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Cross section of a slab showing R as the skier load, ψ the slope angle, h the slab depth and α max the angle from the skier to the maximum induced shear stress.
Source publication
The process of dry-snow slab avalanche formation can be divided into two
phases: failure initiation and crack propagation. Several approaches tried to
quantify slab avalanche release probability in terms of failure initiation
based on shear stress and strength. Though it is known that both the
properties of the weak layer and the slab play a major...
Contexts in source publication
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... R is the line load due to a skier, ψ the slope angle, h the slab depth and α max the angle between the bed surface and the line from the skier to the point of maximum induced shear stress ( Fig. 1 and Appendix). However, the approach proposed by Föhn (1987b) is not accurate for a layered snow cover. For instance, it is clear that a skier will have less influence on the rest of the snowpack if the surface layers are more rigid. Consequently, the stresses in the underlying layers would be lower than the values de- rived with ...
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... a skier at the depth of the weak layer within a snow cover composed of 3 slab layers and a substratum ( Fig. 1) for dif- ferent typical configurations. In particular, they showed that, compared to a uniform slab, the skier-induced stress may de- crease by a factor 2 when taking into account slab stratigra- ...
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... SK ML 38 can be calculated for each layer of the snow- pack. In Fig. 10, two selected examples of simulated snow stratigraphy for 21 January 2002 are shown. The verified re- gional avalanche danger in the vicinity of the AWS was rated as "Moderate; 2". Even if the avalanche danger was only "Moderate" the snow cover (snow depth: 110-120 cm) was quite weak with two unstable parts: one about 40 cm from the ...
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... grain types. However, no significant snowfall was recorded the past month, explaining the relatively low avalanche danger. The relative threshold sum approach (RTA, Monti et al., 2014a) highlighted a po- tential weak layer consisting of faceted crystals about 40 cm deep in both simulated profiles. For the simulated profile at Weissfluhjoch (Fig. 10a), the detected weak layer was found at a depth of 39 cm and both the SK ML 38 and SK 38 predicted a stability of 1.1 (fairly stable). At the weak layer depth the multi-layered additional stress τ ML xz was 680 Pa while the τ xz was 690 Pa with a slab-induced stress τ xz of 416 Pa; thus the difference between the two additional stresses ...
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... the second example (Fig. 10b) the simulation was per- formed for the Gatschiefer AWS. The differences between the skier overload predicted at the slab layers is more impor- tant than for the previous example. For this case, the stability assessment for the weak layer (depth: 37 cm) is different: 1.15 for the SK ML 38 , resulting in a "fair" stability, and 0.98 for ...
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... layer the multi-layered additional stress τ ML xz was 603 Pa, while τ xz was 774 Pa with a slab-induced stress τ xz of 382 Pa; thus the difference between the two additional stresses due to a skier was sufficient to influence the stabil- ity assessment (since the values were close to the threshold of 1). Within the simulated snow profile shown in Fig. 10b the RTA detected a second potential weak layer near the base of the snow cover. In this case the influence depth was largely exceeded so no significant differences between the two ap- proaches was found. However, it is interesting to notice that even if the RTA detected the layer as potentially unstable from a structural point of view ...
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... (1885) and further adapted for a linear load. However, the extension of Boussinesq's theory to the case of an inclined slope (slope angle ψ) was first performed by Fla- mant (1892) by modifying the three-dimensional solution of Boussinesq. The additional stresses predicted by Flamant's solution in polar coordinates for a radius r and an angle α (Fig. 1) are given ...
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... by changing the coordinate system from polar to Carte- sian, the additional stress along the x-z plane for a given depth h and slope angle ψ is given by τ xz = σ rr sin α cos α = − 2R π h cos ψ sin 2 α sin(α + ψ) cos α. (A7) Figure A1. Angle of maximum shear stress α max vs. slope angle ψ. ...
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... a function of the angle α. In order to find the peak shear stress acting in the snow cover, Eq. (A7) has to be differentiated with respect to α and the values of α max can be obtained such as the resulting equation equals zero: (A8) Equation (A8) was numerically solved using Matlab, which gives the relationship between α max and the slope angle ψ (Fig. A1). For slope angles of 0 and 90 • , the re- sulting values of α max are 60 and 45 • , respectively. These two extreme cases correspond to the cases of a purely ver- tical and a horizontal line load in flat terrain. The values in Fig. A1 differ from the ones originally given by Föhn (1987b) since his Eq. (5) still included the radius r ...
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... (A8) was numerically solved using Matlab, which gives the relationship between α max and the slope angle ψ (Fig. A1). For slope angles of 0 and 90 • , the re- sulting values of α max are 60 and 45 • , respectively. These two extreme cases correspond to the cases of a purely ver- tical and a horizontal line load in flat terrain. The values in Fig. A1 differ from the ones originally given by Föhn (1987b) since his Eq. (5) still included the radius r that also depends on α. The value of α max for a typical avalanche slope of angle 38 • is α max = 54.34 • . Consequently, for this partic- ular case of a 38 • slope angle, and for a typical skier load of R = 500 N m −1 , the additional ...
Citations
... For many years, research has been conducted to better understand the process of dry snow slab avalanches to accurately assess the stability of the snowpack (e.g. Föhn, 1987;McClung, 1981;Schweizer et al., 2003;Monti et al., 2016), which is necessary for avalanche forecasting. The release of a dry-snow slab avalanche requires the presence of a weak snow layer buried below cohesive snow slabs. ...
Dry-snow slab avalanches are considered to be the most difficult to predict, yet the deadliest avalanche types. The release of snow slab avalanches starts with a initial failure in a weak layer that may propagate across the slope until the slab fractures and slides. The evaluation of crack propagation area is a primary concern for avalanche forecasters. The purpose of this study is to test the hypothesis that the heterogeneity of snowpack properties is one of the primary factors that may potentially stop dynamic crack propagation. To test this assumption, we use a depth-averaged Material Point Method (DA-MPM) for efficient elasto-plastic modeling of snow slab avalanches. Our analysis includes scenarios involving i) pure-elastic slabs and ii) elasto-plastic slabs. In the first scenario, we report a significant decrease in slab tensile stress with increasing crack speed compared to quasi-static theory. In addition, we quantify the effect of weak layer heterogeneity and softening fracture energy on the crack stopping mechanism. In the second scenario, we analyse the interplay between weak layer heterogeneity and slab tensile fracture and quantify their combined effect on crack arrest. Results are interpreted through a scaling law relating the crack arrest distance to two dimensionless numbers related to weak layer strength variability and slab tensile fracture. Furthermore, the proposed model is applied to field campaigns in which spatial variations of weak layer shear strength were measured. Finally, DA-MPM simulations are performed on three-dimensional terrain with spatial variations revealing interesting release patterns. This research and the proposed methods can not only enhance our comprehension of the factors influencing avalanche release sizes,and possibly, the design of new mitigation measures for avalanche start zones.
... For example, the skier stability index SK38(Monti et al., 2016) and the critical cut length rc(Richter et al., 2019) (both for artificial triggering of dry slab avalanches), the expected time to failure(Conway and Wilbour, 1999) (for natural dry slab avalanche activity), the liquid water content index(Mitterer et al., 2013) and the random forest classifier byHendrick et al. (2023) (for wet slab avalanche activity). ...
Avalanche forecasting is a human judgment process with the goal of describing the nature and severity of avalanche hazard based on the concept of distinct avalanche problems. Snowpack simulations can help improve forecast consistency and quality by extending qualitative frameworks of avalanche hazard with quantitative links between weather, snowpack, and hazard characteristics. Building on existing research on modeling avalanche problem information, we present the first spatial modeling framework for extracting the characteristics of storm and persistent slab avalanche problems from distributed snowpack simulations. Grouping of simulated layers based on regional burial dates allows us to track them across space and time and calculate insightful spatial distributions of avalanche problem characteristics. We applied our approach to ten winter seasons in Glacier National Park, Canada, and compared the numerical predictions to human hazard assessments. Despite good agreement in the seasonal summary statistics, the comparison of the daily assessments of avalanche problems revealed considerable differences between the two data sources. The best agreements were found in the presence and absence of storm slab avalanche problems and the likelihood and expected size assessments of persistent slab avalanche problems. Even though we are unable to conclusively determine whether the human or model data set represents reality more accurately when they disagree, our analysis indicates that the current model predictions can add value to the forecasting process by offering an independent perspective. For example, the numerical predictions can provide a valuable tool for assisting avalanche forecasters in the difficult decision to remove persistent slab avalanche problems. The value of the spatial approach is further highlighted by the observation that avalanche danger ratings were better explained by a combination of various percentiles of simulated instability and failure depth than by simple averages or proportions. Our study contributes to a growing body of research that aims to enhance the operational value of snowpack simulations and provides insight into how snowpack simulations can help address some of the operational challenges of human avalanche hazard assessments.
... To address this limitation, the high-resolution snow micro-penetrometer (SMP) was used to characterize the mechanical and structural properties of the snow, including slab and weak layer thickness, density, elastic modulus, and microstructural strength of the weak layer (Proksch et al., 2015;Löwe and van Herwijnen, 2012;Johnson and Schneebeli, 1999). Several studies have characterized stability based on snow mechanical properties of the slab and the weak layer (Föhn, 1987;Gaume and Reuter, 2017;Reuter et al., 2015b;Monti et al., 2016;Schweizer and Reuter, 2015;Reuter and Schweizer, 2018;Rosendahl and Weißgraeber, 2020). Gaume and Reuter (2017) proposed a stability index that accounts for both failure initiation and propagation propensity, using an analytical method applicable to SMP profiles. ...
... The skier crack length is computed by solving the following equation: τ + τ = τ p , where τ = ρgD sin ψ is the shear stress due to the slab weight with g as the gravitational acceleration. The stress due to the skier τ was originally defined by Föhn (1987) and refined by Monti et al. (2016): ...
... The angle α is defined as the angle between the point at the snow surface under the skier load to the point of maximum induced shear stress at the weak layer. Additionally, D e is the new multilayered slab thickness proposed by Monti et al. (2016), considering that slabs are often made up of multiple layers with different properties, influencing stress redistribution (Habermann et al., 2008). The computation of D e follows Eqs. ...
Snow avalanches represent a natural hazard to infrastructure and backcountry recreationists. Risk assessment of avalanche hazard is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to decision-making and route finding in avalanche terrain for mountain users. Snow cover models can simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5-50 m) remains critical, since slope stability and the possible size of an avalanche are governed by that scale. To better understand and estimate the spatial variability at the slope scale, this work explores links between snow mechanical properties and micro-topographic indicators. Six spatial snow surveys were conducted in two study areas across Canada. Snow mechanical properties, such as snow density, elastic modulus and shear strength, were estimated from high-resolution snow penetrometer (SMP) profiles at multiple locations over several studied slopes, in Rogers Pass, British Columbia, and Mt. Albert, Québec. Point snow stability metrics, such as the skier crack length, critical propagation crack length and a skier stability index, were derived using the snow mechanical properties from SMP measurements. Microtopo-graphic indicators, such as the topographic position index (TPI), vegetation height and proximity, wind-exposed slope index, and potential radiation index, were derived from unoccupied aerial vehicle (UAV) surveys with sub-metre resolution. We computed the variogram and the fractal dimension of the snow mechanical properties and stability metrics and compared them. The comparison showed some similarities in the correlation distances and fractal dimensions between the slab thickness and the slab snow density and also between the weak layer strength and the stability metrics. We then spatially modelled snow mechanical properties, including point snow stability, using spatial generalized additive models (GAMs) with microtopographic indicators as covari-ates. The use of covariates in GAMs suggested that microto-pographic indicators can be used to adequately estimate the variation in the snow mechanical properties but not the stability metrics. We observed a difference in the spatial pattern between the slab and the weak layer that should be considered in snow mechanical modelling.
... The high resolution snow penetrometer, Snowmicropen (SMP), is used to characterize the mechanical 60 properties of the snow,of the snow, such as the thickness of the slab and the weak layer, the density, the elastic modulus, and the microstructural strength of the weak layer (Proksch et al., 2015;Löwe and van Herwijnen, 2012; Johnson and Schneebeli, 1999). Several authors characterized stability based on snow mechanical properties of the slab and the weak layer (Föhn, 1987;Gaume and Reuter, 2017;Reuter et al., 2015b;Monti et al., 2016;Schweizer and Reuter, 2015;Reuter and Schweizer, 2018; Rosendahl and Weißgraeber, 2020). Gaume and Reuter (2017) proposed a stability index that represents both failure initiation ...
... replacing the D only for the skier stress Eq.(4). Slabs are often composed of multiple layers with different properties that can affect stress redistribution and potentially damage the weak layer (Habermann et al., 2008;Monti et al., 2016;Weißgraeber 210 and Rosendahl). To account for this process, the method following equations 2,3,4 in Monti et al. (2016), is used to obtain a new equivalent multilayered slab thickness (D e ) based on each layer elastic modulus E that composed the slab. ...
... Slabs are often composed of multiple layers with different properties that can affect stress redistribution and potentially damage the weak layer (Habermann et al., 2008;Monti et al., 2016;Weißgraeber 210 and Rosendahl). To account for this process, the method following equations 2,3,4 in Monti et al. (2016), is used to obtain a new equivalent multilayered slab thickness (D e ) based on each layer elastic modulus E that composed the slab. For example, a slab can be composed of many soft layers and one rigid layer with a very high elastic modulus (ex. ...
Snow avalanches represent a natural hazard for infrastructures and backcountry recreationists. Risk assessment of avalanche hazard is difficult due to the sparse nature of available observations informing on snowpack mechanical and geo-physical properties and overall stability. The spatial variability of these properties also adds complexity to the decision-making and route finding in avalanche terrain for mountain users. Snow cover models simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5-50 5 m) remains critical, since slope stability and the possible size of an avalanche are governed by such a scale. To better understand and estimate the spatial variability at the slope scale, this work explores existing links between snow mechanical properties and microtopographic indicators. First, we compared the covariance models and the scaling properties. Then, we estimated snow mechanical properties, including point snow stability, using GAM spatial models (Generalized Additives Models) with microtopographic indicators as covariates. Snow mechanical properties such as snow density, elastic modulus, shear modulus 10 and snow microstructural strength were measured at multiple locations over several studied slopes using a high-resolution snow penetrometer (SMP), in Rogers Pass, British-Columbia, and Mt Albert, Québec. Point snow stability such as the skier crack length, critical propagation crack length and a skier stability index were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators such as the topographic position index (TPI), vegetation height and proximity , Up-wind slope index (wind exposed/sheltered area) and potential radiation index were derived from Unmanned Aerial 15 Vehicles (UAV) surveys with sub-meter resolution. We computed the variogram and log-log variogram of snow mechanical properties. The comparison showed some similarities in the correlation distances and fractal dimensions between the slab thickness and the slab snow density and also between the weak layer microstructural strength and the stability metrics. GAM models suggested several significant covariates such as TPI, VRM, Winstral index, vegetation height and distance to vegetation. The snow stability maps that were generated represent good teaching material in avalanche skill training and awareness courses. 20 The difference in spatial pattern between the slab and the weak layer should be considered in snow mechanical modeling.
... Inspired by the work of Monti et al. (2014) and Reuter et al. (2021), our process-based approach used a combination of three indices to assess the instability of a layer: a) the relative threshold sum approach RTA (Monti and Schweizer, 2013; 180 Monti et al., 2014), b) the multi-layered skier stability index SK38 (Monti et al., 2016), and c) the critical crack length r c (Richter et al., 2019). While potential weak layers were pre-selected based on RTA, their propensity for failure initiation and crack propagation was assessed based on SK38 and r c , respectively, which accounts for the two main processes governing slab avalanche release (Viallon-Galinier et al., 2022). ...
Avalanche warning services increasingly employ large-scale snow stratigraphy simulations to improve their insight into the current state of the snowpack. These simulations contain information about thin, persistent critical avalanche layers that are buried within the snowpack and are fundamental drivers of avalanche hazard. However, the data volume, data complexity, and unknown validity have so far limited the value of the simulations for operational decisions. We attribute this at least partially to a lack of research that validates the simulations for their capability to represent the existence and instability of known critical layers at the regional scale. To address this knowledge gap, we present methods that enable meaningful comparisons between regional assessments of avalanche forecasters and snowpack simulations that are distributed across entire forecast regions. We applied these methods to operational data sets of ten winter seasons and three public forecast regions in western Canada and thereby quantified the performance of the Canadian weather and snowpack model chain to represent persistent critical avalanche layers. We found that the overall probability of detecting a known critical layer in the simulations can be as high as 75 % when accepting a low probability of 40 % that any simulated layer is actually of operational concern in reality. Furthermore, we explored patterns that characterize which layers were represented well and which were not. Faceted layers, for example, were captured well but also caused most false alarms, whereas surface hoar layers tended to be less prevalent but in return were mostly of operational concern when modeled. Overall, our results suggest that the simulations provide a valuable starting point for targeted field observations as well as a rich complementary information source that can help alert forecasters about the existence of specific critical layers or provide an independent perspective on their instability. However, we do not believe that the existing model chain is sufficiently reliable to generate assessments purely based on simulations. We conclude by presenting our vision of a real-time operational validation suite that can help forecasters develop a better understanding of the simulations' strengths and weaknesses by continuously comparing assessments and simulations in a user-friendly manner.
... Often, stress fields are obtained by using solutions derived from the Boussinesq solution of an infinite half-plane under a point load (Föhn, 1987;Gaume and Reuter, 2017). Monti et al. (2016) proposed an equivalent-layer approach to allow for the use of solutions of isotropic continua for the stress analysis of layered slabs. Since the early works of Smith and Chu (1972) and Smith and Curtis (1975), finite-element methods have been used to study stratified snowpacks (Schweizer, 1993;Habermann et al., 2008). ...
... The weak layer is 2 cm thick and the slab layers have a thickness of 12 cm each. Similar profiles were used by, e.g., Habermann et al. (2008) and Monti et al. (2016). Here, we complement the homogeneous slab H. weak interfaces can be given as ...
... With reference to the analysis of snowpack layering by Habermann et al. (2008) and Monti et al. (2016), we use three-layered slabs proposed as schematic hardness profiles by Schweizer and Wiesinger (2001) that are composed of soft, medium, and hard snow as benchmark slab configurations (Fig. 5). Assuming bonded slabs (e.g., rounded grains) and considering the density-hand-hardness relations given by Geldsetzer and Jamieson (2000), we assume densities of ρ = 350, 270, and 180 kg m −3 for hard, medium, and soft snow layers with hand hardness indices pencil (P), four fingers (4F), and one finger (1F), respectively. ...
We propose a closed-form analytical model for the mechanical behavior of stratified snow covers for the purpose of investigating and predicting the physical processes that lead to the formation of dry-snow slab avalanches. We represent the system of a stratified snow slab covering a collapsible weak layer by a beam composed of an arbitrary number of layers supported by an anisotropic elastic foundation in a two-dimensional plane-strain model. The model makes use of laminate mechanics and provides slab deformations, stresses in the weak layer, and energy release rates of weak-layer anticracks in real time. The quantities can be used in failure models of avalanche release. The closed-form solution accounts for the layering-induced coupling of bending and extension in the slab and of shear and normal stresses in the weak layer. It is validated against experimentally recorded displacement fields and a comprehensive finite-element model indicating very good agreement. We show that layered slabs cannot be homogenized into equivalent isotropic bodies and reveal the impact of layering on bridging with respect to weak-layer stresses and energy release rates. It is demonstrated that inclined propagation saw tests allow for the determination of mixed-mode weak-layer fracture toughnesses. Our results suggest that such tests are dominated by mode I when cut upslope and comprise significant mode II contributions when cut downslope. A Python implementation of the presented model is publicly available as part of the Weak Layer Anticrack Nucleation Model (WEAC) software package under https://github.com/2phi/weac (last access: 28 March 2023) and https://pypi.org/project/weac (last access: 28 March 2023, Rosendahl and Weißgraeber, 2022).
... In conclusion, Boussinesq's equation is a valuable tool for estimating the vertical stress distribution in elastic layered pavement structures under static loads [50]. It offers simplicity and analytical solutions for stress calculations. ...
This research paper presents an appraisal of Mechanistic-Empirical Models (MEMs) in pavement deterioration assessment. The study examines various MEMs used in the analysis of flexible pavements, including Westergaard's Equation, Boussinesq's Equation, NCHRP 1-37A, AASHTO MEPDG, EICM, and FPO Software. The equations, parameters, limitations, and advantages of each model are thoroughly evaluated. The significance of MEMs in pavement deterioration assessment is highlighted, emphasizing their ability to provide a mechanistic understanding of pavement behavior and enable accurate predictions of performance. The paper offers recommendations for practitioners and researchers, including the adoption and implementation of MEMs, collaboration and data sharing, enhanced model inputs, standardized calibration protocols, continuous model improvement, and training and education. The conclusion emphasizes the value of MEMs in pavement engineering while acknowledging the need for further improvement and research. The findings of this study contribute to advancing the appraisal and utilization of MEMs in pavement deterioration assessment, ultimately leading to improved pavement designs, optimized maintenance strategies, and informed decision-making in the field of pavement engineering.
... In fact, many studies employing the finite-element method have shown that slab layering is important for failure initiation as well as the onset of crack propagation (Habermann et al., 2008;Monti et al., 2016;Schweizer et al., 2011;. In contrast, a homogeneous slab model coming along with an "equivalent modulus" is not able to represent important aspects of the deformation behaviour of a layered slab (e.g. ...
For a slab avalanche to release, we need sustained crack propagation in a weak snow layer beneath a cohesive snow slab – a process we call dynamic crack propagation. Field measurements on crack propagation are very scarce. We therefore performed a series of crack propagation experiments, up to 9 m long, over a period of 10 weeks and analysed these using digital image correlation techniques. We derived the elastic modulus of the slab (0.5 to 50 MPa), the elastic modulus of the weak layer (50 kPa to 1 MPa) and the specific fracture energy of the weak layer (0.1 to 1.5 J m−2) with a homogeneous and a layered-slab model. During crack propagation, we measured crack speed, touchdown distance, and the energy dissipation due to compaction and dynamic fracture (5 mJ m−2 to 0.43 J m−2). Crack speeds were highest for experiments resulting in full propagation, and crack arrest lengths were always shorter than touchdown lengths. Based on these findings, an index for self-sustained crack propagation is proposed. Our data set provides unique insight and valuable data to validate models.
... To assess dry-snow instability, a potential weak layer is determined with the structural stability index (SSI; Schweizer et al., 2006) or the threshold sum approach (Monti et al., 2014), and stability indices are then calculated for this layer. These include the skier stability index (SK 38 ) describing failure initiation (Föhn, 1987b;Jamieson and Johnston, 1998;Monti et al., 2016) and the recently implemented critical cut length (r c ) relating to crack propagation Richter et al., 2019). While SK 38 and r c should capture the most important processes involved in the formation of human-triggered avalanches (step ii), the interpretation of these stability indices (step iii) remains challenging. ...
... While the critical cut length is related to crack propagation, our set of features did not include any parameter related to failure initiation. Indeed, the traditional skier stability index SK 38 (Föhn, 1987b;Jamieson and Johnston, 1998;Monti et al., 2016) and the related failure initiation criterion (Reuter et al., 2015a) had lower feature importance scores (Appendix B, Fig. B1). Recently, Reuter et al. (2022) suggested using a combination of the critical cut length and a failure initiation index to differentiate stable from unstable profiles using a thresholdbased approach. ...
Modeled snow stratigraphy and instability data are a promising source of information for avalanche forecasting. While instability indices describing
the mechanical processes of dry-snow avalanche release have been implemented into snow cover models, there exists no readily applicable method that
combines these metrics to predict snow instability. We therefore trained a random forest (RF) classification model to assess snow instability from
snow stratigraphy simulated with SNOWPACK. To do so, we manually compared 742 snow profiles observed in the Swiss Alps with their simulated
counterparts and selected the simulated weak layer corresponding to the observed rutschblock failure layer. We then used the observed stability test
result and an estimate of the local avalanche danger to construct a binary target variable (stable vs. unstable) and considered 34 features
describing the simulated weak layer and the overlying slab as potential explanatory variables. The final RF classifier aggregates six of these
features into the output probability Punstable, corresponding to the mean vote of an ensemble of 400 classification trees. Although the
subset of training data only consisted of 146 profiles labeled as either unstable or stable, the model classified profiles from an independent
validation data set (N=121) with high reliability (accuracy 88 %, precision 96 %, recall 85 %) using manually predefined weak
layers. Model performance was even higher (accuracy 93 %, precision 96 %, recall 92 %), when the weakest layers of the profiles were
identified with the maximum of Punstable. Finally, we compared model predictions to observed avalanche activity in the region of Davos for
five winter seasons. Of the 252 avalanche days (345 non-avalanche days), 69 % (75 %) were classified correctly. Overall, the results of our
RF classification are very encouraging, suggesting it could be of great value for operational avalanche forecasting.
... While layers of interest are typically weak layers, they can also be other layers such as crusts. These layer labels can be based on grain-type classes (e.g., all persistent grain types), or they can include additional relevant measures like stability thresholds (e.g., threshold sums, SK38, p_unstable; Monti and Schweizer, 2013;Monti et al., 2016;Mayer et al., 2022). If the majority of corresponding layers are labeled as layers of interest, the resulting averaged layer properties are the median properties of all labeled layers regardless of the actual grain types. ...
... The average profile provides access to distributions of layer and profile properties, such as, for example, (b) the distribution of layer stabilities derived from threshold sums, (d) the depth distribution of a SH layer that is starting to get buried, and (e) the elevation distribution of the proportion of profiles that contain layers with poor stability in mid-snowpack. , the multi-layered skier stability index SK38ML (Monti et al., 2016), the joint RTA and SK38ML approach (Monti et al., 2014;Morin et al., 2020), the critical crack length (RC) (Richter et al., 2019), and the most recent random forest classifier p_unstable (PU) (Mayer et al., 2022). We classified each stability index into categories, such as very poor, poor, fair, and good, based on thresholds published in the respective papers. ...
... In the time series implementation of our algorithm the height of the snowpack grows over the course of the season by matching the current day's individual profiles against the previous day's average profile in an open-ended bottom-up alignment approach (for more details, see Herla et al., 2021). Monti et al., 2012Monti et al., , 2014, the relative threshold sum approach (RTA) (Monti and Schweizer, 2013), the multi-layered skier stability index (SK38ML) (Monti et al., 2016), the critical crack length (RC) (Richter et al., 2019), and the most recent random forest classifier p_unstable (PU) (Mayer et al., 2022). Panel (f) shows the corresponding average profile (on 20 January). ...
Snowpack models can provide detailed insight about the evolution of the snow stratigraphy in a way that is not possible with direct observations. However, the lack of suitable data aggregation methods currently prevents the effective use of the available information, which is commonly reduced to bulk properties and summary statistics of the entire snow column or individual grid cells. This is only of limited value for operational avalanche forecasting and has substantially hampered the application of spatially distributed simulations, as well as the development of comprehensive ensemble systems. To address this challenge, we present an averaging algorithm for snow profiles that effectively synthesizes large numbers of snow profiles into a meaningful overall perspective of the existing conditions. Notably, the algorithm enables compiling of informative summary statistics and distributions of snowpack layers, which creates new opportunities for presenting and analyzing distributed and ensemble snowpack simulations.
