1. Introduction
Watershed-based hydrological models provide a practical approach to evaluating the water cycle’s components, particularly snowmelt’s contribution to river flow [
1,
2]. One of the challenges in mountainous regions when modeling watershed hydrology and evaluating water balance components is obtaining weather input data, which are generally among the most essential drivers of watershed models [
3]. Unfortunately, observational climate stations are often sparsely located and thus cannot characterize the climate conditions throughout a catchment, particularly if large hydroclimatic gradients exist. Additionally, climate station measurements often do not cover the proposed modeling period, and there may be gaps in the records. In order to solve this issue, the investigation of alternative climate data is essential in mountainous areas.
The applicability of the climate forecast system reanalysis (CFSR), “Asian Precipitation—Highly Resolved Observational Data Integration Toward the Evaluation of Water Resources” (APHRODITE), and Climatic Research Unit (CRU) datasets for hydrological models in water balance components analysis has not been investigated thus far in the UVRB. Similarly, previous studies on the applicability of models to estimate hydrological components in the highlands of Tajikistan (UVRB) in Central Asia have not been conducted. Various hydrological models at the watershed scale have been used for the estimation of water cycle components, including the Hydrologic Engineering Center hydrologic modeling system [
4], MIKE SHE [
5], the soil and water assessment tool [
6], the hydrologic simulation program Fortran [
7], and the snowmelt runoff model [
2]. The SWAT model is internationally recognized as a robust hydrological model and is widely used, including in several basins that have snowmelt-dominated streamflow [
8,
9,
10,
11,
12,
13,
14].
Previous research indicated that the SWAT model is a common tool to assess the water balance components of watersheds. Combinations of CFSR datasets with the SWAT model and observational datasets with the SWAT model were applied to different watersheds in the Blue Nile Basin in Ethiopia to assess water-balance components, particularly actual evapotranspiration [
15]. In most cases, CFSR weather simulations gave similar or lower evaluations than those obtained when using in situ observations in model inputs. Independent observation datasets and CFSR were used in the SWAT model to estimate water-balance components in the Melka Kuntur watershed in Ethiopia [
16]. Analysis of the mean annual water balance demonstrated that higher values of water-balance components were acquired when applying the CFSR datasets to the Melka Kuntur watershed. This may be associated with the relatively high total precipitation in the CFSR dataset for the Melka Kuntur watershed [
16]. Adeogun et al. noted that the SWAT model could be a promising tool for predicting water balance and water output for sustainable water management in Nigeria [
17]. Gupta et al. noted that SWAT is a powerful tool that very effectively evaluated the hydrological components in a study of water balance and river flow in the Sabarmati River Basin in India [
18]. Goswami et al. used the SWAT model and CFSR datasets from 1984 to 2013 in the Narmada River Basin in India and suggested that the SWAT model was able to simulate the water balance components at the basin and sub-basin scales [
19]. Himanshu et al. [
20] concluded that the SWAT model can accurately simulate the hydrology and water balance components of the Ken River Basin in India. Nasiri et al. [
21] applied the SWAT model to the Samalqan Basin in Iran to assess water-balance components. Actual evapotranspiration contributed to the largest water loss from the basin, which was approximately 86%. Nasiri et al. pointed out that the high evapotranspiration rate that was simulated may be related to the vegetation types in the region [
21]. The applicability of the SWAT model for the simulation of water-balance components, particularly surface runoff, has been assessed in the Heihe mountain river basin in northwest China [
22]. The components of the water balance tended to increase, and the total runoff increased by 30.5% between 1964 and 2013. Rising surface runoff accounted for 42.7% of the total increasing runoff [
22]. Pritchard [
23] used a combination of CFSR temperature and APHRODITE rainfall datasets in the SWAT model to simulate water-balance components, in particular the actual evapotranspiration in five Asian river basins, including the Aral, Indus, Ganges, Brahmaputra, and Tarim, and the lakes of Issyk-Kul and Balkhash. Regarding the Aral Sea Basin in Central Asia, Pritchard reported that summer evaporation is approximately equal to summer precipitation [
23].
The snowmelt runoff model (SRM) and SWAT model with conventional weather data were used to carry out a water balance study of the Karnali River Basin in Nepal and to simulate the contribution of snowmelt to river runoff [
2]. Dhami et al. reported that after comparing the results obtained from the SWAT model and the SRM model, it is recommended to use the results obtained from the SWAT model, which is able to control the volume of melting snow compared to the SRM model [
2]. Siderius et al. [
24] calculated the contribution of snowmelt to river runoff in the Ganges River in the Himalayan arc, using APHRODITE data with the SWAT model. The simulation results showed that approximately 1% and 5% could be considered indicative of the actual total annual contribution of snowmelt to total runoff [
24]. Chiphang et al. [
1] used the SWAT model in the mountainous Mago River basin, located in the Eastern Himalayan region of India, from 2006 to 2009 to compute the contribution of snowmelt to streamflow and evapotranspiration changes in the basin. The results showed that the contribution of snowmelt runoff to the annual streamflow of the basin was about 8% [
1]. Another study was conducted to simulate snowmelt using the SWAT model in the Tizinafu River Basin (TRB) in Xinjiang, in Central Asia, from 2013 to 2014 using observational climate data [
25]. Duan et al. found that about 44.7% of the total runoff comes from snowmelt runoff in the TRB [
25].
Climate data are regarded as among the most important data for setting up the SWAT model. Therefore, assessment of the reliability of the most commonly used gridded climate data in SWAT modeling and water-balance analysis has become a popular theme in recent times, particularly in developing and less developed countries [
26,
27,
28]. Malsy et al. [
29] examined the performance of hydrologic modeling using four datasets, including the Global Precipitation Climatology Center (GPCC) Reanalysis product v6, APHRODITE, WATCH forcing data (WFD), and CRU in a hydrological model named “Water Global Assessment and Prognosis 3” (WaterGAP 3). According to Malsy et al., the GPCC and APHRODITE datasets, coupled with the WaterGAP 3 hydrological model, showed better hydrological results than CRU and WFD datasets at the Tuul River Basin and Khovd River Basin in Mongolia in East Asia. Due to the lack of data on the Upper Helmand Basin in Afghanistan, which is a neighboring country to Tajikistan, the SWAT model and the global CRU dataset were applied to create long-term hydrological conditions [
30]. The results showed the good performance of the SWAT model using CRU data for the study area; therefore, the NSE was 0.84 for the calibration period and 0.82 for the validation period [
30]. It is not known if the same results can be generated with a different hydrological model. For instance, Luo et al. [
31] used the SWAT and the MIKE SHE hydrological models to assess their performance in the Hotan River Basin in southwestern Xinjiang, in Central Asia. The results demonstrated that the SWAT model performs better than the MIKE SHE model for the same climate input. Liu et al. used the SWAT model with climate data from the China meteorological assimilation driving datasets (CMADS V1.0) and CFSR in the Yellow River Source Basin, Qinghai–Tibet Plateau [
32]. The APHRODITE dataset with a SWAT model, in the Yarlung Tsangpo–Brahmaputra River Basin (YTBRS) in Southeast Asia, was used for hydrological modeling. The results showed the validity of APHRODITE estimates in driving the hydrological model in the YTBRB [
33]. Tan et al. [
34] assessed the capabilities of the APHRODITE, CFSR, and PERSIANN datasets to model river flow using the SWAT model for the Kelantan River Basin and the Johor River Basin in Malaysia, in Southeast Asia. The combination of APHRODITE precipitation data and CFSR temperature data resulted in the accurate simulation of river flow. Tan et al. recommended the use of APHRODITE precipitation and CFSR temperature data in the modeling of water resources in Malaysia [
34]. Xu et al. [
35] applied a SWAT model with WFD and APHRODITE datasets to the Xiangjiang River Basin (XRB) in China, to simulate river flow. In XRB, APHRODITE data performed better than WFD data, during both calibration and validation periods [
35]. The Tropical Rainfall Measuring Mission (TRMM), National Center for Environmental Prediction (NCEP), Global Precipitation Climatology Project (GPCP), CFSR, and APHRODITE datasets were used to assess the performance of SWAT in the Wunna Basin in India. In the Wunna Basin, APHRODITE datasets can be an alternative source for hydrological modeling as APHRODITE simulations perform much better than TRMM, NCEP, GPCP and CFSR [
36]. Shen et al. used gridded products, including CFSR, APHRODITE, CRU, TRMM, ERA-Interim and MERRA-2, with the J2000 model to analyze the spatiotemporal patterns of water balance and the distribution of runoff components in the glacierized Kaidu Basin in Central Asia. The results showed that APHRODITE and CRU represented annual and seasonal precipitation dynamics similar to the observational results at most climate points [
37]. However, it should be noted that these results are region- and model-dependent. Many studies show that the accuracy of gridded data results varies by region [
38,
39]. Meanwhile, a hydrological model with a different concept and representation of the streamflow procedure may lead to different conclusions.
The present work focuses on modeling mountainous terrain with insufficient observational climate data. The major goal of this study is to investigate alternative climate data sources for improving the performance of distributed hydrological models, to explore options that could substitute existing observational data in data-scarce areas. The second objective was to investigate the performances of grid-based data combinations of precipitation and temperature data from multiple sources in order to understand the status of water resources by simulating water balance components in general in the UVRB in Central Asia.
3. Methodology
In this study, a physical-based, watershed-scale, continuous-time, semi-distributed hydrological model using a SWAT (soil water and assessment tool) was implemented for the evaluation of water availability in various components of the hydrological cycle in the UVRB. The United States Department of Agriculture’s Agricultural Research Service (USDA-ARS) developed the SWAT model; a detailed description of this model can be found in the theoretical documentation [
52]. The SWAT model has been widely used to support water-resource managers and worldwide research dealing with water quality analyses, hydrological assessment, climate and land-use changes, water supplies, non-point-source pollution, soil erosion/sediment transport, and watershed management impact studies in small- to large-scale river basins [
53]. The model does not have any limitations in terms of the river basin areas of study and is compatible with ArcGIS, QGIS, and MapWindow software, as well as providing reliable and useful theoretical documentation that is readily available. Using the ArcGIS version 10.3 interface of SWAT, named ArcSWAT, the UVRB was divided into sub-basins, based on a digital elevation model. Each sub-basin is connected through a stream channel and the model operates by dividing sub-basins into many HRUs (
Figure 3), according to a unique homogenous combination of land cover, soil properties, and terrain features. The model performs a modification of the soil conservation service curve number (SCS-CN) method, which identifies the surface runoff from daily precipitation, land use, the area of the hydrological group and the antecedent moisture content for each HRU [
54,
55].
The UVRB is a mountainous catchment; hence, the observational climate stations in the UVRB are located at lower altitudes. For instance, the Rasht station is located at an elevation of 1316 m, Bustonobod at an elevation of 1964 m, Lakhsh at an elevation of 1998 m, and Dehavz at an elevation of 2561 m. The orographic features of the UVRB mountainous catchment, in terms of temperature and precipitation, led to the splitting of the UVRB into different elevation bands in the SWAT model. In order to simulate the snowmelt in this study, we used a temperature index algorithm employing the elevation band approach [
56,
57]. We weighted the temperature and precipitation elevation band between the climatic station band and the other elevation band (EB) by using the following mathematical equations:
where 1000 serves as the conversion element from meters to kilometers;
is the precipitation in an EB (mm);
is the precipitation recorded at the measurement gauge (mm);
shows the daily maximum temperature of the EB (
;
indicates the daily minimum temperature of the EB (
;
is the daily mean temperature of the (
;
shows the daily maximum temperature recorded at the measurement gauge (
;
indicates the daily minimum temperature recorded at the measurement gauge (
;
shows the daily average temperature recorded at the measurement gauge (
;
is the lapse rate of temperature (
shows the mean elevation in the EB (m);
indicates the elevation at the measurement gauge (m);
is the precipitation lapse rate (mm/km) and
represents the average annual value of the days when precipitation occurred. The EB approach to the SWAT model has been employed in various mountainous catchments across the globe [
58,
59,
60]. As in our previous study, Gulakhmadov et al. presented the hydrological model calibration results they obtained with the SWAT–CUP tool before and after the EB approach. The application of EB had a positive impact on the modeling of river flow in a mountain watershed [
61].
The model was auto-calibrated for sensitive parameters, such as runoff curve number (CN), Manning’s n, and groundwater (GW) parameters (Soil K, Ch_K, Alpha BF, REVAP, ESCO, soil AWC, GW delay, Recharge_DP, Soil Z), based on their rankings. A multiple regression equation was used to identify the sensitive parameters, as follows:
where
shows the value of the objective function;
indicates the parameter of the calibration;
and
represent the regression coefficients; and m indicates the selected parameter number [
62].
The simulation of the hydrological processes by SWAT is carried out on the basis of the water balance equation:
where
shows the initial soil water content on day
(mm H
2O);
is time in days;
shows the amount of precipitation on day
(mm H
2O);
is the amount of surface runoff on day
(mm H
2O);
is the amount of evapotranspiration (ET) on day
(mm H
2O);
is the amount of water entering the vadose zone from the soil profile on day
(mm H
2O);
is the amount of return flow on day
(mm H
2O); and
shows the final soil water content (mm H
2O).
On the basis of the average daily air temperature, the SWAT model divides the precipitation into rain or snow. The user of the model will give a threshold temperature in order to categorize precipitation as rain or snow. The precipitation, as snow, will be modeled and the equivalent water will be supplemented to the snowpack if the average air temperature is lower than the temperature threshold. The precipitation will be modeled in the form of liquid rain if the average daily temperature is higher than the temperature threshold. If additional snow falls, the snowpack will be raised and if snowmelt or sublimation occur, the snowpack will be reduced, and the water accumulation in the snowpack will be given as the snow water component.
The SWAT model calculates the snowmelt as a linear function of the divergence between the mean maximum temperature of the snowpack and the snowmelt threshold temperature or base. The snowmelt on a given day is calculated based on the following equation:
where
represents the melt factor for the day (mm H
2O/day-°C); the fraction of the HRU area covered by snow is
; the temperature of the snowpack is
(°C); the maximum air temperature is
; the base temperature above which snowmelt is allowed is
(°C); and
indicates the amount of snowmelt (mm). Seasonal differences are allowed by the melt factor, with maximum and minimum indices, taking place towards winter and summer solstices:
where
represents the melt factor for 21 June (mm H
2O/day-°C); the melt factor for 21 December is
(mm H
2O/day-°C);
is the day of the year, and the resulting value (
) shows the melt factor for the day (mm H
2O/day-°C).
The evaluation of evapotranspiration (ET) is essential for water-resource management and hydrological research. The studies of previous researchers suggested that it is acceptable to apply PET (potential evapotranspiration) in models and water allocations [
63,
64]. In order to estimate PET, there are three methods given in the SWAT model, including the radiation-based Priestley and Taylor method [
65], the temperature-based Hargreaves method [
66], and the combined Penman–Monteith method [
67,
68]. For the present study, the Hargreaves method depends on inputted climate data, which were selected to determine the potential evapotranspiration in a mountainous catchment. The Hargreaves approach is the most commonly used method; it is based on temperature and is recommended by the FAO. Li et al. compared the results of the Hargreaves and Penman–Monteith methods in the Ganjiang River Basin in Southern China by using two different datasets [
69]. The results of the analysis showed that there is no significant discrepancy between the Hargreaves and Penman-Monteith methods in terms of streamflow simulations with the same spatial scale. The ET was computed as a function of the corrected potential evapotranspiration, soil depth, soil cover, and plants’ water uptake [
52]. Based on each hydrological response unit, the water balance components were simulated, including precipitation partitioning, precipitation interception, evapotranspiration, snowmelt water, the redistribution of soil water content, return flow from shallow aquifers and lateral subsurface flow from the soil profile.
Model Assessment
The model evaluation was carried out based on the Nash–Sutcliffe efficiency (NSE) measure, the coefficient of determination (R
2), and the percentage bias (PBIAS). Model assessment statistics were evaluated using the NSE, R
2, and Kling–Gupta efficiency (KGE) calculations [
70]. In watershed modeling, the NSE, R
2 and KGE are standard regression statistics [
71]. NSE ranges from −∞ to 1, with 1 being the best performance. The degree of the linear relationship between measured data and model output is R
2 and it ranges between 0 and 1. KGE is the goodness-of-fit measure initiated by Gupta et al. [
70], which gives a decomposition of mean squared error and NSE. In hydrological modeling, the KGE statistic value contributes to the analysis of the relative significance of the correlation, variation, and bias [
72]. The model result is more accurate if the KGE output value is closer to 1, and it ranges from −∞ to 1. Moreover, in the model performance, we used the root mean square error (RMSE) observed standard deviation ratio (RSR) and an error-index statistic. The values of NSE > 0.50 and R
2 > 0.60 are considered satisfactory for river discharge on a monthly scale [
71]. The values of KGE > 0.5 and RSR < 0.60 are also considered satisfactory levels [
70,
71]. To assess the strength of the model calibration and uncertainty, two important factors were computed based on the calibration of soil and water assessment tool model (SWAT–CUP) performance, the P-factor and R-factor [
73,
74]. According to Abbaspour et al. [
74], the P-factor describes the percentage of observational data that is covered with a 95% prediction uncertainty (95PPU). It quantifies the model’s capability in terms of catching uncertainties, and its magnitude ranges between 0 and 1, where 1 demonstrates that 100% of the station-recorded variability is captured by 95PPU. The thickness of 95PPU is the value of the R-factor, which presents the ratio of the mean width of the 95PPU band and the standard deviation of measured variability. The model performance is superior if the R-factor value is low. For discharge modeling, in order to compute prediction uncertainty, the studies of Abbaspour et al. [
74] recommended that the value of the P-factor be > 0.7 and the value of the R-factor be < 1.5.
The NSE, the R
2, the PBIAS, the RSR, KGE, and MSE are frequently applied measures in hydrological modeling studies [
71], which are calculated as:
where n is the whole number of sample couples;
is the station-recorded discharge variable;
is the mean of the station-recorded discharge parameters;
is the simulated discharge parameter;
is the mean of the simulated discharge parameters; and
is the
th station-recorded data or simulated data. Moreover,
and
, while
is the linear regression coefficient of the simulated value against station-recorded value,
and
are the averages of the simulated value against the station-recorded value, and
and
are the standard deviations of the simulated value against the station-recorded value [
70].
5. Discussion
To the best of our knowledge, the simulation of the water balance component, particularly snowmelt runoff and its contribution to total runoff in the Vakhsh River Basin, has not been conducted previously. Similarly, an evaluation of the datasets’ ability to simulate the hydrological behavior of the UVRB from 1982 to 2006 has not previously been performed, especially not when using a combination of the APHRODITE_V1101+CFSR, AHPRODITE_V1101+CRU TS3.1, CFSR, and CRU TS3.1 datasets in a hydrological SWAT model. Water-balance elements in a catchment are influenced by climate and the physical characteristics of the basin, such as topography, land use, soil properties, glaciers, and human economic activities. For any analyses related to sustainable water resource management, understanding all the hydrological components is important. Snowmelt from the Vakhsh River is the main source of groundwater recharge and runoff in the dry seasons of many perennial rivers in Tajikistan, supplying fresh water for drinking, irrigation and hydropower generation. For this reason, it is very important to assess the contribution of snowmelt to the total runoff of UVRB for effective water resources management. Moreover, when modeling mountainous watershed hydrology, the most important determinant is the provision of accurate and alternative climate inputs in modeling. The lack of data has a large impact on modeling in mountainous regions. Ordinary climate stations are often scattered sparsely and cannot fully reflect the climatic conditions in the basin, especially in mountainous areas. In addition, the records of climate stations often do not cover the proposed modeling period or contain gaps. To address/mitigate this issue in UVRB CA, we examined the performance of gridded precipitation and temperature data combinations from various sources for simulating river flow, using the SWAT model.
In this study, the simulation of the observational-based datasets performed better than the gridded products in the UVRB for modeling river flow. Our results revealed that among the applied gridded datasets, the use of the CRU TS3.1 datasets for the overall scale (1982–2006) showed higher accuracy in river flow simulations, followed by the APHRODITE_V1101 and CRU TS3.1 combination, the APHRODITE_V1101 and CFSR combination, and the CFSR in the UVRB in Central Asia. Our results are consistent with those of Hajihosseini et al., who used the global CRU and SWAT models in the Upper Helmand Basin (UHB) in Afghanistan, which is a neighboring country to Tajikistan, to simulate the long-term hydrological conditions of the basin. Hajihosseini et al. used the CRU as an alternative data source, due to the lack of observational data at the UHB [
30]. The results revealed the good performance of the SWAT model, using the CRU dataset for the UHB from 1969 to 1979, while NSE was 0.84 for the calibration period and 0.82 for the validation period [
30]. As is similar to the results of our study, CRU datasets were used in the SWAT model to simulate river flows in the African continent over the period of 1968–1995, and the results showed a “good” performance (NSE > 0.6) [
85]. However, our results for the CRU TS3.1 simulation contradicted the results of Malsy et al. [
29], who examined the performance of hydrological modeling using four datasets, the Global Precipitation Climatology Centre (GPCC) Reanalysis product v6, APHRODITE, WATCH forcing data (WFD), and CRU, in a hydrological model (Water Global Assessment and Prognosis 3, WaterGAP 3) for the period from 1976 to 1999. According to Malsy et al., the GPCC and APHRODITE datasets with the WaterGAP 3 hydrological model showed better hydrological results than the CRU and WFD at the Tuul River Basin and Khovd River Basin in Mongolia in East Asia [
29]. This contradiction may be associated with the use of a different hydrological model, a different selected period and a different study area location. It is not known if the same results could be generated in our study area with a different hydrological model. For instance, in the Hotan River Basin in southwestern Xinjiang in Central Asia, from 2004 to 2008, Luo et al. [
31] assessed the performance of the SWAT and MIKE SHE hydrological models. The results showed that the SWAT model performs better (NSE = 0.77) than the MIKE SHE model (NSE = 0.66) for the same climate input. Besides hydrological modeling, the CRU dataset was evaluated for climatological studies in Central Asia by other researchers and their results showed that the CRU dataset is applicable and satisfactory for climatological studies in Central Asia [
86,
87].
Furthermore, our results indicated that a second alternative source for the hydrological simulation of the UVRB with the SWAT model could be the combination of precipitation with APHRODITE_V1101 and maximum/minimum temperature with CRU TS3.1, followed by the combination of APHRODITE_V1101 and CFSR, and CFSR. These results are in agreement with the findings of Tan et al. [
34], who assessed the capabilities of the APHRODITE, CFSR and the “Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks” (PERSIANN) datasets to model river flow using the SWAT model for the Kelantan River Basin (KRB) and the Johor River Basin (JRB) in Malaysia in Southeast Asia, from 1985 to 1999. The combination of APHRODITE precipitation data and CFSR temperatures resulted in an accurate simulation of the river flow of the KRB and JRB. Tan et al. recommend the use of APHRODITE precipitation and CFSR temperature data when modeling the water resources of Malaysia [
34]. Similarly, the APHRODITE dataset and the SWAT model were used for hydrological modeling from 1980 to 1989 in the Brahmaputra River Basin (BRB) and Yarlung Tsangpo River Basin (YTRB), which is the main international river flowing through China, Bhutan, India, and Bangladesh. The results showed the validity of APHRODITE estimates in driving the hydrological model in the YTRB and BRB [
33]. Comparable results were reported by Xu et al. [
35], who applied the SWAT model with APHRODITE and WFD in the Xiangjiang River Basin (XRB) in China to simulate river flow, particularly high flow and low flow. The APHRODITE simulation (NSE = 0.79, NSE = 0.82) performed better than the WFD dataset (NSE 0.69, NSE = 0.71), both during calibration (1991–2005) and validation (2001–2005) periods. WFD modeling leads to more errors in simulating flood events than APHRODITE [
35]. The TRMM, NCEP, GPCP, CFSR and APHRODITE were used to assess the performance (NSE) of SWAT in the Wunna Basin in India [
36]. APHRODITE dataset simulations performed much better (NSE = 0.68) than TRMM, NCEP, GPCP and CFSR, meaning that APHRODITE can be seen as an alternative source for hydrological modeling in the Wunna Basin [
36]. Our results indicated the superiority of the combination of the APHRODITE_V1101 and CRU TS3.1 in the streamflow simulation in the UVRB from 1981 to 2006. However, these results are somewhat inconsistent with the findings of Eini et al. [
88], who modeled hydrology systems and evaluated the performance of the SWAT model in the Maharlu Lake Basin in Iran by comparing CRU, NCEP CFSR, and APHRODITE, as well as reanalyzing Asfezari rainfall data using conventional data from 1983 to 2010. In this Iranian catchment, a simulation that was achieved through a combination of APHRODITE and CFSR showed superior performance (NSE = 0.91) compared to the other dataset combinations [
88]. However, it should be noted that these results are region- and model-dependent. Many studies show that the accuracy of gridded data results varies by region [
38,
39]. Meanwhile, a hydrological model with a different concept and representation of the streamflow procedure may lead to different conclusions.
Our results demonstrated that for the overall scale (1982–2006), the CFSR simulated the hydrology of the UVRB with lower accuracy than APHRODITE_V1101+CFSR, APHRODITE_V1101+CRU TS3.1, and CRU TS3.1. Correspondingly, the simulation of river flow using a SWAT model based on the characteristics (NSE) of weather products, with reference to the CFSR, showed that despite the small number of observational climate stations in the basin, the modeling of observational datasets was more accurate for representing climate relationships in the basin than the CFSR. These results are very similar to those of Dile et al. [
15] in the Gilgel Abay River and Gumera River in the Lake Tana Basin, and the upper part of the Upper Blue Nile Basin, where SWAT was also set up to assess the performance of CFSR datasets compared with conventional datasets for hydrological predictions from 1994 to 2008. Liu et al. [
32] also used the SWAT model with climate data from the “China Meteorological Assimilation Driving Datasets” (CMADS V1.0) and CFSR in the Yellow River Source Basin, Qinghai–Tibet Plateau, from 2009 to 2013. Liu et al. found that the performance of the hydrological model for the monthly scale of CMADS (NSE = 0.78) was higher than that of the CFSR (NSE = 0.69) [
32]. However, our results, as found in the UVRB, conflict with the findings of Tolere et al. [
16], Fuka et al. [
45], Cuceloglu and Ozturk [
46], and Grusson et al. [
89]. For instance, Tolere et al. [
16] reported more successful SWAT streamflow simulation results using the CFSR than conventional datasets from 1990 to 1995 for the Keleta watershed in Ethiopia, where conventional data are scarce [
16]. Fuka et al. [
45] reported that the SWAT model presented better simulation results with CFSR datasets, compared to using traditional weather-gauging stations in the Catskill Mountains, NY, USA, and the Gumera Watershed in the Blue Nile River in Ethiopia, from 1996 to 2010. Similarly, Cuceloglu and Ozturk [
46] evaluated CFSR using the SWAT model as a hydrological simulator in the Black Sea catchment from 2000 to 2012. The results showed that the CFSR gives quite reasonable agreement between simulated and observed river flow, compared to the observational dataset. Another study was conducted by Grusson et al. [
89] in the Garonne River Watershed in France, employing CFSR and conventional datasets in hydrological simulations using the SWAT model from 2000 to 2010. The results revealed that the CFSR provided better hydrological simulations than conventional datasets [
89]. Differences in climate and geographic conditions are the most likely explanation for such differences between the findings of Tolere et al. [
16], Fuka et al. [
45], Cuceloglu and Ozturk [
46], Grusson et al. [
89], and the results presented in this study.
The applicability of the SWAT model for the simulation of water balance components was assessed in the Heihe mountain river basin in northwest China, from 1961 to 1988 [
22]. Water-balance components over the Narmada River Basin in India were assessed by Goswami et al. using the SWAT model and CFSR, from 1984 to 2013. The results suggested that the SWAT model was able to simulate the water-balance components at the basin and sub-basin scales [
19]. Pathak et al. [
90] applied the SWAT model in nine watersheds in India to validate the annual water yield obtained from diverse water-balance models. Moreover, Pathak et al. assessed the applicability of the Lumped Zhang model and InVEST model, together with the SWAT model, to compute water yield in scenarios before and after climate change for 1980, 1990, 2001, and 2015 [
90]. Gupta et al. [
18] estimated the water-balance components in the Sabarmati River Basin (SRB) in India using the SWAT model, from 1999 to 2005. Gupta et al. noted that SWAT is a powerful tool that very effectively evaluated hydrological components in the study of the water balance and river flow of the SRB [
18]. In Nepal, Thapa et al. used the HBV and BTOPMC models, along with the SWAT model, to assess the components of water balance from 2001 to 2010. The results of the three models were similar [
91]. A predictive study using three models also offered a reasonable range for runoff and evapotranspiration estimates [
91]. Water balance and water yield were predicted by the SWAT model in a basin in the north-central part of Nigeria, from 1985 to 2010 [
17]. Adeogun et al. noted that the SWAT model can be a promising tool for predicting water balance, in terms of sustainable water management in Nigeria [
17]. The SWAT model was applied by Leta et al. to model water balance components in the Heeia watershed in Hawai’i, an island in the Pacific Ocean, from 2006 to 2013 [
92]. This study demonstrated the applicability of SWAT to small island watersheds with large topographic, precipitation, and land-use gradients. Our seasonal and annual precipitation results in the UVRB in Central Asia, using the APHRODITE_V1101 and CRU TS3.1, demonstrated much closer results to the observational dataset. These findings are in agreement with the results of a study by Shen et al. in the Kaidu Basin in Central Asia. Shen et al. [
37] used gridded products, including CFSR, APHRODITE, CRU, TRMM, ERA-Interim and MERRA-2, with the J2000 model to analyze the spatiotemporal patterns of water balance and the distribution of runoff components in the glacierized Kaidu Basin in Central Asia. The results showed that APHRODITE and CRU represented annual and seasonal precipitation dynamics that were similar to the observational dataset at most climate points [
37]. Similarly, a water balance study with the application of the SWAT model and observational datasets was conducted in the Indian Ken River Basin in South Asia, from 1986 to 2005 [
20]. Himanshu et al. concluded that the SWAT model can accurately simulate the hydrology of the Ken River Basin in India. The water balance study of the basin showed that evapotranspiration is more predominant, accounting for about 44.6% of the average annual precipitation [
20].
In this study, considering the more accurate performance of the CRU TS3.1 and observational datasets than other studied datasets, the simulations of the CRU TS3.1 and observational datasets showed that the actual evapotranspiration in July is almost equal to the July catchment precipitation values. These results are in accordance with those of Pritchard [
23], who used a combination of CFSR temperature and APHRODITE precipitation datasets in the SWAT model to simulate water-balance components, especially the actual evapotranspiration in five Asian river basins, including the Aral, Indus, Ganges, Brahmaputra, Tarim, and the lakes of Issyk-Kul and Balkhash. For the Aral Sea Basin in Central Asia, Pritchard reported that summer evaporation is approximately equal to summer precipitation [
23]. In this study, less actual evapotranspiration occurs in December, January, and February for all datasets studied, including CFSR. However, this result contradicts the findings of Goswami et al. [
19], who found that the simulations of actual evapotranspiration showed minimal values in May, using CFSR and the SWAT model, in the Narmada River Basin of India in South Asia between 1984 and 2013. The results of our simulation showed that the average annual actual evapotranspiration is about 2.9% (CFSR), 9.93% (APHRODITE_V1101+CFSR), 21.1% (CRU TS3.1), 25.52% (APHRODITE_V1101+CRU TS3.1), and 27.28% (observational datasets) of the average annual precipitation in the UVRB from 1982 to 2006. The same methodology was applied by Budhathoki et al. [
93] in the West Seti River Basin (WSRB) in South Asia, to simulate the mean annual water balance components, including precipitation and evapotranspiration, using the SWAT model and a combination of conventional weather data and APHRODITE, for the period of 1986–2005. The mean annual total evapotranspiration matched about 36% of the mean annual precipitation in the WSRB [
93]. In another study, Nasiri et al. [
21] applied the SWAT model to the Samalqan Basin in Iran in Western Asia, from 2004 to 2014, to compute actual evapotranspiration using observational weather data. Actual evapotranspiration contributed to the largest water loss from the basin, at approximately 86%. Nasiri et al. pointed out that the high evapotranspiration rate that was simulated may be related to the vegetation types in the region [
21].
Our results indicated that the simulation of the CFSR provided lower actual evapotranspiration values than traditional weather data for the UVRB from 1981 to 2006. Similar results were obtained by Dile et al. [
16] in the Gumera, Rib and Megech River Basins in Ethiopia in the Horn of Africa, where independent observation datasets and CFSR were used in the SWAT model in 1990–1995 [
16]. However, Dile et al. stated that the results in the Melka Kuntur Basin showed higher values for the water-balance components; this may be due to the relatively high total precipitation in the CFSR dataset in the Melka Kuntur Basin [
16]. In general, in our study, CFSR also simulated higher total annual precipitation in the UVRB in Central Asia, compared to other reference datasets. We found the largest deviation between the monthly mean actual evapotranspiration CFSR and the observational datasets, compared to other applied datasets in the UVRB. However, from previous studies, we have observed that in some regions, CFSR, when calculating actual evapotranspiration, produced values very similar to conventional weather data. For instance, the CFSR and observational datasets, with the SWAT model, were applied to different watersheds in the Blue Nile Basin in the northwestern Ethiopian Plateau from 1994 to 2008, to estimate actual evapotranspiration [
15]. For the Megech sub-basins in Ethiopia, the results showed that the deviation between the monthly mean actual evapotranspiration levels, obtained from CFSR simulations, and observational datasets was less than
mm in all months besides August and September, when it reached 12 mm and 19 mm, respectively. Dile et al. noted that CFSR weather data can be a valuable option for hydrological prediction where conventional data are not available, such as in remote parts of the Upper Blue Nile Basin [
15].
Based on the SWAT model application, our results for the simulations of five different climate data combinations, including APHRODITE_V1101+CFSR, APHRODITE_V1101+CRU TS3.1, CFSR, CRU TS3.1, and observational data showed that approximately 81.06%, 63.12%, 82.79%, 81.66%, and 67.67% of annual runoff was contributed by snowmelt runoff from 1982 to 2006 in the UVRB. The largest contribution of snowmelt runoff to the total runoff appears during the spring and summer periods. The monthly APHRODITE_V1101+CFSR simulations showed that from May to August, the snowmelt runoff contribution to river flow was about 68.10% (March), 80.75% (April), 76.97% (May), 83.41% (June), 84.15% (July), and 82.66% (August); for the APHRODITE_V1101+CRU TS3.1 simulations, it was about 72.49%, 73.63%, 62.24%, 63.36%, 63.86%, and 51.29%; for the CFSR simulations, it was about 77.57%, 86.47%, 82.90%, 84.95%, 86.98%, and 88.72%; for the CRU TS3.1 simulations, it was about 89.05%, 83.17%, 78.33%, 79.93%, 83.41%, and 91.75%; and, for the observational simulations, it was about 83.13%, 85.60%, 67.58%, 64.41%, 69.43%, and 60.49%. Many studies have shown that the application of the SWAT model is quite useful in snowmelt simulations [
1,
2,
24,
25]. As far as we are aware, in this study, the use of the SWAT model with various combinations of the respective climate datasets was conducted for the first time as a way to simulate snowmelt runoff contribution to river flow in this mountainous basin.
By using observational climate data in the SWAT model, Duan et al. simulated the snowmelt contribution to total runoff in the Tizinafu River Basin (TRB) in Xinjiang in Central Asia, from 2013 to 2014 [
25]. Duan et al. found that about 44.7% of the total runoff comes from snowmelt runoff in the TRB [
25]. Siderius et al. [
24] calculated the contribution of snowmelt to river runoff using APHRODITE data with the SWAT model, from 1971 to 2000, in the Ganges River in northern India in the Himalayan arc in South Asia. The SWAT results showed that approximately 1% and 5% can be considered to be indicative of the actual total annual contribution of snowmelt to total runoff. The contribution of seasonal snowmelt to total runoff during the dry season before the summer monsoon (MAM) is estimated to range from 12% to 38% [
24]. A similar approach was applied by Chiphang et al. [
1], who used the SWAT model in the mountainous Mago River Basin, located in the Eastern Himalayan region of India, from 2006 to 2009 to compute the contribution of snowmelt to streamflow changes in the basin. Their results showed that the contribution of snowmelt runoff to the annual streamflow of the basin was about 8% [
1]. Another study was performed by Dhami et al. using the snowmelt runoff model (SRM) and SWAT model with conventional weather data, to compute the water balance components of the Karnali River Basin in Nepal in South Asia and to simulate the contribution of snowmelt to river runoff, from 1993 to 2005 [
2]. Dhami et al. reported that, from the comparison of the results obtained from the SWAT model and the SRM model, it is recommended that the results obtained from the SWAT model are used due to its better performance in terms of predicting reality than the SRM model [
2]. The results of the SWAT model indicated that in the Karnali River Basin, about 35% of the total runoff is contributed by snowmelt runoff [
2]. An accurate representation of the snowmelt process could improve the prediction of streamflow in mountainous catchments [
89,
94]. The UVRB has good seasonal snow cover at high altitudes. Snowmelt is usually an important source of river flow at high altitudes. In this study, SWAT appropriately demonstrates both the beginning of snowmelt and the peak of spring snowmelt.
The perennial river basin system, when combined with steep slopes, provides enormous hydropower potential, the exploitation of which requires a deep understanding of the hydrological system of the catchment. The capacity of hydropower is determined by the flow rate of the river, so a change in flow will directly lead to changes in hydropower potential [
95]. Determining the effect of snowmelt on streamflow in the catchment allows an assessment of the hydrological processes within a river basin in a mountainous region. Snowmelt tends to create regular seasonal patterns of river flow during warmer temperatures, with the melting of snowpacks accumulated over the winter. The impact of melting snow on potential flooding, mainly in the spring, is of concern to many inhabitants across the globe. The performance of the SWAT model presents evidence that it can be applied efficiently in the transboundary Vakhsh River Basin in Central Asia for water resources assessment and management. Semi-distributed hydrological models can be used as an essential feature of the water resources monitoring approach and can play a crucial part in the management of transboundary water resources [
96]. Furthermore, well-calibrated models are an important tool for a variety of water management applications, such as for assessing the availability and balance of water resources, modeling water quality and sediment transport, etc.
Uncertainties and Limitations
The SWAT model has been applied in several hydrological modeling studies in various catchments around the world [
14,
25,
97]. There may be a few areas of uncertainty in modeling snow and glacier melt, such as orographic impacts and hydrological model parameterization, as well as heterogeneity in forest cover, slope, and features, which are evident issues in snow and glacier hydrology. All of these aspects are not well represented by a simple temperature index of snowmelt and glacier melt simulation. However, the evaluation of snowmelt based on a temperature index appears to be good enough to compute the physics of snowmelt processes entirely. Snowmelt hydrology is specifically considered as an essential variable in local catchments. Due to the degree-day approach with elevation bands in the SWAT snowmelt module, it is hard to avoid the uncertainties inherent in the model structure, parameters and input data. On the other hand, the absence of an ice-melt module would also cause potential uncertainties. Calibration solely by discharge records might produce good results, as well as potential uncertainties [
8]. Parameter uncertainty occurs when certain physical processes in a hydrological or climatic system cannot be explicitly resolved. As a replacement, they must be included via parameterization, which contains some uncertain variables. The UVRB is situated in a mountainous area, within which a wide range of regions subject to permafrost and seasonal cold is spread. The freezing/thawing processes of the soil also affect the accuracy of the simulation [
98]. Furthermore, measurement-based flow data that have been used in a comparison with a simulated flow may be exposed to human or tool errors in river-level observations, or inaccuracies in estimated curves, all of which are designed for natural river stretches that are subject to erosion and deposition [
99]. The uncertainty of the data source stems from the use of the CRU, which is generated by interpolating data from weather stations in the region. Therefore, the entered climatic data are approximate [
100]. The APHRODITE dataset, which includes multiple climate stations, provides high-resolution, gridded, long-term daily precipitation estimates, as commonly used in South Asia. In the UVRB, most of the climate stations are located in valleys and are not in mountains with a large amount of precipitation. The combination of all of these factors can increase the uncertainty of the APHRODITE estimates. Precipitation data from CFSR showed more heterogeneity than the temperature data. The gridded dataset was obtained directly; thus, its applicability in the study region must be assessed prior to its use. The precipitation error characteristics of datasets vary due to climatic regions, elevations, surface conditions, seasons and storm patterns [
101]. Similarly, these datasets are inevitably prone to inaccuracies caused by sampling uncertainties, indirect measurements, and exploration algorithms [
102]. In future investigations, it might be possible to analyze the effects of bias correction on multiple gridded climate data estimates in SWAT hydrological element simulations.