Challenges and opportunities of ML and explainable AI in large-sample hydrology

Machine learning (ML) is a powerful tool for hydrological modelling, prediction, dataset creation and the generation of insights into hydrological processes. As such, ML has become integral to the field of large-sample hydrology, where hundreds to thousands of river catchments are included within a single ML model to capture diverse hydrological behaviours and improve model generalizability. This manuscript outlines recent advances in ML for large-sample hydrology. We review new tools in explainable AI (XAI) and interpretability approaches, as well as challenges in these areas. Key research avenues for large-sample hydrology include addressing variability in interpretations resulting from different ML models and XAI techniques, enhancing hydrological predictions in data-sparse and human-impacted regions, reducing the ‘cascade of uncertainty’ inherent in hydrological modelling, developing improved methods for multivariate prediction and identifying causal relationships. This article is part of the discussion meeting issue ‘Hydrology in the 21st century: challenges in science, to policy and practice’.