Simulation of spatial flooding disaster on urban roads and analysis of influencing factors: taking main city of Hangzhou as an example

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-27 DOI:10.1007/s00477-024-02796-y

Rikun Wen, Jinjing Sun, Chunling Tao, Hao Tao, Chingaipe N’tani, Liu Yang

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Abstract

This study assessed the risk of urban road waterlogging and the threshold of the influencing factors using software simulation and data analysis. This study selected the road space in the main urban area of Hangzhou City from 2019 to 2021 as the research object. ArcGIS software was used to study the spatial distribution of road waterlogging points. Kernel density analysis and the Geographic Detector (GD) method were used to determine the dominant factors affecting road waterlogging. This study reveals the central clustering distribution characteristics of road waterlogging and the five-level risk zoning of disasters. The simulation results show that the highest-risk areas for road waterlogging in the main urban area of Hangzhou are distributed in Chao Wang Road, Jianguo Middle Road, Jianguo South Road, Hupao Road, Lingyin Road, Fuchunjiang Road, Moganshan Road Sect. 4, and Tianmu Mountain Road Sect. 3. The ranking of the impact factors for road waterlogging was as follows: elevation > vegetation coverage > slope > impervious surface abundance > distance from rivers. Factor threshold for worst flooding is that the elevation of < 15–20 m, a slope of < 8–10°, vegetation coverage of < 10%, and an abundance of impermeable surfaces > 60–70%. Elevation and vegetation coverage were the significant factors with the greatest impact on road space waterlogging. The combination of elevation and vegetation coverage, elevation and slope, and elevation and impervious surface abundance had a greater impact on road waterlogging than the other three combinations. All the interactions of the influencing factors had a nonlinear enhancing effect on urban road waterlogging disasters.

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城市道路空间洪涝灾害模拟及影响因素分析--以杭州主城区为例

本研究通过软件模拟和数据分析，对城市道路内涝风险及影响因素阈值进行了评估。本研究选取杭州市主城区 2019-2021 年道路空间为研究对象。采用 ArcGIS 软件研究道路积水点的空间分布。采用核密度分析和地理检测器（GD）方法确定影响道路积水的主导因素。本研究揭示了道路积水的中心聚类分布特征和五级灾害风险区划。模拟结果表明，杭州市主城区道路积水风险最高的区域分布在潮王路、建国中路、建国南路、湖滨路、灵隐路、富春江路、莫干山路四段和天目山路。天目山路四段和天目山路三段。3.道路内涝影响因素排序为：海拔高度；植被覆盖率；坡度；不透水面积；与河流的距离。最严重内涝的影响因素临界值为：海拔 15-20 米，坡度 8-10 度，植被覆盖率 10%，不透水表面丰度 60-70%。海拔高度和植被覆盖率是对路面积水影响最大的重要因素。高程与植被覆盖率、高程与坡度、高程与不透水表面丰度的组合对道路积水的影响大于其他三种组合。所有影响因素的交互作用对城市道路内涝灾害都有非线性增强效应。

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来源期刊

Stochastic Environmental Research and Risk Assessment 环境科学-工程：环境

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.