Flood footprint assessment: Assessing external assistance’ impact on post‐disaster recovery

Mitigating the risks posed by natural disasters has become a pressing public concern due to their increasingly frequent and intense occurrences, which threaten human lives and socio-economic development. External assistance (EA), as a common form of support for post-disaster reconstruction, has been recognized as significant in several studies. However, the lack of an assessment method has made it unclear to what extent economic recovery can be affected by EA. To address this problem, we adopt the flood footprint as an indicator and improve the flood footprint model by combining it with the consideration of EA. The purpose is to quantify the economic impact of EA on post-disaster recovery. In a successful application case of the 2013 Super Typhoon Haiyan disaster in Hainan province, China, we have validated the feasibility and flexibility of our approach. We found that EA, which accounted for 2.4% of direct economic losses, resulted in a 7.31% reduction of indirect economic losses in the Hainan case. These findings indicate that EA positively impacts short-term economic risk reduction, despite its complex influence on the overall economy. Furthermore, through scenario analysis on alternative EA schemes, we discuss that the ration scheme is a significant factor in determining EA's impact. Policymakers can minimize the indirect flood footprint and facilitate post-disaster economic recovery by implementing alternative EA schemes.

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Publication Details

Journal
Risk Analysis
Published
2025-03-08
DOI
https://doi.org/10.1111/risa.70013
Citations
1
Primary Topic
Infrastructure Resilience and Vulnerability Analysis
Type
article
Field-Weighted Citation Impact
0.95

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article

Flood footprint assessment: Assessing external assistance’ impact on post‐disaster recovery

Huibin Du, Zengkai Zhang, Zhao Zeng, Nan Wang et al.
1 citations
Risk Analysis
Infrastructure Resilience and Vulnerability Analysis
0.95
article

Flood footprint assessment: Assessing external assistance’ impact on post‐disaster recovery

Huibin Du, Zengkai Zhang, Zhao Zeng, Nan Wang, Huimin Wang
article en
1 citations

Abstract

Mitigating the risks posed by natural disasters has become a pressing public concern due to their increasingly frequent and intense occurrences, which threaten human lives and socio-economic development. External assistance (EA), as a common form of support for post-disaster reconstruction, has been recognized as significant in several studies. However, the lack of an assessment method has made it unclear to what extent economic recovery can be affected by EA. To address this problem, we adopt the flood footprint as an indicator and improve the flood footprint model by combining it with the consideration of EA. The purpose is to quantify the economic impact of EA on post-disaster recovery. In a successful application case of the 2013 Super Typhoon Haiyan disaster in Hainan province, China, we have validated the feasibility and flexibility of our approach. We found that EA, which accounted for 2.4% of direct economic losses, resulted in a 7.31% reduction of indirect economic losses in the Hainan case. These findings indicate that EA positively impacts short-term economic risk reduction, despite its complex influence on the overall economy. Furthermore, through scenario analysis on alternative EA schemes, we discuss that the ration scheme is a significant factor in determining EA's impact. Policymakers can minimize the indirect flood footprint and facilitate post-disaster economic recovery by implementing alternative EA schemes.

Risk AnalysisVol. 45(8)
Tianjin University (CN), Xiamen University (CN), Tianjin University of Finance and Economics (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 28%
Infrastructure Resilience and Vulnerability Analysis
0.95
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Flood footprint assessment: Assessing external assistance’ impact on post‐disaster recovery — Huibin Du, Zengkai Zhang, et al. · Risk Analysis (2025) | TGRS Research Map | TGRS