Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China

Snow cover is highly sensitive to global climate change and strongly influences the climate at global and regional scales. Because of limited in situ observations, snow cover dynamics in the Nyang River basin (NRB) have been examined in few studies. Five snow cover indices derived from observation and remote sensing data from 2000 to 2018 were used to investigate the spatial and temporal variation of snow cover in the NRB. There was clear seasonality in the snow cover throughout the entire basin. The maximum snow-covered area was 8,751.35 km, about 50% of the total basin area, and occurred in March. The maximum snow depth (SD) was 5.35 cm and was found at the northern edge of the middle reaches of the basin. Snow cover frequency, SD, and fraction of snow cover area increased with elevation. The decrease in SD was the most marked in the elevation range of 5,000–6,000 m. Above 6,000 m, the snow water equivalent showed a slight upward trend. There was a significant negative correlation between snow cover and temperature. The results of this study could improve our understanding of changes in snow cover in the NRB from multivariate perspectives. It is better for water resources management.


INTRODUCTION STUDY AREA
The NRB is located at 29°28 0 -30°31 0 N, 92°10 0 -94°35 0 E ( Figure 1). It originates from a glacial lake on the southern slope of Mount Nyainqentanglha, flows southeastward, and merges into the Yarlung Zangbo River near Nuxia. It is the second largest tributary of the Yarlung Zangbo River basin. The main channel in the NRB is 309 km long with an average slope of 7.35%. The NRB drainage area is approximately 17,500 km 2 (Zhang et al. 2011). The drainage network is very dense and it is the longest tributary of Yarlung Zangbo River basin. The Ba River is the longest tributary of the NRB, and its drainage area comprises 24% of the total NRB drainage area. Glaciers cover 953 km 2 , which is about 5.3% of the total area of the NRB.
In the NRB, the average altitude is 4,700 m above sea level, and 68.3% of the total area is above 4,500 m. The climate is mild, characterized by cool summers and warm winters. The average annual temperature is 8.6°C ( Jin et al. 2019). In summer, the southwest monsoon from the Indian Ocean is dominant; thus, the rainy season is generally concentrated from May to October (Zhang et al. 2002). The average precipitation in the rainy season is 765 mm, accounting for 90% of the annual precipitation. Internal runoff accounts for about 90% of total annual runoff and mainly occurs between June and September. During nonflood seasons, meltwater from ice and snow is the main source of runoff replenishment in the NRB. Therefore, variations in annual runoff are relatively apparent, and floods caused by rainfall and meltwater occur frequently.

Data acquisition
Meteorological (such as precipitation and air temperature) and spatial datasets (such as the digital elevation model, DEM) were used in this study. Annual meteorological data between 2000 and 2018 were obtained from the National Tibetan Plateau Data Center (Yang & He 2018). Because there are few stations in the basin, and the data time series of hydrological stations are short, the meteorological data of the hydrological stations were mainly used for the correction of meteorological station data. The snow cover indices were derived from observation data and remote sensing data. In this study, we mainly used five indices: SCA, SCD, SD, snow cover frequency (SCF), and SWE, all of which were from 2000 to 2018. The Environmental and Ecological Science Data Center for West China, National Natural Science Foundation of China (http://westdc.westgis.ac.cn) provided the SCA product from passive microwave sensors, which served as the basic dataset for this study. We used the data between 2000 and 2018 from the long-term SD dataset of China, which is observational data and has a spatial resolution of 25 km (Che et al. 2015). Passive microwave sensors are not affected by clouds or darkness and can thus provide daily SCA measurements, although the pixel size is large (Foster 1999). Passive microwave data were used to assess long-term trends in SCA in the NRB, which is our study area (Figure 1). SWE and SCD data were obtained from the Science Data Bank (Qiu et al. 2016).

Snow indices
The SCD of each pixel was calculated by ArcGIS10.3. The cumulative SCD of a single pixel in a given time interval was calculated as follows (Tang et al. 2013): where N is the total number of days (images) within a year and D i is the snow cover fraction (%) in a pixel (0 D i 100). The value Ceil (D i . 50) counts the numbers of D i ! 50. For instance, if the pixel value of the image is 55, the SCD adds 1. If the pixel value on the image is 0, the SCD adds 0 and is unchanged. Snow cover days (SCD, d), the snow start date (SOD), and the snow end date (SED) were three indices used for monitoring seasonal snow cover (Dong 2018); SOD and SED denote the Julian dates of the first and last snowfalls of the snow season; SCD is the number of days between the SOD and SED. For areas with persistent snow cover, the hydrological year was determined in advance. For example, the hydrological year could commence on August 1 and end on the following July 31. In this hydrological year, the SED is the date of the last snowfall of the year.
For regions with little snow, calculating the SOD and SED from January 1, taken as the start of the hydrological year, may be more appropriate. Therefore, values of SOD and SED exceeding 366 indicate that the start of snow accumulation has been delayed or the end of snow accumulation has advanced (366 mainly refers to leap years). Monthly data for SCA in the basin over the study period were obtained from calculations of daily SCA. Monthly cumulative mean composite SCA was derived from average SCA in the month. SWE was mainly determined based on SD and snow particle size. The advantage of SWE is that it can quantitatively describe the amount of water formed after snow melting. The beginning and end dates of snow cover are often closely related to climate change and thus have gradually become snow parameters of interest in the context of global climate change (Wang et al. 2017). Continuous time series of the spatial and temporal parameters of snow cover (SCA, SCD, SOD, and SED) can be used to study the spatial and temporal dynamics of snow cover. In addition, quantity parameters (SWE and SD) can be used to estimate the water storage in the snow cover. These two types of indices are important for assessing snow cover changes under the background of climate change, and thus for water resources management (Niedzielski et al. 2019). The number of times of snow cover for a pixel is the SCF of the pixel (Liang et al. 2019). In this study, the characteristics of snow cover in the NRB were analysed using SCA, SCD, SWE, SD, and SCF. The definition of each index is shown in Table 1.
In this study, Pearson correlation analysis was used to calculate the relationship between the climate variables and snow indices (Pearson 1895).
where x and y are snow indices and climate variables, respectively; R is the correlation coefficient (CC) of these two variables; and n is the number of data pairs. The correlation was computed at the 0.05 and 0.01 statistical significance levels.

Temporal characteristics of snow cover
Seasonal variability Figure 2 shows the SCF in the NRB for different seasons. Over the study period, the SCF decreased in all seasons. SCF was highest in winter, followed by spring, then autumn, and lowest in summer. In winter, SCF increased between 2000 and 2004 and decreased between 2004 and 2018. In the other seasons, there was considerable interannual variability in SCF, and the downward trend was less marked. The decreased rate of SCF in winter was the highest, with a value of 0.34. However, the downward trend was not significant in all seasons (P . 0.05). Figure 3(a) and 3(b) show SCA variations in the NRB. The SCA varied seasonally in the NRB. It was highest in spring and lowest in summer. Over the study period, the monthly SCA decreased. The variation of SCA presented double peaks, generally occurring predominantly in spring, and in winter for a few years. The maximum SCA was 75% of the catchment area.
The multi-year monthly average SCA in the NRB is shown in Figure 3(b). The SCA of the drainage basin was smaller between June and September and larger between January and April. Maxima of monthly SCA occurred in March, whereas minima occurred in July. SCA changes were smaller in summer and larger in winter and spring.
Figure 3(c) shows variations of monthly SWE (divided by 2) in the NRB. Monthly SWE showed a similar trend with SCA. There was clear seasonality. Over the study period, SWE decreased. Figure 3(d) shows that maxima of multi-year monthly SWE occurred between the end of December and the beginning of January; SWE was lower between July and September and higher between November and February. Additionally, both SCA and SWE had lower values in summer and higher values in spring and winter.  Figure 4 shows that the seasonal variability of snow cover in the NRB was very high. The SCA was highest in spring, followed by winter, then autumn, and lowest in summer. In spring, snow covered the whole basin. In summer, snow was mainly concentrated in the high-altitude mountain areas to the south and north. The SCA was higher in autumn than in summer. It was also distributed in the upper reaches of the basin. In winter, the snow cover extended to the downstream area of the drainage basin. The snow season began with the southern and northern marginal mountain areas being covered by snow (summer). Then, the snow cover in the upstream marginal mountain areas increased (autumn). Finally, the snow cover in the downstream marginal mountain areas increased (winter and spring). Figure 5 shows the variations of snow cover indices in the NRB over time. Figure 5(a) shows the variation of SCF. Figure 5(b) shows the variation of annual SCA. Figure 5(c) shows the variation of SWE (divided by 2). The SCF increased between 2000 and 2004 and decreased between 2004 and 2018. Over the study period, annual SCF decreased. The value of annual SCF is constrained between 0.1 and 0.2. Over the study period, SCF was the highest in 2004 and lowest in 2016. This trend is also reflected in the winter SCF. Winter snow cover accounted for the largest proportion of the annual snow cover. Figure 5(b) shows that the annual average SCA decreased over the last 10 years. Over the study period, the SCA was the lowest in 2010, about 4,041 km 2 . The maximum annual SCA was 6,098 km 2 in 2004, accounting for 34.8% of the total basin area. Figure 5(c) shows that fluctuations in SWE over the study period had no obvious trend (P . 0.05). This may also depend on the length of the study period.

Spatial distribution
The study of the spatial distribution of snow cover is key to understanding the runoff process. Figure 6(a) shows the distribution of average annual SWE (divided by 2) in the NRB. The highest SWE was approximately 310 mm and was found at the northern edge of the middle reaches of the NRB; the SWE in the upper reaches was higher than that in the lower reaches. The lowest SWE was approximately 120 mm and was found in the downstream area of the basin.
SD data were pre-processed, and the average SD for 2000-2018 was calculated. Figure 6(b) shows the distribution of average SD for many years in the NRB. The maximum SD was 5.35 cm and was found at the northern edge of the middle reaches of the basin. The minimum SD was 1.82 cm and was found at the western edge of the upper reaches of the basin. The average SD of the basin was 3.83 cm. In general, SD in the middle reaches was higher than that in the upper reaches and lower reaches.
The general situation of the spatial distribution of snow cover in the NRB is represented in Figure 6(c), which shows the annual average SCD between 2000 and 2018. Snow was mainly found in the northern and southeastern regions of the upper  reaches of the NRB, as well as at the northern edges of the middle reaches. In these areas, the SCD exceeded 103 days, and the vegetation is mostly mountain grassland and shrubbery. SCDs were below 103 days in the upper reaches (except in the southeast and at the western edge) and the middle reaches (except at the northern edge) of the NRB. These areas are mostly arid grassland where evaporation is high and precipitation is low.  Figure 7(a) shows the variation of SCF with elevation in the NRB. Elevation was positively correlated with SCF in the basin. The highest SCF was 0.39. Figure 7(b) shows the variation of SD with elevation in the NRB. The annual mean SD was positively correlated with elevation. At low altitudes, SD fluctuated. The highest SD in the basin was 4.7 cm. Figure 7(c) shows the variation of SWE with elevation in the NRB. Below 5,500 m, there was no significant correlation between SWE and elevation. Above 5,500 m, SWE increased with elevation. The highest SWE was approximately 300 mm. These results are partially consistent with those reported by previous studies (Groisman et al. 1994).   Figure 7(d) shows the variations of SCF at different elevations in the NRB. Over 5,000 m above sea level, the SCF slightly increased over the study period. At higher altitudes, the SCF began to exhibit a downward trend in 2008. The rate of decrease of the SCF over the study period increased with altitude.  Figure 7(f) shows variations of SWE in the NRB. The SWE increased above 6,000 m and decreased below 6,000 m. The lowest SWE was recorded in 2,009 below 6,000 m above sea level.

DISCUSSION AND CONCLUSIONS
Snow cover is affected by precipitation and temperature and changes with altitude and season. Because of the complex terrain and changeable climate conditions, the relative importance of temperature and precipitation has changed in space (You et al. 2020).
The main reasons for the decrease of SCA in NRB that showed a downward trend during the study period is the decrease of snowfall and the increases in temperature and liquid precipitation. As shown in Figure 8, the snow cover accumulation in the NRB mainly started in September and snow melt started in April. The maximum value of snow cover area occurred from late January to mid-February each year, and the minimum value occurred between mid-and late August. Figure 8 shows that during the study period, the maximum SCA showed a downward trend, but not an obvious one, and the minimum SCA showed a slight upward trend. The decreasing trend of the maximum SCA may reflect the decrease of the average precipitation rate and maximum precipitation rate, which is related to the decrease of the average temperature and maximum temperature. Previous studies found that there had been no significant trend in SCA in western China since 1957, although climate change in the middle latitudes of the Northern Hemisphere has led to the decrease of SCD on the Tibetan Plateau since the 1970s (Déry & Brown 2007;McCabe & Wolock 2009;Shen et al. 2014). In the study area, SCD and SWE generally decreased, with more marked decreases at high elevation. However, SCA increased with elevation when the elevation was lower than 2,000 m and decreased with elevation at higher elevations. Our results are consistent with the previous study (Shen et al. 2014).
In high-altitude areas, because of the melting of snow, the positive effect of snowfall on snow cover has become more and more important, and the negative effects of precipitation and temperature on snow cover have also become greater (Yang et al. 2019). We observed a significant reduction in snow cover in areas where temperature increased significantly. The decrease of SCA at higher altitudes (especially above 5,000 m) may indicate that continuous snow cover and glacier melting are intensified due to higher temperatures. This is consistent with the study that reported that temperatures around 5,000 m have considerably increased (Rangwala et al. 2010). The increase of annual rainfall, especially in summer, may also be an important factor leading to continuous snow melting in summer.
SCDs exhibited an overall downward trend over most of the basin area. Between 2000 and 2018, there was little change in the SCD spatial distribution, indicating that although there were temporal and spatial interannual variations of snow beginning and melting, the overall distribution of SCD was mainly controlled by the terrain and climate conditions in a certain region. Previous studies on SCD over the Tibetan Plateau based on satellite data found that SCD decreased from 2000 to 2010 (Tang et al. 2013;Qin et al. 2014); our study results are consistent with this finding.
The decreases of SCD and SWE in the NRB during the study period were mainly caused by the decrease in snowfall and increase in rainfall and temperature, which is consistent with a previous study . Based on our research results, it can be inferred that with the increase of temperature and precipitation, SCA gradually decreases; the snow rapidly melts at the end of the snow season, and the permanent snow area of the whole basin continues to decrease.
On a monthly scale, the SCA was mainly negatively correlated with temperature and positively correlated with precipitation, although there were large differences between the correlation coefficients ( Figure 8). The decrease (increase) of SCA was mainly related to the decrease (increase) of precipitation or warming (cooling), which is roughly consistent with the results from a previous study that analysed the relationship between snow cover and climate in the study area using only data from meteorological stations (Singh et al. 2016). The data were de-trended and the following results were obtained (Table 2). In general, the correlation coefficients between SCA and temperature were higher than those between SCA and precipitation (except for in the autumn months), and there is a significant negative correlation between SCA and temperature (Table 2), which indicates that the interannual variability of the snow cover in the NRB was mainly caused by temperature.

CONCLUSION
In this study, we examined variations of different snow cover indices, including SCA, SCD, SCF, SD, and SWE, in the NRB over the study period of 2000-2018. We analysed the changes of snow cover and drew the following conclusions: (1) During the study period, SCF showed a downward trend. Interannual variability in snow cover was highest during midwinter. On the monthly scale, the influence of temperature on snow cover was more significant than that of precipitation. (2) Snow cover of the NRB was mainly concentrated in high-altitude mountain areas to the south and the north. The maximum SWE was also found in such a region, with a value of approximately 310 mm. The SWE in the upper reaches was higher than that in the lower reaches of the basin. (3) In the mountain area to the north, which is in the middle reaches of the basin, the SCD exceeded 103 days, and the maximum SD was about 5.35 cm. The SD in the middle reaches was higher than that in the upper reaches of the basin. (4) There was clear seasonality in snow cover indices. All indices exhibited downward trends. Correlation coefficients showed that the snow cover indices were positively correlated with altitude.
Snow cover indices in the basin were affected by geographical location, topography, and climate change. This study has demonstrated the temporal and spatial distributions of snow cover and their relationships with precipitation and temperature in the NRB, which is helpful for the scientific community to understand the relationship between snow dynamics and climate change, and fills a gap in the study of snow cover in the high-altitude area of this region. Because of the low resolution (25 km) of the snow data used in this study, the number of meteorological stations used, and the short time span, there are some uncertainties. Most of the meteorological stations are located in river valleys or plains, where the altitude is low and there is usually less snow. Conventional meteorological data are limited for areas at high altitudes and in steep terrain. Therefore, measurements of snow cover can be obtained from remote sensing data, which are often limited by the presence of clouds. Thus, more field observations are needed to accurately describe the temporal and spatial changes of snow cover in NRB.