## 4 Results
### Evaluation of Streamflow Simulations
#### 4.2.1 Daily Streamflow  
Figure 8 depicts the daily hydrographs at the Hkamti and Monywa stations that were simulated using different precipitation inputs. All five simulation runs captured the temporal variation patterns of streamflow that were similar to the observed discharge. Frequent high flows occurred from June to September, whereas low flows were mainly observed from December to April. Figure 8 and Table 4 show that although rain gauges are sparsely distributed in the Chindwin River basin (merely five gauges in a controlled area of 110,350 km\({}^{2}\)), the XAJ model, driven by the gridded precipitation data set that was interpolated from the five rain gauges, satisfactorily reproduced the daily streamflow processes at all five stations from 1 April 2014 to 31 December 2015. This simulation run slightly underestimated the daily streamflow by 0.7%-5.2%, with NSE and LogNSE exceeding 0.82 and 0.85. These findings indicate the sound performance of the XAJ model in simulating both high- and low-flow processes. Given that both IMERG and 3B42V7 products contained considerable errors in precipitation estimates, the two modeling runs that utilized the gridded precipitation data sets derived from the original IMERG and 3B42V7 products produced generally poorer simulations than the gauge-based simulations in terms of all statistical indices in Table 4. Moreover, because IMERG underestimated precipitation at all the rain gauges, particularly gaining a considerably large BIAS in upstream gauges (\(-37.1\%\) and \(-41.2\%\) at the Hkamti and Homalin stations, Table 2), the streamflow simulated by the XAJ model with the original IMERG precipitation as its input was approximately 23.5%-31.2% systematically lower than the observed discharge (Table 4). Meanwhile, 3B42V7 presented smaller systematic errors in precipitation at the Homalin, Mawlaik, Kalewa, and Monywa stations (\(-5.2\%\)-6.4% BIAS); however, 3B42V7 significantly underestimated precipitation at Hkamti, which was the source region station (Table 2). Consequently, the XAJ model systematically underestimated the streamflow at Hkamti by 16.6%, and this systematic bias propagated to the downstream stations, causing the simulated discharge at these stations to be 10.9%-16.9% lower than the measurements. Figure 8 illustrates that a few simulated flood peaks were remarkably lower than the actual ones in the two  
Figure 7: Monthly precipitation estimates from 3B42V7 and IMERG satellite precipitation products at the main weather stations against the gauge-based monthly precipitation time series from April 2014 to December 2015.  
modeling runs using the original satellite products. This phenomenon mainly resulted from the underestimation of precipitation by both IMERG and 3B42V7 during heavy rain events. Table 4 shows that the simulation run using the original 3B42V7 product was generally better than that using the original IMERG precipitation. Although the satellite precipitation products and gauge-based precipitation data were in a very poor correlation on daily basis (Tables 2 and 3), the CC values for daily streamflow simulations using the original IMERG and 3B42V7 products were significantly increased (Table 4). The possible reason for this phenomenon is that both IMERG and 3B42V7 are able to effectively capture the seasonal cycle of precipitation in the Chindwin River basin (Figure 7), which facilitates to reproduce rational streamflow processes in both wet and dry seasons. Meanwhile, the observed streamflow time series shows high seasonality, which is generally easy for hydrological models to reproduce. In addition, hydrological models are, to some degree, tolerant to the errors of precipitation inputs, and model parameter calibration is able to partially offset the streamflow biases resulting from inaccurate precipitation inputs. For example, the XAJ model uses the highly sensitive parameter K (the coefficient of PE) to calculate PE, and a larger K tends to produce higher actual ET and thus generate lower total runoff.