Kuolin Hsu

The rapid development of artificial intelligence (AI) is transforming many scientific disciplines, including hydrology and water resources management. AI-based approaches are increasingly applied to water supply assessment, groundwater simulation, hydropower generation, and flood risk management. Despite their considerable potential, the broader adoption of AI in water management remains limited by challenges such as limited representation of extreme events. This presentation reviews recent developments in AI and machine learning methods for water management. In addition, it covers hydrologic modeling and precipitation monitoring using satellite information. This highlights global precipitation monitoring approaches developed at the Center for Hydrology and Remote Sensing (CHRS), which integrate multispectral satellite imagery and ground observations using machine learning and image processing techniques. A global near–real-time satellite precipitation monitoring system is demonstrated, enabling visualization of precipitation patterns and track extreme storm events as they occur. Case studies of extreme storm events are presented to illustrate the practical applications of these methods. The presentation also introduces CHRS precipitation products and data systems, including the near–real-time and climate data record products, and discuss their use in support hydrologic analysis and natural disaster management.