Preprints
https://doi.org/10.5194/nhess-2021-198
https://doi.org/10.5194/nhess-2021-198

  02 Jul 2021

02 Jul 2021

Review status: this preprint is currently under review for the journal NHESS.

Investigating Causal Factors of Shallow Landslides in Grassland Regions of Switzerland

Lauren Zweifel1, Maxim Samarin2, Katrin Meusburger3, and Christine Alewell1 Lauren Zweifel et al.
  • 1Department of Environmental Sciences, University of Basel, Bernoullistrasse 30, 4056 Basel, Switzerland
  • 2Department of Mathematics and Computer Science, University of Basel, Spiegelgasse 1, 4051 Basel, Switzerland
  • 3Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland

Abstract. Mountainous grassland slopes can be severely affected by soil erosion. To better understand the regional differences of soil erosion patterns, we determine the locations of shallow landslides across different sites and aim at identifying their triggering causal factors. Ten sites across Switzerland located in the Alps (8 sites), in foothill regions (1 site), and the Jura mountains (1 site) were selected for statistical evaluations. For the shallow landslide inventory, we used aerial images (0.25 m) with a deep learning approach (U-Net) to map the locations of eroded sites. We used logistic regression with a Group Lasso variable selection method to identify important explanatory variables for predicting the mapped shallow landslides. The set of variables consists of traditional susceptibility modelling factors and climate-related factors to represent local as well as cross-regional conditions. This set of explanatory variables (predictors) are used to develop individual site models (regional evaluation) as well as an all-in-one model (cross-regional evaluation) using all shallow landslide points simultaneously. While the local conditions of the ten sites lead to different variable selections, consistently slope and aspect were selected as the essential explanatory variables of shallow landslide susceptibility. Accuracy scores range between 70.2 and 79.8 % for individual site models. The all-in-one model confirms these findings by selecting slope, aspect as well as roughness as the most important explanatory variables (Accuracy = 72.3 %). Our finding suggest that traditional susceptibility variables describing geomorphological and geological conditions yield satisfactory results for all tested regions. However, for two sites with lower model accuracy, important processes may be under-represented with the available explanatory variables. The regression models for sites with an east-west oriented valley axis performed slightly better than models for north-south oriented valleys, which may be due to the influence of exposition related processes. Additionally, model performance is higher for Alpine sites, suggesting that core explanatory variables are understood for these areas.

Lauren Zweifel et al.

Status: open (until 16 Aug 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-198', Luigi Lombardo, 30 Jul 2021 reply

Lauren Zweifel et al.

Lauren Zweifel et al.

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Short summary
Mountainous grassland areas can be severely affected by soil erosion, such as by shallow landslides. With an automated mapping approach we are able to locate shallow landslide sites on aerial images for 10 different study sites across Swiss mountain regions covering a total of 315 km2. Using a statistical model we identify important explanatory variables for shallow landslide occurrence for the individual sites as well as across all regions, which highlight slope, aspect and terrain roughness.
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