Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes
- Flood damage processes are complex and vary between events and regions. State-of-the-art flood loss models are often developed on the basis of empirical damage data from specific case studies and do not perform well when spatially and temporally transferred. This is due to the fact that such localized models often cover only a small set of possible damage processes from one event and a region. On the other hand, a single generalized model covering multiple events and different regions ignores the variability in damage processes across regions and events due to variables that are not explicitly accounted for individual households. We implement a hierarchical Bayesian approach to parameterize widely used depth-damage functions resulting in a hierarchical (multilevel) Bayesian model (HBM) for flood loss estimation that accounts for spatiotemporal heterogeneity in damage processes. We test and prove the hypothesis that, in transfer scenarios, HBMs are superior compared to generalized and localized regression models. In order to improveFlood damage processes are complex and vary between events and regions. State-of-the-art flood loss models are often developed on the basis of empirical damage data from specific case studies and do not perform well when spatially and temporally transferred. This is due to the fact that such localized models often cover only a small set of possible damage processes from one event and a region. On the other hand, a single generalized model covering multiple events and different regions ignores the variability in damage processes across regions and events due to variables that are not explicitly accounted for individual households. We implement a hierarchical Bayesian approach to parameterize widely used depth-damage functions resulting in a hierarchical (multilevel) Bayesian model (HBM) for flood loss estimation that accounts for spatiotemporal heterogeneity in damage processes. We test and prove the hypothesis that, in transfer scenarios, HBMs are superior compared to generalized and localized regression models. In order to improve loss predictions for regions and events for which no empirical damage data are available, we use variables pertaining to specific region- and event-characteristics representing commonly available expert knowledge as group-level predictors within the HBM.…
Verfasserangaben: | Nivedita SairamORCiD, Kai SchroeterORCiDGND, Viktor RözerORCiDGND, Bruno MerzORCiDGND, Heidi KreibichORCiDGND |
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DOI: | https://doi.org/10.1029/2019WR025068 |
ISSN: | 0043-1397 |
ISSN: | 1944-7973 |
Titel des übergeordneten Werks (Englisch): | Water resources research |
Verlag: | American Geophysical Union |
Verlagsort: | Washington |
Publikationstyp: | Wissenschaftlicher Artikel |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 13.09.2019 |
Erscheinungsjahr: | 2019 |
Datum der Freischaltung: | 23.10.2020 |
Freies Schlagwort / Tag: | flood loss model transfer; flood risk; multilevel probabilistic flood loss model |
Band: | 55 |
Ausgabe: | 10 |
Seitenanzahl: | 15 |
Erste Seite: | 8223 |
Letzte Seite: | 8237 |
Fördernde Institution: | European UnionEuropean Union (EU) [676027]; Deutsche Ruckversicherung; German Research Network Natural Disasters (German Ministry of Education and Research (BMBF))Federal Ministry of Education & Research (BMBF) [01SFR9969/5]; MEDIS project (BMBF)Federal Ministry of Education & Research (BMBF) [0330688]; project "Hochwasser 2013" (BMBF)Federal Ministry of Education & Research (BMBF) [13N13017]; German Research Centre for Geosciences GFZ; University of Potsdam; Deutsche Ruckversicherung AG, Dusseldorf |
Organisationseinheiten: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
DDC-Klassifikation: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
Peer Review: | Referiert |
Publikationsweg: | Open Access |
Open Access / Hybrid Open-Access |