Digital cone penetration measurements can be used to infer snow mechanical properties, for instance, to study snow avalanche formation. The standard interpretation of these measurements is based on ...statistically inferred micromechanical interactions between snow microstructural elements and a well‐calibrated penetrating cone. We propose an alternative continuum model to derive the modulus of elasticity and yield strength of snow based on the widely used cavity expansion model in soils. We compare results from these approaches based on laboratory cone penetration measurements in snow samples of different densities and structural sizes. Results suggest that the micromechanical model underestimates the snow elastic modulus for dense samples by 2 orders of magnitude. By comparison with the cavity expansion‐based model, some of the discrepancy is attributed to low sensitivity of the micromechanical model to the snow elastic modulus. Reasons and implications of this discrepancy are discussed, and possibilities to enhance both methodologies are proposed.
Key Points
We propose the utility of a cavity‐expansion‐based penetration model for interpreting snow mechanical properties from penetration data
We perform a model comparison between a continuum cavity‐expansion‐based penetration model and a discrete microstructural model
We determine sources of model discrepancy and discuss pros and cons from both models as well as propose the utility of combining the models
Wet-snow avalanches are triggered by the infiltration of liquid water which weakens the snowpack. Wet-snow avalanches are among the most destructive avalanches, yet their release mechanism is not ...sufficiently understood for a process-based prediction model. Therefore, we followed a data-driven approach and developed a random forest model, depending on slope aspect, to predict the local wet-snow avalanche activity at the locations of 124 automated weather stations distributed throughout the Swiss Alps. The input variables were the snow and weather data recorded by the stations over the past 20 years. The target variable was based on manual observations over the same 20-year period. To filter out erroneous reports, we defined the days with wet-snow avalanches in a stringent manner, selecting only the most extreme active or inactive days, which reduced the size of the dataset but increased the reliability of the target variable. The model was trained with weather variables and variables computed from simulated snow stratigraphy in 38$^\circ$ slopes facing the 4 cardinal directions. While model development and validation were done in nowcast mode, we also studied model performance in 24-hour forecast mode by using input variables computed from a numerical weather prediction (NWP) model. Overall, the performance was good in both nowcast and forecast mode (f1-score around 0.8). To assess model performance beyond the stringent definition of wet-snow avalanche days, we compared model predictions to wet-snow avalanche activity over the entire Swiss Alps, based on the raw data over 8 winters. We obtained a Spearman correlation coefficient of 0.71. Hence, our model represents a step toward the application of support tools in operational wet-snow avalanche forecasting.
Abstract
Dry-snow slab avalanches result from crack propagation in a highly porous weak layer buried within a stratified and metastable snowpack. While our understanding of slab avalanche mechanisms ...improved with recent experimental and numerical advances, fundamental micro-mechanical processes remain poorly understood due to a lack of non-invasive monitoring techniques. Using a novel discrete micro-mechanical model, we reproduced crack propagation dynamics observed in field experiments, which employ the propagation saw test. The detailed microscopic analysis of weak layer stresses and bond breaking allowed us to define the crack tip location of closing crack faces, analyze its spatio-temporal characteristics and monitor the evolution of stress concentrations and the fracture process zone both in transient and steady-state regimes. Results highlight the occurrence of a steady state in crack speed and stress conditions for sufficiently long crack propagation distances (> 4 m). Crack propagation without external driving force except gravity is possible due to the local mixed-mode shear-compression stress nature at the crack tip induced by slab bending and weak layer volumetric collapse. Our result shed light into the microscopic origin of dynamic crack propagation in snow slab avalanche release that eventually will improve the evaluation of avalanche release sizes and thus hazard management and forecasting in mountainous regions.
To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data are required, which introduce uncertainties. The repercussions of ...these uncertainties on modeled snow stability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty to modeled snow stability by performing a global sensitivity analysis. We used the numerical snow cover model SNOWPACK to simulate two snow instability metrics, i.e., the skier stability index and the critical crack length, for a field site equipped with an automatic weather station providing the necessary input for the model. Simulations were performed for a winter season, which was marked by a prolonged dry period at the beginning of the season. During this period, the snow surface layers transformed into layers of faceted and depth hoar crystals, which were subsequently buried by snow. The early-season snow surface was likely the weak layer of many avalanches later in the season. Three different scenarios were investigated to better assess the influence of meteorological forcing on snow stability during (a) the weak layer formation period, (b) the slab formation period, and (c) the weak layer and slab formation period. For each scenario, 14 000 simulations were performed, by introducing quasi-random uncertainties to the meteorological input. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km or an elevation change of 200 m. Results showed that a weak layer formed in 99.7 % of the simulations, indicating that the weak layer formation was very robust due to the prolonged dry period. For scenario a, modeled grain size of the weak layer was mainly sensitive to precipitation, while the shear strength of the weak layer was sensitive to most input variables, especially air temperature. Once the weak layer existed (case b), precipitation was the most prominent driver for snow stability. The sensitivity analysis highlighted that for all scenarios, the two stability metrics were mostly sensitive to precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For cases b and c, the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e., deep snowpacks, the skier stability index decreased (became less stable). In contrast, the critical crack length increased with increasing precipitation (became more stable). With regard to spatial simulations of snow stability, the high sensitivity to precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.
Sintering rates in snow were investigated by measuring changes in penetration resistance with time and by using a numerical snow metamorphism model. Periodic Snow Micro Penetrometer (SMP) ...measurements were performed on uniform snow samples covering a wide range of densities. The mean penetration resistance of snow increased with time according to a power law with an average exponent of 0.18. Simulated changes in the bond-to-grain ratio for simplified spherical ice grains followed a power law with an average exponent of 0.18, showing that the mean penetration resistance, as measured with the SMP, closely relates to bond-to-grain ratio in snow. For lower-density snow samples, consisting mostly of dendritic snow morphologies, the measured increase in penetration resistance was lower. This is likely the result of two competing processes: (1) strengthening of the snow sample due to the creation and growth of bonds and (2) weakening of the snow sample due to the formation of unbonded small rounded particles at the expense of larger dendritic forms. On the other hand, the rate of increase in penetration resistance for snow samples consisting of closely packed depth hoar was similar to that of rounded grains.
Predicting the timing and size of natural snow avalanches is crucial for local and regional decision makers but remains one of the major challenges in avalanche forecasting. So far, forecasts are ...generally made by human experts interpreting a variety of data and drawing on their knowledge and experience. Using avalanche data from the Swiss Alps and one-dimensional physics-based snowpack simulations for virtual slopes, we developed a model predicting the probability of dry-snow avalanches occurring in the region surrounding automated weather stations based on the output of a recently developed instability model. This new avalanche day predictor was compared with benchmark models related to the amount of new snow. Evaluation on an independent data set demonstrated the importance of snow stratigraphy for natural avalanche release, as the avalanche day predictor outperformed the benchmark model based on the 3 d sum of new snow height (F1 scores: 0.71 and 0.65, respectively). The averaged predictions of both models resulted in the best performance (F1 score: 0.75). In a second step, we derived functions describing the probability for certain avalanche size classes. Using the 24 h new snow height as proxy of avalanche failure depth yielded the best estimator of typical (median) observed avalanche size, while the depth of the deepest weak layer, detected using the instability model, provided the better indicator regarding the largest observed avalanche size. Validation of the avalanche size estimator on an independent data set of avalanche observations confirmed these findings. Furthermore, comparing the predictions of the avalanche day predictors and avalanche size estimators with a 21-year data set of re-analysed regional avalanche danger levels showed increasing probabilities for natural avalanches and increasing avalanche size with increasing danger level. We conclude that these models may be valuable tools to support forecasting the occurrence of natural dry-snow avalanches.
Snow slab avalanches start with a failure in a weak snow layer buried below a cohesive snow slab. After failure, the very porous character of the weak layer leads to its volumetric collapse and thus ...closing of crack faces due to the weight of the overlaying slab. This complex process, generally referred to as anticrack, explains why avalanches that release on steep slopes can be triggered from flat terrain. On the basis of a new elastoplastic model for porous cohesive materials and the Material Point Method, we investigate the dynamics of mixed-mode anticracks, the subsequent detachment of the slab and the flow of the avalanche. In particular, we performed two and three dimensional slope scale simulations of both the release and flow of slab avalanches triggered either directly or remotely. We describe the fracture and flow dynamics on a realistic topography and focus on the volumetric plastic strain, stress invariants, propagation speed and flow velocity. Our simulations reproduce typical observations of “en-echelon” fractures and the propagation speed reached up to three times that measured in field experiments. In addition, slab fracture always started from the top in the Propagation Saw Test while it systematically initiated at the interface with the weak layer at the crown of slope-scale simulations in agreement with limited field observations. During the avalanche flow, snow granulation, erosion and deposition processes were naturally simulated and do not need additional implementations. Our unified model represents a significant step forward as it allows simulating the entire avalanche process, from failure initiation to crack propagation and to solid-fluid phase transitions, which is of paramount importance to forecast and mitigate snow avalanches.
•A material point method based on finite strain elastoplasticity allowed to simulate the release and flow of slab avalanches.•Simulations reproduce “en-echelon” fractures observed in the field•We explain discrepancies in the slab fracture branching mode between the PST and the slope scale, as observed in the field.•Crack propagation speed at the slope scale can be 3 to 4 times larger than that measured in PST experiments.•Our simulations naturally capture snow granulation, erosion and deposition processes without additional implementations.•The run-out distance, maximum velocity and α-angles evaluated in our simulations are in good agreement with field data
In mountainous areas, rockfalls, rock avalanches, and debris flows constitute a risk to human life and property. Seismology has proven a useful tool to monitor such mass movements, while increasing ...data volumes and availability of real-time data streams demand new solutions for automatic signal classification. Ideally, seismic monitoring arrays have large apertures and record a significant number of mass movements to train detection algorithms. However, this is rarely the case, as a result of cost and time constraints and the rare occurrence of catastrophic mass movements. Here, we use the supervised random forest algorithm to classify windowed seismic data on a continuous data stream. We investigate algorithm performance for signal classification into noise (NO), slope failure (SF), and earthquake (EQ) classes and explore the influence of non-ideal though commonly encountered conditions: poor network coverage, imbalanced data sets, and low signal-to-noise ratios (SNRs). To this end we use data from two separate locations in the Swiss Alps: data set (i), recorded at Illgraben, contains signals of several dozen slope failures with low SNR; data set (ii), recorded at Pizzo Cengalo, contains only five slope failure events albeit with higher SNR. The low SNR of slope failure events in data set (i) leads to a classification accuracy of 70 % for SF, with the largest confusion between NO and SF. Although data set (ii) is highly imbalanced, lowering the prediction threshold for slope failures leads to a prediction accuracy of 80 % for SF, with the largest confusion between SF and EQ. Standard techniques to mitigate training data imbalance do not increase prediction accuracy. The classifier of data set (ii) is then used to train a model for the classification of 176 d of continuous seismic recordings containing four slope failure events. The model classifies eight events as slope failures, of which two are snow avalanches, and one is a rock-slope failure. The other events are local or regional earthquakes. By including earthquake detection of a permanent seismic station at 131 km distance to the test site into the decision-making process, all earthquakes falsely classified as slope failures can be excluded. Our study shows that, even for limited training data and non-optimal network geometry, machine learning algorithms applied to high-quality seismic records can be used to monitor mass movements automatically.
If a weak snow layer below a cohesive slab is present in the snow cover, unstable snow conditions can prevail for days or even weeks. We monitored the temporal evolution of a weak layer of faceted ...crystals as well as the overlaying slab layers at the location of an automatic weather station in the Steintälli field site above Davos (Eastern Swiss Alps). We focussed on the crack propagation propensity and performed propagation saw tests (PSTs) on 7 sampling days during a 2-month period from early January to early March 2015. Based on video images taken during the tests we determined the mechanical properties of the slab and the weak layer and compared them to the results derived from concurrently performed measurements of penetration resistance using the snow micro-penetrometer (SMP). The critical cut length, observed in PSTs, increased overall during the measurement period. The increase was not steady and the lowest values of critical cut length were observed around the middle of the measurement period. The relevant mechanical properties, the slab effective elastic modulus and the weak layer specific fracture, overall increased as well. However, the changes with time differed, suggesting that the critical cut length cannot be assessed by simply monitoring a single mechanical property such as slab load, slab modulus or weak layer specific fracture energy. Instead, crack propagation propensity is the result of a complex interplay between the mechanical properties of the slab and the weak layer. We then compared our field observations to newly developed metrics of snow instability related to either failure initiation or crack propagation propensity. The metrics were either derived from the SMP signal or calculated from simulated snow stratigraphy (SNOWPACK). They partially reproduced the observed temporal evolution of critical cut length and instability test scores. Whereas our unique dataset of quantitative measures of snow instability provides new insights into the complex slab-weak layer interaction, it also showed some deficiencies of the modelled metrics of instability – calling for an improved representation of the mechanical properties.
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.
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