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Abstract

Avalanche forecasting is a human judgment process with the goal of describing the nature and severity of avalanche hazard based on the concept of avalanche problems. Snowpack simulations can help improve forecast consistency and quality by extending qualitative frameworks of avalanche hazard with quantitative links between weather, snowpack, and hazard characteristics. Building on existing research on modeling avalanche problems, we present the first spatial modeling framework for extracting the characteristics of storm and persistent slab avalanche problems from distributed snowpack simulations. Grouping of simulated layers based on regional burial dates allows us to track them across space and time and calculate insightful spatial distributions of avalanche problem characteristics.

We applied our approach to ten winter seasons in Glacier National Park, Canada, and compared the numerical predictions to human hazard assessments. Despite good seasonal agreement, the comparison of the daily assessments of avalanche problems revealed considerable differences. Best agreements were found in the presence and absence of storm problems and the likelihood and expected size assessments of persistent problems. Even though we are unable to conclusively determine whether the human or model data set represents reality more accurately when they disagree, our analysis indicates that the current model predictions can add value to the forecasting process by offering an independent perspective. Our study contributes to a growing body of research that aims to enhance the operational value of snowpack simulations and provides insight into how snowpack simulations can help address some of the operational challenges of human avalanche hazard assessments.