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利用综合框架和过程分析评估城市洪水脆弱性:以中国南京为例。

Assessment of Urban Flood Vulnerability Using the Integrated Framework and Process Analysis: A Case from Nanjing, China.

机构信息

Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China.

出版信息

Int J Environ Res Public Health. 2022 Dec 10;19(24):16595. doi: 10.3390/ijerph192416595.

Abstract

Flooding is a serious challenge that increasingly affects residents as well as policymakers. Many studies have noted that decreasing the urban flood vulnerability (UFV) is an indispensable strategy for reducing flood risks; however, some studies have several pertinent assessment limitations. The objective of this study is to assess the UFV of the Xuanwu-Qinhuai-Jianye-Gulou-Yuhua (XQJGY) region from 2012 to 2018 by integrating various indicators into a composite index. This study uses the environment for visualizing images (ENVI) and the geographic information system (GIS) to extract indicators that have geographic attributes for the assessment of UFV and the process analysis method is then used to explore the relationship between these indicators. The results indicated that: (1) The UFV of Xuanwu, Qinhuai, and Gulou decreased from 2012 to 2018 and the UFV of Jianye and Gulou increased from 2012 to 2015 and decreased from 2015 to 2018. (2) The vegetation coverage, precipitation during the flood season, population density, and highway density significantly contributed to the UFV. (3) There also exist transformation pathways between the indicators that led to vulnerability in five districts. This study provides a theoretical basis for the government to manage floods.

摘要

洪水是一个日益严重的挑战,不仅影响居民,也影响政策制定者。许多研究指出,降低城市洪水脆弱性(UFV)是降低洪水风险的不可或缺的策略;然而,一些研究存在一些相关的评估限制。本研究旨在通过将各种指标整合到一个综合指数中,评估 2012 年至 2018 年宣武琴怀建安鼓楼雨花(XQJGY)地区的 UFV。本研究使用环境可视化图像(ENVI)和地理信息系统(GIS)提取具有地理属性的指标,用于评估 UFV,并采用过程分析方法探索这些指标之间的关系。结果表明:(1)2012 年至 2018 年,宣武、秦怀和鼓楼的 UFV 下降,而建安和鼓楼的 UFV 则从 2012 年至 2015 年增加,从 2015 年至 2018 年下降。(2)植被覆盖率、雨季降水量、人口密度和高速公路密度对 UFV 有显著影响。(3)五个区之间的指标也存在脆弱性的转化途径。本研究为政府管理洪水提供了理论依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ea2/9779312/71566f58fd6b/ijerph-19-16595-g001.jpg

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