Methodology

This study consists of two parts: in the first (precipitation product evaluation) we aimed to evaluate the quality of TRMM, IMERG, CMADS, and CFSR precipitation products at grid and watershed-scales based on GO; in the second (streamflow simulation evaluation), 12 precipitation scenarios were created to drive the hydrological model (Table 2 ). Scenarios S1 to S7 were used to study the runoff simulation effect of each precipitation dataset; Scenarios S8 and S9 were SWAT models driven by corrected precipitation data to study the influence of precipitation data correction on runoff simulation (Section 4.2.2 describes the reasons for correcting only CMADS and CFSR precipitation data). Scenarios S10, S11, and S12 cover CMADS precipitation data combined with GO1, corrected CFSR precipitation data combined with GO1, and IMERG precipitation data combined with GO2, respectively: these were designed to study the effects of precipitation data combination on runoff simulation (Section 4.2.3 describes the reasons for choosing these three combinations). The analysis process used herein is shown in Fig. 2 .

3.1 Precipitation data evaluation

To quantitatively evaluate the accuracy of the TRMM, IMERG, CFSR, and CMADS precipitation products in the YRHR, the precipitation derived from the four precipitation products is directly compared with GO. Six statistical metrics, including the root mean square error (RMSE), percent bias (PBIAS), correlation coefficient (CC), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI), were utilized to evaluate the agreement between the GO and the four precipitation products. The calculation equations, units, ranges, and optimal values of the evaluation indicators are listed in Table 3 .

3.2 SWAT model and model setting

The SWAT is a semi-distributed, physics-based eco-hydrological model, which runs in daily, monthly, or annual time steps (Arnold et al ., 1998), and has been widely used in hydrological processes (Grussonet al ., 2015), soil erosion (Song et al ., 2011), and nutrient transportation (Wang et al ., 2018). Previous studies have proven that dividing the YRSR into 25 (Liu et al ., 2018), 29 (Hao et al ., 2013), and 97 (Mengyaun et al ., 2019) sub-basins would yield reliable simulation results. Therefore, the YRSR was divided into 26 sub-basins to reduce unnecessary calculation. SWAT was originally developed to evaluate water resources in large agricultural basins, and was not designed to model heterogeneous mountain basins typical of the western United States (Fontaine et al ., 2002). Ten elevation zones (each covering an change in elevation of 500 m) were established in the present work, to divide each sub-basin to reduce the influence of topography on precipitation. According to previous research (Fontaine et al ., 2002; Zhenchun et al ., 2013), the snowfall temperature (SFTMP), snow melt base temperature (SMTMP), maximum melt rate for snow during year (SMFMX), minimum melt rate for snow during the year (SMFMN), snow pack temperature lag factor (TIMP), and minimum snow water content that corresponds to 100% snow cover (SNOCOVMX) in the snowmelt module have been adjusted to reduce the influence of snowmelt on the model (Table 4 ).

3.3 Parameter calibration and model evaluation

Calibration and uncertainty analyses of the simulation results from the model were performed using Sequential Uncertainty Fitting Version 2 (SUFI2 ) in the SWAT calibration and uncertainty program (SWAT-CUP) (Abbaspour et al ., 2015). According to previous studies on hydrological modeling in alpine basins (Bhatta et al ., 2019; Mengyaun et al , 2019; Shuai et al ., 2019; Zhenchunet al ., 2013), 30 sensitive parameters were initially selected. Sixteen parameters with the highest sensitivity were then selected using the Latin hypercube and one-factor-at-a-time sampling (LH-OAT) method for calibration (Table 5 ). Due to limitations of space, we do not present any analysis of the calibration parameters. According to Abbaspour (2015), the model was calibrated using three iterations with 400 simulations (necessitating a total of 1200 simulations during calibration) using the Nash-Sutcliffe Efficiency (NSE) (Nash and Sutcliffe, 1970) and coefficient of determination (R 2) as the objective function. The range of each parameter was modified after each iteration, according to both new parameters suggested by SWAT-CUP and their reasonable physical ranges. The criteria proposed by Moriasi et al (2015) was adopted to classify model performance into the respective categories, “very good” (NSE > 0.80; PBIAS < ±5%), “good” (0.70 < NSE ≤ 0.80; ±5% ≤ PBIAS < ±10%), “satisfactory” (0.50 < NSE ≤ 0.70; ±10% ≤ PBIAS < ±15%), and “unsatisfactory” (NSE ≤ 0.50; PBIAS ≥ ±15%).

3.4 Precipitation data pre-processing

Before modeling, we preprocessed the precipitation data:
(1) The numbers of grids or stations with precipitation products of TRMM, IMERG, CMADS, and CFSR located in the YRSR are 200, 1027, 198, and 122, respectively. Considering that SWAT only uses data from the one weather station closest to the centroid of the sub-basin (Masih et al ., 2011; Villarán, 2014). It is impractical to divide the watershed into 1027 sub-watersheds and correspond thereto on a one-by-one basis. Therefore, virtual weather stations were constructed for each sub-basin (Ruan et al ., 2017; Tuoet al ., 2016). The specific methods are as follows:
(2) Considering that the starting period of SWAT-CUP calibration must be a whole year, the periods of coincidence of CMADS (1 January 2008 to 31 December 2016) and CFSR (1 January 1974 to 31 December 2014) data are only six years (1 January 2008 to 31 December 2013), deducting the warm-up period of the SWAT model (12 years), the final simulation time will be shorter (45 years), which does not reflect the quality of the data. Therefore, we added meteorological data from 1 January 2008 to 31 December 2010 and 1 January 2006 to 31 December 2007 for the warm-up of the SWAT model, so that the data time-span used for the simulation becomes six years (1 January 2008 to 31 December 2013).