Urban flooding risk assessment based on the impact of land cover spatiotemporal characteristics with hydrodynamic simulation

IF 3.9 3区 环境科学与生态学 Q1 ENGINEERING, CIVIL
Yanfen Geng, Xiao Huang, Xinyu Hu, Yingmeng Zhong, Peng Liu
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Abstract

In recent years, urban flooding has emerged as a major challenge, with land cover change identified as a key contributing factor. This study investigates the sensitivity of urban flooding risk to land cover changes. Seven urban land cover maps from different years and five different rainfall events, were selected as the examples. Based on hydrodynamic model simulations, this study analyzed the relationship between the total area of urban flooding and the proportion of ponding depths across various depth intervals and the land cover change. The study region was divided into 41 sub-areas based on road classifications and building clusters. The urban flood risk considering the aggregation of urban flooding, maximum ponding depth, the extent of the ponded area, and the average ponding depth was quantified within these sub-regions. Additionally, ten characteristic points were extracted from two sub-area with significant risk changes to analyze the logic of urban flooding risk evolution under land use change. The results indicate that: (1) There is a positive correlation between the total area of urban flooding and the proportion of high ponding depths and increasing impervious surfaces. (2) Urbanization significantly increases urban flooding risk, with 28 out of 41 areas experiencing heightened risk, including 6 sub-areas with risk increases exceeding 100%. (3) When the rainfall event changes from a 20-year to a 100-year return period, the maximum ponding depth in cropland stabilizes compared to impervious surfaces. Conversion of cropland to impervious surfaces accelerates increases in ponding depth and can lead to higher maximum ponding depths.

Abstract Image

基于水动力模拟的土地覆被时空特征影响的城市洪水风险评估
近年来,城市洪水已成为一项重大挑战,而土地覆被变化被认为是一个关键因素。本研究调查了城市洪水风险对土地覆被变化的敏感性。研究选取了不同年份的七幅城市土地覆被图和五次不同的降雨事件作为实例。基于水动力模型模拟,本研究分析了城市内涝总面积和不同深度区间的积水深度比例与土地覆被变化之间的关系。研究区域根据道路分类和建筑群划分为 41 个子区域。在这些子区域内,考虑到城市洪水的聚集、最大积水深度、积水区域范围和平均积水深度,对城市洪水风险进行了量化。此外,还从两个风险变化显著的子区域中提取了 10 个特征点,以分析土地利用变化下城市洪水风险的演变逻辑。结果表明(1)城市内涝总面积与高积水深度比例和不透水面积增加呈正相关。(2)城市化明显增加了城市内涝风险,41 个地区中有 28 个地区的风险增加,其中 6 个次级地区的风险增加超过 100%。(3)当降雨事件的重现期从 20 年一遇变为 100 年一遇时,与不透水地面相比,耕地的最大积水深度趋于稳定。耕地转为不透水地面会加速积水深度的增加,并可能导致更大的最大积水深度。
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来源期刊
CiteScore
7.10
自引率
9.50%
发文量
189
审稿时长
3.8 months
期刊介绍: Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.
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