Abstract
Climate change exerts profound impacts on hydrological processes and the spatiotemporal distribution of water resources, and accurately assessing these impacts has become a core issue in global sustainable water resource management and climate adaptation strategies. This study focuses on big data-driven methods for coupling climate models and hydrological models, aiming to enhance the simulation and prediction of water resource evolution under climate change. First, the fundamental principles of typical climate models and hydrological models are systematically reviewed, and the critical role of big data technologies in multi-source data integration, model input optimization, and parameter inversion is analyzed. Second, coupling strategies of climate and hydrological models under one-way driving and two-way feedback frameworks are explored in depth, and their effectiveness in improving simulation accuracy and regional adaptability is evaluated. Through case studies of typical river basins, the coupled models are verified for their effectiveness in characterizing runoff changes, water resource availability, and supply-demand imbalance risks under climate change. Finally, challenges in model uncertainty quantification, data assimilation, and spatiotemporal scale consistency are highlighted, and the future potential of artificial intelligence and deep learning technologies in climate–hydrological coupling simulations is discussed. The findings are expected to provide theoretical and technical support for watershed water resource planning, risk assessment, and climate adaptation policy-making.
