Due to increased data availability and algorithm development, there has been an explosion of research that use Data Science approaches in Hydrology, ranging from direct Machine Learning approaches to post processing of hydrological model residuals. However, as the recent paper by Razavi et al. (2022) highlights, we are still some distance from real digital twins that fully integrate and co-create the physical modelling with data science. While this new area of research is full of promise in terms of better model predictions, quantified uncertainties and increased forecasting abilities, there are also many challenges in applying current Data Science techniques to wicked hydrological problems. Nevertheless, there is need to build the number of examples and exchange ideas on challenges and opportunities. This session seeks contributions with examples in water resources that integrates Data Science with hydrological modelling.
Key topics: Data science, Hydrological modelling