## 3 Results and Discussion
### Streamflow Simulation Scenarios and Results
#### 3.3.2 Scenario II: Hydrological Model Calibrated by Different Precipitation Estimates  
To further assess the utility of multi-source precipitation estimates on model simulations of streamflow, each of the CREST models was re-parameterized using the TMPA-3B42V7, TMPA-3B42 RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, and Average Rain 1 and Rain 2 for the same calibration and validation periods as in Scenario I. As an alternative, the calibration approach can be employed specifically for ungauged catchments, where only remotely sensed precipitation estimates are free to the public [10; 31]. Figures 9 and 10 show comparisons of the daily model-simulated hydrographs using nine satellite- and reanalysis-based precipitation inputs versus the observed ones and their Q-Q plots for the Beijing River Basin, respectively. Meanwhile, for the Dongjiang River Basin, the comparisons of the daily model-simulated hydrographs using nine selected precipitation inputs versus the observed ones and their Q-Q plots are shown in Figures 11 and 12, respectively. Tables 6 and 7 present a summary of the evaluation indicators; these re-calibrated and validated results in Scenario II, compared with those in Scenario I, have been relatively improved. For the Beijing River Basin (as shown in Figures 9b-j and 10b-j), most of the daily flow peaks obtained from the product-specific recalibration for Scenario II could be well captured and fitted to their observed ones comparatively well, whereas the hydrological model tended to underestimate both the high and low flows equally. The statistical performance of discharge simulation displayed in Table 6 shows that there was a slight improvement of NSCE and CC values and an obvious reduction of RMSE values (except for ERA-Interim) during the validation period. ERA5 outperforms ERA-Interim and performs similar to TMPA-3B42 RT. In terms of NSCE, CC, and RMSE, the ranking of the nine selected precipitation estimates was Average Rain 1, GSMaP, Average Rain 2, TMPA-3B42V7, IMERG-cal, ERA5, TMPA-3B42 RT, IMERG-uncal, and ERA-Interim, which are the best suited to Scenario II. This means that these corrected precipitation products have better hydrological simulation utilities than the uncorrected ones using their corresponding re-calibrated model parameter sets. For the Dongjiang River Basin (as shown in Figures 11b-j and 12b-j), the recalibration specific to precipitation products in Scenario II showed reasonable agreement with gauged observations, and the efficiency of hydrologic simulation was also improved. On the basis of model category performances in Table 7, GSMaP, IMERG-cal, and Average Rain 1 showed similar improved performances, followed by Average Rain 2, TMPA-3B42V7, TMP-3B42 RT, ERA5, and IMERG-uncal. However, ERA-Interim showed the worst performance (e.g., the values of NSCE, BIAS, and CC were 0.16, 9.93%, and 0.67 in the validation period, respectively). The findings show that the error magnitudes in streamflow simulations could be relatively big even for small basins [74], which are very sensitive to basin characteristics [43]. The main sources of uncertainties are inherent in multiple source precipitation estimates,