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曹雪健, 戚友存, 李梦迪, 等. 2022. 极端暴雨威胁下的城市内涝风险预警系统研究[J]. 大气科学, 46(4): 953−964. DOI: 10.3878/j.issn.1006-9895.2112.21191
引用本文: 曹雪健, 戚友存, 李梦迪, 等. 2022. 极端暴雨威胁下的城市内涝风险预警系统研究[J]. 大气科学, 46(4): 953−964. DOI: 10.3878/j.issn.1006-9895.2112.21191
CAO Xuejian, QI Youcun, LI Mengdi, et al. 2022. Early Warning System of Urban Waterlogging under the Threat of Extreme Rainfall Events [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 953−964. DOI: 10.3878/j.issn.1006-9895.2112.21191
Citation: CAO Xuejian, QI Youcun, LI Mengdi, et al. 2022. Early Warning System of Urban Waterlogging under the Threat of Extreme Rainfall Events [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 953−964. DOI: 10.3878/j.issn.1006-9895.2112.21191

极端暴雨威胁下的城市内涝风险预警系统研究

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

  • 摘要: 传统内涝风险预报系统多基于单一降雨产品驱动城市水文水动力模型的模式,难以解决由于暴雨观测或数值模拟带来的不确定性问题。综合利用多源降雨(雷达、地面雨量计,地面雨滴谱)、积水观测数据,有利于提高内涝预报精度,改善风险空间描述。因此,为了进一步加强洪涝预测能力以更好地应对极端暴雨威胁,本研究提出了基于综合观测的城市内涝风险预警系统,并在北京市清河流域进行了初步实践和检验。该系统包含六个模块,融合了新兴的降雨积水观测技术,引入了主流的降雨临近预报方法,采用了成熟的城市雨洪模拟手段,可为道路交通提供实时的积水深度和风险等级,为城市内涝灾害应急管理提供内涝风险预测和预警产品。

     

    Abstract: 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.

     

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