Capturing Regional Differences in Flood Vulnerability Improves Flood Loss Estimation - Sairam, Nivedita; Schröter, Kai; Steinhausen, Max; Kreibich, Heidi (Frontiers in Water, 2022)
Flood vulnerability is quantified by loss models which are developed using either empirical or synthetic approaches. Processes influencing flood risk are stochastic and loss predictions bear significant uncertainty, especially due to vulnerability differences across exposed objects and regions. However, many state-of-the-art loss models are deterministic, i.e. they ignore data and model uncertainty. The Bayesian Data-Driven Synthetic (BDDS) model (Sairam et al. 2020) was one of the first approaches that used empirical data to reduce the prediction errors at object-level and enhance the reliability of synthetic loss models. However, the BDDS model does not capture regional vulnerability differences leading to over-/under-estimation of losses in some regions. This study introduces a hierarchical parameterization of the BDDS model which enhances synthetic predictions by quantifying regional vulnerability differences. The hierarchical parameterization makes optimal use of the process information contained in the overall data set for the various regional applications, so that it is particularly suitable for cases in which only a small amount of empirical data is available. This is demonstrated with the Multi-Coloured Manual (MCM) and empirical damage dataset from the UK. The developed model improves prediction accuracy of flood loss compared to MCM by reducing the absolute error and bias by at least 23% and 90%, respectively. The model reliability in terms of hit rate (the probability that the observed value lies in the 90% high density interval of predictions) is between 73% and 88% . The approach is of high practical relevance for all regions with only limited empirical data availability.