## 4 Results
### Assessment of Discharge Simulations Using Multiple SPPs
#### 4.2.2 Scenario II: Discharge Simulations with Satellite-product-specific Parameters  
The GXAJ model was recalibrated using 3B42 RT, 3B42V7, and IMERG-F SPPs to further assess the hydrological utilities of these SPPs. Figures 8 and 9 demonstrate that model recalibration effectively improves most SPP-based discharge simulations at the Monywa station in the calibration and validation periods. The only exception is the 3B42 RT-forced model run. The 3B42 RT-based recalibration improves streamflow simulation in the calibration period but slightly worsens it in the validation period. Its BIAS, NSE, and RMSE values for the entire simulation period are 9.2%, 0.868, and 1937.5 m\({}^{3}\)/s, respectively (Figure 9d-f), which are slightly worse than those under Scenario I (Figure 9a-c). The 3B42V7-specific model calibration alleviates streamflow underestimation under Scenario I and improves the high-flow simulation (Figure 8a,d), which reduces BIAS and RMSE from \(-\)27.4% and 2965.8 m\({}^{3}\)/s to \(-\)18.9% and 2209.6 m\({}^{3}\)/s, respectively, and increases NSE from 0.690 to 0.828 in the entire simulation period (Figure 9). Such improvement is also found in the IMERG-F-based model run. BIAS is reduced from \(-\)32.3% under Scenario I to \(-\)18.5% under Scenario II, and RMSE for the entire period is cut down by 30.9%, and NSE is remarkably increased from 0.664 to 0.840 (Figure 9). Figures S1-S4 indicate that the input-specific model calibration generally enhances the hydrological performance of all three SPPs at the four upstream hydrological stations. Moreover, the satellite-product-specific recalibration generally improves the discharge-duration curves in the high-flow section. Figure 10b shows that model recalibration effectively dampens the magnitudes of discharge underestimation at the quantile level higher than 50% for all SPP-forced simulations, except for the 3B42 RT-based model run. The 3B42 RT-forced model presents even more severe high-flow underestimation after recalibration (Figure 10b) than the situation that uses rain-gauge-calibrated parameters (Figure 10a). The 3B42 RT-forced model presents a considerable overestimation of quite a few flood peaks in the calibration period under Scenario I (Figure 8a). Model recalibration that uses 3B42 RT effectively reduces high flows in the calibration period and consequently decreases high flows for the entire period (Figure 8d). Notably, the satellite-precipitation-based model recalibration does not effectively enhance the low-flow simulations. The reason is probably due to the fact that the objective function for hydrological model optimization,  
Figure 11: Taylor diagrams of the simulated discharge comparison at the Monywa station using the rain-gauge-based precipitation data set, 3B42 RT, 3B42V7, and IMERG-F SPPs based on the observed discharge data: (**a**) Scenario I and (**b**) Scenario II. the maximum of NSE tends to provide high importance to high flows, and thus the optimized model may not accurately simulate low-flow processes. The SPP-specific recalibration generally improves the model's capability in capturing flood peak flows. Table 2 shows that model recalibration obviously augments flood peak flows for all flood events and clearly cuts down the BIAS\({}_{\text{p}}\) values for the model runs driven by all SPPs except 3B42 RT. The 3B42 RT-forced simulation runs reduce the magnitudes of flood peak overestimation for Events 20140523 and 20150908 by 14.6% and 12.0%, respectively, but exacerbate the flood peak underestimation for Events 20150803 and 20160803 by 9.8% and 2.5%, respectively. Although the systmatic errors of the simulated flood peak flows are obviously reduced in the 3B42V7- and IMERG-F-based model runs for all flood events, considerable flood peak underestimations still exist in Events 20150803 (BIAS\({}_{\text{p}}=-35.3\%\) and \(-38.0\%\), respectively) and 20160803 (BIAS\({}_{\text{p}}=-23.3\%\) and \(-17.2\%\), respectively).