GIS-based deterministic models may be used for landslide susceptibility mapping over large areas. However, such efforts require specific strategies to (i) keep computing time at an acceptable level, and (ii) parameterize the geotechnical data. We test and optimize the performance of the GIS-based, 3-D slope stability model r.slope.stability in terms of computing time and model results. The model was developed as a C- and Python-based raster module of the open source software GRASS GIS and considers the 3-D geometry of the sliding surface. It calculates the factor of safety (FoS) and the probability of slope failure (Pf) for a number of randomly selected potential slip surfaces, ellipsoidal or truncated in shape. Model input consists of a digital elevation model (DEM), ranges of geotechnical parameter values derived from laboratory tests, and a range of possible soil depths estimated in the field. Probability density functions are exploited to assign Pf to each ellipsoid. The model calculates for each pixel multiple values of FoS and Pf corresponding to different sliding surfaces. The minimum value of FoS and the maximum value of Pf for each pixel give an estimate of the landslide susceptibility in the study area. Optionally, r.slope.stability is able to split the study area into a defined number of tiles, allowing parallel processing of the model on the given area. Focusing on shallow landslides, we show how multi-core processing makes it possible to reduce computing times by a factor larger than 20 in the study area. We further demonstrate how the number of random slip surfaces and the sampling of parameters influence the average value of Pf and the capacity of r.slope.stability to predict the observed patterns of shallow landslides in the 89.5 km2 Collazzone area in Umbria, central Italy.

This study aims to test the capacity of Flow-3D regarding the simulation of a rockslide-generated impulse wave by evaluating the influences of the extent of the computational domain, the grid resolution, and the corresponding computation times on the accuracy of modelling results. A detailed analysis of the Lituya Bay tsunami event (1958, Alaska, maximum recorded run-up of 524 m a.s.l.) is presented. A focus is put on the tsunami formation and run-up in the impact area. Several simulations with a simplified bay geometry are performed in order to test the concept of a “denser fluid”, compared to the seawater in the bay, for the impacting rockslide material. Further, topographic and bathymetric surfaces of the impact area are set up. The observed maximum run-up can be reproduced using a uniform grid resolution of 5 m, where the wave overtops the hill crest facing the slide source and then flows diagonally down the slope. The model is extended along the entire bay to simulate the wave propagation. The tsunami trimline is well recreated when using (a) a uniform mesh size of 20 m or (b) a non-uniform mesh size of 15 m × 15 m × 10 m with a relative roughness of 2 m for the topographic surface. The trimline mainly results from the primary wave, and in some locations it also results from reflected waves. The denser fluid is a suitable and simple concept to recreate a sliding mass impacting a waterbody, in this case with maximum impact speed of ∼93 m s⁻¹. The tsunami event and the related trimline are well reproduced using the 3D modelling approach with the density evaluation model available in Flow-3D.

Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological–geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

In contrast to the complex nature of slope failures, physically-based slope stability models rely on simplified representations of landslide geometry. Depending on the modelling approach, landslide geometry is reduced to a slope-parallel layer of infinite length and width (e.g., the infinite slope stability model), a concatenation of rigid bodies (e.g., Janbu’s model), or a 3D representation of the slope failure (e.g., Hovland’s model). In this paper, the applicability of four slope stability models is tested at four shallow landslide sites where information on soil material and landslide geometry is available. Soil samples were collected in the field for conducting respective laboratory tests. Landslide geometry was extracted from pre- and post-event digital terrain models derived from airborne laser scanning. Results for fully saturated conditions suggest that a more complex representation of landslide geometry leads to increasingly stable conditions as predicted by the respective models. Using the maximum landslide depth and the median slope angle of the sliding surfaces, the infinite slope stability model correctly predicts slope failures for all test sites. Applying a 2D model for the slope failures, only two test sites are predicted to fail while the two other remain stable. Based on 3D models, none of the slope failures are predicted correctly. The differing results may be explained by the stabilizing effects of cohesion in shallower parts of the landslides. These parts are better represented in models which include a more detailed landslide geometry. Hence, comparing the results of the applied models, the infinite slope stability model generally yields a lower factor of safety due to the overestimation of landslide depth and volume. This simple approach is considered feasible for computing a regional overview of slope stability. For the local scale, more detailed studies including comprehensive material sampling and testing as well as regolith depth measurements are necessary.

Slow‐moving deep‐seated landslides are characterised by continuous deformation, constantly changing topography and sliding‐mass geometry. Deformation rates are predominantly controlled by temporal dynamics of pore pressure. Progressing movements typically cause an over‐steepening of a landslide's foot, making these areas more susceptible to secondary slope failures and piggyback slides that, once they occur, change the geometric boundary conditions of a slope. This study presents an integrated topographic monitoring and geomechanical modelling approach, which is suitable for both model‐based replication of the landslide's hydro‐meteorological drivers and assessment of the long‐term effect of topographic changes on the stability behaviour of a large deep‐seated landslide. Parametrised at the Vögelsberg landslide (Tyrol, Austria) the integrated approach quantified considerable mass relocations between 2007 and 2020 at the landslide's foot and assessed respective effects on slope stability. Additionally, scenarios of past and future topographies were reconstructed and projected. Mass relocations of the order of 25 000 m³ were assessed between multiple airborne laser scanning acquisitions covering a period of 13 years. Based on annual uncrewed aerial vehicle laser scanning campaigns, area‐wide 3D displacements were analysed, exceeding a magnitude of 200 cm a⁻¹ at small parts (2.500 m²) on the steeper foot of the active landslide. The main landslide body (0.28 km²) moves considerably slower with movements of 2–10 cm a⁻¹. Besides spatio‐temporally varying hydrological drivers, topographic changes can have a severe impact on slope stability and therefore modify the spatiotemporal activity of the landslide. It is shown that, besides the hydrometeorological drivers, the varying elevation of the landslide's toe is a key parameter determining the long‐term trend of slope stability. With the presented approach the formation and evolution of the Vögelsberg landslide can be understood and explained.