Can model-based avalanche forecasts match the discriminatory skill of human danger-level forecasts? A comparison from Switzerland

Abstract

In recent years, physics-based snowpack models combined with machine-learning techniques have gained momentum in public avalanche forecasting. When integrated with spatial interpolation methods, these approaches enable fully model-driven predictions of snowpack stability or avalanche danger at any location. This raises a key question: are such spatially detailed model predictions sufficiently accurate for operational use? We evaluated the performance of three spatially interpolated model-driven forecasts of snowpack stability and avalanche danger in Switzerland over three winters. As a benchmark, we used the official public avalanche-danger forecasts, specifically focusing on the forecast danger level that includes the sub-levels. We assessed the ability of both model and human forecasts to discriminate between reference distributions of conditions - typically not associated with avalanche activity - and actual avalanche events that were either naturally released or triggered by humans by calculating event ratios as proxies for release probability. Our results show that event ratios clearly increased with higher predicted avalanche probability, lower snowpack stability, or higher forecast sub-level. Overall, both model predictions and human forecasts showed a comparable ability to discriminate between reference and event conditions, with the event ratio increasing exponentially with increasing model-predicted probabilities or forecast sub-levels. However, the human forecasts - which incorporate model output - achieved a small but statistically significant advantage in discriminatory skill. This indicates that while the models alone have not yet reached the full discriminatory power of human forecasters, their performance is already approaching operational usefulness in a setup such as that used in Switzerland. As model quality is expected to improve further in coming years, it is essential to ensure optimal integration into the operational forecasting workflow to realize the full potential of model-based support. Further research should explore how to implement this effectively, how to integrate real-time avalanche occurrence data into model prediction pipelines, and how to validate increasingly high-resolution avalanche forecasts.