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Developing Flood Vulnerability Functions Through Questionnaire Survey for Flood Risk Assessments in the Meghna Basin, Bangladesh

WATER(2022)

Publ Works Res Inst PWRI

Cited 9|Views6
Abstract
Flood vulnerability is estimated by Flood Damage Functions (FDFs), which are crucial for integrated flood risk assessment for developing sustainable flood management, mitigation, and adaptation strategies under global change. However, the FDFs, either empirical or synthetic, are not available in Bangladesh. Therefore, this paper focused on developing the synthetic type of FDFs for agriculture and rural households through the data of a well–structured questionnaire survey conducted in two pilot sub–districts of northeastern Bangladesh in the Meghna River basin. Multiple regression analyses were performed on the collected data, and the best performing models were selected to establish FDFs. The FDF for agriculture (~196 samples) was developed concerning damage to Boro rice, whereas the FDFs for households (~165 samples) were developed concerning damage to the buildings and household property of three house types (Mud, Brick, and Concrete), separately. The results revealed that there were no yield losses when the water levels were lower than 25 cm (~rice tiller height), and the yield losses were ~100% when the water levels were 70–75 cm deep (~rice grain height). Mud houses and their household property were found the most flood–vulnerable and likely to experience total damage when the water levels exceeded 150 cm above the plinth level, whereas the damage to Brick and Concrete houses and their household property was found likely to remain partial even when the water levels exceeded 150 cm above the plinth level. The developed FDFs can be used to assess potential flood risk in the study area for sustainable and effective management of flood disasters and build back better under global change in the future.
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Key words
northeastern Bangladesh,Meghna basin,flood risk assessment,damage curves,questionnaire survey,regression analysis
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