Primary Convener:
Ziheng Sun, George Mason University Fairfax

Convener:
Nicoleta Cristea, University of Washington
Annie Burgess, Earth Science Information Partners
Justin Pflug, University of Maryland College Park

Chair:
Ziheng Sun, George Mason University
Nicoleta Cristea, University of Washington
Annie Burgess, Earth Science Information Partners
Justin Pflug, University of Maryland College Park

As climate extremes intensify, the need for accurate, actionable hydrological forecasts has never been more urgent. This session invites contributions that focus on real-world applications of AI and machine learning to hydrology forecasting challenges—including snow melting, streamflow, flood risk, drought prediction, and water management. We seek practical solutions that go beyond theory: models deployed in the field, workflows integrated into operations, and systems that are robust to data gaps, uncertainty, and changing climate conditions. We welcome submissions including but not limited to: End-to-end forecasting systems using AI/MLHybrid physics-ML models in hydrology; Forecast post-processing and uncertainty quantification; Scalable workflows for regional and global hydrologic forecasting; Tools and platforms for decision-making support; Open-source contributions and reproducible methods; Case studies and partnerships with water managers or emergency services. This session aims to foster collaboration between researchers, practitioners, and public agencies working to turn AI advances into reliable, equitable, and operational hydrological tools.

Index Terms
0736 Snow
1863 Snow and ice
1872 Time series analysis
1895 Instruments and techniques: monitoring

Cross-Listed:
NH - Natural Hazards
IN - Informatics
C - Cryosphere
GC - Global Environmental Change

Co-Organized Sessions:
Cryosphere
Informatics

Neighborhoods:
3. Earth Covering
1. Science Nexus