Journal metrics

Journal metrics

  • IF value: 2.281 IF 2.281
  • IF 5-year value: 2.693 IF 5-year 2.693
  • CiteScore value: 2.43 CiteScore 2.43
  • SNIP value: 1.193 SNIP 1.193
  • SJR value: 0.965 SJR 0.965
  • IPP value: 2.31 IPP 2.31
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 73 Scimago H index 73
Volume 18, issue 8 | Copyright

Special issue: Global- and continental-scale risk assessment for natural...

Nat. Hazards Earth Syst. Sci., 18, 2127-2142, 2018
https://doi.org/10.5194/nhess-18-2127-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Aug 2018

Research article | 10 Aug 2018

Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates

Michalis I. Vousdoukas1, Dimitrios Bouziotas2, Alessio Giardino2, Laurens M. Bouwer3, Lorenzo Mentaschi1, Evangelos Voukouvalas4, and Luc Feyen1 Michalis I. Vousdoukas et al.
  • 1European Commission, Joint European Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra, Italy
  • 2Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
  • 3Climate Service Center Germany, Fischertwiete 1, 20095 Hamburg, Germany
  • 4Engineering Ingegneria Informatica S.p.A. Via S. Martino della Battaglia, 56, 00185 Rome, Italy

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.

Download & links
Publications Copernicus
Special issue
Download
Short summary
We examine sources of epistemic uncertainty in coastal flood risk models. We find that uncertainty from sea level estimations can be higher than that related to greenhouse gas emissions or climate prediction errors. Of comparable importance is information on coastal protection levels and the topography. In the absence of large datasets with sufficient resolution and accuracy, the last two factors are the main bottlenecks in terms of estimating coastal flood risks at large scales.
We examine sources of epistemic uncertainty in coastal flood risk models. We find that...
Citation
Share