Preprints
https://doi.org/10.5194/nhess-2020-388
https://doi.org/10.5194/nhess-2020-388

  03 Dec 2020

03 Dec 2020

Review status: a revised version of this preprint is currently under review for the journal NHESS.

Residential flood loss estimated from Bayesian multilevel models

Guilherme S. Mohor1, Annegret H. Thieken1, and Oliver Korup1,2 Guilherme S. Mohor et al.
  • 1Institute of Environmental Science and Geography, University of Potsdam, 14476 Potsdam, Germany
  • 2Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany

Abstract. Model predictions of monetary losses from floods mainly use physical metrics like inundation depth or building characteristics but largely ignore indicators of preparedness. The role of such predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may complicate reliable loss estimation from empirical data.

Guilherme S. Mohor et al.

 
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Guilherme S. Mohor et al.

Guilherme S. Mohor et al.

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Short summary
We explored differences in the damaging process across different flood types, regions within Germany, or flood events, through a numerical modelling in which the groups can learn from each other. Differences were found mostly across flood types, indicating the importance of identifying them, but great overlap across regions and flood events, indicating that socioeconomic or temporal information was either not well represented, or they are in fact less different among our case.
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