Abstract
Against the backdrop of intensified global climate change and increasing uncertainty in water resource systems, artificial intelligence (AI) and big data technologies are profoundly reshaping research paradigms in hydrology and geography. This paper systematically reviews key advances in the application of AI and big data to water resource modeling, simulation, and management, with a focus on four frontier areas: spatiotemporal dynamics modeling and forecasting of water resources, identification and early warning of extreme hydrological events, assessment of system vulnerability and adaptability, and intelligent optimization of hydrological-geographical models. The study reveals that AI demonstrates significant advantages in handling nonlinear, multi-source, and heterogeneous data. In contrast, big data technologies enhance the extraction and integration of long-term time series and spatial heterogeneity features. The synergistic development of these technologies is driving a paradigm shift in water resource research from traditional experience-driven approaches to intelligent computation-driven frameworks, thereby enhancing the understanding and management of complex hydrological systems. Looking ahead, building intelligent model systems that integrate physical mechanisms with data-driven approaches is a critical pathway to achieving sustainable water resource utilization and strengthening the explanatory power and decision-support capacity of geographical science.
