Primary Convener:
Hernan Moreno, University of Texas at El Paso

Convener:
Chaopeng Shen, Pennsylvania State University Main Campus
Praveen Kumar, University of Illinois at Urbana-Champaign
Laura Alvarez, University of Texas at El Paso

Early Career Convener:
Leila Hernandez Rodriguez, Lawrence Berkeley National Laboratory

Chair:
Hernan Moreno, The University of Texas at El Paso
Chaopeng Shen, Pennsylvania State University Main Campus

The integration of machine learning (ML), physics-informed AI, and generative artificial intelligence (GenAI) is rapidly advancing hydrology and river system modeling. These emerging approaches enhance predictive capabilities, improve model interpretability, and support data-driven scientific discovery. This session invites contributions on: (1) Data-driven and hybrid AI/ML models; (2) Physics-informed and learnable physical models; (3) Generative AI for data enhancement, downscaling, and uncertainty quantification; (4) AI-integrated Earth system modeling; (5) Scalable AI/ML approaches for watershed to global scales; (6) AI-assisted discovery of hydrologic patterns and relationships; and (7) Trustworthy, explainable, and responsible AI. We seek work that pushes the frontiers of AI for hydrologic science and water resource management.

Index Terms
1805 Computational hydrology
1856 River channels
1942 Machine learning
1954 Natural language processing

Suggested Itineraries:
Machine Learning and AI

Neighborhoods:
3. Earth Covering
1. Science Nexus

Co-Organized Sessions:
Informatics