Coastal vulnerability index of the coastal profile from La Libertad to Salinas and its impact

Abstract

Introduction: The study evaluates and estimates the vulnerability that may arise along the coastal stretch between the cantons of La Libertad and Salinas in the province of Santa Elena (Ecuador), in response to significant impacts such as climate change and anthropogenic influences on the coastal zone, applying the Coastal Vulnerability Index (CVI) through the QGIS cartographic software and the InVEST modeling environment. Objective: To evaluate and estimate the Coastal Vulnerability Index (CVI) using the integrated application of QGIS and InVEST platforms, with the purpose of identifying critical sectors and their current spatial evolution. Materials and Methods: The research is presented through a quantitative approach, geospatial representation, and a multivariate framework, integrating physical, ecological, ocean-atmospheric, and socioeconomic variables. The coastal strip was divided into 10 segments of 250 meters, allowing for a structured and schematic framework in which detailed and replicable analyses could be conducted. Results: The area of interest evidenced an approximate estimation of 70% vulnerability ranging from moderate to high, emphasizing that low-lying areas, fragile ecosystems, and nearshore zones exhibit significantly greater exposure. Among the determining components of the CVI evaluation, geomorphology and the degradation of natural habitats were identified as significant factors influencing the adaptive capacity of the sector against erosion and sea-level rise. Discussion: The methodological application of QGIS combined with InVEST effectively generated an estimation of impacts and supported the risk management necessary to consolidate territorial planning and urban development in a secure manner. Conclusion: The area of interest presented a qualitative index ranging from moderate to high, due to its dynamic nature, and requires priority actions such as the restoration of protective ecosystems, mitigation of anthropogenic pressures, and strengthening of adaptation strategies for future scenarios related to climate change, based on the enhancement of predictive models that can foster resilience in the area of interest.