Comparing and validating modelled snow stability metrics

Abstract. Recent developments in snow stability modelling demonstrated the great potential of numerical snow cover modelling for avalanche forecasting. These recently developed metrics provided promising results, possibly even superseding traditional stability indices. To further validate these results, we compared the temporal evolution of various stability metrics to a unique dataset of measurements, including snow stratigraphy, snow microstructure, and stability obtained at the high Alpine study site Steintälli above Davos (Eastern Swiss Alps) during the winter 2015/16. There, we measured the shear strength of a prominent weak layer of depth hoar crystals using the shear frame and assessed the propagation propensity of this weak layer with propagation saw tests. Concurrently, we characterized snow microstructure with the snow micro-penetrometer (SMP). At the study site, an automated weather station is located, providing the data to run the numerical snow cover model SNOWPACK so that modelled stability metrics can be derived. Field measurements showed that weak layer strength and toughness were initially low but increased over time, in parallel with the increasing slab load; all three parameters were correlated. While field observations focused on the most prominent weak layer, modelled stability metrics were evaluated for the persistent weak layers identified by the respective modelling approaches. These generally corresponded well to the layers visually identified in simulated snow stratigraphy. Data-driven and process-based stability metrics exhibited similar temporal evolution and comparable skill in relation to local avalanche activity, achieving overall accuracies of 60–70 %. This site-specific validation demonstrates that both types of numerical stability metrics provide valuable support for avalanche forecasting, although differences in their behaviour highlight the need for metric-specific interpretation. Hence, a multi-model approach may be advantageous for operational forecasting.