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Quantifying Flood Vulnerability Reduction via Private Precaution
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| Zeitschriftentitel: | Earth's Future |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | Earth's Future, 7, 2019, 3, S. 235-249 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Geophysical Union (AGU)
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| Schlagwörter: |
| author_facet |
Sairam, Nivedita Schröter, Kai Lüdtke, Stefan Merz, Bruno Kreibich, Heidi Sairam, Nivedita Schröter, Kai Lüdtke, Stefan Merz, Bruno Kreibich, Heidi |
|---|---|
| author |
Sairam, Nivedita Schröter, Kai Lüdtke, Stefan Merz, Bruno Kreibich, Heidi |
| spellingShingle |
Sairam, Nivedita Schröter, Kai Lüdtke, Stefan Merz, Bruno Kreibich, Heidi Earth's Future Quantifying Flood Vulnerability Reduction via Private Precaution Earth and Planetary Sciences (miscellaneous) General Environmental Science |
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sairam, nivedita |
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Sairam, Nivedita Schröter, Kai Lüdtke, Stefan Merz, Bruno Kreibich, Heidi 2328-4277 2328-4277 American Geophysical Union (AGU) Earth and Planetary Sciences (miscellaneous) General Environmental Science http://dx.doi.org/10.1029/2018ef000994 <jats:title>Abstract</jats:title><jats:p>Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (<jats:italic>n</jats:italic> = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state‐of‐the‐art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network‐based model BN‐FLEMOps and the rule‐based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.</jats:p> Quantifying Flood Vulnerability Reduction via Private Precaution Earth's Future |
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| title |
Quantifying Flood Vulnerability Reduction via Private Precaution |
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Quantifying Flood Vulnerability Reduction via Private Precaution |
| title_full |
Quantifying Flood Vulnerability Reduction via Private Precaution |
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Quantifying Flood Vulnerability Reduction via Private Precaution |
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Quantifying Flood Vulnerability Reduction via Private Precaution |
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Quantifying Flood Vulnerability Reduction via Private Precaution |
| title_sort |
quantifying flood vulnerability reduction via private precaution |
| topic |
Earth and Planetary Sciences (miscellaneous) General Environmental Science |
| url |
http://dx.doi.org/10.1029/2018ef000994 |
| publishDate |
2019 |
| physical |
235-249 |
| description |
<jats:title>Abstract</jats:title><jats:p>Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (<jats:italic>n</jats:italic> = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state‐of‐the‐art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network‐based model BN‐FLEMOps and the rule‐based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.</jats:p> |
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| author | Sairam, Nivedita, Schröter, Kai, Lüdtke, Stefan, Merz, Bruno, Kreibich, Heidi |
| author_facet | Sairam, Nivedita, Schröter, Kai, Lüdtke, Stefan, Merz, Bruno, Kreibich, Heidi, Sairam, Nivedita, Schröter, Kai, Lüdtke, Stefan, Merz, Bruno, Kreibich, Heidi |
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| description | <jats:title>Abstract</jats:title><jats:p>Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (<jats:italic>n</jats:italic> = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state‐of‐the‐art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network‐based model BN‐FLEMOps and the rule‐based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.</jats:p> |
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| spelling | Sairam, Nivedita Schröter, Kai Lüdtke, Stefan Merz, Bruno Kreibich, Heidi 2328-4277 2328-4277 American Geophysical Union (AGU) Earth and Planetary Sciences (miscellaneous) General Environmental Science http://dx.doi.org/10.1029/2018ef000994 <jats:title>Abstract</jats:title><jats:p>Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (<jats:italic>n</jats:italic> = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state‐of‐the‐art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network‐based model BN‐FLEMOps and the rule‐based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.</jats:p> Quantifying Flood Vulnerability Reduction via Private Precaution Earth's Future |
| spellingShingle | Sairam, Nivedita, Schröter, Kai, Lüdtke, Stefan, Merz, Bruno, Kreibich, Heidi, Earth's Future, Quantifying Flood Vulnerability Reduction via Private Precaution, Earth and Planetary Sciences (miscellaneous), General Environmental Science |
| title | Quantifying Flood Vulnerability Reduction via Private Precaution |
| title_full | Quantifying Flood Vulnerability Reduction via Private Precaution |
| title_fullStr | Quantifying Flood Vulnerability Reduction via Private Precaution |
| title_full_unstemmed | Quantifying Flood Vulnerability Reduction via Private Precaution |
| title_short | Quantifying Flood Vulnerability Reduction via Private Precaution |
| title_sort | quantifying flood vulnerability reduction via private precaution |
| title_unstemmed | Quantifying Flood Vulnerability Reduction via Private Precaution |
| topic | Earth and Planetary Sciences (miscellaneous), General Environmental Science |
| url | http://dx.doi.org/10.1029/2018ef000994 |