A probabilistic approach to estimating residential losses from different flood types

A probabilistic approach to estimating residential losses from different flood types

Paprotny, Dominik (German Research Centre for Geosciences)
Kreibich, Heidi (German Research Centre for Geosciences)
Morales Napoles, O. (TU Delft Hydraulic Structures and Flood Risk)
Wagenaar, Dennis (Deltares)
Castellarin, Attilio (University of Bologna)
Carisi, Francesca (University of Bologna)
Bertin, Xavier (Universite de la Rochelle-CNRS)
Merz, Bruno (German Research Centre for Geosciences; Institute of Physics and Astronomy)
Schröter, Kai (German Research Centre for Geosciences)

2020

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.

Bayesian networks
Coastal floods
Flood damage surveys
Fluvial floods
Pluvial floods

http://resolver.tudelft.nl/uuid:8ce1f8e2-33b5-4218-8a80-a614a28a1b5f

https://doi.org/10.1007/s11069-020-04413-x

0921-030X

Natural Hazards, 105 (3), 2569-2601

Institutional Repository

journal article

© 2020 Dominik Paprotny, Heidi Kreibich, O. Morales Napoles, Dennis Wagenaar, Attilio Castellarin, Francesca Carisi, Xavier Bertin, Bruno Merz, Kai Schröter