Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis

With increasing population growth and urban sprawl, many coastal lowlands are unprecedentedly vulnerable to climate change and its impacts, such as rising sea levels, increasing extreme storm events, and coastal flooding. Quantifying coastal flood vulnerability serves as a tool to identify a system’...

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Autor principal:	Tao Wu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Coastal flood Vulnerability indicator Climate change Principal component analysis Adaptation policy Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/fc2608b1e7234453a7d544ab015aad5c
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spelling	oai:doaj.org-article:fc2608b1e7234453a7d544ab015aad5c2021-12-01T04:57:50ZQuantifying coastal flood vulnerability for climate adaptation policy using principal component analysis1470-160X10.1016/j.ecolind.2021.108006https://doaj.org/article/fc2608b1e7234453a7d544ab015aad5c2021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21006713https://doaj.org/toc/1470-160XWith increasing population growth and urban sprawl, many coastal lowlands are unprecedentedly vulnerable to climate change and its impacts, such as rising sea levels, increasing extreme storm events, and coastal flooding. Quantifying coastal flood vulnerability serves as a tool to identify a system’s weakness, monitor its change, and support making targeted climate adaptation policies. The assessment framework proposed in this research uses principal component analysis (PCA) and a weighting method to build a composite indicator of flood vulnerability index and evaluate the vulnerability for 256 coastal census tracts and 24 municipalities along the coast of Connecticut, USA. The research uses Keiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity to test sample adequacy and performs data standardization for all indicators. Through PCA, 30 coastal vulnerability-related indicators were grouped into four major dimensions: hazard exposure, socio-economic, physical/land use and land cover, and natural. The findings highlight the variations of flood vulnerability across highly urbanized areas, suburban areas, and rural areas; and the gradient from coastal low-elevation region to high-elevation inland area. This variance is unevenly caused by different dimensions although they may trade-off with each other when aggregated, the dominant dimensions play a significant or decisive role in the vulnerability assessment. This research built an automatic and objective assessment framework that is flexible enough to be applied at a smaller scale so as to obtain detailed analysis and it can be used as a decision-making support system.Tao WuElsevierarticleCoastal floodVulnerability indicatorClimate changePrincipal component analysisAdaptation policyEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 108006- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Coastal flood Vulnerability indicator Climate change Principal component analysis Adaptation policy Ecology QH540-549.5
spellingShingle	Coastal flood Vulnerability indicator Climate change Principal component analysis Adaptation policy Ecology QH540-549.5 Tao Wu Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis
description	With increasing population growth and urban sprawl, many coastal lowlands are unprecedentedly vulnerable to climate change and its impacts, such as rising sea levels, increasing extreme storm events, and coastal flooding. Quantifying coastal flood vulnerability serves as a tool to identify a system’s weakness, monitor its change, and support making targeted climate adaptation policies. The assessment framework proposed in this research uses principal component analysis (PCA) and a weighting method to build a composite indicator of flood vulnerability index and evaluate the vulnerability for 256 coastal census tracts and 24 municipalities along the coast of Connecticut, USA. The research uses Keiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity to test sample adequacy and performs data standardization for all indicators. Through PCA, 30 coastal vulnerability-related indicators were grouped into four major dimensions: hazard exposure, socio-economic, physical/land use and land cover, and natural. The findings highlight the variations of flood vulnerability across highly urbanized areas, suburban areas, and rural areas; and the gradient from coastal low-elevation region to high-elevation inland area. This variance is unevenly caused by different dimensions although they may trade-off with each other when aggregated, the dominant dimensions play a significant or decisive role in the vulnerability assessment. This research built an automatic and objective assessment framework that is flexible enough to be applied at a smaller scale so as to obtain detailed analysis and it can be used as a decision-making support system.
format	article
author	Tao Wu
author_facet	Tao Wu
author_sort	Tao Wu
title	Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis
title_short	Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis
title_full	Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis
title_fullStr	Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis
title_full_unstemmed	Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis
title_sort	quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/fc2608b1e7234453a7d544ab015aad5c
work_keys_str_mv	AT taowu quantifyingcoastalfloodvulnerabilityforclimateadaptationpolicyusingprincipalcomponentanalysis
_version_	1718405701701730304

Quantifying coastal flood vulnerability for climate adaptation policy using principal component analysis

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