Building Flood Resilience In Charsadda: An Analysis Of Adaptation Measures

Authors

  • Bahadar Sher, Dr. Muhammad Kaleem

Abstract

Flood is an ever increasing global disaster and affects billions of people every year. Charsadda is one of the 11 high flood-risk district in Pakistan, where people are extremely expose to floods with low-adaptive capacity and low resilience, receive huge human, infrastructural and socio-economic damages. Therefore, the present study has been carried out to analyze the adaption measures and flood-resilience in the district. Three union councils e.g. Kot, Mirzadher and Batagram, one from each tehsil were purposively selected as sample of the study. Through snowball sampling, 300 respondents (100 from each union council) were selected and data were collected through interviewer-administered questionnaires. Various flood-mitigating measures have been observed in the study area. About 66.7 percent of respondents adapted different adaptation measure at households’ level. These adaptations included, evaluation of houses, protection barriers, emergency bags, drains cleanliness/digging, green measures, evacuation of people, moving assets/livestock to high places, repair and[1] rebuilt of houses and restoration of livelihoods. Besides, small proportion (16%) acknowledged nominal role of government and NGOs in adaptation process. A huge proportion (88.3%) denied women role in flood-adaptation due to cultural barriers. All (100%) population size reported flood-adaptation inadequate. Similarly, all (100%) of them considered themselves extremely vulnerable to future floods. The research study recommended establishment of an effective flood-response system and infrastructure modification to make it flood-resilient, and framing of flood-resilient policies.

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Published

2024-08-15

How to Cite

Bahadar Sher, Dr. Muhammad Kaleem. (2024). Building Flood Resilience In Charsadda: An Analysis Of Adaptation Measures. Migration Letters, 21(8), 1246–1257. Retrieved from https://migrationletters.com/index.php/ml/article/view/12362

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Articles