Machine learning (ML) in hydrology has made substantial progress in the past several years, but it is not without its limitations regarding the scope of prediction, interpretability and relatively underexplored theoretical contributions. This session focuses on integrating machine learning approaches with process-based models to address these issues. Topics include, but are not limited to: 1) Approaches integrating machine learning and process-based modeling components; 2) Computational approaches in physics-informed neural networks or learnable models for hydrology or hydrodynamics; 3) Large-scale applications of machine learning and physics-informed ML methods; 4) Examining the role of physics in data-limited or data-rich settings; 5) Deriving scaling relationships or process insights from ML; and 6) Interpretative machine learning.

• IN - Earth and Space Science Informatics