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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 12
Nat. Hazards Earth Syst. Sci., 13, 3169–3184, 2013
https://doi.org/10.5194/nhess-13-3169-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Progress in landslide hazard and risk evaluation

Nat. Hazards Earth Syst. Sci., 13, 3169–3184, 2013
https://doi.org/10.5194/nhess-13-3169-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 09 Dec 2013

Research article | 09 Dec 2013

Shallow landslide's stochastic risk modelling based on the precipitation event of August 2005 in Switzerland: results and implications

P. Nicolet1, L. Foresti1,2, O. Caspar1, and M. Jaboyedoff1 P. Nicolet et al.
  • 1Center of Research on Terrestrial Environment, University of Lausanne, Lausanne, Switzerland
  • 2Royal Meteorological Institute of Belgium, Brussels, Belgium

Abstract. Due to their relatively unpredictable characteristics, shallow landslides represent a risk for human infrastructures. Multiple shallow landslides can be triggered by widespread intense precipitation events. The event of August 2005 in Switzerland is used in order to propose a risk model to predict the expected number of landslides based on the precipitation amounts and lithological units. The spatial distribution of rainfall is characterized by merging data coming from operational weather radars and a dense network of rain gauges with an artificial neural network. Lithologies are grouped into four main units, with similar characteristics. Then, from a landslide inventory containing more than 5000 landslides, a probabilistic relation linking the precipitation amount and the lithology to the number of landslides in a 1 km2 cell, is derived. In a next step, this relation is used to randomly redistribute the landslides using Monte Carlo simulations. The probability for a landslide to reach a building is assessed using stochastic geometry and the damage cost is assessed from the estimated mean damage cost using an exponential distribution to account for the variability. Although the model reproduces well the number of landslides, the number of affected buildings is underestimated. This seems to result from the human influence on landslide occurrence. Such a model might be useful to characterize the risk resulting from shallow landslides and its variability.

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