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Author Index

* Please note this includes full papers only

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

H

  • Hagley, S.
  • Hepworth, G.
  • Hickie, I.B.
    • Prototyping, developing, and iterating a gamified survey to evaluate participatory systems modelling for youth mental health: Quality assurance pilot
      Lee, G.Y., Hickie, I.B., Occhipinti, J., Song, Y.J.C., Huntley, S., Skinner, A., Lawson, K., Hockey, S.J. and Freebairn, L.
      https://doi.org/10.36334/modsim.2021.H2.lee
  • Hockey, S.J.
    • Prototyping, developing, and iterating a gamified survey to evaluate participatory systems modelling for youth mental health: Quality assurance pilot
      Lee, G.Y., Hickie, I.B., Occhipinti, J., Song, Y.J.C., Huntley, S., Skinner, A., Lawson, K., Hockey, S.J. and Freebairn, L.
      https://doi.org/10.36334/modsim.2021.H2.lee
  • Hoek, P.J.
  • Hudson, I.L.
    • Model-based machine learning to explore the nexus between COVID-19 and environmental factors in the United States
      Munir, T., Hudson, I.L., Cheema, S.A., Muhammad, R., Shafqat, M. and Kifayat, T.
      https://doi.org/10.36334/modsim.2021.H1.munir
    • Use of auxiliary information in estimation of the finite population mean: An exponential type of estimator
      Ullah, K., Hudson, I.L., Cheema, S.A., Khan, A., Rahman, A.R. and Hussian, Z.
      https://doi.org/10.36334/modsim.2021.A1.ullah
  • Huntley, S.
    • Prototyping, developing, and iterating a gamified survey to evaluate participatory systems modelling for youth mental health: Quality assurance pilot
      Lee, G.Y., Hickie, I.B., Occhipinti, J., Song, Y.J.C., Huntley, S., Skinner, A., Lawson, K., Hockey, S.J. and Freebairn, L.
      https://doi.org/10.36334/modsim.2021.H2.lee
  • Hussian, Z.
    • Use of auxiliary information in estimation of the finite population mean: An exponential type of estimator
      Ullah, K., Hudson, I.L., Cheema, S.A., Khan, A., Rahman, A.R. and Hussian, Z.
      https://doi.org/10.36334/modsim.2021.A1.ullah

I

  • Irvine, M.
  • Islam, K.M.N.

J

  • Jakeman, A.J.
    • Modelling hydrological change due to wildfires
      Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H.R., Simmons, C.T., Croke, B.F.W., Jakeman, A.J., Gupta, H.V. and Kavetski, D.
      https://doi.org/10.36334/modsim.2021.J8.partington
    • Uncertainty, sensitivity and scenario analysis: how do they fit together?
      Maier, H.R., Guillaume, J.H.A., McPhail, C., Westra, S., Kwakkel, J.H., Razavi, S., van Delden, H., Thyer, M.A., Culley, S.A. and Jakeman, A.J.
      https://doi.org/10.36334/modsim.2021.J5.maier
  • Jeffrey, S.J.
  • Jordan, P.
    • Mapping additional streamflow decline due to shifts in catchment response during the Millennium Drought
      Saft, M., Peel, M., Jordan, P., Shepherd, D., Steendam, G., Lett, R. and Peterson, T.
      https://doi.org/10.36334/modsim.2021.K11.saft

K

  • Karunaratne, S.B.
    • A nationally scalable approach to simulating soil organic carbon in agricultural landscapes
      Coelli, K.M., Karunaratne, S.B., Baldock, J.A., Ugbaje, S., Buzacott, A.J.V., Filippi, P., Cattle, S. and Bishop, T.F.A.
      https://doi.org/10.36334/modsim.2021.B6.coelli
  • Khan, A.
    • Use of auxiliary information in estimation of the finite population mean: An exponential type of estimator
      Ullah, K., Hudson, I.L., Cheema, S.A., Khan, A., Rahman, A.R. and Hussian, Z.
      https://doi.org/10.36334/modsim.2021.A1.ullah
  • Khan, Z.
    • Estimation of road closure risks along the Bruce highway using the AWRA-L water balance model
      Vogel, E., Lerat, J., Gericke, L.A., Russell, C.A., Preece, A., Stuart, G., Pipunic, R., Khan, Z. and Donnelly, C.
      https://doi.org/10.36334/modsim.2021.K5.vogel
  • Kiem, A.S.
    • Robust detection of statistically significant correlations in geophysical timeseries: A Monte Carlo method accounting for serial dependence and sampling uncertainty
      Armstrong, M.S., Kiem, A.S. and Kuczera, G.
      https://doi.org/10.36334/modsim.2021.A1.armstrong
  • Kifayat, T.
    • Model-based machine learning to explore the nexus between COVID-19 and environmental factors in the United States
      Munir, T., Hudson, I.L., Cheema, S.A., Muhammad, R., Shafqat, M. and Kifayat, T.
      https://doi.org/10.36334/modsim.2021.H1.munir
  • Kwakkel, J.H.
    • Uncertainty, sensitivity and scenario analysis: how do they fit together?
      Maier, H.R., Guillaume, J.H.A., McPhail, C., Westra, S., Kwakkel, J.H., Razavi, S., van Delden, H., Thyer, M.A., Culley, S.A. and Jakeman, A.J.
      https://doi.org/10.36334/modsim.2021.J5.maier