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A probabilistic approach to estimating residential losses from different flood types

Author

Listed:
  • Dominik Paprotny

    (Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section Hydrology)

  • Heidi Kreibich

    (Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section Hydrology)

  • Oswaldo Morales-Nápoles

    (Delft University of Technology)

  • Dennis Wagenaar

    (Deltares)

  • Attilio Castellarin

    (University of Bologna)

  • Francesca Carisi

    (University of Bologna)

  • Xavier Bertin

    (UMR 7266 LIENSs CNRS-La Rochelle Université)

  • Bruno Merz

    (Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section Hydrology
    University of Potsdam)

  • Kai Schröter

    (Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section Hydrology)

Abstract

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 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.

Suggested Citation

  • Dominik Paprotny & Heidi Kreibich & Oswaldo Morales-Nápoles & Dennis Wagenaar & Attilio Castellarin & Francesca Carisi & Xavier Bertin & Bruno Merz & Kai Schröter, 2021. "A probabilistic approach to estimating residential losses from different flood types," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(3), pages 2569-2601, February.
  • Handle: RePEc:spr:nathaz:v:105:y:2021:i:3:d:10.1007_s11069-020-04413-x
    DOI: 10.1007/s11069-020-04413-x
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    References listed on IDEAS

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    1. H. Apel & G. Aronica & H. Kreibich & A. Thieken, 2009. "Flood risk analyses—how detailed do we need to be?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 49(1), pages 79-98, April.
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    5. Tina Gerl & Heidi Kreibich & Guillermo Franco & David Marechal & Kai Schröter, 2016. "A Review of Flood Loss Models as Basis for Harmonization and Benchmarking," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-22, July.
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    7. Paprotny, Dominik & Kreibich, Heidi & Morales-Nápoles, Oswaldo & Castellarin, Attilio & Carisi3, Francesca & Schröter, Kai, 2020. "Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe," Earth Arxiv r6dfg, Center for Open Science.
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    Cited by:

    1. Rosa Fernández Ropero & María Julia Flores & Rafael Rumí, 2022. "Bayesian Networks for Preprocessing Water Management Data," Mathematics, MDPI, vol. 10(10), pages 1-18, May.

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