- Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model's ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model's performance for a type of flood not included in the surveyResidential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model's ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model's performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.…
MetadatenAuthor details: | Dominik PaprotnyORCiD, Heidi KreibichORCiDGND, Oswaldo Morales-Napoles, Dennis Wagenaar, Attilio Castellarin, Francesca CarisiORCiD, Xavier Bertin, Bruno MerzORCiDGND, Kai SchröterORCiDGND |
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DOI: | https://doi.org/10.1007/s11069-020-04413-x |
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ISSN: | 0921-030X |
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ISSN: | 1573-0840 |
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Title of parent work (English): | Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards |
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Publisher: | Springer |
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Place of publishing: | New York |
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Publication type: | Article |
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Language: | English |
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Date of first publication: | 2020/12/02 |
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Publication year: | 2020 |
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Release date: | 2022/11/14 |
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Tag: | Bayesian networks; coastal floods; damage surveys; flood; fluvial floods; pluvial floods |
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Volume: | 105 |
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Issue: | 3 |
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Number of pages: | 33 |
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First page: | 2569 |
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Last Page: | 2601 |
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Funding institution: | Climate-KIC through project "SAFERPLACES-Improved assessment of pluvial,; fluvial and coastal flood hazards and risks in European cities as a mean; to build safer and resilient communities" [TC2018B_4.7.3-SAFERPL_P430-1A; KAVA2 4.7.3]; European Union's Horizon 2020 research and innovation; programme [730381]; German Research Network Natural Disasters (German; Ministry of Education and Research (BMBF))Federal Ministry of Education; & Research (BMBF) [01SFR9969/5]; project MEDIS (BMBF)Federal Ministry of; Education & Research (BMBF) [0330688]; project "Hochwasser 2013"; (BMBF)Federal Ministry of Education & Research (BMBF) [13N13017]; project EVUS (BMBF)Federal Ministry of Education & Research (BMBF); [03G0846B]; project URBAS (BMBF)Federal Ministry of Education & Research; (BMBF) [0330701C] |
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Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
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DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
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Peer review: | Referiert |
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Publishing method: | Open Access / Hybrid Open-Access |
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License (German): | CC-BY - Namensnennung 4.0 International |
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