Assessing shallow landslide hazards using the TRIGRS and SHALSTAB models, Serra do Mar, Brazil

  • Bianca Carvalho Vieira
  • Nelson Ferreira Fernandes
  • Oswaldo Augusto Filho
  • Tiago Damas Martins
  • David R. Montgomery
Original Article


The hillslopes of the Serra do Mar, a system of escarpments and mountains that extend more than 1500 km along the southern and southeastern Brazilian coast, are regularly affected by heavy rainfall that generates widespread mass movements, causing large numbers of casualties and economic losses. This paper evaluates the efficiency of susceptibility mapping for shallow translational landslides in one basin in the Serra do Mar, using the physically based landslide susceptibility models SHALSTAB and TRIGRS. Two groups of scenarios were simulated using different geotechnical and hydrological soil parameters, and for each group of scenarios (A and B), three subgroups were created using soil thickness values of 1, 2, and 3 m. Simulation results were compared to the locations of 356 landslide scars from the 1985 event. The susceptibility maps for scenarios A1, A2, and A3 were similar between the models regarding the spatial distribution of susceptibility classes. Changes in soil cohesion and specific weight parameters caused changes in the area of predicted instability in the B scenarios. Both models were effective in predicting areas susceptible to shallow landslides through comparison of areas predicted to be unstable and locations of mapped landslides. Such models can be used to reduce costs or to define potentially unstable areas in regions like the Serra do Mar where field data are costly and difficult to obtain.


Mathematical models Susceptibility mapping Shallow landslides Geotechnical and hydrological parameters Serra do Mar 



The authors thank the journal editor and the reviewers, the São Paulo Research Foundation (FAPESP 2014/10109-2), the Coordination for the Improvement of Higher Education Personnel (CAPES BEX 5188/14-8) and FACEPE (BFP-0072-1.07/16) for partial financial support for this research. We are grateful to those who spent the time Drª. Lupamudra Dasgupsta e a Harvey Greenberg (GisLAB, University of Washington).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of GeographyUniversity of São PauloSão PauloBrazil
  2. 2.Department of GeographyFederal University of Rio de JaneiroRio de Janeiro, Ilha do FundãoBrazil
  3. 3.São Carlos School of EngineeringUniversity of São PauloSão PauloBrazil
  4. 4.Cities InstituteFederal University of São PauloItaquera, São PauloBrazil
  5. 5.Department of Earth and Space ScienceUniversity of Washington, Johnson HallSeattleUSA

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