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.