Dr. Kurt Winkler

The main danger to which backcountry skiers are exposed are dry slab avalanches triggered by skiers themselves. This study introduces SLABS (Screening the Likelihood of Avalanches on Backcountry Ski tours), a novel, statistically derived probabilistic method that strikes a better balance between accident prevention and freedom of movement than traditional approaches. We fitted a Generalised Additive Model (GAM) with a binomial link to over 57.8 thousand km of GPS-tracked ski tours and 1,250 accidents recorded in Switzerland across two decades. SLABS combines the maximum slope angle, elevation, danger level, and aspect into a risk assessment. A comparison of the SLABS method with other methods (Graphical, Professional, and Quantitative Reduction Method) shows that it offers more freedom of movement for a given accident prevention rate. It is the first time that probabilistic methods are compared in terms of their tradeoff between accident prevention and freedom of movement. Because the SLABS method is not suited for mental arithmetic, it is meant to be implemented on the website www.skitourenguru.ch. Every day, this website evaluates the avalanche risk on thousands of backcountry tours throughout the Alps to help skiers plan their next tour. For a single slope the risk assessment of SLABS provides a starting point that should be supplemented with local observations.

The main danger to which backcountry skiers are exposed are dry slab avalanches triggered by skiers themselves. In this paper we present a new probabilistic method to assess the avalanche risk when planning a ski tour: SLABS (Screening the Likelihood of Avalanches on Backcountry Ski tours). The SLABS method is the first fully statistically derived probabilistic method for backcountry skiing. We used a data set with GPS (Global Positioning System) tracks of backcountry ski tours (57.8 thousand km) and a data set of 1250 accidents recorded in Switzerland during the last 20 winter seasons. A GAM (Generalised Additive Model) with binomial link discriminating between accidents and non-accidents was fitted to these data. As predictors the model uses a non-linear relation with the slope angle, and a linear relation with the danger level, the elevation and the aspect. Conditions with elevated and high risk were defined such that the accident prevention rate is 80% and 60%, respectively. A comparison of the SLABS method with the Graphical Reduction Method (GRM), the Professional Reduction Method (PRM) and the Quantitative Reduction Method (QRM) shows that it offers more freedom of movement for a given accident prevention rate. It is the first time that probabilistic methods are compared in terms of their trade-off between accident prevention and freedom of movement. Because the SLABS method is not suited for mental arithmetic it is implemented in the website www.skitourenguru.ch. This website evaluates on a daily basis the avalanche risk along thousands of backcountry tours throughout the Alps to help skiers plan their next tour. For a single slope the risk assessment of SLABS provides a starting point that has to be supplemented with local observations.

In recent years, the integration of physical snowpack models coupled with machine-learning techniques has become more prevalent in public avalanche forecasting. When combined with spatial interpolation methods, these approaches enable fully data- and model-driven predictions of snowpack stability or avalanche danger at any given location. This prompts the question: Are such highly detailed spatial model predictions sufficiently accurate for operational use? To explore this, we assess the performance of interpolated, model-based predictions of snowpack stability and avalanche danger, comparing them to human-generated public avalanche forecasts during the 2023/2024 winter season in Switzerland. To do so, we compare human forecasts and model predictions for locations in avalanche terrain (considering coordinates, aspect, elevation) where skiers triggered avalanches (244 events) or which were skied but where no avalanche was triggered (non-events, 3177 data points from GPX tracks). While this data reflects human behavior to some extent, we consider the event ratio as a proxy for the probability of avalanche release due to human load. We observed that with increasing model-predicted danger level or decreasing model-predicted snowpack stability, the event ratio increased. Comparing model predictions with human-made forecasts showed that the predictive performance of two operationally used models was similar to the performance of human avalanche forecasts: both predicted a strong increase in the probability of human-triggered avalanches. In summary, our results indicate that models capture regional patterns of snowpack (in)stability or avalanche danger well, and that these model chains should therefore be systematically integrated in the forecasting process.

Machine learning (ML) models are revolutionizing avalanche forecasting, providing increasingly reliable predictions. At present, however, such model output can hardly be integrated into the traditional operational forecast workflow. To close this gap, the Swiss Avalanche Warning Service has developed the Snow Avalanche Forecast Editor (SAFE), allowing a flexible integration of both human forecasts and model predictions. In SAFE, each hazard source (e.g., an avalanche problem) is assessed and described as a separate layer. This allows an entirely independent characterization of the severity of avalanche problems including their spatial extent, in line with the European Avalanche Warning Services standards. Once the assessment has been completed, all existing layers are automatically combined and published as regional avalanche forecasts. Forecast accuracy can be increased by minimizing noise. Decision theory advises to calculate an expected value from multiple independent assessments. For this reason, each of the two or three forecasters on duty assesses the regional avalanche danger in the forecast domain independently of each other. Another estimate comes from the danger-level model. To combine these, forecasters and models must assess the same hazard source using the same target variable. SAFE merges these independent assessments to expected values of the danger level including sub-levels, and the particularly affected elevations and aspects. This includes clustering into a limited number of danger regions, characterized by similar expected avalanche conditions. Following the discussion between forecasters, clusters can be refined. Lastly, the avalanche danger of each cluster is manually described. SAFE is conceived as open-source platform and a first version has been operational within the Swiss avalanche warning since winter 2023/24.

In public avalanche forecasts, avalanche danger is commonly summarized using one of five ordinal danger levels. This strong simplification of a complex phenomenon inevitably leads to a loss of information. To compensate for this information loss and to ease interpretation of the forecast, the Swiss avalanche warning service introduced sublevels, that indicate whether the forecast danger is in the lower (-), middle (=) or higher (+) range of the danger level. After a 6-year test period, during which it showed that forecasters can reliably assign sublevels to a danger level, the sublevels were published for the first time in the public avalanche forecast in the winter season of 2022/2023. To evaluate the general understanding and the usefulness of the sublevels, and to understand how they influence people’s backcountry behavior, we performed a user survey towards the end of the season. The survey with 3403 participants showed that participants perceived the sublevels as generally useful (85%) and that they understood them in the intended manner (94%). Sublevels also had a positive influence on the planning of backcountry tours. Faced with a forecast sublevel (+), almost half of the participants claimed they would change their plans to a less exposed tour than they had before when the same danger level was given but without sublevel information. This self-stated influence of the sublevels on the choice of a backcountry tour was especially important for participants with little or no avalanche training, showing that sublevels seem most useful for less experienced forecast users, who may find it difficult to extract similar information from the forecast text description.

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.

Forecasting avalanche danger at a regional scale is a largely data-driven yet also experience-based decision-making process by human experts. In the case of public avalanche forecasts, this assessment process terminates in an expert judgment concerning summarizing avalanche conditions by using one of five danger levels. This strong simplification of the continuous, multi-dimensional nature of avalanche hazard allows for efficient communication but inevitably leads to a loss of information when summarizing the severity of avalanche hazard. Intending to overcome the discrepancy between determining the final target output in higher resolution while maintaining the well-established standard of assessing and communicating avalanche hazard using the avalanche danger scale, avalanche forecasters at the national avalanche warning service in Switzerland used an approach that combines absolute and relative judgments. First, forecasters make an absolute judgment using the five-level danger scale. In a second step, a relative judgment is made by specifying a sub-level describing the avalanche conditions relative to the chosen danger level. This approach takes into account the human ability to reliably estimate only a certain number of classes. Here, we analyze these (yet unpublished) sub-levels, comparing them with data representing the three contributing factors of avalanche hazard: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. We analyze both data used in operational avalanche forecasting and data independent of the forecast, going back 5 years. Using a sequential analysis, we first establish which data are suitable and in which part of the danger scale they belong by comparing their distributions at consecutive danger levels. In a second step, integrating these findings, we compare the frequency of locations with poor snowpack stability and the number and size of avalanches with the forecast sub-level. Overall, we find good agreement: a higher sub-level is generally related to more locations with poor snowpack stability and more avalanches of larger size. These results suggest that on average avalanche forecasters can make avalanche danger assessments with higher resolution than the five-level danger scale. Our findings are specific to the current forecast set-up in Switzerland. However, we believe that avalanche warning services making a hazard assessment using a similar temporal and spatial scale as currently used in Switzerland should also be able to refine their assessments if (1) relevant data are sufficiently available in time and space and (2) a similar approach combining absolute and relative judgment is used. The sub-levels show a rank-order correlation with data related to the three contributing factors of avalanche hazard. Hence, they increase the predictive value of the forecast, opening the discussion on how this information could be provided to forecast users.

Das Lawinenrisiko ist mit dabei auf jeder Skitour. Jetzt zeigt sich, dass es mit der Gefahrenstufe noch stärker ansteigt als bisher angenommen. Die neuen Erkenntnisse unterstreichen, wie wichtig es ist, bei der Entscheidungsfindung das Lawinenbulletin zu berücksichtigen.

Eine Lawinengefahr besteht auf jeder Skitour. Wie gross sie an den einzelnen Orten ist, zeigt die Auswertung von 784 Lawinenunfällen und 20 000 Kilometern GPS-Tracks von Skitouren.

In January 2018, heavy snowfalls caused an extraordinary avalanche situation in Switzerland with an extent not observed since the avalanche winter of 1999. The amount of precipitation was far above the long-term average und in parts of the Swiss Alps even twice the average. For the first time since 1999, the highest danger level 5-Very High was forecast for a 36-hour period and for most regions of the Swiss Alps. We present key findings of the analysis of this extraordinary event and show that the mitigation efforts taken after the catastrophic events in February 1999 were effective. Despite very high avalanche activity, there was no loss of life and damage to property was limited.

Rule-based decision frameworks are widely recommended to estimate the avalanche risk while planning a ski tour. However, these frameworks were developed relying primarily on accident data and usually did not consider backcountry travel data. Hence, they are not risk-based. Here, we address this gap and calculate the risk taken during backcountry touring in avalanche terrain and correlate it to the expected avalanche conditions as described in a public avalanche forecast. For this, we rely on 784 reported avalanche accidents and more than 2.1 million movement points in potential avalanche terrain, based on GPS tracks recorded in Switzerland for 14 winter seasons. Combining this data with the respective avalanche forecast, we show that risk increases fourfold from danger level 1-Low to 2-Moderate, and from 2-Moderate to 3-Considerable. Furthermore, at 2-Moderate and 3-Considerable, in the critical elevations and aspects specified in the avalanche forecast, the risk is nearly six times higher compared to locations outside this, so-called, core zone. For danger level 1-Low, where the Swiss avalanche forecast does not provide any information about the critical elevations and aspects, we derived a risk-based core zone. Within this core zone too, the risk is about six times higher than outside. These findings suggest an adaption of the rule-based decision frameworks to reflect the observed risk better. The proposed framework considers the strong influence of the elevation and reduces the effect of the aspect, compared to former decision frameworks. We emphasize that this new decision framework cannot replace on-site risk assessment. However, it allows backcountry users to come closer to the goal of achieving a minimum of avalanche risk while allowing a maximum of freedom of movement.

Snow instability tests provide valuable information regarding the stability of the snowpack. Test results are key data used to prepare public avalanche forecasts. However, to include them into operational procedures, a quantitative interpretation scheme is needed. Whereas the interpretation of the rutschblock test (RB) is well established, a similar detailed classification for the extended column test (ECT) is lacking. Therefore, we develop a four-class stability interpretation scheme. Exploring a large data set of 1719 ECTs observed at 1226 sites, often performed together with a RB in the same snow pit, and corresponding slope stability information, we revisit the existing stability interpretations and suggest a more detailed classification. In addition, we consider the interpretation of cases when two ECTs were performed in the same snow pit. Our findings confirm previous research, namely that the crack propagation propensity is the most relevant ECT result and that the loading step required to initiate a crack is of secondary importance for stability assessment. The comparison with the RB showed that the ECT classifies slope stability less reliably than the RB. In some situations, performing a second ECT may be helpful when the first test did not indicate rather unstable or stable conditions. Finally, the data clearly show that false-unstable predictions of stability tests outnumber the correct-unstable predictions in an environment where overall unstable locations are rare.

In public avalanche forecasts, avalanche danger is summarized using a five-level ordinal danger scale. However, in Switzerland - but also in other countries - on about 75% of the forecasting days, only two of the five danger levels are actually used, indicating a lack of refinement in the forecast danger level. A refined classification requires the forecasters to assess the avalanche danger in greater detail than the established danger levels. This leads to the fundamental question, whether a reasonable accuracy and consistency of refined danger ratings can be achieved at all. We address this question relying on a data set from Switzerland, where forecasters of the national avalanche warning service have refined the forecast danger level using three sub-levels (minus, neutral, plus) during four forecasting seasons. These sub-levels, which describe where within a danger level the danger was estimated, were not provided to the public. With the goal to assess whether the forecast sub-levels were better than a random assignment of sub-levels, we compared these forecasts with local nowcast estimates of avalanche danger, for days when two observers reported such an estimate (N= 1146), as ground truth. The agreement between the forecast regional danger level and the local danger level estimate was 81%, with a distinct over-forecast bias in cases when forecast and nowcast disagreed. This tendency towards over-forecasting also showed in a spatial and temporal context. Furthermore, some anomalies in the use of the sub-levels were noted, particularly for sub-level plus in combination with danger level 2-Moderate. Despite these anomalies, the forecast sub-levels were clearly better than a randomly assigned sub-level, resulting in a lower misclassification cost. Furthermore, in case of over-forecasting, the forecast sub-level was in 70% of the cases the sub-level closest to the local estimate, and thus the difference between forecast and nowcast danger level was likely less than one "full" danger level. This indicates that forecasters can often forecast avalanche danger at greater detail than the established danger levels, provided that relevant and reliable data is available in sufficient spatial and temporal density, and that the warning regions, the smallest spatial units used in the forecast are sufficiently small. Therefore, we argue, such refinements of the danger level should be made whenever possible, last but not least for an improved internal assessment of avalanche danger.

Im vergangenen Winter gab es auffallend viele tödliche Lawinenunfälle in den westlichen Voralpen. Schuld war ein aussergewöhnliches Altschneeproblem. Das Institut für Schnee- und Lawinenforschung (SLF) informiert nun im Bulletin allgemein präziser vor Lawinenproblemen.

Strategic methods are well established aids for planning backcountry ski tours. They typically combine the avalanche danger level and the slope angle to a "risk category". The broad application of these classical methods reduces the frequency of avalanche accidents. However, they can't represent the risk, because they are based exclusively on accident data and neglect the terrain usage by the skier community. In this paper we present the Quantitative Reduction Method (QRM), which allows the estimation of the relative avalanche risk on backcountry ski tours. The method is based upon data: Human involved avalanche accidents (1469), GPS tracks of backcountry ski tours (47'530 km) and avalanche conditions (taken from 4656 avalanche warning forecasts). First, we introduce two continuous indicators: the "Danger Indicator" (DI) to describe the danger predicted in the avalanche bulletin and the "Terrain Indicator" (TI) to describe the extent to which a certain point and its surroundings are typical avalanche terrain. Then, we compute pairs of DI and TI for both, the release areas of the avalanche accidents and discrete points along the GPS tracks. The latter gives information about the terrain usage by backcountry recreationists. For probabilistic interpretation we use Kernel Density Estimations (KDE). Dividing accident KDE by terrain usage KDE gives the QRM. At a first glance, the QRM resembles earlier strategic methods. However, the QRM shows, that in the orange and red zones the risk increases exponentially with the danger indicator and terrain indicator. On the other hand, the relative risk remains close to zero in the green zone. The new method is suitable for computer applications and separates unambiguously low risk zones from high risk zones. Approximately 50% of the avalanche accidents could be avoided, by abstaining from only 1.9% of route segments.

The Swiss avalanche bulletin is produced twice a day in four languages. Due to the lack of time available for manual translation, a fully automated translation system is employed, based on a catalogue of predefined phrases and predetermined rules of how these phrases can be combined to produce sentences. Because this catalogue of phrases is limited to a small sublanguage, the system is able to automatically translate such sentences from German into the target languages French, Italian and English without subsequent proofreading or correction. Having been operational for two winter seasons, we assess here the quality of the produced texts based on two different surveys where participants rated texts from real avalanche bulletins from both origins, the catalogue of phrases versus manually written and translated texts. With a mean recognition rate of 55 %, users can hardly distinguish between the two types of texts, and give very similar ratings with respect to their language quality. Overall, the output from the catalogue system can be considered virtually equivalent to a text written by avalanche forecasters and then manually translated by professional translators. Furthermore, forecasters declared that all relevant situations were captured by the system with sufficient accuracy. Forecaster’s working load did not change with the introduction of the catalogue: the extra time to find matching sentences is compensated by the fact that they no longer need to double-check manually translated texts. The reduction of daily translation costs is expected to offset the initial development costs within a few years.