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.
Highlights
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
Six of the collected models are simple univariate damage curves (Hydrotec 2001; ICPR 2001; Huizinga 2007; Klijn et al 2007; Luino et al 2009), including at least one per country covered by flood loss surveys. (No model was identified for France.) One model created for coastal floods (Reese et al 2003), MERK, provides curves for 4 different construction types, and we use an average of those linear models
In the analysis of the results from the Bayesian network (BN)-based model, we focus on the variant using a “balanced” sample consisting of all German, Italian and 40% random sample of Dutch data, which provided the best predictions overall
Summary
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. Most damage models rely only on water depth, as it is by far the most important determinant of flood losses (Merz et al 2013; Schröter et al 2014; Amadio et al 2019) It is usually available from flood hazard analyses. Fluvial (riverine) floods, generated by rainfall or snowmelt, are associated with rather large water depths and long inundation duration, but rather low flow velocities and contamination levels unless a dike is breached. In areas affected by dike or dune breaches, the duration of inundation can be long (Apel et al 2016; Chen et al 2010; Kelman and Spence 2004; Webster et al 2014; Zellou and Rahali 2019) Given all those differences between flood types, it is common to separate flood damage models by individual flood types
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