenarios in stories: Communicating flood inundation modelling in the Cooper Creek using interactive StoryMaps	Mateo CMR, Vaze J, Holland K, Marvanek S, Zheng H, Wang B, London A, Campos P, Huddlestone-Holmes C	https://doi.org/10.36334/modsim2025.G09.mateo	https://mssanz.org.au/modsim2025/files/G09.mateo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	L
Marvanek S	Scenarios in stories: Communicating flood inundation modelling in the Cooper Creek using interactive StoryMaps	Mateo CMR, Vaze J, Holland K, Marvanek S, Zheng H, Wang B, London A, Campos P, Huddlestone-Holmes C	https://doi.org/10.36334/modsim2025.G09.mateo	https://mssanz.org.au/modsim2025/files/G09.mateo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Mateo CMR	Scenarios in stories: Communicating flood inundation modelling in the Cooper Creek using interactive StoryMaps	Mateo CMR, Vaze J, Holland K, Marvanek S, Zheng H, Wang B, London A, Campos P, Huddlestone-Holmes C	https://doi.org/10.36334/modsim2025.G09.mateo	https://mssanz.org.au/modsim2025/files/G09.mateo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	M
Vaze J	Scenarios in stories: Communicating flood inundation modelling in the Cooper Creek using interactive StoryMaps	Mateo CMR, Vaze J, Holland K, Marvanek S, Zheng H, Wang B, London A, Campos P, Huddlestone-Holmes C	https://doi.org/10.36334/modsim2025.G09.mateo	https://mssanz.org.au/modsim2025/files/G09.mateo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	V
Wang B (Bill)	Scenarios in stories: Communicating flood inundation modelling in the Cooper Creek using interactive StoryMaps	Mateo CMR, Vaze J, Holland K, Marvanek S, Zheng H, Wang B, London A, Campos P, Huddlestone-Holmes C	https://doi.org/10.36334/modsim2025.G09.mateo	https://mssanz.org.au/modsim2025/files/G09.mateo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	W
Zheng H (Hongzing)	Scenarios in stories: Communicating flood inundation modelling in the Cooper Creek using interactive StoryMaps	Mateo CMR, Vaze J, Holland K, Marvanek S, Zheng H, Wang B, London A, Campos P, Huddlestone-Holmes C	https://doi.org/10.36334/modsim2025.G09.mateo	https://mssanz.org.au/modsim2025/files/G09.mateo.pdf	G	Global change and natural hazards	G9	Advancing Flood and Tsunami Modelling: Techniques, Applications, and Emerging Technologies	Z
Chiera BA	SCOPE: An integrated early warning system for combat casualty prediction	Tiong JKR, Chiera BA	https://doi.org/10.36334/modsim2025.M02.tiong	https://mssanz.org.au/modsim2025/files/M02.tiong.pdf	M	Operations Research	M2	Simulation modelling and analysis	C
Tiong JKR	SCOPE: An integrated early warning system for combat casualty prediction	Tiong JKR, Chiera BA	https://doi.org/10.36334/modsim2025.M02.tiong	https://mssanz.org.au/modsim2025/files/M02.tiong.pdf	M	Operations Research	M2	Simulation modelling and analysis	T
Lin Z (Zhaohui)	Seasonal prediction of summer rainfall anomalies over China using CAS-ESM and machine learning technique (INVITED SPEAKER)	Lin Z, Shao Y, Zhang H, Zhang H	https://doi.org/10.36334/modsim2025.K08.lin	https://mssanz.org.au/modsim2025/files/K08.lin.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	L
Shao Y (Yuhang)	Seasonal prediction of summer rainfall anomalies over China using CAS-ESM and machine learning technique (INVITED SPEAKER)	Lin Z, Shao Y, Zhang H, Zhang H	https://doi.org/10.36334/modsim2025.K08.lin	https://mssanz.org.au/modsim2025/files/K08.lin.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	S
Zhang H (He)	Seasonal prediction of summer rainfall anomalies over China using CAS-ESM and machine learning technique (INVITED SPEAKER)	Lin Z, Shao Y, Zhang H, Zhang H	https://doi.org/10.36334/modsim2025.K08.lin	https://mssanz.org.au/modsim2025/files/K08.lin.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Zhang H (Huiwei)	Seasonal prediction of summer rainfall anomalies over China using CAS-ESM and machine learning technique (INVITED SPEAKER)	Lin Z, Shao Y, Zhang H, Zhang H	https://doi.org/10.36334/modsim2025.K08.lin	https://mssanz.org.au/modsim2025/files/K08.lin.pdf	K	Hydroclimate	K8	Watershed flood regime changes and their prediction	Z
Graham TDJ	Selective incorporation of trend into seasonal streamflow climatology	Graham T, Wang Q, Western A, Wu W	https://doi.org/10.36334/modsim2025.K06.graham	https://mssanz.org.au/modsim2025/files/K06.graham.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	G
Wang QJ	Selective incorporation of trend into seasonal streamflow climatology	Graham T, Wang Q, Western A, Wu W	https://doi.org/10.36334/modsim2025.K06.graham	https://mssanz.org.au/modsim2025/files/K06.graham.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	W
Western AW	Selective incorporation of trend into seasonal streamflow climatology	Graham T, Wang Q, Western A, Wu W	https://doi.org/10.36334/modsim2025.K06.graham	https://mssanz.org.au/modsim2025/files/K06.graham.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	W
Wu W (Wenyan)	Selective incorporation of trend into seasonal streamflow climatology	Graham T, Wang Q, Western A, Wu W	https://doi.org/10.36334/modsim2025.K06.graham	https://mssanz.org.au/modsim2025/files/K06.graham.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	W
Biggs J	Sensibility testing of a NH3 volatilisation model for APSIM	Pasley H, Verburg K, Biggs J, Cichota R, Schwenke G, Vogeler I, Holzworth D, Snow V	https://doi.org/10.36334/modsim2025.B03.pasleyh	https://mssanz.org.au/modsim2025/files/B03.pasleyh.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	B
Cichota R	Sensibility testing of a NH3 volatilisation model for APSIM	Pasley H, Verburg K, Biggs J, Cichota R, Schwenke G, Vogeler I, Holzworth D, Snow V	https://doi.org/10.36334/modsim2025.B03.pasleyh	https://mssanz.org.au/modsim2025/files/B03.pasleyh.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	C
Holzworth D	Sensibility testing of a NH3 volatilisation model for APSIM	Pasley H, Verburg K, Biggs J, Cichota R, Schwenke G, Vogeler I, Holzworth D, Snow V	https://doi.org/10.36334/modsim2025.B03.pasleyh	https://mssanz.org.au/modsim2025/files/B03.pasleyh.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	H
Pasley H	Sensibility testing of a NH3 volatilisation model for APSIM	Pasley H, Verburg K, Biggs J, Cichota R, Schwenke G, Vogeler I, Holzworth D, Snow V	https://doi.org/10.36334/modsim2025.B03.pasleyh	https://mssanz.org.au/modsim2025/files/B03.pasleyh.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	P
Schwenke G	Sensibility testing of a NH3 volatilisation model for APSIM	Pasley H, Verburg K, Biggs J, Cichota R, Schwenke G, Vogeler I, Holzworth D, Snow V	https://doi.org/10.36334/modsim2025.B03.pasleyh	https://mssanz.org.au/modsim2025/files/B03.pasleyh.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	S
Snow V	Sensibility testing of a NH3 volatilisation model for APSIM	Pasley H, Verburg K, Biggs J, Cichota R, Schwenke G, Vogeler I, Holzworth D, Snow V	https://doi.org/10.36334/modsim2025.B03.pasleyh	https://mssanz.org.au/modsim2025/files/B03.pasleyh.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	S
Verburg K	Sensibility testing of a NH3 volatilisation model for APSIM	Pasley H, Verburg K, Biggs J, Cichota R, Schwenke G, Vogeler I, Holzworth D, Snow V	https://doi.org/10.36334/modsim2025.B03.pasleyh	https://mssanz.org.au/modsim2025/files/B03.pasleyh.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	V
Vogeler I	Sensibility testing of a NH3 volatilisation model for APSIM	Pasley H, Verburg K, Biggs J, Cichota R, Schwenke G, Vogeler I, Holzworth D, Snow V	https://doi.org/10.36334/modsim2025.B03.pasleyh	https://mssanz.org.au/modsim2025/files/B03.pasleyh.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	V
Ani CJ	Sensitivity analysis of a local-scale coral larval connectivity model to biological parameters	Ani CJ, Robson BJ	https://doi.org/10.36334/modsim2025.F03.ani	https://mssanz.org.au/modsim2025/files/F03.ani.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	A
Robson BJ	Sensitivity analysis of a local-scale coral larval connectivity model to biological parameters	Ani CJ, Robson BJ	https://doi.org/10.36334/modsim2025.F03.ani	https://mssanz.org.au/modsim2025/files/F03.ani.pdf	F	Environment and ecology	F3	Modelling for effective climate change adaptation	R
Beh T	Sensor-driven control of UAS using AI in simulation	Pywell S, Beh T, Crase S, Dansie J	https://doi.org/10.36334/modsim2025.C02.pywell	https://mssanz.org.au/modsim2025/files/C02.pywell.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	B
Crase S	Sensor-driven control of UAS using AI in simulation	Pywell S, Beh T, Crase S, Dansie J	https://doi.org/10.36334/modsim2025.C02.pywell	https://mssanz.org.au/modsim2025/files/C02.pywell.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	C
Dansie J	Sensor-driven control of UAS using AI in simulation	Pywell S, Beh T, Crase S, Dansie J	https://doi.org/10.36334/modsim2025.C02.pywell	https://mssanz.org.au/modsim2025/files/C02.pywell.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	D
Pywell S	Sensor-driven control of UAS using AI in simulation	Pywell S, Beh T, Crase S, Dansie J	https://doi.org/10.36334/modsim2025.C02.pywell	https://mssanz.org.au/modsim2025/files/C02.pywell.pdf	C	Computer science and engineering	C2	Modelling and simulation of Robotic and Autonomous Systems (RAS)	P
Pincombe W	Sentiment analysis of historical Australian parliamentary speeches	Pincombe W	https://doi.org/10.36334/modsim2025.A03.pincombe	https://mssanz.org.au/modsim2025/files/A03.pincombe.pdf	A	Applied and computational mathematics	A3	Modelling and simulation for trustworthy data science on challenging datasets	P
Bennett FR	ShapleyX – a Python package for the efficient estimation of Shapley effects and Sobol indices from tabular data	Bennett FR	https://doi.org/10.36334/modsim2025.A04.bennett	https://mssanz.org.au/modsim2025/files/A04.bennett.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	B
Huth NI	Shedding light on complexity using sensitivity analysis	Huth NI, Simwaka P, Lana M	https://doi.org/10.36334/modsim2025.B05.huth	https://mssanz.org.au/modsim2025/files/B05.huth.pdf	B	Biological systems	B5	Agricultural Systems	H
Lana M	Shedding light on complexity using sensitivity analysis	Huth NI, Simwaka P, Lana M	https://doi.org/10.36334/modsim2025.B05.huth	https://mssanz.org.au/modsim2025/files/B05.huth.pdf	B	Biological systems	B5	Agricultural Systems	L
Simwaka P	Shedding light on complexity using sensitivity analysis	Huth NI, Simwaka P, Lana M	https://doi.org/10.36334/modsim2025.B05.huth	https://mssanz.org.au/modsim2025/files/B05.huth.pdf	B	Biological systems	B5	Agricultural Systems	S
Maier HR	Shifting from modelling changes in climate to better understanding risks to achieving desired outcomes	Maier HR	https://doi.org/10.36334/modsim2025.J07.maier	https://mssanz.org.au/modsim2025/files/J07.maier.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	M
Brennan E	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Cressall B	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	C
De Kleermaeker S	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Dixon S	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Fuller J	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	F
Gijsbers P	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	G
Peralta L	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Price N	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Rahman J (Joel)	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Urich C	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	U
Vorreiter D	Shifting Left: Building a collaborative integrated hydrological model for the Murray‑Darling Basin	Brennan E, Peralta L, Vorreiter D, Fuller J, Price N, Dixon S, Gijsbers P, De Kleermaeker S, Rahman J, Cressall B, Urich C	https://doi.org/10.36334/modsim2025.J02.brennan	https://mssanz.org.au/modsim2025/files/J02.brennan.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	V
Adams MP	Simple models can reveal insightful science: data-fitted models of intertidal seagrass as a case study	Adams MP, Wang X, Shao D	https://doi.org/10.36334/modsim2025.F12.adams	https://mssanz.org.au/modsim2025/files/F12.adams.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	A
Shao D	Simple models can reveal insightful science: data-fitted models of intertidal seagrass as a case study	Adams MP, Wang X, Shao D	https://doi.org/10.36334/modsim2025.F12.adams	https://mssanz.org.au/modsim2025/files/F12.adams.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	S
Wang X (Xinyan)	Simple models can reveal insightful science: data-fitted models of intertidal seagrass as a case study	Adams MP, Wang X, Shao D	https://doi.org/10.36334/modsim2025.F12.adams	https://mssanz.org.au/modsim2025/files/F12.adams.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	W
Beletse Y	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	B
Biggs JS	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	B
Cichota R	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	C
Garba II	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	G
Holzworth DP	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	H
Huth NI	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	H
Pasley HR	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	P
Snow V	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	S
Thorburn PJ	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	T
Verburg K	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	V
Vogeler I	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	V
Wang E (Enli)	Simulating nitrogen dynamics for sustainable farming systems	Verburg K, Pasley HR, Garba II, Biggs JS, Beletse Y, Snow V, Vogeler I, Holzworth DP, Cichota R, Huth NI, Wang E, Thorburn PJ	https://doi.org/10.36334/modsim2025.B01.verburg	https://mssanz.org.au/modsim2025/files/B01.verburg.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	W
Cichota R	Simulating the effects of microclimate on the water balance of understorey plant communities in orchards	Cichota R, Sharp J, Gentile R, Müller K	https://doi.org/10.36334/modsim2025.B05.cichota	https://mssanz.org.au/modsim2025/files/B05.cichota.pdf	B	Biological systems	B5	Agricultural Systems	C
Gentile R	Simulating the effects of microclimate on the water balance of understorey plant communities in orchards	Cichota R, Sharp J, Gentile R, Müller K	https://doi.org/10.36334/modsim2025.B05.cichota	https://mssanz.org.au/modsim2025/files/B05.cichota.pdf	B	Biological systems	B5	Agricultural Systems	G
Müller K	Simulating the effects of microclimate on the water balance of understorey plant communities in orchards	Cichota R, Sharp J, Gentile R, Müller K	https://doi.org/10.36334/modsim2025.B05.cichota	https://mssanz.org.au/modsim2025/files/B05.cichota.pdf	B	Biological systems	B5	Agricultural Systems	M
Sharp J	Simulating the effects of microclimate on the water balance of understorey plant communities in orchards	Cichota R, Sharp J, Gentile R, Müller K	https://doi.org/10.36334/modsim2025.B05.cichota	https://mssanz.org.au/modsim2025/files/B05.cichota.pdf	B	Biological systems	B5	Agricultural Systems	S
Alam J	Simulating transmission losses during environmental flow events in the northern Murray-Darling Basin	Kim SSH, Crosbie RS, Dawes W, Vaze J, Wang B, Mateo C, May R, Nair S, Alam J	https://doi.org/10.36334/modsim2025.J02.kims	https://mssanz.org.au/modsim2025/files/J02.kims.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	A
Crosbie RS	Simulating transmission losses during environmental flow events in the northern Murray-Darling Basin	Kim SSH, Crosbie RS, Dawes W, Vaze J, Wang B, Mateo C, May R, Nair S, Alam J	https://doi.org/10.36334/modsim2025.J02.kims	https://mssanz.org.au/modsim2025/files/J02.kims.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	C
Dawes W	Simulating transmission losses during environmental flow events in the northern Murray-Darling Basin	Kim SSH, Crosbie RS, Dawes W, Vaze J, Wang B, Mateo C, May R, Nair S, Alam J	https://doi.org/10.36334/modsim2025.J02.kims	https://mssanz.org.au/modsim2025/files/J02.kims.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Kim SSH	Simulating transmission losses during environmental flow events in the northern Murray-Darling Basin	Kim SSH, Crosbie RS, Dawes W, Vaze J, Wang B, Mateo C, May R, Nair S, Alam J	https://doi.org/10.36334/modsim2025.J02.kims	https://mssanz.org.au/modsim2025/files/J02.kims.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	K
Mateo C	Simulating transmission losses during environmental flow events in the northern Murray-Darling Basin	Kim SSH, Crosbie RS, Dawes W, Vaze J, Wang B, Mateo C, May R, Nair S, Alam J	https://doi.org/10.36334/modsim2025.J02.kims	https://mssanz.org.au/modsim2025/files/J02.kims.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	M
May R	Simulating transmission losses during environmental flow events in the northern Murray-Darling Basin	Kim SSH, Crosbie RS, Dawes W, Vaze J, Wang B, Mateo C, May R, Nair S, Alam J	https://doi.org/10.36334/modsim2025.J02.kims	https://mssanz.org.au/modsim2025/files/J02.kims.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	M
Nair S	Simulating transmission losses during environmental flow events in the northern Murray-Darling Basin	Kim SSH, Crosbie RS, Dawes W, Vaze J, Wang B, Mateo C, May R, Nair S, Alam J	https://doi.org/10.36334/modsim2025.J02.kims	https://mssanz.org.au/modsim2025/files/J02.kims.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	N
Vaze J	Simulating transmission losses during environmental flow events in the northern Murray-Darling Basin	Kim SSH, Crosbie RS, Dawes W, Vaze J, Wang B, Mateo C, May R, Nair S, Alam J	https://doi.org/10.36334/modsim2025.J02.kims	https://mssanz.org.au/modsim2025/files/J02.kims.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	V
Wang B (Bill)	Simulating transmission losses during environmental flow events in the northern Murray-Darling Basin	Kim SSH, Crosbie RS, Dawes W, Vaze J, Wang B, Mateo C, May R, Nair S, Alam J	https://doi.org/10.36334/modsim2025.J02.kims	https://mssanz.org.au/modsim2025/files/J02.kims.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	W
Clarke W	Simulating wind-driven greenhouse gas exchange in landfills	Pjetri M, Clarke W, Gibbes B	https://doi.org/10.36334/modsim2025.A06.pjetri	https://mssanz.org.au/modsim2025/files/A06.pjetri.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	C
Gibbes B	Simulating wind-driven greenhouse gas exchange in landfills	Pjetri M, Clarke W, Gibbes B	https://doi.org/10.36334/modsim2025.A06.pjetri	https://mssanz.org.au/modsim2025/files/A06.pjetri.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	G
Pjetri M	Simulating wind-driven greenhouse gas exchange in landfills	Pjetri M, Clarke W, Gibbes B	https://doi.org/10.36334/modsim2025.A06.pjetri	https://mssanz.org.au/modsim2025/files/A06.pjetri.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	P
Cunningham L	Simulation of ember transport around urban structures	Cunningham L, Hilton J, Rudman M	https://doi.org/10.36334/modsim2025.G08.cunningham	https://mssanz.org.au/modsim2025/files/G08.cunningham.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	C
Hilton J	Simulation of ember transport around urban structures	Cunningham L, Hilton J, Rudman M	https://doi.org/10.36334/modsim2025.G08.cunningham	https://mssanz.org.au/modsim2025/files/G08.cunningham.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	H
Rudman M	Simulation of ember transport around urban structures	Cunningham L, Hilton J, Rudman M	https://doi.org/10.36334/modsim2025.G08.cunningham	https://mssanz.org.au/modsim2025/files/G08.cunningham.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	R
Rojas AM	Simulation of residential energy-efficiency star rating and thermal requirement using NatHERS certificate data	Wang C‑H, Rojas AM	https://doi.org/10.36334/modsim2025.A04.wang	https://mssanz.org.au/modsim2025/files/A04.wang.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	R
Wang C‑H	Simulation of residential energy-efficiency star rating and thermal requirement using NatHERS certificate data	Wang C‑H, Rojas AM	https://doi.org/10.36334/modsim2025.A04.wang	https://mssanz.org.au/modsim2025/files/A04.wang.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	W
Barber A	Simulation-based decision support for nitrogen management in vegetable production: The Sustainable Vegetable Systems (SVS) Tool	Khaembah EN, Brown H, Barber A, Stenning H, Dellow S, Sharp J, Searle B	https://doi.org/10.36334/modsim2025.B05.khaembah	https://mssanz.org.au/modsim2025/files/B05.khaembah.pdf	B	Biological systems	B5	Agricultural Systems	B
Brown H (Hamish)	Simulation-based decision support for nitrogen management in vegetable production: The Sustainable Vegetable Systems (SVS) Tool	Khaembah EN, Brown H, Barber A, Stenning H, Dellow S, Sharp J, Searle B	https://doi.org/10.36334/modsim2025.B05.khaembah	https://mssanz.org.au/modsim2025/files/B05.khaembah.pdf	B	Biological systems	B5	Agricultural Systems	B
Dellow S	Simulation-based decision support for nitrogen management in vegetable production: The Sustainable Vegetable Systems (SVS) Tool	Khaembah EN, Brown H, Barber A, Stenning H, Dellow S, Sharp J, Searle B	https://doi.org/10.36334/modsim2025.B05.khaembah	https://mssanz.org.au/modsim2025/files/B05.khaembah.pdf	B	Biological systems	B5	Agricultural Systems	D
Khaembah EN	Simulation-based decision support for nitrogen management in vegetable production: The Sustainable Vegetable Systems (SVS) Tool	Khaembah EN, Brown H, Barber A, Stenning H, Dellow S, Sharp J, Searle B	https://doi.org/10.36334/modsim2025.B05.khaembah	https://mssanz.org.au/modsim2025/files/B05.khaembah.pdf	B	Biological systems	B5	Agricultural Systems	K
Searle B	Simulation-based decision support for nitrogen management in vegetable production: The Sustainable Vegetable Systems (SVS) Tool	Khaembah EN, Brown H, Barber A, Stenning H, Dellow S, Sharp J, Searle B	https://doi.org/10.36334/modsim2025.B05.khaembah	https://mssanz.org.au/modsim2025/files/B05.khaembah.pdf	B	Biological systems	B5	Agricultural Systems	S
Sharp J	Simulation-based decision support for nitrogen management in vegetable production: The Sustainable Vegetable Systems (SVS) Tool	Khaembah EN, Brown H, Barber A, Stenning H, Dellow S, Sharp J, Searle B	https://doi.org/10.36334/modsim2025.B05.khaembah	https://mssanz.org.au/modsim2025/files/B05.khaembah.pdf	B	Biological systems	B5	Agricultural Systems	S
Stenning H	Simulation-based decision support for nitrogen management in vegetable production: The Sustainable Vegetable Systems (SVS) Tool	Khaembah EN, Brown H, Barber A, Stenning H, Dellow S, Sharp J, Searle B	https://doi.org/10.36334/modsim2025.B05.khaembah	https://mssanz.org.au/modsim2025/files/B05.khaembah.pdf	B	Biological systems	B5	Agricultural Systems	S
Anderson R	Simulation-based decision-support tools in digital agriculture: Converting APSIM into point-and-click online tools	Grewer U, Anderson R, Lai Y, Lyons B, Zull A, Pembleton K	https://doi.org/10.36334/modsim2025.B04.grewer	https://mssanz.org.au/modsim2025/files/B04.grewer.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	A
Grewer U	Simulation-based decision-support tools in digital agriculture: Converting APSIM into point-and-click online tools	Grewer U, Anderson R, Lai Y, Lyons B, Zull A, Pembleton K	https://doi.org/10.36334/modsim2025.B04.grewer	https://mssanz.org.au/modsim2025/files/B04.grewer.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	G
Lai Y	Simulation-based decision-support tools in digital agriculture: Converting APSIM into point-and-click online tools	Grewer U, Anderson R, Lai Y, Lyons B, Zull A, Pembleton K	https://doi.org/10.36334/modsim2025.B04.grewer	https://mssanz.org.au/modsim2025/files/B04.grewer.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	L
Lyons B	Simulation-based decision-support tools in digital agriculture: Converting APSIM into point-and-click online tools	Grewer U, Anderson R, Lai Y, Lyons B, Zull A, Pembleton K	https://doi.org/10.36334/modsim2025.B04.grewer	https://mssanz.org.au/modsim2025/files/B04.grewer.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	L
Pembleton K	Simulation-based decision-support tools in digital agriculture: Converting APSIM into point-and-click online tools	Grewer U, Anderson R, Lai Y, Lyons B, Zull A, Pembleton K	https://doi.org/10.36334/modsim2025.B04.grewer	https://mssanz.org.au/modsim2025/files/B04.grewer.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	P
Zull A	Simulation-based decision-support tools in digital agriculture: Converting APSIM into point-and-click online tools	Grewer U, Anderson R, Lai Y, Lyons B, Zull A, Pembleton K	https://doi.org/10.36334/modsim2025.B04.grewer	https://mssanz.org.au/modsim2025/files/B04.grewer.pdf	B	Biological systems	B4	APSIM Developments: Livestock, pests and diseases, whole farm modelling, cloud computing, etc.	Z
Carozzi M	Soil carbon sequestration potential of Queensland under land use change and future climate scenarios	Leo S, De Antoni Migliorati M, Carozzi M	https://doi.org/10.36334/modsim2025.F13.leo	https://mssanz.org.au/modsim2025/files/F13.leo.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	C
De Antoni Migliorati M	Soil carbon sequestration potential of Queensland under land use change and future climate scenarios	Leo S, De Antoni Migliorati M, Carozzi M	https://doi.org/10.36334/modsim2025.F13.leo	https://mssanz.org.au/modsim2025/files/F13.leo.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	D
Leo S	Soil carbon sequestration potential of Queensland under land use change and future climate scenarios	Leo S, De Antoni Migliorati M, Carozzi M	https://doi.org/10.36334/modsim2025.F13.leo	https://mssanz.org.au/modsim2025/files/F13.leo.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	L
Bishop T	Soil property prediction under data scarcity using a tabular foundation model	Deo R, Hoskin N, Filippi P, Bishop T	https://doi.org/10.36334/modsim2025.F11.deo	https://mssanz.org.au/modsim2025/files/F11.deo.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	B
Deo R	Soil property prediction under data scarcity using a tabular foundation model	Deo F, Chiew F, Vaze J	https://doi.org/10.36334/modsim2025.F11.deo	https://mssanz.org.au/modsim2025/files/F11.deo.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	D
Filippi P	Soil property prediction under data scarcity using a tabular foundation model	Deo F, Chiew F, Vaze J	https://doi.org/10.36334/modsim2025.F11.deo	https://mssanz.org.au/modsim2025/files/F11.deo.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	F
Hoskin N	Soil property prediction under data scarcity using a tabular foundation model	Deo F, Chiew F, Vaze J	https://doi.org/10.36334/modsim2025.F11.deo	https://mssanz.org.au/modsim2025/files/F11.deo.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	H
Gardner B	Space-time image velocimetry for remote streamflow measurement using satellite video	Gibbs M, Guglielmo M, Gardner B, Hughes J, Petheram C	https://doi.org/10.36334/modsim2025.J03.gibbs	https://mssanz.org.au/modsim2025/files/J03.gibbs.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	G
Gibbs MS	Space-time image velocimetry for remote streamflow measurement using satellite video	Gibbs M, Guglielmo M, Gardner B, Hughes J, Petheram C	https://doi.org/10.36334/modsim2025.J03.gibbs	https://mssanz.org.au/modsim2025/files/J03.gibbs.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	G
Guglielmo M	Space-time image velocimetry for remote streamflow measurement using satellite video	Gibbs M, Guglielmo M, Gardner B, Hughes J, Petheram C	https://doi.org/10.36334/modsim2025.J03.gibbs	https://mssanz.org.au/modsim2025/files/J03.gibbs.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	G
Hughes J	Space-time image velocimetry for remote streamflow measurement using satellite video	Gibbs M, Guglielmo M, Gardner B, Hughes J, Petheram C	https://doi.org/10.36334/modsim2025.J03.gibbs	https://mssanz.org.au/modsim2025/files/J03.gibbs.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	H
Petheram C	Space-time image velocimetry for remote streamflow measurement using satellite video	Gibbs M, Guglielmo M, Gardner B, Hughes J, Petheram C	https://doi.org/10.36334/modsim2025.J03.gibbs	https://mssanz.org.au/modsim2025/files/J03.gibbs.pdf	J	Water resources	J3	Novel methods for data collection and predictive modelling in data sparse environments	P
Chiew FHS	Spatial and temporal variability of weather system contributions to rainfall in the Murray-Darling Basin	Fu G, Peña-Arancibia JL, Zheng H, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.G02.fu	https://mssanz.org.au/modsim2025/files/G02.fu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	C
Fu G (Guobin)	Spatial and temporal variability of weather system contributions to rainfall in the Murray-Darling Basin	Fu G, Peña-Arancibia JL, Zheng H, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.G02.fu	https://mssanz.org.au/modsim2025/files/G02.fu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	F
Peña-Arancibia JL	Spatial and temporal variability of weather system contributions to rainfall in the Murray-Darling Basin	Fu G, Peña-Arancibia JL, Zheng H, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.G02.fu	https://mssanz.org.au/modsim2025/files/G02.fu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	P
Post D	Spatial and temporal variability of weather system contributions to rainfall in the Murray-Darling Basin	Fu G, Peña-Arancibia JL, Zheng H, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.G02.fu	https://mssanz.org.au/modsim2025/files/G02.fu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	P
Yu Y (YingYing)	Spatial and temporal variability of weather system contributions to rainfall in the Murray-Darling Basin	Fu G, Peña-Arancibia JL, Zheng H, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.G02.fu	https://mssanz.org.au/modsim2025/files/G02.fu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Y
Zheng H (Hongzing)	Spatial and temporal variability of weather system contributions to rainfall in the Murray-Darling Basin	Fu G, Peña-Arancibia JL, Zheng H, Yu Y, Chiew FHS, Post D	https://doi.org/10.36334/modsim2025.G02.fu	https://mssanz.org.au/modsim2025/files/G02.fu.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Z
Li X (Xiaojuan)	Spatial and temporal variation of surface temperatures in China based on long-term reanalysis data	Zhang Y, Li X, Yin L	https://doi.org/10.36334/modsim2025.G02.zhangy	https://mssanz.org.au/modsim2025/files/G02.zhangy.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	L
Yin L (Lianwang)	Spatial and temporal variation of surface temperatures in China based on long-term reanalysis data	Zhang Y, Li X, Yin L	https://doi.org/10.36334/modsim2025.G02.zhangy	https://mssanz.org.au/modsim2025/files/G02.zhangy.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Y
Zhang Y (Yunfei)	Spatial and temporal variation of surface temperatures in China based on long-term reanalysis data	Zhang Y, Li X, Yin L	https://doi.org/10.36334/modsim2025.G02.zhangy	https://mssanz.org.au/modsim2025/files/G02.zhangy.pdf	G	Global change and natural hazards	G2	Recent Advances of Earth Observations in Climate Change Impact Research	Z
Bennett JC	Spatially weighted fine-tuning of LSTMs for improved streamflow predictions in ungauged catchments	Shokri A, Bennett JC, Robertson DE	https://doi.org/10.36334/modsim2025.C07.shokri	https://mssanz.org.au/modsim2025/files/C07.shokri.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	B
Robertson DE	Spatially weighted fine-tuning of LSTMs for improved streamflow predictions in ungauged catchments	Shokri A, Bennett JC, Robertson DE	https://doi.org/10.36334/modsim2025.C07.shokri	https://mssanz.org.au/modsim2025/files/C07.shokri.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	R
Shokri A	Spatially weighted fine-tuning of LSTMs for improved streamflow predictions in ungauged catchments	Shokri A, Bennett JC, Robertson DE	https://doi.org/10.36334/modsim2025.C07.shokri	https://mssanz.org.au/modsim2025/files/C07.shokri.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	S
Clark S	Spatiotemporal groundwater predictions using explainable AI	Clark S, Janardhanan S, Fu G	https://doi.org/10.36334/modsim2025.C06.clark	https://mssanz.org.au/modsim2025/files/C06.clark.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	C
Fu G (Guobin)	Spatiotemporal groundwater predictions using explainable AI	Clark S, Janardhanan S, Fu G	https://doi.org/10.36334/modsim2025.C06.clark	https://mssanz.org.au/modsim2025/files/C06.clark.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	F
Janardhanan S	Spatiotemporal groundwater predictions using explainable AI	Clark S, Janardhanan S, Fu G	https://doi.org/10.36334/modsim2025.C06.clark	https://mssanz.org.au/modsim2025/files/C06.clark.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	J
Jason K	Statistical modelling of property and crop losses	Jason K, Raveendran N, Zhu H, Li H, Sofronov G	https://doi.org/10.36334/modsim2025.A04.jason	https://mssanz.org.au/modsim2025/files/A04.jason.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	J
Li H	Statistical modelling of property and crop losses	Jason K, Raveendran N, Zhu H, Li H, Sofronov G	https://doi.org/10.36334/modsim2025.A04.jason	https://mssanz.org.au/modsim2025/files/A04.jason.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	L
Raveendran N	Statistical modelling of property and crop losses	Jason K, Raveendran N, Zhu H, Li H, Sofronov G	https://doi.org/10.36334/modsim2025.A04.jason	https://mssanz.org.au/modsim2025/files/A04.jason.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	R
Sofronov G	Statistical modelling of property and crop losses	Jason K, Raveendran N, Zhu H, Li H, Sofronov G	https://doi.org/10.36334/modsim2025.A04.jason	https://mssanz.org.au/modsim2025/files/A04.jason.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	S
Zhu H	Statistical modelling of property and crop losses	Jason K, Raveendran N, Zhu H, Li H, Sofronov G	https://doi.org/10.36334/modsim2025.A04.jason	https://mssanz.org.au/modsim2025/files/A04.jason.pdf	A	Applied and computational mathematics	A4	Computational statistics and data analysis	Z
Bouraoui F	Step-wise calibration technique using SWAT model	Pagliero L, Willems P, Bouraoui F, Malagò A, Diels J, McIntyre N	https://doi.org/10.36334/modsim2025.J10.pagliero	https://mssanz.org.au/modsim2025/files/J10.pagliero.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	B
Diels J	Step-wise calibration technique using SWAT model	Pagliero L, Willems P, Bouraoui F, Malagò A, Diels J, McIntyre N	https://doi.org/10.36334/modsim2025.J10.pagliero	https://mssanz.org.au/modsim2025/files/J10.pagliero.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	D
Malagò A	Step-wise calibration technique using SWAT model	Pagliero L, Willems P, Bouraoui F, Malagò A, Diels J, McIntyre N	https://doi.org/10.36334/modsim2025.J10.pagliero	https://mssanz.org.au/modsim2025/files/J10.pagliero.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	M
McIntyre N	Step-wise calibration technique using SWAT model	Pagliero L, Willems P, Bouraoui F, Malagò A, Diels J, McIntyre N	https://doi.org/10.36334/modsim2025.J10.pagliero	https://mssanz.org.au/modsim2025/files/J10.pagliero.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	M
Pagliero L	Step-wise calibration technique using SWAT model	Pagliero L, Willems P, Bouraoui F, Malagò A, Diels J, McIntyre N	https://doi.org/10.36334/modsim2025.J10.pagliero	https://mssanz.org.au/modsim2025/files/J10.pagliero.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	P
Willems P	Step-wise calibration technique using SWAT model	Pagliero L, Willems P, Bouraoui F, Malagò A, Diels J, McIntyre N	https://doi.org/10.36334/modsim2025.J10.pagliero	https://mssanz.org.au/modsim2025/files/J10.pagliero.pdf	J	Water resources	J10	Advancements in Hydrological Predictions through Novel Sensing and Innovative Modelling Techniques	W
Wong M	Strategic adversarial analysis of a game theory security model	Wong M	https://doi.org/10.36334/modsim2025.M02.wong	https://mssanz.org.au/modsim2025/files/M02.wong.pdf	M	Operations Research	M2	Simulation modelling and analysis	W
Carlier R	Strategic environmental water use: optimising wetland outcomes with ecological feedbacks	Carlier R, Kuczera G, Rodriguez JF, Saco P	https://doi.org/10.36334/modsim2025.F04.carlier	https://mssanz.org.au/modsim2025/files/F04.carlier.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	C
Kuczera G	Strategic environmental water use: optimising wetland outcomes with ecological feedbacks	Carlier R, Kuczera G, Rodriguez JF, Saco P	https://doi.org/10.36334/modsim2025.F04.carlier	https://mssanz.org.au/modsim2025/files/F04.carlier.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	K
Rodriguez JF	Strategic environmental water use: optimising wetland outcomes with ecological feedbacks	Carlier R, Kuczera G, Rodriguez JF, Saco P	https://doi.org/10.36334/modsim2025.F04.carlier	https://mssanz.org.au/modsim2025/files/F04.carlier.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	R
Saco P	Strategic environmental water use: optimising wetland outcomes with ecological feedbacks	Carlier R, Kuczera G, Rodriguez JF, Saco P	https://doi.org/10.36334/modsim2025.F04.carlier	https://mssanz.org.au/modsim2025/files/F04.carlier.pdf	F	Environment and ecology	F4	Exploring the multiple roles of managed water within river basins	S
Amorsi N	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	A
Bokal S	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	B
Čerkasova N	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	Č
Cvejić R	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	C
Farkas C	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	F
Fribourg‑Blanc B	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	F
Fučík P	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	F
Glavan M	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	G
Honzak L	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	H
Krzeminska D	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	K
Lemann T	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	L
Monaco F	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	M
Nemes A	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	N
Nesheim I	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	N
Piniewski M	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	P
Schürz C	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	S
Strauch M	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	S
Szabó B	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	S
Volk M	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	V
Witing F	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	W
Wittekind C	Strategies for water and nutrient retention in agricultural catchments linking modelling and optimization	Volk M, Amorsi N, Bokal S, Čerkasova N, Cvejić R, Farkas C, Fribourg‑Blanc B, Fučík P, Glavan M, Honzak L, Krzeminska D, Lemann T, Monaco F, Nemes A, Nesheim I, Piniewski M, Schürz C, Strauch M, Szabó B, Witing F, Wittekind C	https://doi.org/10.36334/modsim2025.F07.volk	https://mssanz.org.au/modsim2025/files/F07.volk.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	W
Stancu M	Streamlining modelling & reporting for continuous simulation modelling of stormwater pollutants and runoff	Stancu M	https://doi.org/10.36334/modsim2025.L03.stancu	https://mssanz.org.au/modsim2025/files/L03.stancu.pdf	L	Water quality	L3	Modelling water quality treatment and management	S
Chrystal C	Sunwater Smart Schemes: Improving efficiency in water delivery	Morales Y, Chrystal C, Hughes M	https://doi.org/10.36334/modsim2025.F05.morales	https://mssanz.org.au/modsim2025/files/F05.morales.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	C
Hughes M	Sunwater Smart Schemes: Improving efficiency in water delivery	Morales Y, Chrystal C, Hughes M	https://doi.org/10.36334/modsim2025.F05.morales	https://mssanz.org.au/modsim2025/files/F05.morales.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	H
Morales Y	Sunwater Smart Schemes: Improving efficiency in water delivery	Morales Y, Chrystal C, Hughes M	https://doi.org/10.36334/modsim2025.F05.morales	https://mssanz.org.au/modsim2025/files/F05.morales.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	M
Balasoiu DA	SWAR: Evaluating the effect of distributed communication approaches on application effectiveness	Balasoiu DA, Honhaga I, Judd G, Craggs DW, Campbell B, Szabo C	https://doi.org/10.36334/modsim2025.M02.balasoiu	https://mssanz.org.au/modsim2025/files/M02.balasoiu.pdf	M	Operations Research	M2	Simulation modelling and analysis	B
Campbell BC (Benjamin)	SWAR: Evaluating the effect of distributed communication approaches on application effectiveness	Balasoiu DA, Honhaga I, Judd G, Craggs DW, Campbell B, Szabo C	https://doi.org/10.36334/modsim2025.M02.balasoiu	https://mssanz.org.au/modsim2025/files/M02.balasoiu.pdf	M	Operations Research	M2	Simulation modelling and analysis	C
Craggs DW	SWAR: Evaluating the effect of distributed communication approaches on application effectiveness	Balasoiu DA, Honhaga I, Judd G, Craggs DW, Campbell B, Szabo C	https://doi.org/10.36334/modsim2025.M02.balasoiu	https://mssanz.org.au/modsim2025/files/M02.balasoiu.pdf	M	Operations Research	M2	Simulation modelling and analysis	C
Honhaga I	SWAR: Evaluating the effect of distributed communication approaches on application effectiveness	Balasoiu DA, Honhaga I, Judd G, Craggs DW, Campbell B, Szabo C	https://doi.org/10.36334/modsim2025.M02.balasoiu	https://mssanz.org.au/modsim2025/files/M02.balasoiu.pdf	M	Operations Research	M2	Simulation modelling and analysis	H
Judd G	SWAR: Evaluating the effect of distributed communication approaches on application effectiveness	Balasoiu DA, Honhaga I, Judd G, Craggs DW, Campbell B, Szabo C	https://doi.org/10.36334/modsim2025.M02.balasoiu	https://mssanz.org.au/modsim2025/files/M02.balasoiu.pdf	M	Operations Research	M2	Simulation modelling and analysis	J
Szabo C	SWAR: Evaluating the effect of distributed communication approaches on application effectiveness	Balasoiu DA, Honhaga I, Judd G, Craggs DW, Campbell B, Szabo C	https://doi.org/10.36334/modsim2025.M02.balasoiu	https://mssanz.org.au/modsim2025/files/M02.balasoiu.pdf	M	Operations Research	M2	Simulation modelling and analysis	S
Leopold M	Synthesising hydrological knowledge in a semi-dry region with low-permeability unsaturated zone using Modflow-6	Miotlinski K, Leopold M, Thompson S	https://doi.org/10.36334/modsim2025.A02.miotlinski	https://mssanz.org.au/modsim2025/files/A02.miotlinski.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	L
Miotlinski K	Synthesising hydrological knowledge in a semi-dry region with low-permeability unsaturated zone using Modflow-6	Miotlinski K, Leopold M, Thompson S	https://doi.org/10.36334/modsim2025.A02.miotlinski	https://mssanz.org.au/modsim2025/files/A02.miotlinski.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	M
Thompson S	Synthesising hydrological knowledge in a semi-dry region with low-permeability unsaturated zone using Modflow-6	Miotlinski K, Leopold M, Thompson S	https://doi.org/10.36334/modsim2025.A02.miotlinski	https://mssanz.org.au/modsim2025/files/A02.miotlinski.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	T
Cochrane T	Systems modelling of technological opportunities for a sustainable and profitable dairy sector in New Zealand	Ekanayake C, Cochrane T, Vannier C	https://doi.org/10.36334/modsim2025.B05.ekanayake	https://mssanz.org.au/modsim2025/files/B05.ekanayake.pdf	B	Biological systems	B5	Agricultural Systems	C
Ekanayake C (Chavi)	Systems modelling of technological opportunities for a sustainable and profitable dairy sector in New Zealand	Ekanayake C, Cochrane T, Vannier C	https://doi.org/10.36334/modsim2025.B05.ekanayake	https://mssanz.org.au/modsim2025/files/B05.ekanayake.pdf	B	Biological systems	B5	Agricultural Systems	E
Vannier C	Systems modelling of technological opportunities for a sustainable and profitable dairy sector in New Zealand	Ekanayake C, Cochrane T, Vannier C	https://doi.org/10.36334/modsim2025.B05.ekanayake	https://mssanz.org.au/modsim2025/files/B05.ekanayake.pdf	B	Biological systems	B5	Agricultural Systems	V
Kavetski D	Tailored calibration of stochastic weather generators for enhanced hydrological system evaluation	McInerney D, Westra S, Leonard M, Kavetski D, Thyer M, Maier HR	https://doi.org/10.36334/modsim2025.K04.mcinerney	https://mssanz.org.au/modsim2025/files/K04.mcinerney.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	K
Leonard M (Michael)	Tailored calibration of stochastic weather generators for enhanced hydrological system evaluation	McInerney D, Westra S, Leonard M, Kavetski D, Thyer M, Maier HR	https://doi.org/10.36334/modsim2025.K04.mcinerney	https://mssanz.org.au/modsim2025/files/K04.mcinerney.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	L
Maier HR	Tailored calibration of stochastic weather generators for enhanced hydrological system evaluation	McInerney D, Westra S, Leonard M, Kavetski D, Thyer M, Maier HR	https://doi.org/10.36334/modsim2025.K04.mcinerney	https://mssanz.org.au/modsim2025/files/K04.mcinerney.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	M
McInerney D	Tailored calibration of stochastic weather generators for enhanced hydrological system evaluation	McInerney D, Westra S, Leonard M, Kavetski D, Thyer M, Maier HR	https://doi.org/10.36334/modsim2025.K04.mcinerney	https://mssanz.org.au/modsim2025/files/K04.mcinerney.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	M
Thyer M	Tailored calibration of stochastic weather generators for enhanced hydrological system evaluation	McInerney D, Westra S, Leonard M, Kavetski D, Thyer M, Maier HR	https://doi.org/10.36334/modsim2025.K04.mcinerney	https://mssanz.org.au/modsim2025/files/K04.mcinerney.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	T
Westra S	Tailored calibration of stochastic weather generators for enhanced hydrological system evaluation	McInerney D, Westra S, Leonard M, Kavetski D, Thyer M, Maier HR	https://doi.org/10.36334/modsim2025.K04.mcinerney	https://mssanz.org.au/modsim2025/files/K04.mcinerney.pdf	K	Hydroclimate	K4	Understanding and modelling catchment behaviour in a variable and changing climate	W
Gan C	Tariff risks and stock market performance: Evidence from multivariable simultaneous quantile	Ho LT, Gan C	https://doi.org/10.36334/modsim2025.D04.ho	https://mssanz.org.au/modsim2025/files/D04.ho.pdf	D	Economics and finance	D4	International Trade, Supply Chains, and Global Markets	G
Ho LT	Tariff risks and stock market performance: Evidence from multivariable simultaneous quantile	Ho LT, Gan C	https://doi.org/10.36334/modsim2025.D04.ho	https://mssanz.org.au/modsim2025/files/D04.ho.pdf	D	Economics and finance	D4	International Trade, Supply Chains, and Global Markets	H
Chen M (Menghan)	Temporal dynamics and controlling factors of CO2 fluxes of the Asia’s largest artificial freshwater lake	Lu M, Cheng L, Xu L, Fu C, Qin S, Chen M, Xu C, Zhang L, Liu P	https://doi.org/10.36334/modsim2025.F10.lu	https://mssanz.org.au/modsim2025/files/F10.lu.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	C
Cheng L (Lei)	Temporal dynamics and controlling factors of CO2 fluxes of the Asia’s largest artificial freshwater lake	Lu M, Cheng L, Xu L, Fu C, Qin S, Chen M, Xu C, Zhang L, Liu P	https://doi.org/10.36334/modsim2025.F10.lu	https://mssanz.org.au/modsim2025/files/F10.lu.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	C
Fu C (Chenhao)	Temporal dynamics and controlling factors of CO2 fluxes of the Asia’s largest artificial freshwater lake	Lu M, Cheng L, Xu L, Fu C, Qin S, Chen M, Xu C, Zhang L, Liu P	https://doi.org/10.36334/modsim2025.F10.lu	https://mssanz.org.au/modsim2025/files/F10.lu.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	F
Liu P (Pan)	Temporal dynamics and controlling factors of CO2 fluxes of the Asia’s largest artificial freshwater lake	Lu M, Cheng L, Xu L, Fu C, Qin S, Chen M, Xu C, Zhang L, Liu P	https://doi.org/10.36334/modsim2025.F10.lu	https://mssanz.org.au/modsim2025/files/F10.lu.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	L
Lu M (Mingshen)	Temporal dynamics and controlling factors of CO2 fluxes of the Asia’s largest artificial freshwater lake	Lu M, Cheng L, Xu L, Fu C, Qin S, Chen M, Xu C, Zhang L, Liu P	https://doi.org/10.36334/modsim2025.F10.lu	https://mssanz.org.au/modsim2025/files/F10.lu.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	L
Qin S (Shujing)	Temporal dynamics and controlling factors of CO2 fluxes of the Asia’s largest artificial freshwater lake	Lu M, Cheng L, Xu L, Fu C, Qin S, Chen M, Xu C, Zhang L, Liu P	https://doi.org/10.36334/modsim2025.F10.lu	https://mssanz.org.au/modsim2025/files/F10.lu.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	Q
Xu C (Changjiang)	Temporal dynamics and controlling factors of CO2 fluxes of the Asia’s largest artificial freshwater lake	Lu M, Cheng L, Xu L, Fu C, Qin S, Chen M, Xu C, Zhang L, Liu P	https://doi.org/10.36334/modsim2025.F10.lu	https://mssanz.org.au/modsim2025/files/F10.lu.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	X
Xu L (Liyong)	Temporal dynamics and controlling factors of CO2 fluxes of the Asia’s largest artificial freshwater lake	Lu M, Cheng L, Xu L, Fu C, Qin S, Chen M, Xu C, Zhang L, Liu P	https://doi.org/10.36334/modsim2025.F10.lu	https://mssanz.org.au/modsim2025/files/F10.lu.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	X
Zhang L (Lu)	Temporal dynamics and controlling factors of CO2 fluxes of the Asia’s largest artificial freshwater lake	Lu M, Cheng L, Xu L, Fu C, Qin S, Chen M, Xu C, Zhang L, Liu P	https://doi.org/10.36334/modsim2025.F10.lu	https://mssanz.org.au/modsim2025/files/F10.lu.pdf	F	Environment and ecology	F10	Applications of artificial neural networks and generative artificial intelligence to environmental problems	Z
Kankanige D	Terrestrial water storage in wildfire forecast	Kankanige D, Liu Y, Sharma A	https://doi.org/10.36334/modsim2025.G08.kankanige	https://mssanz.org.au/modsim2025/files/G08.kankanige.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	K
Liu Y (Yi)	Terrestrial water storage in wildfire forecast	Kankanige D, Liu Y, Sharma A	https://doi.org/10.36334/modsim2025.G08.kankanige	https://mssanz.org.au/modsim2025/files/G08.kankanige.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	L
Sharma A	Terrestrial water storage in wildfire forecast	Kankanige D, Liu Y, Sharma A	https://doi.org/10.36334/modsim2025.G08.kankanige	https://mssanz.org.au/modsim2025/files/G08.kankanige.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Cook S	The addition of large-scale WSUD alternatives and their effect on kerbside stormwater systems	Cook S	https://doi.org/10.36334/modsim2025.L03.cook	https://mssanz.org.au/modsim2025/files/L03.cook.pdf	L	Water quality	L3	Modelling water quality treatment and management	C
Ashbolt SC	The challenges and rewards of collaborative environmental flow modelling	Ashbolt SC, Szemis J, May R	https://doi.org/10.36334/modsim2025.J02.ashbolt	https://mssanz.org.au/modsim2025/files/J02.ashbolt.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	A
May R	The challenges and rewards of collaborative environmental flow modelling	Ashbolt SC, Szemis J, May R	https://doi.org/10.36334/modsim2025.J02.ashbolt	https://mssanz.org.au/modsim2025/files/J02.ashbolt.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	M
Szemis J	The challenges and rewards of collaborative environmental flow modelling	Ashbolt SC, Szemis J, May R	https://doi.org/10.36334/modsim2025.J02.ashbolt	https://mssanz.org.au/modsim2025/files/J02.ashbolt.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	S
Blachut C	The effect of ignition protocol on dynamic fire propagation in the Bridger Foothills fire	Blachut C, Sharples JJ, Cheung KKW, Del Favero D	https://doi.org/10.36334/modsim2025.G08.blachut	https://mssanz.org.au/modsim2025/files/G08.blachut.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	B
Cheung KKW	The effect of ignition protocol on dynamic fire propagation in the Bridger Foothills fire	Blachut C, Sharples JJ, Cheung KKW, Del Favero D	https://doi.org/10.36334/modsim2025.G08.blachut	https://mssanz.org.au/modsim2025/files/G08.blachut.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	C
Del Favero D	The effect of ignition protocol on dynamic fire propagation in the Bridger Foothills fire	Blachut C, Sharples JJ, Cheung KKW, Del Favero D	https://doi.org/10.36334/modsim2025.G08.blachut	https://mssanz.org.au/modsim2025/files/G08.blachut.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	D
Sharples JJ	The effect of ignition protocol on dynamic fire propagation in the Bridger Foothills fire	Blachut C, Sharples JJ, Cheung KKW, Del Favero D	https://doi.org/10.36334/modsim2025.G08.blachut	https://mssanz.org.au/modsim2025/files/G08.blachut.pdf	G	Global change and natural hazards	G8	Modelling of bushfire dynamics, fire weather, impact and risk	S
Buber A (Alexander)	The impact of climate change on the hydrological and water management situation in the Lower Don	Buber A, Buber A	https://doi.org/10.36334/modsim2025.J02.buber	https://mssanz.org.au/modsim2025/files/J02.buber.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Buber A (Alina)	The impact of climate change on the hydrological and water management situation in the Lower Don	Buber A, Buber A	https://doi.org/10.36334/modsim2025.J02.buber	https://mssanz.org.au/modsim2025/files/J02.buber.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	B
Bell ST	The Minerva data platform – towards a data platform for research applications	Bell ST, Liu D, Philip A, Cerecke C, Tang J, Nichols M, Gilmore Z, Lin HT	https://doi.org/10.36334/modsim2025.C05.bell	https://mssanz.org.au/modsim2025/files/C05.bell.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	B
Cerecke C	The Minerva data platform – towards a data platform for research applications	Bell ST, Liu D, Philip A, Cerecke C, Tang J, Nichols M, Gilmore Z, Lin HT	https://doi.org/10.36334/modsim2025.C05.bell	https://mssanz.org.au/modsim2025/files/C05.bell.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	C
Gilmore Z	The Minerva data platform – towards a data platform for research applications	Bell ST, Liu D, Philip A, Cerecke C, Tang J, Nichols M, Gilmore Z, Lin HT	https://doi.org/10.36334/modsim2025.C05.bell	https://mssanz.org.au/modsim2025/files/C05.bell.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	G
Lin HT	The Minerva data platform – towards a data platform for research applications	Bell ST, Liu D, Philip A, Cerecke C, Tang J, Nichols M, Gilmore Z, Lin HT	https://doi.org/10.36334/modsim2025.C05.bell	https://mssanz.org.au/modsim2025/files/C05.bell.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	L
Liu D	The Minerva data platform – towards a data platform for research applications	Bell ST, Liu D, Philip A, Cerecke C, Tang J, Nichols M, Gilmore Z, Lin HT	https://doi.org/10.36334/modsim2025.C05.bell	https://mssanz.org.au/modsim2025/files/C05.bell.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	L
Nichols M	The Minerva data platform – towards a data platform for research applications	Bell ST, Liu D, Philip A, Cerecke C, Tang J, Nichols M, Gilmore Z, Lin HT	https://doi.org/10.36334/modsim2025.C05.bell	https://mssanz.org.au/modsim2025/files/C05.bell.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	N
Philip A	The Minerva data platform – towards a data platform for research applications	Bell ST, Liu D, Philip A, Cerecke C, Tang J, Nichols M, Gilmore Z, Lin HT	https://doi.org/10.36334/modsim2025.C05.bell	https://mssanz.org.au/modsim2025/files/C05.bell.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	P
Tang J	The Minerva data platform – towards a data platform for research applications	Bell ST, Liu D, Philip A, Cerecke C, Tang J, Nichols M, Gilmore Z, Lin HT	https://doi.org/10.36334/modsim2025.C05.bell	https://mssanz.org.au/modsim2025/files/C05.bell.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	T
Ananthapavan J	The modelled cost-effectiveness of a prevention program targeting both eating disorders and high BMI	Le LK, Tan EJ, Hay P, Ananthapavan J, Lee YY, Mihalopoulos C	https://doi.org/10.36334/modsim2025.H05.le	https://mssanz.org.au/modsim2025/files/H05.le.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	A
Hay P	The modelled cost-effectiveness of a prevention program targeting both eating disorders and high BMI	Le LK, Tan EJ, Hay P, Ananthapavan J, Lee YY, Mihalopoulos C	https://doi.org/10.36334/modsim2025.H05.le	https://mssanz.org.au/modsim2025/files/H05.le.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	H
Le LK-D	The modelled cost-effectiveness of a prevention program targeting both eating disorders and high BMI	Le LK, Tan EJ, Hay P, Ananthapavan J, Lee YY, Mihalopoulos C	https://doi.org/10.36334/modsim2025.H05.le	https://mssanz.org.au/modsim2025/files/H05.le.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	L
Lee YY	The modelled cost-effectiveness of a prevention program targeting both eating disorders and high BMI	Le LK, Tan EJ, Hay P, Ananthapavan J, Lee YY, Mihalopoulos C	https://doi.org/10.36334/modsim2025.H05.le	https://mssanz.org.au/modsim2025/files/H05.le.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	L
Mihalopoulos C	The modelled cost-effectiveness of a prevention program targeting both eating disorders and high BMI	Le LK, Tan EJ, Hay P, Ananthapavan J, Lee YY, Mihalopoulos C	https://doi.org/10.36334/modsim2025.H05.le	https://mssanz.org.au/modsim2025/files/H05.le.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	M
Tan EJ	The modelled cost-effectiveness of a prevention program targeting both eating disorders and high BMI	Le LK, Tan EJ, Hay P, Ananthapavan J, Lee YY, Mihalopoulos C	https://doi.org/10.36334/modsim2025.H05.le	https://mssanz.org.au/modsim2025/files/H05.le.pdf	H	Health and biosecurity	H5	Development and applications of simulation models in health economics	T
Nathan R	The need to avoid determinism and weak links in modelling chains when assessing climate impacts	Nathan R	https://doi.org/10.36334/modsim2025.J07.nathan	https://mssanz.org.au/modsim2025/files/J07.nathan.pdf	J	Water resources	J7	The path forward for hydroclimate risk assessments - advancing science, modelling and management to support decision making in large river basins	N
Weng H	The numerical modeling study of discharging brine into the Persian Gulf of Saudi Arabia	Weng H, Yu M	https://doi.org/10.36334/modsim2025.L02.weng	https://mssanz.org.au/modsim2025/files/L02.weng.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	W
Yu M	The numerical modeling study of discharging brine into the Persian Gulf of Saudi Arabia	Weng H, Yu M	https://doi.org/10.36334/modsim2025.L02.weng	https://mssanz.org.au/modsim2025/files/L02.weng.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	Y
Gan C (Christopher)	The relationship between FinTech adoption, financial inclusion and poverty in rural Thailand	Khwankaew N, Gan C, Ho LT	https://doi.org/10.36334/modsim2025.D06.khwankaew	https://mssanz.org.au/modsim2025/files/D06.khwankaew.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	G
Ho LT	The relationship between FinTech adoption, financial inclusion and poverty in rural Thailand	Khwankaew N, Gan C, Ho LT	https://doi.org/10.36334/modsim2025.D06.khwankaew	https://mssanz.org.au/modsim2025/files/D06.khwankaew.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	H
Khwankaew N	The relationship between FinTech adoption, financial inclusion and poverty in rural Thailand	Khwankaew N, Gan C, Ho LT	https://doi.org/10.36334/modsim2025.D06.khwankaew	https://mssanz.org.au/modsim2025/files/D06.khwankaew.pdf	D	Economics and finance	D6	Applications of new technologies or methods in economics and finance	K
Ghasemi M	The role of artificial intelligence techniques in forecasting renewable energy sources	Johnson SW, Nazari A, Ghasemi M, Thiruvay D, van der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.johnson	https://mssanz.org.au/modsim2025/files/E04.johnson.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	G
Johnson SW	The role of artificial intelligence techniques in forecasting renewable energy sources	Johnson SW, Nazari A, Ghasemi M, Thiruvay D, van der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.johnson	https://mssanz.org.au/modsim2025/files/E04.johnson.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	J
Nazari A	The role of artificial intelligence techniques in forecasting renewable energy sources	Johnson SW, Nazari A, Ghasemi M, Thiruvay D, van der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.johnson	https://mssanz.org.au/modsim2025/files/E04.johnson.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	N
Shiri F	The role of artificial intelligence techniques in forecasting renewable energy sources	Johnson SW, Nazari A, Ghasemi M, Thiruvay D, van der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.johnson	https://mssanz.org.au/modsim2025/files/E04.johnson.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	S
Thiruvay D	The role of artificial intelligence techniques in forecasting renewable energy sources	Johnson SW, Nazari A, Ghasemi M, Thiruvay D, van der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.johnson	https://mssanz.org.au/modsim2025/files/E04.johnson.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	T
van der Meer R	The role of artificial intelligence techniques in forecasting renewable energy sources	Johnson SW, Nazari A, Ghasemi M, Thiruvay D, van der Meer R, Shiri F	https://doi.org/10.36334/modsim2025.E04.johnson	https://mssanz.org.au/modsim2025/files/E04.johnson.pdf	E	Energy, integrated infrastructure and urban planning	E4	AI assessment of distributed energy resources and variable renewable energy systems	v
Brown H (Hamish)	The struggles of software engineering in a science-based context	Holzworth D, Huth N, Snow V, Brown H, Cichota R, Paroz A, Rich J	https://doi.org/10.36334/modsim2025.B01.holzworth	https://mssanz.org.au/modsim2025/files/B01.holzworth.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	B
Cichota R	The struggles of software engineering in a science-based context	Holzworth D, Huth N, Snow V, Brown H, Cichota R, Paroz A, Rich J	https://doi.org/10.36334/modsim2025.B01.holzworth	https://mssanz.org.au/modsim2025/files/B01.holzworth.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	C
Holzworth D	The struggles of software engineering in a science-based context	Holzworth D, Huth N, Snow V, Brown H, Cichota R, Paroz A, Rich J	https://doi.org/10.36334/modsim2025.B01.holzworth	https://mssanz.org.au/modsim2025/files/B01.holzworth.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	H
Huth N	The struggles of software engineering in a science-based context	Holzworth D, Huth N, Snow V, Brown H, Cichota R, Paroz A, Rich J	https://doi.org/10.36334/modsim2025.B01.holzworth	https://mssanz.org.au/modsim2025/files/B01.holzworth.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	H
Paroz A	The struggles of software engineering in a science-based context	Holzworth D, Huth N, Snow V, Brown H, Cichota R, Paroz A, Rich J	https://doi.org/10.36334/modsim2025.B01.holzworth	https://mssanz.org.au/modsim2025/files/B01.holzworth.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	P
Rich J	The struggles of software engineering in a science-based context	Holzworth D, Huth N, Snow V, Brown H, Cichota R, Paroz A, Rich J	https://doi.org/10.36334/modsim2025.B01.holzworth	https://mssanz.org.au/modsim2025/files/B01.holzworth.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	R
Snow V	The struggles of software engineering in a science-based context	Holzworth D, Huth N, Snow V, Brown H, Cichota R, Paroz A, Rich J	https://doi.org/10.36334/modsim2025.B01.holzworth	https://mssanz.org.au/modsim2025/files/B01.holzworth.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	S
Holzworth D	Thirty years of APSIM modelling: Reflections on the journey, past, present and future	Keating B, Holzworth D	https://doi.org/10.36334/modsim2025.B01.keating	https://mssanz.org.au/modsim2025/files/B01.keating.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	H
Keating B	Thirty years of APSIM modelling: Reflections on the journey, past, present and future	Keating B, Holzworth D	https://doi.org/10.36334/modsim2025.B01.keating	https://mssanz.org.au/modsim2025/files/B01.keating.pdf	B	Biological systems	B1	APSIM 30-Year Symposium: Shaping the Future of Agricultural Modelling	K
Bishop AN	Time-uniform universal approximation and recurrent neural networks	Bishop AN	https://doi.org/10.36334/modsim2025.C06.bishop	https://mssanz.org.au/modsim2025/files/C06.bishop.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	B
Evans N	Toward an Internet of Things data governance approach	Seo S, Evans N, Velasquez D	https://doi.org/10.36334/modsim2025.C05.seo	https://mssanz.org.au/modsim2025/files/C05.seo.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	E
Seo S	Toward an Internet of Things data governance approach	Seo S, Evans N, Velasquez D	https://doi.org/10.36334/modsim2025.C05.seo	https://mssanz.org.au/modsim2025/files/C05.seo.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	S
Velasquez D	Toward an Internet of Things data governance approach	Seo S, Evans N, Velasquez D	https://doi.org/10.36334/modsim2025.C05.seo	https://mssanz.org.au/modsim2025/files/C05.seo.pdf	C	Computer science and engineering	C5	Systems for Supporting the Re-use of Data – The Backbone of Digital Transformation	V
Freebairn A	Toward long-range forecasting of ecological outcomes	Robertson D E, Teng J, Peña‑Arancibia J L, Schepen A, McGinness H, Jackson M V, Ticehurst C, Freebairn A, Shokri A	https://doi.org/10.36334/modsim2025.K06.robertson	https://mssanz.org.au/modsim2025/files/K06.robertson.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	F
Jackson MV	Toward long-range forecasting of ecological outcomes	Robertson D E, Teng J, Peña‑Arancibia J L, Schepen A, McGinness H, Jackson M V, Ticehurst C, Freebairn A, Shokri A	https://doi.org/10.36334/modsim2025.K06.robertson	https://mssanz.org.au/modsim2025/files/K06.robertson.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	J
McGinness H	Toward long-range forecasting of ecological outcomes	Robertson D E, Teng J, Peña‑Arancibia J L, Schepen A, McGinness H, Jackson M V, Ticehurst C, Freebairn A, Shokri A	https://doi.org/10.36334/modsim2025.K06.robertson	https://mssanz.org.au/modsim2025/files/K06.robertson.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	M
Peña‑Arancibia JL	Toward long-range forecasting of ecological outcomes	Robertson D E, Teng J, Peña‑Arancibia J L, Schepen A, McGinness H, Jackson M V, Ticehurst C, Freebairn A, Shokri A	https://doi.org/10.36334/modsim2025.K06.robertson	https://mssanz.org.au/modsim2025/files/K06.robertson.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	P
Robertson DE	Toward long-range forecasting of ecological outcomes	Robertson D E, Teng J, Peña‑Arancibia J L, Schepen A, McGinness H, Jackson M V, Ticehurst C, Freebairn A, Shokri A	https://doi.org/10.36334/modsim2025.K06.robertson	https://mssanz.org.au/modsim2025/files/K06.robertson.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	R
Schepen A	Toward long-range forecasting of ecological outcomes	Robertson D E, Teng J, Peña‑Arancibia J L, Schepen A, McGinness H, Jackson M V, Ticehurst C, Freebairn A, Shokri A	https://doi.org/10.36334/modsim2025.K06.robertson	https://mssanz.org.au/modsim2025/files/K06.robertson.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	S
Shokri A	Toward long-range forecasting of ecological outcomes	Robertson D E, Teng J, Peña‑Arancibia J L, Schepen A, McGinness H, Jackson M V, Ticehurst C, Freebairn A, Shokri A	https://doi.org/10.36334/modsim2025.K06.robertson	https://mssanz.org.au/modsim2025/files/K06.robertson.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	S
Teng J	Toward long-range forecasting of ecological outcomes	Robertson D E, Teng J, Peña‑Arancibia J L, Schepen A, McGinness H, Jackson M V, Ticehurst C, Freebairn A, Shokri A	https://doi.org/10.36334/modsim2025.K06.robertson	https://mssanz.org.au/modsim2025/files/K06.robertson.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	T
Ticehurst C	Toward long-range forecasting of ecological outcomes	Robertson D E, Teng J, Peña‑Arancibia J L, Schepen A, McGinness H, Jackson M V, Ticehurst C, Freebairn A, Shokri A	https://doi.org/10.36334/modsim2025.K06.robertson	https://mssanz.org.au/modsim2025/files/K06.robertson.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	T
Farooq U	Toward modelling termite impacts on carbon stability and methane emissions in greenhouse gas modelling	Farooq U, Pasut C, Wang YP, Macdonald B, Karunaratne S	https://doi.org/10.36334/modsim2025.F01.farooq	https://mssanz.org.au/modsim2025/files/F01.farooq.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	F
Karunaratne S	Toward modelling termite impacts on carbon stability and methane emissions in greenhouse gas modelling	Farooq U, Pasut C, Wang YP, Macdonald B, Karunaratne S	https://doi.org/10.36334/modsim2025.F01.farooq	https://mssanz.org.au/modsim2025/files/F01.farooq.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	K
Macdonald B	Toward modelling termite impacts on carbon stability and methane emissions in greenhouse gas modelling	Farooq U, Pasut C, Wang YP, Macdonald B, Karunaratne S	https://doi.org/10.36334/modsim2025.F01.farooq	https://mssanz.org.au/modsim2025/files/F01.farooq.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	M
Pasut C	Toward modelling termite impacts on carbon stability and methane emissions in greenhouse gas modelling	Farooq U, Pasut C, Wang YP, Macdonald B, Karunaratne S	https://doi.org/10.36334/modsim2025.F01.farooq	https://mssanz.org.au/modsim2025/files/F01.farooq.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	P
Wang Y-P	Toward modelling termite impacts on carbon stability and methane emissions in greenhouse gas modelling	Farooq U, Pasut C, Wang YP, Macdonald B, Karunaratne S	https://doi.org/10.36334/modsim2025.F01.farooq	https://mssanz.org.au/modsim2025/files/F01.farooq.pdf	F	Environment and ecology	F1	Ecological and environmental modelling using a combination of mathematical and statistical approaches	W
Heal MR	Towards a globally nested EMEP4UK model	Tan DY, Vieno M, Nemitz E, Heal MR, Stevenson DS, Reis S	https://doi.org/10.36334/modsim2025.G07.tan	https://mssanz.org.au/modsim2025/files/G07.tan.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	H
Nemitz E	Towards a globally nested EMEP4UK model	Tan DY, Vieno M, Nemitz E, Heal MR, Stevenson DS, Reis S	https://doi.org/10.36334/modsim2025.G07.tan	https://mssanz.org.au/modsim2025/files/G07.tan.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	N
Reis S	Towards a globally nested EMEP4UK model	Tan DY, Vieno M, Nemitz E, Heal MR, Stevenson DS, Reis S	https://doi.org/10.36334/modsim2025.G07.tan	https://mssanz.org.au/modsim2025/files/G07.tan.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	R
Stevenson DS	Towards a globally nested EMEP4UK model	Tan DY, Vieno M, Nemitz E, Heal MR, Stevenson DS, Reis S	https://doi.org/10.36334/modsim2025.G07.tan	https://mssanz.org.au/modsim2025/files/G07.tan.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	S
Tan DYT	Towards a globally nested EMEP4UK model	Tan DY, Vieno M, Nemitz E, Heal MR, Stevenson DS, Reis S	https://doi.org/10.36334/modsim2025.G07.tan	https://mssanz.org.au/modsim2025/files/G07.tan.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	T
Vieno M	Towards a globally nested EMEP4UK model	Tan DY, Vieno M, Nemitz E, Heal MR, Stevenson DS, Reis S	https://doi.org/10.36334/modsim2025.G07.tan	https://mssanz.org.au/modsim2025/files/G07.tan.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	V
Hamilton D	Towards a reference GLM-AED model for wetlands in Great Barrier Reef catchments	Pagliero L, Legesse N, Hamilton D, Weber T, Nguyen H, Rahman J, Waltham N	https://doi.org/10.36334/modsim2025.L02.pagliero	https://mssanz.org.au/modsim2025/files/L02.pagliero.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	H
Legesse N	Towards a reference GLM-AED model for wetlands in Great Barrier Reef catchments	Pagliero L, Legesse N, Hamilton D, Weber T, Nguyen H, Rahman J, Waltham N	https://doi.org/10.36334/modsim2025.L02.pagliero	https://mssanz.org.au/modsim2025/files/L02.pagliero.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	L
Nguyen H	Towards a reference GLM-AED model for wetlands in Great Barrier Reef catchments	Pagliero L, Legesse N, Hamilton D, Weber T, Nguyen H, Rahman J, Waltham N	https://doi.org/10.36334/modsim2025.L02.pagliero	https://mssanz.org.au/modsim2025/files/L02.pagliero.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	N
Pagliero L	Towards a reference GLM-AED model for wetlands in Great Barrier Reef catchments	Pagliero L, Legesse N, Hamilton D, Weber T, Nguyen H, Rahman J, Waltham N	https://doi.org/10.36334/modsim2025.L02.pagliero	https://mssanz.org.au/modsim2025/files/L02.pagliero.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	P
Rahman J (Joel)	Towards a reference GLM-AED model for wetlands in Great Barrier Reef catchments	Pagliero L, Legesse N, Hamilton D, Weber T, Nguyen H, Rahman J, Waltham N	https://doi.org/10.36334/modsim2025.L02.pagliero	https://mssanz.org.au/modsim2025/files/L02.pagliero.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	R
Waltham N	Towards a reference GLM-AED model for wetlands in Great Barrier Reef catchments	Pagliero L, Legesse N, Hamilton D, Weber T, Nguyen H, Rahman J, Waltham N	https://doi.org/10.36334/modsim2025.L02.pagliero	https://mssanz.org.au/modsim2025/files/L02.pagliero.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	W
Weber T	Towards a reference GLM-AED model for wetlands in Great Barrier Reef catchments	Pagliero L, Legesse N, Hamilton D, Weber T, Nguyen H, Rahman J, Waltham N	https://doi.org/10.36334/modsim2025.L02.pagliero	https://mssanz.org.au/modsim2025/files/L02.pagliero.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	W
Abellan E	Towards a sustainable workflow for post-processing development and evaluation	Owen B, Abellan E, Johnson R, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owenb	https://mssanz.org.au/modsim2025/files/K02.owenb.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	A
Johnson R	Towards a sustainable workflow for post-processing development and evaluation	Owen B, Abellan E, Johnson R, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owenb	https://mssanz.org.au/modsim2025/files/K02.owenb.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	J
Mentiplay D	Towards a sustainable workflow for post-processing development and evaluation	Owen B, Abellan E, Johnson R, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owenb	https://mssanz.org.au/modsim2025/files/K02.owenb.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	M
Owen B	Towards a sustainable workflow for post-processing development and evaluation	Owen B, Abellan E, Johnson R, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owenb	https://mssanz.org.au/modsim2025/files/K02.owenb.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	O
Trotta B	Towards a sustainable workflow for post-processing development and evaluation	Owen B, Abellan E, Johnson R, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owenb	https://mssanz.org.au/modsim2025/files/K02.owenb.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	T
Whelan J	Towards a sustainable workflow for post-processing development and evaluation	Owen B, Abellan E, Johnson R, Mentiplay D, Trotta B, Whelan J	https://doi.org/10.36334/modsim2025.K02.owenb	https://mssanz.org.au/modsim2025/files/K02.owenb.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	W
Borevitz J	Towards generalizable crop models: an eco-evolutionary optimality approach to carbon allocation	Norton AJ, Borevitz J	https://doi.org/10.36334/modsim2025.B02.norton	https://mssanz.org.au/modsim2025/files/B02.norton.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	B
Norton AJ	Towards generalizable crop models: an eco-evolutionary optimality approach to carbon allocation	Norton AJ, Borevitz J	https://doi.org/10.36334/modsim2025.B02.norton	https://mssanz.org.au/modsim2025/files/B02.norton.pdf	B	Biological systems	B2	APSIM-Development: Advances in Plant and Vegetation Modelling	N
Batelaan O	Towards rapid 3D soil moisture mapping with multiple on-the-go proximal sensors	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.F11.zhang	https://mssanz.org.au/modsim2025/files/F11.zhang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	B
Guan H	Towards rapid 3D soil moisture mapping with multiple on-the-go proximal sensors	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.F11.zhang	https://mssanz.org.au/modsim2025/files/F11.zhang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	G
Singha K	Towards rapid 3D soil moisture mapping with multiple on-the-go proximal sensors	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.F11.zhang	https://mssanz.org.au/modsim2025/files/F11.zhang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	S
Veneklaas E	Towards rapid 3D soil moisture mapping with multiple on-the-go proximal sensors	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.F11.zhang	https://mssanz.org.au/modsim2025/files/F11.zhang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	V
Zhang Y	Towards rapid 3D soil moisture mapping with multiple on-the-go proximal sensors	Zhang Z, Guan H, Veneklaas E, Singha K, Batelaan O	https://doi.org/10.36334/modsim2025.F11.zhang	https://mssanz.org.au/modsim2025/files/F11.zhang.pdf	F	Environment and ecology	F11	Agricultural Artificial Intelligence Modelling	Z
Baker P	Towards seamless provenance in collaborative modelling: Integrating Airflow with Provena	Petridis R, Yu J, Baker P, Munnerley J, Mirza F	https://doi.org/10.36334/modsim2025.C01.petridis	https://mssanz.org.au/modsim2025/files/C01.petridis.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	B
Mirza F	Towards seamless provenance in collaborative modelling: Integrating Airflow with Provena	Petridis R, Yu J, Baker P, Munnerley J, Mirza F	https://doi.org/10.36334/modsim2025.C01.petridis	https://mssanz.org.au/modsim2025/files/C01.petridis.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	M
Munnerley J	Towards seamless provenance in collaborative modelling: Integrating Airflow with Provena	Petridis R, Yu J, Baker P, Munnerley J, Mirza F	https://doi.org/10.36334/modsim2025.C01.petridis	https://mssanz.org.au/modsim2025/files/C01.petridis.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	M
Petridis R	Towards seamless provenance in collaborative modelling: Integrating Airflow with Provena	Petridis R, Yu J, Baker P, Munnerley J, Mirza F	https://doi.org/10.36334/modsim2025.C01.petridis	https://mssanz.org.au/modsim2025/files/C01.petridis.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	P
Yu J	Towards seamless provenance in collaborative modelling: Integrating Airflow with Provena	Petridis R, Yu J, Baker P, Munnerley J, Mirza F	https://doi.org/10.36334/modsim2025.C01.petridis	https://mssanz.org.au/modsim2025/files/C01.petridis.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	Y
Green D	Towards the generalised compression of weather data for technoeconomic optimisations	Green D	https://doi.org/10.36334/modsim2025.E03.green	https://mssanz.org.au/modsim2025/files/E03.green.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	G
Bruce LC	Tracking the cure: Finding the right numerical tool for bath treatment dispersion	Gunaratne GL, Stones GL, Bruce LC	https://doi.org/10.36334/modsim2025.F08.gunaratne	https://mssanz.org.au/modsim2025/files/F08.gunaratne.pdf	F	Environment and ecology	F8	GIS and environmental modelling	B
Gunaratne GL	Tracking the cure: Finding the right numerical tool for bath treatment dispersion	Gunaratne GL, Stones GL, Bruce LC	https://doi.org/10.36334/modsim2025.F08.gunaratne	https://mssanz.org.au/modsim2025/files/F08.gunaratne.pdf	F	Environment and ecology	F8	GIS and environmental modelling	G
Stones GL	Tracking the cure: Finding the right numerical tool for bath treatment dispersion	Gunaratne GL, Stones GL, Bruce LC	https://doi.org/10.36334/modsim2025.F08.gunaratne	https://mssanz.org.au/modsim2025/files/F08.gunaratne.pdf	F	Environment and ecology	F8	GIS and environmental modelling	S
Arndt S	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	A
Griebel A	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	G
Hinko‑Najera N	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	H
Holzworth D	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	H
Inbar A	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	I
Isaac P	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	I
Knauer J	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	K
Medlyn B	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	M
Moore C	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	M
Pendall E	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	P
Stephens C	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	S
Williams L	Tracking the path of carbon through ecosystems: modelling vegetation carbon dynamics at Australian flux sites	Inbar A, Williams L, Holzworth D, Knauer J, Stephens C, Arndt S, Griebel A, Hinko‑Najera N, Isaac P, Moore C, Pendall E, Medlyn B	https://doi.org/10.36334/modsim2025.F12.inbar	https://mssanz.org.au/modsim2025/files/F12.inbar.pdf	F	Environment and ecology	F12	The Future of Vegetation: Advances in Modelling Plant Function and Distribution	W
Bartkowski B	Transforming agriculture under climate change: A coupled framework of process-based, optimization and agent-based models	Kaim A, Bartkowski B, Gütschow M, Heiß I, Linde J	https://doi.org/10.36334/modsim2025.F07.kaim	https://mssanz.org.au/modsim2025/files/F07.kaim.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	B
Gütschow M	Transforming agriculture under climate change: A coupled framework of process-based, optimization and agent-based models	Kaim A, Bartkowski B, Gütschow M, Heiß I, Linde J	https://doi.org/10.36334/modsim2025.F07.kaim	https://mssanz.org.au/modsim2025/files/F07.kaim.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	G
Heiss I	Transforming agriculture under climate change: A coupled framework of process-based, optimization and agent-based models	Kaim A, Bartkowski B, Gütschow M, Heiß I, Linde J	https://doi.org/10.36334/modsim2025.F07.kaim	https://mssanz.org.au/modsim2025/files/F07.kaim.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	H
Kaim A	Transforming agriculture under climate change: A coupled framework of process-based, optimization and agent-based models	Kaim A, Bartkowski B, Gütschow M, Heiß I, Linde J	https://doi.org/10.36334/modsim2025.F07.kaim	https://mssanz.org.au/modsim2025/files/F07.kaim.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	K
Linde J	Transforming agriculture under climate change: A coupled framework of process-based, optimization and agent-based models	Kaim A, Bartkowski B, Gütschow M, Heiß I, Linde J	https://doi.org/10.36334/modsim2025.F07.kaim	https://mssanz.org.au/modsim2025/files/F07.kaim.pdf	F	Environment and ecology	F7	Coupling Models for Agricultural Transformation and Climate Change Adaptation: Challenges, Opportunities, and Best Practices	L
Bennett J	Trends in hydrological forecast skill in a changing climate	Bennett J, Robertson D, Shokri A	https://doi.org/10.36334/modsim2025.K06.bennett	https://mssanz.org.au/modsim2025/files/K06.bennett.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	B
Robertson D	Trends in hydrological forecast skill in a changing climate	Bennett J, Robertson D, Shokri A	https://doi.org/10.36334/modsim2025.K06.bennett	https://mssanz.org.au/modsim2025/files/K06.bennett.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	R
Shokri A	Trends in hydrological forecast skill in a changing climate	Bennett J, Robertson D, Shokri A	https://doi.org/10.36334/modsim2025.K06.bennett	https://mssanz.org.au/modsim2025/files/K06.bennett.pdf	K	Hydroclimate	K6	Advancing hydroclimate forecasting: methods and applications	S
Allingham D	Turning cross-sectoral agricultural climate change modelling into actionable insights for industry and government	Allingham D, Ellis B, Kelley J, Lawson J, Young R	https://doi.org/10.36334/modsim2025.G03.allingham	https://mssanz.org.au/modsim2025/files/G03.allingham.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	A
Ellis B	Turning cross-sectoral agricultural climate change modelling into actionable insights for industry and government	Allingham D, Ellis B, Kelley J, Lawson J, Young R	https://doi.org/10.36334/modsim2025.G03.allingham	https://mssanz.org.au/modsim2025/files/G03.allingham.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	E
Kelley J	Turning cross-sectoral agricultural climate change modelling into actionable insights for industry and government	Allingham D, Ellis B, Kelley J, Lawson J, Young R	https://doi.org/10.36334/modsim2025.G03.allingham	https://mssanz.org.au/modsim2025/files/G03.allingham.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	K
Lawson J	Turning cross-sectoral agricultural climate change modelling into actionable insights for industry and government	Allingham D, Ellis B, Kelley J, Lawson J, Young R	https://doi.org/10.36334/modsim2025.G03.allingham	https://mssanz.org.au/modsim2025/files/G03.allingham.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	L
Young R	Turning cross-sectoral agricultural climate change modelling into actionable insights for industry and government	Allingham D, Ellis B, Kelley J, Lawson J, Young R	https://doi.org/10.36334/modsim2025.G03.allingham	https://mssanz.org.au/modsim2025/files/G03.allingham.pdf	G	Global change and natural hazards	G3	Climate risk assessment for government and industry: Bridging the gap between climate science and actionable insights for decision makers	Y
de Groot A	Two-dimensional hydrodynamic modelling of wetlands in non-urban catchments	Nguyen H, Weber T, Egger F, de Groot A	https://doi.org/10.36334/modsim2025.L02.nguyen	https://mssanz.org.au/modsim2025/files/L02.nguyen.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	d
Egger F	Two-dimensional hydrodynamic modelling of wetlands in non-urban catchments	Nguyen H, Weber T, Egger F, de Groot A	https://doi.org/10.36334/modsim2025.L02.nguyen	https://mssanz.org.au/modsim2025/files/L02.nguyen.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	E
Nguyen H (Ha)	Two-dimensional hydrodynamic modelling of wetlands in non-urban catchments	Nguyen H, Weber T, Egger F, de Groot A	https://doi.org/10.36334/modsim2025.L02.nguyen	https://mssanz.org.au/modsim2025/files/L02.nguyen.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	N
Weber T	Two-dimensional hydrodynamic modelling of wetlands in non-urban catchments	Nguyen H, Weber T, Egger F, de Groot A	https://doi.org/10.36334/modsim2025.L02.nguyen	https://mssanz.org.au/modsim2025/files/L02.nguyen.pdf	L	Water quality	L2	Advanced Lake, Reservoir, Wetland and River System Modelling	W
Chen M (Min)	Uncertainty assessment in water quality modelling: towards fulsome practices	Jakeman T, Sun X, Croke B, Chen M	https://doi.org/10.36334/modsim2025.J05.jakeman	https://mssanz.org.au/modsim2025/files/J05.jakeman.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	C
Croke B	Uncertainty assessment in water quality modelling: towards fulsome practices	Jakeman T, Sun X, Croke B, Chen M	https://doi.org/10.36334/modsim2025.J05.jakeman	https://mssanz.org.au/modsim2025/files/J05.jakeman.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	C
Jakeman T	Uncertainty assessment in water quality modelling: towards fulsome practices	Jakeman T, Sun X, Croke B, Chen M	https://doi.org/10.36334/modsim2025.J05.jakeman	https://mssanz.org.au/modsim2025/files/J05.jakeman.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	J
Sun X	Uncertainty assessment in water quality modelling: towards fulsome practices	Jakeman T, Sun X, Croke B, Chen M	https://doi.org/10.36334/modsim2025.J05.jakeman	https://mssanz.org.au/modsim2025/files/J05.jakeman.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	S
Hughes JD	Uncertainty estimation for river system models	Kim SSH, Hughes JD, Lerat J, Regan‑Beasley D	https://doi.org/10.36334/modsim2025.J02.kim	https://mssanz.org.au/modsim2025/files/J02.kim.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	H
Kim SSH	Uncertainty estimation for river system models	Kim SSH, Hughes JD, Lerat J, Regan‑Beasley D	https://doi.org/10.36334/modsim2025.J02.kim	https://mssanz.org.au/modsim2025/files/J02.kim.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	K
Lerat J	Uncertainty estimation for river system models	Kim SSH, Hughes JD, Lerat J, Regan‑Beasley D	https://doi.org/10.36334/modsim2025.J02.kim	https://mssanz.org.au/modsim2025/files/J02.kim.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	L
Regan‑Beasley D	Uncertainty estimation for river system models	Kim SSH, Hughes JD, Lerat J, Regan‑Beasley D	https://doi.org/10.36334/modsim2025.J02.kim	https://mssanz.org.au/modsim2025/files/J02.kim.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	R
Dzator J	Uncovering major barriers for not seeking mathematics assistance in the education industry: DEMATEL modelling approach	Dzator M,Simelane P, Dzator J	https://doi.org/10.36334/modsim2025.A06.dzator	https://mssanz.org.au/modsim2025/files/A06.dzator.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	D
Simelane P	Uncovering major barriers for not seeking mathematics assistance in the education industry: DEMATEL modelling approach	Dzator M,Simelane P, Dzator J	https://doi.org/10.36334/modsim2025.A06.dzator	https://mssanz.org.au/modsim2025/files/A06.dzator.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	S
Dzator M	Uncovering major barriers for not seeking mathematics assistance in the education industry: DEMATEL modelling approach	Dzator M,Simelane P, Dzator J	https://doi.org/10.36334/modsim2025.A06.dzator	https://mssanz.org.au/modsim2025/files/A06.dzator.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	D
Attarzadeh S	Understanding catchment hydrology approaches for flow estimation	Singh J, Attarzadeh S	https://doi.org/10.36334/modsim2025.G06.singh	https://mssanz.org.au/modsim2025/files/G06.singh.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	A
Singh J	Understanding catchment hydrology approaches for flow estimation	Singh J, Attarzadeh S	https://doi.org/10.36334/modsim2025.G06.singh	https://mssanz.org.au/modsim2025/files/G06.singh.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	S
Lu Y	Understanding structural change from transitioning to a low-carbon economy: an integrated multi-model approach for Australia	Mariano MJM, Verikios G, Lu Y	https://doi.org/10.36334/modsim2025.E03.mariano	https://mssanz.org.au/modsim2025/files/E03.mariano.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	L
Mariano MJM	Understanding structural change from transitioning to a low-carbon economy: an integrated multi-model approach for Australia	Mariano MJM, Verikios G, Lu Y	https://doi.org/10.36334/modsim2025.E03.mariano	https://mssanz.org.au/modsim2025/files/E03.mariano.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	M
Verikios G	Understanding structural change from transitioning to a low-carbon economy: an integrated multi-model approach for Australia	Mariano MJM, Verikios G, Lu Y	https://doi.org/10.36334/modsim2025.E03.mariano	https://mssanz.org.au/modsim2025/files/E03.mariano.pdf	E	Energy, integrated infrastructure and urban planning	E3	Economic modelling for a sustainable energy transition	V
Brooks S	Understanding the impacts of climate change through the lens of climate and ecological analogues	Sengupta A, Brooks S, Doble R, Doody T, Gibbs M, Gao S, Pritchard J, Round V, O’Sullivan J	https://doi.org/10.36334/modsim2025.K01.sengupta	https://mssanz.org.au/modsim2025/files/K01.sengupta.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	B
Doble R	Understanding the impacts of climate change through the lens of climate and ecological analogues	Sengupta A, Brooks S, Doble R, Doody T, Gibbs M, Gao S, Pritchard J, Round V, O’Sullivan J	https://doi.org/10.36334/modsim2025.K01.sengupta	https://mssanz.org.au/modsim2025/files/K01.sengupta.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	D
Doody T	Understanding the impacts of climate change through the lens of climate and ecological analogues	Sengupta A, Brooks S, Doble R, Doody T, Gibbs M, Gao S, Pritchard J, Round V, O’Sullivan J	https://doi.org/10.36334/modsim2025.K01.sengupta	https://mssanz.org.au/modsim2025/files/K01.sengupta.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	D
Gao S (Sicong)	Understanding the impacts of climate change through the lens of climate and ecological analogues	Sengupta A, Brooks S, Doble R, Doody T, Gibbs M, Gao S, Pritchard J, Round V, O’Sullivan J	https://doi.org/10.36334/modsim2025.K01.sengupta	https://mssanz.org.au/modsim2025/files/K01.sengupta.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	G
Gibbs MS	Understanding the impacts of climate change through the lens of climate and ecological analogues	Sengupta A, Brooks S, Doble R, Doody T, Gibbs M, Gao S, Pritchard J, Round V, O’Sullivan J	https://doi.org/10.36334/modsim2025.K01.sengupta	https://mssanz.org.au/modsim2025/files/K01.sengupta.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	G
O’Sullivan J	Understanding the impacts of climate change through the lens of climate and ecological analogues	Sengupta A, Brooks S, Doble R, Doody T, Gibbs M, Gao S, Pritchard J, Round V, O’Sullivan J	https://doi.org/10.36334/modsim2025.K01.sengupta	https://mssanz.org.au/modsim2025/files/K01.sengupta.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	O
Pritchard J	Understanding the impacts of climate change through the lens of climate and ecological analogues	Sengupta A, Brooks S, Doble R, Doody T, Gibbs M, Gao S, Pritchard J, Round V, O’Sullivan J	https://doi.org/10.36334/modsim2025.K01.sengupta	https://mssanz.org.au/modsim2025/files/K01.sengupta.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	P
Round V	Understanding the impacts of climate change through the lens of climate and ecological analogues	Sengupta A, Brooks S, Doble R, Doody T, Gibbs M, Gao S, Pritchard J, Round V, O’Sullivan J	https://doi.org/10.36334/modsim2025.K01.sengupta	https://mssanz.org.au/modsim2025/files/K01.sengupta.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	R
Sengupta A	Understanding the impacts of climate change through the lens of climate and ecological analogues	Sengupta A, Brooks S, Doble R, Doody T, Gibbs M, Gao S, Pritchard J, Round V, O’Sullivan J	https://doi.org/10.36334/modsim2025.K01.sengupta	https://mssanz.org.au/modsim2025/files/K01.sengupta.pdf	K	Hydroclimate	K1	Modelling Complexity: New Approaches for Water, Environment and Society in Large Basins	S
Farrell M	Unpacking soil carbon stabilization: From measurement to modelling	Liu J, Farrell M, Weng H, Pasut C	https://doi.org/10.36334/modsim2025.F13.liu	https://mssanz.org.au/modsim2025/files/F13.liu.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	F
Liu J (Jingjing)	Unpacking soil carbon stabilization: From measurement to modelling	Liu J, Farrell M, Weng H, Pasut C	https://doi.org/10.36334/modsim2025.F13.liu	https://mssanz.org.au/modsim2025/files/F13.liu.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	L
Pasut C	Unpacking soil carbon stabilization: From measurement to modelling	Liu J, Farrell M, Weng H, Pasut C	https://doi.org/10.36334/modsim2025.F13.liu	https://mssanz.org.au/modsim2025/files/F13.liu.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	P
Weng H	Unpacking soil carbon stabilization: From measurement to modelling	Liu J, Farrell M, Weng H, Pasut C	https://doi.org/10.36334/modsim2025.F13.liu	https://mssanz.org.au/modsim2025/files/F13.liu.pdf	F	Environment and ecology	F13	Reimagining Croplands: Modeling Carbon Storage and Biodiversity Benefits	W
Bai H (Huiqing)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	B
Chen D (Di)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Chen X (Xianguan)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	C
Han R (Rui)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	H
Li G (Guoqiang)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
Li Y (Yang)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
Liu B (Buchun)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
Liu E (Enke)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	L
Yang F (Fan)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	Y
Yang X (Xiaojuan)	Unraveling varietal resilience: integrating APSIM and RF to assess wheat yield responses to extreme temperatures	Bai H, Chen X, Li Y, Yang F, Yang X, Chen D, Han R, Liu E, Liu B, Li G	https://doi.org/10.36334/modsim2025.B08.bai	https://mssanz.org.au/modsim2025/files/B08.bai.pdf	B	Biological systems	B8	Innovative Modelling Approaches for Agricultural Systems Sustainability under Climate Change	Y
Cheng X (Xiang)	Updated climate inputs for rainfall-runoff modelling in northern Victoria	Cheng X, Szabo K, Finger MJ	https://doi.org/10.36334/modsim2025.J02.cheng	https://mssanz.org.au/modsim2025/files/J02.cheng.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	C
Finger MJ	Updated climate inputs for rainfall-runoff modelling in northern Victoria	Cheng X, Szabo K, Finger MJ	https://doi.org/10.36334/modsim2025.J02.cheng	https://mssanz.org.au/modsim2025/files/J02.cheng.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	F
Szabo K	Updated climate inputs for rainfall-runoff modelling in northern Victoria	Cheng X, Szabo K, Finger MJ	https://doi.org/10.36334/modsim2025.J02.cheng	https://mssanz.org.au/modsim2025/files/J02.cheng.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	S
Dutta D	Urban demand models for the Fish River-Wywandy System	Trim A, Podger GM, Dutta D	https://doi.org/10.36334/modsim2025.J02.trim	https://mssanz.org.au/modsim2025/files/J02.trim.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	D
Podger GM	Urban demand models for the Fish River-Wywandy System	Trim A, Podger GM, Dutta D	https://doi.org/10.36334/modsim2025.J02.trim	https://mssanz.org.au/modsim2025/files/J02.trim.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	P
Trim A	Urban demand models for the Fish River-Wywandy System	Trim A, Podger GM, Dutta D	https://doi.org/10.36334/modsim2025.J02.trim	https://mssanz.org.au/modsim2025/files/J02.trim.pdf	J	Water resources	J2	River System Modelling for Water Resources Management: Advances and Challenges	T
Andualem TG	Urbanisation and climate variability driving hydrological and geomorphological changes: Implications for flood risk (INVITED SPEAKER)	Andualem TG, Hewa GA, Peters S, Myers BR, Boland J	https://doi.org/10.36334/modsim2025.G06.andualem	https://mssanz.org.au/modsim2025/files/G06.andualem.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	A
Boland J	Urbanisation and climate variability driving hydrological and geomorphological changes: Implications for flood risk (INVITED SPEAKER)	Andualem TG, Hewa GA, Peters S, Myers BR, Boland J	https://doi.org/10.36334/modsim2025.G06.andualem	https://mssanz.org.au/modsim2025/files/G06.andualem.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	B
Hewa GA	Urbanisation and climate variability driving hydrological and geomorphological changes: Implications for flood risk (INVITED SPEAKER)	Andualem TG, Hewa GA, Peters S, Myers BR, Boland J	https://doi.org/10.36334/modsim2025.G06.andualem	https://mssanz.org.au/modsim2025/files/G06.andualem.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	H
Myers BR	Urbanisation and climate variability driving hydrological and geomorphological changes: Implications for flood risk (INVITED SPEAKER)	Andualem TG, Hewa GA, Peters S, Myers BR, Boland J	https://doi.org/10.36334/modsim2025.G06.andualem	https://mssanz.org.au/modsim2025/files/G06.andualem.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	M
Peters S	Urbanisation and climate variability driving hydrological and geomorphological changes: Implications for flood risk (INVITED SPEAKER)	Andualem TG, Hewa GA, Peters S, Myers BR, Boland J	https://doi.org/10.36334/modsim2025.G06.andualem	https://mssanz.org.au/modsim2025/files/G06.andualem.pdf	G	Global change and natural hazards	G6	Urbanization, Hydro-Meteorological Hazards, and Risk Mitigation	P
Cichota R	Use of mass balance to test and improve system models: Case study with APSIM	Sharp J, Cichota R	https://doi.org/10.36334/modsim2025.B03.sharp	https://mssanz.org.au/modsim2025/files/B03.sharp.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	C
Sharp J	Use of mass balance to test and improve system models: Case study with APSIM	Sharp J, Cichota R	https://doi.org/10.36334/modsim2025.B03.sharp	https://mssanz.org.au/modsim2025/files/B03.sharp.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	S
Horne A	Using both top-down and bottom-up approaches to assess environmental outcomes under climate change – a case study	Traill L, Lang S, John A, Horne A	https://doi.org/10.36334/modsim2025.J05.traill	https://mssanz.org.au/modsim2025/files/J05.traill.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	H
John A	Using both top-down and bottom-up approaches to assess environmental outcomes under climate change – a case study	Traill L, Lang S, John A, Horne A	https://doi.org/10.36334/modsim2025.J05.traill	https://mssanz.org.au/modsim2025/files/J05.traill.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	J
Lang S	Using both top-down and bottom-up approaches to assess environmental outcomes under climate change – a case study	Traill L, Lang S, John A, Horne A	https://doi.org/10.36334/modsim2025.J05.traill	https://mssanz.org.au/modsim2025/files/J05.traill.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	L
Traill L	Using both top-down and bottom-up approaches to assess environmental outcomes under climate change – a case study	Traill L, Lang S, John A, Horne A	https://doi.org/10.36334/modsim2025.J05.traill	https://mssanz.org.au/modsim2025/files/J05.traill.pdf	J	Water resources	J5	Fit for purpose modelling for water resources management	T
Hughes JD	Using energy balance functions to constrain future climate runoff trajectories	Hughes JD, Petheram C, Yang A	https://doi.org/10.36334/modsim2025.A02.hughes	https://mssanz.org.au/modsim2025/files/A02.hughes.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	H
Petheram C	Using energy balance functions to constrain future climate runoff trajectories	Hughes JD, Petheram C, Yang A	https://doi.org/10.36334/modsim2025.A02.hughes	https://mssanz.org.au/modsim2025/files/A02.hughes.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	P
Yang A	Using energy balance functions to constrain future climate runoff trajectories	Hughes JD, Petheram C, Yang A	https://doi.org/10.36334/modsim2025.A02.hughes	https://mssanz.org.au/modsim2025/files/A02.hughes.pdf	A	Applied and computational mathematics	A2	Advances in Numerical Solutions for Water Resources Models	Y
Jeanneau A	Using foresight and modelling to stress-test risk reduction options under various climate and socio-economic scenarios	van Delden F, Smith P, Jones R	https://doi.org/10.36334/modsim2025.F05.vandelden	https://mssanz.org.au/modsim2025/files/F05.vandelden.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	J
Maier HR	Using foresight and modelling to stress-test risk reduction options under various climate and socio-economic scenarios	van Delden H, Vanhout R, Jeanneau A, Maier HR, Zecchin AC, McNaught T	https://doi.org/10.36334/modsim2025.F05.vandelden	https://mssanz.org.au/modsim2025/files/F05.vandelden.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	M
McNaught T	Using foresight and modelling to stress-test risk reduction options under various climate and socio-economic scenarios	van Delden H, Vanhout R, Jeanneau A, Maier HR, Zecchin AC, McNaught T	https://doi.org/10.36334/modsim2025.F05.vandelden	https://mssanz.org.au/modsim2025/files/F05.vandelden.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	M
van Delden H	Using foresight and modelling to stress-test risk reduction options under various climate and socio-economic scenarios	van Delden F, Smith P, Jones R	https://doi.org/10.36334/modsim2025.F05.vandelden	https://mssanz.org.au/modsim2025/files/F05.vandelden.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	v
Vanhout R	Using foresight and modelling to stress-test risk reduction options under various climate and socio-economic scenarios	van Delden F, Smith P, Jones R	https://doi.org/10.36334/modsim2025.F05.vandelden	https://mssanz.org.au/modsim2025/files/F05.vandelden.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	V
Zecchin AC	Using foresight and modelling to stress-test risk reduction options under various climate and socio-economic scenarios	van Delden H, Vanhout R, Jeanneau A, Maier HR, Zecchin AC, McNaught T	https://doi.org/10.36334/modsim2025.F05.vandelden	https://mssanz.org.au/modsim2025/files/F05.vandelden.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	Z
Lemiale V	Using machine learning to emulate physics-inspired agent-based models: a case study in emergency evacuations	Smith D, Lemiale V, Singh D, Rahman A, Pagendam D	https://doi.org/10.36334/modsim2025.C06.smith	https://mssanz.org.au/modsim2025/files/C06.smith.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	L
Pagendam D	Using machine learning to emulate physics-inspired agent-based models: a case study in emergency evacuations	Smith D, Lemiale V, Singh D, Rahman A, Pagendam D	https://doi.org/10.36334/modsim2025.C06.smith	https://mssanz.org.au/modsim2025/files/C06.smith.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	P
Rahman A	Using machine learning to emulate physics-inspired agent-based models: a case study in emergency evacuations	Smith D, Lemiale V, Singh D, Rahman A, Pagendam D	https://doi.org/10.36334/modsim2025.C06.smith	https://mssanz.org.au/modsim2025/files/C06.smith.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	R
Singh D	Using machine learning to emulate physics-inspired agent-based models: a case study in emergency evacuations	Smith D, Lemiale V, Singh D, Rahman A, Pagendam D	https://doi.org/10.36334/modsim2025.C06.smith	https://mssanz.org.au/modsim2025/files/C06.smith.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	S
Smith D	Using machine learning to emulate physics-inspired agent-based models: a case study in emergency evacuations	Smith D, Lemiale V, Singh D, Rahman A, Pagendam D	https://doi.org/10.36334/modsim2025.C06.smith	https://mssanz.org.au/modsim2025/files/C06.smith.pdf	C	Computer science and engineering	C6	Emulation of Dynamic Models	S
Dennis G	Using markerless motion capture to identify unsuitable static body postures during work activities	Harrison SM, Dennis G, Garcia-Flores R, Smith D, Kandanaarachchi S, Rahman A	https://doi.org/10.36334/modsim2025.M03.harrison	https://mssanz.org.au/modsim2025/files/M03.harrison.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	D
Garcia-Flores R	Using markerless motion capture to identify unsuitable static body postures during work activities	Harrison SM, Dennis G, Garcia-Flores R, Smith D, Kandanaarachchi S, Rahman A	https://doi.org/10.36334/modsim2025.M03.harrison	https://mssanz.org.au/modsim2025/files/M03.harrison.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	G
Harrison SM	Using markerless motion capture to identify unsuitable static body postures during work activities	Harrison SM, Dennis G, Garcia-Flores R, Smith D, Kandanaarachchi S, Rahman A	https://doi.org/10.36334/modsim2025.M03.harrison	https://mssanz.org.au/modsim2025/files/M03.harrison.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	H
Kandanaarachchi S	Using markerless motion capture to identify unsuitable static body postures during work activities	Harrison SM, Dennis G, Garcia-Flores R, Smith D, Kandanaarachchi S, Rahman A	https://doi.org/10.36334/modsim2025.M03.harrison	https://mssanz.org.au/modsim2025/files/M03.harrison.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	K
Rahman A	Using markerless motion capture to identify unsuitable static body postures during work activities	Harrison SM, Dennis G, Garcia-Flores R, Smith D, Kandanaarachchi S, Rahman A	https://doi.org/10.36334/modsim2025.M03.harrison	https://mssanz.org.au/modsim2025/files/M03.harrison.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	R
Smith D	Using markerless motion capture to identify unsuitable static body postures during work activities	Harrison SM, Dennis G, Garcia-Flores R, Smith D, Kandanaarachchi S, Rahman A	https://doi.org/10.36334/modsim2025.M03.harrison	https://mssanz.org.au/modsim2025/files/M03.harrison.pdf	M	Operations Research	M3	AI for Optimization: Methodologies and Applications	S
Gelsinari S	Using models and high resolution monitoring to estimate recharge in south-west Western Australia	Hall J, Silberstein R, Gelsinari S	https://doi.org/10.36334/modsim2025.J01.hall	https://mssanz.org.au/modsim2025/files/J01.hall.pdf	J	Water resources	J1	Long term shifts in hydrological systems	G
Hall J (Joel)	Using models and high resolution monitoring to estimate recharge in south-west Western Australia	Hall J, Silberstein R, Gelsinari S	https://doi.org/10.36334/modsim2025.J01.hall	https://mssanz.org.au/modsim2025/files/J01.hall.pdf	J	Water resources	J1	Long term shifts in hydrological systems	H
Silberstein R	Using models and high resolution monitoring to estimate recharge in south-west Western Australia	Hall J, Silberstein R, Gelsinari S	https://doi.org/10.36334/modsim2025.J01.hall	https://mssanz.org.au/modsim2025/files/J01.hall.pdf	J	Water resources	J1	Long term shifts in hydrological systems	S
Lintern A	Using rainfall characteristics to evaluate sizing of rainwater tanks under climate change	Manuelpillai DP, Winfrey B, Lintern A	https://doi.org/10.36334/modsim2025.L03.manuelpillai	https://mssanz.org.au/modsim2025/files/L03.manuelpillai.pdf	L	Water quality	L3	Modelling water quality treatment and management	L
Manuelpillai DP	Using rainfall characteristics to evaluate sizing of rainwater tanks under climate change	Manuelpillai DP, Winfrey B, Lintern A	https://doi.org/10.36334/modsim2025.L03.manuelpillai	https://mssanz.org.au/modsim2025/files/L03.manuelpillai.pdf	L	Water quality	L3	Modelling water quality treatment and management	M
Winfrey B	Using rainfall characteristics to evaluate sizing of rainwater tanks under climate change	Manuelpillai DP, Winfrey B, Lintern A	https://doi.org/10.36334/modsim2025.L03.manuelpillai	https://mssanz.org.au/modsim2025/files/L03.manuelpillai.pdf	L	Water quality	L3	Modelling water quality treatment and management	W
Bolger M	Using Workspace to Explore Large Point Cloud Datasets	Hetherton L, Bolger M, Cleary PW, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.hetherton	https://mssanz.org.au/modsim2025/files/C01.hetherton.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	B
Cleary PW	Using Workspace to Explore Large Point Cloud Datasets	Hetherton L, Bolger M, Cleary PW, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.hetherton	https://mssanz.org.au/modsim2025/files/C01.hetherton.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	C
Hetherton L	Using Workspace to Explore Large Point Cloud Datasets	Hetherton L, Bolger M, Cleary PW, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.hetherton	https://mssanz.org.au/modsim2025/files/C01.hetherton.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	H
Thomas D (David)	Using Workspace to Explore Large Point Cloud Datasets	Hetherton L, Bolger M, Cleary PW, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.hetherton	https://mssanz.org.au/modsim2025/files/C01.hetherton.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	T
Watkins D	Using Workspace to Explore Large Point Cloud Datasets	Hetherton L, Bolger M, Cleary PW, Thomas D, Watkins D	https://doi.org/10.36334/modsim2025.C01.hetherton	https://mssanz.org.au/modsim2025/files/C01.hetherton.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	W
Abram N	Utilising CORDEX projections to assess runoff changes in Victoria	Devanand G, Abram N, Pitman A	https://doi.org/10.36334/modsim2025.G04.devanand	https://mssanz.org.au/modsim2025/files/G04.devanand.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	A
Devanand G	Utilising CORDEX projections to assess runoff changes in Victoria	Devanand G, Abram N, Pitman A	https://doi.org/10.36334/modsim2025.G04.devanand	https://mssanz.org.au/modsim2025/files/G04.devanand.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	D
Pitman A	Utilising CORDEX projections to assess runoff changes in Victoria	Devanand G, Abram N, Pitman A	https://doi.org/10.36334/modsim2025.G04.devanand	https://mssanz.org.au/modsim2025/files/G04.devanand.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	P
Cresswell A	Utilising satellite-derived maps to partition coral reefs into meaningful spatial units	Haller-Bull V, Cresswell A, Gonzalez-Rivero M, Ortiz J	https://doi.org/10.36334/modsim2025.F05.hallerbull	https://mssanz.org.au/modsim2025/files/F05.hallerbull.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	C
Gonzalez-Rivero M	Utilising satellite-derived maps to partition coral reefs into meaningful spatial units	Haller-Bull V, Cresswell A, Gonzalez-Rivero M, Ortiz J	https://doi.org/10.36334/modsim2025.F05.hallerbull	https://mssanz.org.au/modsim2025/files/F05.hallerbull.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	G
Haller-Bull V	Utilising satellite-derived maps to partition coral reefs into meaningful spatial units	Haller-Bull V, Cresswell A, Gonzalez-Rivero M, Ortiz J	https://doi.org/10.36334/modsim2025.F05.hallerbull	https://mssanz.org.au/modsim2025/files/F05.hallerbull.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	H
Ortiz J	Utilising satellite-derived maps to partition coral reefs into meaningful spatial units	Haller-Bull V, Cresswell A, Gonzalez-Rivero M, Ortiz J	https://doi.org/10.36334/modsim2025.F05.hallerbull	https://mssanz.org.au/modsim2025/files/F05.hallerbull.pdf	F	Environment and ecology	F5	Advancing decision support: Decision tools for building robust and resilient futures	O
Kirkegaard JA	Validated simulation of a long-term experiment reveals a pathway for improved crop productivity	Lilley JM, Kirkegaard JA	https://doi.org/10.36334/modsim2025.B03.lilley	https://mssanz.org.au/modsim2025/files/B03.lilley.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	K
Lilley JM	Validated simulation of a long-term experiment reveals a pathway for improved crop productivity	Lilley JM, Kirkegaard JA	https://doi.org/10.36334/modsim2025.B03.lilley	https://mssanz.org.au/modsim2025/files/B03.lilley.pdf	B	Biological systems	B3	APSIM Developments: Soil modelling capability (water, nutrients, temperature, climatic inputs)	L
Leahy MJ	Validation of a numerical model of a ship and cable-towed body system	Leahy MJ, Logan S, Sgarioto D	https://doi.org/10.36334/modsim2025.A06.leahy	https://mssanz.org.au/modsim2025/files/A06.leahy.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	L
Logan S	Validation of a numerical model of a ship and cable-towed body system	Leahy MJ, Logan S, Sgarioto D	https://doi.org/10.36334/modsim2025.A06.leahy	https://mssanz.org.au/modsim2025/files/A06.leahy.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	L
Sgarioto D	Validation of a numerical model of a ship and cable-towed body system	Leahy MJ, Logan S, Sgarioto D	https://doi.org/10.36334/modsim2025.A06.leahy	https://mssanz.org.au/modsim2025/files/A06.leahy.pdf	A	Applied and computational mathematics	A6	Industrial mathematical modelling and simulation	S
Schaerf TM	Vision and body as drivers of topological interactions in collective motion	Zvezdin AV, Welch MC, Schaerf TM	https://doi.org/10.36334/modsim2025.B09.zvezdin	https://mssanz.org.au/modsim2025/files/B09.zvezdin.pdf	B	Biological systems	B9	Advances in agent-based modelling and their statistical challenges in biological, ecological and agricultural systems	S
Welch MC	Vision and body as drivers of topological interactions in collective motion	Zvezdin AV, Welch MC, Schaerf TM	https://doi.org/10.36334/modsim2025.B09.zvezdin	https://mssanz.org.au/modsim2025/files/B09.zvezdin.pdf	B	Biological systems	B9	Advances in agent-based modelling and their statistical challenges in biological, ecological and agricultural systems	W
Zvezdin AV	Vision and body as drivers of topological interactions in collective motion	Zvezdin AV, Welch MC, Schaerf TM	https://doi.org/10.36334/modsim2025.B09.zvezdin	https://mssanz.org.au/modsim2025/files/B09.zvezdin.pdf	B	Biological systems	B9	Advances in agent-based modelling and their statistical challenges in biological, ecological and agricultural systems	Z
Taggart RJ	Warnings based on risk matrices: a coherent framework with consistent evaluation	Taggart RJ, Wilke D	https://doi.org/10.36334/modsim2025.K02.taggart	https://mssanz.org.au/modsim2025/files/K02.taggart.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	T
Wilke D	Warnings based on risk matrices: a coherent framework with consistent evaluation	Taggart RJ, Wilke D	https://doi.org/10.36334/modsim2025.K02.taggart	https://mssanz.org.au/modsim2025/files/K02.taggart.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	W
Ashbolt SC	Water resources modelling in Melbourne: Current status and future directions	Kularathna MDU, Ashbolt SC, Vu K	https://doi.org/10.36334/modsim2025.J04.kularathnam	https://mssanz.org.au/modsim2025/files/J04.kularathnam.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	A
Kularathna MDU	Water resources modelling in Melbourne: Current status and future directions	Kularathna MDU, Ashbolt SC, Vu K	https://doi.org/10.36334/modsim2025.J04.kularathnam	https://mssanz.org.au/modsim2025/files/J04.kularathnam.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	K
Vu K	Water resources modelling in Melbourne: Current status and future directions	Kularathna MDU, Ashbolt SC, Vu K	https://doi.org/10.36334/modsim2025.J04.kularathnam	https://mssanz.org.au/modsim2025/files/J04.kularathnam.pdf	J	Water resources	J4	Responding to the Challenges and Complexities of Urban Water Management	V
Egger F	What’s the point of education in the age of AI?	O’Brien KR, Egger F, Maier HR, Elsawah S, Gibbes B	https://doi.org/10.36334/modsim2025.I04.obrien	https://mssanz.org.au/modsim2025/files/I04.obrien.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	E
Elsawah S	What’s the point of education in the age of AI?	O’Brien KR, Egger F, Maier HR, Elsawah S, Gibbes B	https://doi.org/10.36334/modsim2025.I04.obrien	https://mssanz.org.au/modsim2025/files/I04.obrien.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	E
Gibbes B	What’s the point of education in the age of AI?	O’Brien KR, Egger F, Maier HR, Elsawah S, Gibbes B	https://doi.org/10.36334/modsim2025.I04.obrien	https://mssanz.org.au/modsim2025/files/I04.obrien.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	G
Maier HR	What’s the point of education in the age of AI?	O’Brien KR, Egger F, Maier HR, Elsawah S, Gibbes B	https://doi.org/10.36334/modsim2025.I04.obrien	https://mssanz.org.au/modsim2025/files/I04.obrien.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	M
O’Brien KR	What’s the point of education in the age of AI?	O’Brien KR, Egger F, Maier HR, Elsawah S, Gibbes B	https://doi.org/10.36334/modsim2025.I04.obrien	https://mssanz.org.au/modsim2025/files/I04.obrien.pdf	I	Participatory decision-making, socioecological systems, and education	I4	Training future modellers for an uncertain future	O
Sowden M	When dust meets cloud: overcoming satellite monitoring gaps in high-impact pollution events across Australasia	Sowden M	https://doi.org/10.36334/modsim2025.G07.sowdenm	https://mssanz.org.au/modsim2025/files/G07.sowdenm.pdf	G	Global change and natural hazards	G7	Tackling the challenges associated with modelling increases in landscape fire effects on human and ecosystem health under climate change conditions	S
Higgins A	When floods disrupt freight: modelling supply chain risks under future climates	Tong M, Higgins A, McFallan S, Islam Z, Marquez L	https://doi.org/10.36334/modsim2025.G04.tong	https://mssanz.org.au/modsim2025/files/G04.tong.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	H
Islam Z	When floods disrupt freight: modelling supply chain risks under future climates	Tong M, Higgins A, McFallan S, Islam Z, Marquez L	https://doi.org/10.36334/modsim2025.G04.tong	https://mssanz.org.au/modsim2025/files/G04.tong.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	I
Marquez L	When floods disrupt freight: modelling supply chain risks under future climates	Tong M, Higgins A, McFallan S, Islam Z, Marquez L	https://doi.org/10.36334/modsim2025.G04.tong	https://mssanz.org.au/modsim2025/files/G04.tong.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	M
McFallan S	When floods disrupt freight: modelling supply chain risks under future climates	Tong M, Higgins A, McFallan S, Islam Z, Marquez L	https://doi.org/10.36334/modsim2025.G04.tong	https://mssanz.org.au/modsim2025/files/G04.tong.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	M
Tong M	When floods disrupt freight: modelling supply chain risks under future climates	Tong M, Higgins A, McFallan S, Islam Z, Marquez L	https://doi.org/10.36334/modsim2025.G04.tong	https://mssanz.org.au/modsim2025/files/G04.tong.pdf	G	Global change and natural hazards	G4	Projections of regional climate change: from modelling to applications	T
Athukorala R	When more isn’t always better: Lessons from multi-variable calibration in hydrology	Athukorala R, Nervi E, Vervoort RW	https://doi.org/10.36334/modsim2025.C07.athukorala	https://mssanz.org.au/modsim2025/files/C07.athukorala.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	A
Nervi E	When more isn’t always better: Lessons from multi-variable calibration in hydrology	Athukorala R, Nervi E, Vervoort RW	https://doi.org/10.36334/modsim2025.C07.athukorala	https://mssanz.org.au/modsim2025/files/C07.athukorala.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	N
Vervoort RW	When more isn’t always better: Lessons from multi-variable calibration in hydrology	Athukorala R, Nervi E, Vervoort RW	https://doi.org/10.36334/modsim2025.C07.athukorala	https://mssanz.org.au/modsim2025/files/C07.athukorala.pdf	C	Computer science and engineering	C7	Advancing Machine Learning in Hydrology: Ensuring Reliable, Reproducible, and Validated Applications	V
Deng J	When rain returns but rivers don't: Non-stationary response of streamflow to rainfall	Deng J, Wang L, Chen Y	https://doi.org/10.36334/modsim2025.J01.deng	https://mssanz.org.au/modsim2025/files/J01.deng.pdf	J	Water resources	J1	Long term shifts in hydrological systems	D
McCullough DP	When rain returns but rivers don't: Non-stationary response of streamflow to rainfall	Deng J, Wang L, Chen Y	https://doi.org/10.36334/modsim2025.J01.deng	https://mssanz.org.au/modsim2025/files/J01.deng.pdf	J	Water resources	J1	Long term shifts in hydrological systems	M
Louis S	When will it happen? On the evaluation of time-to-event, survival time and first-time passage forecasts	Taggart RJ, Loveday N, Louis S	https://doi.org/10.36334/modsim2025.K02.taggartr	https://mssanz.org.au/modsim2025/files/K02.taggartr.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	L
Loveday N	When will it happen? On the evaluation of time-to-event, survival time and first-time passage forecasts	Taggart RJ, Loveday N, Louis S	https://doi.org/10.36334/modsim2025.K02.taggartr	https://mssanz.org.au/modsim2025/files/K02.taggartr.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	L
Taggart RJ	When will it happen? On the evaluation of time-to-event, survival time and first-time passage forecasts	Taggart RJ, Loveday N, Louis S	https://doi.org/10.36334/modsim2025.K02.taggartr	https://mssanz.org.au/modsim2025/files/K02.taggartr.pdf	K	Hydroclimate	K2	Statistical, post-processing and verification methods for environmental prediction	T
Bolger M	Workspace: 20 years retrospective and prospective	Watkins D, Bolger M, Hetherton L, Thomas D, Cleary PW	https://doi.org/10.36334/modsim2025.C01.watkins	https://mssanz.org.au/modsim2025/files/C01.watkins.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	B
Cleary PW	Workspace: 20 years retrospective and prospective	Watkins D, Bolger M, Hetherton L, Thomas D, Cleary PW	https://doi.org/10.36334/modsim2025.C01.watkins	https://mssanz.org.au/modsim2025/files/C01.watkins.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	C
Hetherton L	Workspace: 20 years retrospective and prospective	Watkins D, Bolger M, Hetherton L, Thomas D, Cleary PW	https://doi.org/10.36334/modsim2025.C01.watkins	https://mssanz.org.au/modsim2025/files/C01.watkins.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	H
Thomas D (David)	Workspace: 20 years retrospective and prospective	Watkins D, Bolger M, Hetherton L, Thomas D, Cleary PW	https://doi.org/10.36334/modsim2025.C01.watkins	https://mssanz.org.au/modsim2025/files/C01.watkins.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	T
Watkins D	Workspace: 20 years retrospective and prospective	Watkins D, Bolger M, Hetherton L, Thomas D, Cleary PW	https://doi.org/10.36334/modsim2025.C01.watkins	https://mssanz.org.au/modsim2025/files/C01.watkins.pdf	C	Computer science and engineering	C1	Using workflow platforms in modelling and simulation	W
Lerat J	The tumultuous relationship between flood frequency analysis and streamflow data uncertainty	Lerat J	https://doi.org/10.36334/modsim2025.K05.lerat	https://mssanz.org.au/modsim2025/files/K05.lerat.pdf	K	Hydroclimate	K5	When models fail: lessons learned from false starts, wrong turns and dead ends	L
Wasko C	Learnings from an update to Australia’s flood guidance	Wasko C	https://doi.org/10.36334/modsim2025.K05.wasko	https://mssanz.org.au/modsim2025/files/K05.wasko.pdf	K	Hydroclimate	K5	When models fail: lessons learned from false starts, wrong turns and dead ends	W

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