Process‐Based Flood Risk Assessment for Germany. Issue 10 (5th October 2021)

• Record Type: Journal Article
• Authors: Sairam, Nivedita; Brill, Fabio; Sieg, Tobias; Farrag, Mostafa; Kellermann, Patric; Nguyen, Viet Dung; Lüdtke, Stefan; Merz, Bruno; Schröter, Kai; Vorogushyn, Sergiy; Kreibich, Heidi
• Is Part Of: Earth's future. Volume 9:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-05
• Subjects: risk model chain -- continuous simulation -- expected annual damage -- risk curves -- multi‐sector risk; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2021EF002259
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: 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. Plain Language Summary: We provide spatially consistent flood risk estimates for the residential, commercial and agricultural sectors of Germany. The Regional Flood Model (RFM) simulates a 5000‐year flood event catalogue from which the flood risk curves are derived based on the losses per economic sector. The RFM is a process‐based model chain, that couples the weather generator providing spatially consistent precipitation fields with the hydrological and hydrodynamic models considering processes such as dike overtopping and hinterland storage. The coherent heterogeneous return period flows result in flood maps consisting of inundation depth and duration. These are intersected with sector specific assets at high spatial resolution. Detailed flood loss models are used to estimate losses. From the risk curves, we estimate the Expected Annual Damage and losses corresponding to a 200‐year return period 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. Owing to the process‐based, spatially consistent approach implemented, our risk estimates for extreme events are more realistic compared to other large‐scale assessments. Key Points: Regional Flood Model provides spatially consistent flood risk estimates for residential, commercial and agriculture sectors for Germany Flood risk is derived using a 5000‐year event catalog, yielding a realistic representation of risk along with uncertainty quantification The median Expected Annual Damage and Value At Risk at 99.5% confidence for Germany is estimated to be €0.53 bn and €8.87 bn, respectively