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Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras

Quantitative disaster risk studies for slow-moving rotational and translational landslides in small regions (e.g., cities and watersheds) are very scarce. The limitations of risk modeling associated with these hazards include (i) the lack of data for physical modeling, (ii) methodological restrictio...

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Bibliographic Details
Published in:Applied sciences 2024-10, Vol.14 (19), p.9114
Main Authors: Suárez, Ginés, Domínguez-Cuesta, María José
Format: Article
Language:English
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Summary:Quantitative disaster risk studies for slow-moving rotational and translational landslides in small regions (e.g., cities and watersheds) are very scarce. The limitations of risk modeling associated with these hazards include (i) the lack of data for physical modeling, (ii) methodological restrictions on estimating landslide intensity with statistical models and determining the temporal probability of landslides, and (iii) the absence of characterizations of the physical vulnerability of exposed assets. The present study combines and updates different methodologies to overcome these limitations for quantitative landslide disaster risk estimation, creating a novel methodological approach that was applied in a pilot study in Tegucigalpa city, Honduras. Tegucigalpa, the capital city of Honduras, has the highest number of recorded landslides in the country. In a previous study, landslides were found to be mainly concentrated in areas with colluvium and residual soils. As an input for the disaster risk assessment, this study generated landslide risk vulnerability functions based on empirical data. The application of the proposed methodology allowed us to estimate the average annual loss (AAL) caused by landslides in the study area—a key disaster risk metric that is lacking in other landslide disaster risk studies—enabling comparisons with disaster risk estimates associated with other hazards. In particular, the AAL value obtained for the study region was USD 7.26 million.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14199114