Abstract. Climate change impact on avalanches is ambiguous. Fewer, wetter, and smaller avalanches are expected in areas where snow cover is declining, while in higher-altitude areas where snowfall prevails, snow avalanches are frequently and spontaneously triggered. In the present paper, we (1) analyse trends in frequency, magnitude, and orientation of wet- and slab-avalanche activity during 59 winter seasons (1962–2021) and (2) detect the main meteorological and snow drivers of wet and slab avalanches for winter seasons from 1979 to 2020 using machine learning techniques – decision trees and random forest – with a tool that can balance the avalanche-day and non-avalanche-day dataset. In terms of avalanches, low to medium–high mountain ranges are neglected in the literature. Therefore we focused on the low-altitude Czech Krkonoše mountain range (Central Europe). The analysis is based on an avalanche dataset of 60 avalanche paths. The number and size of wet avalanches in February and March have increased, which is consistent with the current literature, while the number of slab avalanches has decreased in the last 3 decades. More wet-avalanche releases might be connected to winter season air temperature as it has risen by 1.8 ∘C since 1979. The random forest (RF) results indicate that wet avalanches are influenced by 3 d maximum and minimum air temperature, snow depth, wind speed, wind direction, and rainfall. Slab-avalanche activity is influenced by snow depth, rainfall, new snow, and wind speed. Based on the balanced RF method, air-temperature-related variables for slab avalanches were less important than rain- and snow-related variables. Surprisingly, the RF analysis revealed a less significant than expected relationship between the new-snow sum and slab-avalanche activity. Our analysis allows the use of the identified wet- and slab-avalanche driving variables to be included in the avalanche danger level alerts. Although it cannot replace operational forecasting, machine learning can allow for additional insights for the decision-making process to mitigate avalanche hazard.

Abstract. Climate change impact on avalanches is ambiguous. Fewer, wetter, and smaller avalanches are expected in areas where snow cover is declining, while in higher altitude areas where snowfall prevails, snow avalanches are frequently and spontaneously triggered. In the present paper, we assess 39 years (winters of 1979–1999 to 2002–2020) of avalanche activity related to meteorological and snow drivers in the Krkonoše Mountains, Czechia, Central Europe. The analysis is based on an avalanche occurrence dataset for mostly south, south-easterly oriented 60 avalanche paths and related meteorological and snowpack data. Since 1979, 179/531 wet-snow/slab avalanches have been recorded. The aim is to analyze changes in avalanche activity: frequency and magnitude, and detect driving weather variables of wet and slab avalanches with quantification of variable importance. Especially, the number of wet avalanches in February and March has increased in the last three decades, while the number of slab avalanches has decreased with decadal variability. Medium, large, and very large slab avalanches seem to decline with decadal variability since 1961. The results indicate that wet avalanches are influenced by 3-day maximum and minimum air temperature, snow depth, wind speed, wind direction, and rainfall. Slab avalanche activity is determined by snow depth, rainfall, new snow, and wind speed. Air temperature-related variables for slab avalanches were less important than rain and snow-related variables based on the balanced random forest (RF) method. Surprisingly, the RF analysis revealed less significant relationship between new snow sum and slab avalanche activity. This could be because of the wind redistributing snow in storms in low altitude mountains. Our analysis allows the use of the identified wet and slab avalanche driving variables to be included in the avalanche danger levels alerts. Although it cannot replace operational forecasting, machine learning can allow for additional insights for the decision-making process to mitigate avalanche hazard.