Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model

<p>Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes, one of the regions of South America most affected by landslides every year. Currently in Colombia, physically based methods for landslide hazard mapping are mandatory for...

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Main Authors: J. Palacio Cordoba, M. Mergili, E. Aristizábal
Format: Article
Language:English
Published: Copernicus Publications 2020-03-01
Series:Natural Hazards and Earth System Sciences
Online Access:https://www.nat-hazards-earth-syst-sci.net/20/815/2020/nhess-20-815-2020.pdf
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spelling doaj-510480510758485888aa3fa1614e0ee82020-11-25T02:38:28ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812020-03-012081582910.5194/nhess-20-815-2020Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability modelJ. Palacio Cordoba0M. Mergili1M. Mergili2E. Aristizábal3Departamento de Ingeniería Civil, Facultad de Minas, Universidad Nacional de Colombia sede Medellín, Medellín, ColombiaGeomorphological Systems and Risk Research, Department of Geography and Regional Research, University of Vienna, Universitätsstraße 7, 1010 Vienna, AustriaInstitute of Applied Geology, University of Natural Resources and Life Sciences (BOKU), Peter-Jordan-Straße 82, 1190 Vienna, AustriaDepartamento de Geociencias y Medio Ambiente, Facultad de Minas, Universidad Nacional de Colombia sede Medellín, Medellín, Colombia<p>Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes, one of the regions of South America most affected by landslides every year. Currently in Colombia, physically based methods for landslide hazard mapping are mandatory for land use planning in urban areas. In this work, we perform probabilistic analyses with r.slope.stability, a spatially distributed, physically based model for landslide susceptibility analysis, available as an open-source tool coupled to GRASS GIS. This model considers alternatively the infinite slope stability model or the 2.5-D geometry of shallow planar and deep-seated landslides with ellipsoidal or truncated failure surfaces. We test the model in the La Arenosa catchment, northern Colombian Andes. The results are compared to those yielded with the corresponding deterministic analyses and with other physically based models applied in the same catchment. Finally, the model results are evaluated against a landslide inventory using a confusion matrix and receiver operating characteristic (ROC) analysis. The model performs reasonably well, the infinite slope stability model showing a better performance. The outcomes are, however, rather conservative, pointing to possible challenges with regard to the geotechnical and geo-hydraulic parameterization. The results also highlight the importance to perform probabilistic instead of – or in addition to – deterministic slope stability analyses.</p>https://www.nat-hazards-earth-syst-sci.net/20/815/2020/nhess-20-815-2020.pdf
collection DOAJ
language English
format Article
sources DOAJ
author J. Palacio Cordoba
M. Mergili
M. Mergili
E. Aristizábal
spellingShingle J. Palacio Cordoba
M. Mergili
M. Mergili
E. Aristizábal
Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model
Natural Hazards and Earth System Sciences
author_facet J. Palacio Cordoba
M. Mergili
M. Mergili
E. Aristizábal
author_sort J. Palacio Cordoba
title Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model
title_short Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model
title_full Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model
title_fullStr Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model
title_full_unstemmed Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model
title_sort probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model
publisher Copernicus Publications
series Natural Hazards and Earth System Sciences
issn 1561-8633
1684-9981
publishDate 2020-03-01
description <p>Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes, one of the regions of South America most affected by landslides every year. Currently in Colombia, physically based methods for landslide hazard mapping are mandatory for land use planning in urban areas. In this work, we perform probabilistic analyses with r.slope.stability, a spatially distributed, physically based model for landslide susceptibility analysis, available as an open-source tool coupled to GRASS GIS. This model considers alternatively the infinite slope stability model or the 2.5-D geometry of shallow planar and deep-seated landslides with ellipsoidal or truncated failure surfaces. We test the model in the La Arenosa catchment, northern Colombian Andes. The results are compared to those yielded with the corresponding deterministic analyses and with other physically based models applied in the same catchment. Finally, the model results are evaluated against a landslide inventory using a confusion matrix and receiver operating characteristic (ROC) analysis. The model performs reasonably well, the infinite slope stability model showing a better performance. The outcomes are, however, rather conservative, pointing to possible challenges with regard to the geotechnical and geo-hydraulic parameterization. The results also highlight the importance to perform probabilistic instead of – or in addition to – deterministic slope stability analyses.</p>
url https://www.nat-hazards-earth-syst-sci.net/20/815/2020/nhess-20-815-2020.pdf
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