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. 2023 Dec 14;10(1):e23555.
doi: 10.1016/j.heliyon.2023.e23555. eCollection 2024 Jan 15.

Cyclone vulnerability assessment in the coastal districts of Bangladesh

Affiliations

Cyclone vulnerability assessment in the coastal districts of Bangladesh

Showmitra Kumar Sarkar et al. Heliyon. .

Abstract

This research aims to assess the vulnerability to cyclones in the coastal regions of Bangladesh, employing a comprehensive framework derived from the Intergovernmental Panel on Climate Change (IPCC, 2007). The study considers a total of eighteen factors, categorized into three critical dimensions: exposure, sensitivity, and adaptive capacity. These factors are crucial in understanding the potential impact of cyclones in the region. In order to develop a cyclone vulnerability map, Principal Component Analysis (PCA) was applied, primarily focusing on the dimensions of sensitivity and adaptive capacity. The findings of this analysis revealed that sensitivity and adaptive capacity components accounted for a significant percentage of variance in the data, explaining 90.00 % and 90.93 % of the variance, respectively. Despite the lack of details about data collection, the study identified specific factors contributing significantly to each dimension. Notably, proximity to the coastline emerged as a highly influential factor in determining cyclone exposure. The results of this research indicate that certain areas, such as Jessore, Khulna, Narail, Gopalgonj, and Bagerhat, exhibit low exposure to cyclones, whereas regions like Chandpur and Lakshmipur face a high level of exposure. Sensitivity was found to be high in most areas, with Noakhali, Lakshmipur, and Chandpur being the most sensitive regions. Adaptive capacity was observed to vary significantly, with low values near the sea, particularly in locations like Cox's Bazar, Shatkhira, Bagerhat, Noakhali, and Bhola, and high values in regions farther from the coast. Overall, vulnerability to cyclones was found to be very high in Noakhali, Lakshmipur, Chandpur, and Bhola, low in Jessore and Khulna, and moderate in Barisal, Narail, Gopalgonj, and Jhalokati. These findings are expected to provide valuable insights to inform decision-makers and authorities tasked with managing the consequences of cyclones in the region.

Keywords: Coastal communities; Disaster preparedness; Disaster risk; Emergency response; GIS.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Study area.
Fig. 2
Fig. 2
Methodological framework.
Fig. 3
Fig. 3
Exposure Factors: (a) Distance to coastline, (b) Distance to Cyclone track.
Fig. 4
Fig. 4
Sensitivity Factors: (a) Elevation, (b) Slope, (c) Population density, (d) Rural population, (e) Female population, (f) Population between 0 and 6 years old, (g) Population ages 65 and above, (h) Poverty head count ratio, (i) Agriculture dependent people.
Fig. 4
Fig. 4
Sensitivity Factors: (a) Elevation, (b) Slope, (c) Population density, (d) Rural population, (e) Female population, (f) Population between 0 and 6 years old, (g) Population ages 65 and above, (h) Poverty head count ratio, (i) Agriculture dependent people.
Fig. 5
Fig. 5
Adaptive Capacity Factors: (a) Industrial worker, (b) Working age population, (c) Literacy rate, (d) Households with electricity, (e) Distance from coastal vegetation, (f) Distance from health center, (g) Distance from major road.
Fig. 5
Fig. 5
Adaptive Capacity Factors: (a) Industrial worker, (b) Working age population, (c) Literacy rate, (d) Households with electricity, (e) Distance from coastal vegetation, (f) Distance from health center, (g) Distance from major road.
Fig. 6
Fig. 6
(a) exposure, (b) sensitivity, (c) adaptive capacity, (d) vulnerability.

References

    1. Hoque M.A., Phinn S., Childs I. Tropical cyclone disaster management using remote sensing and spatial analysis: a review. Int. J. Disaster Risk Reduct. 2017 doi: 10.1016/j.ijdrr.2017.02.008. - DOI
    1. Hoque M.A., Pradhan B., Ahmed N., Roy S. Tropical cyclone risk assessment using geospatial techniques for the eastern coastal region of Bangladesh. Sci. Total Environ. 2019;692:10–22. doi: 10.1016/j.scitotenv.2019.07.132. - DOI - PubMed
    1. Chowdhury H., Scholar G. 2023. Human-Robot Collaboration in Manufacturing Assembly Tasks. - DOI
    1. Li K., Sheng G. 2013. Risk Assessment on Storm Surges in the Coastal Area of Guangdong Province; pp. 1129–1139. - DOI
    1. Weinkle Jessica R.M. 2012. Historical Global Tropical Cyclone Landfalls; pp. 4729–4735. - DOI

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