摘要：Considering the likely increase in coastal flooding in small islanddeveloping states (SIDSs) due to climate change, coastal managers at thelocal and global levels have been developing initiatives aimed atimplementing disaster risk reduction (DRR) and adaptation measures.Developing science-based adaptation policies requires accurate coastal floodrisk (CFR) assessments, which in the case of insular states are oftensubject to input uncertainty. We analysed the impact of a number ofuncertain inputs on coastal flood damage estimates: (i) significant waveheight, (ii) storm surge level and (iii) sea level rise (SLR) contributionsto 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 sensitivityanalysis using an ensemble of hydrodynamic models (XBeach and SFINCS)coupled with a direct impact model (Delft-FIAT) for a case study of a numberof villages on the islands of São Tomé and Príncipe. Modelresults indicate that for the current time horizon, depth damage functions(DDFs) and digital elevation models (DEMs) dominate the overall damageestimation uncertainty. When introducing climate and socioeconomicuncertainties to the analysis, SLR projections become the most relevantinput for the year 2100 (followed by DEM and DDF). In general, the scarcityof reliable input data leads to considerable predictive uncertainty in CFRassessments in SIDSs. The findings of this research can help to prioritizethe allocation of limited resources towards the acquisitions of the mostrelevant input data for reliable impact estimation.
