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Volume 18, issue 3 | Copyright

Special issue: Risk and uncertainty estimation in natural hazards

Nat. Hazards Earth Syst. Sci., 18, 969-982, 2018
https://doi.org/10.5194/nhess-18-969-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Mar 2018

Research article | 27 Mar 2018

A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

Shaojie Zhang1, Luqiang Zhao2, Ricardo Delgado-Tellez3, and Hongjun Bao4 Shaojie Zhang et al.
  • 1Key Laboratory of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
  • 2Public Meteorological Service Center, China Meteorological Administration, Beijing 100081, China
  • 3Nipe Sagua Baracoa Mountain Office, Ministry of Science, Technology and Environment of Cuba, Guantanamo, Cuba
  • 4National Meteorological Center, China Meteorological Administration, Beijing 100081, China

Abstract. Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

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