Prof. Dr. Johan Gaume

The dynamic behaviour of granular media can be observed widely in nature and in many industrial processes. Yet, the modelling of such media remains challenging as they may act with solid-like and fluid-like properties depending on the rate of the flow and can display a varying apparent friction, cohesion and compressibility. Over the last two decades, the μ(I)-rheology has become well established for modelling granular liquids in a fluid mechanics framework where the apparent friction μ depends on the inertial number I. In the geo-mechanics community, modelling the deformation of granular solids typically relies on concepts from critical state soil mechanics. Along the lines of recent attempts to combine critical state and the μ(I)-rheology, we develop a continuum model based on modified cam-clay in an elastoplastic framework which recovers the μ(I)-rheology under flow. This model permits a treatment of plastic compressibility in systems with or without cohesion, where the cohesion is assumed to be the result of persistent inter-granular attractive forces. Implemented in a two- and three-dimensional material point method, it allows for the trivial treatment of the free surface. The proposed model approximately reproduces analytical solutions of steady-state cohesionless flow and is further compared with previous cohesive and cohesionless experiments. In particular, satisfactory agreements with several experiments of granular collapse are demonstrated, albeit with shear bands which can affect the smoothness of the surface. Finally, the model is able to qualitatively reproduce the multiple steady-state solutions of granular flow recently observed in experiments of flow over obstacles.

A snow slab avalanche releases after failure initiation and crack propagation in a highly porous weak snow layer buried below a cohesive slab. While our knowledge of crack propagation during avalanche formation has greatly improved over the last decades, it still remains unclear how snow mechanical properties affect the dynamics of crack propagation. This is partly due to a lack of non-invasive measurement methods to investigate the micro-mechanical aspects of the process. Using a DEM model, we therefore analyzed the influence of snow cover properties on the dynamics of crack propagation in weak snowpack layers. By focusing on the steady-state crack speed, our results showed two distinct fracture process regimes that depend on slope angle, leading to very different crack propagation speeds. For long experiments on level terrain, weak layer fracture is mainly driven by compressive stresses. Steady-state crack speed mainly depends on slab and weak layer elastic moduli as well as weak layer strength. We suggest a semi-empirical model to predict crack speed, which can be up to 0.6 times the slab shear wave speed. For long experiments on steep slopes, a supershear regime appeared, where the crack propagation speed reached approximately 1.6 times the slab shear wave speed. A detailed micro-mechanical analysis of stresses revealed a fracture principally driven by shear. Overall, our findings provide new insight into the micro-mechanics of dynamic crack propagation in snow, and how these are linked to snow cover properties.

Glide-snow avalanches involve significant volumes of moving snow and present a major challenge to infrastructure in snow-covered mountain regions. Their initiation consists of a sequence of processes that remains poorly understood. It is conceptualized as starting with a loss of friction along the snow-ground interface due to the presence of liquid water. To shed more light on the effect of liquid water at the snow-ground interface on friction, we tested snow samples on a temperature-controlled substratum in a cold laboratory for different conditions of roughness and snow liquid water content (LWC). We performed 51 interface shear tests, analyzed the stress-displacement behavior, and studied the effect of interfacial water on the friction angles (static and kinetic). A sort of stick-slip behavior was observed on lubricated glass. On a dry and rough surface, the snow failed across the sample and no sliding occurred at the interface. Under dry conditions on a smooth surface or lubricated conditions on a rough surface, a strain-softening behavior with an interfacial strength was observed. For the tests where the frictional strength was exceeded, the friction angle, both static and kinetic, showed no dependence on the liquid water content. In the future, we plan to refine and extend the experimental setup to investigate snow compaction and analyze the effect of snowpack properties on gliding behavior.

In the rapidly evolving mountain cryosphere, snow avalanches threaten livelihoods, settlements and infrastructure. In this Review, we analyse past and projected impacts of climate change on avalanche activity and the associated risks. The limited availability of comprehensive datasets, the potential confounding factors and the limitations of statistical approaches can make it difficult to identify trends in avalanche activity. However, available data indicate a general decrease in avalanche number, size, seasonality and active paths at low elevations, and an increase in the proportion of wet avalanches relative to dry avalanches. Increased snowfall at high elevations can lead to peaks in avalanche activity and an increase in the number of wet and slush-like avalanches. Activity patterns gradually shift from low to high elevations under continued warming. These changes affect avalanche risk; however, risk is also influenced by factors such as land use and the growth or decline of human settlements. The impact of these factors varies across diverse mountain environments, making it challenging to predict how risk will evolve under a changing climate. Therefore, future research should aim to couple an improved systemic understanding of the impacts of these factors with slope-scale projections of avalanche hazards and risks to support sustainable mountain development and adaptation strategies.

In a recent review of the available scientific literature, impacts of climate change on avalanche activity and the associated risks were analyzed. The work done includes an evaluation of existing data and approaches, a synthesis of past and future changes in avalanche hazards and risks in various mountain environments worldwide with focus on their relation to climate change, elaboration of a conceptual model of trends in avalanche activity and related risks and the formulation of research priorities to address critical knowledge gaps. This paper synthetizes the main outcomes of this review.

In mountainous regions, snow avalanches pose significant threats to both populations and infrastructure. As the flow progresses, it can accumulate additional mass by entraining bed material along its path, thereby amplifying its destructive potential. Volume increases of over tenfold the initial volume have been documented from avalanche observation and measurements. Entrainment is usually accounted for in depth-averaged models through a source term in the mass conservation equation and simplified entrainment criteria. However, quantitative assessment of erosion/entrainment rates with various flow types and bed material properties has seldomly been explored under well-controlled conditions. In this work, we investigate avalanche dynamics as well as erosion/entrainment mechanisms on the basis of depth-resolved particle-based simulations. Through such a modeling approach, processes like frontal ploughing, basal abrasion as well as erosion-deposition waves naturally emerge. Our findings highlight that, the interaction of the flow with the initially static bed material may have multiple and diverse effects on the flow behavior. First, the bed material can be eroded, but only partially entrained in the flow. Second, while the flow mobility is generally reduced by the presence of the bed, it may be enhanced in some cases due to the mechanical weakening of the bed. Finally, we propose a relationship linking the entrainment rate to a dimensionless parameter that compares the flow-induced stress to the bed shear resistance. This work contributes to a better understanding of the dynamics of snow avalanches and entrainment process and can lead to a refinement of depth-averaged formulations for practical purposes. Future research should consider factors such as rate-dependent snow rheology, air pore pressure and bed fluidization mechanisms to further refine our understanding and predictive capabilities in avalanche modeling.

In the domain of avalanche road safety, local hazard experts have to assess the risk of an avalanche hitting the road given the predicted state of the snowpack and changing weather conditions. This project aims to present a proof of concept for a decision-support tool that facilitates risk assessment, provides a reproducible base for expert discussions and enhances the decision-making process by applying numerical models and providing probabilistic maps of avalanche run-out. These maps show the prediction of maximum avalanche impact for specific snow cover conditions. For this feasibility investigation, we consider one avalanche cycle from January 2019, in the Dischma Valley, located in Davos, Switzerland, where three cold avalanches got artificially triggered and reached the road. Photos of the powder cloud and meteorological data from stations nearby the avalanche tracks exist. Within one day after the avalanche events, drone-based photogrammetric measurements of the avalanche outlines and their mass balance, snow pit profiles at the top and bottom of the avalanche tracks were acquired. This unique dataset allows for a robust assessment of the avalanche modelling results. To represent different data availability cases, we investigate two options; a) a dataset in which the input data (new snow, snow temperature) are based on the weather station data collected nearby, and b) a data set derived from SNOWPACK simulation outputs, using weather data from a nearby station. We perform a set of RAMMS avalanche simulations with different values for the predicted average new snow height in the release zone and wind effects. By running multiple simulations for different input parameters, each assigned a specific probability that represents realistic snowcover conditions, we can produce probability-based avalanche run-out results that can be used for decision-making support. To assess the predictive power of the calculated maps, we compare modelled and observed avalanche core outlines.

Slushflow consists of a mixture of snow, water, and ice and often entrain debris or sediments. The high mobility and high density of the flows make them a considerable natural hazard, endangering settlements and line infrastructures. They are primarily associated with higher latitudes, e.g., Norway, Iceland, or Alaska, but they have been reported in many other countries, e.g. from the Alps. In this paper, we discuss a number of slushflow events near Atigun Pass, Alaska, which were well documented in a study for the Alyeska Pipeline Service Company (APSC) in 1982. This information has been privately held and is now being released for research purposes. This paper describes these observations, and we apply modeling techniques to the described flows. We specifically consider both depth-averaged and depth-resolved numerical methods with viscoplastic and elasto-viscoplastic rheological models. These developments can help hazard mapping practitioners better account for the hazards originating from slushflows in the future.

Anthropogenic climate change is rapidly altering high mountain environments, including changing the frequency, dynamic behavior, location, and magnitude of alpine mass movements. Here, we review three decades of scientific literature (1995 to early 2024) to assess to what degree observational records from the European Alps – as the region with the most comprehensive records – reveal these changes. We do this for the processes that are most common in this region, namely rockfall, rock avalanches, debris flows, ice avalanches, and snow avalanches. The systematic literature search and review yielded 335 publications, of which we omitted publications that did not focus primarily on observational records. The remaining 103 publications used observations from over 100 sites and 30 inventories to investigate the connection between climate change and mass movements. About one third of the relevant studies found a measurable impact of climate change on the investigated alpine mass movement processes (with the exception of large rock avalanches). The clearest climate-controlled trends are (i) increased rockfall frequency in high-alpine areas due to higher temperatures, (ii) fewer and smaller snow avalanches due to scarcer snow conditions at low and mid elevations, and (iii) a shift towards avalanches with more wet snow and fewer powder clouds. While there is (iv) despite a clear increase in debris-flow triggering precipitation, debris-flow activity has not been found to uniformly increase, though there is some evidence for increasing activity above treeline and at locations without historical precedence. The trends for (v) ice avalanches are spatially very variable with no clear direction. Ice temperatures are measurably increasing, but – despite a theoretical expectation – this has not impacted ice avalanche activity to date. The reviewed literature also reveals that quantifying the impact of climate change on these mass movements remains difficult in part due to the complexities of the natural system, but also because of limitations in the available datasets, confounding effects, and existing statistical processing techniques. Better assessments could be achieved if we would more broadly support the compilation and maintenance of large standardized data catalogs, bring together various dispersed datasets (in said catalogs), including from social and citizen science projects, invest in long-term natural observatories, and develop suitable processing techniques. Better observations will additionally support the development and performance of process-based models. If we can advance natural hazard research on these fronts, more quantitative predictions of future change are well within our reach.

In densely populated mountain regions, snow avalanches are among the main natural hazards and have long been a threat to settlements and critical infrastructure. Yet over the past decades, avalanche fatalities on roads and in settlements have been reduced through extensive avalanche hazard assessment and mitigation measures. In the protection scheme, fast dry-snow avalanches have long been considered the most destructive and thus the determining avalanches. In this case, impact pressure depends primarily on flow velocity, which is commonly obtained from depth-averaged avalanche simulations in engineering practice. With a warming climate, however, slower wet-snow avalanches are becoming more frequent. The impact pressure of these avalanches depends more on flow height and rheological properties of snow, such as friction, cohesion, and compressibility. In the midst of these changing conditions, current impact pressure calculation procedures involving numerical models based on depth-averaged mass and momentum balance equations struggle to capture the complex rheological behavior of snow and three-dimensional avalanche-obstacle interaction effects. We argue that a paradigm shift is needed if we are to meet the challenges posed by wet-snow avalanches. To that end, we propose a highly efficient 3D model based on the Material Point Method (MPM). Through its continuum formulation, it allows for the incorporation of established elastoplastic constitutive models and naturally handles collisions and fractures in a large deformation framework without mesh distortion problems. We demonstrate the relevance and the potential of this modeling approach by simulating key avalanche processes observed at the Vall ´ee de la Sionne avalanche test site. Furthermore, we quantitatively compare calculated impact pressure magnitude and distribution to measurements to show the accuracy of our approach and address the consequences of common modeling assumptions in avalanche engineering, such as neglecting snow compressibility.
This work aims to highlight the complex rheological composition of avalanche impact pressure and demonstrates the importance of a more fundamental and general impact pressure assessment in light of climate change.

With advancements in computational power and recent developments in particle-based numerical modeling, it is now possible to perform fully three-dimensional (3D) and real-scale snow avalanche simulations using methods such as the Material Point Method (MPM). These 3D MPM simulations explicitly model flow processes and velocity components in the slope-normal direction, allowing for accurate capture of phenomena such as flow detachment from the terrain in response to abrupt topography variations. In a recent case study where we applied MPM to simulate a snow avalanche at slope scale in 3D from release to run-out, we observed significant slope-normal velocity components, particularly in regions with pronounced variations in terrain curvature. This observation raised the question of the practical significance of slope-normal flow velocity components, especially in terms of their interaction with objects susceptible to uprooting, protection galleries, or other structures where slope-normal velocities might exert enough force to dislodge or damage roofs. To investigate this further, we use the flow conditions from a slope-scale MPM simulation of the 2019 “Salez” avalanche in Davos, Switzerland, which overflows a road gallery, as boundary conditions for Discrete Element Method (DEM) simulations. In these DEM simulations, we focused on the avalanche flow on the gallery roof, analyzing the resulting normal and shear forces as well as the velocity components normal and parallel to the roof. A comparison between the stresses obtained from DEM simulations and the calculations based on the Swiss Guidelines reveals that the guidelines provide conservative yet relatively accurate stress estimates. By distinguishing between the normal and parallel velocity components relative to the gallery roof, we find that the normal component is closely correlated with the resulting stresses, highlighting the potential significance of normal velocity components in determining the load on galleries.

Avalanche modeling is an essential tool to assess snow avalanche hazard. Today, most popular numerical approaches adopt depth-Averaged equations. These methods are computationally efficient but limited in cap turing processes occurring in the flow depth direction, e.g., erosion or deposi tion, which are often considered using ad hoc parameterizations or neglected completely. However, processes such as snow erosion, can crucially influence the flow dynamics and run-out and are often not negligible. We address these issues by using a new three-dimensional model, based on the Material Point Method (MPM) and finite strain elastoplasticity. To assess the possibilities and challenges associated with these highly detailed but computationally ex pensive calculations, we simulated the "Salezer" snow avalanche that released in Davos, Switzerland, in 2019. To reproduce the event in our simulations, we use the release areas mapped in a photogrammetric drone survey and es timate the snow conditions on the day of the event. We compare macroscopic features, such as flow outline and snow deposition, of the simulated avalanche to field observations. An in-depth analysis of transient 3D flow structures at the avalanche head demonstrates the degree of physical detail in the model, but also highlight challenges which still need to be addressed.

Waves are omnipresent in avalanches on Earth and other planets. The dynamic nature of waves makes them dangerous in geological hazards such as debris flows, turbidity currents, lava flows, and snow avalanches. Extensive research on granular waves has been carried out by using theoretical and numerical approaches with idealized assumptions. However, the mechanism of waves in realistic complex situations remains intangible, as it is notoriously difficult to capture complex granular waves on real terrain. Here, we leverage a recently developed hybrid Eulerian-Lagrangian numerical scheme and an elastoplastic constitutive model to investigate the processes involved in waves of snow avalanches, including erosion, deposition, and flow instability induced by terrain irregularity. This enables us to naturally simulate roll-waves, erosion-deposition waves, and their transitions in a single large-scale snow avalanche on real terrain. Simulated wave features show satisfactory consistency with field data obtained with different radar technologies. Based on a dimensionless analysis, the wave mechanics is not only controlled by the Froude number and local topography but also by the mass of the wave which governs the entrainment propensity. This study offers new insights into wave mechanisms of snow avalanches and provides a novel and promising pathway for exploring transient waves in granular mass movements.

Gravitational mass movements may erode and/or entrain a significant amount of bed material that can strongly affect the flow dynamics until the moving mass eventually deposits and comes to rest. Snow avalanches generally release on slopes covered by a metastable and thus potentially erodible snow cover that can have a wide range of strength – or cohesion – depending on the type of snow and its physical properties. As the avalanche flows, the snow cover is fully or partially entrained at the front and at the base of the flow, increasing the mass of the avalanche. Conversely, at the tail, snow may be deposited along the track, reducing the overall flowing mass. The balance between entrainment and deposition therefore determines the growing or decaying of the avalanche in terms of mass. To date, it remains unclear how cohesion influences these processes and what consequences it has for avalanche dynamics and run-out. Here, we perform simulations based on the Discrete Element Method (DEM) to analyze the influence of cohesion and slope angle on the erosion, entrainment, mixing and deposition processes. This method makes it possible to follow the dynamics of the particles within the flow very precisely, something that cannot be done in real experiments. In the model, the cohesion is represented as the combined effects of a fragmentation potential associated with the strength of the bonds, and an aggregation potential associated with the stickiness of the particles. For various combinations of input parameters and material properties, we release a heap of particles over an erodible bed and simulate the entrainment and deposition mechanisms. Our results show on the one hand that a low strength (< 3 kPa) promotes a ploughing entrainment mechanism and a large entrainment velocity, up to 3 m/s. On the other hand a high strength (> 3 kPa) favors basal abrasion as the flow front is not able to destabilize the erodible bed at once. In this case, the entrainment velocity decreases typically below 1 m/s. This has important consequences on mixing: the front in granular flows with low strength and adhesion is typically made of freshly entrained material coming from the whole depth of the bed, while remains of the released material can be found at the front of highly cohesive avalanches. Finally, the deposition process is analyzed by evaluating the relationship between the deposit thickness h_stop and slope angle θ which extends the framework of the model of h_stopθ from cohesionless to cohesive granular flows. We find that a higher bond strength of the flowing material increases the deposition height. Our work improves our understanding of the mechanics of cohesive granular flows and may contribute to improving parameterizations in depth-averaged models used to simulate geophysical mass flows such as rock, ice, snow avalanches, debris flows and landslides.

We demonstrate how high spatial and temporal resolution spaceborne synthetic aperture radar (SAR) imagery can improve slope deformation monitoring. We process ICEYE data over the Brienz/Brinzauls slope instability in the Swiss Alps, where a catastrophic failure occurred on 15 June 2023. Image correlation applied to retrieve surface velocities shows results comparable to in situ measurements. Pre- and post-failure acquisitions used to map areas invaded by debris and to compute associated volumetric changes are in good agreement with photogrammetric data. This study sets the baseline for new-generation satellite SAR data aimed at providing timely information in landslide early warning scenarios.

Spatial variability of snowpack properties adds uncertainty in the evaluation of avalanche hazard. We propose a combined mechanicalstatistical approach to study how spatial variation of slab depth affects the skier-triggering probability and possible release size. First, we generate multiple slab depth maps on a plane fictional slope based on Gaussian Random Fields (GRF) for a specific set of mean, variance and correlation length. For each GRF, we derive analytically the Skier Propagation Index SPI. We then simulate multiple skier tracks and computed the probability based on t h e number of skier hits where SPI is below 1. Finally, we use a Depth-Averaged Material Point Method to evaluate the possible avalanche size for given slab depth variations. The results of this analysis show that large correlation lengths and small variances lead to a lower probability of skier-triggering as it reduces the size and the number of areas with low slab depth. Then, we show the effect of skiing style and skier group size on skier-triggering probability. Spatial variability also affects the possible avalanche size by adding stress fluctuation causing early or late tensile failure. Finally, we demonstrate with our models the well-known relationship between the probability and the size in avalanche forecasting.

A continuous upward shift of the upper treeline is observed in many mountain regions worldwide. Such forest expansion is causing significant impacts on ecosystem services, especially as it may reduce avalanche hazards. Forests mitigate the release of snow avalanches mainly through snowpack stabilization. Trees prevent the creation of continuous weak layers thus decreasing the probability of slab release. Forest density, size of forest gaps, tree height and tree species are key factors to estimate the forest protective effect against snow avalanches. Field observations indicate that a certain tree height is crucial for the prevention of avalanche release in young forest stands and that tree height should be at least one-and-a-half to two times taller than the snow cover, with required tree height increasing with slope inclination. In this study, we analysed the relationship between avalanche occurrence, tree height and snow height within the long-term afforestation research area Stillberg near Davos, Switzerland. A decreasing number of avalanche releases was observed in relation to changes in tree height in the afforestation during the last 49 years. Furthermore, the spatial distribution of avalanche release areas shifted from covering most of the studied area of Stillberg to three local avalanche release areas within gullies. These gullies were characterized by lower tree densities but larger tree heights and larger snow accumulation. The analysis of all avalanches at Stillberg indicated that the ratio of tree height to snow height had contrasting effects on the release of loose snow avalanches and slab avalanches; a high ratio hindered the formation of slab avalanches, but under certain conditions some loose snow avalanches released. Areas prone to loose snow avalanches typically have few or small Larix and lack evergreen trees. Overall, the Stillberg afforestation after almost 50 years, together with local temporary protection measures for the gullies could be effective in mitigating avalanche activity.

Herein, fast fracture initiation in glacier ice is modeled using a Material Point Method and a simplified constitutive law describing tensile strain softening. Relying on a simple configuration where ice flows over a vertical step, crevasse patterns emerge and are consistent with previous observations reported in the literature. The model's few parameters allows identification of a single dimensionless number controlling fracture spacing and depth. This scaling law delineates two regimes. In the first one, ice thickness does not play a role and only ice tensile strength controls the spacing, giving rise to numerous surface crevasses, as observed in crevasse fields. In this regime, scaling can recover classical values for ice tensile strength from macroscopic field observations. The second regime, governed by ice bending, produces large-scale, deep fractures resembling serac falls or calving events.

Dry-snow slab avalanches release due to crack propagation in a weak snow layer under a cohesive snow slab. Crack propagation speeds can provide insights into the potential size of avalanches and inform fracture and avalanche release models. Despite their importance, slope-scale crack speed measurements from real avalanches are limited. Further, most existing slope-scale measurements utilize the appearance of slab fractures on the snow surface. However, we have no evidence that the appearance of surface cracking is a good indicator of the weak layer crack propagation tip. Here we present a novel method to estimate crack propagation speed from snow surface movements in avalanche videos. Our technique uses changes in frame pixel intensity, allowing us to detect the location of weak layer cracks well before slab fractures appear on the snow surface. We use field experiments and numerical simulations to validate our method before applying it to five avalanches. Our estimates show that cracks propagate faster up and down the slope than in the cross-slope direction; this suggests that different propagation regimes likely govern crack propagation up/down the slope, cross-slope and in flat terrain.

Skiing or snowboarding down a mountain covered with fluffy new snow can provide a unique experience,  eminiscent of floating above clouds. This sensation is often intuitively attributed to the supporting effect of the interstitial air loaded by the skis or snowboard, creating a feeling of weightlessness. Interestingly, this same principle may also account for the rapid descent and extensive runout distance of much less pleasant phenomena, such as powerful mixed snow avalanches. These avalanches exhibit a complex structure, characterized by a dense basal regime overlaid and preceded by an intermittent layer of coherent snow clusters, along with a lighter suspension layer of fine particles mixed with air. Despite their significance, the  processes driving the formation of these structures and the reasons for the remarkable mobility of mixed snow avalanches remain incompletely understood. One prevailing theory proposes that this mobility could result from the fluidization of the porous snow cover, which is transiently weakened by the development of excess pore air pressures upon undrained loading by the avalanche. We test this hypothesis, by means of two-phase simulations. The solid ice grains are simulated using either the Discrete Element Method (DEM) or as a continuum through the Material Point Method (MPM). Compared to previous works, we simulate explicitly the air both within the pores and in the ambient, using CFD (specifically the Finite Volume Method) which is coupled to the conservation equations of the solid phase. Simple preliminary simulations are presented in this work. We simulate a snowboarder dropping over a fresh snow cover. The simulations show that, if the permeability of the snow cover is low enough, the pressurized pore air may lead to fluidization of the snow, favoring its mechanical weakening and suspension as a vertical jet. Instead, if the permeability of the snow is too high, pore air pressure quickly diffuses, which cannot generate significant suspension of the particles. Similar modelling approaches will be applied in future to model mixed snow avalanches interacting with an erodible snow layer. Ultimately, we expect this research to contribute to improved entrainment and fluidization models in depth-averaged numerical methods.