## 4 Results and Discussion
### Spatio-Temporal Distribution of Rainfall
The comparison of PERSIANN and IMD depicted the R\({}^{2}\), RMSE, NSE, and PBIAS values as 0.79, 44.09, 0.45, and \(-46.6\), respectively. However, the comparison of CHIRPS and IMD showed the R\({}^{2}\), RMSE, NSE, and PBIAS values as 0.72, 48.77, 0.58, and \(-32.94\), respectively, at the outlet (Figure 5). Further, the comparison was also made for the entire basin. The comparison of PERSIANN and IMD showed R\({}^{2}\), RMSE, NSE, and PBIAS values as 0.83, 48.35, 0.81, and \(-6.75\), respectively. However, the comparison of CHIRPS and IMD showed a good correlation of R\({}^{2}\), RMSE, NSE, and PBIAS values as 0.94, 29.92, 0.92, and \(-7.80\), respectively, for the entire basin (Figure 6). It was found that CHIRPS precipitation showed its usefulness, followed by PERSIANN, over the entire basin and at the outlet. Researchers such as [PERSON] and [PERSON] [51], [PERSON] [52], [PERSON] et al. [53], [PERSON] et al. [54], and [PERSON] et al. [55] have also compared the CHIRPS satellite-based precipitation data with IMD for hydrological analysis over different Indian regions and reported a significant positive correlation between them. In this study, the CHIRPS dataset showed better and positive correlation (>0.5) with IMD for the entire basin and at the outlet, as compared to the PERSIANN, which showed the positive correlation. Hence, the CHIRPS precipitation data was found to be more reliable for estimating total water consumption in the basin. Figure 4: Figure portraying average annual precipitation derived from PERSIANN and CHIRPS datasets for the whole study period. The precipitation products from PERSIANN and CHIRPS were also evaluated for their seasonality, maxima, and minima with IMD from 2003 to 2019. The time series plots have been developed between the observed precipitation data of IMD, PERSIANN, and CHIRPS precipitation datasets at the outlet of the basin (Figure 7) and for the entire basin (Figure 8). The mean and standard deviation of the precipitation data are \(\sigma_{\text{max}}=0.001\), \(\sigma_{\text{max}}=0.000\), and \(\sigma_{\text{max}}=0.000\). The  
(Figure 8). The time series plots depict the overestimation of precipitation by both PERSIANN and CHIRPS precipitation datasets, compared to observed data at the outlet of the basin. Compared to the observed data, the PERSIANN precipitation data underestimated the precipitation with bias of \(-6.75\%\), while CHIRPS precipitation data overestimated the precipitation with bias of \(-07.80\%\) over the entire basin. Analysis showed that CHIRPS could detect a high percentage of precipitation in flat areas, but not in hills or mountains [56]. Further, the accuracy of CHIRPS satellite-based precipitation data provided a positive correlation of 0.94, compared to the positive correlation of 0.83 of PERSIANN satellite-based precipitation data with the observed data of IMD. The analysis showed that CHIRPS satellite-based precipitation data provided more reliable, temporally consistent, and spatially distributed information about precipitation in the catchment, compared to the PERSIANN satellite-based precipitation data. Figure 8: The time series plot of observed (IMD) vs. satellite precipitations (PERSIANN and CHIRPS) datasets for the entire basin over a period from 2003–2019. Figure 7: The time series plot of a single location at the basin outlet for observed (IMD) vs. satellite precipitations (PERSIANN and CHIRPS) datasets over a period from 2003–2019.