Dear authors and editors, 

The manuscript introduces an innovative framework to support operational decision making for avalanche forecasting and road closures combining probabilistic avalanche runout modeling with meteorological input and snow cover simulations. Specifically, combining weather station data and snow cover simulation outputs to better assess potential fracture depths, snowpack characteristics and snow conditions within the avalanche path displayed in a probability indication map provide a huge potential for daily avalanche forecasting.  

The maps present a hit probability for the avalanche path and runout that is calculated mainly based on the frequency approach. Only one scenario is additionally calculated based on the stability approach. The frequency approach considers the probability for a certain snow loading and weak-layer interaction to define fracture depth classes to calculate the hit probability.  This approach does weigh the probabilities of new snow and buried weak layer equally and is less complex on necessary input variables as compared to the stability approach. The results for the 2019 event show that the higher probability values match the observed avalanche paths well using the frequency approach. In 2025, the probability indication maps differ from the real avalanche runout mainly due to differences in weak layer characteristics influenced by the slope exposure. This shows us how sensitive the framework is to the quality and availability of input variables.  

The authors describe the background and the objectives well in the Introduction. The Methods are very detailed describing the used approaches. However, this section would benefit from additional clarification, particularly regarding the definition of the validation events (see comments). The Results are comprehensive, and the Discussion provides a reasonable interpretation of the findings and limitations. Additional detail on the study design, results, and broader context could further strengthen the overall manuscript (see comments). 

I am very grateful for the opportunity to review this manuscript. Overall, I think this is a promising manuscript that provides a framework combining daily snowpack conditions and terrain with runout probability that can potentially be used in avalanche operations. The authors’ approach to identifying the least amount of input parameter necessary for the most representative daily snowpack conditions makes this paper particularly meaningful and potentially applicable in different regions. After minor revisions, this paper is a valuable contribution and relevant advancement for operational decision-making processes for avalanche risk management along infrastructure corridors.   

This is a co-review with my supervisor, Dr. Erich Peitzsch. 

General Comments 

• Validation dataset 
  
  Considering the complexity, data availability, time requirements, and resources necessary to analyze each event in the level of detail presented in this manuscript, the reduced number of analyzed events appears justified. Nevertheless, it may be beneficial to acknowledge this more explicitly as a potential limitation within the discussion section, rather than only briefly mentioning it in the conclusion. While the arguments for the selective approach are logical, clarifying this point earlier in the manuscript could strengthen the transparency of the study. 

• Dataset description 
  
  As a reader, I found the description of the analyzed dataset somewhat difficult to follow. In the Test Site and Data section (Section 2), four avalanche events for validation are introduced, although only three of these are described in greater detail. It is not entirely clear whether these refer to the same avalanches, whether each avalanche event contains multiple avalanches, or whether each case study includes several individual events, as implied later in the manuscript (L255). 
  
  In Figure 5, I can only identify three events. The dashed and dotted lines may indicate additional avalanches occurring on specific days; if so, this could be clarified in the figure caption. Furthermore, Section 4.3 introduces additional historical avalanche events. Although this is briefly mentioned in the final paragraph of Section 2, Figure 3 gives the impression that more avalanches were analyzed, rather than only four additional years. 
  
  To improve clarity, it may be helpful to display only the avalanches included in the analyses and to distinguish more clearly between validation data and additional datasets. In addition, displaying all events in a table including event, location, trigger and runout location and consistently referring to each event throughout the manuscript using a standardized naming code could greatly improve readability and help the reader follow the analysis. 

• MAE for data-sparse areas 
  
  Many data-sparse forecasting regions often rely on only one or two weather stations. Therefore, rather than emphasizing the reduction from seven to six or five weather stations, a more detailed comparison using only one or two stations may provide a more practically relevant assessment. 

• Frequency and stability approach 
  
  It would be helpful to include a brief explanation around L256 regarding the decision to use the frequency approach instead of the stability approach. Although both methods are described in detail in the methods section, the stability approach is only applied in one specific scenario. Expanding the discussion of the two approaches (L336-L342), particularly emphasizing the advantages and applicability of the frequency approach, would strengthen the manuscript. 

• Meteorological input data
  
  I would suggest including an overview of the parameters and variables derived from the weather stations that were used to force SNOWPACK, as well as the parameters applied for forecasting daily conditions.
  
  In addition to weather station density, the availability of specific meteorological variables may represent a critical limitation for implementing this approach in other regions. Discussing this aspect could improve the broader applicability of the study. 

• Artificial Triggering vs. Natural Avalanches
  
  It appears that the three avalanches as described in Section 2 were all artificially triggered. How does using artificially triggered avalanches to validate the probability maps whether using frequency or stability approach factor into the results? In other words, are the release probability and subsequent runout independent of triggering mechanism (natural vs. explosive/artificial)? When using the historical data (Section 4.3) for evaluating the workflow/model chain, were these avalanches naturally occurring or artificially triggered? Additionally, the authors mention that “the release zones are derived using a return-period-based algorithm” (Line 360). Is this return period based on natural or artificial triggering. I think this topic deserves some explanation given the validation avalanches are all artificially triggered. 

Specific Comments 

• Figure 2a: Without reading the accompanying text, it is not immediately clear what the values 0, 1, 2, and 3 represent, or why the distances of 1 km, 2 km, and 3 km are important in this context. 
  
  Additionally, from the map alone it is difficult to identify the terrain flattening where most avalanches stop. Including a slope profile may help illustrate this more clearly. 

• Figure 2b: It is unclear what the blue line represents. Does it indicate dependencies of the avalanches? For example, from 3.4 to 4.0 km the line remains at one avalanche: does this indicate that one single avalanche reached this road section, and it was the same avalanche for this entire section? Maybe you could include a short explanation in the figure description? 

• L136: “on January 28” please use the standard English date format consistently throughout the manuscript (Month Day, Year). This comment applies generally across the manuscript.  

• Figure 3: The distinction between “Road closures” and “Days with road closures” is not immediately clear and could be clarified further. 
  
  Additionally, consider replacing “November till May” with “November to May” for more formal language. 

• Figure 4: I think this conceptual diagram of the workflow could use some improvement. I think including the specific input data for ‘Meteorological Data & Forecast’ that is used in SNOWPACK is important. Additionally, include where the frequency and stability approaches are used in the workflow. Finally, include which data (historical and 2019 and 2025 events) are used for validation and how that is incorporated to tune the workflow. 

• Figure 5: Please clarify the meaning of the dashed versus dotted lines in the caption. Additionally, the x-axis may not need to be labeled as “Date.”  

• Figure 7: Consider labeling each panel as a, b, c, etc. The graphs at the bottom may benefit from including y-axis scales. Additionally, the legend order of weather stations should begin with seven stations and decrease sequentially, as placing seven at the end is somewhat counterintuitive. Including a one-weather-station scenario may also be informative. In the final graph, the lines are very close together, making differences between scenarios difficult to distinguish.  

• L301: Instead of stating “stronger influence on the result,” could this effect be described more specifically? For example, does it reduce the hit probability in the avalanche runout, and if so, by approximately how much?  

• L313: What was the reason for including the 2017 event that evolved into a wet-snow avalanche? This is very interesting: is RAMMS::EXTENDED capable of simulating the transition in avalanche type?  

• L366–L369: Consider including supporting citations in this section.  

• L373: A citation may also be appropriate here.  

• L401: It may be worth considering an assessment of data sparsity in a climate more comparable to Davos, rather than addressing the additional caveats associated with high-altitude or Arctic climates. 

Many thanks and best regards,  

Andrea Bruckmeier 

Dear authors and editors, 

The manuscript introduces an innovative framework to support operational decision making for avalanche forecasting and road closures combining probabilistic avalanche runout modeling with meteorological input and snow cover simulations. Specifically, combining weather station data and snow cover simulation outputs to better assess potential fracture depths, snowpack characteristics and snow conditions within the avalanche path displayed in a probability indication map provide a huge potential for daily avalanche forecasting.  

The maps present a hit probability for the avalanche path and runout that is calculated mainly based on the frequency approach. Only one scenario is additionally calculated based on the stability approach. The frequency approach considers the probability for a certain snow loading and weak-layer interaction to define fracture depth classes to calculate the hit probability.  This approach does weigh the probabilities of new snow and buried weak layer equally and is less complex on necessary input variables as compared to the stability approach. The results for the 2019 event show that the higher probability values match the observed avalanche paths well using the frequency approach. In 2025, the probability indication maps differ from the real avalanche runout mainly due to differences in weak layer characteristics influenced by the slope exposure. This shows us how sensitive the framework is to the quality and availability of input variables.  

The authors describe the background and the objectives well in the Introduction. The Methods are very detailed describing the used approaches. However, this section would benefit from additional clarification, particularly regarding the definition of the validation events (see comments). The Results are comprehensive, and the Discussion provides a reasonable interpretation of the findings and limitations. Additional detail on the study design, results, and broader context could further strengthen the overall manuscript (see comments). 

I am very grateful for the opportunity to review this manuscript. Overall, I think this is a promising manuscript that provides a framework combining daily snowpack conditions and terrain with runout probability that can potentially be used in avalanche operations. The authors’ approach to identifying the least amount of input parameter necessary for the most representative daily snowpack conditions makes this paper particularly meaningful and potentially applicable in different regions. After minor revisions, this paper is a valuable contribution and relevant advancement for operational decision-making processes for avalanche risk management along infrastructure corridors.   

This is a co-review with my supervisor, Dr. Erich Peitzsch. 

General Comments 

• Validation dataset 
  
  Considering the complexity, data availability, time requirements, and resources necessary to analyze each event in the level of detail presented in this manuscript, the reduced number of analyzed events appears justified. Nevertheless, it may be beneficial to acknowledge this more explicitly as a potential limitation within the discussion section, rather than only briefly mentioning it in the conclusion. While the arguments for the selective approach are logical, clarifying this point earlier in the manuscript could strengthen the transparency of the study. 

• Dataset description 
  
  As a reader, I found the description of the analyzed dataset somewhat difficult to follow. In the Test Site and Data section (Section 2), four avalanche events for validation are introduced, although only three of these are described in greater detail. It is not entirely clear whether these refer to the same avalanches, whether each avalanche event contains multiple avalanches, or whether each case study includes several individual events, as implied later in the manuscript (L255). 
  
  In Figure 5, I can only identify three events. The dashed and dotted lines may indicate additional avalanches occurring on specific days; if so, this could be clarified in the figure caption. Furthermore, Section 4.3 introduces additional historical avalanche events. Although this is briefly mentioned in the final paragraph of Section 2, Figure 3 gives the impression that more avalanches were analyzed, rather than only four additional years. 
  
  To improve clarity, it may be helpful to display only the avalanches included in the analyses and to distinguish more clearly between validation data and additional datasets. In addition, displaying all events in a table including event, location, trigger and runout location and consistently referring to each event throughout the manuscript using a standardized naming code could greatly improve readability and help the reader follow the analysis. 

• MAE for data-sparse areas 
  
  Many data-sparse forecasting regions often rely on only one or two weather stations. Therefore, rather than emphasizing the reduction from seven to six or five weather stations, a more detailed comparison using only one or two stations may provide a more practically relevant assessment. 

• Frequency and stability approach 
  
  It would be helpful to include a brief explanation around L256 regarding the decision to use the frequency approach instead of the stability approach. Although both methods are described in detail in the methods section, the stability approach is only applied in one specific scenario. Expanding the discussion of the two approaches (L336-L342), particularly emphasizing the advantages and applicability of the frequency approach, would strengthen the manuscript. 

• Meteorological input data
  
  I would suggest including an overview of the parameters and variables derived from the weather stations that were used to force SNOWPACK, as well as the parameters applied for forecasting daily conditions.
  
  In addition to weather station density, the availability of specific meteorological variables may represent a critical limitation for implementing this approach in other regions. Discussing this aspect could improve the broader applicability of the study. 

• Artificial Triggering vs. Natural Avalanches
  
  It appears that the three avalanches as described in Section 2 were all artificially triggered. How does using artificially triggered avalanches to validate the probability maps whether using frequency or stability approach factor into the results? In other words, are the release probability and subsequent runout independent of triggering mechanism (natural vs. explosive/artificial)? When using the historical data (Section 4.3) for evaluating the workflow/model chain, were these avalanches naturally occurring or artificially triggered? Additionally, the authors mention that “the release zones are derived using a return-period-based algorithm” (Line 360). Is this return period based on natural or artificial triggering. I think this topic deserves some explanation given the validation avalanches are all artificially triggered. 

Specific Comments 

• Figure 2a: Without reading the accompanying text, it is not immediately clear what the values 0, 1, 2, and 3 represent, or why the distances of 1 km, 2 km, and 3 km are important in this context. 
  
  Additionally, from the map alone it is difficult to identify the terrain flattening where most avalanches stop. Including a slope profile may help illustrate this more clearly. 

• Figure 2b: It is unclear what the blue line represents. Does it indicate dependencies of the avalanches? For example, from 3.4 to 4.0 km the line remains at one avalanche: does this indicate that one single avalanche reached this road section, and it was the same avalanche for this entire section? Maybe you could include a short explanation in the figure description? 

• L136: “on January 28” please use the standard English date format consistently throughout the manuscript (Month Day, Year). This comment applies generally across the manuscript.  

• Figure 3: The distinction between “Road closures” and “Days with road closures” is not immediately clear and could be clarified further. 
  
  Additionally, consider replacing “November till May” with “November to May” for more formal language. 

• Figure 4: I think this conceptual diagram of the workflow could use some improvement. I think including the specific input data for ‘Meteorological Data & Forecast’ that is used in SNOWPACK is important. Additionally, include where the frequency and stability approaches are used in the workflow. Finally, include which data (historical and 2019 and 2025 events) are used for validation and how that is incorporated to tune the workflow. 

• Figure 5: Please clarify the meaning of the dashed versus dotted lines in the caption. Additionally, the x-axis may not need to be labeled as “Date.”  

• Figure 7: Consider labeling each panel as a, b, c, etc. The graphs at the bottom may benefit from including y-axis scales. Additionally, the legend order of weather stations should begin with seven stations and decrease sequentially, as placing seven at the end is somewhat counterintuitive. Including a one-weather-station scenario may also be informative. In the final graph, the lines are very close together, making differences between scenarios difficult to distinguish.  

• L301: Instead of stating “stronger influence on the result,” could this effect be described more specifically? For example, does it reduce the hit probability in the avalanche runout, and if so, by approximately how much?  

• L313: What was the reason for including the 2017 event that evolved into a wet-snow avalanche? This is very interesting: is RAMMS::EXTENDED capable of simulating the transition in avalanche type?  

• L366–L369: Consider including supporting citations in this section.  

• L373: A citation may also be appropriate here.  

• L401: It may be worth considering an assessment of data sparsity in a climate more comparable to Davos, rather than addressing the additional caveats associated with high-altitude or Arctic climates. 

Many thanks and best regards,  

Andrea Bruckmeier