EGU2020-19633
https://doi.org/10.5194/egusphere-egu2020-19633
EGU General Assembly 2020
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

SlideforMap – a regional scale probabilistic model for shallow landslide onset analysis and protection forest management

Feiko van Zadelhoff, Luuk Dorren, and Massimiliano Schwarz
Feiko van Zadelhoff et al.
  • Bern University of Applied sciences, Bern, Switzerland (feiko.vanzadelhoff@bfh.ch)

In the Alps, shallow landslides repeatedly pose a risk to infrastructure and residential areas. For example, dozens of shallow landslides led to the destruction of several houses, killed one person and led to the evacuation of more than 50 houses, multiple road closure for several days in Austria in Nov. 2019. To analyse and predict the risk posed by shallow landslide, a wide range of scientific methods and tools for modelling disposition and runout exists, both for local and regional scale analyses. Most of these tools, however, do not take the protective effect, i.e. root reinforcement, of vegetation into account. Therefore, we developed SlideforMap (SfM), a probabilistic model that allows for a regional assessment of the disposition of shallow landslides while considering the effect of different scenarios of forest cover and management and of rainfall intensity.

SfM uses a probabilistic approach by attributing landslide surface areas, randomly selected from a gamma shaped distribution published by Malamud (2004), to random coordinates within a given study area. For each generated landslide, SfM calculates a factor of safety using the limit equilibrium infinite slope approach. Thereby, the relevant soil parameters, i.e. angle of internal friction, soil cohesion and soil depth, are defined by normal distributions based on mean and standard deviation values representative for the study area. Hydrology is implemented using a stationary flow approach and the topographical wetness index. Root reinforcement is computed based on root distribution and root strength derived from single tree detection data and the root bundle model of Schwarz et al. (2013). Finally, the fraction of unstable landslides to the number of generated slides per raster cells is calculated and used as an index for landslide onset susceptibility. Inputs for the model are a Digital Terrain Model, a topographical wetness index and a file containing positions and sizes of trees.

Validation of SfM has been done by calculating the AUC (Metz, 1978) for three test areas with a reliable landslide inventory in Switzerland. These test areas are in mountainous areas ranging 0.5 – 7.5 km2 with varying mean slope gradients (18 - 28°). The density of inventoried historical landslides varied from 0.4 – 59 slides/km2. This resulted in AUC values between 0.64 and 0.86. Our study showed that the approach used in SfM can reproduce shallow landslide onset susceptibility on a regional scale observed in reality.

SfM was developed to quantify the stabilizing effect of vegetation at regional scale and localize potential areas where the protective effect of forests can be improved. A first version of the model will be released in 2020 by the ecorisQ association (www.ecorisq.org).

How to cite: van Zadelhoff, F., Dorren, L., and Schwarz, M.: SlideforMap – a regional scale probabilistic model for shallow landslide onset analysis and protection forest management, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19633, https://doi.org/10.5194/egusphere-egu2020-19633, 2020.

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