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Open AccessArticle

Unravelling the Importance of Uncertainties in Global-Scale Coastal Flood Risk Assessments under Sea Level Rise

1
BRGM, 45060 Orléans, France
2
Global Climate Forum, 10829 Berlin, Germany
3
Royal Dutch Meteorological Institute (KNMI), 3731 De Bilt, The Netherlands
4
Geography Institute, 24098 Kiel, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Tony Wong and Vivek Srikrishnan
Water 2021, 13(6), 774; https://doi.org/10.3390/w13060774
Received: 13 January 2021 / Revised: 26 February 2021 / Accepted: 2 March 2021 / Published: 12 March 2021
Global scale assessments of coastal flood damage and adaptation costs under 21st century sea-level rise are associated with a wide range of uncertainties, including those in future projections of socioeconomic development (shared socioeconomic pathways (SSP) scenarios), of greenhouse gas concentrations (RCP scenarios), and of sea-level rise at regional scale (RSLR), as well as structural uncertainties related to the modelling of extreme sea levels, data on exposed population and assets, and the costs of flood damages, etc. This raises the following questions: which sources of uncertainty need to be considered in such assessments and what is the relative importance of each source of uncertainty in the final results? Using the coastal flood module of the Dynamic Interactive Vulnerability Assessment modelling framework, we extensively explore the impact of scenario, data and model uncertainties in a global manner, i.e., by considering a large number (>2000) of simulation results. The influence of the uncertainties on the two risk metrics of expected annual damage (EAD), and adaptation costs (AC) related to coastal protection is assessed at global scale by combining variance-based sensitivity indices with a regression-based machine learning technique. On this basis, we show that the research priorities in terms of future data/knowledge acquisition to reduce uncertainty on EAD and AC differ depending on the considered time horizon. In the short term (before 2040), EAD uncertainty could be significantly decreased by 25 and 75% if the uncertainty of the translation of physical damage into costs and of the modelling of extreme sea levels could respectively be reduced. For AC, it is RSLR that primarily drives short-term uncertainty (with a contribution ~50%). In the longer term (>2050), uncertainty in EAD could be largely reduced by 75% if the SSP scenario could be unambiguously identified. For AC, it is the RCP selection that helps reducing uncertainty (up to 90% by the end of the century). Altogether, the uncertainty in future human activities (SSP and RCP) are the dominant source of the uncertainty in future coastal flood risk. View Full-Text
Keywords: future coastal flooding; damage costs; dyke costs; global sensitivity analysis; machine learning future coastal flooding; damage costs; dyke costs; global sensitivity analysis; machine learning
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MDPI and ACS Style

Rohmer, J.; Lincke, D.; Hinkel, J.; Le Cozannet, G.; Lambert, E.; Vafeidis, A.T. Unravelling the Importance of Uncertainties in Global-Scale Coastal Flood Risk Assessments under Sea Level Rise. Water 2021, 13, 774. https://doi.org/10.3390/w13060774

AMA Style

Rohmer J, Lincke D, Hinkel J, Le Cozannet G, Lambert E, Vafeidis AT. Unravelling the Importance of Uncertainties in Global-Scale Coastal Flood Risk Assessments under Sea Level Rise. Water. 2021; 13(6):774. https://doi.org/10.3390/w13060774

Chicago/Turabian Style

Rohmer, Jeremy; Lincke, Daniel; Hinkel, Jochen; Le Cozannet, Gonéri; Lambert, Erwin; Vafeidis, Athanasios T. 2021. "Unravelling the Importance of Uncertainties in Global-Scale Coastal Flood Risk Assessments under Sea Level Rise" Water 13, no. 6: 774. https://doi.org/10.3390/w13060774

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