Glide-snow avalanches are generally thought to come in two flavors: “cold” and “warm”. The main difference between them is the mechanism by which liquid water is generated and reaches the basal snowpack. For warm avalanches, the water comes from the snow surface via rain or surface melt. For cold avalanches, the water is thought to be generated by capillary suction or geothermal melting. Here, we focus on cold avalanches and address the role of capillary forces at the soil-snow interface. To do so, we combine theoretical considerations, snowpack simulations, and field data. Calculations based on basic principles show that the conditions necessary for capillary suction are unlikely for the representative soil types, because high soil saturation is required. Field data from the “Dorfberg” field site above Davos (eastern Swiss Alps) confirms that these saturated conditions rarely occur. Simulations of two “cold” glide-snow avalanches at the field site further confirm (i) the absence of capillary suction and (ii) the presence of geothermal melting. Thus, we suggest that in the absence of a distinct water source (e.g. spring), geothermal melting is likely responsible for the formation of liquid water in cold avalanches, while the capillary forces are responsible for the retention of this water within the basal snowpack layers.

How to cite: Lombardo, M., Fees, A., Lehmann, P., van Herwijnen, A., and Schweizer, J.: The role of capillary forces in the formation of interfacial water layers in “cold” glide-snow avalanches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16637, https://doi.org/10.5194/egusphere-egu24-16637, 2024.

Snow deforms naturally in a large range of strain rates covering ductile to brittle regimes. In all situations, snow deformation is characterized by complex mechanisms taking place at the microstructure scale, with interplay between metamorphism, sintering and grain rearrangements. Current modeling efforts require better understanding and formulation of the microstructure-scale complexity.
We performed compression experiments on snow samples in order to follow the microstructure evolution at various imposed displacement velocities. The resulting strain-rates varied between 10^-2 s^-1 and 10^-7 s^-1. Samples (15 mm height, 15 mm diameter) were made out of rounded grains with an initial density of about 250 kg m^-3. Samples evolution was followed by means of micro-computed X-Ray tomography (microCT) with full 3D scans performed during the slower tests and simple radiographies at a high frequency for faster tests.
In this presentation we will focus on the various metrics used to analyse the microstructure evolution on one side, in particular specific surface area (SSA) and the minimum cut area. On the other side, we will present recent developments based on digital image and volume correlations (DIC and DVC) performed on the radiographies and the 3D microCT images with the open access SPAM software (https://hal.univ-grenoble-alpes.fr/hal-03020460), in order to follow the local strain field.
We will provide analyses of the interplay between metamorphism and strain in the microstructure evolution and of the impact of mechanisms at bonds in the various strain-rate regimes explored (ductile to brittle). Snow deformation localisations revealed by DIC and DVC observations will be presented. They can be inherited from initial sample heterogeneities or take the shape of compaction bands, depending on the strain rate.
All these data and analyses will be further interpreted regarding the understanding of the small scale mechanisms of metamorphism and deformation of snow and their modeling frame.

How to cite: Georges, D., Védrine, L., Bernard, A., Bonnetier, M., Montagnat, M., Hagenmuller, P., and Chambon, G.: Follow-up at the small scale during snow deformation. Microstructure evolution and local heterogeneities at various strain-rates., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10420, https://doi.org/10.5194/egusphere-egu24-10420, 2024.

Slab avalanches result from the failure of a weak snowpack layer buried underneath a cohesive slab. Determining the material properties of different weak layer morphologies is therefore necessary to better understand and model slab avalanche formation. Natural weak layers exhibit a variety of different microstructures and densities, and thus show different mechanical behavior. Up to now, mechanical properties of snow have been mainly evaluated based on bulk proxies such as snow density, while relevant microstructural characteristics have not been accounted for.

To establish a link between the microstructure of weak layers and their mechanical properties, we performed displacement-controlled laboratory experiments using a uniaxial testing machine. The compression experiments were recorded using a high-speed camera, allowing us to derive the strain within the weak layer. The microstructure of each batch of specimens was analyzed using micro-tomography to obtain density, specific surface area, anisotropy and correlation lengths. As testing a wide range of microstructural morphologies is difficult due to seasonal availability and the need to transport the fragile samples to the laboratory, we used both natural and artificially grown weak layers. We tested weak layers composed of facetted grains, depth hoar, surface hoar, precipitation particles and rounded grains.  

The compressive strength of more than 200 tested samples covered two orders of magnitude (0.5 kPa to 150 kPa) for weak layer densities ranging from 110 kg/m³ to 380 kg/m³. As expected, our results show a strong correlation between weak layer density and compressive strength, but also a dependence on other microstructural quantities. These results will help us improve our understanding of the mechanical properties of weak snowpack layers and will ultimately allow us to better forecast avalanche release probability.

How to cite: Schöttner, J., Walet, M., Adam, V., Rheinschmidt, F., Rosendahl, P., Weißgraeber, P., Schweizer, J., and van Herwijnen, A.: Influence of snow microstructure on the compressive strength of weak layers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12238, https://doi.org/10.5194/egusphere-egu24-12238, 2024.

Dry-snow slab avalanches release due to crack propagation in a weak layer inside the snowpack. Understanding the fracture characteristics of the weak layer is essential for describing the onset of crack propagation and hence for predicting avalanche release. Avalanches release on steep slopes, thus crack propagation is a mixed mode fracture problem. Yet, thus far little is known about the mixed-mode fracture toughness of weak layers, a material property describing the resistance to crack growth under different loading conditions, from mode I normal to the crack faces to mode II parallel to the crack face.

Here, we present experiments that were conducted to derive a full range interaction between mode I and mode II fracture toughness of natural weak layers. Using a mechanical model, we derived fracture toughness values under different mixed-mode loading conditions. Crucial model variables are the elastic properties of the slab and the weak layer, which we retrieved from high-speed video recordings of the experiments and digital image correlation. These elastic properties allow for optimization of the estimates for weak layer fracture toughness values. Our results show that the specific fracture energy is larger in mode II than in mode II. This agrees with the behavior observed in other materials.

In future we will investigate the fracture properties of numerous weak layer microstructures. Since the snow microstructure most likely controls the mechanical properties, a characterization of the microstructure is essential. The connection between weak layer fracture and the microstructure of weak snowpack layers can be used to ultimately improve slab avalanche forecasting.

How to cite: Walet, M., Schöttner, J., Adam, V., Rheinschmidt, F., Schweizer, J., Rosendahl, P., Weissgraeber, P., and van Herwijnen, A.: Elastic snow properties for the optimization of weak layer fracture toughness estimates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18590, https://doi.org/10.5194/egusphere-egu24-18590, 2024.

Redistribution of snow by wind is an important factor influencing the avalanche danger. However, it is challenging to get detailed information on variations of snow depth in avalanche release areas with sufficiently high spatiotemporal resolution. We have developed a distributed measurement system containing two low cost LiDAR sensors, cameras and meteorological sensors. In autumn 2023 we have deployed this system at a first test site, in the area of a frequently released avalanche. Two stations equipped with the sensors cover an area of around 20'000 m² and provide the snow depth distribution once per hour with a spatial resolution on the cm to m-level. The (near) real time data transmission to a local server allows for an up-to-date assessment of the conditions in the slope. First analyses show the small temporal changes of average snow depth from epoch to epoch for small areas (1m²), including some local avalanche events. We will present first results obtained from the unique dataset resulting from acquisition at high spatio-temporal resolution over the entire winter season 2023/2024, focusing particularly on the snow depth variations before and after avalanche events. In the future, the newly built up snow depth database and the additionally recorded meteorological parameters will be used to model, predict and evaluate the snow depth redistribution on a slope scale level. The data collected directly within the release areas will improve the process understanding of avalanche formation and forecasting, and will thus contribute to better protection of people and infrastructure.

How to cite: Ruttner-Jansen, P., Glaus, J., Voordendag, A., Wieser, A., and Bühler, Y.: Snow depth distribution measurements using low cost LiDAR sensors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15088, https://doi.org/10.5194/egusphere-egu24-15088, 2024.

Our research is aimed at improving the prediction accuracy of avalanches caused by the stability of snow cornices developed by blowing snow in the Niseko region, one of Japan's international ski resorts. For this purpose, several studies were conducted in cooperation with local authorities and ski resorts in the Niseko region. Specifically, a network of anemometers was installed and a system was developed to estimate areal wind conditions and snow redistribution over the entire mountain area from wind observation data. To validate the developed system, the snow cover distribution over the entire mountain area for two winters was obtained by laser survey using an aircraft. In addition, several portable ultrasonic anemometers were installed on the slopes where snow cornices develop to observe detailed wind conditions, and small LiDAR was used to continuously survey snow cornice development. We sampled snow in the developing snow cornice and analyzed its microstructure using X-ray computed tomography imaging. The presentation presents a first analysis.

How to cite: Yamaguchi, S., Ito, Y., Tanabe, T., Nishimura, K., Adachi, S., Sunako, S., Saito, Y., Okaze, T., Niiya, H., Tsunematsu, K., and Nishimori, H.: Research on avalanches caused by stability of snow cornices developed by blowing snow, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7008, https://doi.org/10.5194/egusphere-egu24-7008, 2024.

Snow loads exert a significant influence on the structural integrity of buildings in the northern hemisphere, necessitating precise assessment methodologies to ensure the reliability of roofs under this environmental stressor. The determination of roof snow load is intricately linked to evaluating the weight of accumulated snow on the roof surface, a critical consideration in the design and construction of buildings. The reliability of a roof structure is conventionally gauged through the computation of the reliability index, denoted as beta. This index integrates the characteristic ground snow load and an estimation of the associated accuracy, forming a crucial metric for structural engineers. Traditionally, the characteristic ground snow load is determined by fitting a series of yearly maximum ground snow load data to a Gumbel distribution, enabling the extraction of the 50-year return period value. This process traditionally relies on data obtained from weather stations, where meticulous measurements of snow depth are conducted alongside either direct measurements or modeling of snow density. However, the landscape of snow load determination is evolving with the advent of more sophisticated hydrological models. In this context, the paper investigates the impact of transitioning from traditional station data to utilizing gridded simulation data for estimating the characteristic snow load on the ground. The hydrological model "SeNorge" serves as a pivotal tool in this investigation, offering simulated ground snow load data at a 1 km grid. The objective is to scrutinize whether this shift in methodology affects the reliability of buildings and infrastructure subjected to snow loads. The study extends its reach across various climatic zones in Norway, comparing results obtained from the hydrological model with measured data from diverse sources. The fundamental question is whether the adoption of simulated ground snow load data, as generated by advanced hydrological models, translates into a corresponding level of reliability when compared to the established paradigm of utilizing standardized ground snow load data. The results demonstrate a variable uncertainty in the quantification of the snow load depending on the climate region and elevation. When this uncertainty is applied to a reliability calculation a straightforward application of hydrological model may not maintain the same level of reliability as the traditional approach employing standardized ground snow load data. The shift in the structural reliability implies that the partial factors should be adjusted achieve the target reliability criteria when moving from measured to simulated snow load maps. This revelation holds substantial implications for the engineering community, urging a cautious approach to the adoption of newer methodologies in snow load assessments.

How to cite: Thiis, T., Frimannslund, I., Nori, H., and Mustafa, Z.: Quantification of reliability of roofs subjected to snow loadsdetermined by hydrological models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21128, https://doi.org/10.5194/egusphere-egu24-21128, 2024.

Snow avalanches rank among the deadliest natural hazards in mountain environments worldwide. To date, forecasting is mostly based on measuring meteorological forcing, aiming at assessing the probability of event triggering in a certain area. To validate forecast models, information on avalanche occurrence is critical. However, real-time avalanche detection is still challenging and generally limited to radar or visual surveillance of one or a few known channels; here the need for novel monitoring solutions. In the last decades, infrasound has proven to be one of the most promising tools for real-time detection of avalanches. Indeed, snow avalanches, moving downhill, generate acoustic pressure waves in the air, which can be recorded with an array of infrasonic sensors that allows to detect and characterize the source. However, many difficulties still exist, mostly connected to the discrimination of the avalanche infrasound among the signals radiated by other natural or anthropic infrasonic sources active at the Earth's surface or in the atmosphere.

Here we present an analysis of >10 years of data recorded by a small-aperture infrasonic array deployed at an altitude of ~2000 m in Valle d'Aosta (Itay). To detect snow-avalanche events, we develop an algorithm aimed at identifying avalanche signals in the recorded infrasound dataset and calibrated on two avalanche crises occurred in the site. The identified avalanche-type infrasonic signals are then compared to local meteorological data and avalanche bulletins, to test the accuracy of our algorithm. Several clusters of avalanche-type infrasonic signals are identified on days with favourable weather conditions for the triggering of snow avalanches. Our study also allows us to investigate the meteorological forcing of snow avalanches in the Pennine Alps, showing that avalanche storms are induced preferentially as a result of the destabilisation of thick snow accumulations, but also highlighting the importance of weather patterns at seasonal scale.

This study was financially supported by the National Recovery and Resilience Plan, Mission 4 Component 2 - Investment 1.4 - NATIONAL CENTER FOR HPC, BIG DATA AND QUANTUM COMPUTING - funded by the European Union - NextGenerationEU - CUPB83C22002830001.

How to cite: Belli, G., Gheri, D., Marchetti, E., Dellavedova, P., Durand, N., and Bovet, E.: Decennial infrasonic array analysis of snow-avalanche activity and its weather forcing in Pennine Alps: implications for forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16056, https://doi.org/10.5194/egusphere-egu24-16056, 2024.

Snow avalanches pose significant threats in alpine regions, leading to considerable human and economic losses. The ability to promptly identify the locations and timing of avalanche events is essential for effective prediction and risk mitigation. Conventional automatic avalanche detection systems typically rely on radars and/or seismo-acoustic sensors. While these systems operate successfully regardless of weather conditions, their coverage is often confined to a single slope or a small catchment (distances < 3 km).

In our study, we demonstrate the feasibility of detecting snow avalanches using Distributed Acoustic Sensing (DAS) through existing fiber-optic telecommunication cables. Our pilot experiment, conducted over the 2021/2022 winter, involved a 10km long fiber-optic dark cable running parallel to the Flüelapass road in the eastern Swiss Alps close to Davos. The DAS data reveal distinct evidence of numerous dry- and wet-snow avalanches, even when they do not reach the cable, as confirmed photographically. We show that avalanches can be distinguished from other signals (e.g., vehicle traffic) using a frequency-dependent STA/LTA attribute, enabling their detection with high spatiotemporal resolution. These findings pave the way for cost-effective and near-real-time avalanche monitoring over extensive distances, leveraging existing fiber-optic infrastructure.

How to cite: Edme, P., Paitz, P., Fichtner, A., van Herwijnen, A., and Walter, F.: Toward automatic avalanche detection with Distributed-Acoustic-Sensing leveraging telecommunication infrastructure, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11202, https://doi.org/10.5194/egusphere-egu24-11202, 2024.

Monitoring snow avalanche activity is essential for operational avalanche forecasting and the successful implementation of mitigation measures to ensure safety in mountain regions. To facilitate and automate the monitoring process, avalanche detection systems equipped with seismic sensors provide a cost-effective solution. Still, automatically differentiating avalanche signals from other sources in seismic data remains rather challenging. This is mainly due to the complexity of the seismic signals generated by avalanches, the relatively rare occurrence of avalanches and the presence of multiple sources in the continuous recordings.

To discriminate avalanches from other sources in the continuous seismic recordings, we test three random forest classifiers using two feature sets extracted with two autoencoders and a set of 57 statistical features. We extract these features from 10s windows of the seismograms recorded with an array of five seismometers installed in Davos, Switzerland. The statistical feature set includes waveform, spectral and spectrogram attributes. The first autoencoder is composed of convolutional layers and a long short-term memory unit. This neuronal network automatically extracts 64 features from the raw waveform signal. The second autoencoder applies a sequence of fully connected layers to extract the same number of features from the spectrum of the signals. We assess the performance of each classifier and compare the results. To improve the predictive performance of the seismic system, we employ different post-processing, e.g. adaption of classification thresholds and ensembling the predictions from the three classifiers. The final model is tested with the continuous seismic data of the last winter season to potentially be used as an operational, near-real-time detection system.

How to cite: Simeon, A., Pérez-Guillén, C., Volpi, M., Seupel, C., and van Herwijnen, A.: Assessing avalanche activity in seismic data with modern machine learning methods., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16711, https://doi.org/10.5194/egusphere-egu24-16711, 2024.

Snow avalanches are one of the principal glacial threats, which are limited to high snow-covered alpine terrain. Mapping of Avalanche hazard and its modeling are useful in minimizing the fall risk. The current study assesses the utility of satellite imagery and GIS-based analytical hierarchical process (AHP) for mapping of possible avalanche locations for Draupadi ka Danda peak in Garhwal Himalaya, Uttarakhand. Various protruding terrain factors such as elevation, aspect, slope, curvature and land use land cover are used in this model and are derived from ALOS PALSAR DEM and Sentinel-2 images. Sensitivity analysis was performed on the chosen parameters and maximum weightage was set to slope, trailed by elevation, aspect, curvature and land use land cover. Using weighted overlay in ArcGIS avalanche susceptibility maps are formulated and distributed into five zones i.e. very low, low, moderate, high and very high zones and their validation was done by the listed avalanche occasions. Consecutively, Rapid Mass Movement Simulation (RAMMS) which is a three dimensional numerical model is used which generates parameters such as flow distance, height, velocity, pressure and momentum. The model requires a DEM of high resolution, release area of avalanche, friction parameter and was executed on the very high and high zones of avalanche susceptibility map.

How to cite: Mishra, N., Keshari, A. K., and Chahar, B. R.: Remote Sensing based mapping and modelling of potential avalanche zones in Draupadi ka Danda, Garhwal Himalayas, Uttarakhand, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19817, https://doi.org/10.5194/egusphere-egu24-19817, 2024.

The ISeeSnow pilot-study aims at bringing together and starting a conversation among the different groups in the field of gravitational mass flow simulations, with a focus on snow avalanches. These simulation tools are an integral part of engineering practice, scientific development and academic education.

At its core, an objective comparison of simulation results is performed for three different test cases, based on a generic, idealized topography as well as a real-world simulation scenario. In this initial effort, we focus on thickness-integrated shallow water models using a simple Coulomb- or classical Voellmy rheology. In this manner, comparing simulation results for the test cases, prescribing the friction parameters, topography, release area and release thickness, allows us to analyze common features and differences stemming from the various implementations, i.e. formulation of model equations, choice of numerical methods and their implementation into computer code as well as geo-data handling (input/output). We also include simulation tools that rely on a different mathematical formulation and basic assumptions (e.g. 3D models or conceptual approaches) and perform a qualitative comparison for a specially designed test case. Furthermore, performing this pilot-study helps to identify common data needs, come up with standard result formats and discuss helpful visualization options. As a third outcome, we summarize ideas on what is needed to perform a more comprehensive model intercomparison study which also tackles model verification and validation tests, with respect to test designs, required input data as well as model configuration options. In this community-based contribution, we present the concept of the ISeeSnow pilot-study, show preliminary results of the simulation comparison and give an outlook on potential avenues for a future comprehensive model intercomparison project.

How to cite: Wirbel, A., Oesterle, F., Fischer, J.-T., Chambon, G., Faug, T., Gaume, J., Glaus, J., Hergarten, S., Issler, D., Jarosch, A., Jóhannesson, T., Martini, M., Mergili, M., Rauter, M., Robl, J., Rosatti, G., Spannring, P., Tollinger, C., Vicari, H., and Zugliani, D.: ISeeSnow - initiating an avalanche simulation tool intercomparison, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17750, https://doi.org/10.5194/egusphere-egu24-17750, 2024.

Observations in various regions worldwide document a decline in mean snow depth, spatial extent, and duration of snow cover, indicating a connection to climate change, especially at low elevations. Climate scenarios project further changes, but the exact consequences on future snow cover and avalanche patterns remain unknown. Our work investigates the influence of climate change on the snow cover, specifically focusing on its impact on avalanches and the associated risk to buildings. To compare the consequences of these potential changes on snow avalanche hazard and risk with the current situation, we have developed a framework to model avalanche risk on a large scale. We applied an algorithm to generate a protection forest layer, potential release areas, and conduct snow analysis for current climatic conditions. The RAMMS::LSHIM algorithm within the RAMMS avalanche model produces avalanche hazard indication maps. They are combined with the CLIMADA risk assessment platform, incorporating exposure and vulnerability data, to create spatially explicit risk maps under different avalanche return period scenarios.
To address climate change impacts, we have integrated the CH2018 climate scenario data including various model chains into avalanche hazard mapping, using the SNOWPACK snow cover model. Snow cover simulations cover the years from 1997 to 2100 and deliver three day snow accumulation data and layer temperatures for potential future avalanches. We used this data to run the RAMMS::EXTENDED avalanche model with modified snow and temperature parameters. This enabled us to create hazard indication maps considering climate change.
Results indicate a potential decrease in the spatial extent of avalanches, especially at lower altitudes, due to rising snowline, particularly in model chains with reduced snowfall. However, within CH2018, other climate model chains suggest increased snow accumulation, resulting in larger avalanches and increased pressure in high-altitude areas.
Applying the CLIMADA risk tool to climate change hazard analysis using an enhanced vulnerability curve and uncertainty analysis results in various risk outcomes. An average approach over all model chains suggests a decrease in risk, particularly in low-altitude side valleys. Single model chains with increased snowfall project higher risks despite a reduced affected area. The study underlines the need to incorporate climate change into practical avalanche hazard assessment and subsequently risk analysis.
Overall, this research, for the first time, quantifies the impact of climate change on the potential future spatial distribution of avalanches and associated changes in potential risk. The practical applicability of climate change avalanche hazard assessment was demonstrated, offering insights for stakeholders to assess future risks and consider climate change risk appraisal options. 

How to cite: Ortner, G., Michel, A., Kropf, C. M., Bühler, Y., Christen, M., Bründl, M., and Bresch, D. N.: Climate change impacts on large scale avalanche risk in alpine regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9222, https://doi.org/10.5194/egusphere-egu24-9222, 2024.