A probabilistic approach to estimating residential losses from different flood types
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A probabilistic approach to estimating residential losses from different flood types Dominik Paprotny1 · Heidi Kreibich1 · Oswaldo Morales‑Nápoles2 · Dennis Wagenaar3 · Attilio Castellarin4 · Francesca Carisi4 · Xavier Bertin5 · Bruno Merz1,6 · Kai Schröter1 Received: 3 July 2020 / Accepted: 28 October 2020 © The Author(s) 2020
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
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1106 9-020-04413-x) contains supplementary material, which is available to authorized users. * Dominik Paprotny paprotny@gfz‑potsdam.de Extended author information available on the last page of the article
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Natural Hazards
1 Introduction Floods affect many types of assets, but residential buildings and their contents are usually the most exposed to extreme events due to their sheer number. For example, after the extensive 2016 floods in the Loire and Seine river basins in France, damages to dwellings constituted 68% of the number of all claims and 52% of the total value of losses (Fédération Française de l’Assurance 2017). Similarly, the vast majority of buildings damaged by the 1993 Meuse river flood in the Netherlands were residential buildings, which contributed 38% to total flood losses (Wind et al. 1999). Numerous damage models have been used to predict losses to residential assets. Accurate estimation, especially at the scale of individual buildings, is difficult as it
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