## 4 Results
### Assessment of Discharge Simulations Using Multiple SPPs
#### 4.2.1 Scenario I: Discharge Simulations with Rain-Gauge-Calibrated Parameters  
Figure 8a depicts the 3-hourly hydrographs at the watershed outlet (Monywa station, Figure 1) that were simulated using the gauge-based gridded precipitation data set and 3B42 RT SPP under Scenario I. The figure illustrates that the simulated discharge forced by rain gauge data agrees well \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Satellite Precipitation Products**} & \multicolumn{3}{c}{**Diagnostic Statistics**} \\ \cline{2-4}  & **BIAS (\%)** & **CC** & **RMSE (mm)** \\ \hline
3B42 RT & 11.1 & 0.281 & 3.0 \\
3B42V7 & \(-\)4.8 & 0.277 & 3.0 \\ IMERG-E & \(-\)27.0 & 0.323 & 2.9 \\ IMERG-L & \(-\)30.6 & 0.319 & 2.9 \\ IMERG-F & \(-\)6.8 & 0.330 & 2.9 \\ GSMaP-NRT & \(-\)37.6 & 0.232 & 3.0 \\ GSMaP-MVK & \(-\)28.7 & 0.211 & 3.1 \\ GSMaP-GAUGE & \(-\)39.0 & 0.215 & 3.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Diagnostic statistics of the basin-averaged 3-hourly precipitation estimates from the eight SPPs from 1 April 2014 to 31 December 2016. Figure 7: Non-exceedance frequency distributions of the 3-hourly watershed-averaged precipitation estimates from the rain-gauge-based precipitation data and eight SPPs. with the measured discharge in the calibration (1 April 2014-31 December 2015) and validation periods (January 1 2016-31 December 2016). Figure 9a shows that the GXAJ model driven by the rain gauge data produces a slight runoff underestimation of 3.7% in the calibration period and a moderate underestimation of 15.0% in the validation period, and the BIAS value for the entire simulation period is \(-\)8.3%. The rain-gauge-forced simulation run provides high NSEs, with 0.917, 0.856, and 0.895 for the calibration, validation, and entire periods (Figure 9b), respectively, and corresponding RMSE values of 1517.2, 2048.5, and 1729.5 m\({}^{3}\)/s, respectively. Figures S1a, S2a, S3a, and S4a show that the rain-gauge-driven XAJ model can accurately reproduce the historical 3-hourly discharge processes at the four upstream hydrological stations. The GXAJ model using the rain-gauge-benchmarked parameters was forced by 3B42 RT, 3B42V7, and IMERG-F SPPs for 3-hourly discharge simulations. Figure 8a shows that the 3B42 RT-forced simulated hydrograph is fairly in line with the observed data, with BIAS, NSE, and RMSE values of \(-\)0.4%, 0.871, and 1913.1 m\({}^{3}\)/s, respectively, for the entire simulation period (Figure 9a-c), and the performance of the 3B42 RT-based model run is nearly comparable with that of the rain-gauge-based model run, except for a noticeable underestimation of high flows in 2016. However, the 3B42V7- and IMERG-F-driven simulation runs presents an obvious flow underestimation in most of the simulation period (Figure 8b,c), with the BIAS values of \(-\)27.4% and \(-\)31.3% for the entire period, respectively (Figure 9a). The 3B42V7- and IMERG-F-based model runs also obtain lower NSEs (0.690 and 0.664, respectively) and higher RMSEs (2965.8 m\({}^{3}\)/s and 3089.5 m\({}^{3}\)/s, respectively) than the 3B42 RT-based model run (Figure 9b,c). Figure 10a compares the simulated discharge-duration curves using the rain-gauge-based precipitation data, 3B42 RT, 3B42V7, and IMERG-F SPPs with the observations. This figure indicates  
Figure 8: Comparison of the measured and simulated 3-hourly hydrographs at the Monywa hydrological station using the rain-gauge-based gridded precipitation data set, 3B42 RT, 3B42V7, and IMERG-F SPPs in the calibration (1 April 2014–31 December 2015) and validation periods (1 January 2016–31 December 2016) under two simulation scenarios. that the rain-gauge-based simulation generates the 3-hourly discharge frequency distribution that basically agrees with the observations, except for a moderate discharge overestimation at the quantile levels of <12.6% and >98.7% and a slight underestimation of the intermediate discharge quantiles (12.6-98.7%).