AI-Enabled Digital Twin Framework for Real-Time Water Resource Management Using Multi-Source Remote Sensing Data

Traditional water resource management relies on statically configured models and sparse in-situ networks, creating critical gaps in situational awareness that lead to operational failures during floods, droughts, and water quality incidents. This review synthesizes advancements in AI-enabled digital twins—constantly updated, stateful digital representations that synchronize with physical water systems through continuous assimilation of multi-source remote sensing data. Unlike conventional modeling workflows, these closed-loop systems integrate heterogeneous observations (optical, Synthetic Aperture Radar (SAR), thermal, microwave, and altimetry) with in-situ Internet of Things (IoT) measurements to maintain real-time alignment with evolving conditions. We propose a unified reference architecture spanning data ingestion, AI-driven downscaling and retrieval, quality-aware multi-modal fusion, state synchronization via data assimilation, probabilistic forecasting using physics-AI hybrids, and decision support with continuous monitoring. The framework explicitly addresses operational constraints, including latency, missing data, and non-stationarity, while prioritizing uncertainty calibration over point accuracy. Our synthesis evaluates design trade-offs across flood response, reservoir operations, drought monitoring, irrigation management, and water quality applications. We conclude by identifying research priorities: standardized state schemas and uncertainty metrics, interoperable application programming interfaces (APIs), robust domain adaptation, and governance frameworks incorporating human-in-the-loop safeguards. This review provides a roadmap for transforming heterogeneous remote sensing streams into reliable, actionable intelligence for real-time water resource management.