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Improving society's ability to prepare for, respond to and recover from flooding requires integrated, anticipatory flood risk management (FRM). However, most countries still focus their efforts on responding to flooding events if and when they occur rather than addressing their current and future vulnerability to flooding. Flood insurance is one mechanism that could promote a more ex ante approach to risk by supporting risk reduction activities. This paper uses an adapted version of Easton's System Theory to investigate the role of insurance for FRM in Germany and England. We introduce an anticipatory FRM framework, which allows flood insurance to be considered as part of a broader policy field. We analyze if and how flood insurance can catalyze a change toward a more anticipatory approach to FRM. In particular we consider insurance's role in influencing five key components of anticipatory FRM: risk knowledge, prevention through better planning, property‐level protection measures, structural protection and preparedness (for response). We find that in both countries FRM is still a reactive, event‐driven process, while anticipatory FRM remains underdeveloped. Collaboration between insurers and FRM decision‐makers has already been successful, for example in improving risk knowledge and awareness, while in other areas insurance acts as a disincentive for more risk reduction action. In both countries there is evidence that insurance can play a significant role in encouraging anticipatory FRM, but this remains underutilized. Effective collaboration between insurers and government should not be seen as a cost, but as an investment to secure future insurability through flood resilience.
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.
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.