Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates
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Published:2018-08-10
Issue:8
Volume:18
Page:2127-2142
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ISSN:1684-9981
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Container-title:Natural Hazards and Earth System Sciences
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language:en
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Short-container-title:Nat. Hazards Earth Syst. Sci.
Author:
Vousdoukas Michalis I.ORCID, Bouziotas Dimitrios, Giardino AlessioORCID, Bouwer Laurens M., Mentaschi Lorenzo, Voukouvalas EvangelosORCID, Feyen Luc
Abstract
Abstract. An upscaling of flood risk assessment frameworks beyond regional and national
scales has taken place during recent years, with a number of large-scale
models emerging as tools for hotspot identification, support for
international policymaking, and harmonization of climate change adaptation
strategies. There is, however, limited insight into the scaling effects and
structural limitations of flood risk models and, therefore, the underlying
uncertainty. In light of this, we examine key sources of epistemic
uncertainty in the coastal flood risk (CFR) modelling chain: (i) the
inclusion and interaction of different hydraulic components leading to
extreme sea level (ESL), (ii) the underlying uncertainty in the digital
elevation model (DEM), (iii) flood defence information, (iv) the assumptions
behind the use of depth–damage functions that express vulnerability, and
(v) different climate change projections. The impact of these uncertainties
on estimated expected annual damage (EAD) for present and future climates is
evaluated in a dual case study in Faro, Portugal, and on the Iberian
Peninsula. The ranking of the uncertainty factors varies among the different
case studies, baseline CFR estimates, and their absolute and relative
changes. We find that uncertainty from ESL contributions, and in particular
the way waves are treated, can be higher than the uncertainty of the two
greenhouse gas emission projections and six climate models that are used. Of
comparable importance is the quality of information on coastal protection
levels and DEM information. In the absence of large datasets with
sufficient resolution and accuracy, the latter two factors are the main
bottlenecks in terms of large-scale CFR assessment quality.
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
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