%0 Journal article
%A Sairam, Nivedita
%A Brill, Fabio
%A Sieg, Tobias
%A Farrag, Mostafa
%A Kellermann, Patric
%A Nguyen, Viet Dung
%A Lüdtke, Stefan
%A Merz, Bruno
%A Schröter, Kai
%A Vorogushyn, Sergiy
%A Kreibich, Heidi
%T Process‐Based Flood Risk Assessment for Germany
%R 10.1029/2021EF002259
%J Earth's Future
%V 9
%N 10

%I 
%X Large‐scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process‐based Regional Flood Model (RFM) to simulate a 5000‐year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D–2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector‐wise exposure data and empirical multi‐variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5% confidence for Germany to be €0.529 bn and €8.865 bn, respectively. The commercial sector dominates by making about 60% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3% to the total risk. The overall EAD is comparable to other large‐scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large‐scale assessments with homogeneous return periods. Thus, the process‐based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German‐wide flood risk assessments.
%U http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9781
%~  FID GEO-LEO e-docs