Shifting climate patterns are accelerating hydrological cycles, challenging the planning and management of natural systems (watersheds, streams, lakes) and built water infrastructures (reservoirs, dams, large-scale water transferring systems). This session explores integrating AI/ML with traditional hydrologic models and rule-based reservoir operations to enhance water supply decision-making under climate uncertainty. We invite studies on: (1) AI/DL applications in hydrologic and/or reservoir modeling, including streamflow simulation, flood forecasting, water supply planning, and reservoir and hydropower system decision-making; (2) multi-scale forecasting, i.e., from short-term to subseasonal-to-seasonal (S2S) and climate projections, to support surface water management and planning; and (3) using new global hydrometeorological datasets, climate models, and observation tools to study the variabilities and behaviours of watersheds, streams, rivers, lakes, reservoirs, and associated hydropower assets. We encourage studies showcasing frameworks that navigate trade-offs between immediate demands and future weather and climate uncertainty to ensure water security in both natural and built water systems.

• A - Atmospheric Sciences
• GC - Global Environmental Change
• NH - Natural Hazards