## 2 Materials and Methods
### Model Calibration and Validation
#### 2.4.1 SUFI-2 Algorithm  
We calibrated and validated the SWAT model using the SUFI-2 algorithm, which considers several uncertainties in driving variables, measured data sets, conceptual data, and parameters [46]. A sensitivity analysis was carried out by using the Latin hypercube and one factor at a time (LH-OAT) sampling method in SWAT-CUP, where a set of model parameters was selected to identify the sensitive parameters that were capable of significantly affecting the model output [52; 53]. In this study, 12 parameter sets were selected initially. Then, the model was run for 1000 iterations, on a monthly scale, at each gauge station during calibration and validation. Calibration and validation were performed using the observed and global climate model (historical) data sets with LULC, USGS (2001-2010), ESA (1995, 2005, 2015), and global land cover (GLC) China 2000. The \(p\)-value and t-state value were used to indicate the significance of sensitive parameters. A larger absolute t-stat and lower \(p\)-value (approaching zero) signifies higher sensitivity. The 95% percent prediction uncertainty (95 PPU) was calculated through Latin hypercube sampling (LHS), which restricts 5% of the bad simulations. Further, the strength of the model calibration and validation was measured by the p-factor and r-factor, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \cline{2-10} \multirow{2}{*}{**Objective Function**} & \multicolumn{6}{c}{**LULC**} \\ \cline{2-10}  & **USGS** & **ESA** & **ESA** & **ESA** & **ESA** & **GLC** \\  & **(2001–2010)** & **1995** & **2005** & **2015** & **2000** \\ \hline  & **Cal** & **Val** & **Cal** & **Val** & **Cal** & **Val** & **Cal** & **Val** & **Cal** \\ \hline R\({}^{2}\) & 0.85 & 0.8 & 0.89 & 0.89 & 0.89 & 0.86 & 0.90 & 0.91 & 0.90 & 0.83 \\ NSE & 0.84 & 0.87 & 0.88 & 0.82 & 0.88 & 0.85 & 0.89 & 0.86 & 0.83 & 0.77 \\ PPIAS & 7.7 & 11.9 & -11.5 & -15.1 & 2.8 & 6.5 & 0.7 & 18.3 & 22.5 & 21.3 \\ KGE & 0.89 & 0.87 & 0.87 & 0.79 & 0.93 & 0.90 & 0.95 & 0.81 & 0.71 & 0.75 \\ RSR & 0.40 & 0.36 & 0.35 & 0.42 & 0.35 & 0.39 & 0.33 & 0.37 & 0.42 & 0.48 \\ P-Factor & 0.64 & 0.71 & 0.67 & 0.83 & 0.73 & 0.69 & 0.64 & 0.48 & 0.68 & 0.58 \\ R-Factor & 0.42 & 0.48 & 0.68 & 0.81 & 0.44 & 0.50 & 0.32 & 0.36 & 0.42 & 0.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Objective function values with observed rainfall using LULC USGS-(2001–2010), European Space Agency (ESA) (1995, 2005, 2015), and global land cover (GLC)-2000 China data for the Qianjiang Hydrological Station. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \cline{2-10} \multirow{2}{*}{**Objective Function**} & \multicolumn{6}{c}{**LULC**} \\ \cline{2-10}  & **USGS** & **ESA** & **ESA** & **ESA** & **ESA** & **GLC** \\  & **(2001–2010)** & **1995** & **2005** & **2015** & **2000** \\ \hline  & **Cal** & **Val** & **Cal** & **Val** & **Cal** & **Val** & **Cal** & **Val** & **Cal** \\ \hline R\({}^{2}\) & 0.80 & 0.83 & 0.88 & 0.86 & 0.83 & 0.76 & 0.83 & 0.83 & 0.82 & 0.84 \\ NSE & 0.78 & 0.79 & 0.79 & 0.85 & 0.82 & 0.74 & 0.82 & 0.77 & 0.81 & 0.78 \\ PPIAS & 12.1 & 14.8 & \(-\)20.2 & \(-\)2.6 & 0.8 & 12.8 & 0.8 & 17.2 & 8.1 & 22.2 \\ KGE & 0.83 & 0.83 & 0.75 & 0.83 & 0.91 & 0.80 & 0.91 & 0.79 & 0.87 & 0.75 \\ RSR & 0.47 & 0.47 & 0.45 & 0.39 & 0.42 & 0.51 & 0.42 & 0.48 & 0.4 & 0.47 \\ P-Factor & 0.61 & 0.67 & 0.67 & 0.50 & 0.74 & 0.71 & 0.73 & 0.50 & 0.65 & 0.71 \\ R-Factor & 0.47 & 0.54 & 0.82 & 0.44 & 0.51 & 0.56 & 0.51 & 0.42 & 0.48 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Objective function values with observed rainfall using LULC USGS-(2001–2010), ESA (1995, 2005, 2015), and GLC-2000 China for the Tianr Hydrological Station. the values of which vary from 0 to 1. The p-factor indicates the measured data covered in a 95% range of uncertainty (95 PPU). Highly sensitive parameters have lower p-factor values, where parameters with a near-zero p-factor are the most sensitive [54].