Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes

TY - JOUR

T1 - Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes

AU - Sairam, Nivedita

AU - Schröter, Kai

AU - Rözer, Viktor

AU - Merz, Bruno

AU - Kreibich, Heidi

N1 - Funding Information: This research has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement 676027 MSCA ETN System‐Risk. Flood damage data of the 2005, 2006, 2010, 2011, and 2013 events along with instructions on how to access the data are available via the German flood damage database, HOWAS21 ( http://howas21.gfz‐potsdam.de/howas21/ ). Flood damage data of the 2002 event was partly funded by the reinsurance company Deutsche Rückversicherung ( www.deutscherueck.de ) and may be obtained upon request. The surveys were supported by the German Research Network Natural Disasters (German Ministry of Education and Research (BMBF), 01SFR9969/5), the MEDIS project (BMBF; 0330688) the project “Hochwasser 2013” (BMBF; 13N13017), and by a joint venture between the German Research Centre for Geosciences GFZ, the University of Potsdam, and the Deutsche Ruckversicherung AG, Dusseldorf. Funding Information: This research has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement 676027 MSCA ETN System-Risk. Flood damage data of the 2005, 2006, 2010, 2011, and 2013 events along with instructions on how to access the data are available via the German flood damage database, HOWAS21 (http://howas21.gfz-potsdam.de/howas21/). Flood damage data of the 2002 event was partly funded by the reinsurance company Deutsche Rückversicherung (www.deutscherueck.de) and may be obtained upon request. The surveys were supported by the German Research Network Natural Disasters (German Ministry of Education and Research (BMBF), 01SFR9969/5), the MEDIS project (BMBF; 0330688) the project “Hochwasser 2013” (BMBF; 13N13017), and by a joint venture between the German Research Centre for Geosciences GFZ, the University of Potsdam, and the Deutsche Ruckversicherung AG, Dusseldorf. Publisher Copyright: ©2019. American Geophysical Union. All Rights Reserved.

PY - 2019/10/1

Y1 - 2019/10/1

N2 - 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 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.

AB - 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 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.

KW - flood loss model transfer

KW - flood risk

KW - multilevel probabilistic flood loss model

UR - http://www.scopus.com/inward/record.url?scp=85074588060&partnerID=8YFLogxK

U2 - 10.1029/2019WR025068

DO - 10.1029/2019WR025068

M3 - Article

AN - SCOPUS:85074588060

SN - 0043-1397

VL - 55

SP - 8223

EP - 8237

JO - WATER RESOURCES RESEARCH

JF - WATER RESOURCES RESEARCH

IS - 10

ER -