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Uncertainties in coastal flood risk assessments in small island developing states
Natural Hazards and Earth System Sciences ( IF 4.6 ) Pub Date : 2020-09-11 , DOI: 10.5194/nhess-20-2397-2020 Matteo U. Parodi , Alessio Giardino , Ap van Dongeren , Stuart G. Pearson , Jeremy D. Bricker , Ad J. H. M. Reniers
Natural Hazards and Earth System Sciences ( IF 4.6 ) Pub Date : 2020-09-11 , DOI: 10.5194/nhess-20-2397-2020 Matteo U. Parodi , Alessio Giardino , Ap van Dongeren , Stuart G. Pearson , Jeremy D. Bricker , Ad J. H. M. Reniers
Abstract. Considering the likely increase in coastal flooding in small island
developing states (SIDSs) due to climate change, coastal managers at the
local and global levels have been developing initiatives aimed at
implementing disaster risk reduction (DRR) and adaptation measures.
Developing science-based adaptation policies requires accurate coastal flood
risk (CFR) assessments, which in the case of insular states are often
subject to input uncertainty. We analysed the impact of a number of
uncertain inputs on coastal flood damage estimates: (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 a direct impact model (Delft-FIAT) for a case study of a number
of villages on the islands of Sao Tome and Principe. Model
results indicate that for the current time horizon, depth damage functions
(DDFs) and digital elevation models (DEMs) dominate the overall damage
estimation uncertainty. 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 uncertainty in CFR
assessments in SIDSs. The findings of this research can help to prioritize
the allocation of limited resources towards the acquisitions of the most
relevant input data for reliable impact estimation.
更新日期：2020-09-11
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