Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025068 |
Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes | |
Sairam, Nivedita1,2; Schroeter, Kai1; Roezer, Viktor3; Merz, Bruno1,4; Kreibich, Heidi1 | |
2019-10-29 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
文章类型 | Article;Early Access |
语种 | 英语 |
国家 | Germany; England |
英文摘要 | 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. |
英文关键词 | flood risk flood loss model transfer multilevel probabilistic flood loss model |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000493036400001 |
WOS关键词 | AFFECTED RESIDENTS ; GERMANY ; PREPAREDNESS ; UNCERTAINTY ; RECOVERY ; LOSSES |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/187838 |
专题 | 资源环境科学 |
作者单位 | 1.GFZ German Res Ctr Geosci, Sect Hydrol, Potsdam, Germany; 2.Humboldt Univ, Geog Dept, Berlin, Germany; 3.London Sch Econ, Grantham Res Inst Climate Change & Environm, London, England; 4.Univ Potsdam, Inst Environm Sci & Geog, Potsdam, Germany |
推荐引用方式 GB/T 7714 | Sairam, Nivedita,Schroeter, Kai,Roezer, Viktor,et al. Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes[J]. WATER RESOURCES RESEARCH,2019. |
APA | Sairam, Nivedita,Schroeter, Kai,Roezer, Viktor,Merz, Bruno,&Kreibich, Heidi.(2019).Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes.WATER RESOURCES RESEARCH. |
MLA | Sairam, Nivedita,et al."Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes".WATER RESOURCES RESEARCH (2019). |
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