2.2.1 High spatial resolution data set of monthly air temperatures and precipitation

Data with a high spatial resolution of 0.5^′ (approximately 1 km), including the monthly minimum, maximum and mean temperatures (T_min, T_max and T_a, respectively) and precipitation, were obtained from Zenodo in the Network Common Data Form (NetCDF; https://doi.org/10.5281/zenodo.3114194, for precipitation, Peng, 2019a; https://doi.org/10.5281/zenodo.3185722, for air temperature, Peng, 2019b). The data were spatially downscaled from the 0.5^∘ Climatic Research Unit time series data, with the 0.5^′ climatology data set of WorldClim, using delta spatial downscaling. Peng et al. (2019) evaluated this data set using observations collected from 1951 to 2016 by 496 meteorological stations across China, and the average values of the coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE), for the monthly mean temperature at all meteorological stations are 0.996, 0.820, 0.969 and 0.981 ^∘C, respectively, and for the monthly precipitation are 0.863, 13.269, 21.941 and 0.808 mm, respectively. Detailed information on the data set was given by Peng et al. (2019). Although the original data set covers the 1901 to 2017 period, we selected 1951 to 2017 as the study period, as only data from after 1951 have been validated by observations from meteorological stations (Table 1).

Table 1Data used in the snowmelt model.

^* Minimum, maximum and mean temperatures. Note: CMA – China Meteorological Administration; NTPDC – National Tibetan Plateau Data Center; NCDDC – National Cryosphere Desert Data Center.

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2.2.2 Data sets for model parameterization

The observational air temperature data used to determine positive degree–day (PDD) parameters were collected from 824 meteorological stations (Fig. 1b) in the 1951–2017 period and were provided by the China Meteorological Administration (http://data.cma.cn/, last access: 27 December 2019). The positive degree–day is defined as the cumulative temperature above 0 ^∘C over a given period (Wake and Marshall, 2017), and in this study, it was the monthly positive accumulated temperature.

The observational snow density data used to determine degree–day factors (DDFs) were collected from 417 meteorological stations in China (Fig. 1c) during the 1999–2008 period and were provided by the China Meteorological Administration.

The threshold temperature parameters for determining the precipitation types at meteorological stations (Fig. S2) were obtained from Han et al. (2010), who derived threshold temperature parameters using air temperature and precipitation data from 485 meteorological stations in China from 1961 to 1979. The threshold temperatures of each calculated cell were interpolated using the parameters from the meteorological stations via the inverse distance weighting (IDW) method.

2.2.3 Data sets for model evaluation

The observational snowfall (snow depth) data used to validate the model were collected from 475 (557) meteorological stations in China (Fig. 1d (1a)) during the 1961–1979 (1951–2009) period and were provided by the China Meteorological Administration (Table 1). Because of the scarce data or the very low snowfall (shallow snow) at some stations, snowfall (snow depth) observations from 457 (264) stations were selected to verify the snowfall (snow depth) from the model.

A long time series of daily snow depth derived from passive microwave remote sensing data (1979–2018) was obtained from the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn, last access: 26 November 2019; Che and Dai, 2015; Table 1). The data set covers the entire land surface of China, with a spatial resolution of 0.25^∘. Detailed information on the data set sources and product processes can be found in Che et al. (2008) and Dai et al. (2012). This data set has been widely utilized in climatic and hydrological research in China (e.g., Liu et al., 2020; Wu et al., 2021; Zhu et al., 2021). The spatial resolution of this data set is significantly different from that of the air temperature and precipitation data used in this study. The snow cover extent measures generated from this data set were used to validate the model.

Additionally, a long time series of a daily snow water equivalent data set derived from passive microwave remote sensing data (1980–2020) was provided by the National Cryosphere Desert Data Center (https://www.ncdc.ac.cn, last access: 9 April 2021; Jiang et al., 2020; Table 1). The snow water equivalent data set was produced from the passive microwave remote sensing data using the mixed-pixel method. The data set covers the entire land surface of China, with a spatial resolution of 25 km. Detailed information on the data set sources and product processes are given by Jiang et al. (2014) and Yang et al. (2019).

2.2.4 Climate projections and downscaling

In total, five CMIP5 models under three different representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5), namely GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M, were selected to predict the future snowmelt changes in China. The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) selected these five CMIP5 models to span the space of global mean temperature change and relative precipitation changes (Warszawski et al., 2014). The aforementioned climate projections have been bias-corrected and downscaled to a grid with a resolution of 0.5^∘ by ISIMIP (Hempel et al., 2013) and widely utilized in climate change research in China (e.g., Li et al., 2016; Yuan et al., 2017; Chang et al., 2020). In this study, we use the delta downscaling method to determine the monthly future meteorological data (2006–2099) based on the high spatial resolution temperature and precipitation data set and the simulations of the five CMIP5 models during the historical period (1951–2005). Detailed information on the delta method was given in Immerzeel et al. (2012) and Zhao et al. (2019). The projected precipitation and temperature changes in China and the three main stable snow cover regions can be seen in Figs. S3 and S4, respectively.

Figure 2The relationship between the observed monthly accumulated positive air temperature (PDD) and mean air temperature (T_a) (a) and the scatterplot between the calculated and observed monthly PDD (b) at meteorological stations in the mountain plateau zone of China. R² is the coefficient of determination, MAE is the mean absolute error (degrees Celsius), RMSE is the root mean square error (degrees Celsius) and NSE is the Nash–Sutcliffe efficiency.

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3.1.1 Snowfall

The determination of the precipitation types is the first crucial step, and the distinction between rainfall and snowfall is based on the assumption that precipitation falls either as rain, as snow or as a mix, depending on two threshold temperature parameters, as follows:

where P_snow and P are the monthly snowfall and precipitation (millimeters), respectively. T_a is the monthly mean temperature (degrees Celsius). T_rain and T_snow are the threshold temperature (degrees Celsius) for rainfall and snowfall, respectively.

3.1.2 DDF

DDF is the key parameter of the temperature index snowmelt model, which depends on the snow density and varies with space and time. We used the following the empirical equation to determine the DDF after Rajkumari et al. (2019):

where ρ_s and ρ_w are the density of snow and water (grams per cubic centimeter; hereafter g cm⁻³), respectively. The observed snow density is variable and the density of water is constant. DDF is the degree–day factor ($\mathrm{mm}({}^{\circ }\mathrm{C}{)}^{-\mathrm{1}}{\mathrm{d}}^{-\mathrm{1}}$). For taiga regions (Fig. 1c), the equation was corrected as follows:

The DDF values at the meteorological stations were calculated for each month and were then interpolated to each calculated cell.

3.1.3 PDD

Based on the daily mean air temperatures from 1960 to 2018 at meteorological stations in China, Liu et al. (2021) proposed a simple method to calculate the monthly PDD according to the monthly mean temperature, as follows:

where T₁ and T₂ are threshold temperatures (degrees Celsius), and a, b and c are empirical parameters.

First, T₁, T₂, a, b and c were calculated according to the measured data from meteorological stations in the four different climate zones (Fig. 1b) and were then interpolated to each calculated cell. Table 2 shows the different threshold temperatures and empirical parameters in the four climate zones, and Fig. 2 shows the relationship between the measured monthly PDD and the mean temperature at meteorological stations and the relationship between the calculated and measured monthly PDD, taking the MPZ as an example. A statistical analysis of the calculated monthly PDD against observed PDD at 824 meteorological stations shows the excellent performance at almost all of the stations (Fig. S5). Statistical analysis shows that Eq. (6) can adequately calculate the monthly PDD (Table 2).

Table 2The parameters required for the calculation of the monthly accumulated positive air temperature (PDD) and the statistical analysis between the calculated and measured monthly PDD in four different climatic zones of China.

MPZ – mountain plateau zone; TMZ – temperate monsoon zone; TCZ – temperate continental zone; SMZ – subtropical monsoon zone; T₁, T₂, a, b and c – parameters in Eq. (6); R² – coefficient of determination; MAE – mean absolute error (degrees Celsius); RMSE – root mean square error (degrees Celsius); NSE – Nash–Sutcliffe efficiency.

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3.1.4 Snow sublimation

As a loss of water from the snowpack to the atmosphere, snow sublimation is difficult to quantify by measurement or modeling (Stigter et al., 2018). The bulk aerodynamic method and aerodynamic profile methods are the common methods to calculate snow sublimation (Svoma, 2016). Some researchers used evapotranspiration equations (e.g., the Penman–Monteith method) to estimate sublimation (Stigter et al., 2018). In general, these methods require accurate meteorological data and are very difficult to scale up from the microscale to the macroscale (Svoma, 2016). Many studies have reported the ratio of snow sublimation to snowfall (e.g., Zhang et al., 2008; Zhu et al., 2014; Sexstone et al., 2018). Therefore, considering the limited data availability and the monthly scale of this study, the following simple equation was used to estimate the monthly snow sublimation:

where k⋅S_acc and PET are the amount of snow accumulation available for sublimation and the potential sublimation (millimeters), respectively. The empirical parameter k was set according to studies reporting the ratio of snow sublimation to snowfall in China and surrounding areas (Table S1 and Fig. S6), as shown in Table 3.

Table 3The values of the empirical parameter k used to estimate snow sublimation (Eq. 7) for different climate zones and different snow types.

MPZ – mountain plateau zone; TMZ – temperate monsoon zone; TCZ – temperate continental zone; SMZ – subtropical monsoon zone.

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PET was calculated by the Hargreaves–Samani method, as follows (Hargreaves and Samani, 1985):

where T_a, T_max and T_min are the monthly average temperature, maximum temperature and minimum temperature (degrees Celsius), respectively; R_a is the extraterrestrial radiation (megajoules per square meter per month; hereafter $\mathrm{MJ}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{month}}^{-\mathrm{1}}$), which is a function of the latitude and the Julian day, and λ is the latent heat of vaporization (megajoules per kilogram; hereafter MJ kg⁻¹).

4.1.1 Snowfall validation using observational data from meteorological stations

Figure 3 shows the statistical criteria between the calculated and observed snowfall at meteorological stations in China from 1961 to 1979. The MAE and RMSE vary greatly between stations due to the huge difference in the amount of snowfall at each station. The ratios of MAE and RMSE to monthly mean snowfall ($\text{MAE}/\text{Mean}$ and $\text{RMSE}/\text{Mean}$, respectively) were selected to analyze the model performance. Of the 457 stations, 315 (68.9 %) have R²>0.4, 263 (57.5 %) have $\text{MAE}/\text{Mean}<\mathrm{1}$, 274 (60.0 %) have $\text{RMSE}/\text{Mean}<\mathrm{1}$, and 353 (77.2 %) have NSE>0. Among China's three main stable snow cover regions, the most accurate snowfall simulation is obtained for Northeast China, followed by North Xinjiang and then the Tibetan Plateau.

Figure 3Statistical criteria of the calculated snowfall against observed snowfall at 457 meteorological stations in China. Panel (a) shows the R² (coefficient of determination). Panel (b) shows $\text{MAE}/\text{Mean}$, where MAE is the mean absolute error, and Mean is the monthly mean snowfall. Panel (c) shows $\text{RMSE}/\text{Mean}$, where RMSE is the root mean square error. Panel (d) shows the NSE (Nash–Sutcliffe efficiency).

4.1.2 Snow depth validation using observational data from meteorological stations

The monthly snow depth in each grid was calculated from the snow accumulation and snow density. As the snow depth was observed at meteorological stations daily, the snow depth observation on the last day of each month was selected to verify the model's snow depth output. Figure 4 shows the statistical analyses of the calculated and observed snow depths in China from 1951 to 2009. Similar to snowfall validation, besides R² and NSE, the ratios of MAE and RMSE to the mean snow depth ($\text{MAE}/\text{Mean}$ and $\text{RMSE}/\text{Mean}$, respectively) were selected to analyze the model performance. Of the 264 stations, 108 (40.9 %) have R²>0.2, 221 (83.7 %) have $\text{MAE}/\text{Mean}<\mathrm{2}$, 52 (19.7 %) have $\text{RMSE}/\text{Mean}<\mathrm{2}$, and 105 (39.8 %) have NSE>0. Better snow depth simulations are obtained in regions with larger snow depths, such as Northeast China and North Xinjiang.

Figure 4Statistical criteria of the calculated snow depth against observed snow depth at 264 meteorological stations in China. Panel (a) shows the R² (coefficient of determination). Panel (b) shows $\text{MAE}/\text{Mean}$, where MAE is the mean absolute error, and Mean is the monthly mean snow depth. Panel (c) shows $\text{RMSE}/\text{Mean}$, where RMSE is the root mean square error. Panel (d) shows the NSE (Nash–Sutcliffe efficiency).

4.1.3 Snow cover extent validation using the data set derived from passive microwave remote sensing

As the extent of the snow cover derived from remote sensing has a daily timescale and the snowmelt model has a monthly timescale, the remote sensing data on the last day of each month were selected to verify the model's snow cover extent output. The mean monthly snow cover extent in China output by the model and the information derived from passive microwave remote sensing from 1979 to 2017 are 1.74×10⁶ and 1.62×10⁶ km², respectively. The R², MAE, RMSE and NSE between the snow cover extent output by the model against the data set derived from remote sensing in China from 1979 to 2017 are 0.93, 0.45×10⁶, 0.64×10⁶ km² and 0.89, respectively. Among the three main stable snow cover regions, the R², MAE, RMSE and NSE are, respectively, 0.81, 0.06×10⁶, 0.09×10⁶ km² and 0.76, for Northern Xinjiang, 0.93, 0.13×10⁶, 0.21×10⁶ km² and 0.87 for Northeast China, and 0.90, 0.32×10⁶, 0.40×10⁶ km² and 0.81 for the Tibetan Plateau (Fig. 5).

Figure 5Scatterplots of the snow cover extent output by the model and information derived from remote sensing in China and its three main stable snow cover regions. The red and blue solid lines are the linear fit and the 1:1 line, respectively.

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4.1.4 Snow water equivalent validation using the data set derived from passive microwave remote sensing

The snow water equivalent derived from passive microwave remote sensing on the last day of each month was used to verify the snow accumulation output by the model. As the spatial resolution of the snow water equivalent data set is 25 km, and the grid scale of the model is about 1 km, to facilitate comparison, they were uniformly converted into water equivalent units of cubic meters for different regions. The mean monthly snow accumulation in China output by the model from 1980 to 2017 is 2.55×10¹⁰ m³, while the mean snow water equivalent derived from passive microwave remote on the last day of each month is 1.47×10¹⁰ m³. The R², MAE, RMSE and NSE between the snow accumulation output by the model against snow water equivalent data set in China from 1980 to 2017 are 0.62, 1.27×10¹⁰, 1.27×10¹⁰ m³ and 0.80, respectively. The R², MAE, RMSE and NSE for the three main stable snow cover regions are shown in Fig. 6. A large number of glaciers are distributed in the Tibetan Plateau, many of which may not be recorded in the remote-sensing-based snow water equivalent data set because its spatial resolution of about 25 km is larger than the width of most glaciers. This can explain the fact that a snow water equivalent of 0 is observed in some months in this region. However, the spatial scale of the model is about 1 km, meaning that the snow accumulation in the glacier areas is always detected. This can explain the observation that, in the Tibetan Plateau, the modeled snow water equivalent is higher than the remote-sensing-based values. When the snow accumulation in the glacier areas was not considered, the model performance improved in both the Tibetan Plateau and the whole of China (MAE and RMSE decreased and NSE increased; see Fig. S7).

Figure 6Scatterplots of the snow water equivalent output by the model and derived from remote sensing in China and its three main stable snow cover regions. The red and blue solid lines are the linear fit and the 1:1 line, respectively.

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4.2.1 Spatial distribution

The mean annual snowmelt in China from 1951 to 2017 is about 2.41×10¹¹ m³ yr⁻¹. The three main stable snow cover regions account for about 72.62 % of the total snowmelt in China, with the mean annual snowmelt in Northern Xinjiang, Northeast China and the Tibetan Plateau being about 0.18×10¹¹, 0.42×10¹¹ and 1.15×10¹¹ m³ yr⁻¹, respectively. Southeast China (mainly the Huaihe River basin and the lower reaches of the Yangtze River) also has high snowmelt due to heavy snowfall (Fig. 7a). The areas with the lowest snowmelt are mainly distributed in the arid region of Northwest China, because of this region's low precipitation, and in the humid region of South China, because of this region's high air temperature and low snowfall.

Figure 7Spatial distribution of mean annual snowmelt (a), the month of maximum snowmelt (b), trends in annual snowmelt based on the Mann–Kendall method (c) and Sen's slope of the annual snowmelt in China during the 1951–2017 period.

Figure 8Spatial distribution of the mean monthly snowmelt (millimeters) for 12 months in China during the 1951–2017 period.

The spatial pattern of snowmelt differs considerably during the year (Fig. 8). In the three stable snow cover regions, in winter, the air temperature is low, and there is little snowmelt. The winter snowmelt mainly occurs in South China. In spring, North Xinjiang and Northeast China are the main regions of snowmelt in China. With further warming, the Tibetan Plateau becomes the main region of snowmelt until May. In summer, there is no snowfall in most of China, and snowmelt occurs only in the high mountains of the Tibetan Plateau and the Tianshan mountains.

Figure 9Interannual variability in the mean annual snowmelt in China and its three main stable snow cover regions from 1951 to 2017.

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The month of maximum snowmelt differs between regions (Fig. 7b). In Northern Xinjiang and Northeast China, due to the warming in spring, the maximum monthly snowmelt generally occurs in March, April or May. Meanwhile, in North and Southeast China, which are ephemeral snow regions where the snow melts quickly after falling, the maximum snowmelt generally occurs in the month with the largest snowfall (which occurs in winter). Because of the Tibetan Plateau's complex terrain and varied climate, the months of maximum snowmelt vary greatly across this region. In the southeastern part of the Tibetan Plateau, the month of maximum snowmelt occurs during the winter snowfall period because of the warm and humid climate. In the area between the Himalayas and the Gangdise Mountains, the Qaidam Basin and other colder regions (Fig. S8), the month of maximum snowmelt generally occurs in spring. Meanwhile, in the Qiangtang plateau, which has a drier climate, most snowfall occurs in summer, and the month of maximum snowmelt also occurs in this season. There are many high-elevation mountains in the Tibetan Plateau, with air temperatures above 0 ^∘C for only a few days in the summer, and the month of maximum snowmelt in those high elevations areas also occurs in summer.

4.2.2 Temporal variations

From 1951 to 2017, the annual snowmelt increased significantly in some areas of Northern Xinjiang, although it did not increase significantly in Northern Xinjiang as a whole. In Northeast China, although the central and eastern regions showed a significant increasing trend, the southwestern regions showed a decreasing trend, leading to a slight decreasing trend (although not a significant trend) in the whole of Northeast China. Annual snowmelt increased significantly in most parts of the Tibetan Plateau, leading to a significant increasing trend in the Tibetan Plateau as a whole. In Southeast China, the annual snowfall decreased due to climate warming, and snowmelt also decreased significantly in this region, leading to a decreasing trend (although not a significant trend) of annual snowmelt in China (Figs. 7c and d and 9). From 1951 to 2017, the linear trends of annual snowmelt in China, Northern Xinjiang, Northeast China and the Tibetan Plateau are $-\mathrm{2.7}\times {\mathrm{10}}^{\mathrm{9}}$, 0.2×10⁹, $-\mathrm{0.3}\times {\mathrm{10}}^{\mathrm{9}}$ and 1.4×10⁹ m³ per decade, respectively.

The temporal trend of snowmelt in each month from 1951 to 2017 varies across China (Figs. S9 and S10). In Northern Xinjiang and Northeast China, snowmelt increased significantly in March but decreased significantly in April. This might imply that the warming of these two regions led to the earlier onset of snowmelt. Regarding seasonal changes between 1951 and 2017, at the Tibetan Plateau, spring snowmelt increased significantly, whereas summer snowmelt decreased significantly. This may be due to the warming in spring and the reduction of snowfall in summer. In Southeast China, snowmelt experienced a decreasing trend in almost all months because of the reduction in snowfall due to climate warming.

4.3.1 Spatial distribution

Of the 210 third-level basins in China, only nine small basins near the tropical monsoon region have no snowmelt runoff from 1951 to 2017 (as snowmelt was not calculated in Taiwan, it was assumed that there was no snowmelt there). In West China, which contains two main stable snow cover regions – Northern Xinjiang and the Tibetan Plateau – the mean annual snowmelt runoff ratios are more than 10 % in all basins, except those in the Taklimakan Desert. In the basins in North and Northeast China, the snowmelt runoff ratios are generally more than 5 %, whereas, due to heavy rainfall, the snowmelt runoff ratios in basins in South China are generally less than 2 % (Fig. 10a).

Figure 10Spatial distribution of the mean annual snowmelt runoff ratio (percent) (a), and Sen's slope of the annual snowmelt runoff ratio (percent per year; hereafter % yr⁻¹) (b) in the third-level basins in China during the 1951–2017 period.

Figure 11Spatial distribution of the monthly mean snowmelt runoff ratio (percent) for 12 months in the third-level basins in China during the 1951–2017 period.

The monthly snowmelt runoff ratio also shows large spatial differences (Fig. 11). In the cold months of November, December, January and February, the snowmelt runoff ratios in the basins of Central and North China are over 30 % due to the precipitation being dominated by snowfall because of the extremely low temperatures in the cold months, and there is no snowmelt or rainfall in some basins of the three stable snow cover regions. In March, although the rainfall increased, the snowmelt from the snow that accumulated in winter also increased, and the snowmelt runoff ratios are still relatively high in most basins of North China. In April, the snowmelt runoff ratios begin to decline in most basins, and basins with snowmelt runoff ratios greater than 30 % are mainly located in the three stable snow cover regions. In May and June, the snowmelt runoff ratios further decrease, dropping to zero in most of South, East, and Northeast China; however, they are still more than 30 % in some basins in Northern Xinjiang and the Tibetan Plateau. In July and August, there is no snowmelt runoff in any basins, except for some in Xinjiang and the Tibetan Plateau, and almost no basins have a snowmelt runoff ratio of more than 30 %. In September and October, snowfall and snowmelt gradually increase, leading to a gradual increase in the snowmelt runoff ratio in many basins.

4.3.2 Temporal variations

From 1951 to 2017, the Sen's slope in third-level basins shows that the annual snowmelt runoff ratio decreased in most basins (Fig. 10b). Additionally, the Mann–Kendall test shows that the basins with a significant decreasing trend of the snowmelt runoff ratio are mainly distributed in central Inner Mongolia, the southern slope of the Tianshan Mountains and South China (Fig. S11). The basins with an increased snowmelt runoff ratio are mainly distributed in the southeastern part of the Tibetan Plateau, the Heihe River basin in the Qilian Mountains, the Gurbantünggüt Desert and Wulungu River in Northern Xinjiang, and the Songhua River basin in Northeast China. Among these basins, the Mann–Kendall test shows that only four basins had significant increasing trends, i.e., the source region of the Yellow River and three subbasins of the Songhua River.

The temporal trend in the snowmelt runoff ratio from 1951 to 2017 shows spatial variations in China in every month (Figs. S12 and S13). In the third-level basins in Central and East China, the snowmelt runoff ratio decreased in almost every month. In December, January and February, the snowmelt runoff ratio decreased significantly in the subbasins of the Huaihe River and the middle and lower reaches of the Yangtze River. In March, the snowmelt runoff ratio decreased mainly in the middle reaches of the Yellow River and in Northwest China. In April and May, the snowmelt runoff ratios decreased significantly in Northern Xinjiang and Northeast China, and in June, July and August, they decreased significantly in the Tibetan Plateau, Tianshan mountains and Altai Mountains. In September, the snowmelt runoff ratios decreased in only a few basins, mainly in the Tianshan mountains and at the edge of the Tibetan Plateau. In October, the snowmelt runoff ratios decreased significantly, mainly in Northern Xinjiang and central Inner Mongolia in October, and in November, they decreased significantly in Southern Xinjiang and the middle reaches of the Yellow River. In some months, the snowmelt runoff ratio increased in a few basins, mainly in the Tibetan Plateau and nearby areas. For example, in May, the snowmelt runoff ratios showed a large increase in the upper reaches of four rivers, namely the Yangtze River, the Nu River, the Lancang River and the Yarlung Zangbo River; however, the Mann–Kendall test shows that these monthly increasing trends in third-level basins are rarely significant (Fig. S13).

4.4.1 Snowmelt

The snowmelt in Northern Xinjiang and Northeast China show a significant decreasing trend from 2006 to 2099 under RCP8.5 but shows no significant changes under RCP2.6 or RCP4.5. The snowmelt in the Tibetan Plateau and the whole of China show significant decreasing trends from 2006 to 2099 under all three RCPs, with the most drastic decline being under RCP8.5 (Fig. 12). The changes in snowmelt in China and its three main stable snow cover regions in the near-future (2011–2040), mid-future (2041–2070), far future (2071–2099) periods under the three RCPs are shown in Table 4. In Northern Xinjiang, the total projected snowmelt in these three future periods under the three RCPs are not very different than the snowmelt in the reference period (1981–2010). The models project that snowmelt will increase in low-elevation arid areas and decrease in the higher-elevation Tianshan and Altai Mountains (Fig. 13). In Northeast China, the total projected snowmelt in different future periods under the three RCPs is lower than in the reference period. The models project that snowmelt will increase in the Greater Khingan Range and the Songliao Plain and decrease in the Lesser Khingan and Changbai mountains. In most areas of the Tibetan Plateau, the models project a large decrease in snowmelt (Fig. 13). Under RCP2.6, RCP4.5 and RCP8.5, the snowmelt in the Tibetan Plateau in the near future (mid-future; far future) is projected to decrease by 16.7 % (20.8 %; 19.2 %), 18.8 % (24.8 %; 26.1 %) and 18.3 % (31.1 %; 44.2 %), respectively, compared to the reference period. Southeast China is another area where snowmelt is projected to decrease to a large degree (Fig. 13). The models project that the total snowmelt in China will decrease in different future periods. Under RCP2.6, RCP4.5 and RCP8.5, the projected decrease in snowmelt in China in the near future (mid-future; far future) is 10.4 % (15.8 %; 13.9 %), 12.0 % (17.9 %; 21.1 %) and 11.7 % (24.8 %; 36.5 %), respectively, compared to the reference period.

Figure 12Projected future changes in snowmelt in China and its three main stable snow cover regions. The historical period is 1951–2017, and RCP is the representative concentration pathway.

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Table 4Changes in mean annual air temperature (T_a; degrees Celsius), precipitation (P; millimeters) and snowmelt (M; ×10¹¹ m³) in China and its three main stable snow cover regions during the reference period (1981–2010), near future (2011–2040), mid-future (2041–2070) and far future (2071–2099) under the three representative concentration pathways (RCPs).

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Figure 13Spatial distributions of differences (millimeters) between the projected mean annual snowmelt and the reference period (1981–2010) for the near future (2011–2040), mid-future (2041–2070) and far future (2071–2099) under RCP2.6, RCP4.5 and RCP8.5 in China.

4.4.2 Snowmelt runoff ratio

Under the three RCPs, the projected mean annual snowmelt runoff ratios in the third-level basins in different future periods are mostly smaller than those in the reference period, except for a few basins in Xinjiang and North China (Fig. 14). In general, the largest decreases in snowmelt runoff ratio in the basins are projected to occur by in far future, followed by the mid-future and near future. The largest decreases are projected under RCP8.5, followed by RCP4.5 and RCP2.6. Among the three main stable snow cover regions, the snowmelt runoff ratios are projected to decrease the most in basins in the Tibetan Plateau, followed by basins in Northern Xinjiang and Northeast China. Under RCP8.5, the projected mean annual snowmelt runoff ratios in the far future are lower than those in the reference period in all basins, except the three basins near the Taklimakan Desert and one basin in central Inner Mongolia. Under RCP8.5, relative to the reference period, the snowmelt runoff ratios in the Tibetan Plateau and Tianshan mountains are projected to decrease by more than 5 % in most basins and by more than 10 % in a few basins in the far future (Fig. 14i).

Figure 14Spatial distributions of differences (percent) between the projected mean annual snowmelt runoff ratio and the reference period (1981–2010) for the near future (2011–2040), mid-future (2041–2070) and far future (2071–2099) under RCP2.6, RCP4.5 and RCP8.5 in China.

5.3.1 Northern Xinjiang

Northern Xinjiang is located in the TCZ, and snowmelt is an important source of fresh water in this region (Chen et al., 2020; Wu et al., 2021). This study shows that between 1951 and 2017 the mean annual snowmelt runoff ratios in the third-level basins in Northern Xinjiang are above 10 % and exceeded 30 % in some basins. Although the annual snowmelt showed a significant increasing trend from 1951 to 2017, the increase was mainly in November. Because of the earlier onset of snowmelt under climate warming, the monthly snowmelt increased in March and decreased significantly in April. The snowmelt runoff ratios in April are generally more than 30 % and also show a significant decreasing trend. March and April are times of plant and crop cultivation, and the water requirements for agricultural irrigation therefore increase sharply in these months. The shift of the snowmelt time may reduce the water that is available for agriculture because of the mismatch between snowmelt and crop growing (Notarnicola, 2020) and, therefore, introduce agricultural risks to the Northern Xinjiang.

5.3.2 Northeast China

In Northeast China, the runoff in March and April is mainly generated from snowmelt. From 1951 to 2017, snowmelt in March increased while that in April decreased significantly. The snowmelt runoff ratios in the basins in Northeast China are generally about 10 %, and the importance of snowmelt as a water resource in this region may be less than in Northern Xinjiang. However, because of the cold and long winter, snow plays a role as a natural reservoir to store water in winter, and melting snow releases water in spring, which can influence agriculture by affecting soil moisture (Qi et al., 2020). Snowmelt is vital for the seasonal water supply in Northeast China, which is an important region for agricultural production in China. Because of the changes in snowmelt documented in this study, changes in water resource allocation in spring should be considered to meet the demands of spring crop planting.

5.3.3 Tibetan Plateau

The Tibetan Plateau is known as the water tower of Asia, since many of the continent's major rivers originate there (e.g., the Yangtze, Yellow, Indus, Mekong, Brahmaputra, Salween and Ganges rivers). The western and northern parts of the Tibetan Plateau are the source of many arid inland rivers such as the Hotan and Heihe rivers. Additionally, the Tibetan Plateau contains arid inland basins (e.g., the Qiangtang inland basin). These waters from the Tibetan Plateau have been sustaining life, agricultural, and industrial water usage for nearly 40 % of the world's population (Xu et al., 2008). Due to its high elevation and low temperature, the mean annual snowmelt runoff ratios in the basins of the Tibetan Plateau are generally greater than 10 %. Snowmelt contributes to runoff every month, and in some months, there is no rainfall to generate runoff. The snowmelt in the Tibetan Plateau showed an increasing trend from 1951 to 2017, and as the temperature is projected to rise further in the future, it is likely that both the snowmelt and snowmelt runoff ratio will decrease in the near future. Other studies have similarly concluded that the snowmelt and snowmelt runoff ratio in the Tibetan Plateau will decline in the future (Qin et al., 2020; Kraaijenbrink et al., 2021). The Tibetan Plateau and its surrounding downstream areas are projected to experience declines in the share of water from snowmelt and will thus require increases in alternative water supplies.

5.3.4 Southeast China

Southeast China is located in the SMZ, where precipitation is relatively high. The snowfall in winter is high, resulting in a large amount of snowmelt in this region. However, due to this region's high precipitation, snowmelt contributes relatively little to its water resources, with the snowmelt runoff ratios being less than 2 % in most basins. Although climate change has caused a significant decrease in snowmelt in this region, and this trend is projected to continue in the future, the impact on the water supply is likely to be much smaller than in other regions of China. The increased frequency and intensity of flooding that is projected to be caused by climate change is a major concern for this region (Qin and Lu, 2014).