Abstract
Abstract. Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how 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 unduly generalise the model and systematically bias loss estimations from empirical data.
Highlights
The estimation of flood losses is a key requirement for assessing flood risk and for the evaluation of mitigation strategies like the design of relief funds, structural protection, or insurance design
We explore the suitability of a Bayesian multilevel model to estimate relative building loss from models with different predictor combinations
Judging from the predictive capacity using leave-one-out cross-validation (LOO-CV), we arrived at a number of models worth further inspection
Summary
The estimation of flood losses is a key requirement for assessing flood risk and for the evaluation of mitigation strategies like the design of relief funds, structural protection, or insurance design. Without standard loss documentation procedures in place, the highly variable losses caused by different flood types (e.g. pluvial, fluvial, coastal) can make loss modelling challenging, especially where data are limited or heterogeneous This lack of detailed or structured data motivates most modelling studies concerned with flood loss to assign just a single type of flooding to each event (Gerl et al, 2016). Another confounding issue is scale: inventories of flood damage are often aggregated at administrative levels such as municipalities or states (Spekkers et al, 2014; Bernet et al, 2017; Gradeci et al, 2019). Previous work has emphasised this challenge of transferring models with respect to different flood types, events, or locations (Jongman et al, 2012; Cammerer et al, 2013; Schröter et al, 2014; Figueiredo et al, 2018)
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