Open AccessUnderstanding risks in the light of uncertainty: low-probability, high-impact coastal events in cities
Author(s) -
Luis M. Abadie,
Ibon Galarraga,
Elisa Sainz de Murieta
Publication year - 2017
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/aa5254
Subject(s) - damages , climate change , flood myth , adaptation (eye) , coastal flood , environmental science , climate change adaptation , risk management , environmental resource management , risk analysis (engineering) , computer science , geography , sea level rise , business , ecology , physics , archaeology , optics , finance , political science , law , biology
A quantification of present and future mean annual losses due to extreme coastal events can be crucial for adequate decision making on adaptation to climate change in coastal areas around the globe. However, this approach is limited when uncertainty needs to be accounted for. In this paper, we assess coastal flood risk from sea-level rise and extreme events in 120 major cities around the world using an alternative stochastic approach that accounts for uncertainty. Probability distributions of future relative (local) sea-level rise have been used for each city, under three IPPC emission scenarios, RCP 2.6, 4.5 and 8.5. The approach allows a continuous stochastic function to be built to assess yearly evolution of damages from 2030 to 2100. Additionally, we present two risk measures that put low-probability, high-damage events in the spotlight: the Value at Risk (VaR) and the Expected Shortfall (ES), which enable the damages to be estimated when a certain risk level is exceeded. This level of acceptable risk can be defined involving different stakeholders to guide progressive adaptation strategies. The method presented here is new in the field of economics of adaptation and offers a much broader picture of the challenges related to dealing with climate impacts. Furthermore, it can be applied to assess not only adaptation needs but also to put adaptation into a timeframe in each city