Altitudinal Gradient Characteristics of Spatial and Temporal Variations of Snowpack in the Changbai Mountain and Their Response to Climate Change

: The variations in the snowpack in water towers of the world due to climate change have threatened the amount and timing of freshwater supplied downstream. However, it remains to be further investigated whether snowpack variation in water towers exhibits elevational heterogeneity at different altitude gradients and which climatic factors mainly inﬂuence these differences. Therefore, Changbai Mountain, a high-latitude water tower, was selected to analyze the changes in the snowpack by the methods of modiﬁed Mann–Kendall based on the daily meteorological data from the China Meteorological Data Service Centre. Meanwhile, the responses of snowpack change to climatic factors over recent decades were assessed and generalized using additive models. The results showed that the snow depth was greater in the higher altitude areas than in the lower elevation areas at different times. Areas with a snow depth of over 70 mm increased signiﬁcantly in the 2010s. Increasing trends were shown at different altitudes from December to March of the next year during 1960~2018. However, a signiﬁcant decreasing trend was shown in April, except for altitudes of 600–2378 m. The snow cover time at different altitudes showed a trend of ﬁrst increasing and then decreasing during 1960~2018. The date of maximum snow depth appears to be more lagged as the altitude increases. In addition, the spring snowpack melted signiﬁcantly faster in the 2010s than that in the 1960s. The snowpack variation in low-altitude regions is mainly inﬂuenced by ET and relative humidity. However, the mean temperature gradually became an important factor, affecting the snow depth variation with the increase in altitude. Therefore, the results of this study will be beneﬁcial to the ecological protection and sustainable development of water towers. The same phenomenon was observed at other altitudes; only the change was different with increasing altitudes.


Introduction
Mountains are the water towers of the world and are important sources of freshwater; they supply a substantial proportion of both natural and anthropogenic water demands [1,2]. In these high-altitude regions, snow is the main component of the hydrological cycle, because changes in the snowpack have a great impact on streamflow, groundwater recharge, hydropower production [3], agricultural irrigation and ecosystem function [4]. Due to climate change over the past few decades, the duration, depth and cover of snowpacks in these mountain regions have all changed, which has threatened the amount and timing of the freshwater supply [5]. Therefore, it is very important to maintain the stability of the ecological environment in a water tower and provide sustainable water

Study Site Description
This research was conducted in the Changbai Mountains, which are the source of Second Songhua River (SSR), Tumen River (TR), and Yalu River (YR); the area is approximately 17.16 × 10 4 km 2 , and they are located at 39. 8-45.4 • N and 123.5-131.3 • E (Figure 1a). The Tumen River and Yalu River are international boundary rivers between China and North Korea and Russia, respectively.
Changbai Mountains exhibit an obvious elevation gradient, ranging from −8 m to 2738 m (Figure 1b). This area is characterized by a temperate continental mountain climate affected by monsoons. It is cold and dry in winter. The climate exhibits an annual average temperature of −7~3 • C, and annual average snowfall of more than 600 mm in certain areas.

Data
Daily meteorological data from 54 stations (Table 1) in the Changbai Mountains were obtained for the period of 1960-2018 from the China Meteorological Data Service Centre. The variables included snow depth (SD), snowfall (SF), evapotranspiration (ET 0 ), relative humidity (RH), wind (W), sunlight hours (SH) and mean temperature (MT). The missing rate of snow depth is 0.98%, and the suspicious rate is 0.002‰.

Data
Daily meteorological data from 54 stations (Table 1) in the Changbai Mountains were obtained for the period of 1960-2018 from the China Meteorological Data Service Centre. The variables included snow depth (SD), snowfall (SF), evapotranspiration (ET0), relative humidity (RH), wind (W), sunlight hours (SH) and mean temperature (MT). The missing rate of snow depth is 0.98%, and the suspicious rate is 0.002‰.

Modified Mann-Kendall Method
A modified Mann-Kendall (MMK) test was applied to analyze the long-term trends in snow depth throughout the entire study period. To correct serial correlations in the data, the trend-free pre-whitening procedure was used: a more specific process of this methodology is described by Qi et al. [17]. After the Z value was calculated for all meteorological stations, they were spatially aggregated to obtain probability maps of the statistical differences.

Generalized Additive Model
Generalized additive models (GAM) are a nonparametric generalized multiple linear regression method which can overcome limitations of the multiple linear regression model and predict nonlinear relationships as a flexible regression technique using nonparametric smoothers. The general formula of a GAM is: where g µ y represents a function of the conditional mean of the response variable y, and the term β 0 is recognized as any strictly parametric component in the model, such as the intercept. The component f i (X i ) is designated as the variable explained by the nonparametric smoothing function, and ε i is identically and independently distributed as a normal random variable. The detailed calculation process can be found in the paper by Liu et al. [6].

Spatial Interpolation
In recent years, based on meteorological station data, some spatial interpolation methods have been applied to study the spatial distributions of snow depth and the climate variations. Due to the inverse distance weighting method gave the lowest mean error than other methods, the Inverse Distance Weight method (IDW) was applied widely in all over world [17,18]. Thus, in this study, the IDW method was selected to analyze the spatial variation of snow depth.

Interdecadal Variation of Annual Mean Snow Depth
As shown in Figure 2, snow depth in the Changbai Mountains was greater in the higher altitude areas than in the lower elevation areas at different times. It showed that snow distribution in the Changbai Mountains exhibits obvious altitude gradient characteristics. In the past 60 years, the spatial distribution of the interannual average snow depth of the Changbai Mountains has changed greatly. A trend of increasing and then decreasing was shown in the Changbai Mountains at different times. Compared with the 1960s (Figure 2a Figure 3d); however, the snow in some stations at high altitudes reached 239 mm deep, which is higher than the maximum in January. This indicates that the snow season was delayed, and the snow period was longer in the high-altitude area than that of the low altitude area. The snow depth decreases significantly in April (Figure 3f), and most of the areas have between 0 and 10 mm of snow, which is significantly affected by the increase in temperature.

Interannual Evolution of Snow Depth at Different Altitudes in the Changbai Mountains
There was no significant trend in snow depth at different altitudes in November from 1960 to 2018 (Figure 4a). The trend in variation was not consistent under each altitude gradient. Increasing trends were observed at −8~200 m (slope = 0.04 mm/10 years) and 200-400 m (slope = 3.5 mm/10 year). Meanwhile, decreasing trends were observed at 400~600 m (slope = −0.18 mm/10 year) and 600~2738 m (slope = −0.17 mm/10 year). Increasing trends in snow depth were observed at different altitudes in December during 1960~2018 (Figure 4b). The trend in increasing snow depth at −8~200 m is significant (Z = 2.05), with a rate of 2.37 mm/10 year, and there was no significant change at all other altitudes. Similar to December, only the snow depth from −8 to 200 m showed a significant increasing trend (Z = 2.38) at a rate of 4.09 mm/10 year in January (Figure 4c). There was As shown in Figure 2, snow depth in the Changbai Mountains was greater in the higher altitude areas than in the lower elevation areas at different times. It showed that snow distribution in the Changbai Mountains exhibits obvious altitude gradient characteristics. In the past 60 years, the spatial distribution of the interannual average snow depth of the Changbai Mountains has changed greatly. A trend of increasing and then decreasing was shown in the Changbai Mountains at different times. Compared with the 1960s (Figure 2a

Variation of Monthly Mean Snow Depth
Snow depth in the Changbai Mountains has obvious seasonal variation characteristics. In November, the entire region was completely covered with snow, with snow depths reaching 30-50 mm at higher elevations ( Figure 3a). After that, snow depths increased month by month (Figure 3b), reaching a maximum in January (Figure 3c), with an average

Interannual Evolution of Snow Depth at Different Altitudes in the Changbai Mountains
There was no significant trend in snow depth at different altitudes in November from

Intra-Annual Evolution of Snow Depth at Different Altitudes in the Changbai Mountains
The Changbai Mountains are a seasonal frozen soil area; the snow period lasts from October to May. It can clearly be seen that as the altitude rises, the date of maximum snow depth appears more lagged, with 0~200 m presenting at the end of January and 600~2738 m presenting in mid-February ( Figure 5). At the same time, the start and end times of the snowpack were different in different years. In the 2010s, the snow began on October 17 at an altitude of −8~200 m; this start time lagged behind that in the 1960s by 11 days. The spring snowpack melted significantly faster in the 2010s than that in the 1960s. The snow disappeared on 9 April in 2010s, 10 days earlier than in the 1960s at −8~200 m.
The same phenomenon was observed at other altitudes; only the change was different with increasing altitudes.

Intra-Annual Evolution of Snow Depth at Different Altitudes in the Changbai Mountai
The Changbai Mountains are a seasonal frozen soil area; the snow period lasts f October to May. It can clearly be seen that as the altitude rises, the date of maximum s depth appears more lagged, with 0~200 m presenting at the end of January and 600~ m presenting in mid-February ( Figure 5). At the same time, the start and end times o snowpack were different in different years. In the 2010s, the snow began on October an altitude of −8~200 m; this start time lagged behind that in the 1960s by 11 days. spring snowpack melted significantly faster in the 2010s than that in the 1960s. The s disappeared on 9 April in 2010s, 10 days earlier than in the 1960s at −8~200 m. The s phenomenon was observed at other altitudes; only the change was different with incr at −8~200 m, 195 days at 200~400 m, 193 days at 400~600 m and 198 days at 600~2738 m Compared with the 1960s, the average snow cover time in the 2010s had decreased by days at −8~200 m, 6 days at 200~400 m, 5 days at 400~600 m and 2 days at 600~2738 m.

Spatio-Temporal Changes of Snow in the Changbai Mountains
The Changbai Mountains are an important, functional ecological area in East Asia, with a typical vertical belt spectrum, from temperate broad-leaved forest to tundra in the east of Eurasia. Snow is one of the most important water sources for maintaining ecological function of the region. It has been shown that snowmelt runoff can account for 14.1% to 59.8% of the total runoff in watersheds in the source area of the Changbai Mountains [19]. In recent decades, the snowpack of Changbai Mountain has changed considerably with climate change. In general, the snow depth showed a trend of decreasing and then increasing during 1961~2018; it was at a minimum value in the 1980s, which has been proven by Li and Ke [20]. In terms of space, the area of snow cover with an annual average of more than 70 mm has been expanding since the 1990s (Figure 3). Temporally, snow depth showed a trend of increasing and then decreasing during 1961-2018. This finding was consistent with the existing research results [21][22][23]. However, previous studies have lacked the heterogeneity of snow depth variation at different altitudes. The most significant increase in snow depth was observed at lower altitudes (−8~200 m), but the increased value was greater at higher altitudes than in other regions (600~2738 m) in December, January and February (Figure 4) during 1961~2018. The duration of snow cover also first increased and then decreased at different altitudes. The duration increased before 1980 and then decreased. The spring snowpack melted significantly faster in the 2010s compared with that of the 1960s.

Climatic Drivers for Changes in Snow Depth
Changbai Mountains are a mountainous area with little human activity; thus, snow depth is mainly influenced by climate change [22]. In recent decades, significant changes in climate factors have occurred in the Changbai Mountains [24]. Wind speed has decreased year by year (Figure 7a), snowfall has increased (Figure 7b), ET has increased (Figure 7c), SH has decreased (Figure 7d), RH has decreased (Figure 7e) and Tm has increased (Figure 7f) at different altitudes over the past few decades. It has been found that there is a negative relationship between snow depth and average temperature [20]. Another study concluded that snowfall has a greater effect on snow depth than temperature [21]. We found that the dominant factors affecting snow depth variability were not consistent across different months at different altitudes. The snowpack variation was mainly influenced by snowfall in November at −8~200 m during 1961~2018 (Table 1). However, the dominant factor was ET 0 from December to March of the next year, and RH in April (Table 1). Variations in snow depth from 200 m to 400 m and 400 m to 600 m are mainly influenced by ET 0 and RH (Tables 2 and 3); the gradual increase in altitude temperature has become an important factor affecting the variation in snow depth. As shown in Table 4, the dominant factor affecting snow depth variability was Tm in December, March and April at 600~2738 m. At this time, a significant negative correlation was observed between snow depth and temperature.

Climatic Drivers for Changes in Snow Depth
Changbai Mountains are a mountainous area with little human activity; thus, snow depth is mainly influenced by climate change [22]. In recent decades, significant changes in climate factors have occurred in the Changbai Mountains [24]. Wind speed has decreased year by year (Figure 7a), snowfall has increased (Figure 7b), ET has increased (Figure 7c), SH has decreased (Figure 7d), RH has decreased (Figure 7e) and Tm has increased (Figure 7f) at different altitudes over the past few decades. It has been found that there is a negative relationship between snow depth and average temperature [20]. Another study concluded that snowfall has a greater effect on snow depth than temperature [21]. We found that the dominant factors affecting snow depth variability were not consistent across different months at different altitudes. The snowpack variation was mainly influenced by snowfall in November at −8~200 m during 1961~2018 (Table 1). However, the dominant factor was ET0 from December to March of the next year, and RH in April (Table 1). Variations in snow depth from 200 m to 400 m and 400 m to 600 m are mainly influenced by ET0 and RH (Tables 2 and 3); the gradual increase in altitude temperature has become an important factor affecting the variation in snow depth. As shown in Table  4, the dominant factor affecting snow depth variability was Tm in December, March and April at 600~2738 m. At this time, a significant negative correlation was observed between snow depth and temperature.

Conclusions
This study identified the trend in changes in the snowpack at different altitudes during recent decades and their driving factors in a high-latitude water tower. Based on these findings, the following conclusions can be drawn: (1) Altitude gradient characteristics of the snow distribution were identified in the Changbai Mountains. Snow depth was greater in the higher altitude areas than in the lower elevation areas at different times of the year. Compared with other years, the area with a snow over 70 mm deep increased significantly in the 2010s. (2) The changing trend in snow depth is not consistent under each altitude gradient. Increasing trends were observed at different altitudes from December to March of the next year during 1960~2018. The most significant change was in April, which presented a significant decreasing trend except for at 600-2378 m. (3) The Changbai Mountains are a seasonal frozen soil area, and the snow cover duration at different altitudes showed a trend of first increasing and then decreasing from 1960 to 2018. The date of maximum snow depth appeared to become more lagged as the altitude increased. In addition, the spring snowpack melted significantly faster in the 2010s compared with that of the 1960s. Meanwhile, the change was different with increasing altitudes. (4) Snowpack variation in low-altitude regions is mainly influenced by ET and relative humidity. However, the mean temperature gradually became an important factor, affecting the snow depth variation with an increase in altitude.
Author Contributions: Y.C. and P.Q. conceived the idea of the study and wrote the manuscript; Z.C., S.X., Y.S. and X.T. carried out data collection and analysis; P.Q., D.L. and Y.C. contributed valuable analysis and manuscript review; all authors approved the final manuscript. All authors have read and agreed to the published version of the manuscript.
Funding: This research was supported by National Key R&D Program of China (2019YFC0409104) and National Natural Science Foundation of China (42001032). In addition, we would like to express our gratitude to both the editors and reviewers for their efforts and suggestions.

Institutional Review Board Statement:
The study did not involve humans or animals.

Informed Consent Statement:
The study did not involve humans.

Data Availability Statement:
The study did not report any data.

Conflicts of Interest:
The authors declare no conflict of interest.

Conclusions
This study identified the trend in changes in the snowpack at different altitudes during recent decades and their driving factors in a high-latitude water tower. Based on these findings, the following conclusions can be drawn: (1) Altitude gradient characteristics of the snow distribution were identified in the Changbai Mountains. Snow depth was greater in the higher altitude areas than in the lower elevation areas at different times of the year. Compared with other years, the area with a snow over 70 mm deep increased significantly in the 2010s. (2) The changing trend in snow depth is not consistent under each altitude gradient.
Increasing trends were observed at different altitudes from December to March of the next year during 1960~2018. The most significant change was in April, which presented a significant decreasing trend except for at 600-2378 m. (3) The Changbai Mountains are a seasonal frozen soil area, and the snow cover duration at different altitudes showed a trend of first increasing and then decreasing from 1960 to 2018. The date of maximum snow depth appeared to become more lagged as the altitude increased. In addition, the spring snowpack melted significantly faster in the 2010s compared with that of the 1960s. Meanwhile, the change was different with increasing altitudes. (4) Snowpack variation in low-altitude regions is mainly influenced by ET and relative humidity. However, the mean temperature gradually became an important factor, affecting the snow depth variation with an increase in altitude.
Author Contributions: Y.C. and P.Q. conceived the idea of the study and wrote the manuscript; Z.C., S.X., Y.S. and X.T. carried out data collection and analysis; P.Q., D.L. and Y.C. contributed valuable analysis and manuscript review; all authors approved the final manuscript. All authors have read and agreed to the published version of the manuscript.
Funding: This research was supported by National Key R&D Program of China (2019YFC0409104) and National Natural Science Foundation of China (42001032). In addition, we would like to express our gratitude to both the editors and reviewers for their efforts and suggestions.

Institutional Review Board Statement:
The study did not involve humans or animals.

Informed Consent Statement:
The study did not involve humans.

Data Availability Statement:
The study did not report any data.

Conflicts of Interest:
The authors declare no conflict of interest.