Global Spatiotemporal Multicriteria and Data-Driven Analyses Identify Current and Future Coastal Risk Hot Spots and Clusters
Carlo Giupponi (Department of Economics, Ca Foscari University of Venice and Fondazione Eni Enrico Mattei); Marco Bidoia (Fondazione Eni Enrico Mattei)
Coastal zones, climate change, extreme sea levels, scenario, multicriteria analysis, data-driven analysis, principal component analysis, K-means, policy, Adaptation
Coastal zones are among the environments most threatened by climate change. Several efforts for global mapping and classification of coastal social and ecological systems have been attempted, but there has been limited capabilities to analyze and describe the spatial heterogeneity and multidimensionality of the phenomena. In this work, we present a methodological framework for the assessment of risks from extreme sea levels (ESL) at the global level, comparing the current scenario with two future combinations of shared socioeconomic (SSP2 and 5) and representative concentration pathways (RCP4.5 and 8.5). Risk maps deriving from the intersection between hazard, vulnerability, and exposure allow for the identification of global hot spots, that is, large areas with high risk. Furthermore, multivariate analysis of the indicators used to calculate risk identifies spatial clusters with common risk features. The results contribute to improving the knowledge required for planning adaptation strategies and sharing solutions between areas with similar situations.