Dr. Bettina Richter

The cryosphere in mountain regions is rapidly transforming due to climate change, yet the impact of these changes on snow avalanche activity remains uncertain. Here, we use a snow cover model driven by downscaled climate projections to evaluate future alterations in dry- and wet-snow avalanche occurrences throughout the 21st century in the Swiss Alps. We assess avalanche activity by employing machine learning models trained with observed records of avalanches. Our findings indicate an overall decline in the occurrence of dry-snow avalanches during the months of December to May that is partially compensated for by an increase in wet-snow avalanche activity. Depending on elevation and the emission scenario considered, we anticipate a net reduction in the total avalanche activity ranging from under 10 % to as much as 60 % by the end of the century compared to 45–75 avalanche days per year at the beginning of the century. Projections further reveal a shift of wet-snow avalanche activity to earlier winter months. Analysis of changes in prominent snow grain types offers a coherent explanation of projected changes beyond a mere decrease in snow depth and snow cover duration. Overall, our study quantifies for the first time the significant influence of climate change on snow avalanche activity in the Swiss Alps and may serve as a benchmark for further mountain regions with similar avalanche climates.

Predicting natural avalanche activity and verifying avalanche forecasts have long been major challenges. However, the advent of new technologies, such as radar-based avalanche detection systems and prediction models, are beginning to fill these critical gaps. In this study, we describe the integration of these technologies into our operational workflow, assess their perceived value by forecasters, and provide a quantitative comparison between model predictions and radar detections in southern Valais. A recently developed natural avalanche model (NAM), which predicts natural avalanche activity, was used operationally by the Swiss Avalanche Warning Service in the 2023/24 winter season. The model was displayed next to avalanche observations reported by the observer network and avalanches detected by automated radar systems. This visualization was crucial in helping forecasters develop a better appreciation of the new model. The approach also provided immediate feedback when obvious deviations from predicted and actual conditions became apparent. In this work, after one winter of operational use, we quantitatively evaluated model predictions by comparing these with avalanche activity data from two radar avalanche detection systems in Southern Valais. The analysis confirmed that the natural avalanche model was related to regional avalanche activity, but also highlighted areas for improvement. In particular, after reaching a peak in predicted probability, the model often maintained a high probability while measured avalanche activity had ceased. Our findings suggest that incorporating a trend of predicted probability could improve the model’s accuracy. For further model development, we recommend integrating detection data from multiple sites and over multiple winters. In addition, we recommend the development of automated methods to classify detected avalanches as dry or wet and to link these events to explosives logs. This would greatly enhance the real-time utility of detection data for operational avalanche forecasting services and support ongoing analysis and model refinement.

Climate change strongly affects the seasonal snow cover in mountainous areas worldwide. Warming-induced changes of snowfall and snow cover are expected to influence the frequency and type of snow avalanches, but research on the effect of climate change on avalanches is scarce. To investigate the effect of climate change on avalanche activity in the region of Davos, Switzerland, we simulated snow stratigraphy until the end of the 21^st century using downscaled climate projections for two automatic weather stations at 2147 m a.s.l. and 2540 m a.s.l. Projections were based on eight different climate model chains and three different emission scenarios and snow stratigraphy was simulated with the snow cover model SNOWPACK. We interpreted simulated snow stratigraphy in terms of wet- and dry-snow instability using two recently developed random forest models. Results suggest that, depending on the emission scenario, dry-snow instability expressed as avalanche days will on average decrease by 20 to 60% until the end of the century compared to the reference period (1991-2020). Wet-snow instability, on the other hand, will slightly increase, by up to 20% for the higher-elevation site, and decrease by up to 50% for the lower-elevation site. Detailed analysis of the monthly changes of avalanche activity revealed a shift of wet-snow instability to earlier winter months. Overall, our results contribute to a better understanding of the influence of climate warming on avalanche activity which is crucial to revisit long-standing avalanche risk mitigation strategies.

When riding out of bounds or touring in the backcountry, many recreationists and professionals plan their trip with the aim of finding a tour at a personally acceptable level of risk. Particularly during the planning phase, information from the public avalanche forecast is considered. In Switzerland, the avalanche forecast summarizes avalanche conditions with a danger level (DL) and the aspects and elevations (AE) where the DL applies. Further, it informs about the relevant avalanche problems, including a text description. Previous studies have shown that most of this information is related to avalanche risk. However, it is poorly understood which information users consider helpful for trip planning and whether these (often) self-stated claims relate to what is remembered at the beginning of a trip. We therefore conducted two independent surveys. The online survey, with 3400 participants, showed that all information provided in the forecast is perceived as valuable for planning. The DL was considered the most important, followed by the avalanche problem(s). In comparison, the AE were considered less important. This ranking of the users is not in line with recent findings regarding avalanche risk. For the second survey, we went outdoors, either in a popular area for out of bounds skiing or at trail heads of ski tours and asked the users directly. Compared to the responses from the online survey, a different picture emerged: While the DL was well-remembered (90 % correct), only around half of the participants (44 %) could correctly name the avalanche problems. In an application exercise, a clear minority (24 %) could say whether slopes of a particular aspect and elevation were assessed as critical in the forecast. This apparent discrepancy between the self-stated use of forecast information during trip planning and the retention of the information at the start of a trip comes partly as a surprise. Especially the avalanche problems, intended to emphasize and focus on the cause of instability resulting in impact-oriented travel advice, are remembered poorly. The findings are supported by other recent surveys in Europe and show that the information from the forecast is not effectively retained, even at the start of a trip. It highlights the need to rethink and improve the communication of avalanche danger, including the level of detail. While providing detailed information may be helpful for experts, it may be overwhelming for less experienced users. Our study aims to contribute to the ongoing discussion on how to best communicate avalanche danger to reduce risks for recreational skiers.

Assessing the avalanche danger level requires snow stratigraphy and instability data. As such data are usually sparse, we investigated whether distributed snow cover modeling can be used to provide information on spatial instability patterns relevant for regional avalanche forecasting. Using Alpine3D, we performed spatially distributed simulations to evaluate snow instability for the winter season 2016-17 in the region of Davos, Switzerland. Meteorological data from automatic weather stations were interpolated to 100 m horizontal resolution and precipitation was scaled with snow depth measurements from airborne laser scanning. Modeled snow instability metrics assessed for two different weak layers suggested that the weak layer closer to the snow surface was more variable. Initially, it was less stable than the weak layer closer to the ground, yet it stabilized faster as the winter progressed. In spring, the simulated snowpack on south-facing slopes stabilized faster than on north-facing slopes, in line with the regional avalanche forecast. In the winter months January to March 2017, simulated instability metrics did not suggest that the snowpack on south-facing slopes was more stable, as reported in the regional avalanche forecast. Although a validation with field data is lacking, these model results still show the potential and challenges of distributed modeling for supporting operational avalanche forecasting.

The necessity of characterizing snow through objective, physically motivated parameters has led to new model formulations and new measurement techniques. Consequently, essential structural parameters such as density and specific surface area (for basic characterization) or mechanical parameters such as the critical crack length (for avalanche stability characterization) gradually replace the semiempirical indices acquired from traditional stratigraphy. These advances come along with new demands and potentials for validation. To this end, we conducted the RHOSSA field campaign, in reference to density (ρ) and specific surface area (SSA), at the Weissfluhjoch research site in the Swiss Alps to provide a multi-instrument, multi-resolution dataset of density, SSA and critical crack length over the complete winter season of 2015-2016. In this paper, we present the design of the campaign and a basic analysis of the measurements alongside predictions from the model SNOWPACK. To bridge between traditional and new methods, the campaign comprises traditional profiles, density cutter, IceCube, SnowMicroPen (SMP), micro-computed-tomography, propagation saw tests and compression tests. To bridge between different temporal resolutions, the traditional weekly to biweekly (every 2 weeks, used in this sense throughout the paper) snow pits were complemented by daily SMP measurements. From the latter, we derived a recalibration of the statistical retrieval of density and SSA for SMP version 4 that yields an unprecedented spatiotemporal picture of the seasonal evolution of density and SSA in a snowpack. Finally, we provide an intercomparison of measured and modeled estimates of density and SSA for four characteristic layers over the entire season to demonstrate the potential of high-temporal-resolution monitoring for snowpack model validation.

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.

Observed snow stratigraphy and snow stability are of key importance for avalanche forecasting. Such observations are rare and snow cover models can improve the spatial and temporal resolution. To evaluate snow stability, failure initiation and crack propagation have to be considered. Recently, a new stability criterion relating to crack propagation, namely the critical crack length, was implemented into the snow cover model SNOWPACK. The critical crack length can also be measured in the field with a propagation saw test, which allows for an unambiguous comparison. To validate and improve the parameterization for the critical crack length, we used data from 3 years of field experiments performed close to two automatic weather stations above Davos, Switzerland. We monitored seven distinct weak layers and performed in total 157 propagation saw tests on a weekly basis. Comparing modeled to measured critical crack length showed some discrepancies stemming from model assumption. Hence, we replaced two variables of the original parameterization, namely the weak layer shear modulus and thickness, with a fit factor depending on weak layer density and grain size. With these adjustments, the normalized root-mean-square error between modeled and observed critical crack lengths decreased from 1.80 to 0.28. As the improved parameterization accounts for grain size, values of critical crack lengths for snow layers consisting of small grains, which in general are not weak layers, become larger. In turn, critical weak layers appear more prominently in the vertical profile of critical crack length simulated with SNOWPACK. Hence, minimal values in modeled critical crack length better match observed weak layers. The improved parameterization of critical crack length may be useful for both weak layer detection in simulated snow stratigraphy and also providing more realistic snow stability information-and hence may improve avalanche forecasting.

To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data is required, which can introduce uncertainties. The repercussions of these uncertainties on modeled snow instability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty on modeled snow instability by performing a global sensitivity analysis. We used the model SNOWPACK to simulate snow instability, 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. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km and an elevation change of 200 m. Two different scenarios were investigated to better understand the influence of meteorological forcing on snow instability during a) the weak layer formation period and b) the slab formation period. Results showed that during the weak layer formation period, the evolution of weak layer properties and subsequent snow instability were sensitive to all input parameters. In particular, increasing air temperature and increasing precipitation led to higher stability indices. Once a certain weak layer had formed, precipitation was the most prominent driver for snow instability during the slab formation period. While with increasing precipitation the skier stability index decreased, the critical crack length increased. Such findings help with selecting model resolution and complexity, interpreting spatial snow simulations and understanding the evolution of snow instability.

Forecasting snow avalanches requires a detailed understanding of the influence of meteorological boundary conditions on avalanche formation. For instance, while it is widely known that avalanche activity generally increases during snow storms, much less is known about typical time scales involved in avalanche formation. In other words, we generally do not know how quickly avalanche activity rises or when peak activity has been reached. Whereas meteorological data are nowadays readily available with high temporal resolution, the corresponding avalanche occurrence data are lacking. This is mainly because data on avalanche activity obtained through conventional visual observations are incomplete and inaccurate, especially regarding the release time. To identify characteristic time scales and dominant meteorological drivers associated with avalanche activity, we therefore used unique avalanche activity catalogues obtained through seismic and radar monitoring in combination with local meteorological measurements and snow cover simulations. Results show that different meteorological drivers related to avalanche activity throughout a season: energy input into the snow cover was a dominant driver late in the season, while modeled precipitation correlated best with avalanche activity earlier in the season. While these findings are not surprising, having more accurate release times for the avalanche events allowed us to cross-correlate meteorological drivers with avalanche activity to identify time scales. For precipitation, time scales were on the order of several days, while for energy input later in the season, time scales were on the order of several hours to one day. Finally, by using a moving windowed cross-correlation approach, we clearly identified regime changes throughout the season. Overall, our findings show that accurate avalanche activity data can provide novel insight into avalanche formation processes and ultimately can help improve avalanche forecasting using readily available meteorological data and snow cover simulations.

Forecasting natural avalanche activity is challenging. Events are relatively rare and several contributory factors affecting the formation of dry- or wet-snow avalanches have to be evaluated. In contrast to previous statistical avalanche forecast models which focus on specific avalanche types or small scales, in this study we developed a model to predict avalanche days for an entire winter season at the regional scale. We therefore analysed an avalanche catalogue consisting of observed avalanches in the region of Davos, Switzerland, over the last 13 years (2003/04 to 2015/16). In combination with data from an automatic weather station and simulated snow cover properties from the model SNOWPACK, we trained random forest models by applying different methods to predict avalanche days. Overall, the predictive performance was in line with other similar studies. However, substantial differences in performance were observed among the 13 winter seasons. Surprisingly, the performance of models without snow cover data from the SNOWPACK simulations was very similar. These results suggest that there is no strong correlation between avalanche activity and snow cover properties, highlighting the limitations of obtained avalanche activity data through visual observations. While more work is still required, the reasonable performance of our statistical model to predict avalanche days show that automatically predicting regional avalanche activity using an automatic weather stations is feasible.

Spatial and temporal variations are inherent characteristics of the alpine snow cover. Spatial heterogeneity is supposed to control the avalanche release probability by either hindering extensive crack propagation or facilitating localized failure initiation. Though a link between spatial snow instability variations and meteorological forcing is anticipated, it has not been quantitatively shown yet. We recorded snow penetration resistance profiles with the snow micropenetrometer at an alpine field site during five field campaigns in Eastern Switzerland. For each of about 150 vertical profiles sampled per day a failure initiation criterion and the critical crack length were calculated. For both criteria we analyzed their spatial structure and predicted snow instability in the basin by external drift kriging. The regression models were based on terrain and snow depth data. Slope aspect was the most prominent driver, but significant covariates varied depending on the situation. Residual autocorrelation ranges were shorter than the ones of the terrain suggesting external influences possibly due to meteorological forcing. To explore the causes of the instability patterns we repeated the geostatistical analysis with snow cover model output as covariate data for one case. The observed variations of snow instability were related to variations in slab layer properties which were caused by preferential deposition of precipitation and differences in energy input at the snow surface during the formation period of the slab layers. Our results suggest that 3-D snow cover modeling allows reproducing some of the snow property variations related to snow instability, but in future work all relevant micrometeorological spatial interactions should be considered.

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 Steintalli 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.