E3S Web Conf.
Volume 9, 20163rd European Conference on Unsaturated Soils – “E-UNSAT 2016”
|Number of page(s)||5|
|Published online||12 September 2016|
Probabilistic assessment of precipitation-triggered landslides: the role of vegetation
1 Departamento de Geociencias y Medio Ambiente, Universidad Nacional de Colombia, Colombia
2 Departamento de Engenharia Civil e Ambiental, Universidade de Brasília, Brazil
a Corresponding author: email@example.com
Landslides triggered by rainfall are one of the most common causes of disaster in tropical regions, characterized by having deep weathering soil profiles, steep slopes and high-intensity storms. The increasing number of landslides during wet season evidences the close relationship between hydro-climatic conditions as a triggering factor for the occurrence of landslides. In addition, the type of vegetation covering the slope affects the soil shear strength by the roots reinforcement, plants weight and changes in soil moisture due to transpiration and interception. This paper proposes a probabilistic methodology to study the slope stability on the long-term, considering different hydro-climatic conditions and the effect of vegetation cover in the soil moisture. The ecohydrological model developed by Rodríguez-Iturbe et al.  was used to determine the boundary conditions of the problem. To generate the pore pressure field, the flow equation was solved using the Finite Element Method and Finite Differences Method. Finally, the Limit Equilibrium Method was used to find the Factor of Safety. The stability of a hypothetical slope under certain hydro-climatic conditions and two types of vegetation was evaluated. The analysis showed that it is more likely that a grass-covered slope slides than a tree-covered slope, and that the average FS of the slope during wet and dry season is very similar, but the FS dispersion is higher when the probability and intensity of rainfall events increases.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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