Dr. Yves Bühler

The Indian Himalayan region is the origin of numerous natural hazards, including landslides, earthquakes, floods, cloudbursts, etc. (Dash et al. 2021; Falae et al. 2021; Kanungo and Sharma 2014). Uttarakhand, located in the northwest Indian Himalayas, is one of the most landslide-prone states in India. In a recently published report by the National Remote Sensing Centre (NRSC) India, all the 13 districts of Uttarakhand state are severely affected by landslides and the Chamoli district ranks 3 among 13 districts of Uttarakhand and ranks 19 among 147 landslide-prone districts in the country according to the landslide density (Jain et al. 2023). Over the past three decades, Chamoli has experienced several destructive mass movement events, including the 2013 Kedarnath tragedy, the Chamoli rock and ice avalanche on February 7, 2021, land subsidence in Joshimath during 2023, etc. In 1999, Chamoli was struck by an earthquake with a magnitude of 6.8 on the Richter scale, with its epicenter near Gopeswar township. In addition to landslides and earthquakes, Chamoli has also faced numerous disasters caused by cloudbursts and flash floods. National Highway 58 connects Uttarakhand with the rest of the country, entering Chamoli district through Karnaprayag and extending to Badrinath. This highway passes through the Alakananda Valley, traversing many landslide-prone zones. In a recent study, Ramiz et al. (2023) found that about 27% of the areas along the highway corridor from Rishikesh to Badrinath is highly susceptible to landslides. Frequent landslides, including debris flows, rockfalls, debris slides, and subsidence, disrupt traffic and deposit materials in the river, increasing the likelihood of river damming (Dash et al. 2022, 2023) and traffic disruption. A similar traffic disruption occurred due to a rock avalanche near Patalganga Langsi Tunnel on the Badrinath Highway of Chamoli district on July 10, 2024. This is an area known for frequent rockfall, and debris slide events, as noted by earlier researchers.

We present an hourly hydrometeorological and snow dataset with 100 m spatial resolution from the alpine Dischma watershed and its surroundings in eastern Switzerland, including station measurements of variables such as snow depth and catchment runoff. This dataset is particularly suited for different modelling experiments using distributed and process-based models, including physics-based snow and hydrological models. Additionally, the data are highly useful for testing various snow data assimilation schemes and for developing models representing snow–forest interactions. The dataset covers 7 water years from 1 October 2016 to 30 September 2023. The complete domain spans an area of 333 km² with altitudes ranging from 1250 to 3228 m. The Dischma Basin, with its outlet at 1671 m elevation, occupies 42.9 km². Included in the dataset are high-resolution (100 m) hourly meteorological data (air temperature, relative humidity, wind speed and direction, precipitation, and long- and shortwave radiation) from a numerical weather predication model and rain radar, land cover characteristics (primarily forest properties), and a digital elevation model. Notably, the dataset includes snow depth acquisitions obtained from airborne lidar and photogrammetry surveys, constituting the most extensive spatial snow depth dataset derived using such techniques in the European Alps. Along with these gridded datasets, we provide daily quality-controlled snow depth recordings from seven sites, biweekly snow water equivalent measurements from two locations, and hourly runoff and stream temperature observations for the Dischma watershed. The data compiled in this study will be useful to further develop our ability to forecast snow and hydrological conditions in high-alpine headwater catchments that are particularly sensitive to ongoing climate change. All data are available for download at https://doi.org/10.16904/envidat.568 (Magnusson et al., 2024).

The influence of climate change on snow avalanches, particularly for the end of this century, remains uncertain, underscoring the need for further research. To assess the possible consequences of potential changes in snow accumulation and temperature and their impact on avalanche hazard, we introduce a comprehensive multi-step framework. It includes the analysis of climate change scenarios as well as the modeling of future snow covers and the simulation of avalanches in a case study region in central Switzerland.
Using a downscaling and a quantile mapping approach, we considered the high emission RCP8.5 from the CH2018 Swiss climate change scenarios and simulated a potential snow cover of more than 100 future winters with the snow cover model SNOWPACK. Changing snow accumulation and snow cover temperature was taken into account for two future time frames. The changed parameters were used in the RAMMS::EXTENDED avalanche simulation software on large scale.
The results indicate that changes in snow accumulation and temperature have a considerable impact on the run-out of avalanches. The results strongly depend on the climate model, without a clear overall trend in snow accumulation across the selected model chains. Snow accumulation and layer temperature can increase or decrease. However, for snow cover temperature, an increase in the mean snow temperature, especially towards the end of the century, can be expected. In future scenarios with reduced snow accumulation and rising temperatures, avalanche simulations show a decrease in the affected area.
The workflow from climate scenario analysis to avalanche hazard modeling serves as an initial method for estimating future avalanche extents in the context of climate change on a large scale and can be useful for achieving future protection and adaptation goals.

Snow avalanches threaten people and infrastructure in mountainous areas. For the assessment of temporal protection measures of infrastructure when the avalanche danger is high, local and up-to-date information from the release zones and the avalanche track is crucial. One main factor influencing the avalanche situation is wind-drifted snow, which causes variations in snow depth across a slope. We developed a monitoring system using low-cost lidar and optical sensors to measure snow depth variations in an avalanche release area at a high spatiotemporal scale (centimetre to low decimetre spatial resolution and hourly temporal resolution). We analyse data obtained from such a monitoring system, installed within an avalanche release area at 2200 m a.s.l. close to Davos in the Swiss Alps. The system comprises two measurement stations and has been operational since November 2023. We present the experiences and insights gained from a preliminary analysis of the data obtained so far. The temporal variations of the spatial coverage show the potential and limitations of the system under varying weather conditions. A comparison of the surface elevation models derived from the lidar data and from photogrammetric processing of UAV-based images shows a good agreement, with a mean vertical difference of 0.005 m and standard deviation of 0.15 m. An avalanche event and a period of snowfall with strong winds, chosen as case studies, show the potential and limitations of the proposed system to detect changes in the snow depth distribution on a low decimetre level or better. The results obtained so far indicate that a measurement system with a few setups in or near an avalanche slope can provide information about the small-scale snow depth distribution changes in near real time. We expect that such systems and the related data processing have the potential to support experts in their decisions on avalanche safety measures in the future.

Snow avalanches are the primary mountain hazard for mechanized skiing operations. Helicopter and snowcat ski guides are tasked with finding safe terrain to provide guests with enjoyable skiing in a fast-paced and highly dynamic and complex decision environment. Based on years of experience, ski guides have established systematic decision-making practices that streamline the process and limit the potential negative influences of time pressure and emotional investment. While this expertise is shared within guiding teams through mentorship, the current lack of a quantitative description of the process prevents the development of decision aids that could strengthen the process. To address this knowledge gap, we collaborated with guides at Canadian Mountain Holidays (CMH) Galena Lodge to catalogue and analyze their decision-making process for the daily run list, where they code runs as green (open for guiding), red (closed), or black (not considered) before heading into the field. To capture the real-world decision-making process, we first built the structure of the decision-making process with input from guides and then used a wide range of available relevant data indicative of run characteristics, current conditions, and prior run list decisions to create the features of the models. We employed three different modeling approaches to capture the run list decision-making process: Bayesian network, random forest, and extreme gradient boosting. The overall accuracies of the models are 84.6 %, 91.9 %, and 93.3 % respectively compared to a testing dataset of roughly 20 000 observed run codes. The insights of our analysis provide a baseline for the development of effective decision support tools for backcountry avalanche risk management that can offer independent perspectives on operational terrain choices based on historic patterns or as a training tool for newer guides.

Swiss geosciences are at the heart of major national and global challenges that our society is facing, including climate change and weather extremes, long-term environmental trends, natural hazards (e.g. landslides, floods,
seismic and volcanic events), the energy transition towards a green and blue economy, sustainable management of natural resources (e.g. water, soils, ecosystems, critical metals, building materials), and the increasing interest
towards extra-terrestrial life and resources. In order to remain at the forefront of geosciences research and provide the fertile environment necessary for groundbreaking findings addressing these societal challenges, the Swiss Geosciences Community proposes five large infrastructures, organised into four pillars built on a common datadriven research foundation. [...]

This paper explores the application of grain flow theory to snow avalanche dynamics, focusing on the interrelation between shear stress and fluctuation energy. The study employs a system of differential equations to model the mean velocity (U) and fluctuation energy (R) within snow avalanches. The key physical constraint highlighted is the relationship dR · S = −R₀ · dS, which provides a physical constraint on the  omplex relationship between avalanche shear stress and the production of fluctuation energy. When fluctuation energy is produced by shearing, it generates not only directionless random kinetic energy but also directional energy fluxes that alter the configuration of the granular ensemble, leading to changes in shear stress. The analysis of the R-U phase plane reveals that avalanche behavior can be characterized by equilibrium points that shift with slope angle, highlighting the role of fluctuation energy in determining the flow regime. The R-U phase plane not only advances our understanding of avalanche dynamics but also facilitates the examination of flow regime transitions, driven by changes in the potential energy of the granular ensemble, and the emergence of different avalanche types.

Die Ereignisse Ende Juni 2024 haben gezeigt, dass neue, konsistente Methoden zur Bewertung von Murgang- Schutzmassnahmen benötigt werden. Die Wahl geeigne­ter Gefahrenszenarien und die Modellierung der Mur­gänge sind zentral, um Schutzmassnahmen effektiv zu planen. Ein neues numerisches Zwei-Schichten-Modell, das feste und flüssige Phasen trennt, wurde eingesetzt, um die komplexe Dynamik von Murgängen besser nachzu­bilden. Zwei Fallstudien im Val Greva (GR) und in Sorte (GR) zeigen realistische Ergebnisse und verdeutlichen die Bedeutung der Erosion für die Mobilität von Mur­gängen. Die Ergebnisse weisen darauf hin, dass künftige Schutzmassnahmen differenziertere Informationen zum Fliessverhalten erfordern. Eine stetige Verbesserung der Modelle durch eine gründliche Analyse vergangener Er­eignisse ist entscheidend, um die Genauigkeit und Zu­verlässigkeit der Gefahrenbewertung zu gewährleisten.

Automated weather stations (AWS) measuring snow-related parameters are essential for avalanche warning in many regions. Key data include new snow accumulation, wind direction, and wind velocity, especially in remote, high-elevation terrain. This information is critical for decisions such as when to close and reopen roads. Given the high spatial variability of snow depth distribution in mountain areas, the positioning of AWS is crucial. High-resolution, spatially coherent snow depth measurements acquired by drones, airplanes, or satellites reveal significant variability within short distances of just a few meters. Therefore, it is important to place weather stations at relatively flat locations with representative snow depth values. Areas where the snowpack is strongly influenced by wind or avalanches, either removing or depositing large amounts of snow, are unsuitable.

We developed an automated approach combining remotely sensed snow depth maps with terrain characteristics (e.g., slope or homogeneity) and simulated avalanche scenarios to identify optimal positions for AWS. We demonstrate how this approach can enhance safety-relevant information in the Dischma Valley near Davos, Switzerland. This approach could be applied globally wherever high-quality digital elevation models are available and spatially coherent snow depth maps can be acquired. Currently, the positioning of AWS relies heavily on expert judgment. Our tool could help make these decisions more comprehensible and serve as a second opinion, ensuring the optimal placement of AWS.

Snow avalanches are a major natural hazard in mountainous areas, posing a significant risk to lives and infrastructure. Knowing the location, frequency, and magnitude of past snow avalanche occurrences is vital to mitigate these risks. Avalanche mapping is therefore critical in risk mitigation. Previous research has explored various techniques to automate snow avalanche mapping from remote sensing sources, including satellite synthetic aperture radar (SAR), optical airborne, and satellite imagery. However, employing historical optical data sources for full snow avalanche delineation has received less attention. These datasets, created in the analog era, could shed light on the patterns of past avalanche activity. The winter of 1999 in the European Alps is notable for its significant avalanche activity with extreme avalanche runouts. It is well documented through aerial images covering over 12,000 square kilometers in the Swiss Alps and neighboring Austria. Our study leverages deep learning techniques to map the release, path, and deposition areas of visible snow avalanches in these historical images. Building upon previous work in deep learning for automated mapping with optical SPOT 6/7 satellite imagery, we have applied an avalanche segmentation model to historical data. The model is based on a convolutional neural network and relies on digital elevation data and orthorectified optical imagery to produce a pixel-level binary snow avalanche segmentation mask. This task requires the adaption of the methods, originally developed and trained on multi-spectral satellite data, for application on grey-scale data, a problem referred to as a domain gap. We do this by augmenting the historical training set with contemporary samples. The contemporary data is transformed to match the target data domain using a Generative Adversarial Network and coupled with the historical data to train the model. We also offer insights into the estimated uncertainty associated with labels and predictions, serving as a resource for future dataset users. Our research shows how cutting-edge mapping techniques can be adapted to historical imagery.

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.

Humans have been exposed to snow avalanches ever since they inhabited or travelled through mountainous regions. For centuries, ways of managing risk and mitigating damage due to avalanches have been limited. For example, forests prevented the release of avalanches, protecting the villages below. In the past ca. 150 years, avalanche protection measures and risk reduction strategies have become increasingly sophisticated. With a growing population, the rise of alpine tourism, and major transportation corridors running through the mountains, dealing with avalanches has become more important than ever. Operational services such as the avalanche warning service, hazard mapping, or the installation of protection and mitigation measures for endangered zones are important tools for avalanche risk management. The efficiency of these tools depends on knowledge about past avalanche occurrences. In this thesis, novel methods are developed to automatically provide this information over large regions. Specifically, state-of-the-art deep learning technology is used to extract such information from optical satellite and webcam imagery. The avalanches required to train the deep learning models are identified and mapped by human experts in the domain. Therefore, the thesis also focuses on the consistency of the avalanche area identified by various domain experts.

In the first part of the thesis, a DeeplabV3+ model is adapted to automatically identify and map avalanches from optical SPOT 6/7 satellite imagery (1.5 m resolution). The model is trained, validated, and tested with more than 24'000 manually annotated avalanche polygons. The data originate from two avalanche periods in January 2018 and January 2019 and cover an area of more than 22'000 km2. In addition, the quality of the model and the reproducibility of avalanches manually annotated by experts are assessed for a small subset of the data.

The second part of the thesis investigates in more detail the reproducibility of estimates of avalanche dimensions by human experts in three user studies. The first study analyzes the classification of ten avalanches into five standardized size categories by each of 170 avalanche experts from Europe and North America. The second and the third study examine avalanches manually mapped from oblique photographs (6 avalanches, 10 participants) or from remotely sensed imagery (2.9 km², 5 participants), respectively.

The third part of the thesis leverages interactive avalanche segmentation (IAS) to combine human expert knowledge with deep learning. Here, when mapping avalanches from webcam imagery, the user collaborates with the previously trained model. The use of the model is supposed to make avalanche mapping more accurate and efficient. For this purpose, we adapt a state-of-the-art interactive segmentation model based on HRNet+OCR and train it for avalanche segmentation from webcam imagery. The human user interacts with the model through confirming or corrective feedback.

In summary, this thesis makes a substantial contribution to the development of an operational automatic avalanche mapping service. The thesis provides the first automatic avalanche mapping from optical satellite imagery with deep learning, it quantifies, for the first time to this extent, the reproducibility of human avalanche estimates, and it presents a first interactive approach for the mapping of avalanches from webcam imagery. Thereby, this thesis contributes to a more efficient use of data and better provision of information on past avalanche occurrences, and thus it benefits decision making in safety-relevant applications.

For many safety-related applications such as hazard mapping or road management, well-documented avalanche events are crucial. Nowadays, despite the variety of research directions, the available data are mostly restricted to isolated locations where they are collected by observers in the field. Webcams are becoming more frequent in the Alps and beyond, capturing numerous avalanche-prone slopes. To complement the knowledge about avalanche occurrences, we propose making use of this webcam imagery for avalanche mapping. For humans, avalanches are relatively easy to identify, but the manual mapping of their outlines is time intensive. Therefore, we propose supporting the mapping of avalanches in images with a learned segmentation model. In interactive avalanche segmentation (IAS), a user collaborates with a deep-learning model to segment the avalanche outlines, taking advantage of human expert knowledge while keeping the effort low thanks to the model’s ability to delineate avalanches. The human corrections to the segmentation in the form of positive clicks on the avalanche or negative clicks on the background result in avalanche outlines of good quality with little effort. Relying on IAS, we extract avalanches from the images in a flexible and efficient manner, resulting in a 90 % time saving compared to conventional manual mapping. The images can be georeferenced with a mono-photogrammetry tool, allowing for exact geolocation of the avalanche outlines and subsequent use in geographical information systems (GISs). If a webcam is mounted in a stable position, the georeferencing can be reused for all subsequent images. In this way, all avalanches mapped in images from a webcam can be imported into a designated database, making them available for the relevant safety-related applications. For imagery, we rely on current data and data archived from webcams that cover Dischma Valley near Davos, Switzerland, and that have captured an image every 30 min during the daytime since the winter of 2019. Our model and the associated mapping pipeline represent an important step forward towards continuous and precise avalanche documentation, complementing existing databases and thereby providing a better base for safety-critical decisions and planning in avalanche-prone mountain regions.

Systematic mapping of avalanches using remote-sensing technologies has become a reliable and – when combined with machine-learning algorithms – also time-effective way to document avalanche activity over large areas. Making use of these technological advances, we compared the avalanche activity for three periods with widespread activity of very large and extremely large avalanches, when the highest danger level 5 (very high) was forecast for large parts of the Swiss Alps. In these three periods (in 1999, 2018, 2019), avalanches were captured with different remote-sensing instruments: in 1999, with airplane imagery, in 2018 and 2019 with multispectral satellite data. Comparing the activity of all three periods to Large Scale Hazard Indication Modelling (LSHIM) of avalanches with a 100-year return period allowed to compare how much of the potential avalanche terrain was active and to quantitatively compare activity between these periods. We found that 19% of the avalanche release areas showed activity in 1999 compared to 16% in 2018 and 8% in 2019. Considering the avalanche area delimited using the 100-year return period, 17% were active in 1999, 11% in 2018 and 6% in 2019. By comparing the three avalanche periods, we contribute to better understand the extent and return periods of very large and extremely large avalanches (size 4 and 5) and demonstrate, how avalanche mapping from remotely sensed data can make avalanche databases more complete.

Avalanche terrain maps are becoming increasingly common for large areas due to the availability of high-resolution and high-quality digital elevation models (DEM). Many of these maps use the Avalanche Terrain Exposure Scale (ATES) classification system, which categorizes terrain from simple to extreme, often using simple runout models such as the statistical alpha-beta model to identify potential runout areas. The current automated ATES classification models do not include avalanche dynamics models such as RAMMS, SAMOS or r.avaflow. Since 2018, Classified Avalanche Terrain (CAT) and Avalanche Terrain Hazard (ATH) maps have been introduced in Switzerland, delineating avalanche terrain into potential release areas and runout zones for size class 3 avalanches determined with the numerical avalanche simulation model RAMMS. While these maps have been very well received by recreationists and are widely used in Switzerland, the experience gained over the last years has shown that there is still room for improvements. Specifically, (i) potential release areas were not always well classified, (ii) runout zones may be too long for typical skier-triggered avalanches, and (ii) the ATH map, combining various factors, is complex and the information can be ambiguous. Therefore, we present updated versions of the CAT and ATH maps addressing these issues and allowing users to better assess avalanche risk and plan backcountry trips. These revised maps provide refined representations of potential release areas and improved mapping of avalanche runout zones driven by a new version of RAMMS::EXTENDED. Validation in different regions, focusing on size 3 avalanches, confirm the improved accuracy in delineating potential avalanche runout zones. Additionally, a new iteration of the ATH map simplifies the complexity of avalanche terrain by categorizing the terrain into easy-to-understand classifications that provide a concise overview, which also incorporates the ATES system. The new maps are generated using various layers provided by the RAMMS output, which can also support automatic risk assessment at cruxes. All that is required to produce these new maps is a high-quality elevation model at 5-m resolution, and a reliable map layer for the protective forest cover.

One of the primary causes of non-uniform snowfall deposition on the ground in mountainous regions is the preferential deposition of snow, which results from the interaction of near-surface winds with topography and snow particles. However, producing high-resolution snowfall deposition patterns can be computationally expensive due to the need to run full atmospheric models. To address this, we developed two statistical downscaling schemes that can efficiently downscale near-surface, low-resolution snowfall data to fine-scale snow deposition accounting for the effect of preferential deposition in mountainous regions. Our approach relies on a comprehensive, model database generated using 3D wind fields from an atmospheric model and a preferential deposition model on several thousand simulated topographies covering a broad range in terrain characteristics. Both snowfall downscaling schemes rely on fine-scale topographic scaling parameters and low-resolution wind speed as input. While one scheme, referred to as the “wind scheme”, further necessitates fine-scale vertical wind components, a second scheme, referred to as the “aspect scheme”, does not require fine-scale temporal input. We achieve this by additionally downscaling near-surface vertical wind speed solely using topographic scaling parameters and low-resolution wind direction. We assess the performance of our downscaling schemes using an independent subset of the model database on simulated topographies, model data on actual terrain, and spatially measured new snow depth obtained through a photogrammetric drone survey following a snowfall event on previously snow-free ground. While the assessments show that our downscaling schemes perform well (relative errors ≤ ±3% with modeled and ≤ ±6% with measured snowfall deposition), they also demonstrate comparable results to benchmark downscaling models. However, our schemes notably outperform the benchmark models in representing fine-scale patterns. Our downscaling schemes possess several key features, including high computational efficiency, versatility enabled by the comprehensive model database, and independence from fine-scale temporal input data (aspect scheme), indicating their potential for widespread applicability. Therefore, our downscaling schemes for near-surface snowfall and vertical wind speed can be beneficial for various applications at fine grid resolutions such as in atmospheric and climate sciences, snow hydrology, glaciology, remote sensing, and avalanche sciences.

We present an innovative modelling approach, which allows to derive probability-based hazard information at any location in avalanche-prone areas. Such an event-based modelling approach allows for comprehensive risk assessments as it provides continuous intensity-frequency-curves at any location and allows to calculate continuous loss-frequency-curves for individual objects as well as for groups of objects.
We simulate numerous possible avalanches, covering the entire range of release volumes, from small to very large. The probabilities of the different avalanche events are based on the exceedance probabilities of different snow accumulation heights, represented by the three-day snow accumulation, the key information currently used for the generation of hazard maps in Switzerland. The combined evaluation of avalanche intensities and probabilities in the area of impact allows to derive continuous intensity- frequency-curves at any given location. This approach contrasts with the current practice in Switzerland of assigning the return period of a single snow accumulation to all possible avalanche outcomes of this snow accumulation. In combination with exposed values and their vulnerability functions, this modelling approach allows to derive continuous loss-frequency-curves for comprehensive risk assessment and risk management.
We present the simulation results of this novel approach from a case study in the Bernese Highland in the Swiss Alps. The case study results highlight the advantages of this new modelling approach in hazard and risk assessment. We show that climate change can be included in this modelling approach and that uncertainties can be quantified.

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.

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.

Flow transitions from rock avalanches to multi-material flows and water saturated debris flows are frequently observed. In 2017, a rock mass of Piz Cengalo (CH) collapsed on a glacier triggering a rock/ice avalanche that resulted into a series of debris flows. At Flüela Wisshorn (CH), in 2019, a rock face collapsed in winter, resulting in a snow avalanche. These transitions depend on the composition of the release and entrainment zones, and on the terrain as ice and snow can melt via frictional heating, increasing the flow water content.
Here, we compute the water content and localisation for the Piz Cengalo event depending on the input parameters. We show that water localizes at the front of the flow, favoring a transition to a debris flow. At Flüela Wisshorn, we show the transition from a rock/snow release to a snow avalanche. We highlight the importance of field and remote sensing data for models, as entrainment is critical for the simulations' outcome and hazard assessment.