1950–2100 climate trends in avalanche activity in Haute-Maurienne, French Alps

Avalanche activity in alpine regions is sensitive to climate change. However, without consistent historical data, it is challenging to estimate past trends in avalanche activity and assess future avalanche scenarios from climate projections. To tackle this challenge, we use avalanche observations and simulated snowpack conditions to train a machine learning gradient-boosting regression model, which predicts the number of avalanches per day. We focus on a small alpine domain with high-quality data: the Haute-Maurienne valley in the French Alps, where avalanche paths span elevations from approximately 1800 to 2700 m a.s.l. First, we demonstrate that accounting for the uncertainties in avalanche occurrence dates and using only the most recent period (2006–2023) with homogeneous observations during the training step is essential for achieving consistent results. We then use this machine learning model to reconstruct the past avalanche activity (1958–2023) from reanalysed meteorological and snow data, and to project future avalanche activity (1950–2100) from a downscaled ensemble of snow-climate simulations. We evaluate climatic trends in avalanche activity using three indicators: the number of avalanches per winter season, the number of avalanches per month, and the annual maximum number of avalanches in one week, which quantifies the largest avalanche cycles. Based on reanalysed snow-climate simulations, the model estimates that avalanche activity decreased in the past: the mean number of avalanches per year declined by 4.8 % per decade between 1958 and 2023, with a stronger decrease in spring avalanche activity, and the 30-year return level associated with large avalanche cycles decreased at a slower rate of 1.4 % per decade. In the future, avalanche activity is also expected to decrease. For the emission scenarios RCP4.5 and RCP8.5, the annual number of avalanches is expected to decrease by 4.6 % per decade and 8.8 % per decade, respectively, mainly due to a reduction in spring avalanche activity. Large avalanche cycles, quantified by the 30-year return level, are also expected to decrease in intensity but at slower rates: 2.2 % per decade for RCP4.5 and 5.5 % per decade for RCP8.5. This study quantifies the impact of climate change on avalanche activity in an exemplary alpine valley. It demonstrates that combining statistical learning with climate simulations can help produce reference scenarios for mitigation strategies in high mountain environments.