Abstract. 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.

Abstract. 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 GISbased, 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 (P f ) 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 P f to each ellipsoid. The model calculates for each pixel multiple values of FoS and P f corresponding to different sliding surfaces. The minimum value of FoS and the maximum value of P f 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 P f and the capacity of r.slope.stability to predict the observed patterns of shallow landslides in the 89.5 km 2 Collazzone area in Umbria, central Italy.

Abstract. 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 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 allows 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.

No abstract

No abstract

The aim of this paper is to numerically investigate the development, thickness and orientation of shear bands, in biaxial test with two approaches towards solving problems of continuum mechanics, namely the meshless “Soft PARticle” method and the mesh based Finite Element method. Soft PArticle Code (SPARC) is a straightforward collocation numerical method based on strong formulation, in which a first order polynomial basis is adopted for the evaluation of spatial derivatives in partial differential equations. A novel nonlinear constitutive model— barodesy for clay, is adopted in this study. The biaxial test, which involves homogeneous, and later inhomogeneous localized deformation is simulated using the Soft PArticle Code and the Finite Element method. The inclination and thickness of the shear bands are evaluated and analysed with the earlier experimental, theoretical and numerical investigations. Furthermore, simulation results are compared and presented to demonstrate the advantages and limitations of SPARC in comparison to FE method.

On 17 October 2015, a large-scale subaerial landslide occurred in Taan Fiord, Alaska, which released about 50 Mm3 of rock. This entered the water body and triggered a tsunami with a runup of up to 193 m. This paper aims to simulate the possible formation of a weak layer in this mountainous slope until collapse, and to analyze the possible triggering factors of this landslide event from a geotechnical engineering perspective so that a deeper understanding of this large landslide event can be gained. We analyzed different remote-sensing datasets to characterize the evolution of the coastal landslide process. Based on the acquired remote-sensing data, Digital Elevation Models were derived, on which we employed a 2D limit equilibrium method in this study to calculate the safety factor and compare the location of the associated sliding surface with the most probable actual location at which this landslide occurred. The calculation results reflect the development process of this slope collapse. In this case study, past earthquakes, rainfall before this landslide event, and glacial melting at the toe may have influenced the stability of this slope. The glacial retreat is likely to be the most significant direct triggering factor for this slope failure. This research work illustrates the applicability of multi-temporal remote sensing data of slope morphology to constrain preliminary slope stability analyses, aiming to investigate large-scale landslide processes. This interdisciplinary approach confirms the effectiveness of the combination of aerial data acquisition and traditional slope stability analyses. This case study also demonstrates the significance of a climate change for landslide hazard assessment, and that the interaction of natural hazards in terms of multi-hazards cannot be ignored.

In this work, a simple methodology for preliminarily assessing the magnitude of potential landslide-induced impulse waves’ attenuation in mountain lakes is presented. A set of metrics is used to define the geometries of theoretical mountain lakes of different sizes and shapes and to simulate impulse waves in them using the hydrodynamic software Flow-3D. The modeling results provide the ‘wave decay potential’, a ratio between the maximum wave amplitude and the flow depth at the shoreline. Wave decay potential is highly correlated with what is defined as the ‘shape product’, a metric that represents lake geometry. The relation between these two parameters can be used to evaluate wave dissipation in a natural lake given its geometric properties, and thus estimate expected flow depth at the shoreline. This novel approach is tested by applying it to a real-world event, the 2007 landslide-generated wave in Chehalis Lake (Canada), where the results match well with those obtained using the empirical equation provided by ETH Zurich (2019 Edition). This work represents the initial stage in the development of this method, and it encourages additional research and modeling in which the influence of the impacting characteristics on the resulting waves and flow depths is investigated.