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郝莹, 陈靖, 王元, 王皓, 邱学兴, 王东勇, 翟振芳. 2019. 基于高时空分辨率降水预报产品的城市内涝预警研究[J]. 暴雨灾害, 38(3): 229-237. DOI: 10.3969/j.issn.1004-9045.2019.03.005
引用本文: 郝莹, 陈靖, 王元, 王皓, 邱学兴, 王东勇, 翟振芳. 2019. 基于高时空分辨率降水预报产品的城市内涝预警研究[J]. 暴雨灾害, 38(3): 229-237. DOI: 10.3969/j.issn.1004-9045.2019.03.005
HAO Ying, CHEN Jing, WANG Yuan, WANG Hao, QIU Xuexing, WANG Dongyong, ZHAI Zhenfang. 2019. Research on urban waterlogging risk early warning based on high spatial-temporal resolution precipitation forecast products[J]. Torrential Rain and Disasters, 38(3): 229-237. DOI: 10.3969/j.issn.1004-9045.2019.03.005
Citation: HAO Ying, CHEN Jing, WANG Yuan, WANG Hao, QIU Xuexing, WANG Dongyong, ZHAI Zhenfang. 2019. Research on urban waterlogging risk early warning based on high spatial-temporal resolution precipitation forecast products[J]. Torrential Rain and Disasters, 38(3): 229-237. DOI: 10.3969/j.issn.1004-9045.2019.03.005

基于高时空分辨率降水预报产品的城市内涝预警研究

Research on urban waterlogging risk early warning based on high spatial-temporal resolution precipitation forecast products

  • 摘要: 使用高精度高程、路网、河网、排水管网、工程设施以及防洪调度等数据,将各类空间信息剖分为7 287个无结构不规则网格及相应通道,并针对城市立体化交通设施,对模型进行调参,最终构建了合肥城市暴雨内涝数值模型。采用城市地表、明渠河道、排水管网等主要水文水动力学物理过程,模拟积水深度及演进情况。在此基础上,将短时临近预报系统INCA(Integrated Nowcasting through Comprehensive Analysis)的降水估测产品和降水预报产品(空间分辨率1 km,时间分辨率1 h)作为驱动条件,得到未来6 h逐时的积水深度预报及内涝风险预警产品。研究结果表明:城市内涝模型对积水深度及积水演进过程的模拟和实况较为吻合,体现出对河网、路网、社区积水良好的模拟能力。对2017年8月25日合肥西南部严重内涝过程的检验表明,积水深度预报效果很大程度上依赖INCA的降水预报质量,对于短时强降水,INCA在临近时效预报效果相对较好,因此积水深度预报产品可在临近时效内较为准确的预报积水区域以及积水变化过程。可见利用高时空分辨率降水预报产品和城市暴雨内涝数值模型耦合制作内涝风险预警,可有效增加内涝灾害的预见期,为城市防涝减灾提供科学参考。

     

    Abstract: High resolution elevation data, road network, river network, drainage network, engineering facilities and flood control countermeasures in Hefei city were integrated and divided into 7 287 unstructured irregular grids and corresponding channels. By means of adjusting the parameters according to the urban three-dimensional traffic facilities, Hefei Urban Waterlogging Numerical Model was constructed. The waterlogging depth and evolution of water accumulation were simulated according to the main hydro-hydrodynamic physical processes in urban surface, open channel and drainage network. Furthermore the quantitative precipitation evaluation and forecast products of INCA (Integrated Nowcasting through Comprehensive Analysis) were utilized to drive this model and obtained 1-6 hours waterlogging depth prediction and risk early warning products with one-hour interval. The results show that the simulations of both water depth and evolution of water accumulation are in good agreement with the observations. Examination of the severe waterlogging event in southwestern Hefei on August 25, 2017 shows that the accuracy of water depth forecast largely depends on the quality of precipitation forecast by INCA. For short-term heavy rainfall, INCA has a relatively good performance in nowcasting. Therefore, the product could more accurately predict the falling area and the evolution of waterlogging when the leading time is less than 2 hours. It was proved that waterlogging risk early warning can effectively prolong the lead time of waterlogging disaster and provide a scientific reference for urban waterlogging prevention and mitigation by using high spatial and temporal resolution precipitation forecast products coupled with urban rainstorm waterlogging numerical model.

     

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