This session explores how geospatial data science, artificial intelligence, and remote sensing are reshaping water resources research and management. Topics include AI-driven estimation of evapotranspiration, soil moisture, and groundwater; modeling floods, droughts, and water quality under climate and land-use change; and integrating physics-informed machine learning, data assimilation, and digital twins for improved prediction and interpretability. Contributions may highlight deep learning applications (e.g., CNNs, RNNs, Transformers), multi-source data fusion from satellites and in situ sensors, and decision-support tools for irrigation, drainage, and watershed planning. Emphasis is placed on reproducible, transparent, and ethical AI workflows. Through case studies spanning agricultural and urban systems, the session aims to bridge cutting-edge GeoAI methods with real-world implementation, supporting informed, adaptive, and equitable water management decisions.

• B - Biogeosciences
• H - Hydrology
• SY - Science and Society