Landslide Susceptibility Analysis by Applying TRIGRS to a Reliable Geotechnical Slope Model

This paper presents the results of a research aimed at analysing the susceptibility to shallow landslides of a study area in the Calabria region (Southern Italy). These shallow landslides, which in some cases evolve as debris flows, periodically affect the study area, causing damage to structures and infrastructure. The involved soils come from the weathering of gneissic rocks and cover about 60% of the study area. To fulfil the goal of the research, the Transient Rainfall Infiltration and Grid-based Slope-Stability (TRIGRS) model was first used, assuming input data (including physical and mechanical parameters of soils) provided by the scientific literature. Then, the preliminary results obtained were used to properly locate in situ investigations that included sampling. Geotechnical laboratory tests allowed characterising the investigated soils, and related parameters were used as new input data of the TRIGRS model. The generated shallow landslide susceptibility scenario showed a good predictive capability based on the adoption of a cutoff-independent performance technique.

The Effects of Rainfall, Soil Type and Slope on the Processes and Mechanisms of Rainfall-Induced Shallow Landslides

Landslides are a serious geohazard worldwide, causing many casualties and considerable economic losses every year. Rainfall-induced shallow landslides commonly occur in mountainous regions. Many factors affect an area’s susceptibility, such as rainfall, the soil, and the slope. In this paper, the effects of rainfall intensity, rainfall pattern, slope gradient, and soil type on landslide susceptibility are studied. Variables including soil volumetric water content, matrix suction, pore water pressure, and the total stress throughout the rainfall were measured. The results show that, under the experimental conditions of this paper, no landslides occurred on a 5° slope. On a 15° slope, when the rainfall intensity was equal to or less than 80 mm/h with a 1 h duration, landslides also did not happen. With a rainfall intensity of 120 mm/h, the rainfall pattern in which the intensity gradually diminishes could not induce landslides. Compared with fine soils, coarser soils with gravels were found to be prone to landslides. As the volumetric water content rose, the matrix suction declined from the time that the level of infiltration reached the position of the matrix. The pore water pressure and the total stress both changed drastically either immediately before or after the landslide. In addition, the sediment yield depended on the above factors. Steeper slopes, stronger rainfall, and coarser soils were all found to increase the amount of sediment yield.

Natural and human-induced landslides in a tropical mountainous region: the Rift flank west of Lake Kivu (DR Congo)

Tropical mountainous regions are often identified as landslide hotspots with particularly vulnerable populations. Anthropogenic factors are assumed to play a role in the occurrence of landslides in these populated regions, yet the relative importance of these human-induced factors remains poorly documented. In this work, we aim to explore the impact of forest cover dynamics, roads and mining activities on the occurrence of landslides in the Rift flank west of Lake Kivu in the DR Congo. To do so, we compile an inventory of 2730 landslides using © Google Earth imagery, high resolution topographic data, historical aerial photographs from the 1950’s and extensive field surveys. We identify old and recent (post 1950’s) landslides, making a distinction between deep-seated and shallow landslides, road landslides and mining landslides. We find that susceptibility patterns and area distributions are different between old and recent deep-seated landslides, which shows that natural factors contributing to their occurrence were either different or changed over time. Observed shallow landslides are recent processes that all occurred in the past two decades. The analysis of their susceptibility indicates that forest dynamics and the presence of roads play a key role in their regional distribution pattern. Under similar topographic conditions, shallow landslides are more frequent, but of smaller size, in areas where deforestation has occurred since the 1950’s as compared to shallow landslides in forest areas, i.e. in natural environments. We attribute this size reduction to the decrease of regolith cohesion due to forest loss, which allows for a smaller minimum critical area for landsliding. In areas that were already deforested in 1950’s, shallow landslides are less frequent, larger, and occur on less steep slopes. This suggests a combined role between regolith availability and soil management practices that influence erosion and water infiltration. Mining activities increase the odds of landsliding. Mining and road landslides are larger than shallow landslides but smaller than the recent deep-seated instabilities. The susceptibility models calibrated for shallow and deep-seated landslides do not predict them well, highlighting that they are controlled by environmental factors that are not present under natural conditions. Our analysis demonstrates the role of human activities on the occurrence of landslides in the Lake Kivu region. Overall, it highlights the need to consider this context when studying hillslope instability characteristics and distribution patterns in regions under anthropogenic pressure. Our work also highlights the importance of considering the timing of landslides over a multi-decadal period of observation.

Investigating causal factors of shallow landslides in grassland regions of Switzerland

Mountainous grassland slopes can be severely affected by soil erosion, among which shallow landslides are a crucial process, indicating instability of slopes. We determine the locations of shallow landslides across different sites to better understand regional differences and to identify their triggering causal factors. Ten sites across Switzerland located in the Alps (eight sites), in foothill regions (one site) and the Jura Mountains (one 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 (local evaluation) as well as an all-in-one model (cross-regional evaluation) using all shallow-landslide points simultaneously. While the local conditions of the 10 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 and roughness as the most important explanatory variables (accuracy = 72.3 %). Our findings 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.