Assessing coastal vulnerability at the village level using a robust framework, the example of Canacona in South Goa, India

Ritwik Nigam; Alvarinho J Luis; Alexandre S Gagnon; Eric Vaz; Bruno Damásio; Mahender Kotha

doi:10.1016/j.isci.2024.109129

Assessing coastal vulnerability at the village level using a robust framework, the example of Canacona in South Goa, India

iScience. 2024 Feb 13;27(4):109129. doi: 10.1016/j.isci.2024.109129. eCollection 2024 Apr 19.

Authors

Ritwik Nigam¹, Alvarinho J Luis², Alexandre S Gagnon³, Eric Vaz⁴, Bruno Damásio⁵, Mahender Kotha¹

Affiliations

¹ School of Earth, Ocean and Atmospheric Sciences (SEAOS), Goa University, Taleigao Plateau, Goa 403206, India.
² Division of Polar Remote Sensing, National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Government of India, Headland Sada, Vasco-da-Gama, Goa 403804, India.
³ School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK.
⁴ Department of Geography & Environmental Studies, Ryerson University, Toronto, ON M5B 2K3, Canada.
⁵ NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa, Portugal.

Abstract

Climate change poses a significant threat to coastal regions worldwide. This study presents and applies a modified Coastal Vulnerability Index (CVI) to assess coastal vulnerability at the village level, focusing on Canacona, a taluka in South Goa, India. It adapts the existing CVI methodology by incorporating additional variables to better represent the various dimensions of vulnerability, resulting in 21 variables split into a Physical Vulnerability Index (PVI) and a Social Vulnerability Index (SoVI). The results show spatial variability in coastal vulnerability across the studied villages, with Agonda and Nagercem-Chaudi found to be highly vulnerable and Loliem to be the least vulnerable. A hydrological modeling approach is also used to compare the CVI of every village with their susceptibility to inundation due to rising sea levels. The results demonstrate the influence of local factors on vulnerability, challenging previous taluka-level assessments given the scale upon which adaptation typically takes place.

Keywords: Global change; Hazard identification; Remote sensing.