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

Abstract

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.

National-Scale Built-Environment Exposure to 100-Year Extreme Sea Levels and Sea-Level Rise

“…While this study delivers a first assessment of built-environment exposure to ESLs and SLR in New Zealand, information on social and economic impacts and criticality of built-assets is not provided. Several national-and continental-scale flood impact assessments represent built-environments as continuous land cover maps of simplified built-asset typologies for applying economic vulnerability functions [36,[48][49][50]. Our asset-level approach enabled exposure metrics to be aggregated at scales consistent with local authority decision-making.…”

“…Another limitation of the assessment is applying a bathtub model that assumes all coastal land below a given water level and hydrologically connected to the ocean or behind protection structures such as levees, as potentially exposed directly or indirectly to flooding from extreme coastal water levels (including groundwater). The bathtub approach tends to overestimate coastal flooding extents at local-scales when caused by episodic events and to a larger extent along mildly sloped terrains [36,37]. In New Zealand, these terrains are representative of most major urban areas with >30,000 population.…”

From Hazard to Consequences: Evaluation of Direct and Indirect Impacts of Flooding Along the Emilia-Romagna Coastline, Italy

“…Finally, the numerical models are applied in default mode, except for the parameters previously described. It was demonstrated that the hazard modeling input (and, therefore, the output flood maps) can act as important factors affecting the overall uncertainty of flooding risk assessments (e.g., Vousdoukas et al, 2018a). On the other hand, other studies (e.g., Apel et al, 2009;De Moel and Aerts, 2011) conclude that it does not represent a dominant factor, when compared with the uncertainty due to the assumptions on the impact assessment component.…”

A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas

“…For example, we expect uncertainties in the water surface elevation imposed at the downstream boundary of the hydraulic model domain to strongly influence extreme water levels in the downstream reach. Wave contributions, such as wave setup and wave propagation, are also not directly represented by the BN but can strongly influence water levels [88][89][90]. While this study can be improved by using complementary data at these stations, this typically requires dedicated and extensive studies to properly capture complex coastal and hydrological processes [64,91,92] and is left for future studies.…”