Articles | Volume 10, issue 9
Nat. Hazards Earth Syst. Sci., 10, 1829–1837, 2010
https://doi.org/10.5194/nhess-10-1829-2010

Special issue: Approaches to hazard evaluation, mapping, and mitigation

Nat. Hazards Earth Syst. Sci., 10, 1829–1837, 2010
https://doi.org/10.5194/nhess-10-1829-2010

  06 Sep 2010

06 Sep 2010

Shallow landslide prediction in the Serra do Mar, São Paulo, Brazil

B. C. Vieira1, N. F. Fernandes2, and O. A. Filho3 B. C. Vieira et al.
  • 1Department of Geography, University of São Paulo, São Paulo, Brazil
  • 2Department of Geography, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
  • 3Geotechnical Department, University of São Paulo, São Paulo, Brazil

Abstract. Various methods are currently used in order to predict shallow landslides within the catchment scale. Among them, physically based models present advantages associated with the physical description of processes by means of mathematical equations. The main objective of this research is the prediction of shallow landslides using TRIGRS model, in a pilot catchment located at Serra do Mar mountain range, São Paulo State, southeastern Brazil. Susceptibility scenarios have been simulated taking into account different mechanical and hydrological values. These scenarios were analysed based on a landslide scars map from the January 1985 event, upon which two indexes were applied: Scars Concentration (SC – ratio between the number of cells with scars, in each class, and the total number of cells with scars within the catchment) and Landslide Potential (LP – ratio between the number of cells with scars, in each class, and the total number of cells in that same class). The results showed a significant agreement between the simulated scenarios and the scar's map. In unstable areas (SF≤1), the SC values exceeded 50% in all scenarios. Based on the results, the use of this model should be considered an important tool for shallow landslide prediction, especially in areas where mechanical and hydrological properties of the materials are not well known.

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