Building climate change into risk assessments

Alex Coletti (), Antonio De Nicola and Maria Luisa Villani
Additional contact information
Alex Coletti: SM Resources Corporation
Antonio De Nicola: ENEA
Maria Luisa Villani: ENEA

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2016, vol. 84, issue 2, No 29, 1307-1325

Abstract: Abstract Community managers and planners have an increasing need for assessing system failure risks as they relate to fact-based information on weather extremes and climate change. We illustrate a model that defines the services a software system can provide to facilitate the discovery of useful information by stakeholders with different technical background wanting to reach a fact-based consensus on risks, hazards, and vulnerabilities. Decision support systems succeed in facilitating the analysis of past severe weather events but provide limited support for the analysis of hazards related to climate change. Severe weather data enable estimates of the ability of an exposed system to withstand environmental extreme values, but the estimates of their impact on communities remain largely undetermined and prone to divergent interpretations. This study proposes a model that is built on the experience of a decision support system (DSS) that is dedicated to guide users through a stakeholder-based vulnerability assessment of community water systems. The DSS integrated data sources into an online environment so that perceived risks—defined and prioritized qualitatively by users—could be compared and discussed against the impacts that past events have had on the community. To make DSS useful for practical decision making related to the complex issues, such as those encountered in the case of climate change, we propose a model with a prototype design suitable for semantic web applications where the various entities are connected by an ontology that defines relative concepts and relationships.

Keywords: Vulnerability; Water systems; Climate; Ontology (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11069-016-2487-6

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