The proposal of the Coast-RiskBySea: COASTal zones RISK assessment for built environment bY extreme SEA level, based on the new Copernicus Coastal Zones data. Case study: Naples, Italy

International Journal of Disaster Risk Reduction(2022)

引用 4|浏览0
暂无评分
摘要
Natural hazards and the long-term effects of climate change could have disastrous consequences for urban settlements, in this context, risk assessment is a key factor in supporting urban designers and planners. The research proposes a simplified model for the assessment of coastal risk on built environment expressed in terms of potential, direct and tangible, economic damages. The Coast-RiskBySea (COASTal zones RISK assessment for Built environment bY extreme SEA level) is developed in GIS and the risk is evaluated based on the IPCC AR5 and AR6 frameworks through an equation that combines exposure, vulnerability and hazard. The first step is the creation of the land use grid derived from the Copernicus Coastal Zones database, after which then damage scenarios are calculated using depth-damage functions that enable to translate climate impacts into economic damages (exposure). Vulnerability is then assessed in relation to coastal elevation through the DTM and, finally, climate projections of Extreme Sea Level (ESL) are introduced (hazard). To test and verify the approach, the model is applied to the metropolitan city of Naples, Italy, the results show multiple areas at medium and high risk for ESL events with significant economic impacts. From the output maps it is possible to identify the higher risk areas and to understand how, with significant mitigation actions, the risks could be significantly reduced. In conclusion, the Coast-RiskBySea, as it has been developed with input data characterised by a homogeneous spatial coverage, it can be replicable in all EU coastal zones that dispose of a DTM with adequate vertical accuracy.
更多
查看译文
关键词
Coastal zones risk assessment,Copernicus coastal zones data,Extreme sea level,Depth-damage function,DTM,GIS,Coast-RiskBySea
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要