ArticlePloS one2026
Vulnerability assessment to tropical cyclones in the North Caribbean Coast of Nicaragua (1988-2022).
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The North Caribbean Coast Autonomous Region of Nicaragua (RACCN) is one of the most vulnerable areas to tropical cyclones (TCs), a condition exacerbated by climate change and internal structural disparities. Despite its historical exposure to TCs, significant gaps remain in the systematic analysis of its vulnerability. This research assesses the vulnerability levels of RACCN municipalities to TCs by constructing a Tropical Cyclone Vulnerability Index (VItc), and a municipal typology based on exposure, susceptibility, and adaptive capacity factors. Twelve indicators were selected, normalized, and analyzed using factor analysis with varimax rotation. The resulting sub-indices were integrated through weighted aggregation, and a municipal typology was developed using the Natural Breaks (Jenks) classification method. Findings reveal marked territorial disparities among municipalities. Bonanza exhibits high exposure and susceptibility combined with low adaptive capacity while Waslala demonstrates greater structural resilience. The VItc enabled the identification of three municipality clusters, with differentiated vulnerability profiles, facilitating the design of targeted interventions. The results underscore the role of social, demographic, and infrastructure factors in shaping local vulnerability. Despite limitations in data availability, the VItc provides a diagnostic tool that may help identify priority areas and the development of strategies for disaster risk reduction and resilience in the RACCN. The study highlights the need to improve data availability and strengthen institutional capacities. It also points to important directions for future research, including the integration of territorial, socioeconomic, and gender-sensitive approaches, as well as dynamic variables such as climate projections, land-use change, and migration, together with participatory methods that integrate local knowledge.
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