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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. 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. Torrential Rain and Disasters, 38(3): 229-237. DOI: 10.3969/j.issn.1004-9045.2019.03.005

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

  • 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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