## 5 Discussion  
The latest GPM Core Observatory sensors and new calibration algorithms were developed to improve the performance of GPM constellation satellites in observing precipitation at much finer spatiotemporal scales than their predecessor, TRMM. A few recent studies have proven that the GPM IMERG products are generally superior to TRMM in several regions, such as the Xinjiang region [30] and the Qinghai-Tibetan Plateau in China [30; 31]; Mainland China [33; 34]; Guilan, Bushehr, Kermanshah, and Tehran regions in Iran [37]; Far-East Asia [35]; and India [36]. However, in the present study, although the daily precipitation data of both the IMERG final run and TMPA 3B42V7 captured the spatiotemporal variation patterns of rainfall events in the Chindwin River basin in Myanmar, no significant improvements were found in IMERG. Furthermore, the 3B42V7 data set even outperformed IMERG at the daily and monthly time scales and in heavy rain event detections at four out of the five gauges. These findings strengthen the urgent need to further improve GPM algorithms, particularly for data-sparse regions with complex terrains in Myanmar. This study demonstrates that the non-negligible errors in the two satellite precipitation products significantly propagated to streamflow simulations. The significant negative biases in total precipitation amount and heavy rain estimates, to a large extent, caused the noticeable underestimation of total runoff and high flow, with IMERG-based simulations demonstrating a considerably worse performance than 3B42V7. Despite the inherent considerable biases, both the original IMERG and 3B42V7 products were, in certain periods, superior to the gauge-based data in hydrologic applications. Figure 11 indicates that the two satellite products estimated the areal mean precipitation in the region controlled by the Hkamti station to be 303.5 mm and 286.3 mm in May 2014. Thus, the corresponding simulated hydrographs agreed well with the observed data. By contrast, only one gauge was situated in the drainage area of Hkamti, and the basin-averaged precipitation derived from gauge data was negatively biased to be 57.0 mm, thus producing a much lower streamflow than the measured values. This result proves that both IMERG and 3B42V7 accurately captured the spatiotemporal features of precipitation in May 2014 and therefore facilitated streamflow simulations. It also highlights the necessity for the local government to build a denser rain gauge network in the basin. In addition, a monthly-based simple bias-correction method was applied to the two satellite data sets and effectively improved the overall performance of streamflow simulations. The corrected 3B42V7-based simulations performed slightly better than that using the gauge-based precipitation, implying that the 3B42V7 product can potentially improve the discharge simulations over the data-sparse Chindwin River. In regions where a sufficiently dense gauge network is unavailable, satellite-derived rainfall can be a critical data source for identifying hazards from small-scale rainfall and flood events [52]. The hydrological utility of satellite precipitation products is associated with large uncertainties from bias-correction methods, parameter estimations, and model structures. In this study, improved streamflow simulations were achieved via bias correction. However, the method adopted in this study sometimes produced irrationally high precipitation values at a few grid cells, resulting in overestimated discharge in certain periods. Therefore, a better bias-correction method should be developed in the future, and the uncertainties introduced by bias-correction procedures should be quantified. Ideally, the parameters of a hydrological model should be calibrated using the \"perfect\" observed precipitation data set measured in a dense gauge network.