Primary Convener:
Dipankar Dwivedi, Lawrence Berkeley National Laboratory

Convener:
Soumendra Bhanja, Oak Ridge National Laboratory
Alison Appling, U.S. Geological Survey, Water Mission Area
Hoshin Gupta, Hydrology and Atmospheric Sciences, The University of Arizona
Olufemi Omitaomu, Oak Ridge National Laboratory

Chair:
Dipankar Dwivedi, Lawrence Berkeley National Laboratory
Soumendra Bhanja, Oak Ridge National Laboratory

Machine learning (ML) applications are increasingly used in watershed system science due to their speed, efficiency, adaptability, and flexibility. However, their effectiveness may be limited in data-scarce regions or when they fail to capture complex process interactions in high-dimensional spaces adequately. To address these challenges, integrating physics-based models with ML offers a promising and comprehensive approach. Such hybrid models leverage the strengths of ML in both data-rich and data-poor environments by enabling rapid execution and improving our understanding of complex watershed processes.To advance the discussion on the benefits and challenges of hybrid modeling, we invite submissions related to watershed science, including hydrology, biogeochemistry, river corridors, and hydrologic connectivity. Relevant topics include, but are not limited to: (a) Physics-informed ML modeling, (b) Interpretable and explainable ML applications, (c) Deep learning and generative AI, and (d) Transfer learning.

Index Terms
0483 Riparian systems
1804 Catchment
1839 Hydrologic scaling
1879 Watershed

Co-Sponsored Sessions:
ESA： Ecological Society of America
AMS： American Meteorological Society
EGU： European Geosciences Union

Suggested Itineraries:
Machine Learning and AI

Cross-Listed:
B - Biogeosciences

Neighborhoods:
3. Earth Covering