Primary Convener:
Xiaofeng Liu, University of Michigan Ann Arbor

Convener:
William Currie, University of Michigan
Tiantian Yang, University of Oklahoma Norman Campus
Yi Hong, Cooperative Institute for Great Lakes Research, University of Michigan

Early Career Convener:
Alyssa Schubert, University of Michigan Ann Arbor

Chair:
Xiaofeng Liu, Georgia Institute of Technology Main Campus, Georgia Water Resources Institute, Georgia Tech
William Currie, University of Michigan
Tiantian Yang, University of Oklahoma Norman Campus
Yi Hong, Cooperative Institute for Great Lakes Research, University of Michigan

Artificial intelligence (AI) is rapidly transforming our ability to understand, predict, and manage water resources. Recent advances in machine learning, deep learning, and hybrid modeling approaches have shown exceptional promise in capturing complex spatiotemporal dynamics. As these tools increasingly transition from research to real-world applications, the water community faces critical questions: What challenges arise when deploying AI tools in operational settings? How can AI contribute to new scientific discoveries? What are the next frontiers? This session invites critical reflections and practical insights into what works, what doesn’t, and why. We welcome contributions from hydrologists, environmental scientists, and AI researchers on topics including: model training and operational deployment, integrating domain knowledge into AI, quantifying model trustworthiness, and AI-driven scientific discovery. We are especially interested in frontiers such as foundation models and multimodal models that integrate sensor, text, and image data for comprehensive prediction and reasoning in water systems.

Index Terms
0470 Nutrients and nutrient cycling
1816 Estimation and forecasting
1847 Modeling
1860 Streamflow

Neighborhoods:
3. Earth Covering