사회적 취약성을 고려한 도시홍수 취약요인 연구: 서울시 행정동을 중심으로

김혜령; 김윤정

doi:10.15531/ksccr.2021.12.2.187

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한국기후변화학회
한국기후변화학회지 학술저널
Journal of Climate Change Research Vol.12 No.2
2021.4 187 - 202 (16page)
DOI : 10.15531/ksccr.2021.12.2.187

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김혜령
김윤정 (한국환경정책평가연구원)

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초록·키워드

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Urban areas are experiencing considerable damage from urban flooding due to climate change. Vulnerable groups (e.g., older people and infants) are susceptible to such damage, as they generally exhibit higher social vulnerability. Moreover, damage from urban floods differs in terms of type and scale based on the distribution features of local adaptation resources and characteristics of the urban environment. Therefore, the purpose of this study is to identify the major vulnerability factors along with their spatial clustering considering the social vulnerability of socio-economically vulnerable groups, as well as the urban and residential environments under increasing urban flooding. Specifically, the regional distribution features of these vulnerable groups, conditions of the urban and residential environments, and provision status of adaptation resources should be comprehensively studied. This research identified the major vulnerability factors through correlations, principal components, and cluster analysis of the considered vulnerable elements. In particular, our results show that the elderly to are the most vulnerable group. Furthermore, elderly people living in poverty require the most active adaptation measures. Improvements in land cover distribution (e.g., increases in green areas and protected areas) considering regional spatial plans and provision in adaptation resources are also required to reduce vulnerability. The results of this study can be used to assess the status of social vulnerability and improve previous flood vulnerability assessments due to climate change. We expect that consideration of social vulnerability may contribute to successful adaptation policies for urban floods.

#Vulnerable Group #Urban Flood #Social Vulnerability #Principal Component Analysis #Cluster Analysis