## 3 Results and Discussion
### Streamflow Simulation Scenarios and Results
#### 3.3.1 Scenario I: Hydrological Model Calibrated by Gauge Observations  
1. Simulated streamflow forced by CGDPA  
First, we used the daily gauge observations of both rainfall and streamflow data from April 2006 to December 2010 to calibrate the CREST model parameters, where the previous 3 months were used to warm up the hydrological model [70]. For the Beijing and Dongjiang River Basins, Tables 4 and 5 indicate the considerable agreement of model streamflow simulations with their observations during the calibration and validation periods, respectively. As shown in Table 4 and Figures 9a and 10a, there was a good match between most magnitudes and timings of flood peaks that were forced by CGDPA precipitation over the whole period (e.g., NSCE, BIAS, CC, and RMSE values were 0.82-0.89, \(-\)9.37%--10.47%, 0.91-0.95, and 546.91-662.95 m\({}^{3}\)/s, respectively). Under increasing observed streamflow (over 5690 m\({}^{3}\)/s) for the Beijinga River Basin, it is obvious that the modeled streamflow was almost underestimated. According to Table 5 and Figures 11a and 12a  
Figure 8: Taylor diagrams of (**a**) calibration over the Beijinga River, (**b**) validation over the Beijinga River, (**c**) calibration over the Dongjiang River, and (**d**) validation over the Dongjiang River (i.e., the centered root-mean-square difference, correlation coefficient, and standard deviation) for the performance of multi-source precipitation products against CGDPA. for the Dongjiang River Basin, the majority of flood magnitudes and timings were a close match during the calibration period (e.g., NSCE, BIAS, CC, and RMSE values were 0.87, \(-\)2.99%, 0.93, and 305.77 m\({}^{3}\)/s, respectively), whereas the model streamflow simulation was underestimated as their observed streamflow increased during the validation period (e.g., NSCE, BIAS, CC, and RMSE values were 0.62, \(-\)9.76%, 0.81, and 379.28 m\({}^{3}\)/s, respectively). As discussed in Section 3.2, the uncertainty in rainfall products along with topographical and geophysical characteristics can contribute to the discrepancy in hydrological performance between these two watersheds and--particularly for small catchments--degrade the model-simulated streamflow [61; 71]. Using the CGDPA precipitation inputs, the CREST model could be well simulated at a daily scale during both the calibration and validation periods over the Beijing and Dongjiang River Basins, respectively. Hence, CGDPA could serve as an appropriate reference to assess the hydrological accuracy of various satellite- and reanalysis-based precipitation products for this study region. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & **Precipitation Input** & **NSCE** & **BIAS (\%)** & **CC** & **RMSE (m\({}^{3}\)/s)** \\ \hline Calibration & CGDPA & 0.89 & \(-\)10.47 & 0.95 & 546.91 \\ \cline{2-5}  & CGDPA & 0.82 & \(-\)9.37 & 0.91 & 662.95 \\  & 3B42V7 & 0.71 & \(-\)8.97 & 0.86 & 835.39 \\  & 3B42 RT & 0.61 & 1.69 & 0.84 & 972.68 \\  & IMERG-cal & 0.73 & \(-\)11.41 & 0.86 & 810.01 \\  & IMERG-uncal & 0.63 & \(-\)21.94 & 0.81 & 951.71 \\ Validation & ERA5 & 0.66 & \(-\)5.32 & 0.82 & 902.55 \\  & ERA-Interim & 0.45 & 0.85 & 0.70 & 1154.42 \\  & GSMAP & 0.78 & \(-\)13.23 & 0.89 & 724.98 \\  & GSMAP-RNL & 0.40 & \(-\)50.94 & 0.77 & 1200.90 \\  & PERSIANN-CCS & \(-\)0.30 & \(-\)83.84 & 0.57 & 1769.10 \\  & Average Rain 1 & 0.77 & \(-\)12.33 & 0.89 & 746.78 \\  & Average Rain 2 & 0.71 & \(-\)12.75 & 0.86 & 830.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 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 I for the Beijing River Basin.