## 4 Results
### Comparison of IMERG Series Products with Rain Gauge Data
#### 4.2.1 Simulation Driven by Rain Gauge Data (Scenario I)  
Rain Gauge Calibration and Validation  
In Scenario I, gauge observations (2000-2012) are first used as input for driving the model and optimizing the parameter values. Next, in the validation period (2013-2019), the gauge observations are used to evaluate the effectiveness of the hydrological model constructed based on the SWAT model for the XRRB. Two years of warm-up period (2000-2001, 2013-2014) are reserved in the calibration and validation process. Finally, the ER, LR, and FR products are used to drive the model with the same parameter values, and the effectiveness of the IMERG series in the hydrological application of the XRRB is evaluated. An article providing a detailed description of the model construction process is currently in press. The model is constructed with a spatial resolution of 90 m and a total watershed area of 5734 km\({}^{2}\). The subbasin division threshold is set to 160 km\({}^{2}\), and the watershed is divided into a total of 23 subbasins; the thresholds for land use types, soil types, and slope types are set to 8%, 8%, and 10%, respectively, and the watershed is divided into 409 hydrological response units (HRUs). In this study, the skewed normal method is used for the simulation of rainfall, the Penman-Monteith method is selected for the simulation of potential evapotranspiration, the SCS runoff curve method is utilized for the simulation of runoff, and Muskingum is applied in the calculation of river evolution. Twelve parameters that are more sensitive to the runoff simulation of the XRRB are selected for parameter rate determination, and the results of parameter preferences are shown in Table 1.  
Figures 5 and 6 show the comparisons of the observed streamflow with rain gauge observations on daily and monthly scales during the calibration period and validation period, respectively. Table 4 shows the evaluation results of the modeling effect for the calibration period and the validation period. In general, good agreement is observed between the simulated streamflow and the observed streamflow (inflow streamflow data) at the daily scale during both the calibration and validation periods, with daily CORRs of 0.91 and 0.85, NSEs of 0.91 and 0.83, and BIASs of 0.04 and 0.13 for the calibration and validation periods, respectively. Good agreement is also observed between the simulated streamflow and observed streamflow at the monthly scale, with CORR and NSE values of the rate period and validation period both reaching above 0.90 and above 0.80, respectively, and a BIAS within 20%. As seen in Table 4, both \(p\)-factors are greater than 0.6 and R-factors are less than 1 during both the calibration and validation periods. The \(p\)-factor is greater than 0.6 and the R-factor is less than 1, indicating that most of the observed data are captured by the 95 PPU, and the SWAT model is capable of simulating large flows and extreme events in the XRRB. Figure 5: Comparison of observed and simulated hydrographs using daily rain gauge data during the calibration period and validation period in the XRRB (Scenario I). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Indexes**} & \multicolumn{2}{c}{**Daily**} & \multicolumn{2}{c}{**Monthly**} \\ \cline{2-5}  & **Calibration Period** & **Validation Period** & **Calibration Period** & **Validation Period** \\ \hline R\({}^{2}\) & 0.91 *** & 0.85 *** & 0.96 *** & 0.90 *** \\ NSE & 0.91 & 0.83 & 0.95 & 0.80 \\ BIAS & +0.04 & +0.13 & +0.06 & +0.20 \\ \(p\)-factor & 0.74 & 0.78 & 0.83 & 0.82 \\ R-factor & 0.39 & 0.56 & 0.48 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Indices for the performances of the SWAT model in both the calibration and validation periods (Scenario I). Figure 6: Comparison of observed and simulated hydrographs using monthly rain gauge data during the calibration period and validation period in the XRRB (Scenario I). In general, the SWAT model constructed in Scenario I meets the requirements of hydrological simulation.