## 3 Results and Discussion
### Evaluation of Streamflow Simulation Capacity of Different Precipitation Products
The underestimations of rainfall from these precipitation products can be a good reason subsequently for such underestimations in simulated streamflow. [PERSON] and [PERSON] [38] have also carried out hydrologic modeling for the Upper Nan River Basin using the Rainfall-Runoff Inundation Model (RRI) to simulate an extreme rainfall event. The results of the study in the Upper Nan demonstrated that the PERSIANN product significantly underestimates observed streamflow. In addition, [PERSON] et al. [64; 65] showcased similar cases for the PERSIANN group of products over the Seethawaka watershed, a sub-watershed of the Kelani watershed of Sri Lanka. Through the results of the current study, it is clear that the spatial resolution of SbPPs products does not have a clear impact on streamflow simulations. For instance, in terms of SbPPs, the TMPA-3B42 product which had the lowest spatial resolutions (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) outperformed the CCS which had a comparatively high spatial resolution (0.04\({}^{\circ}\)\(\times\) 0.04\({}^{\circ}\)). The CMORPH product which had the highest spatial resolution (0.027\({}^{\circ}\)\(\times\) 0.027\({}^{\circ}\)) demonstrated the worst skills among all. Interestingly, the IMERG product which had a spatial resolution of 0.10\({}^{\circ}\)\(\times\) 0.10\({}^{\circ}\) did not perform well in simulating streamflow when compared to other SbPPs (ice PERSIANN, PERSIANN-CDR, and MSWEP) which had a comparatively lower spatial resolution (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)) to IMERG. PERSIANN, PERSIANN-CDR, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Precipitation** & \multicolumn{2}{c}{**For Calibration (2007–2010)**} & \multicolumn{2}{c}{**For Validation (2011–2014)**} \\
**Product** & **NSE** & **R2** & **NSE** & **R2** \\ \hline Rain gauge & 0.82 & 0.83 & 0.77 & 0.78 \\ PERSIANN & 0.19 & 0.49 & 0.50 & 0.73 \\ CCS & 0.27 & 0.57 & 0.35 & 0.76 \\ CDR & 0.15 & 0.60 & 0.40 & 0.81 \\
3B42 & 0.55 & 0.72 & 0.68 & 0.85 \\ TMPA-3B42 RT & 0.01 & 0.63 & 0.10 & 0.62 \\ IMERG & 0.08 & 0.74 & 0.13 & 0.82 \\ MSWEP & 0.55 & 0.75 & 0.30 & 0.77 \\ CHIRPS & 0.55 & 0.69 & 0.14 & 0.61 \\ CMORPH & \(-\)0.17 & 0.53 & -0.07 & 0.68 \\ APHRODITE\_V1901 & 0.61 & 0.72 & 0.53 & 0.91 \\ APHRODIE\_V1801 & 0.21 & 0.66 & 0.49 & 0.90 \\ GPCC & 0.32 & 0.73 & 0.45 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hydrologic performance of different precipitation products. MSWEP, TMPA-3B42, which had the same spatial resolution performed slightly differently in hydrologic model simulations. However, with a slight exception, in terms of the GbGPPs, it is clear that among the GbGPPs which had the highest spatial resolution (0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\)), APHRODIE_V1901 outperformed two other GbGPPs in reproducing observed streamflow with NSE values greater than 0.50 for both calibration and validation time periods. The authors of this paper believe that the uncertainties in the hydrologic model might have also induced some errors (to a certain extent). The sampling, instrumental and algorithmic errors in estimating rainfall are some of the reasons which might be attributed to the mismatches between observed and simulated streamflow. However, [PERSON] et al. [66] demonstrated that 3B42 product has outperformed PERSIANN and CMORPH products over Malaysia. These results are comparable with the results obtained in this study for HBS watershed. Furthermore, [PERSON] et al. [38] have demonstrated that TRMM and IMERG simulated streamflow over-estimated observed streamflow in the Chi River Basin of the north-eastern part of Thailand. This observation is also similar to the present study. Therefore, the results of this presented paper are justifiable.