城市内涝风险动态评估与实时预警方法探讨（Discussion on dynamic assessment and real-time early warning methods for urban inland flooding risk）

With global warming and accelerated urbanization, extreme rainfall events are becoming more frequent, and urban flooding has become a problem affecting urban safety and hindering normal operations. Most past flood risk assessments were based on static scenarios and historical data, which could not match the dynamic changes and uncertainties of rainfall processes, resulting in insufficient timeliness and limited accuracy in early warning. To address these shortcomings, this paper studies a dynamic assessment and real-time early warning method for urban flood risk that combines real-time monitoring data and dynamic simulation technology. A coupled hydrological and hydrodynamic model is constructed, integrating high-resolution terrain, pipe network data, and real-time rainfall radar information to dynamically simulate the scope, depth, and evolution process of floodwater accumulation. At the same time, key node water level and flow data collected by the Internet of Things (IoT) sensor network are integrated to perform online calibration and data assimilation for the model, thereby improving simulation accuracy and timeliness.