## 4 Results
### Evaluation of Streamflow Simulations
#### 4.2.1 Daily Streamflow
Table S1 shows that the calibrated parameter K in the area controlled by the Hkamti station decreased from 1.379 in the gauge-based model run to 0.838 in the original IMERG-based simulation. As a result, model recalibration effectively damped the magnitude of runoff reduction that resulted from the significant precipitation underestimation in IMERG. Another example is the sensitive parameter SM (the areal mean free water storage capacity), and a smaller SM usually generates higher flood peaks. As indicated in Table S1, SM in the original IMERG-based run was much lower than that in the gauged-based run. The decreased SM partially compensated for the reduction in high flows arising from the systematical underestimation of heavy rain events in IMERG. Figure 8: Simulated daily hydrographs using the gauge-based precipitation data, the original 3B42V7 and IMERG precipitation estimates, and their corrected data sets at: Hkamti (**a**); and Monywa (**b**) streamflow stations. Qobs represents the observed streamflow. Qcal (gauged prec), Qcal (original 3B42V7), Qcal (corrected 3B42V7), Qcal (original IMERG), and Qcal (corrected IMERG) represent the calculated streamflow using the gauge-based precipitation data, the original and corrected 3B42V7 data sets, and the original and corrected IMERG data sets, respectively. Figure 8 and Table 4 demonstrate that bias correction of satellite precipitation products significantly improved the performance of streamflow simulations. The simulations using the corrected 3B42V7 precipitation achieved an even higher accuracy than those using the gauge-based precipitation, implying that the 3B42V7 product can effectively characterize the spatiotemporal features of precipitation in the study area. It also suggests that the bias-corrected 3B42V7 data set likely improved the hydrological simulations in the studied watershed where rain gauges are sparsely distributed. The XAJ model generally performed better in terms of BIAS and NSE when driven by the corrected IMERG data set than when driven by the original IMERG precipitation input. However, the CC and LogNSE values substantially decreased at the Mawlaik, Kalewa, and Monywa stations. Figure 8b shows that the modeling performance at Monywa even worsened in May-June 2015, overestimating the calculated streamflow presumably because of the large uncertainty of the bias-correction method for satellite precipitation products. As mentioned in Section 3.2, the bias correction of satellite precipitation was conducted under the assumption that the satellite precipitation product at the grid cells near a specified rain gauge has the same magnitude of biases (BIAS) as the precipitation estimates at that rain gauge. In this study, IMERG estimated the total precipitation amount at Homalin and Kalewa in May 2015 to be 9.6 mm and 12.0 mm, whereas the corresponding observations were 84.0 mm and 92.0 mm. According to Equations (7) and (8), the bias correction factor \(r\) was calculated to be 8.75 and 7.67 for the grid boxes surrounding Homalin and Kalewa. The original IMERG precipitation estimates at the grid cells near Homalin and Kalewa were amplified by 8.75 and 7.67 times to reach the corresponding corrected precipitation. This procedure of utilizing extremely large bias correction factors produced irrationally high precipitation values at a few grid cells, thereby generating distinct higher basin-averaged precipitation and ultimately leading to discharge overestimation at the basin outlet in May-June 2015. To further investigate the performance of high- and low-flow simulations using the satellite precipitation inputs, we defined the observed daily streamflow exceeding its 90% quantile as high flow and that less than its 50% quantile as low flow. The observed high- and low-flow data points