Improving the science and practice of hydrological modelling and forecasting

Many hydrological modelling groups face similar challenges, with untapped opportunities to share code and concepts across different model development groups. An active community of practice is emerging, where the focus is not so much on developing a community hydrological model, and more on advancing the science and practice of community hydrological modelling. This presentation will summarize our recent efforts to develop open-source models, methods, and datasets to enable process-based hydrological prediction across large geographical domains. The focus will be on recent work to (1) develop multi-source probabilistic hydrometeorological forcing datasets on continental and global domains; (2) advance a flexible approach to represent a myriad of physical processes in a unified modelling framework; (3) improve the numerical robustness and efficiency of large-domain terrestrial system model simulations; (4) develop extensible and reproducible modeling workflows; and (5) develop a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting. The presentation will highlight major scientific challenges, future research needs, and some key opportunities for community collaboration.