Early Warning System of Urban Waterlogging under the Threat of Extreme Rainfall Events

Traditional urban flood forecasting mostly adopts the urban hydrodynamic model with a single rainfall product, making it difficult to solve the uncertainty due to rainfall measurements or numerical modeling. Comprehensive utilization of multisource precipitation (radar, rain gage, and distrometer) and surface ponding observation will help improve the forecasting accuracy of waterlogging and the spatial description of risk. Therefore, to cope with the threat of extreme storms in a better manner, this study proposes a warning system of urban waterlogging based on comprehensive observations to further strengthen the ability of flood forecasting. Moreover, the authors have conducted the preliminary practice and validation in the Qing River basin of Beijing. The system contains six modules, integrates emerging observation technologies of both rainfall and waterlogging, introduces a mainstream method of rainfall nowcasting, and adopts well-established simulation methods of urban flooding. It can provide real-time water depth for road traffic and early warning products for urban emergency management.