## 3 Results and Discussion
### Streamflow Simulation Scenarios and Results
#### 3.3.2 Scenario II: Hydrological Model Calibrated by Different Precipitation Estimates
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & **Precipitation Input** & **NSCE** & **BIAS (\%)** & **CC** & **RMSE (m3/s)** \\ \hline \multirow{8}{*}{Calibration} & 3B42V7 & 0.84 & \(-\)9.59 & 0.92 & 637.68 \\  & 3B42 RT & 0.78 & \(-\)13.26 & 0.89 & 753.08 \\  & IMERG-cal & 0.84 & \(-\)10.47 & 0.92 & 651.98 \\  & IMERG-uncal & 0.75 & \(-\)17.31 & 0.88 & 811.29 \\  & ERA5 & 0.79 & \(-\)7.92 & 0.89 & 735.02 \\  & ERA-Interim & 0.70 & \(-\)6.19 & 0.84 & 889.43 \\  & GSMaP & 0.87 & \(-\)10.01 & 0.94 & 574.55 \\  & Average Rain 1 & 0.87 & \(-\)10.31 & 0.94 & 587.44 \\  & Average Rain 2 & 0.85 & \(-\)14.56 & 0.93 & 628.41 \\ \hline \multirow{8}{*}{Validation} & 3B42V7 & 0.73 & \(-\)8.17 & 0.86 & 813.05 \\  & 3B42 RT & 0.67 & \(-\)11.57 & 0.84 & 895.53 \\ \cline{1-1}  & IMERG-cal & 0.73 & \(-\)9.62 & 0.86 & 802.20 \\ \cline{1-1}  & IMERG-uncal & 0.64 & \(-\)12.35 & 0.81 & 936.56 \\ \cline{1-1}  & ERA5 & 0.67 & \(-\)5.90 & 0.83 & 897.44 \\ \cline{1-1}  & ERA-Interim & 0.22 & 33.68 & 0.71 & 1372.53 \\ \cline{1-1}  & GSMaP & 0.78 & \(-\)10.97 & 0.89 & 722.47 \\ \cline{1-1}  & Average Rain 1 & 0.78 & \(-\)9.61 & 0.89 & 735.70 \\ \cline{1-1}  & Average Rain 2 & 0.73 & \(-\)15.29 & 0.87 & 809.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of daily simulated streamflow during the calibration (1 April 2006–31 December 2010) and validation (1 January 2011–31 December 2018) periods under Scenario II for the Beijing River Basin. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & **Precipitation Input** & **NSCE** & **BIAS (\%)** & **CC** & **RMSE (m3/s)** \\ \hline \multirow{8}{*}{Calibration} & 3B42V7 & 0.78 & \(-\)3.58 & 0.89 & 389.41 \\  & 3B42 RT & 0.76 & \(-\)4.98 & 0.87 & 414.30 \\  & IMERG-cal & 0.82 & \(-\)4.74 & 0.91 & 353.33 \\  & IMERG-uncal & 0.76 & \(-\)7.60 & 0.88 & 411.61 \\  & ERA5 & 0.74 & \(-\)3.33 & 0.86 & 431.49 \\  & ERA-Interim & 0.65 & \(-\)11.02 & 0.82 & 494.52 \\  & GSMAP & 0.83 & \(-\)1.42 & 0.91 & 346.75 \\  & Average Rain 1 & 0.82 & \(-\)9.93 & 0.91 & 352.03 \\  & Average Rain 2 & 0.82 & \(-\)6.18 & 0.91 & 352.15 \\ \hline \multirow{8}{*}{Validation} & 3B42V7 & 0.67 & \(-\)5.30 & 0.83 & 354.25 \\  & 3B42 RT & 0.64 & \(-\)6.52 & 0.82 & 367.73 \\ \cline{1-1}  & IMERG-cal & 0.71 & \(-\)9.84 & 0.86 & 332.75 \\ \cline{1-1}  & IMERG-uncal & 0.61 & \(-\)18.44 & 0.81 & 387.06 \\ \cline{1-1}  & ERA5 & 0.63 & \(-\)8.76 & 0.80 & 372.93 \\ \cline{1-1}  & ERA-Interim & 0.16 & 19.93 & 0.67 & 565.66 \\ \cline{1-1}  & GSMaP & 0.70 & \(-\)2.00 & 0.85 & 335.17 \\ \cline{1-1}  & Average Rain 1 & 0.71 & \(-\)13.04 & 0.86 & 334.78 \\ \cline{1-1}  & Average Rain 2 & 0.69 & \(-\)13.62 & 0.85 & 341.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics of daily simulated streamflow during the calibration (1 April 2006–31 December 2010) and validation (1 January 2011–31 December 2018) periods under Scenario II for the Dongjiang River Basin. including sampling, geography, climate, seasonal conditions, and algorithmic error; these contribute to the influence of streamflow estimations along with basin areas [61]. Above all, for both the Beijing and Dongjiang River Basins, the results of comparing Scenarios I and II demonstrated that the hydrologic utility of all corrected precipitation datasets had been improved regardless of whether the CREST model was calibrated by CGDPA or re-calibrated using satellite and reanalysis precipitation inputs. By referring to performance indicators, after gauge (CGDPA), Average Rain 1, and GSMaP proved to be better than other precipitation estimates for simulating the streamflow at the daily scale during the calibration and validation periods from 2006 to 2018. Combined with the results in Section 3.1, Average Rain 2 was found to significantly improve the evaluation indicators of both precipitation and hydrologic simulations compared to TMPA-3B42 RT, IMERG-uncal, and ERA5. In a way, reasonably merged precipitation products can help reduce the uncertainty of individual precipitation datasets and even improve hydrologic applications.