Abstract
The spatiotemporal dynamics of water resources are increasingly governed by the coupled effects of climate change and human activities, creating substantial challenges for accurate monitoring, prediction, and equitable management. This paper discusses four major research frontiers in big data-driven water resource science: multi-scale predictive modeling using deep learning and multi-source data fusion, multimodal intelligent sensing for resilient urban water systems, data-driven assessment of water allocation equity through geospatial analytics, and long-term monitoring and causal attribution of water storage dynamics using remote sensing and causal inference. By integrating advances in artificial intelligence, geospatial technologies, and data analytics, the study proposes a unified research framework linking intelligent sensing, predictive simulation, system assessment, and adaptive governance across multiple spatial and temporal scales. This synthesis highlights the transition from data-rich observation to mechanism-informed, equity-oriented water resource management, providing an integrated theoretical framework to advance big data-driven research on the spatiotemporal dynamics of water resources.
