A probabilistic approach to estimating residential losses from different flood types

Dominik Paprotny (), Heidi Kreibich, Oswaldo Morales-Nápoles, Dennis Wagenaar, Attilio Castellarin, Francesca Carisi, Xavier Bertin, Bruno Merz and Kai Schröter
Additional contact information
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
Kai Schröter: Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Section Hydrology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 105, issue 3, No 11, 2569-2601

Abstract: 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.

Keywords: Fluvial floods; Coastal floods; Pluvial floods; Bayesian networks; Flood damage surveys (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-020-04413-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:105:y:2021:i:3:d:10.1007_s11069-020-04413-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-020-04413-x

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().