Understanding how natural and human-induced drivers will contribute to rising vulnerability and risks in coastal areas requires a broader use of future projections capturing the spatio-temporal dynamics which drive changes in the different vulnerability dimensions, including the solo-demographic and economic spheres.To go beyond the traditional approaches for coastal vulnerability appraisal, a Multi-dimensional Coastal Vulnerability Index (MDim-CVI) - integrating a composite set of physical, environmental and socio-economic indicators - is proposed to rank Italian coastal provinces according to their relative vulnerability to extreme sea level scenarios, in 2050. Specifically, information on hazard-prone areas, potentially inundated by sea level rise and extreme water levels (under the RCP8.5 climate scenario) is combined with indicators of geomorphic vulnerability (e.g. elevation, distance from coastline, shoreline evolution trend) exposure, and adaptive capacity (e.g. sensible segments of the population, GDP, land use patterns). The methodology is applied to a reference timeframe, representing current climate and land use condition, and a future scenario for the year 2050, integrating both climate projections and data simulating potential evolution of the environmental and socio-economic systems.Results show that most vulnerable provinces are located in the North Adriatic, the Gargano area and other Southern parts of Italy, mostly due to the very high vulnerability scores reported by climate-related indicators (e.g. extreme sea level). The number of vulnerable provinces as well as the magnitude of vulnerability is expected to increase in the future due to the worsening of climate, environmental, and socio-economic conditions (e.g. land use variations and increase of the elderly population). These outcomes can timely inform integrated coastal zone management and support climate adaptation planning. (C) 2021 Elsevier B.V. All rights reserved.

Development of a Multi-Dimensional Coastal Vulnerability Index: Assessing vulnerability to inundation scenarios in the Italian coast

Furlan, E;Michetti, M;Critto, A
;
Marcomini, A
2021

Abstract

Understanding how natural and human-induced drivers will contribute to rising vulnerability and risks in coastal areas requires a broader use of future projections capturing the spatio-temporal dynamics which drive changes in the different vulnerability dimensions, including the solo-demographic and economic spheres.To go beyond the traditional approaches for coastal vulnerability appraisal, a Multi-dimensional Coastal Vulnerability Index (MDim-CVI) - integrating a composite set of physical, environmental and socio-economic indicators - is proposed to rank Italian coastal provinces according to their relative vulnerability to extreme sea level scenarios, in 2050. Specifically, information on hazard-prone areas, potentially inundated by sea level rise and extreme water levels (under the RCP8.5 climate scenario) is combined with indicators of geomorphic vulnerability (e.g. elevation, distance from coastline, shoreline evolution trend) exposure, and adaptive capacity (e.g. sensible segments of the population, GDP, land use patterns). The methodology is applied to a reference timeframe, representing current climate and land use condition, and a future scenario for the year 2050, integrating both climate projections and data simulating potential evolution of the environmental and socio-economic systems.Results show that most vulnerable provinces are located in the North Adriatic, the Gargano area and other Southern parts of Italy, mostly due to the very high vulnerability scores reported by climate-related indicators (e.g. extreme sea level). The number of vulnerable provinces as well as the magnitude of vulnerability is expected to increase in the future due to the worsening of climate, environmental, and socio-economic conditions (e.g. land use variations and increase of the elderly population). These outcomes can timely inform integrated coastal zone management and support climate adaptation planning. (C) 2021 Elsevier B.V. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/5004135
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