## 5 Discussion
### Evaluation of SPPs against Ground Rainfall Observations
[78] concluded that GSMaP-GAUGE version 06 (V06) performs better and has more stable quality results than IMERG-F version 04 (V04) on daily and monthly scales in Mainland China in terms of mean error (ME), RMSE, CC, and POD. [PERSON] et al. [79] also found that GSMaP-GAUGE V06 generally outperforms IMERG-F V04 with high CC and low absolute ME values on daily and monthly scales over eight river basins in China. IMERG-F V04 performs relatively better than GSMaP-GAUGE V06 in most areas in China but tends to overestimate precipitation when rainfall intensity exceeds 20 mm/d in four regions of China [72]. Among the four near-real-time products (PERSIANN, 3B42 RT, IMERG-L, and GSMaP-NRT), [PERSON] et al. [80] found that IMERG-L and GSMaP-NRT perform closest-to-ground observations in South China and recommended IMERG-F as the best satellite product in capturing flood hazard events with low false alarms. [PERSON] et al. [29] evaluated IMERG-F V05, GSMaP-GAUGE V07, and 3B42V7 in six Brazilian regions and the entire Brazil territory and found that GSMaP-GAUGE presents better performance than IMERG-F and 3B42V7 in all regions and at various precipitation intensity levels with respect to CSI and equitable threat score (ETS). Regarding CSI, ETS, POD, FAR and BIAS, IMERG-F and 3B42V7 demonstrate similar behavior with better performance for IMERG-F [29]. [PERSON] et al. [29] inferred that the different precipitation characteristics from these three products might partially result from the quality of the CPC and GPCC global precipitation data sets. In Brazil, the mean gauge number of the CPC data used to calibrate GSMaP is approximately 1000, whereas only approximately 300 rain gauges are included in the GPCC data set for bias-correcting the IMERG and TMPA products [29]. In Bangladesh, GSMaP-GAUGE V07 was found to have better correspondence with the rain gauge measurements than IMERG-F version 04A [74]. This observation is partly ascribed to the situation where GSMaP-GAUGE uses auxiliary data sources, such as topographic data, JMA Global Analysis (GANAL) data, and JMA Merged Satellite and in situ Global Daily Sea Surface Temperatures (MGDSST) forecast data set [74]. Our study demonstrates that IMERG is generally superior to GSMaP in retrieving near- and post-real-time precipitation in the Chindwin River basin (Figure 6). The relatively better performance of IMERG-F in contrast with GSMaP-GAUGE might be partially attributed to the fact that around five rain gauges are included in the GPCC data, but the CPC data set do not contain local stations for bias-correction. Other causes for this phenomenon should be analyzed in the future, particularly the comparison and analysis of the algorithms of the near-real-time versions of IMERG and GSMaP with consideration of monsoon activities and local topographical effects. The auxiliary data sets for GSMaP gauge-adjustment, such as GANAL and MGDSST, should also be evaluated. Despite the inherent non-negligible biases, nearly all SPPs (excluding GSMaP-GAUGE) have an advantage over the gauge-based gridded precipitation data in effectively identifying the low precipitation region in the basin (the Arakan highland region, Figure 3), where rainfall occurrence is strongly affected by the southwestern monsoon activities and large topographic relief. This phenomenon reflects that ground precipitation observations at a limited number of rain gauges (seven stations in this study) might not sufficiently capture the actual precipitation distribution in the watershed. Therefore, the watershed-averaged precipitation time series derived from the gauge-based gridded precipitation data are likely to contain considerable biases, and caution should be taken on the findings of the watershed-scale precipitation evaluation. In the light of the strength of SPPs in detecting reasonable spatial distribution of precipitation over the studied basin, reproducing rational precipitation data sets by merging the satellite precipitation data, ground precipitation observations, and other data sources is desirable. [PERSON] et al.