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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2019-392
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-392
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Dec 2019

Submitted as: research article | 16 Dec 2019

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A revised version of this preprint is currently under review for the journal NHESS.

Uncertainties in Coastal Flood Risk Assessments in Small Island Developing States

Matteo U. Parodi1, Alessio Giardino1, Ap van Dongeren1, Stuart G. Pearson1,2, Jeremy D. Bricker2, and Ad J. H. M. Reniers2 Matteo U. Parodi et al.
  • 1Deltares, Unit Marine and Coastal Systems, Boussinesweg 1, 2629 HV Delft, the Netherlands
  • 2Civil Engineering and Geoscience, Delft University of Technology, 2628 CN Delft, the Netherlands

Abstract. Considering the likely increase of coastal flooding in Small Island Developing States (SIDS), coastal managers at the local and global level have been developing initiatives aimed at implementing Disaster Risk Reduction (DRR) measures and adapting to climate change. Developing science-based adaptation policies requires accurate coastal flood risk (CFR) assessments, which are often subject to the scarcity of sufficiently accurate input data for insular states. We analysed the impact of uncertain inputs on coastal flood damage estimates, considering: (i) significant wave height, (ii) storm surge level and (iii) sea level rise (SLR) contributions to extreme sea levels, as well as the error-driven uncertainty in (iv) bathymetric and (v) topographic datasets, (vi) damage models and (vii) socioeconomic changes. The methodology was tested through a sensitivity analysis using an ensemble of hydrodynamic models (XBeach and SFINCS) coupled with an impact model (Delft-FIAT) for a case study at the islands of São Tomé and Príncipe. Model results indicate that for the current time horizon, depth damage functions (DDF) and digital elevation model (DEM) dominate the overall damage estimation uncertainty. We find that, when introducing climate and socioeconomic uncertainties to the analysis, SLR projections become the most relevant input for the year 2100 (followed by DEM and DDF). In general, the scarcity of reliable input data leads to considerable predictive error in CFR assessments in SIDS. The findings of this research can help to prioritise the allocation of limited resources towards the acquisitions of the most relevant input data for reliable impact estimation.

Matteo U. Parodi et al.

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Matteo U. Parodi et al.

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Short summary
We investigate sources of uncertainty in coastal flood risk assessment in São Tomé and Príncipe, a Small Island Developing State. We find that, for the present day scenario, uncertainty from depth damage functions and digital elevation models can be more significant than that related to the estimation of significant wave height or storm surge level. For future scenarios (year 2100), sea level rise prediction becomes the input with the strongest impact on coastal flood damage estimate.
We investigate sources of uncertainty in coastal flood risk assessment in São Tomé and...
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