This article describes a new method of urban pluvial flood modeling by coupling the 1D storm water management model(SWMM)and the 2D flood inundation model(ECNU Flood-Urban).The SWMM modeling results(the overflow of th...This article describes a new method of urban pluvial flood modeling by coupling the 1D storm water management model(SWMM)and the 2D flood inundation model(ECNU Flood-Urban).The SWMM modeling results(the overflow of the manholes)are used as the input boundary condition of the ECNU Flood-Urban model to simulate the rainfall–runoff processes in an urban environment.The analysis is applied to the central business district of East Nanjing Road in downtown Shanghai,considering 5-,10-,20-,50-,and 100-year return period rainfall scenarios.The results show that node overflow,water depth,and inundation area increase proportionately with the growing return periods.Water depths are mostly predicted to be shallow and surface flows generally occur in the urban road network due to its low-lying nature.The simulation result of the coupled model proves to be reliable and suggests that urban surface water flooding could be accurately simulated by using this methodology.Adaptation measures(upgrading of the urban drainage system)can then be targeted at specific locations with significant overflow and flooding.展开更多
建成环境的高空间异质性与致灾过程的复杂性给城市暴雨内涝研究带来巨大的挑战,具体表现为模型代表性不够、计算效率低、基础数据和验证数据匮乏。以机器学习为代表的人工智能技术、高分遥感和互联网大数据的快速发展则为城市暴雨内涝...建成环境的高空间异质性与致灾过程的复杂性给城市暴雨内涝研究带来巨大的挑战,具体表现为模型代表性不够、计算效率低、基础数据和验证数据匮乏。以机器学习为代表的人工智能技术、高分遥感和互联网大数据的快速发展则为城市暴雨内涝研究提供了新的契机。论文结合人工智能、高分遥感和互联网大数据等新技术发展,从特征、机理、数据与方法4个维度对暴雨内涝的研究现状和发展趋势进行了系统总结,主要结论包括:(1)暴雨内涝具有短历时性、空间散布性、连锁性和突变性,其热点呈现空间上的动态迁移特征。(2)降雨时空特征和城市化程度决定暴雨内涝灾害的量级,地形条件尤其是微地形则决定发生位置和内涝频率。地形控制作用指数(topographic control index,TCI)对暴雨内涝发生位置具有良好的指示能力。(3)排水管网、高精度地形和不透水面分布是暴雨内涝模拟的关键基础数据;降雨过程的高时空变异性是暴雨内涝近实时预报预警的主要瓶颈,需要充分利用天气雷达观测提高其精准度;互联网众包大数据是获取高空间覆盖度暴雨内涝灾情信息的新途径,但也面临不同类型信息融合、提炼和质量控制的挑战。(4)结合水动力模拟与机器学习可建立兼具物理基础和计算效率的暴雨内涝模拟方法,是实现近实时模拟与快速预报预警的有效途径。展开更多
基金supported by the National Key Research and Development Program of China(Grant Nos.2018YFC1508803,2017YFE0107400,2017YFE0100700)the National Natural Science Foundation of China(Grant Nos.41871164,51761135024)+3 种基金the National Social Science Fund of China(Grant No.18ZDA105)the Humanities and Social Sciences Project of the Ministry of Education of China(Grant No.17YJAZH111)the Key Project of Soft Science Research of Shanghai(Grant No.19692108100)the Fundamental Research Funds for the Central Universities(Grant Nos.2018ECNU-QKT001,2017ECNUKXK013)。
文摘This article describes a new method of urban pluvial flood modeling by coupling the 1D storm water management model(SWMM)and the 2D flood inundation model(ECNU Flood-Urban).The SWMM modeling results(the overflow of the manholes)are used as the input boundary condition of the ECNU Flood-Urban model to simulate the rainfall–runoff processes in an urban environment.The analysis is applied to the central business district of East Nanjing Road in downtown Shanghai,considering 5-,10-,20-,50-,and 100-year return period rainfall scenarios.The results show that node overflow,water depth,and inundation area increase proportionately with the growing return periods.Water depths are mostly predicted to be shallow and surface flows generally occur in the urban road network due to its low-lying nature.The simulation result of the coupled model proves to be reliable and suggests that urban surface water flooding could be accurately simulated by using this methodology.Adaptation measures(upgrading of the urban drainage system)can then be targeted at specific locations with significant overflow and flooding.
文摘建成环境的高空间异质性与致灾过程的复杂性给城市暴雨内涝研究带来巨大的挑战,具体表现为模型代表性不够、计算效率低、基础数据和验证数据匮乏。以机器学习为代表的人工智能技术、高分遥感和互联网大数据的快速发展则为城市暴雨内涝研究提供了新的契机。论文结合人工智能、高分遥感和互联网大数据等新技术发展,从特征、机理、数据与方法4个维度对暴雨内涝的研究现状和发展趋势进行了系统总结,主要结论包括:(1)暴雨内涝具有短历时性、空间散布性、连锁性和突变性,其热点呈现空间上的动态迁移特征。(2)降雨时空特征和城市化程度决定暴雨内涝灾害的量级,地形条件尤其是微地形则决定发生位置和内涝频率。地形控制作用指数(topographic control index,TCI)对暴雨内涝发生位置具有良好的指示能力。(3)排水管网、高精度地形和不透水面分布是暴雨内涝模拟的关键基础数据;降雨过程的高时空变异性是暴雨内涝近实时预报预警的主要瓶颈,需要充分利用天气雷达观测提高其精准度;互联网众包大数据是获取高空间覆盖度暴雨内涝灾情信息的新途径,但也面临不同类型信息融合、提炼和质量控制的挑战。(4)结合水动力模拟与机器学习可建立兼具物理基础和计算效率的暴雨内涝模拟方法,是实现近实时模拟与快速预报预警的有效途径。
