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![Slope angle in starting zone of human-triggered avalanches ( N ϭ 809, fi rst quartile, 37 Њ , with median of 39 Њ ; third quartile, 41 Њ , with mean of 38.8 Њ Ϯ 3.8 Њ ). The mean thickness of the sampled slabs was 0.49 Ϯ 0.22 m (reprinted from Schweizer and Jamieson [2001] with permission from Elsevier).](profile/Martin-Schneebeli/publication/235977824/figure/fig3/AS:299882519777288@1448508972271/Slope-angle-in-starting-zone-of-human-triggered-avalanches-N-809-fi-rst-quartile-37.png)
Slope angle in starting zone of human-triggered avalanches ( N ϭ 809, fi rst quartile, 37 Њ , with median of 39 Њ ; third quartile, 41 Њ , with mean of 38.8 Њ Ϯ 3.8 Њ ). The mean thickness of the sampled slabs was 0.49 Ϯ 0.22 m (reprinted from Schweizer and Jamieson [2001] with permission from Elsevier).
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Snow avalanches are a major natural hazard, endangering human life and infrastructure in mountainous areas throughout the world. In many countries with seasonally snow-covered mountains, avalanche-forecasting services reliably warn the public by issuing occurrence probabilities for a certain region. However, at present, a single avalanche event can...
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... avalanche forecasting the critical slope is the steepest angle from the horizon- tal averaged over about 20 m in the starting zone. Schweizer and Jamieson [2001] analyzed a large data set of skier-triggered avalanches from Switzerland and Can- ada including data on aspect (compass direction that the slope faces) and slope angle (Figure 4). The results do not differ substantially from previous studies on natural avalanches or data sets of avalanches with different types of triggering [Perla, 1977]. ...
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... Besides that occurring in spring by snowmelt discussed in the previous paragraph, winter avalanches might contribute strongly to soil erosion in alpine grasslands (Ceaglio et al., 2011;Jomelli and Bertran, 2001). Identifying and classifying avalanche formation is complex; its multifactorial nature means that local conditions influence its pathway and dynamics (Schweizer et al., 2003). Snow avalanches are both an erosional and flooding process, and may modify or produce other erosion processes, such as gullies and landslides (Luckman, 1977). ...
Permanent grasslands are widely recognized for their role in protecting the landscape against soil erosion and flooding. However, this role has not yet been comprehensively quantified. Also, the degradation of grasslands is accelerating at an alarming pace, leading to erosion and runoff generation. This study aims to (i) quantify the erosion and flooding mitigation effect of permanent grasslands in the EU and the UK, compared to other land uses; (ii) review all soil erosion and runoff generating processes on permanent grasslands. First, a meta-analysis compared four erosion and flooding-related indicators: bulk density, hydraulic conductivity, runoff and soil loss between permanent grasslands, arable land and forests. The results show that permanent grassland soils had generally lower bulk density and higher hydraulic conductivity than arable soils, and generated less runoff and soil loss. Differences are less clear-cut in comparison with forests, although permanent grasslands had higher bulk density and runoff values. Secondly, a qualitative, in-depth review was performed to identify knowledge gaps related to the characteristics, importance and driving factors behind relevant soil erosion processes affecting grasslands in the EU. This identified six processes with appreciable knowledge gaps: trampling-induced erosion, gullying, piping, landsliding, snowmelt erosion, and avalanche erosion. Additionally, three processes were identified that promote runoff generation and soil erosion: compaction, hydrophobicity and wildfires.
... They are critical to forecast and anticipate snow-related hazards, especially avalanche triggering (Schweizer et al., 2003;Morin et al., 2020a). At a larger scale, snow melt-out is an important source of water for downflow hydrological catchment, with impacts on water availability for agriculture and ecosystems, human consumption and hydropower (IPCC, 2022). ...
Wind-induced snow transport has a strong influence on snow spatial variability especially at spatial scales between 1 and 500 m in alpine environments. Thus, the evolution of snow modelling systems towards 100–500 m resolutions requires representing this process. We developed SnowPappus, a parsimonious blowing snow model coupled to the Crocus state-of-the-art snow model, able to be operated over large domains and entire snow seasons. SnowPappus simulates blowing snow occurrence, horizontal transport flux and sublimation rate on each grid cell as a function of 2D atmospheric forcing and snow surface properties. Then, it computes a mass balance using an upwind scheme to provide eroded or accumulated snow amounts to Crocus. Parameterizations used to represent the different processes are described in detail and discussed against existing literature. A point-scale evaluation of blowing snow fluxes was conducted, mainly at the Col du Lac Blanc observatory in French Alps. Blowing snow occurrence evaluation showed SnowPappus performs as well as a currently operational scheme. Evaluation of the simulated suspension fluxes highlighted a strong sensitivity to the suspended particles terminal fall speed. Proper calibrations allows the model to reproduce the correct order of magnitude of the mass flux in the suspension layer. Numerical performances of gridded simulations of Crocus coupled with SnowPappus were assessed, showing the feasibility of using it for operational snow forecast at the scale of the entire French Alps.
... The instabilities evolved within a snowpack are major cause of a dry slab avalanche [5]. The instabilities were built within a snowpack due to overloading by new snow precipitation as well as due to the presence of persistent weak layer [6] and due to initiation followed by propagation of a crack in the weak layer [7]. The prediction of snow avalanche has been a challenge since long time; however, many conventional methods are employed but having limitations on accuracy and resolution both in temporal as well as in spatial scales. ...
The stato-dynamic instability evolution is a process in which a system undergoes transition from a static (equilibrium) phase to dynamical state. The snow avalanche formation preceding release may pass through different phases of instabilities evolving within a snowpack on a slope from an initial static to dynamic phase of avalanche release. In this research work, the instability evolution within snow mass carried out under the laboratory conditions, monitored using Acoustic emission (AE), is presented to understand the mechanism of avalanche formation and release. For this purpose, the segregated (natural) snow particles were deposited in layers over a tilt-plane arrangement fixed at a particular angle using a remotely controlled mechanical system. Under the effect of gravitational pull on an inclined plane, the evolution of stato-dynamic instabilities prior to avalanche release (lab-scale) were monitored within the snow block using multi sensor AE network. Several resonant types AE sensors coupled to needle shaped waveguides were used to detect the AE activities within snow using broadband multichannel AE data acquisition system. In this work, a number of lab scale avalanche experiments were recorded in terms of the prominent AE parameters and voltage waveforms during the course of stato-dynamic instability evolution. The interpretation and analysis of AE voltage waveforms is carried out in terms of AE entropy (Hs), based on Shannon’s information theory. The values of AE entropy are associated with the AE waveforms recorded at different stages of instability development which tend to follow an increasing trend until the failure. Further, a new parameter, Instability index (Iw) based on AE voltage waveforms, is formulated to measure the proportion of instability building up within a system prior to its failure. The rising trends of Instability index are consistent with the trends of AE entropy values during the stato-dynamic instability evolution within the snow block and the maximum values attributed to the lab scale avalanche. To predict the failure of snow mass, regression models are formulated based on the values of AE entropy and instability index. The results of experiments have verified that both the methods are efficient to elucidate the stato-dynamic instability development in the snowpack. The research work carried out is quite insightful to understand the instabilities evolving within an unstable snowpack on a slope during the avalanching process in terms of Acoustic emissions.
... Avalanches are defined by a rapid movement of snow within a path (Ancey, 2006). Avalanche characteristics are directly related to climatic, meteorological, and geomorphological conditions (Gaume et al., 2012;Mock & Birkeland, 2000;Schweizer et al., 2003). The final avalanche deposit extent and volume are highly variable, driven by several factors, including snow depth, temperature and initial snowpack stratigraphy, modified by the avalanche flow and the path morphology. ...
Snow avalanches are a major component of the mountain cryosphere that frequently create road obstructions. Deposit characteristics determine the extent of damage to the road infrastructures and the period of disruption of the road network, but the factors controlling snow-deposit volumes remain largely unknown. This study investigates the influence of meteorological and snowpack conditions on snow-avalanche deposits and road-network vulnerability based on 1986 deposit volumes from 182 paths located in two regions of the French Alps between 2003 and 2017: the Guil and Haute-Maurienne valleys. During the period, 195 avalanches impacted the road network in these areas, leading to major disruptions. In the Haute-Maurienne, correlations between deposit volumes and meteorological and snowpack conditions are high in winter. However, the relationships differ with path elevation and orientation. Results do not show any significant relationship between volumes and meteorological or snowpack conditions for the spring season. Focusing on deposits that disturbed the road network in winter and spring reveals a distinct influence of meteorological and snow variables compared to the overall dataset, with snowfall intensity as the predominant control variable of deposit volumes leading to road cuts. When the same analysis is conducted by considering Guil valley separately or by aggregating the Haute-Maurienne with Guil valley area data, results do not show any significant relationship, highlighting the specific local nature of relations between deposit volumes and meteorological and snowpack conditions.
K E Y W O R D S avalanches deposit, meteorological control, road network vulnerability, snow avalanches
... Having a closer look at snow, one discovers many microstructural patterns and realizes that snow on the ground undergoes constant evolution [17]. The snow microstructure fully controls its physical and mechanical properties, which are essential for diverse applications, such as avalanche forecasting [78]. A snowpack is typically structured in numerous layers composed of different snow types, where such stratigraphy will determine the snowpack stability. ...
Stochastic behavior can be observed in nature in the context of the interactions of nonlinear complex dynamical systems. These can be described by mathematical models and their stochastic nature can generally be separated into continuous and discontinuous contributions. In this thesis, the continuous contribution is considered to be a classical Brownian motion and the discontinuous part as a compound Poisson process. With these assumptions, a jump-diffusion stochastic differential equation is applied. These methods are applied in the field of snow physics and the energy conversion processes in wind turbines. Additionally, more thorough mathematical characterization is performed in order to introduce robust criteria to distinguish the continuous or discontinuous nature of the given data. Our studies approach the complex dynamics from a new perspective which allows us to have a better understanding of their complex nature.
... They also feed springs and rivers, which makes them an important part of the hydrosphere, as well as by feeding glaciers and ice caves, which a narrower sense makes them part of the cryosphere. In addition, avalanches are an important erosional and biological factor (UNESCO, 1981;Laternser & Schneebeli, 2002;Schweizer et al., 2003;Volk Bahun, 2016, 2017. ...
The article discusses avalanche occurrence in the Slovenian Alps (SE Alps) in the context of climate change. It analyses the relationship between the North Atlantic Oscillation and maximum snow depth over the last two centuries, and the relationships between maximum snow depth and avalanches over the last three decades. We argue that higher temperatures lead to precipitation in the form of rain at higher elevations even in winter, so that major wet avalanches occur already in winter rather than in early spring, as was more common in the past. A case study of extreme avalanches in January 2021 is presented to support the hypothesis.
... Snow avalanches can be defined as rapid mass movement of snow, ice, rock and soil on snow covered steep slopes. Snow avalanche occurs depending on terrain, meteorological and snowpack properties (Schweizer et al., 2003). A snow avalanche may contain soil, rocks, vegetation, or ice particles, all of which can have profoundly devastating socioeconomic and environmental effects. ...
Snow avalanche refers to rapid snow mass movement under the influence of gravity and is a frequently observed natural phenomenon in mountainous regions. An area affected by a snow avalanche consists of starting, track (or transition) and runout (or deposition) zones, which have different geomorphologic characteristics that need to be considered in hazard modelling. This study attempted to analyze the impact of the different zones in producing snow avalanche susceptibility maps (ASMs) with data-driven methods. The random forest (RF) method was applied to the data from Gross Spannort Mountain region (Switzerland) for this purpose. Avalanche inventories from two dates were manually delineated to separate the three zones and two RF models were trained with learning datasets; i.e. Inventory-A which includes all snow avalanche zones (original inventory), and Inventory-B with starting and track zones. The conditioning factors were defined based on the literature, data availability and study area characteristics. The trained models (Model-A with Inventory-A and Model-B with Inventory-B) were evaluated with the area under the receiver operating characteristic (ROC), precision, recall and F1 score. The results show that Model-A has AUC of 0.97, precision of 0.89, recall of 0.95 and F1 score of 0.92 and the Model-B has AUC of 0.98, precision of 0.90, recall of 0.94 and F1 score of 0.92 that indicate high prediction performances for both cases. Furthermore, feature importance values were calculated by the Mean Decrease in Impurity (MDI) method, and elevation, aspect and valley depth were found the most influencing conditioning factors.
... Maggioni and Gruber, 2003). Yet, the range of slopes to be retained in automated PRA detection remains debated, as (i) the true range of slopes over which avalanche release is actually possible remains uncertain (e.g. it varies with snow conditions; Schweizer et al., 2003;Naaim et al., 2013) and (ii) it is not independent of the chosen digital elevation model (DEM) resolution. Hence, the 28-60 • range is selected in Veitinger et al. (2016) using a DEM with a 2 m resolution, but 28-55 • is preferred in Aydin and Eker (2017) using a DEM with a resolution of 10 m, and 25-40 • is used in Kumar et al. (2019). ...
... Indeed, PRA detection methods mostly focus on terrain characteristics at the pixel scale. Hence, they do not easily segment large areas where factors are favourable to avalanche release (suitable slope, roughness, etc.) into different PRAs compatible with the physical processes involved in snow avalanching (Schweizer et al., 2003). This may lead to unrealistically wide PRAs that, in reality, correspond to different avalanche paths/events. ...
... Yet, this threshold is dependent on the local climate and should be adapted for other regions. Our other choices regarding slope and distance to ridge are (i) largely in accordance with the state of the art (Maggioni and Gruber, 2003;Bühler et al., 2013) and (ii) compatible with broader knowledge regarding snow avalanche formation (Schweizer et al., 2003). Regarding the DEM, a 25 m resolution appeared to be a good compromise between high efficiency in the detection and the computational burden, even if, arguably, it leads to less realistic PRA boundaries than finer DEM resolutions. ...
Snow avalanches are a prevalent threat in mountain territories. Large-scale mapping of avalanche-prone terrain is a prerequisite for land-use planning where historical information about past events is insufficient. To this aim, the most common approach is the identification of potential release areas (PRAs) followed by numerical avalanche simulations. Existing methods for identifying PRAs rely on terrain analysis. Despite their efficiency, they suffer from (i) a lack of systematic evaluation on the basis of adapted metrics and past observations over large areas and (ii) a limited ability to distinguish PRAs corresponding to individual avalanche paths. The latter may preclude performing numerical simulations corresponding to individual avalanche events, questioning the realism of resulting hazard assessments. In this paper, a method that accurately identifies individual snow avalanche PRAs based on terrain parameters and watershed delineation is developed, and confusion matrices and different scores are proposed to evaluate it. Comparison to an extensive cadastre of past avalanche limits from different massifs of the French Alps used as ground truth leads to true positive rates (recall) between 80 % and 87 % in PRA numbers and between 92.4 % and 94 % in PRA areas, which shows the applicability of the method to the French Alps context. A parametric study is performed, highlighting the overall robustness of the approach and the most important steps/choices to maximize PRA detection, among which the important role of watershed delineation to identify the right number of individual PRAs is highlighted. These results may contribute to better understanding avalanche hazard in the French Alps. Wider outcomes include an in-depth investigation of the issue of evaluating automated PRA detection methods and a large data set that could be used for additional developments, and to benchmark existing and/or new PRA detection methods.
... Slab avalanches can occur wherever there is sufficient snow on the ground and a slope steep enough to slide. The systematic identification of starting zones has been restricted to slope angle (terrain between 30 • and 50 • , occasionally 60 • ) and vegetation (no forest) (Schweizer et al., 2003). While the location where the avalanche stops, i.e., the runout distance, can be estimated using a diverse range of numerical models (e.g., Bartelt et al., 2013;Christen et al., 2010;Volk andKleemayr, 1999, Mergili et al., 2017), these models require estimates of a number of parameters, including the snowpack conditions, depth and area of release, and rely on assumptions on the friction and turbulence parameters. ...
Accurate prediction of snow avalanche runout-distances in a deterministic sense remains a challenge due to the complexity of all the physical properties involved. Therefore, in many locations including Norway, it has been common practice to define the runout distance using the angle from the starting point to the end of the runout zone (α-angle). We use a large dataset of avalanche events from Switzerland (N = 18,737) acquired using optical satellites to calculate the α-angle for each avalanche. The α-angles in our dataset are normally distributed with a mean of 33° and a standard deviation of 6.1°, which provides additional understanding and insights into α-angle distribution. Using a feature importance module in the Random Forest framework, we found the most important topographic parameter for predicting α-angles to be the average gradient from the release area to the β-point. Despite the large dataset and a modern machine learning (ML) method, we found the simple linear regression model to yield a higher performance than our ML attempts. This means that it is better to use a simple linear regression in an operational context.
... Victims either underestimate the avalanche risk or overestimate their abilities to deal with it. As a result, the primary cause of the unusually high number of fatalities is the lack of information among many skiers, another meteorological factor whose variations create layers of varying density or hardness, resulting in stress concentrations within the stratified snowpack (Schweizer et al. 2003). The last meteorological factor is solar radiation (Fig. 5e). ...
This study examines the use of snow avalanche susceptibility maps (SASMs) to identify areas prone to avalanches and develop measures to mitigate the risk in the Province of Sondrio, Italy. Various machine learning classifiers such as Random Forest, Gradient Boosting Machines, and AdaBoost, as well as newer classifiers like XGBoost, LightGBM, and NGBoost, were used with 17 conditioning factors and 1880 snow avalanche samples. The XGBoost classifier was found to have the best performance and McNemar’s test results indicated that certain classifier pairs, such as RF-AdaBoost, RFXGBoost, and XGBoost-LightGBM, produced significant predictions while others did not. The XGBoost classifier found that 19.31% of Sondrio was very susceptible to avalanches. Instead of providing a global explanation of the classifier models, the study employs a local eXplainable Artificial Intelligence (XAI) approach called SHapley Additive eXplanations (SHAP) to give insight into how each conditioning factor contributes to the likelihood of snow avalanches. According to the SHAP values, the three most important factors in the XGBoost classifier model for determining the likelihood of snow avalanches are elevation, maximum temperature, and slope. The model shows that as elevation increases, the likelihood of avalanches also increases. On the other hand, a higher maximum temperature is found to decrease the likelihood of an avalanche. Slope is found to have a positive effect on the likelihood of an avalanche, meaning that steeper slopes increase the likelihood of an avalanche. This study also analyzes the avalanche susceptibility of ski resorts in the province and found that the majority of them are located in low and moderately susceptible areas, but some are in highly susceptible areas. The study used SHAP force plots to examine the local factors that contribute to the likelihood of avalanches in these specific ski resorts. The results show that ski resorts with elevations greater than 2000 m and slopes greater than 30 degrees, such as Livigno, Santa Caterina-Valfurva and Passo dello Stelvio, have a higher susceptibility to avalanches due to higher positive SHAP values. Conversely, ski resorts with elevations less than 2000 m and slopes less than 30 degrees, such as Aprica and Bormio, have a lower susceptibility to avalanches because of negative SHAP values. This study provides a valuable tool for creating new strategies to reduce the harm and damage caused by slow avalanches in the region.
