Primary Convener:
Alyssa Dausman, The Water Institute

Convener:
Marta Zaniolo, Stanford University
Julie Shortridge, Virginia Tech
Tiantian Yang, University of Oklahoma Norman Campus
Ali Nazemi, Concordia University
Newsha Ajami, Lawrence Berkeley National Laboratory
Chiyuan Miao, Beijing Normal University
Alvar Escriva-Bou, Public Policy Institute of California
Hannu Marttila, University of Oulu

Early Career Convener:
Chung-Yi Lin, Clemson University

Chair:
Alyssa Dausman, The Water Institute
Marta Zaniolo, Stanford University
Julie Shortridge, Virginia Tech
Ximing Cai, University of Illinois
Andrea Cominola, Politecnico di Milano
Marie Philine Gross, Technical University of Berlin

This session explores the transformative potential of emerging technologies, including digital twins, AI/ML, AI/DL, and real-time data processing, in reshaping water management and governance. The integration of advanced modeling techniques with understanding of human behavior, governance dynamics, and multi-sector resilience is crucial for effective decision-making in complex, coupled human-water systems. We invite interdisciplinary contributions addressing the application of these technologies in water management, including risk analysis, planning, and decision support, as well as the role of data analytics in informing policy frameworks. Topics of interest encompass AI-driven innovations, the water-energy nexus, governance dynamics, and approaches for minimizing inequities in resource allocation such as potential biases when using AI to inform decision-making processes. This session aims to advance strategies for managing water systems in the face of socio-economic, climatic, and urban changes, with a focus on adaptive planning, multi-scale system resilience, and stakeholder engagement which incorporates their values in decision-making processes.

Index Terms
1880 Water management
1884 Water supply
1918 Decision analysis
1942 Machine learning

Cross-Listed:
NH - Natural Hazards
IN - Informatics
SY - Science and Society
A - Atmospheric Sciences
GC - Global Environmental Change

Suggested Itineraries:
Machine Learning and AI
Community and People-Powered Science
Global Impacts‚ Solutions‚ & Policies

Co-Sponsored Sessions:
EGU： European Geosciences Union

Neighborhoods:
3. Earth Covering