| 英文摘要： |
Identifying and tracking the potential avalanche types is the first step toward characterizing an avalanche problem and a fundamental principle of effective avalanche risk management and forecasting. However, identifying potential avalanche types has been a challenging and unsolved problem. Taking into account the characteristics of snow and position of failure layer during the avalanche activity, avalanches were classified into full-depth dry snow avalanche (FDA), full-depth wet snow avalanche (FWA), surface-layer dry snow avalanche (SDA), and surface-layer wet snow avalanche (SWA). Based on a unique data set of avalanche, weather, soil, and snowpack records from four snow seasons (2015/2016-2018/2019) in the upper reaches of the Kunse River Valley in the central Tianshan Mountains with continental snow climate, this study analyzed the time intervals and triggering factors of various avalanches. Moreover, the characteristics of snowpack and the conditions of weather and soil in the time intervals of various avalanches have been compared. The results showed that FDA and FWA prevailed at the beginning and end of the snow season, which was characterized by high surface soil moisture content (>= 0.32 m(3)/m(3)) and high surface soil temperature (>= 0 degrees C). During the periods of FWA activity, the increase of air temperature and the decrease of snow depth were significantly stronger than that during the periods of FDA. SDA and SWA mainly occurred in the middle of the snow season, which was characterized by low surface soil moisture content (<= 0.32 m(3)/m(3)), high surface soil temperature (<= 0 degrees C), and persistent weak layer in the snowpack. Compared with the period of SDA activity, the prevalence period of SWA was characterized by the moisture content of surface snow at more than 3% and the infiltration from snowmelt water appeared in the snowpack. These results provide detailed information on the identification of potential avalanche types by monitoring snowpack, soil, and weather data, which would help avalanche risk management and forecasting. |
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