Quantification of uncertainties in flood risk assessments

Abstract By a common definition, flood risk assessments are comprised of two parts: a hazard and vulnerability assessment. The hazard assessment investigates the extent and magnitude of usually large flood events, which are associated to a certain exceedance probability, whereas the vulnerability part assesses the impact of the flooding on specified targets, e.g., building, people or infrastructure. Being inherently speculative flood risk assessments should always be accompanied by an uncertainty assessment in order to assist consequent decision properly. In this paper a dynamic‐probabilistic method is proposed, which enables a cumulated flood risk assessment of a complete river reach considering dike failures at all dike locations. The model uses simple but computational efficient modules to simulate the complete process chain of flooding. These modules are embedded into a Monte Carlo framework thus enabling a risk assessment which is physically based thus mapping the real flooding process, and which is also probabilistic and not based on scenarios. The model also provides uncertainty estimates by quantifying various epistemic uncertainty sources of the hazard as well as the vulnerability part in a second layer of Monte Carlo simulations. These uncertainty estimates are associated to defined return intervals of the model outputs, i.e., the derived flood frequencies at the end of the reach and the risk curves for the complete reach, thus providing valuable information for the interpretation of the results. By separating single uncertainty sources a comparison of the contribution of different uncertainty sources to the overall predictive uncertainty in terms of derived flood frequencies and monetary risks could be performed. This revealed that the major uncertainties are extreme value statistics, resp. the length of the data series used and the discharge‐stage relation used for the transformation of discharge into water levels in the river. Keywords: Flood risk assessmentdynamic‐probabilistic modeluncertainty analysisMonte Carlo analysis