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Achieving a sustainable future for water resources demands accurate models that address the interdisciplinary nature of water dynamics. The eco-hydrological-socioeconomic (ECHO) framework integrates physics-based hydrological models with data-driven machine learning techniques, leveraging reanalysis and multi-source remote sensing data. This enables dynamic estimation of sector-specific water demand and interaction with hydrological estimates. ECHO's modular structure allows coupling with grid-based models and includes modules for runoff, evapotranspiration (ET), groundwater flow, surface water routing, and water demand estimation. Calibration and validation demonstrate robust performance in simulating rainfall-runoff processes, with strong agreement observed for monthly ET estimates and gravity recovery and climate experiment-follow on (GRACE-FO) data on total water storage changes. The model accurately estimates total water demand across sectors and aligns with recorded water use data. Simulation outputs of water stress closely match findings from the China Water Resources Bulletin, while also showing promise to enhance projections aligned with sustainable development goals (SDGs) for global water management strategies. By providing high-resolution, dynamic assessments, ECHO offers a scalable tool for policymakers to identify water stress hotspots and optimize allocation strategies essential for meeting SDG targets.

ECHO: an integrated model fusing remote sensing and AI for dynamic water resource assessment