## 4 Results
### Comparison of IMERG Series Products with Rain Gauge Data
#### 4.2.1 Simulation Driven by Rain Gauge Data (Scenario I)
All the results show that the SWAT model based on the site data is suitable for the XRRB. Simulation Driven by Multiple Precipitation Datasets with the Optimal Parameter Set from Scenario I  
The gauge-benchmarked model based on Scenario I is then forced by rain gauge data (STA) and three IMERG products for daily and monthly streamflow simulations for the period from 1 April 2014 to 31 December 2017. Figure 6 depicts the daily hydrographs that are simulated using the STA, ER, LR, and FR products for the validation period based on the optimal parameter in Scenario I. The results show that the simulation of runoff from rainfall gauge data is in good agreement with the observations in the XRRB from Figure 7. The simulated streamflow forced by the three IMERG products has good performance capturing the spatial and temporal trends of heavy rainfall that lead to runoff generation, although there are significant overestimations in individual periods. Additionally, Figure 8 shows the evaluation statistics of the hydrological simulation results when the four precipitation pieces of data are used as inputs to drive the model. In the validation period, the CORR values of the simulated streamflow driven by the three IMERG products ranged from 0.50 to 0.61 during the validation period, the NSE values of the three products were considerably low and ranged from 0.25 to 0.39, and the BIAS values were all within \(\pm\)20%. The FR-based streamflow presents a better CORR value than the ER and LR but worse BIAS and RMSE values, with a significant runoff overestimation of 18% and relatively low NSE (0.25). This phenomenon may occur for the following reason. The GPCC precipitation dataset, which is used for bias correction of the FR, is monthly-scale precipitation data, and may not sufficiently capture the spatiotemporal variation in daily-scale precipitation in the XRRB from 2014-2017. The potential large errors associated with the precipitation input will propagate in the streamflow simulations. Therefore, FR has not shown an obvious advantage in hydrological applications at daily scale. In terms of the index results, the simulation results of the directly input IMERG products are acceptable at the daily scale but are not satisfactory. With regard to the results at the monthly temporal scale, the monthly observed and simulated streamflows driven by the four precipitation inputs (STA, ER, LR, and FR products) for the validation period are shown in Figures 9 and 10. The simulated runoff curves for the STA, ER, LR, and FR products are generally consistent with the observed streamflow curves, especially for FR, which has a CORR of 0.87, a value that is much higher than that of ER (0.73) and LR (0.71). The NSE values of all three IMERG products are in the range of 0.69-0.72. The BIAS of FR (0.26) is significantly overestimated, much higher than ER (0.07) and LR (0.04). Similar to the significance test results reported in 4.1, the simulation results based on ER, LR and FR also exhibit significant differences under the \(t\)-test at the 95% confidence level. Overall, the IMERG series products have better simulations at the monthly scale than at the daily scale. Compared with the two near-real-time products (ER and LR), the simulations driven by the FR show better performance in hydrological applications. As seen from Figure 8, the markers (May- 2015, March-2017 and January-2016) show that the FR simulation is closer to the STA simulation than ER and LR simulations, which indicates the advantage of the post-real-time FR product. However, it is also necessary to note that all four products (STA, ER, LR, and FR) showed higher simulated flows than observations (the locations marked in May-2015, January-2016, October-2016 and March-2017), which may be related to measurement error in discharge. Figure 7: Daily streamflow simulations based on the optimal parameter in Scenario I driven by. **(a)** STA, **(b)** ER, **(c)** LR, and **(d)** FR during the validation period (2014–2017).