Residential flood loss estimated from Bayesian multilevel models
- 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 affectedModels 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.…
Verfasserangaben: | Guilherme Samprogna MohorORCiDGND, Annegret ThiekenORCiDGND, Oliver KorupORCiDGND |
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URN: | urn:nbn:de:kobv:517-opus4-517743 |
DOI: | https://doi.org/10.25932/publishup-51774 |
ISSN: | 1866-8372 |
Titel des übergeordneten Werks (Deutsch): | Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe |
Schriftenreihe (Bandnummer): | Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (1148) |
Publikationstyp: | Postprint |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 15.09.2021 |
Erscheinungsjahr: | 2021 |
Veröffentlichende Institution: | Universität Potsdam |
Datum der Freischaltung: | 15.09.2021 |
Freies Schlagwort / Tag: | Germany; damage; insurance; preparedness; recovery; transferability |
Seitenanzahl: | 18 |
Erste Seite: | 1599 |
Letzte Seite: | 1614 |
Quelle: | Natural Hazards and Earth System Sciences 21 (2021) 1599–1614 DOI: 10.5194/nhess-21-1599-2021 |
Organisationseinheiten: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
Mathematisch-Naturwissenschaftliche Fakultät / Institut für Umweltwissenschaften und Geographie | |
DDC-Klassifikation: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
9 Geschichte und Geografie / 91 Geografie, Reisen / 910 Geografie, Reisen | |
Peer Review: | Referiert |
Publikationsweg: | Open Access / Green Open-Access |
Lizenz (Deutsch): | CC-BY - Namensnennung 4.0 International |
Externe Anmerkung: | Bibliographieeintrag der Originalveröffentlichung/Quelle |