1. Introduction
An avalanche is a natural phenomenon widely occurring in mountains at middle and high latitudes, exhibiting seasonal, sudden, potential, and unpredictable characteristics [
1,
2,
3,
4]. The western Tianshan Mountains of Xinjiang, China, are limited by tectonic belts, which are conducive to the invasion of westerly airflows, thereby generating abundant precipitation. These typically folded fault block mountains widely contain ancient cirques and snow depressions, while a large amount of snow can accumulate in these mountains, becoming the source of avalanches [
5,
6,
7]. With the continuous development of transportation and tourism in mountain areas, avalanches have become one of the main natural disasters hindering development [
8]. In March 2008, an avalanche induced by strong winds destroyed construction camps and buried construction tunnels in the Guozigou region of the Ili Kazakh Autonomous Prefecture in Xinjiang, resulting in the deaths of 16 people, injuries to 8 individuals, and the loss of the natural gas supply. In December 2010, an avalanche triggered by heavy snowfall impacted passing vehicles and cut off roads in the G218 Tianshan section, killing two people and trapping thousands of vehicles in the mountainous area. In April 2019, an avalanche buried a mountain road in Urumqi’s Nanshan Scenic area, injuring and trapping 10 people [
9]. Therefore, analyzing avalanche-triggering factors could facilitate the accurate control of regional main disaster factors, which has very important research value and practical significance for disaster prevention and reduction, disaster management, and decision-making.
The initiation of avalanches is closely related to the topography and geomorphology, snow characteristics, meteorological conditions, and human activities [
5,
10,
11,
12]. Among the above factors, the topography and landform as the basic conditions can trigger avalanches under the joint coupling and combination of snow characteristics, meteorology, human activities, etc. Many scholars have widely researched the triggering factors of avalanches. Schweizer et al. [
10,
13] stated that because the terrain is the only constant parameter during avalanche triggering, the slope in most avalanche formation zones ranges from 30~45°. Linke et al. [
5], who studied the disaster mechanism of tabular avalanches, mentioned that the change in the shear force between the snowboard and the lower snow layer is the main cause of the rupture between snow layers and the triggering of avalanches. Schweizer, De Quervain [
6,
10,
14], and others studied the relationship between wind-blown snow and avalanches, noting that wind-blown snow is more fragmented than naturally settled snow; hence, the snow layer formed by wind-blown snow is more susceptible to avalanches. Gratton et al. [
15] found that the avalanche-triggering factors of the eastern slope and the western slope differ when considering the avalanche-triggering mechanism of slope direction. The avalanche triggering of the eastern slope depends on the accumulated snow amount of the slope area, while the western slope depends on occasional snowfall and temperature. At present, research on avalanche-triggering factors mostly focuses on the influences of individual factors on avalanche triggering, but rarely examines the influences of disaster factors on avalanches on small regional scales. In different disaster-prone environments, disaster-causing factors provide both advantages and disadvantages, leading to different triggering mechanisms.
Analyzing disaster-causing factors on a small scale is a prerequisite for improving the accuracy of regional disaster prediction. Since avalanche triggering is a complex nonlinear system, the degree of influence between causative factors varies among different regions and under different physical and geographic conditions. Therefore, mathematical models can be used to consider the relationship between the distribution of avalanches and causative factors and to analyze the dominant causative factors of avalanche triggering [
16,
17] to further elucidate avalanche-triggering factors within small-scale ranges and provide scientific references for avalanche disaster prevention and control.
At present, many scholars have used certainty factor (CF) models to study disaster-prone environments in the region and have achieved certain results. Mohsen Rezaei et al. [
18] used the CF model and analytic hierarchy process (AHP) method to analyze the land subsidence sensitivity in the Neshapur Plain of Iran and found that the prediction accuracy of the CF model was much higher than that of the AHP method, which can be used to accurately distinguish areas with low, medium, high and very high susceptibility levels, thus providing high disaster prevention and control guidance value. Lin et al. [
19] solved the problem of selecting and quantifying hazard factors in the process of landslide susceptibility evaluation by coupling the CF model and Stability INdex MAPping (SINMAP) model and applied this method in the Wulingshan area of Cili County, Hunan Province, which provided a new idea for regional rainfall-induced landslide forecasting. Wang Zhiheng [
16] found that the geotechnical type, elevation, and slope control the distribution of rainfall-type landslides in the study area using the CF model, and the results could be quantitatively analyzed to reflect the degree of influence of each disaster-inducing environmental factor on landslide destabilization. Based on the Google Earth Engine (GEE) cloud computing platform, Mao Zhengjun [
20] selected eight influencing factors of terraced loess landslides and used the deterministic coefficient method to analyze these factors, revealing that the main control factors of their formation were rainfall and stratigraphic lithology. Xiang Lingzhi et al. [
21] used the coefficient of certainty to analyze the environmental factors that can cause disasters in the Wenchuan earthquake disaster area and further determined the dominant factor interval affecting the occurrence of disasters in the area. The above research results indicate that the CF value can be considered to more objectively determine the main control factors of disaster triggering on a small regional scale. This method is simple and easy to apply and can be suitably employed to study the influences of disaster-causing factors. Therefore, the CF model can be utilized to explore the degree of influence of each causative factor in the avalanche-prone environment to reveal the causative mechanism of avalanches on a small scale.
Using high-resolution remote sensing image interpretation and field survey analysis, a spatial distribution map of avalanche disasters in Aerxiangou was established. Based on the CF model, the triggering factors in the avalanche-prone environment within the study area were analyzed, the main control factors were clarified, and the influence of disaster factors on avalanche triggering was quantified, thereby aiming to reveal the disaster mechanism in the avalanche area of Aerxiangou. The RAMMS-avalanche model was used to simulate and analyze the activity characteristics at typical avalanche points to determine the attributes of the avalanche motion trajectory and disaster formation process. The research results could provide theoretical support and a scientific basis for avalanche disaster prevention.
5. Discussion
In the study of the mechanism of avalanche disasters, the physical and mechanical nature and trends of the dynamic change process from the steady state to unstable sliding are analyzed under the joint action of the inherent geological conditions of the mountain area and meteorological and human-induced factors, which provides a theoretical basis for avalanche prediction and early warning and effective prevention [
26,
35]. At present, many scholars have examined various avalanche-causing mechanisms, such as the analysis of avalanche formation conditions and influencing factors [
28,
36,
37], analysis of snow layer forces [
38], analysis of snow mechanical properties and spatial and temporal variation patterns [
27,
39,
40], and analysis of the impact of skiing on the snow layer stability [
10,
41]. Avalanche hazards are most likely to occur in parts of mountain slopes with favorable snowfall conditions and slopes ranging from 35° to 45°, and these parts of mountain slopes are key potential hazard sites. After clarifying the main types of disaster occurrence, activity characteristics, and movement characteristic values, targeted development of measures for early identification and prediction of disaster hazards could further improve the efficiency of disaster prevention and mitigation efforts and reduce the direct losses caused by avalanche disasters. In this paper, we analyzed avalanche-triggering factors in the small-scale area of Aerxiangou, aiming to reveal the disaster-causing mechanisms and activity characteristics at multiple avalanche hazard sites in the study area and to provide a reliable scientific basis for avalanche disaster prevention and control.
Based on several field surveys aimed at exploring the distribution and scale of avalanches, the correlation between eight hazard-causing factors and the number of avalanches was studied using the CF model, the influence index (E) of each hazard-causing factor was derived using CF values, and the main control factors of avalanche triggering under different periods were proposed. Notably, different categories of avalanches could be induced under the influence of the main control factors under different periods, which is consistent with the findings of Wenlinke et al. [
5] Multiple factors control avalanche triggering, and different categories of avalanches can be formed for different reasons. Since disaster triggering results from multiple factors, there are complex interrelationships among the considered factors. Zhou Jin et al. [
42] used the geographic detection method to reveal that the results of a two-by-two interaction analysis of causative factors could be enhanced, with a significantly higher impact degree and a higher probability of disaster occurrence under the interaction of factors. Bruce Jamieson et al. [
43] suggested that there exists a stronger correlation between the temperature and snow thickness than with other factors and that the snow thickness is the main factor controlling the occurrence of slab avalanches. In this paper, only eight influential factors with a notable impact on avalanche triggering were independently analyzed to achieve factor elucidation. The secondary role of factors and the interaction among factors should be studied further.
To identify the activity characteristics at multiple avalanche hazard sites, theory and simulation experiments were combined to determine the formation process of avalanches of various categories under the influence of the main controlling factors. The RAMMS-avalanche simulation experiment revealed that, based on considering depositional effects on secondary avalanche paths, the influence range of the avalanche flow deposition area changes slightly. In contrast, the avalanche path greatly changes in the movement area, and the different categories of avalanches exhibit high variability in their kinematic eigenvalues. Overall, wet snow avalanches are more hazardous and generate higher pressure. Fresh snow avalanches exhibit shorter start-up times and higher sliding speeds, while the difference in the avalanche sliding surface (the whole and surface layers) mainly manifests in the difference in the flow height. The movement paths derived from the simulation results conform to the actual situation of avalanche activity. Therefore, the RAMMS-avalanche model is a very effective tool for simulating avalanche movement and has promising application prospects. However, when this model is used in the design of disaster prevention and mitigation engineering, the destructive force of the turbulent flow structure formed by the high-speed movement of snow particles should not be ignored. On this basis, disaster prevention and mitigation work should be further studied to determine a certain proportion of the impact range buffer zone, and the characteristic value of protection engineering design work should be prioritized over simulated design values.
Recently, regarding avalanche hazards, researchers have focused on prevention and mitigation efforts at sites where hazards have already occurred, ignoring the issue of potential avalanche sites. As a result of global climate change, extreme weather events are occurring frequently, especially extreme snowfall, wind-blown snow, and a sudden rise in spring temperatures, resulting in avalanches frequently occurring in areas not traditionally prone to avalanches in recent years. Mechanisms such as disaster risk identification and early warning have not been adapted to the new requirements of the new situation, which could cause incalculable losses to the safety of people’s property and infrastructure construction. Therefore, by emphasizing risk identification and assessment management of avalanche hazards at highways, scenic spots, and important infrastructure areas, we could effectively avoid disasters and provide a scientific basis for infrastructure construction and planning in disaster areas.