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作 者:杜佳锴 徐梦华[1] 陈昱宏 蒋智弘 肖素芬[4] 王兆礼[1,2] DU Jiakai;XU Menghua;CHEN Yuhong;JIANG Zhihong;XIAO Sufen;WANG Zhaoli(State Key Laboratory of Subtropical Building Science,School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510641,China;Pazhou Lab,Guangzhou 510335,China;State Power Investment Corporation Limited Guangdong Electric Power Company Limited,Guangzhou 510710,China;Guangdong Technology Center of Water Resources and Hydropower,Guangzhou 510610,China)
机构地区:[1]华南理工大学土木与交通学院亚热带建筑科学国家重点实验室,广州510641 [2]琶洲实验室,广州510335 [3]国家电力投资集团广东电力有限公司,广州510710 [4]广东省水利水电技术中心,广州510610
出 处:《水力发电学报》2023年第2期87-96,共10页Journal of Hydroelectric Engineering
基 金:国家自然科学基金项目(51879107,U1911204);广东省科技计划项目(2020A0505100009)。
摘 要:为解决由于城市管网数据难以获取导致的城市内涝模拟困难的问题,本文基于SWMM模型、二维TELEMAC-2D模型以及两者耦合的TSWM模型,以广州市猎德涌流域为例,利用不同概化方法构建无管网情景下的模型并与有管网情景的模型进行对比分析,探讨了不同概化方法的适用性。研究结果表明:有管网模型的一维流量与二维淹没水深结果与实测结果基本相符,证明该模型在研究区内有较好的可靠性;降雨折减法、下渗增加法、管网概化法在不同重现期下模拟的淹没范围与有管网情景的模拟结果具有较好的相关性,其中下渗增加法与有管网情景的相关系数最高,淹没范围与淹没水深也最为接近,表明该方法在高度城镇化的无管网资料地区有较好的适用性。To solve the problem in urban waterlogging simulations caused by the difficulty in obtaining urban sewer network data, this paper uses different generalization methods to construct numerical models for areas with no sewer network data, based on the SWMM model, a two-dimensional TELEMAC-2D model, and the coupled model TSWM. Using a case study of the Liede River basin in Guangzhou, we compare the results with the scenario of sewer network data available, and discuss the applicability of different generalization methods. The results show that the flows of one-dimensional simulations and the submergence depths of two-dimensional simulations by the models with a sewer network basically agree with the measurements, demonstrating a good reliability of the models for the study area. Our simulations of the scenario with the sewer network for different return periods of rainfall are correlated satisfactorily with the inundation extents simulated by the rainfall discount method, the infiltration increase method,and the pipe network generalizing method. Of the three methods, the infiltration increase method has the largest correlation coefficient and its inundation extent and depth are the closest to the scenario with the sewer network, showing its better applicability to highly urbanized areas with no sewer network data.
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