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Vulnerability and risk perceptions of hydrometeorological disasters: a study of a coastal district of Odisha, India

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Abstract

Climate change has increased hydrometeorological disasters all over the world. The increase in the occurrences of hydrometeorological disasters has brought into focus the preservation and protection of the coasts. This study, thus, makes a comprehensive assessment of the Puri district on the east coast of India, which is most affected by hydrometeorological disasters. We identify the hotspots by applying the analytical hierarchical process to the integrated coastal vulnerability index in ArcGIS. Further, employing the ordered logistic regression model, we identify the household characteristics determining disaster risk perception among the vulnerable households. Results tend to indicate that the region falling in and around Puri-Konark coastal belt are the most vulnerable ones. The households, which are economically deprived, engaged in the primary sector, and having more dependent members, perceive greater risks of hydrometeorological disasters. The proximity to the sea and river also increases their perceived risks. The findings suggest risk-mitigating efforts by prioritizing the needs of the poor and vulnerable communities while guaranteeing access to opportunities.

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Source: Author’s calculation from primary survey data)

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Notes

  1. AHP method was first proposed by Saaty (1977). It is a method of deriving priority vectors in a hierarchical form. Additive normalization method in AHP is the simplest method of estimating the priority vector w or, the weights. Srdjevic (2005) has shown evidence of the additive normalization method’s acceptability in comparison with other methods. We normalize the column by dividing the elements of each column of pairwise comparison matrix by the sum of that column, then we add the elements in each resulting row and divide the sum by the number of elements in the row.

  2. PCA is a statistical data extraction tool to convert several correlated factors contributing to an attribute into a set of uncorrelated variables that capture the variability of the attribute. It is a non-parametric analysis and is independent of any distribution. The principal components are the total variability within the data.

  3. OLM is applied for categorical variables, which in this study is the risk perception of the households. It is a nonlinear model, and is similar to binary regression models. The magnitude of change in the outcome probability in one of the independent variables depends on the levels of all the independent variables.

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This study was funded by the Ministry of Human Resource Development, Government of India, New Delhi.

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  1. Department of Humanities and Social Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    Sweta Sen, Narayan Chandra Nayak, William Kumar Mohanty & Chhotu Kumar Keshri

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  1. Sweta Sen
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Sen, S., Nayak, N.C., Mohanty, W.K. et al. Vulnerability and risk perceptions of hydrometeorological disasters: a study of a coastal district of Odisha, India. GeoJournal 88, 711–731 (2023). https://doi.org/10.1007/s10708-022-10637-0

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