Determining the drivers for snow gliding

Fromm, Reinhard; Baumgärtner, Sonja; Leitinger, Georg; Tasser, Erich; Höller, Peter

doi:10.5194/nhess-2018-3

Cited by 1 publication

(1 citation statement)

References 14 publications

(22 reference statements)

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“…While the explanatory variables for this study were chosen based on data availability, this is not an exclusive list of possible predictors. Many studies have worked towards identifying triggering factors in varying Alpine regions, such as the effects of land-use, snow processes, precipitation events or vegetation cover (Newesely et al, 2000;Tasser et al, 2003;Rickli and Graf, 2009;Geitner, 2010, 2013;Meusburger and Alewell, 2008;Meusburger et al, 2013;Von Ruette et al, 2013;Höller, 2014;Ceaglio et al, 2017;Fromm et al, 2018;Geitner et al, 2021). Therefore, it is difficult to fully quantify all ongoing processes simultaneously in such a complex system, as triggering factors are often interlaced (Zweifel et al, 2019).…”

Section: Performance Of All-in-one Modelmentioning

confidence: 99%

Investigating Causal Factors of Shallow Landslides in Grassland Regions of Switzerland

Zweifel¹,

Samarin²,

Meusburger³

et al. 2021

Preprint

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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.

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