To ensure safe living and travel in mountain regions during the winter season, effective avalanche protection systems are essential . However, continuously protecting infrastructure such as roads, railways, power lines, and ski runs is not feasible due to financial restrictions and the need to preserve the natural landscape . In such cases, safety protocols define the operational procedures to evaluate avalanche danger and deciding whether traffic routes can remain open, have to be temporarily closed, and require active control measures, such as artificial avalanche releases. These safety protocols currently rely on avalanche forecasts, data from measurement networks, and expert knowledge . However, climate-driven shifts to snowpack conditions and avalanche hazard, coupled with generational turnover among avalanche safety practitioners, are making experience-based decision-making in avalanche safety increasingly difficult to sustain . Meanwhile, advances in real-time monitoring, data integration and artificial intelligence–based approaches are transforming and providing growing support for operational decision-making .

Avalanche hazard maps are a central tool in natural hazard management and form the basis for long-term planning and safety protocols. They provide a spatial representation of avalanche intensity, including impact pressure, velocity, flow height, and runout extent, thereby identifying areas that are repeatedly exposed to avalanches. Hazard maps are mainly intended for inhabited areas and not for all road sections. In Switzerland, avalanche hazard maps are traditionally based on discrete return periods (10, 30, 100 and 300 years) assigned to specific snow accumulation scenarios. Hazard maps are developed from historical event records and are increasingly complemented by numerical simulation tools for avalanche dynamics, such as RAMMS::Avalanche , SamosAT , r.avaflow , OpenFOAM , MoT-Voellmy , and ELBA . A more complete list of depth-averaged tools is provided in the model intercomparison project in . The calculated avalanche intensities depend on topography, snow and weather conditions, and vegetation cover . After the extreme avalanche winter of 1999 in Switzerland, the Swiss government strengthened its organizational risk management framework to better translate hazard information into operational measures, with a focus on the coordination and procedures of road closures .

Beyond spatial hazard maps, several methods have been developed to systematize hazard assessment by explicitly integrating terrain, snowpack, and meteorological information. The Avalanche-Hazard Index (AHI) and the Wilhelm method are widely used in the United States for systematic avalanche risk assessment. The AHI quantifies avalanche danger by integrating snowpack stability, terrain characteristics, and meteorological factors into a standardized hazard score. In contrast, the Wilhelm method relies more on qualitative field observations and records of past avalanche activity to classify slope-specific risk, thereby complementing the numerical framework of the AHI for the hazard map creation.

In many regions, particularly those with large and sparsely populated areas, traditional hazard mapping is constrained by the scarcity of comprehensive historical avalanche databases, motivating the increasing use of automated and model-based methods. A critical first step in adapting these approaches is to identify spatial and temporal hotspots; where and when avalanches are likely to occur. Hotspots can be identified using terrain based analysis models by and or by analysing historical event records . developed an approach for large scale hazard indication modelling (LSHIM), consistent with the Swiss approach used for hazard mapping. In Norway, the region independent system NAtural hazard Knowledge System for INundation and avalanches (NAKSIN) has been developed to produce Avalanche Hazard Indication Maps (AHIMs). NAKSIN is automatically identifying potential release areas, estimating avalanche release probabilities from empirical relationships involving terrain, long-term climatic characteristics, and forest cover, and simulating avalanche runout using numerical flow models .

While automated hazard indication maps improve spatial coverage and objectivity, they still rely on discrete scenarios and simplified frequency assumptions. Early statistical approaches attempted to overcome these limitations by analysing avalanche occurrence and runout in a probabilistic framework. For example, applied extreme value statistics and frequency–magnitude relationships to characterize avalanche size distributions and to estimate exceedance probabilities for different runout distances. Building on this line of research, reconsidered hazard mapping by moving away from the traditional use of return periods. Instead, probabilities are linked to each avalanche, depending on snow accumulation and exposition of the release zones, resulting in a wide range of possible avalanche release volumes. Return periods of avalanche intensity (flow height and pressure) in the runout region are derived from all simulated avalanches and their probabilities. By explicitly combining avalanche intensities with their associated probabilities, continuous intensity–frequency curves are obtained at any location, thereby replacing the discrete, scenario-based representation of hazard. Similarly, developed a probabilistic hazard mapping framework that transmits uncertainties in avalanche release volume through three-dimensional runout simulations to derive probability maps of impact and exceedance for exposed road sections. In addition, recent work by and by has extended probabilistic mass-flow modelling by systematically propagating uncertainties within initial conditions and model parameters through ensemble-based, process-oriented simulations to derive spatially distributed runout and impact probabilities across complex terrain. demonstrate a probabilistic back-calculation framework with r.avaflow, where uncertainties in observed avalanche data and model parameters are propagated into conditional runout probabilities.

Today, operational avalanche safety decisions are still taken very differently in various regions. A basic approach is to measure the new snow accumulation and empirically derived thresholds for the height of new snow . This approach has reduced avalanche-related fatalities . describe a more advanced observation-based approach for a daily prediction of the potential avalanche runout area. They derived fracture depths from documented avalanche events and statistically analysed weak-layer measurements to estimate typical fracture depth. From the recurrence of these fracture depths, they obtained probability distributions, which they combined with numerical simulations to predict avalanche runout.

An operational forecasting system, integrating meteorological station data into numerical avalanche dynamics simulations, is currently implemented in the Central Andes. Developed for a mining region, where avalanche activity threatens a critical access road, the system is highly site-specific and tailored to local topographic and climatic conditions . It applies RAMMS::Extended to simulate wet snow avalanche runout distances, differentiating between cold and warm flow regimes based on the mean snow temperature in the release zone, while fracture depths are estimated from SNOWPACK simulations at adjacent weather stations .

Advancing towards operational daily forecasting, present an approach that integrates automated identification of potential release areas (PRAs) with numerical snowpack modelling. The system employs an automatic PRA-detection algorithm and combines its outputs with SNOWPACK simulations to assess snow cover stability. Based on this integration, potential avalanche runout lengths are determined for a range of typical avalanche problem types.

The described studies demonstrate that integrating daily observations with numerical simulation tools can effectively enhance operational avalanche mitigation processes. Previous work investigated the required model complexity and sensitivity needed to accurately capture daily variations in avalanche runout distances.

Both, hazard mapping approaches and operational forecasting systems rely on numerical avalanche dynamic models to estimate potential runout distances. In hazard mapping, model input is typically derived from statistically evaluated snow heights based on long-term meteorological records, whereas operational applications increasingly use near-real-time measurements from meteorological stations. However, these observations are point measurements, often located in flat terrain, and cannot fully capture the spatial variability of snow conditions related to topographic parameters, such as elevation and aspect or terrain features causing variations in wind exposure within avalanche release areas .

As a result, considerable uncertainty is inherently accepted in the specification of snow-related input parameters for avalanche simulations. Furthermore, numerical avalanche dynamics models introduce additional uncertainties related to model structure, parametrization, and simplifications of physical processes. To meaningfully interpret simulation results for decision-making, these sources of uncertainty need to be explicitly represented and carried on within the modelling framework.

In this study, we develop an automated workflow that integrates weather station data, weather forecast data, SNOWPACK simulations, avalanche dynamics simulation models, and visualization tools for cold powder avalanche conditions, aiming to improve traffic safety applications in high-risk avalanche hazard areas. The weather station and forecast data is used to derive potential fracture depths, snowpack characteristics, and the estimated snow conditions within the avalanche path. The probability of release for each potential release area is determined based on weak-layer stability outputs from SNOWPACK. To address uncertainties in the input data and model results, we implement a probabilistic framework that generates daily hazard indication maps across multiple meteorological and snow cover scenarios. Because the probabilistic framework relies on a limited ensemble of input scenarios derived from measurement stations, the resulting values should be interpreted as relative probability indicators rather than absolute probabilities. We therefore refer to the outputs as probability indication maps.

To test the proposed tool for creating a probability indication map, we required a study site with well-documented avalanche events against which the resulting probability indication maps could be evaluated. The site needs records of avalanche occurrences and road closures, mapped avalanche outlines and runout distances, and sufficient meteorological and snow-measurement data to drive snowpack simulations.

The Dischma road in the Davos region of Switzerland is used as the main study site, with a specific focus on the Brämabüel section, attributable to the availability of long-term documentation of avalanche activity and road closures (see Fig. ). The area is characterized by a dense network of meteorological measurement stations called Interkantonales Mess- und Informationssystem (IMIS) as shown in Fig. . Additionally, two new stations independent of the IMIS network, were installed with low-cost LiDAR sensors in November 2023, close to the release zone of one of the avalanche paths. These stations measure the snow depth distribution on an hourly basis over an area of 14 500 m² . Along the road, avalanche risk is managed through a combination of avalanche control measures and temporary closures. At the Brämabüel section, active avalanche control with explosives from a helicopter platform is used, whereas on the south-facing slope no mitigation measures are implemented and risk management relies solely on road closures.

In Fig. , we show the mapped avalanches from the Davos avalanche database that occurred over the past 30 years at the Dischma road below the Brämabüel slope. There are four main avalanche paths marked as path 0, 1, 2 and 3. Avalanches originating from track 0 are excluded from the subsequent analysis because avalanche fences have been installed in this area in 1997.

Due to limited data availability and the time required for detailed event reconstruction, we focus on two winter seasons with well-documented avalanche events. To test the framework beyond these seasons, we also include older events from the same region, although these cases provide fewer input data (see Table ). We have runout information for all avalanches, but the available release information differs between events. For the validation events, we derive the average fracture depth in the release zone from lidar and drone measurements. For two older avalanches, reports indicate release within the new snow.

Figure a shows the number of observed avalanches reaching zones classified according to the total number of documented avalanches, while Fig. b displays the number of avalanches reaching the road measured from north to south of the road. It is clearly visible that most avalanches stop in the flatter runout zones (slope angle < 10°). A graphic of the slope steepness and avalanche paths is attached in the Appendix . Only 12 avalanches are documented as reaching the road section between Duchli and Chaiseren from 1999 to 2025. In Fig. , avalanche path 3 is influenced by the avalanche path descending from the opposing slope.

We validate our approach using historical avalanche events, where – at the time – fewer measurement stations were active. The first event we focus on occurred on 15 January 2019 at Brämabüel, a north-east-facing slope in the Dischma Valley. After the New Year in 2019, a continuous snowfall period began. Within 36 h, an increase in snow height of 0.56 m was measured at the flat-field site of the Weissfluhjoch (WFJ) station (see Fig. ). The avalanche bulletins were issued twice daily, at 08:00 and 17:00 LT. From 11 January 2019 onwards, danger level 3 was forecast above 2000 m for all aspects due to new snow and wind-drifted snow. The danger level increased to 4 in the bulletin issued on 13 January 2019 at 17:00 LT, with medium-sized spontaneous avalanches expected and warnings that soft snow below weak layers could be easily eroded. By the evening bulletin of 14 January 2019, very large spontaneous avalanches were expected in nearby warning regions at danger level 5, while Brämabüel itself remained in a region forecast at danger level 4, close to the level-5 warning region. During avalanche control operations with helicopter three powder-snow avalanches (Track 1, 2, and 3) were artificially triggered, with estimated release volumes between 5000 and 10 000 m³. All three avalanches reached the road even if they had rather small release volumes, primarily due to substantial snow entrainment along their paths .

The second and third validation events occurred on 28 and 29 January 2025. The first of these avalanches was released by a Wyssen tower at Breitzug (east- to south-east-facing slope) at approximately 17:00 CET on 28 January 2025. This was the first avalanche release at Breitzug during the winter season and the avalanche reached the river. The third avalanche was artificially triggered at Brämabüel on 29 January 2025 08:00 CET, approximately 9 km horizontal distance from Breitzug. There was little to no skiing activity on that slope prior to the release and no artificial avalanche releases in that season were performed, allowing the snowpack to develop largely under natural conditions. The avalanche remained relatively small and stopped well before reaching the base of the slope.

In order to assess the robustness of the proposed data flow under varying data availability, we compare recent data-rich avalanche events with historical cases for which information on weak layers and snowpack conditions is more limited. We consider four historical events from 1999, 2017, 2018, and 2021. The 1999 and 2018 events include avalanches that reached the road, whereas the 2017 and 2021 events stopped within the runout zone shortly before reaching the road (Fig. ). These cases therefore provide a useful test set for evaluating whether the proposed workflow can distinguish between road-hitting and non-road-hitting avalanches under less complete input-data conditions.

The proposed framework aims to support daily avalanche runout predictions by coupling information on avalanche release conditions with avalanche dynamics. We only simulate dry slab avalanche releases. The occurrence of a slab avalanche requires sufficiently steep terrain, a weak-layer with a cohesive slab above, and a triggering mechanism. Snowpack stability is estimated using SNOWPACK, a physically based snow cover model , to constrain the likelihood, frequency, and characteristic size of potential releases. This release information is then used in combination with a numerical avalanche simulation tool to estimate runout distances.

Our operational workflow is shown in Fig. . The design of the workflow depends on the availability of input data and the local measurement infrastructure. In a first step, we present a configuration tailored to the Davos region as an example for a region where a large number of measurements are available. In a second step, we reduced the number of weather stations to show a more realistic picture of data availability for more data sparse regions.

Terrain information is derived from a digital elevation model (DEM) with a spatial resolution of 5 m. The potential release areas are identified using the automated algorithm by , which identifies release areas based on steepness, terrain shape, and surface roughness for small avalanches (volumes 5000–25 000 m³) and medium-sized avalanches (25 000–60 000 m³) . Larger release volumes are not considered, as such events are typically associated with very high avalanche danger levels, under which road closures are implemented as a precautionary measure.

Snowpack conditions are derived from SNOWPACK simulations based on meteorological observations from the available IMIS stations. Two release scenarios are considered throughout the study: release of newly accumulated fresh snow and release on the most unstable buried weak-layer. Potential fracture depths are derived from flat-field weather stations and adjusted for elevation differences using a snow-depth gradient inferred from valley and mountain stations. A correction accounting for slope inclination is applied to the conversion of flat-field measurements to release-zone conditions following .

For the release scenario of new snow, fracture depth corresponds to the depth of snow accumulated during the most recent snowfall period, including settling of the snowpack, with the weak-layer defining the interface between the old and the new snow. For the weak-layer release scenario, the most unstable weak-layer is identified using the method proposed by . Snowpack stability is evaluated for a virtual slope with a reference slope angle of 38° and for the four main slope aspects. As e.g the Brämabüel release zone is oriented towards the northeast, weak-layer properties are estimated as the average of the north- and east-facing simulations.

The high temporal resolution measurements of the snow depth distribution in the Brämabüel release area reveals the substantial influence of snow redistribution by wind . Wind-induced snow redistribution is accounted for when wind speeds exceed a threshold of 7.5 m s⁻¹ and originate from directions that allow snow transport into the avalanche path. The amount of wind-drifted snow remains uncertain because existing approaches either neglect settling processes or operate at spatial resolutions which are too coarse to resolve local terrain effects . We therefore apply additional snow loading of 0.25 and 0.5 m to account for moderate and strong wind drifts respectively.

Avalanche propagation is simulated for all potential release areas and fracture depth scenarios using RAMMS::Extended. The model explicitly accounts for erosion and entrainment along the avalanche path and allows the simulation of multiple flow regimes . Because entrainment strongly influences avalanche mass and runout distance , we limit erosion depth to the fracture depth associated with each release scenario, accounting for the erosion of new snow or erosion of the snowpack above the weak-layer.

Meteorological forecast data are used to estimate precipitation (new snow) and the evolution of air temperature prior to avalanche release. For both events, we used data from numerical weather prediction models run by MeteoSwiss, which were based on COSMO for the 2019 event and on ICON for the 2025 event . Both models have a spatial resolution of around 1 km for a forecast lead time of 24 h. We apply the predicted precipitation and air temperature to estimate cooling or warming of the snowpack. Based on the predicted temperature evolution (warming, cooling, constant), we include an additional colder or warmer simulation scenario to account for uncertainty in snowpack temperature.

Probability indication maps are calculated in a predictive mode based on the frequency approach for the data-rich validation events from 2019 and 2025. The 2019 event is used to assess how well the predicted impact probabilities reproduce observed avalanche release areas and runout distances. In 2025, two avalanches were artificially triggered within a 24 h prediction period, but on different slopes with different aspects and terrain characteristics. This allows for a targeted evaluation of whether the probability indication maps capture distinct avalanche responses under comparable meteorological conditions. The 2025 event at Breitzug is additionally recalculated based on the stability approach. We focus only on one example case for the stability approach due to the additional calculation and storage needs. To further evaluate the proposed framework, the analysis is extended to four additional historical avalanche events, with the primary aim of assessing whether the probability indication maps correctly indicate road impact in cases where SNOWPACK simulations were not available.

For the calculation of the probability indication maps, we performed one large-scale simulation for each predicted snow height at a station in the flat field. Each simulation represents a scenario in which all PRAs are triggered as independent avalanches. Figure  shows an example of the maximum core height across all scenarios and the maximum powder pressure for a flat-field snow height of 1.60 m at -8 °C in the Brämabüel region, assuming 96 medium-sized release zones.

Following the implementation of our framework for both recent and historical avalanche events, this section evaluates its ability to predict runout distances from daily snow and weather conditions. The discussion subsequently examines the sensitivity of the results to input data and the robustness of the probability calculations.

The Brämabüel slope offers a useful case for comparing the avalanche conditions and consequences in 2019 and 2025. In 2019, the Dischma road was closed for more than 2 days and the avalanches reached the road, while in 2025 the road was only briefly closed during artificial release, and the avalanche stopped partway down the slope. The contrasting outcomes align with the probability indication maps where some avalanche scenarios are expected to stop partway down the slope while others are likely to produce much longer runouts (see Figs.  and ).

A limitation of this study is that the framework was evaluated using a limited number of avalanche events from one specific region, Davos. The selected cases primarily involve situations in which the avalanche tracks were snow-covered from the release area to the runout zone. These conditions may not hold in other cases, for example, in late-season conditions, where avalanches subsequently flow over snow-scarce or grass-covered slopes at lower elevations. Such conditions make snow-height information along the full elevation gradient essential, requiring representative input from both valley and mountain stations.

A general challenge in evaluating probability indication maps using documented avalanche events is that an observed runout confirms that a scenario was possible under the given conditions but does not directly provide information about its occurrence probability. To assign such a probability, the non-occurrence probability of comparable avalanches under similar conditions would also need to be known. This limitation is particularly relevant for organized measurement campaigns, where avalanches are artificially triggered to collect detailed measurements. The triggering method and explosive type may influence which weak layer is released and, therefore, the resulting fracture depth. Once the fracture depth and release area are defined, however, the subsequent runout is mainly governed by the released volume, terrain, snow cover conditions, and flow parameters. Artificially triggered avalanches can therefore be used to assess whether simulated runout scenarios are plausible, but they cannot directly validate the absolute probability of a natural release. The probability indication maps presented here should thus be interpreted primarily as conditional scenario maps that indicate which runout may result if a release occurs, rather than as direct estimates of the absolute probability of avalanche occurrence. However, we acknowledge that this may lead to a conservative interpretation.

The availability and quality of the input data can have a strong influence on the resulting probability indication maps. The maps are based on forecasted snow and weather conditions and therefore inherit uncertainties from meteorological predictions. Deviations between forecasted and actual conditions, or changes in the snowpack due to snowfall, wind redistribution, or temperature changes, can substantially reduce the representativeness of the maps. Consequently, probability indication maps should be regarded as time-dependent products valid only for the specific forecast window for which they are generated. The appropriate issuing time depends on the operational application. For example, a map may be useful before the onset of a forecasted snowfall event to support early road-closure decisions, or it may be updated during or after the event as new meteorological and snowpack information becomes available.

The spatial representativeness of snow and weather measurement stations strongly influences the model performance. Although a dense station network is available, most stations are located in flat terrain and may not adequately represent the prevailing conditions in steep avalanche release zones . Our analysis shows that simply increasing the number of measurement stations does not improve predictive skill. The calculations could also be run with a single weather station by setting the gradient of the erodible snowcover and snow temperature to zero, as illustrated in Fig. S1 of the Supplement. However, this would prevent the system from representing situations in which avalanches flow into lower-elevation areas with little or no erodible snow cover, or into zones with a warmer snowpack. The model performance improves when meteorological measurements at a station location are well correlated with the conditions within a specific avalanche path. In this respect, the DAV2 station, which is located in steeper terrain, shows a notably stronger correspondence with the Brämabüel avalanche slope. Snow temperature measurements are particularly sensitive to sensor placement and radiative effects, which impacts the transferability of the data to release areas and introduces additional uncertainty. Still, if we compare the predicted fracture depth with the snow depth distribution measurements from photogrammetric drone data for the Brämabüel cases 2019 and 2025, we had a high probability around the measured snow height.

The results of our framework implementation further reveal a strong sensitivity to several model input parameters. In particular, the definition and spatial extent of the release zones exert a major influence on the resulting probability indication maps. In the current implementation, release zones are derived using a return-period-based algorithm. While suitable for large-scale assessments, such static delineations are suboptimal for operational forecasting. Release zones should instead be dynamically adapted to prevailing snow and weather conditions .

Moreover, the avalanche dynamics simulations are highly sensitive to the assumed snow temperature, a parameter that is difficult to infer reliably from valley-based observations. This sensitivity constitutes a key source of uncertainty in operational applications, where direct measurements in avalanche release areas are rarely available.

The presented dataflow is not limited to a specific avalanche dynamics model. However, applying it with another numerical simulation tool requires that the model is sufficiently sensitive to the snow-cover and flow regime represented in the input scenarios. In RAMMS::EXTENDED, the frictional behavior is dynamically calculated for different snow conditions, so we treat the friction parameterization as part of the calibrated model setup. If the workflow was applied with a model based on fixed Voellmy friction parameters, such as prescribed values of μ and ξ for cold snow, very cold snow, or highly erosive conditions, the uncertainty associated with these calibrated parameters would become more important. In such an application, μ and ξ should be included as additional uncertain input variables in the probabilistic calculation.

Accurate representation of forest cover and vegetation is also important to achieve realistic avalanche extents , as these strongly influence avalanche flow behaviour. In the Brämabüel simulations, forest density was underestimated compared to the actual conditions, which allowed simulated avalanches to propagate into areas that are classified as non-affected in the official hazard map.

Interpretation of modelled results requires consideration of the avalanche history of individual tracks as it affects the potential amount of erodible snow . The Breitzug case, for instance, represents the first avalanche of the season and occurred on an undisturbed snowpack within a well-defined terrain bowl–conditions conducive to large avalanches. In contrast, the frequently skied Brämabüel slope experiences snowpack compaction from ski traffic, which disrupts weak-layer continuity and reduces the likelihood of large releases. These local effects are not explicitly represented in the current modelling framework, but strongly influence real-world avalanche behaviour.

We tested two approaches to weight the release scenarios: a frequency-based approach, in which storm-snow and old-snow releases are weighted equally, and a stability-based approach, in which the weighting depends on the natural stability index derived from SNOWPACK calculations. Overall, we get similar probability indication maps from these two approaches, but the map generated based on the stability approach shows a more differentiated picture of the runout distances. The frequency-based approach is intentionally simple and computationally efficient, as it requires only an aggregated avalanche overlap map for each scenario. This simplicity is advantageous for operational applications and avoids introducing additional uncertainty from simulated weak-layer stability. In contrast, the stability approach is more data-intensive because it requires storing individual avalanches and introduces an additional source of uncertainty by relying on weak-layer stability information simulated with SNOWPACK. Moreover, the stability approach is based on the natural stability index and therefore provides a more nuanced representation of spontaneous release conditions. For artificially triggered releases, however, this information is not directly transferable, since the released weak layer and fracture depth may depend on the applied release method. In such cases, the stability approach may yield a conservative probability indication map. Nevertheless, the stability approach may be advantageous in situations with a broad range of release-zone steepness, as it reduces the influence of comparatively flat release zones in the probability indication map. In practical applications where limited information on weak-layers is available – for example, when only a single snowpit provides indications of weak-layer properties – this weighting could be adjusted based on expert judgement. Alternatively, the stability of the weak-layer could be parametrized based on the shear strength as a function of fracture depth, following the formulation proposed by and further refined by and . This would allow the stability index to be estimated in a manner more aligned with the stability approach. With evolving research on the prediction of snowpack stability and release-zone size, additional statistical models could be coupled to the dataflow .

This study presents a proof of concept for a model-driven framework that combines meteorological measurements, weather forecasts, snowpack simulations, and dynamic avalanche simulations to generate spatially explicit probability indication maps of avalanche runout. These maps are intended to support decision-making concerning traffic safety management and daily avalanche mitigation strategies in high risk avalanche hazard areas.

The results demonstrate that the framework can reliably distinguish between events in which avalanches reach the road and events in which they stop higher up on the slope. The analysis highlights the importance of the spatial relationship between weather stations and individual avalanche paths, as well as the potential for substantial uncertainties to propagate through our framework, from local measurements to dynamic avalanche simulations.

In this first implementation, snowpack stability was not explicitly included in the probability calculation. While integrating stability information from snowpack models or observations is a promising next step, caution is warranted, as additional modelling layers may further increase overall uncertainty and calibration traceability.

For practical implementation, such a framework would need to be evaluated and refined over several seasons in close interaction with practitioners, in order to characterize systematic model deficiencies, including potential over- or underestimation of runout distances, and to isolate the sources of error. In this context, data-driven and machine-learning approaches may offer additional opportunities to support pattern recognition and uncertainty assessment, provided that sufficient high-quality training data is available. We see considerable potential for this approach in regions where a single decision-maker must assess avalanche hazard over large areas within a short time frame, as the framework can provide a rapid overview of potential avalanche hot spots and it can highlight tracks that may not have been active in recent years. Future work should first test the workflow along the full road corridor and across different Alpine regions to assess its transferability under comparable climatic conditions, before adapting it to more data-sparse regions such as Alaska or the Himalayas.