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A comparison of factors driving flood losses in households affected by different flood types
- Flood loss data collection and modeling are not standardized, and previous work has indicated that losses from different flood types (e.g., riverine and groundwater) may follow different driving forces. However, different flood types may occur within a single flood event, which is known as a compound flood event. Therefore, we aimed to identify statistical similarities between loss-driving factors across flood types and test whether the corresponding losses should be modeled separately. In this study, we used empirical data from 4,418 respondents from four survey campaigns studying households in Germany that experienced flooding. These surveys sought to investigate several features of the impact process (hazard, socioeconomic, preparedness, and building characteristics, as well as flood type). While the level of most of these features differed across flood type subsamples (e.g., degree of preparedness), they did so in a nonregular pattern. A variable selection process indicates that besides hazard and building characteristics,Flood loss data collection and modeling are not standardized, and previous work has indicated that losses from different flood types (e.g., riverine and groundwater) may follow different driving forces. However, different flood types may occur within a single flood event, which is known as a compound flood event. Therefore, we aimed to identify statistical similarities between loss-driving factors across flood types and test whether the corresponding losses should be modeled separately. In this study, we used empirical data from 4,418 respondents from four survey campaigns studying households in Germany that experienced flooding. These surveys sought to investigate several features of the impact process (hazard, socioeconomic, preparedness, and building characteristics, as well as flood type). While the level of most of these features differed across flood type subsamples (e.g., degree of preparedness), they did so in a nonregular pattern. A variable selection process indicates that besides hazard and building characteristics, information on property-level preparedness was also selected as a relevant predictor of the loss ratio. These variables represent information, which is rarely adopted in loss modeling. Models shall be refined with further data collection and other statistical methods. To save costs, data collection efforts should be steered toward the most relevant predictors to enhance data availability and increase the statistical power of results. Understanding that losses from different flood types are driven by different factors is a crucial step toward targeted data collection and model development and will finally clarify conditions that allow us to transfer loss models in space and time. <br /> Key Points <br /> Survey data of flood-affected households show different concurrent flood types, undermining the use of a single-flood-type loss model Thirteen variables addressing flood hazard, the building, and property level preparedness are significant predictors of the building loss ratio Flood type-specific models show varying significance across the predictor variables, indicating a hindrance to model transferability…
Author details: | Guilherme Samprogna MohorORCiDGND, Paul HudsonORCiDGND, Annegret ThiekenORCiDGND |
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DOI: | https://doi.org/10.1029/2019WR025943 |
ISSN: | 0043-1397 |
ISSN: | 1944-7973 |
Title of parent work (English): | Water resources research |
Publisher: | American Geophysical Union |
Place of publishing: | Washington |
Publication type: | Article |
Language: | English |
Date of first publication: | 2020/03/13 |
Publication year: | 2020 |
Release date: | 2023/01/13 |
Tag: | Compound flood event; Groundwater; Levee; Loss modeling; Riverine floods; Surface floods; breaches |
Volume: | 56 |
Issue: | 4 |
Article number: | e2019WR025943 |
Number of pages: | 20 |
Funding institution: | DAAD Deutscher Akademischer Austausch Dienst (DAAD) European Commission; [57320205]; German Ministry for Education and Research (BMBF)Federal; Ministry of Education & Research (BMBF) [DFNK 01SFR9969/5, MEDIS; 0330688, Flood 2013 13N13017] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
Peer review: | Referiert |
Publishing method: | Open Access / Hybrid Open-Access |