Understanding the snow accumulation and melting process is of great significance for the assessment and regulation of water resources and the prevention of meltwater flooding, especially for the semiarid region in the Manas River Basin. However, the lack of long snow measurement time series in this semiarid region prevents a full understanding of the detailed local-scale snow ablation process. Additionally, the modeling of snow accumulation and melting is challenging due to parameter uncertainty. In this study, the snow ablation process in the Manas River Basin was quantitatively explored with long time-series of 3-h measurements of snow depth, snow density and snow water equivalent (SWE) at the Wulanwusu (WLWS), Hanqiazi (HQZ), and Baiyanggou (BYG) sites. This study explored the ability of the Utah energy balance (UEB) snow accumulation and melt model to simulate SWE, energy flux and water loss in the study area. Furthermore, the uncertainty in the ground surface aerodynamic roughness index zos in the UEB model was also analyzed. The results showed that: (
Keywords: Manas River Basin; measurements; local scale; energy balance; snow ablation processes
The Tianshan Mountains are a large, high-elevation mountain range stretching across the central part of Xinjiang, China, and these mountains represent the main area of runoff formation in Xinjiang. This mountain range has complex natural geographic, hydrologic, and climatic characteristics. The winter is cold and long, and snow cover is abundant. Rapid warming in the spring leads to rapid melting of the snow cover in low- and intermediate-elevation areas and the piedmont plain, which can cause snowmelt floods and disrupt or damage traffic, animal husbandry, agricultural facilities, and people's lives and properties [[
Generally, snowmelt models can be divided into degree-day models and energy balance models. Degree-day models serve as typical statistical models and have been extensively applied in research on ice and snow melting [[
In contrast, energy balance models have a better model structure with more complete parameters and error sources that are more readily detected than those in degree-day models. In energy balance models, energy inputs are the factors that drive snowmelt, and these factors mainly include incident and reflected shortwave radiation, incident and reflected longwave radiation, geothermal flux, and sensible and latent heat fluxes. Energy balance models can effectively explain the physical factors within the snow cover, atmospheric boundary conditions, and ground surface status, and thus, these models serve as reliable and accurate methods for simulating the snow accumulation process [[
Additionally, energy balance models can be applied to single points or larger distributed areas [[
Considering the ease of calibration, the UEB model was employed in this study to quantitatively analyze the typical underlying surface and local scalar snow accumulation and ablation processes at different elevation zones in the Manas River Basin based on snow variables, meteorological parameters, and geographical environment data within the basin. The Nash–Sutcliffe model efficiency (NSE), root mean square error (RMSE), and RMSE-observed standard deviation ratio (RSR) were used to quantitatively assess the accuracy of the model based on the measured snow water equivalent (SWE) and snow surface temperature. In addition to obtaining the snow ablation of a single point through observation and simulation, the main objective of this study is to obtain the model parameters and theoretical basis for the regional snow ablation process and to better understand the contribution of snow and glaciers to the water resources of the arid middle Tianshan Mountains using the UEB model by taking advantage of remote sensing and high-resolution climate data products to compensate for the scarcity of ground-based observation data.
The Manas River Basin is located on the northern slope of the Tianshan Mountains in Xinjiang, China. The catchment has an area of approximately 5274 km
Observations and experiments serve as indispensable measures for research on the formation, development and melting of snow cover. In 2010, a set of automatic weather stations and a snow variable measurement system were established at WLWS, HQZ, and BYG, all of which are located in the middle section of the Tianshan basin but differ in their underlying surfaces and elevations [[
In addition, four net radiometers were installed at WLWS to measure air incidence and reflected shortwave radiation and incoming and outgoing longwave radiation. The snow variable measurement system was equipped with real-time measurement sensors for snow depth, SWE and snow surface temperature (Table 2). All sensors were connected to a data recorder (CR1000; Campbell Scientific Inc., Logan, UT, USA). The snow surface temperature was recorded every 1 min, and the snow depth and SWE were recorded every 30 min (however, because of serious battery losses due to low winter temperatures and wire rupture due to damage by rats in spring and summer, limited or anomalous data were acquired at some time points and the acquired snow depth and snow density data were subject to scattered and negative records). Based on the method proposed in the literature [[
Additionally, due to the malfunction of the automatic weather observation instrument, there was data loss in the HQZ and BYG stations, and the missing data were extracted from the China meteorological forcing dataset, which is generated from the Princeton reanalysis data, GLDAS (Global Land Data Assimilation System) Data, GEWEX(Global Energy and Water Cycle Experiment)-Surface Radiation Budget radiation data, and the Tropical Rainfall Measuring Mission (TRMM) precipitation data as the background field, which is combined with conventional meteorological observation data from the China Meteorological Administration [[
The UEB model employed water equivalence W (m), energy content U (kJ m
The temporal evolutions of U and W can be determined based on the following energy and mass balance equation:
(
where the units of all terms are kJ m
(
where the units of all terms are m h
Based on the observations and the energy balance model, the characteristics of the local-scale snow accumulation and ablation processes in the piedmont clinoplain (WLWS), mountain desert and grassland belt (HQZ), and mountain forest belt (BYG) in the Manas River Basin were analyzed. First, the seasonal and interannual features of snow depth, snow density and SWE were analyzed based on the data obtained by the snow variable measurement system. Second, based on model parameters in the literature [[
The measured SWE and snow surface temperature were used for an accuracy assessment of the model outputs. The SWE data were from the snow pillow measurement system, which provided the initial precipitation input for the evolution of the snowfall events and SWE. The characteristics of the energy variables within one snow accumulation year (2014–2015) were analyzed. The similarities and differences among the piedmont clinoplain, mountain desert grassland belt, and mountain forest belt in terms of local-scale energy and its contributions were obtained.
The NSE, RMSE, and RSR were used to compare the simulated values with observations and assess the goodness-of-fit of the model. These indexes are defined as follows:
(
(
(
where
NSE ranges from −∞ to 1. A value close to 1 indicates high quality and high model reliability. According to Andreadis and Lettenmaier [[
Figure 2 shows the observed interannual and seasonal variability of the snow depth, SWE, and snow density at WLWS, HQZ, and BYG during the five snow accumulation periods from 1 November to 31 March of the following year between 2012 and 2017. However, there are missing values for the BYG site after January 2016. Additionally, some values are missing from the HQZ site in February 2016 and in the winter season of 2012–2013 (November–March).
As shown in Figure 2, after the snowfall occurred in the three regions at the beginning of November within one snow accumulation year, the snow depth and SWE increased in a step-like manner, indicating that the changes in the snow variables were not gradual and consistent increases. When there was a snowfall event or temperature increase, the increases or decreases were significant, and the data before and after the process showed a steep increase or decrease. The maximum snow depth occurred in February. Subsequently, the snow depth decreased rapidly, and the snowmelt was mostly completed by the end of March, although noticeable seasonal variations occurred. The snowmelt was accompanied by decreases in snow depth and SWE. Variations in the snow depth occurred continuously under the joint action of snow cover and external meteorological factors. Differences in the melting time and melting ratios were present on short time scales. The rapid decrease in the snow depth did not influence the variations in SWE, and snowmelt occurred during different time periods, even in winter, which demonstrated the snow accumulation features in an arid environment.
From the first snowfall to snow melting and disappearance within a single snow accumulation year, the snow density in the three regions continuously changed. Without new snowfall and snow melting, the density continuously increased under the action of gravity. The density further increased under the actions of settling and wind. The change in snow density is the result of the continuous change in snow grain diameter caused by the densification and metamorphism of the snow cover [[
In different regions within the same snow accumulation year, the snowfall amount increased with increasing elevation. The spatial distribution features regarding the maximum snow depth and SWE showed the following sequence: WLWS < HQZ < BYG. Within the corresponding snow accumulation year, HQZ had the minimum snow density, whereas BYG had the maximum snow density. Within the snow accumulation periods between 2012 and 2017, the average snow depths of WLWS, HQZ, and BYG were 14.8 cm, 24.3 cm, and 21.6 cm, respectively. The average maximum snow depths were 31.1 cm, 46.0 cm, and 48.1 cm, respectively. The average SWE values were 21.8 mm, 30.1 mm, and 38.8 mm, respectively. The maximum SWE values were 54.2 mm, 59.6 mm, and 70.9 mm, respectively. The average snow densities were 151.7 g cm
Within different snow accumulation years in the same region, the snow variables showed noticeable interannual differences. Based on the time series of snow variables after removing outliers, the maximum and average values of snow depth, SWE and snow density from 1 November to 30 April of each year from 2012 to 2017 at the three stations were calculated. The statistical results were as follows. WLWS showed maximum snow depth and SWE values within the 2012–2013 snow accumulation year during the investigation, with maximum values of 34.6 mm and 66.86 mm, respectively. HQZ and BYG showed maximum snow depth and SWE values within the 2014–2015 snow accumulation year during the investigation, with maximum values of 60.57 mm and 92.38 mm at HQZ, respectively, and 52.51 mm and 99.13 mm at BYG, respectively. The average snow density at WLWS, HQZ, and BYG from 2012–2017 were 147.4 g cm
Figure 3 shows the comparative outcomes of SWE (red line) simulated by the UEB model and SWE (black line) measured by snow pillow measurements. As shown in Figure 3, unlike the measured value, the simulation was somewhat delayed, showing postponed melting. The simulated values were generally lower than the measured values during the snow accumulation period, and a general lag phenomenon was observed during the snowmelt period (examples can be found at WLWS and HQZ during the 2014–2015 snow accumulation year). The reason for this phenomenon may be explained as follows. In the snow melting stage, the liquid water content increases, resulting in a noticeable increase in the snow density, whereas the density variations in the parameterization protocol of the model were slow. However, the trend based on the simulation experiment was consistent with that based on the observations, which indicates that snow melting demonstrated remarkable seasonal variations and interannual differences.
The average SWE of the five snow accumulation years (2012–2017) obtained from snow pillow measurements and simulations are summarized in Table 4. In the table, "/" indicates missing data. With regard to the NSE, except for the minimum NSE coefficient of BYG during the 2013–2014 snow accumulation year (NSE = 0.140; "unsatisfactory"), the coefficients of the three regions were all higher than 0.5 (most were higher than 0.6, which indicates "excellent"). These results indicate that the UEB-simulated SWE agreed with the measured data and that the UEB model possessed a satisfactory capacity to simulate the snow accumulation process. For the RSR index, the simulated outcomes during the three snow accumulation years at WLWS were "satisfactory", and those at HQZ during two snow accumulation years were considered "very excellent". However, the simulated outcomes at BYG during the three snow accumulation years varied greatly, with one being "very excellent" and two being "unsatisfactory". The RMSE values were low at WLWS and HQZ (approximately 10 mm), whereas they were high at BYG, which featured the highest actual SWE value in this region. Based on the NSE, RMSE, and RSR indexes, the SWE simulation accuracy varied greatly among the years as well as among the stations. For instance, the simulated values for WLWS in the snowmelt periods of 2014 and 2015 were lower than the measured values, but the degree of agreement between the simulated peak and measured peak was moderate, which accounted for 61% of the maximum measured peak value. Comparisons among stations and years showed that the model could reasonably predict the SWE and snow melting processes at these stations.
The UEB model calculates the snow surface temperature by balancing the energy flux of the snow surface [[
Figure 4 shows the scatter diagrams and time series of the 3-h average measured and simulated snow surface temperature at WLWS, HQZ, and BYG. As shown in the scatter diagrams of Figure 4 (left), the simulated 3-h average snow surface temperatures in the three stations all perform well against the observed snow temperature variable with mean absolute error (MAE) values between 4.69 and 5.40 °C and RMSE values between 6.09 and 6.79 °C. The fitting outcomes of the scatter diagrams show that the correlation coefficients between the measured data and simulated data at WLWS, HQZ, and BYG were 0.71, 0.67, and 0.69, respectively. The values of the three evaluation indexes RMSE, MAE, and R quantitatively show that the degree of agreement between the measured data and simulated data was satisfactory. Figure 5 shows the time series of the measured and simulated data at WLWS, HQZ, and BYG at different time points. The simulation accuracies at 0:00, 3:00, and 6:00 were all satisfactory for the three regions. Although a low simulation accuracy was observed at HQZ for 15:00 and 18:00, the correlation coefficients were both approximately 0.6. The correlation coefficients at BYG for 6:00 and 9:00 were approximately 0.7, and the linear fitting coefficients at WLWS for all time points were greater than 0.9 (Table 5).
Figure 6 and Figure 7 show the time series of energy variables of WLWS, HQZ, and BYG during the 2014–2015 snow accumulation year. As shown in Figure 6 and Figure 7, the local-scale net radiation, absolute sensible heat fluxes and latent heat fluxes, and the sum of energy fluxes into/out of snow for the three typical underlying surfaces during the accumulation period were all noticeably lower than those during the melting period. The snow net radiation presented noticeable daily variations, and the values were positive during the daytime and negative during the nighttime. Thus, the snow absorbed radiation energy while melting in the daytime and released energy while cooling and freezing at night. Net radiation serves as the primary energy source for snow melting. In this study, the contributions of net radiation to different regions formed the following order: piedmont clinoplain < mountain forest vegetation belt < mountain dessert grassland belt, with contribution percentages of 75%, 86%, and 88%, respectively (Table 6).
As shown in Figure 7, the variation in the turbulence flux was manifested by similar fluctuations with comparable magnitudes but opposite directions in the latent and sensible heat fluxes. The higher the wind speed was, the higher the absolute values of latent and sensible heat fluxes. During the melting stage, the sensible heat fluxes were positive, and the latent heat fluxes were negative. During the melting process, net radiation is the most important energy source for snow cover, and latent heat is the primary energy source for sublimation and evaporation of snow cover. According to the contribution percentages of net turbulence, the different regions were ordered as follows: piedmont clinoplain > mountain forest vegetation belt > mountain desert grassland belt (with percentages of 25%, 14%, and 12% at WLWS, BYG, and HQZ, respectively (Table 6).
The snowmelt amount and air temperature are the factors most associated with the snowmelt outflow rate (m/h). The rate showed a strong relationship with the increase in SWE, particularly during the stage where SWE noticeably decreased (Figure 8). Incoming longwave radiation led to a change in the average snow temperature because of an increase in the snow energy content, which promoted snow melting, and only a small portion of the input energy was used for snow runoff generation [[
To validate the accuracy of the outflow model, the water balance in March was calculated in this study. According to the mass conservation principle, the snow outflow and sublimation amount should increase with the decrease in the snow amount. When snowfall occurs, the amount of snow increases. In this study, the snow outflow and sublimation amounts were defined as the amount of loss during the snowmelt process, and the initial SWE and precipitation were defined as the original water amount. WLWS was taken as an example. According to the observed data, the initial SWE of the snow melting process was 0.02289 m, and the precipitation was 0.01500 m. The modeled snowmelt outflow loss was 0.03556 m, and the sublimation loss was 0.00436 m, with a total modeled loss of 0.03992 m. The modeled loss agreed with the measured loss (0.03789 m), which indicates that the water content remained balanced during model establishment and that the outflow model was reliable (Table 7).
The local-scale average monthly air temperatures in the piedmont clinoplain (WLWS), mountain desert grassland belt (HQZ), and mountain forest vegetation belt (BYG) between November 2014 and February 2015 were below 0 °C. During this time, even in winter, several melting periods occurred (Figure 10), which is the typical feature of snow melting in arid environments. In the piedmont clinoplain (WLWS), the first melting period occurred on 9 November, which was due to an increase in air temperature (from 1 to 8 °C) and an increase in wind speed (from 1.4 m·s
First, we performed a preliminary simulation with the reference model parameters of Tarboton et al. [[
The Taylor diagrams in Figure 11 summarize model performance at each site when all parameters but z
(
where
The correspondences between the letters in the diagrams in Figure 11 and the simulated SWE values are as follows: B, SWEz
Unlike z
In this study, the characteristics of the local-scale snow depth, SWE, snow surface temperature and snow density in the piedmont clinoplain, mountain desert grassland belt, and mountain forest vegetation belt of the semiarid Manas River Basin were analyzed using the snow variable measurement system. Additionally, the local-scale snow accumulation and melting processes in the three typical underlying surfaces were simulated using the UEB model. We investigated the skill and utility of the UEB model in terms of its ability to model snow accumulation and ablation, and we also obtained local model parameters, which may be directly applied in regional simulations. The main findings of this study are as follows.
- On the local scale, the variations in snow depth, SWE, and snow density in the piedmont clinoplain, mountain desert grassland belt, and mountain forest vegetation belt show similarities as well as differences. The snow variables in the three typical underlying surfaces above present noticeable seasonal and interannual characteristics. Within a single snow accumulation year, snow depth increases with increasing elevation, and multiple snow melting events occur and are primarily driven by air temperature, a typical feature of snow melting in arid environments. In terms of snow depth, the three typical underlying surfaces exhibit the following order: piedmont clinoplain < mountain desert grassland belt < mountain forest vegetation belt.
- The UEB model is a simple but useful model with only a few data requirements and no (or minimal) calibration and uses a limited number of state variables, which is convenient for spatial applications. Similar to the studies of Tarboton and Luce [[
26 ]] and Wu et al. [[32 ]], our analysis also found that the model is highly sensitive to the air temperature above which all precipitation is rain (Tr) and the surface aerodynamic roughness (zos ), thus, these parameters should be modified when applying the model in different regions. However, at the local catchment scale of the Manas River Basin, consistent parameters were applied to the selected three typical underlying surfaces to model the snow ablation process, and the simulation results were demonstrated to be accurate. Moreover, the UEB model requires incoming radiation fluxes and wind speed, which are not measured at all weather stations, especially those in high-elevation, rugged terrain. Therefore, the sparse meteorological data in the area motivated the development of a methodology for driving the UEB model using globally (especially regionally) available reanalysis data. - According to the simulation accumulation and ablation results during the 2012–2017 snow accumulation years, the minimum NSE values for WLWS, HQZ, and BYG were 0.645, 0.8755, and 0.526, respectively, and the predicted SWE values were mostly consistent with the measured values, indicating that the model could reasonably simulate SWE evolution characteristics in the selected three typical underlying surfaces. The correlation coefficients between the measured snow surface temperature and simulated outcomes at WLWS, HQZ, and BYG were 0.71, 0.67, and 0.69, respectively, and the energy parameter could be used for the characteristic analysis of the surface energy budgets. The net radiation served as the main energy source for the melting of snow layers. The net radiation contribution percentages for WLWS, HQZ, and BYG were 75%, 86%, and 88%, and the net turbulence contribution percentages for the three observed locations were 25%, 14%, and 12%, respectively. The net radiation contribution to snow melting differed regionally from that of the net turbulence: the lowest contribution of the net radiation occurred in the piedmont clinoplain, followed by the mountain desert grassland belt and mountain forest belt, whereas the order for the net turbulence was the opposite.
MAP: Figure 1 Map of the study area and locations of the three automatic snow and weather stations used in this study. Photos are also shown for the Wulanwusu (WLWS), Hanqiazi (HQZ), and Baiyanggou (BYG) stations.
Graph: Figure 2 Time series of snow water equivalent (SWE), snow density, and snow depth for 3-h time steps at the three automatic snow stations for five winter periods from 1 November to 30 April, 2012–2017.
Graph: Figure 3 SWE simulated by the Utah energy balance (UEB) model (red line) for a single 3-h time step compared with the observations (black line) at the three automatic snow stations for the 2012–2017 effective snowmelt periods.
Graph: Figure 4 The scatter diagrams and time series data of the measured (three automatic snow variable measurement systems) and simulated 3-h average snow surface temperatures between 0:00 on 1 November 2014 and 21:00 on 31 March 2015.
Graph: Figure 5 The time series data of the measured (three automatic snow variable measurement systems) and simulated 3-h average snow surface temperatures between 0:00 on 1 November 2014 and 21:00 on 31 March 2015.
Graph: Figure 6 Variation characteristics of the simulated net radiation and measured SWE at 3-h intervals between 0:00 on 1 November 2014 and 21:00 on 31 March 2015.
Graph: Figure 7 Characteristics of the variations in net radiation, sensible heat flux, latent heat flux, and wind speed at 3-h intervals between 0:00 on 1 November 2014 and 21:00 on 31 March 2015.
Graph: Figure 8 Trends in the simulated snowmelt outflow and SWE at 3-h intervals between 0:00 on 1 November 2014 and 21:00 on 31 March 2015.
Graph: water-11-01058-g008b.tif
Graph: Figure 9 Trends in the simulated snowmelt outflow and related energy items at 3-h intervals between 0:00 on 1 November 2014 and 21:00 on 31 March 2015.
Graph: Figure 10 Trends in the simulated cumulative precipitation, evaporation, and melt outflow amount at 3-h intervals between 0:00 on 1 November 2014 and 21:00 on 31 March 2015.
Graph: water-11-01058-g010b.tif
Graph: Figure 11 Taylor diagrams showing statistical comparisons between the observations and simulated SWE at 3-h intervals between 0:00 on 1 November 2014 and 21:00 on 31 March 2015 using different surface aerodynamic roughness (zos) values at the three sites.
Graph: Figure 12 Taylor diagrams showing statistical comparisons between the observations and simulated SWE at 3-h intervals between 0:00 on 1 November 2014 and 21:00 on 31 March 2015 using different air temperatures above which all precipitation is rain (Tr) at the three sites.
Table 1 Site parameters of the three automatic snow and weather stations.
Site Variables Values WLWS HQZ BYG Slope (°) 15.0 62.0 73.1 Aspect (° clockwise from N) 191.3 11.3 63.4 Latitude (°) 44.28 43.93 43.85 Longitude (°) 85.82 86.21 85.98 Elevation (m) 466 1337 1547 Average atmospheric pressure (Pa) 98,530 93,164 94,164 Average winter precipitation (mm) from December 2017 to February 2018 25.62 21.32 21.3
Table 2 Technical specifications of the sensors used at the three automatic snow and weather stations.
Quantity Instrument Type Sensitivity Range Accuracy Resolution Air temperature (T) WUSH-TW100A (−50 °C to +50 °C) 0.1 °C 0.01 °C Relative humidity (RH) DHC2 5 to 100% RH 1% RH ±2% RH (≤80%) Wind direction ZQZ-TF 0 to 360° 3° ±5° Wind speed (u) ZQZ-TF 0 to 60 m/s 0.1 m/s ±0.5 m/s (≤5 m/s) Precipitation SL3-1 0 to 4 mm/min 0.1 mm ±0.4 m (≤10 mm) Geonor T-200 B 0 to 0.05 mm/min 0.1% (FS) 0.1 mm Atmospheric pressure (Pa) DYC1 500 to 1100 hPa 0.1 hPa 0.2 hPa Snow depth SR50A 0.5 to 10.0 m ±1.0 cm 0.25 mm Surface temperature SI-111 Infrared Radiometer (−40 °C to 70 °C) ±0.5 °C 0.1 °C Snow water equivalent Sommer Snow pillow (0, 25bar) 0.25% (FS) 1 mm
Table 3 Model parameters.
Name Values Basis Air temperature above which all precipitation is rain (Tr) 0.3 °C Adjusted in this study Air temperature below which all precipitation is snow (Tsn) –1 °C You et al. [ Emissivity of snow (es) 0.98 Mahat and Tarboton [ Ground heat capacity (Cg) 2.09 kJ kg−1 °C−1 You et al. [ Nominal measurement of height for air temperature and humidity (zms) 2.0 m You et al. [ Surface aerodynamic roughness (zos) 0.01 Adjusted in this study Soil density (rg) 1700 kg m−3 Tarboton et al. [ Snow density (r s) 150 kg m−3 Adjusted in this study Liquid holding capacity of snow (Lc) 0.05 Tarboton et al. [ Snow saturated hydraulic conductivity (Ks) 25 m h−1 Wu et al. [ Visual new snow albedo (avo) 0.89 Wu et al. [ Near-infrared new snow albedo (airo) 0.63 Wu et al. [ Bare ground albedo (Abg) 0.25 You et al. [ Thermally active depth of soil (de) 0.1 m You et al. [ Thermal conductivity of snow (ls) 1 kJ m−1 °C−1 h−1 Mahat and Tarboton [ Thermal conductivity of soil (lg) 4 kJ m−1 °C−1 h−1 Mahat and Tarboton [
Table 4 Model error statistics at the three automatic snow stations.
Station/Season 1 November 2012–30 April 2013 1 November 2013–30 April 2014 1 November 2014–30 April 2015 1 November 2015–30 April 2016 1 November 2016–30 April 2017 WLWS / / 9.24 10.56 5.866 HQZ / 5.186 12.9 / / BYG 27.59 13.4 21.64 / / WLWS / / 0.626 0.663 0.480 HQZ / 0.3024 0.396 / / BYG 0.249 0.921 0.706 / / WLWS / / 0.607 0.559 0.769 HQZ / 0.908 0.843 / / BYG 0.937 0.140 0.501 / /
Table 5 The correlation coefficients between the measured snow surface temperature and simulated values at different time points.
Stations/Time 0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 ALL WLWS 0.973 0.972 0.969 0.962 0.948 0.943 0.940 0.922 0.910 HQZ 0.883 0.880 0.860 0.822 0.836 0.603 0.690 0.874 0.654 BYG 0.840 0.819 0.747 0.778 0.842 0.822 0.856 0.840 0.745
Table 6 Contribution percentages (%) of the net radiation and net turbulence flux to snow mass loss during the snow accumulation period.
Month WLWS BYG HQZ Qe + Qh NetRad Qe + Qh NetRad Qe + Qh NetRad Accumulation November 39.94 60.06 50.82 49.18 40.91 59.09 December 49.39 50.61 49.97 50.03 47.88 52.12 January 51.12 48.88 54.89 45.11 53.03 46.97 February 48.83 51.17 44.31 55.69 48.76 51.24 Melting stage March 24.99 75.01 13.55 86.45 11.89 88.11
Table 7 Comparison between the simulated water loss and measured water loss in March 2015.
Stations BYG HQZ WLWS Obtained water Initial SWE (m) 0.08460 0.08468 0.02289 Precipitation (m) 0.00771 0.00857 0.01500 Sum of these two terms (m) 0.09231 0.09325 0.03789 Lost water Sublimation (m) 0.015402 0.015024 0.00436 Melt (m/h) 0.074499 0.073759 0.03556 Sum of these two terms (m) 0.089901 0.088783 0.03992
Table 8 Different energy component contributions to snow mass loss.
Snow accumulation period Accumulation stage November 65.78 57.80 40.80 10.18 9.17 2.64 41.09 30.15 28.90 December 83.67 76.49 52.20 12.32 10.28 6.07 41.09 30.15 28.90 January 107.25 97.86 62.40 17.46 12.64 9.30 41.09 30.15 28.90 February 129.08 118.67 72.60 22.80 15.39 15.72 41.09 30.15 31.96 Melting stage March 154.91 141.92 87.60 37.82 23.68 20.08 114.85 74.40 67.52 Snow accumulation period Accumulation stage November 1.73 1.90 1.39 84.11 81.56 86.31 −2.86 −4.59 −0.20 December 1.15 1.33 0.88 70.72 69.25 69.14 −14.11 −15.49 −13.96 January 1.70 1.89 1.39 83.97 81.47 86.14 −11.84 −13.14 −11.44 February 1.68 1.78 1.10 75.39 74.33 75.50 −10.46 −11.07 −9.06 Melting stage March 2.26 2.43 1.65 87.19 85.17 89.92 −0.86 −2.14 2.15
Y.L. and P.Z. conceived and designed the experiments; Y.L. and L.N. performed the experiments; Y.L., L.N., and J.X. processed the data and analyzed the experimental results; Y.L. wrote the manuscript; P.Z., X.L., and S.L. reviewed the manuscript and made helpful suggestions; J.X. and Y.L. revised the manuscript.
This study was financially supported by the National Natural Science Foundation of China and Xinjiang Joint Fund (U1703121), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100306), the National Natural Science Foundation of China (41671351), the Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06D336), and the Science and Technology Planning Project of Guangdong Province (2018B020207012 and 2018B020207002).
The authors declare no conflict of interest.
We would like to thank the anonymous reviewers and the editor for providing valuable suggestions and comments, which have greatly improved this manuscript. We are thankful to the science team of NASA for the DEM data (
By Yan Liu; Pu Zhang; Lei Nie; Jianhui Xu; Xinyu Lu and Shuai Li
Reported by Author; Author; Author; Author; Author; Author