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

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.