## 3 Results and Discussion
### Analysis of Merge Data at Five Flood Levels  
To assess the performance of various merged data across different flood levels according to national flood-control standards, the daily flood stages were further divided into five levels based on probability of exceedance (i.e., I: 25%; II: 20%; III: 10%; IV: 5%; and V: 2%). Figures 13 and 14 show the overall evaluation results (CC, BIAS, RMSE, and KGE\({}^{\prime}\)) for streamflow, based on the merged and original satellite/reanalysis-based precipitation products under Scenario II (including CGDPA under Scenario I) in the five flood levels, as measured against the observed discharges from 2006 to 2018 over the Beijing and Dongjiang River Basins, respectively.  
For the Beijing River Basin, except for ERA-Interim, all the CC, BIAS, and KGE\({}^{\prime}\) (MAE) showed descending (increasing) trend lines from Flood Level I to V (Figure 13a-d). After gauge (CGDPA) and GSMaP showed better performances than the others (e.g., CC, BIAS, MAE, and KGE\({}^{\prime}\) values were 0.67-0.86, \(-\)4.67%-20.97%, 791.14-2083.07 m\({}^{3}\)/s, and 0.61-0.83, respectively), followed by Average Rain 1 and Average Streamflow 1 (e.g., KGE\({}^{\prime}\) values ranged from 0.55 to 0.81 and from 0.50 to 0.78, respectively). However, with the KGE\({}^{\prime}\) values of 0.29-0.51, ERA-Interim showed the worst performances of all and was not good for flood simulations. In terms of KGE\({}^{\prime}\) values exceeding 0.59, the highest rankings of the twelve streamflow series from Flood Level I to IV were CGDPA, GSMaP, Average Rain 1, Average Streamflow 1, Average Rain 2, TMPA-3B42V7, IMERG-cal, and Average Streamflow 2 (Figure 11d). Additionally, ERA5 and 3B42 RT showed similar performances from Flood Level I to V, based on three indicators (CC, MAE, and KGE\({}^{\prime}\)).  
For the Dongjiang River Basin, Figure 14a-d shows that the performances of all twelve streamflow series were reduced from Flood Level I to V. For KGE\({}^{\prime}\) values exceeding 0.57, the highest rankings of the twelve streamflow series from Flood Level I to IV were CGDPA, IMERG-cal, Average Rain 1, Average Rain 2, GSMaP, Average Streamflow 1, TMPA-3B42V7, and Average Streamflow 2. However, all the KGE\({}^{\prime}\) values except CGDPA were less than 0.47 in Flood Level V over the Dongjiang River Basin (Figure 12d). Overall, it is noted that the performances of the two merged precipitation products were slightly  
Figure 13: Statistical metrics of (**a**) CC, (**b**) BIAS, (**c**) MAE, and (**d**) KGE\({}^{\prime}\) of the daily CREST-simulated and two merged streamflow series versus the observations for five flood categories during 2006–2018 in the Beijing River Basin.  
better than those of the two merged streamflow series in the five flood levels for both the Beijing and Dongjiang River Basins. Furthermore, Average Rain 1 (TMPA-3B42V7, IMERG-cal, and GSMaP) outperformed Average Rain 2 (TMPA-3B42 RT, IMERG-uncal, and ERA5) for hydrologic applications in the whole study. These findings validate the feasibility of merging satellite/reanalysis precipitation estimates for better understanding of flood characteristics.  
On the whole, the results suggested that directly merging modeled streamflow outputs was more effective than merging multi-source precipitation inputs in the Beijing and Dongjiang River Basins, which demonstrates that the streamflow simulation generated by Scenario III was superior to those from both Scenario II and I.