Suchergebnisse

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.

Abstract. Effective flood risk management requires a realistic estimation of flood losses. However, available flood damage estimates are still characterized
by significant levels of uncertainty, questioning the capacity of flood damage models to depict real damages. With a joint effort of eight
international research groups, the objective of this study was to compare, in a blind-validation test, the performances of different models for the
assessment of the direct flood damage to the residential sector at the building level (i.e. microscale). The test consisted of a common flood case
study characterized by high availability of hazard and building data but with undisclosed information on observed losses in the implementation
stage of the models. The nine selected models were chosen in order to guarantee a good mastery of the models by the research teams, variety of the
modelling approaches, and heterogeneity of the original calibration context in relation to both hazard and vulnerability features. By avoiding
possible biases in model implementation, this blind comparison provided more objective insights on the transferability of the models and on the
reliability of their estimations, especially regarding the potentials of local and multivariable models. From another perspective, the exercise
allowed us to increase awareness of strengths and limits of flood damage modelling, which are summarized in the paper in the form of take-home messages
from a modeller's perspective.