Process‐Based Flood Risk Assessment for Germany

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 ca...

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Autores principales:	Nivedita Sairam, Fabio Brill, Tobias Sieg, Mostafa Farrag, Patric Kellermann, Viet Dung Nguyen, Stefan Lüdtke, Bruno Merz, Kai Schröter, Sergiy Vorogushyn, Heidi Kreibich
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	risk model chain continuous simulation expected annual damage risk curves multi‐sector risk Environmental sciences GE1-350 Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/7afd357302b642278762ebe084ebf291
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spelling	oai:doaj.org-article:7afd357302b642278762ebe084ebf2912021-11-23T22:36:10ZProcess‐Based Flood Risk Assessment for Germany2328-427710.1029/2021EF002259https://doaj.org/article/7afd357302b642278762ebe084ebf2912021-10-01T00:00:00Zhttps://doi.org/10.1029/2021EF002259https://doaj.org/toc/2328-4277Abstract 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.Nivedita SairamFabio BrillTobias SiegMostafa FarragPatric KellermannViet Dung NguyenStefan LüdtkeBruno MerzKai SchröterSergiy VorogushynHeidi KreibichAmerican Geophysical Union (AGU)articlerisk model chaincontinuous simulationexpected annual damagerisk curvesmulti‐sector riskEnvironmental sciencesGE1-350EcologyQH540-549.5ENEarth's Future, Vol 9, Iss 10, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	risk model chain continuous simulation expected annual damage risk curves multi‐sector risk Environmental sciences GE1-350 Ecology QH540-549.5
spellingShingle	risk model chain continuous simulation expected annual damage risk curves multi‐sector risk Environmental sciences GE1-350 Ecology QH540-549.5 Nivedita Sairam Fabio Brill Tobias Sieg Mostafa Farrag Patric Kellermann Viet Dung Nguyen Stefan Lüdtke Bruno Merz Kai Schröter Sergiy Vorogushyn Heidi Kreibich Process‐Based Flood Risk Assessment for Germany
description	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.
format	article
author	Nivedita Sairam Fabio Brill Tobias Sieg Mostafa Farrag Patric Kellermann Viet Dung Nguyen Stefan Lüdtke Bruno Merz Kai Schröter Sergiy Vorogushyn Heidi Kreibich
author_facet	Nivedita Sairam Fabio Brill Tobias Sieg Mostafa Farrag Patric Kellermann Viet Dung Nguyen Stefan Lüdtke Bruno Merz Kai Schröter Sergiy Vorogushyn Heidi Kreibich
author_sort	Nivedita Sairam
title	Process‐Based Flood Risk Assessment for Germany
title_short	Process‐Based Flood Risk Assessment for Germany
title_full	Process‐Based Flood Risk Assessment for Germany
title_fullStr	Process‐Based Flood Risk Assessment for Germany
title_full_unstemmed	Process‐Based Flood Risk Assessment for Germany
title_sort	process‐based flood risk assessment for germany
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/7afd357302b642278762ebe084ebf291
work_keys_str_mv	AT niveditasairam processbasedfloodriskassessmentforgermany AT fabiobrill processbasedfloodriskassessmentforgermany AT tobiassieg processbasedfloodriskassessmentforgermany AT mostafafarrag processbasedfloodriskassessmentforgermany AT patrickellermann processbasedfloodriskassessmentforgermany AT vietdungnguyen processbasedfloodriskassessmentforgermany AT stefanludtke processbasedfloodriskassessmentforgermany AT brunomerz processbasedfloodriskassessmentforgermany AT kaischroter processbasedfloodriskassessmentforgermany AT sergiyvorogushyn processbasedfloodriskassessmentforgermany AT heidikreibich processbasedfloodriskassessmentforgermany
_version_	1718416101461721088

Process‐Based Flood Risk Assessment for Germany

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