Reply on RC1

Abstract

Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how the combination of snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a Random Forest approach to predict avalanche days at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent test set, confusion matrices, and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the statistical model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically-based stability indices and their time-derivatives in addition to simple snow and meteorological variables increases the recall from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche days in the training data set, the precision remains low, around 3.5 %, due to the scarcity of avalanche days. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current cutting-edge data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting.