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- Publisher Website: 10.1016/j.scitotenv.2020.136704
- Scopus: eid_2-s2.0-85078008848
- PMID: 32019039
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Article: Spatial heterogeneities of current and future hurricane flood risk along the U.S. Atlantic and Gulf coasts
Title | Spatial heterogeneities of current and future hurricane flood risk along the U.S. Atlantic and Gulf coasts |
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Authors | |
Keywords | Climate change Coastal resilience Geographic information system (GIS) Hurricane storm surge Natural hazards Spatial risk distribution |
Issue Date | 2020 |
Citation | Science of the Total Environment, 2020, v. 713, article no. 136704 How to Cite? |
Abstract | We evaluate the spatial heterogeneities of hurricane flood risk along the United States (U.S.) Atlantic and Gulf coasts under two different climate scenarios (current and future). The flood hazard is presented as the hurricane surge flood level with 1% annual exceedance probability (100-year flood) under the two scenarios, where the future scenario considers the effect of hurricane climatology change and sea level rise towards late-21st-century under a high emission scenario (RCP8.5). This hazard information is combined with estimated vulnerability and disaster resilience of coastal communities to map the relative current and future risk employing different risk definitions. Several geographical techniques and spatial distributional models (e.g., spatial autocorrelation, spatial hotspot analysis, and spatial multivariate clustering analysis) are applied to systematically analyze the risk and identify statistically significant hotspots of the highest risk. Most of the high-risk hotspots are found in the Gulf coast region, particularly along the west coast of Florida. However, two out of three risk evaluation approaches also indicate New York City as a risk hotspot under the future climate—showing that the resultant risk is sensitive to the consideration of evaluation factors (i.e., hazard, vulnerability, and resilience). Additionally, we apply a machine-learning algorithm-based spatial multivariate approach to map the spatially distinct groups based on the values of risk, hazard, vulnerability, and resilience. The results show that the counties in the highest risk group (value >3rd quartile, 15% of total counties, including New York City) in the future lack specifically in the community capital and the social components of community resilience. This assessment of coastal risk to hurricane flood has important policy-relevant implications to provide a focus-for-action for risk reduction and resilience enhancement for the U.S., where 6.5 million households live in the hurricane flood-prone areas. |
Persistent Identifier | http://hdl.handle.net/10722/349390 |
ISSN | 2023 Impact Factor: 8.2 2023 SCImago Journal Rankings: 1.998 |
DC Field | Value | Language |
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dc.contributor.author | Sajjad, Muhammad | - |
dc.contributor.author | Lin, Ning | - |
dc.contributor.author | Chan, Johnny C.L. | - |
dc.date.accessioned | 2024-10-17T06:58:13Z | - |
dc.date.available | 2024-10-17T06:58:13Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Science of the Total Environment, 2020, v. 713, article no. 136704 | - |
dc.identifier.issn | 0048-9697 | - |
dc.identifier.uri | http://hdl.handle.net/10722/349390 | - |
dc.description.abstract | We evaluate the spatial heterogeneities of hurricane flood risk along the United States (U.S.) Atlantic and Gulf coasts under two different climate scenarios (current and future). The flood hazard is presented as the hurricane surge flood level with 1% annual exceedance probability (100-year flood) under the two scenarios, where the future scenario considers the effect of hurricane climatology change and sea level rise towards late-21st-century under a high emission scenario (RCP8.5). This hazard information is combined with estimated vulnerability and disaster resilience of coastal communities to map the relative current and future risk employing different risk definitions. Several geographical techniques and spatial distributional models (e.g., spatial autocorrelation, spatial hotspot analysis, and spatial multivariate clustering analysis) are applied to systematically analyze the risk and identify statistically significant hotspots of the highest risk. Most of the high-risk hotspots are found in the Gulf coast region, particularly along the west coast of Florida. However, two out of three risk evaluation approaches also indicate New York City as a risk hotspot under the future climate—showing that the resultant risk is sensitive to the consideration of evaluation factors (i.e., hazard, vulnerability, and resilience). Additionally, we apply a machine-learning algorithm-based spatial multivariate approach to map the spatially distinct groups based on the values of risk, hazard, vulnerability, and resilience. The results show that the counties in the highest risk group (value >3rd quartile, 15% of total counties, including New York City) in the future lack specifically in the community capital and the social components of community resilience. This assessment of coastal risk to hurricane flood has important policy-relevant implications to provide a focus-for-action for risk reduction and resilience enhancement for the U.S., where 6.5 million households live in the hurricane flood-prone areas. | - |
dc.language | eng | - |
dc.relation.ispartof | Science of the Total Environment | - |
dc.subject | Climate change | - |
dc.subject | Coastal resilience | - |
dc.subject | Geographic information system (GIS) | - |
dc.subject | Hurricane storm surge | - |
dc.subject | Natural hazards | - |
dc.subject | Spatial risk distribution | - |
dc.title | Spatial heterogeneities of current and future hurricane flood risk along the U.S. Atlantic and Gulf coasts | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.scitotenv.2020.136704 | - |
dc.identifier.pmid | 32019039 | - |
dc.identifier.scopus | eid_2-s2.0-85078008848 | - |
dc.identifier.volume | 713 | - |
dc.identifier.spage | article no. 136704 | - |
dc.identifier.epage | article no. 136704 | - |
dc.identifier.eissn | 1879-1026 | - |