## 4 Results and Discussion
### Flood Discharge Simulation  
Model setup: The BTOP model was run at an hourly time step for five cases (i.e., July 2002, August 2003, October 2004, July 2007, and October 2009). Figure 6 and Table 2 show the hydrographs and evaluation criteria of simulations with five precipitation datasets (i.e., Gauge, GSMaP, IMERG, GSMaP-RBC, IMERG-RBC). We considered that the best-fit parameters of the model are calibrated with well-gauged rainfall input. Therefore, we adopted the parameters calibrated with gauge precipitation to set up the model to simulate discharges with SPPs. The model performed excellently in calibration (October 2004) with an \(NSE\) of 0.95, a CC of 0.98, a \(BIAS\) of \(-72.42\) m\({}^{3}\)/s, and an \(RMSE\) of \(165.23\) m\({}^{3}\)/s. The results of validation with gauge rainfall were also satisfactory with \(NSE\) of 0.94, 0.94, 0.96, and 0.74 for July 2002, August 2003, July 2007, and October 2009, respectively. It was found that the gauged precipitation performed best among five rainfall data sources. This means  
Figure 5: Taylor diagram for the original and best-corrected SPPs of five flood cases. (**a**)–(**e**) represent five cases respectively. the BTOP model has the capability to simulate flood discharge with sufficiently accurate rainfall input. Simulation with/without bias correction: The bias-corrected NRT SPPs were used in discharge simulation, and the results were compared with the cases without bias correction. It was found the bias correction significantly improved the performance. In particular, as shown in Table 2, the \(NSE\) of runoff simulation with GSMaP-RBC rose from 0.14 to 0.83, \(CC\) increased from 0.49 to 0.93, \(BIAS\) decreased from \(-152.59\) m\({}^{3}\)/s to \(-6.32\) m\({}^{3}\)/s, and \(RMSE\) improved from 576.5 m\({}^{3}\)/s to 259.14 m\({}^{3}\)/s in August 2003. Similarly, the \(NSE\) of simulation with IMERG-RBC in July 2002 and August 2003 improved from 0.2 to 0.81 and 0.21 to 0.82, respectively. As shown in Figure 6a-c, the simulated discharges with corrected SPPs have a good agreement with the observed. In July 2002, the simulated runoff with GSMaP-RBC overestimated the flood peak due to the corrected SPPs enlarged the extreme value of precipitation. Figure 6b shows that the simulated discharge of the second flood peak with GSMaP-RBC and IMERG-RBC matches the observed discharge perfectly, even better than gauge simulations. In October 2004, although the results significantly improved after bias correction, there is still a discrepancy of the second flood peak. Figure 6: Flood discharge simulation at Kitamatsuo by using five precipitation datasets for five cases. (**a**)–(**e**) represent five cases respectively. (**c**) is the calibration period, while others are the validation period. However, in the cases of July 2007 and October 2009, the performance of corrected SPPs is not satisfactory. As shown in Figure 6d,e, GSMaP and IMERG overestimated the rainfall before the flood peak and underestimated the actual precipitation event that caused the flood. It caused the simulated discharge to fail to meet the flood peak and also caused the simulated flood peak to move forward. From the aspect of rainfall distribution in these two cases, the NRT SPPs duration is longer than Gauge. It leads the light rainfall heavier and heavy rainfall lighter, and makes the simulated discharge higher than observed when in the low-flow stage and fail to capture the flood peak. Moreover, looking at the rainfall evaluation of five flood cases, Figure 5d,e, the improvement of original SPPs obviously smaller than other cases. In the view of _NSD_ in these two cases, the corrected SPPs become worse except GSMaP in July 2007. The results show that such statistical bias correction techniques cannot effectively improve all precipitation events when the distribution of SPPs and measured precipitation is inconsistent. Another problem that should be noticed is that in the case of October 2009, even the gauge rainfall cannot capture the flood peak well with an _NSE_ of 0.74.