Advances in artificial intelligence (AI) are transforming how we observe, model, and forecast the state of land, water, and agricultural systems — from snowpack and streamflow to soil health, crop stress, carbon flux, and ecosystem productivity — with direct implications for food security, water management, and environmental resilience. These advances are extending how far into the future these systems can reliably forecast, making predictions more useful for a broader range of decision-makers. This session brings together different domains to examine how emerging AI methods are pushing the frontiers of forecasting and prediction across the water-agriculture-ecosystem nexus. We invite contributions spanning predictions and forecasts of diverse aspects across different scales (fields-watersheds-geographies). We aim to showcase AI's role in advancing forecasting across domains — through transferable methods, open datasets, and decision support tools — while welcoming submissions that bridge research and practice, address data-scarce regions, and improve uncertainty communication for decision-makers.

• H - Hydrology
• NH - Natural Hazards