1. INTRODUCTION
Mountainous environments are very complex, with various physical and chemical processes interacting with each other on a vertical scale, playing an important role in the environment, and also being an important source of water for downstream areas. Seasonal changes in the cryosphere play an important role in regulating rivers and sediments in mountainous areas and downstream areas, and also have a significant impact on the production and living of downstream residents and other types of social needs(Huss et al. 2017).
Snowpack plays a crucial role in the cryosphere, and its response to climate change has profound implications for the regional and global energy balance as well as the water cycle. The high albedo of snowpack effectively reflects solar radiation, thereby reducing the amount of radiation absorbed by the ground. This, in turn, affects the climate system. Additionally, the melting of snowpack has a substantial impact on the water cycle (Brown et al. 2009,Yasunari et al. 1991,Zuo et al. 2011). Snowmelt is an important freshwater resource, providing 17 per cent of the global population with water for productive use. As the climate changes, the snowpack in many regions undergoes drastic changes. The significant decrease in the number of snow days and the increasing trend towards ”snowlessness” in some areas have had a significant impact on the regional water cycle and on the interaction of the various layers. The temperature increase is particularly severe at high altitudes at low latitudes. The snowpack in the northern hemisphere has shown a decreasing trend in recent decades(Wang et al. 2018,Xiao et al. 2020). However, there is heterogeneity in snowpack changes in different regions, for example, in parts of Central Asia there is a significant increasing trend in snowpack, and snowpack anomalies are increasing from year to year (Gong et al. 2007,Tang et al. 2017). Snowpack anomalies can lead to significant changes in spatial distribution patterns, with important implications for regional runoff: when the spatial distribution of snowpack is not uniform, and the rate of snowmelt varies in different regions, this leads to an uneven spatial and temporal distribution of surface runoff and subsurface runoff, which has an impact on the allocation and utilisation of water resources within the basin(de Jong et al. 2009). Warming temperatures lead to changes in snowpack phenology that have a greater impact on river flows in downstream areas. Currently, the first day of snowpack is significantly earlier in most areas, and some of the snowpack is melting earlier, which increases the risk of flooding due to higher flood levels in the spring floods (Peng et al. 2013,Stewart. 2009). At the same time, it may exacerbate summer drought conditions in some areas. The Tibetan Plateau is located in Central Asia, with an average altitude of 4,000 metres above sea level and widespread glaciers and snow, and is known as the Third Pole. It is the source of many large rivers and is known as the ”water tower of Asia”. In addition, the snow pattern on the Tibetan Plateau has a significant impact on the Asian monsoon(Qian et al. 2011,Zhao et al. 2004). Due to its unique geographical location, has become a hotspot for global snow research (Yang et al. 2015). Studying the distribution pattern of stable snow accumulation in the region contributes to understanding the response of snow to climate change, changes in regional ecological environment, and socio-economic development.
Due to the unique geographical conditions of the Tibetan Plateau, most of the snow exists for a short period of time and melts quickly, often instantaneously (Zhang et al. 2014), The distribution of snowpack exhibits significant heterogeneity due to the substantial variation in environmental factors across different regions (Liu et al. 2019). To address this issue, numerous scholars have conducted studies on the partitioning of snowpack on the Tibetan Plateau. In the 1980s, Li et al proposed a classification system for the snowpack, categorizing it into stable and unstable snowpack based on the accumulation of snow days over a span of 60 days per year (Li et al. 1983). In the 1990s, based on SSRM remote sensing data, it was found that most of the Tibetan Plateau is an unstable snowpack area, and the distribution of the stable snowpack area is small and scattered in the western Sichuan Plateau (Li. 1995). Considering the significant inter-annual variability of snowpack in the majority of Tibetan Plateau regions, He et al. (Year) proposed a method for classifying snowpack. This method combines the annual cumulative number of snow days with the inter-annual variability of snowpack. The study revealed that stable snowpack areas on the Tibetan Plateau are primarily concentrated in the central and eastern regions (He et al. 2012). In addition, the temporal continuity of the snowpack serves as a significant indicator for classifying its stable characteristics. Zhang et al employed the number of consecutive snow days as a method to classify the snowpack in Eurasia. Their findings demonstrated that this method exhibits superior applicability (Zhang et al. 2014). There is an urgent need to study the distribution pattern of stable snowpack, as it provides a more accurate reflection of the regional snowpack distribution in the context of climate change, where significant changes in snowpack are occurring.
Currently, four types of snow data are commonly used on the Tibetan Plateau: station data, remotely sensed data, reanalyzed data, and model data (Gao et al. 2012,Huang et al. 2020,Shi et al. 2011,Zhang et al. 2021). Station data remain highly reliable sources of information for current snowpack studies due to their field measurements and daily observations. However, the uneven distribution of stations on the Tibetan Plateau and the significant spatial heterogeneity of snowpack in certain areas contribute to substantial errors in the interpolation process. While reanalyzed and modeled data help mitigate errors associated with individual data points, they are not ideal for small- and medium-scale snowpack studies due to their high resolution (Bian et al. 2020). Remote sensing data compensates for the uneven distribution of station data sites because it has the ability to monitor a larger area. Furthermore, the Moderate Resolution Imaging Spectroradiometer (MODIS) possesses not only a high resolution of 500 meters, but also performs daily observations with consistent time intervals. This makes the data ideal for investigating the formation of stable snow and its patterns across various topographic conditions on the western Sichuan Plateau.
The Western Sichuan Plateau is situated in the eastern part of the Tibetan Plateau within the Hengduan Mountains. It exhibits a complex topography and is primarily divided into two regions: the Northwest Sichuan Plateau and the Western Sichuan Mountains. The Northwest Sichuan Plateau is characterized by high altitudes and flat terrain, whereas the West Sichuan Mountains have a complex terrain with significant elevation changes and distinct vertical zoning characteristics. The environmental conditions in the Western Sichuan Plateau are unique, leading to inconsistent spatial and temporal continuity of the snowpack and significant year-to-year variability. Consequently, there is an urgent need to investigate the current distribution pattern of stable snowpack and the factors influencing it under different topographic conditions in the western Sichuan Plateau. In this study, we selected the Mamukao River basin in the northwestern part of the western Sichuan Plateau and the Hanliu River basin in the east-central part of the western Sichuan Plateau as representative areas of hilly plateaus and alpine valleys, respectively (see Fig. 1). We conducted an investigation into the distribution pattern of stable snowpack and the influencing factors during a single snowfall event in both spring and winter in these two areas. The aim was to explore the variations in distribution patterns and influencing factors of stable snowpack across different seasons and areas within the western Sichuan Plateau. The findings from this study will serve as a reference for effective resource utilization and ecological conservation in the region, thereby contributing to the overall sustainable development of the area.