Understanding the hydrological processes related to snow
in global mountainous regions under climate change is necessary for
achieving regional water and food security (e.g., the United Nation's
Sustainable Development Goals 2 and 6). However, the impacts of
future snow changes on the hydrological processes in the high mountains of
the “Third Pole” are still largely unclear. In this study, we aimed to project
future snow changes and their impacts on hydrology in the upstream
region of the Salween River (USR) under two shared socioeconomic pathway (SSP) scenarios
(SSP126 and SSP585) using a physically based cryosphere–hydrology model. We
found that the climate would become warmer (0.2
Snow, a key component of the cryosphere, is widely distributed in high mountainous regions around the world, which are particularly sensitive to climate change; thus, it is an important indicator of regional climate change (Nepal et al., 2021; Pulliainen et al., 2020). The snowpack can store a large amount of solid precipitation in the cold season and can melt in the warm season. Accordingly, it not only has a strong effect on the regional hydrological cycle (Huning and AghaKouchak, 2020; Musselman et al., 2021) but also provides abundant water resources for industry, agriculture, and residents in basins as well as supporting about one-sixth of the world's population (Yan et al., 2022; Barnett et al., 2005). In addition, snow cover (SC) affects the radiation balance and the thermal regime of the underlying ground due to the high albedo of snow, thereby changing the regional energy balance and, in turn, affecting regional climate (Jia et al., 2021; You et al., 2020; Henderson et al., 2018; Xiao et al., 2017). Changes in snowfall, snow storage, and snowmelt under climate change would not only change the total annual and seasonal runoff at mountain outlets but would also affect and change mountain glaciers and the availability of water resources in downstream regions (Immerzeel et al., 2020; Li et al., 2019). These factors may increase the frequency, intensity, and range of natural disasters and may further threaten the security of the water supply, flood/drought control, and ecological security in downstream areas (Qin et al., 2020; Biemans et al., 2019). Therefore, an in-depth understanding of the impact of climate change on the snow-related hydrological regime in mountainous areas is crucial to regional ecological protection, water resource management, disaster prevention, and sustainable socioeconomic development (Nepal et al., 2014; Biemans et al., 2019; Yao et al., 2019; Tang et al., 2019; Viviroli et al., 2020; Qi et al., 2020).
Over the last few decades, the Tibetan Plateau (TP) has experienced intense warming, which has exceeded the global warming rate (IPCC, 2019). In addition, precipitation has also exhibited an increasing trend in the central TP and a decreasing trend in the southern and eastern TP under climate warming (Chen et al., 2015). Moreover, several studies have predicted that the TP will continue to warm in the future, accompanied by increased precipitation, especially in the monsoon-controlled regions, with an increased frequency and intensity of extreme events (Panday et al., 2015; Sanjay et al., 2017). These influences not only change the spatiotemporal distribution and magnitude of precipitation but also alter several of the key variables (e.g., solid precipitation) that drive snow occurrence and development as well as the variables (e.g., radiation and temperature) that control snow ablation (Hock et al., 2019). Yao et al. (2019) and Bibi et al. (2018) reported that the melting of the snowpack, especially in low- and mid-elevation areas (because more of the precipitation occurs as rainfall in these regions), has accelerated in recent decades on the TP, which has increased the instability and uncertainty of the inter-annual and seasonal runoff and will cause future changes in the spatiotemporal pattern and availability of water resources in the TP river basins (Tang et al., 2019; Immerzeel et al., 2010; Kraaijenbrink et al., 2021). Previous studies have assessed several snow variables (e.g., snowfall, snow storage, SC, and snowmelt) and the hydrological processes related to snow under climate change on the TP based on in situ observations and land surface snow–hydrology models (Bian et al., 2020; Xu et al., 2017; Barnhart et al., 2016; Su et al., 2016). However, the impact of future snow changes on runoff is still unclear due to a lack of reliable data. Moreover, most of these models did not fully consider the physical processes of snow accumulation and ablation (Wang et al., 2017; Liu et al., 2018). Therefore, a comprehensive cryosphere–hydrological model and high-quality forcing datasets are urgently needed to improve our understanding of snow-related hydrological processes on the TP in order to better support the sustainable development of this region.
To better understand the effect of snow changes on runoff on the TP under climate change, the upstream region of Salween River (USR) was selected as the study area for the present work. The USR is located in the alpine region of the TP and Hengduan Mountains, which has a complex underlying surface and is very sensitive to climate change (Liu et al., 2017; Chen et al., 2020). Moreover, as a transboundary river with great influence, the reasonable utilization and coordination of water resources in the Salween Basin have received a great deal of attention from relevant countries and organizations worldwide (Yao et al., 2012). Previous studies have shown that the complex underlying surface, ecosystem, and hydrological processes of the basin have undergone intense changes in recent years, which have gradually changed the total water resources and runoff within the basin, further affecting the rationing and allocation of water resources and hydropower development (Liu et al., 2017; Fan and He, 2012; Luo et al., 2017; Hong and He, 2019). However, the working conditions in the USR are very difficult due to the complexity of the topography and the harsh environment, which has resulted in an irregular and relatively sparse distribution of national meteorological stations in the basin. In addition, due to a lack of continuous snow and hydrological observations in the basin resulting from strict national data policies, it is difficult to quantify and reveal the mechanisms of snow-related hydrological processes and verify the results of numerical simulations (Wang et al., 2021). Moreover, the precipitation and snow products used in previous studies have low accuracy and poor applicability in this basin and are often insufficient for the analysis of trends due to the short periods of the records. These factors have seriously restricted our understanding of snow-related hydrological processes and the development and utilization of water resources in this basin (Mao et al., 2019; Liu et al., 2016; Ding et al., 2015).
Previous studies have used simple statistical methods to study the variations in the historical runoff in the USR based on observation data from individual sites; the dry season and annual runoff have been found to exhibit an increasing trend as a result of the increase in precipitation and meltwater (glacier and snow), wherein the precipitation contributes the most (Zhang et al., 2007; You et al., 2008; Yao et al., 2012; Cuo et al., 2014; Luo et al., 2016; Liu et al., 2017; Zhang et al., 2019). However, these studies did not separate glacier melt and snowmelt, consider the corresponding physical processes, nor predict future changes in runoff and its components. In addition, Su et al. (2016), Lutz et al. (2014), Zhao et al. (2019), and Khanal et al. (2021) used hydrological models to predict the future runoff on the TP, and they all found that the future runoff would exhibit an increasing trend; however, there are great uncertainties in the meltwater contribution and seasonal variations in these models. This may be due to the different descriptions of the cryospheric processes used in these models and the large differences in the driving datasets used. Moreover, these studies did not consider the intermediate snow change processes, which may lead to a partial understanding of snow and hydrological processes.
The main objective of this study was to simulate the changes in the
snow-related hydrological processes in the USR on the southeastern TP,
China. First, based on meteorological observation data, we evaluated the
performance of four reanalysis precipitation products and selected the most
reliable products. Second, we constructed a distributed
cryosphere–hydrological model of the USR basin, namely the Water and Energy Budget-based Distributed Hydrological Model with improved Snow and Frozen ground processes (WEB-DHM-sf) (Wang et al.,
2009a, b, 2010, 2016, 2017), and evaluated its performance using observed
discharge and remote sensing data (land surface temperature, LST, Moderate
Resolution Imaging Spectroradiometer, MODIS, SC). Third, we used
WEB-DHM-sf driven by a Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) to predict the changes
in the snowfall, snow cover, snow water equivalent (SWE), total snowmelt,
snowmelt runoff, and total runoff during different periods under the following different shared socioeconomic pathway
(SSP) scenarios:
SSP126, which is a combination of the RCP2.6
low-emission scenario and the SSP1 sustainable socioeconomic pathway,
representing low vulnerability, low mitigation pressure, and low
radiative forcing (2.6 W m SSP585, which is a combination of the
RCP8.5 high-emission scenario and the SSP5 fossil fuel development pathway
and is the only SSP that can achieve an
anthropogenic radiative forcing of 8.5 W m
We also further analyzed the impact of the snow
changes on runoff.
The Salween River, also known as the Nujiang River in China, is a
free-flowing international river, and it is also an important strategic
hydropower and water resource reserve area in Southeast Asia (Lu et al.,
2021). It originates from the southern foot of the Tanggula Mountains on the
Tibetan Plateau in China, flows across Myanmar and Thailand from north to
south, and finally flows into the Andaman Sea (He, 2004). In China, its
length and basin areas are 2013 km and 137 800 km
WEB-DHM-sf has been further improved compared with the first version of the Water and Energy Budget-based Distributed biosphere Hydrological Model (WEB-DHM; Wang et al., 2009a, b), which coupled and improved the three-layer energy balance snow module of the Simplified Simple Biosphere 3 (SSib3) model (Shrestha et al., 2010) and the empirical frozen soil parameterization scheme (Wang et al., 2010). The three-layer snow module divides the snow pack (at each model grid) into layers, with snow depths of greater than 5 cm being divided into three layers and shallower depths being regarded as single-layer snow. It considers the energy exchange between the snow layers and the influences of the incidence angle of the solar radiation and the age of the snow on the snow albedo. Therefore, this model can describe the physical processes of snow in detail, including the phase transition, compaction, albedo, temperature, and melt runoff of each layer. Thus, we can obtain the SC, snowfall, SWE, snowmelt, and other variables from the model outputs (Shrestha et al., 2014). The frozen ground processes are characterized by the frozen soil hydrothermal transfer parametrization scheme, in which the thermal conductivity scheme used is the Johansen scheme (Wang et al., 2017). In both snow parameterization schemes, enthalpy is used instead of temperature to establish the energy equation or as a new predictive variable, which reduces the uncertainty when calculating the latent heat released by the changes in the water phase and also enhances the stability of the model (Wang et al., 2017; Song et al., 2020). In this version of the model, glaciers are considered to be snow with a thickness of 100 m (the details of this model have been described by Wang et al., 2016, and Shrestha et al., 2010, 2014). Due to the continuous improvements in recent years, this model can better describe the cryospheric–hydrological processes in the alpine region, and it has been verified to have a good performance in several basins in the TP (Liu et al., 2018; Qi et al., 2019, 2022; Zhong et al., 2020; Wang et al., 2021; Zhou et al., 2022), especially in areas where observations are very scarce. In addition, the outputs of the model can be verified using multisource data, such as in situ observations and satellite remote sensing data. Therefore, this model was used in this study to analyze the impact of the snow changes on the runoff with a temporal resolution of 1 h and a spatial resolution of 5 km.
The meteorological forcing data included the near-surface air temperature,
total precipitation, downward shortwave and longwave radiation, wind speed,
surface pressure, and specific humidity. For the historical period
(1995–2014), except for the precipitation data, the data for the
meteorological variables were obtained from the China Meteorological Forcing
Dataset (CMFD), which has a high spatiotemporal resolution (3 h and
0.1
A digital elevation model (DEM) was used to extract the basin's boundary
and calculate the topographic parameters. The DEM was the National
Aeronautics and Space Administration (NASA) Shuttle Radar Topography Mission
(SRTM) DEM with a spatial resolution of 90 m. The soil type and land use
maps with a spatial resolution of 1 km used to calculate the soil hydraulic
parameters and land use types were obtained from the Food and Agriculture
Organization (FAO, 2003) and the U.S. Geological Survey (USGS) (Fig. 1a, b), respectively. The fraction of photosynthetically active radiation
(FPAR) and the leaf area index (LAI) used to calculate the vegetation dynamics were downloaded from the Global Land Surface Satellite (GLASS)
datasets, both with respective temporal and spatial resolutions of 8 d and 0.05
The daily observed discharge data at the outlet (JYQ) of the USR basin were
obtained from the National Hydrology Almanac of China and were used to
calibrate and validate the hydraulic parameters of the hydrological model
during 1981–1987. There are six meteorological stations within the basin.
Among them, Naqu and Dingqing stations are the international exchange
stations (Fig. 1a). The meteorological observation data used to evaluate
the precipitation products were obtained from the China Meteorological
Administration (CMA). The land surface temperature (LST) data were obtained
from the MODIS and MOD11A2 products, with respective spatial and temporal resolutions
of 1 km and 8 d from 2001 to 2018 (Wan et al., 2014). The SC data were
obtained from an improved Terra–Aqua MODIS snow cover and Randolph Glacier
Inventory 6.0 combined product (MOYDGL06
The evaluation criteria used to evaluate the performance of the hydrological
model outputs (e.g., discharge, LST, and snow cover) mainly included the
Nash–Sutcliffe coefficient (NSE), the Kling–Gupta coefficient (KGE), the correlation
coefficient (CC), mean bias (MB), root-mean-square error (RMSE), and
relative bias (RB). The NSE and KGE can complement each other and make the
evaluation more reasonable (Gupta et al., 2009). The equations used to
calculate these evaluation criteria are as follows:
Simulated and observed
Simulated
First, the model parameters were calibrated using the daily observed
discharge data measured at JYQ station from 1981 to 1983, which mainly
included the following soil hydraulic parameters: the saturated soil
moisture content (
Due to the lack of observed discharge data from 1988 to the present, remote
sensing data (MODIS LST and snow cover) were used to further validate the
performance of the hydrological model at the basin scale. Figure 3 shows the
comparison of the results for the basin-averaged time series between the
simulated and MODIS LST values at daytime (10:30 LT, local time) and nighttime
(22:30 LT) during 2001–2018. The results show that the simulated
LST is in good agreement with the MODIS LST at daytime (
From the perspective of the spatial distribution patterns (Fig. 4), the
simulated multiyear seasonal average LST during the daytime and nighttime
were similar to those of the MODIS LST from 2001 to 2018. Similarly, the
simulated nighttime LST was better than the simulated daytime LST. The
basin-average value of simulated seasonal LST during the nighttime was
closer to the MODIS LST (December–January–February, DJF: MB
The fraction of simulated snow-covered area (FSCA) was also verified using the
MOYDGL06
As can be seen from Figs. 6 and S3, the simulated SC reproduced the spatial
distribution of the seasonal evolution of the SC in the USR basin, and it
also captured the snow ablation (April–August) and accumulation
(September–March) processes well. Moreover, the simulated SC also reflected
the rapid melting of snow after 2 June and the rapid accumulation of snow
after 8 October, but there was a difference on some days – that is, the SC
was overestimated in the high mountains in summer and underestimated in the
low valleys in winter. If the uncertain grids in MOYDGL06
Comparison of the spatial distributions of the land surface temperature (LST) simulation and MODIS data for the USR basin from 2001 to 2018 at daytime (first and second rows) and nighttime (third and fourth rows). From left to right, the columns show winter (DJF), summer (JJA), spring (MAM), and autumn (SON).
Comparison of the simulated fraction of the snow-covered
area (FSCA) and the MOYDGL06
Overall, the verification results show that the simulation results of the hydrological model are reasonable and can be used to simulate the historical and future snow and hydrological processes in the USR basin.
Due to the systematic bias and coarse spatial resolution of the GCM, there
was great uncertainty in using it directly to drive the hydrological model
at the basin scale (Zhang et al., 2016; Liu et al., 2018). Thus, it was
necessary to evaluate these data before conducting the hydrometeorological
analysis. Four GCM datasets with bias adjustment conducted using the delta
method were used as the meteorological forcing data to drive the WEB-DHM-sf
hydrological model; the results are shown in Fig. 7. Although the simulated
values of the single GCM in the wet season were overestimated (GFDL-ESM4 and
IPSL-CM6A-LR) or underestimated (MPI-ESM1-2-HR and MRI-ESM2-0), the daily
simulated discharge of each GCM was relatively consistent with the
inter-annual variations in the observations during 1981–1987 (Fig. 7a).
Moreover, the daily hydrograph curve of the multi-GCM ensemble mean (MEM)
reproduced the discharge at JYQ well, with reasonable NSE (0.61), KGE
(0.77),
As shown in Table S1, the temperature exhibits significant growth
trends under SSP126 (0.2
Comparison of the MODIS 8 d snow cover product (MOYDGL06*) and the simulated 8 d snow cover in the USR basin in 2017; the time interval used is 16 d.
Figure 8 and Table S2 show the relative changes in the annual precipitation
and mean annual temperature in the USR during 1995–2100, with 1995–2014 as
the baseline, under SSP126 and SSP585. Compared with the reference period,
the warming amplitude increases in the near, mid- and long-term
periods, but the rate of increase slows down and does not exceed
2
Comparison of simulated discharge of four GCMs and the
observed discharge at JYQ station during 1981–1987. The red line is the
multi-GCM ensemble mean discharge, and the gray line is the observed data on
The seasonal cycles of the projected temperature and precipitation in the
USR exhibit different changes under different scenarios (Fig. 9). Intense warming occurs in
all of the seasons under SSP126 and SSP585. Compared with the reference
period, the changes in the seasonal warming rates for each period are
relatively small under SSP126, but these changes are greater under
SSP585, with a distinct gradient. The warming rates during each season do
not exceed 2
Relative changes in the annual precipitation and mean annual temperature from 1995 to 2100. The black, blue, and green lines represent the precipitation and temperature during the reference period (1995–2014) and under the CMIP6 SSP585 (2015–2100) and SSP126 (2015–2100) scenarios, respectively. The rectangular shaded areas are the near-term (pink: 2021–2040), midterm (yellow: 2041–2060), and long-term (red: 2081–2100) periods. The shading around the lines represents the interquartile range of the data, and the upper and lower ranges are 95 % and 5 %, respectively.
There is a significant decreasing trend in the annual snowfall and the
snow / precipitation ratio (Snow / Pre) in the USR under all of the SSPs during
1995–2100 (Fig. 10a, b). The rate of decrease in snowfall under SSP585
(
Seasonal changes in the temperature and precipitation in
the reference period, the near term, the midterm, and the long term under
Figure 10c and d show the changes in the monthly snowfall and rainfall at the basin scale. The rainfall mainly occurs from May to October (monsoon season), accounting for more than 70 % of the total annual precipitation, and the changes in rainfall and the pattern of rainfall in the future are similar to those of the total precipitation. However, snowfall exhibits a distinct bimodal pattern, with the first peak appearing in May (accounting for about 21 % of the annual snowfall) and the second occurring in October (accounting for about 14 % of the annual snowfall). Snowfall is projected to decrease by less than 10 % from November to April compared with the reference period, and there will likely be no snowfall in July and August after the midterm. Moreover, the projected Snow / Pre exhibits a consistent decrease in all of the months compared with the reference period (Fig. 10e, f). Further analysis of the seasonal variations in snowfall revealed that snowfall would be the heaviest in spring, and it is projected to decrease by about 30 % (1 %) under SSP585 (SSP126) by the end of the century. The largest reduction in snowfall occurs in the summer and autumn seasons under all of the SSPs, and it is likely to decrease by approximately 85 % (44 %) and 60 % (21 %) in these respective seasons under SSP585 (SSP126) by the end of the century (Table 1). The above results indicate that precipitation in the USR is less likely to occur in the form of snow in the future under climate warming.
Changes and trends in seasonal snowfall in the USR basin during different periods under SSP126 and SSP585 compared to the reference period (1995–2014).
“
Snowfall changes and changes in the snowfall / precipitation ratio (Snow / Pre):
The trends and relative changes in the annual FSCA at the basin scale
under the different SSPs during 1995–2100 are shown in Fig. S4 and Table
S3. The annual FSCA exhibits a significantly decreasing trend at the
basin scale during 1995–2100, and this trend is more obvious under
SSP585 (
Changes in the monthly and seasonal FSCA in the USR basin
during different periods under SSP126 and SSP585 compared to the reference
period:
The results obtained from the analysis of the monthly-scale decadal FSCA from 1995 to the 2090s are shown in Fig. 11a and b. Figure 11 shows that the cycle of accumulation and ablation of the FSCA increases gradually from September to March and decreases rapidly from April to August. Under SSP585, the FSCA is projected to decrease in almost all months. However, the FSCA increases in June–November and decrease in December–April under SSP126. This pattern also occurs for the monthly-scale FSCA during the different periods (Fig. 11c, d). Similarly, the changes in the FSCA are more pronounced under SSP585 than under SSP126. For example, compared with the reference period, the FSCA becomes snow-free from July to September by the end of the century under SSP585.
Change in the monthly fraction of snow-covered area (FSCA) in the different elevation bands and periods under SSP126 and SSP585 compared to the reference period. The shading around the lines represents the interquartile range of the data, and the upper and lower ranges are 95 % and 5 %, respectively.
Changes in the seasonal SWE in the USR basin under SSP126 and SSP585 compared to the reference period (1995–2014).
Figure 11e and f and Table S4 present the change trends in the seasonal FSCA, i.e., in spring (MAM), summer (JJA), autumn (SON), and winter (DJF). The results show that there is a decrease in all of the seasons during 1995–2100, but the decrease is more significant in winter and spring. Compared with the reference period, the FSCA is projected to decrease most (by about 61 % on average) in winter and spring under SSP585. Even under SSP126, the FSCA will decrease by about 15 % on average in winter and spring. However, there are differences between SSP126 and SSP585 in summer and autumn. From the near term to the long term, the FSCA is projected to increase in summer and autumn under SSP126, but this would not offset the large losses in spring and winter. This shows that the reduction in the SC in winter and spring would cause a reduction in the annual SC, which may further lead to a decrease in the snow storage in the basin, thereby affecting the amount of snowmelt during the ablation period.
From the perspective of different elevations, the SC is mainly
distributed at middle–high elevations (
Seasonal changes in the SWE in the USR basin during different periods under SSP126 and SSP585 compared to the reference period.
Further statistical analysis of the monthly-scale FSCA at all elevations
during all of the periods revealed that there is a consistent decrease
in the FSCA at all elevations compared with the reference period (1995–2014),
except in areas with elevations of 4500–5500 m a.s.l. in the near term
under SSP126 (Table S3, Fig. 12). Under all of the SSPs, the FSCA at
middle–high elevations (
Changes in the snowmelt in the USR basin during the
different periods under SSP126 and SSP585 compared to the reference period:
comparison of snowmelt runoff (SR) and total
runoff at the basin outlet under
Figure 13 and Table 2 show the monthly- and seasonal-scale changes in the SWE in the different periods under SSP126 and SSP585. Compared with the reference period, the SWE is projected to decrease by about 39 % in the near term and midterm, and it slightly rebounds in the long term (about 31 %) under SSP126. However, there is a clear decrease in the SWE from the near term to the long term under SSP585, and the SWE decreases by more than 85 % by the end of the century. Similarly, the characteristics of the abovementioned changes are also reflected in the seasonal changes in the SWE (Fig. 13c, d). The SWE in the USR is the highest in spring and the lowest in autumn. Further analysis of the data revealed that the SWE decreases in all of the seasons and all of the periods under all of the SSPs, and the largest decrease occurs in summer and autumn, with a decrease of more than 91 % (30 %) under SSP585 (SSP126) by the end of the century. Furthermore, the peak in the SWE shifts from June to May under SSP585 by the middle of the century, and the SWE is almost zero from July to September. However, this does not occur under SSP126. Moreover, it can be seen that the peak in the SWE after the near term shifts from June to May under SSP585, whereas it remains in June under SSP126, and the peak value of the SWE weakens after the reference period.
Changes in the total runoff (blue line) and snowmelt
runoff (orange line) during the different periods under SSP126
The trends in the annual total runoff, total snowmelt, and snowmelt runoff
during 1995–2100 are shown in Fig. S5. The total runoff increases
under all of the SSPs, and it is faster under SSP585 (4.4 mm yr
Table S5 presents the changes in the annual total runoff, total snowmelt,
and snowmelt runoff from the near term to the long term under SSP126 and
SSP585. In the near term, the total runoff slightly increases under
SSP126 (1.07 %) and SSP585 (1.40 %). In the midterm, the total runoff
increases by twice as much under SSP585 (22.86 %) compared with
SSP126 (10.4 %). By the end of the century, the total runoff is projected
to increase by 112.29 % under SSP585 compared with the reference period
(341.0 mm), whereas there is only a small increase under SSP126
(4.7 %). The total snowmelt consistently decreases from the near term
to the long term under all of the SSPs. The reduction in the snowmelt
remains below 20 % under SSP126, whereas it decreases by 22.5 % and
43.01 % in the midterm and the long term under SSP585, respectively. The
snowmelt runoff increases in the near term and long term and decreases
in the midterm (by
The intra-annual changes in the total runoff are very similar to those of the precipitation, and the hydrograph remains largely consistent in all periods under SSP126 and SSP585, with 60 % of the annual total runoff occurring in summer and the peak flow occurring in July (Fig. 14a–h). There is a very obvious change in the total snowmelt – that is, the peak snowmelt shifts from July to June after the reference period (Fig. 14i–p). The snowmelt runoff in summer is the largest compared with the other seasons, accounting for about 65 %–85 % of the annual snowmelt runoff, and it sharply increases in May, with a peak in June (Fig. 14i–p). From Table 3, compared with the reference period, it can be seen that there is a consistent decrease in the spring and winter total runoff in the future periods under all of the SSPs, except in the long term for SSP585, whereas total runoff increases in summer and autumn in all of the periods. The snowmelt consistently increases in winter and spring in the future periods under all of the SSPs, whereas it markedly decreases in summer and autumn. Similarly, the snowmelt runoff in spring is projected to increase in all of the periods under all of the SSPs, whereas it decreases in summer and autumn, except in the near term and long term under SSP126.
Changes in the seasonal total runoff, snowmelt, and snowmelt runoff in the USR basin under SSP126 and SSP585 compared to the reference period (1995–2014).
Table S6 and Fig. 15 show the contributions of the multiyear average and seasonal snowmelt runoff to the annual total runoff (SR / TR) in all of the periods under SSP126 and SSP585. The SR / TR was about 17.6 % in the reference period, and this value reaches about 19 % by the end of the century under SSP126. However, the SR / TR constantly decreases in all periods under SSP585, reaching about 2 % by the end of this century. Compared with the reference period, the SR / TR in spring was projected to increase in all of the periods, except in the long term under SSP585. Although the SR in summer contributes the most to the TR at the annual scale, the spring SR contributed the most to the spring TR at the seasonal scale, which is more obvious after the midterm under all of the SSPs due to climate warming. (Table S7). At the monthly scale (Fig. 14a–h), the SR / TR was the largest in June during the reference period, while it gradually shifted to May after the near term under SSP585 and SSP126. In short, the abovementioned results indicate that change in the SR under all of the SSPs is not the primary reason for the increase in the annual total runoff, but its contribution cannot be ignored.
According to the climate projections of the two SSPs, the future climate of the USR basin will become warmer and wetter, leading to a continuous reduction in the snowfall and accelerated melting of the SC during 2021–2100. In addition, the reduction in the annual snowfall and SC will be greater under SSP585 than under SSP126. Moreover, it was found that there are inconsistencies in the seasonal fluctuations of the simulated result in the different periods under SSP126 and SSP585. For example, the summer and autumn SC increases in all of the periods under SSP126 compared with the reference period, but it significantly decreases under SSP585. Moreover, a snow-free summer would likely occur in the long term under SSP585. The above phenomena were also reflected in the snow storage, snowmelt, and snowmelt runoff. This is mainly due to the differences in the GCM models – that is, the warming rate of SSP58 is stronger than that of SSP126 during 1995–2100 (Fig. 8). Although SSP126 includes warming in the different periods, it has very stable changes in temperature and precipitation. SSP585, in contrast, is accompanied by strong warming and significant precipitation from the near term to the long term, which prompts a shift from more solid precipitation to liquid precipitation in each season, resulting in dramatic decreases in the snowfall, SC, and snow storage (Figs. 9, 10; Table 1).
In addition, significant differences in the response of the
SC to climate warming are predicted in the different elevation ranges, and it was found
that the SC in the low-elevation regions (
For both SSPs, the increase in the temperature leads to a reduction in
the snow storage over the projected period, resulting in a continued reduction
in the total snowmelt and further causing a reduction in the snowmelt
runoff, particularly in the cold season (Figs. 13, S4, S5). We found that,
in the projected period, the spring and winter snowmelt and snowmelt runoff increase compared with the reference period, especially in May (Fig. S6), Moreover, the reduction in the spring snowfall leads to a
reduction in the amount of snowpack that can be stored in the spring. These
factors are the reasons why the meltwater and runoff in the summer are
greatly reduced during the projected periods. However, the increased
snowmelt in spring would cause an increase in the snowmelt runoff, which
would make up for the reduction in the total runoff in spring to a certain
extent and would play an important role in alleviating the drought before
the monsoon period (Table S7, S8). Although the precipitation
increases in autumn, the amount of snowfall is very limited due to the
influence of the increase in temperature, which results in a decrease
in the snowmelt and snowmelt runoff in autumn during the projected period (a
significant decrease under SSP585 and a weak balance under SSP126) (Table S8, Fig. S6). Hock et al. (2019) and Nepal et al. (2021) pointed out a
similar pattern of snowpack meltwater in alpine regions, and they attributed
this pattern to increased rainfall due to the increase in temperature.
During the projected period, the peak snowmelt in the USR shifts from
July to June, but the pattern of the total runoff differs from that of
the snowmelt. The total runoff still peaks in July (Figs. 14a–d and
15a–d), which is mainly influenced by the monsoon precipitation (Figs. 9
and 10c–d). Thus, the annual hydrological curve remains unchanged in
the future. This finding is consistent with that of Su et al. (2016), who reported that the runoff patterns of rivers in monsoon regions would remain stable in
the future. We also found that the proportion of snowmelt runoff to snowmelt
would continue to decrease. In addition to climate warming, another possible
explanation for this is that most of the snowmelt infiltrates into the soil
and is stored in the snowpack, and it can also evaporate during runoff
according to the calculations of the WEB-DHM-sf model. Under future climate
warming and a continuous increase in precipitation, in view of the very
small glacial area in the basin (
Although there are some differences in the driving data, models, and periods
used in previous studies, the overall trends and patterns in the snow
variables and runoff in the USR are still comparable and consistent. In this
study, it was found that the total runoff exhibited an insignificant
decreasing trend (
The main uncertainties and limitations of this study were as follows. Very limited observed runoff data (for only 6 years) were used in the calibration and validation of the model. The lack of long-term runoff observations may cause uncertainties in the simulation results for the other periods. The SSP245 scenario, which is closer to the current state of development, was not considered here due to data limitations. Although ERA5 precipitation data were better than the other products in terms of the temporal and spatial distributions, most studies have pointed out that this product overestimates the precipitation during the monsoon period in high mountains (Yang et al., 2021; Khanal et al., 2021). The ERA5 product was evaluated at a single point, and the historical forcing data were not corrected. Although this effect could be reduced through model parameter calibration, it would inevitably lead to the overestimation of variables such as snow cover and runoff. For example, Khanal et al. (2021) pointed out that the cold bias in the ERA5 (even after bias correction) is the main cause of the overestimation of the winter snow cover, but an insufficient description of the snow sublimation and other physical mechanisms in the model may also lead to excessive snow cover. In addition, although the climate model data selected in the study can reflect the uncertainty range of all of the GCM datasets, there is still some uncertainty at the basin scale. Moreover, a simple bias correction method was used to conduct a secondary correction of the GCM based on the ERA5, which only ensured the consistency of the relative change trend; it did not improve the accuracy of the future predictions of the meteorological variables, such as the precipitation and temperature frequency distribution and seasonal variations, which may cause some uncertainty in the simulation results (Khanal et al., 2021; Zhao et al., 2019; Su et al., 2016). Therefore, to improve our understanding of the effects of the synergistic changes in the cryospheric components under climate change on the hydrological processes in high mountainous areas, it is necessary to enhance our understanding of the related hydrological processes through in situ experiments and to calibrate more reliable parameters to further improve the physical processes in the model.
In addition, the spatial resolution of the model has an important effect on
the snow hydrological simulation in high mountainous regions with complex
environments (Etchevers et al., 2001). Here, the accuracy of snow
hydrological simulation between the model resolutions of 3 and 5 km was
compared. The NSE,
In this study, we used multisource reanalysis data and four GCMs under two
different SSPs to drive a validated cryosphere–hydrological model
(WEB-DHM-sf) in order to analyze the impact of future snow changes on runoff in the
USR basin. The main conclusions of this study are as follows:
From 1995 to 2100, the annual average temperature in the USR is projected
to significantly increase under SSP126 (0.2 From the perspective of the entire study period, the annual and seasonal
snowfall in the basin will significantly decrease, i.e., by 13 %–16 % in
the near term and 16 %–49 % in the long term. Overall, the snowfall will
also decrease in all of the seasons, with the greatest decrease occurring in
summer and autumn, i.e., by 44 %–85 % and 21 %–59 % in the abovementioned respective seasons by the end of the
century. The decrease in snowfall will directly affect the
changes in the snow cover. The snow cover in the low-elevation areas will
decrease significantly due to the warming, especially in winter and spring.
Melting will also accelerate in the middle- to high-elevation areas due to the
increase in temperature under SSP585, especially in the long term, and the
ablation of snow will shift from May to March. A large amount of snowpack is
melting in the USR, which will further lead to a continuous reduction in snow
storage. Compared with the reference period, the snow storage in the USR in
the future will exhibit negative growth in terms of both annual average
(a During the reference period, the contribution of the snowmelt runoff to the
total runoff was about 17.6 %. Under the large temperature increase
scenario, this contribution will become continuously smaller in the future
and be about 2 % by the end of the century, leading to changes in the
total runoff from a snow- and rain-dominated pattern to a rain-dominated pattern.
Under the SSPs, the snow–rain runoff pattern would be maintained. The annual
total runoff in the USR will increase significantly in the future, which
will also increase the availability of water resources in the basin. At the
seasonal scale, the total runoff will decrease in winter and spring and
increase in summer and autumn, but the total annual hydrograph will remain
unchanged. The increasing peak runoff may increase the risk of flooding in
the future. The snowmelt runoff will significantly decrease, except in the
long term under SSP126, and the meltwater peak will advance to June, with
the largest proportion occurring in May. The increase in the spring
snowmelt will make up for the reduction in the spring total runoff caused
by the reduction in rainfall, thereby ensuring the availability of water
resources in the basin during the growing season in the spring and
alleviating the spring drought to a certain extent. The advance in the
snowmelt will also lead to a decline in snowmelt in summer and change
the pattern of snowmelt runoff in this season. These changes will have some
impacts on the availability of water resources, ecosystem, and agriculture
in high mountainous regions. As such, to reduce the risk of summer rain floods
and spring droughts, it would be necessary to actively adjust the water
resource allocation scheme to adapt to the impacts of climate change.
The data used in this paper and their respective sources are as follows: CMFD and the second glacier inventory dataset of China, version 1.0 (
The supplement related to this article is available online at:
CC and LW designed the research and organized the text. CC analyzed data and wrote the manuscript. JZ, HL, and JZ provided technical guidance. LW, DC, JZ, YW, and TC provided suggestions and reviewed the manuscript.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19070301), the National Natural Science Foundation of China (grant nos. 41988101 and 92047301), and the Second Tibetan Plateau Scientific Expedition and Research Program (STEP; grant no. 2019QZKK020604). We sincerely thank Hylke E. Beck, who generously provided the MSWEP 2.8 precipitation datasets.
This research has been supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19070301), the National Natural Science Foundation of China (grant nos. 41988101 and 92047301), and the Second Tibetan Plateau Scientific Expedition and Research Program (STEP; grant no. 2019QZKK020604).
This paper was edited by Hongkai Gao and reviewed by two anonymous referees.