HYDROLEARN: A SELF-SUPERVISED SPATIOTEMPORAL LEARNING FRAMEWORK FOR PREDICTING AND MANAGING WATER RESOURCES UNDER CLIMATE VARIABILITY

Water resource management faces unprecedented challenges due to climate variability and increasing demand.Traditional hydrological models often fail to capture complex spatiotemporal dependencies in water systems.We propose HydroLearn, a novel selfsupervised learning framework that integrates multi-scale spatiotemporal attention mechanisms with adaptive climate encoding to predict water resource availability and optimize management strategies.Our methodology introduces three key innovations:(1) Hierarchical Spatiotemporal Graph Neural Networks (HST-GNN) that model watershed connectivity at multiple scales, ( 2) Climate-Aware Contrastive Learning (CACL) for self-supervised representation learning from unlabeled meteorological data, and (3) Dynamic Water Balance Optimization (DWBO) that adapts management policies in real-time.Extensive experiments on five major watersheds demonstrate that HydroLearn achieves 23.7% improvement in prediction accuracy over state-of-the-art methods, with 31.2% better performance during extreme climate events.The framework