## 4 Results and Discussion
### Spatio-Temporal Variability of Evapotranspiration and Water Yield in the Basin  
Evapotranspirationiration (ET) defines the turbulent transfer of water in the form of transpiration from vegetation surfaces and evaporation from ground surfaces [57]. It is one of the important components of the hydrologic cycle. Calculation of ET is typically based on the conservation of either energy or mass, or both. Evapotranspiration is one of the important processes of energy balance because it requires latent heat (energy) to evaporate. It is simply too expensive to install equipment measuring ETs in various locations across river basins. To deal with such problems, the USGS Earth Resources Observation and Science (EROS) Center estimates actual evapotranspiration based on surface energy balance methods from the earth land surface using satellite remote sensing techniques and the operational simplified surface energy balance (SSEBop) model. [PERSON] et al. [58] have developed a method to estimate the actual evapotranspiration, based on SSEBop model, with a unique parameterization using (MODIS) land surface temperature (LST) data. SSEBop's ET was available publicly via the famine early warning system [48]. In this study, seasonal and inter-annual variability of evapotranspiration ET of the basin was calculated using SSEBop ET datasets. The maximum and minimum values of ET, that is, 125 mm/month and 12 mm/month in the basin, were obtained during May and November, respectively (Figure 9). Higher and lower ET values in the basin were obtained during post-monsoon and monsoon seasons, respectively. The analysis shows that the October and November months of the post-monsoon season generate minimum ET from the basin. The seasonal variability of water yield derived from PERSIANN precipitation data depicts that the high water yield in the basin is associated with the July and August months of the monsoon season, with water yields of 240 mm/month and 120 mm/month, respectively (Figure 9). The analysis of PERSIANN precipitation data also shows that the pre-monsoon and post-monsoon seasons generate almost zero to negative water yield in the basin. The spatial variability in mean monthly water yield (mm/month) obtained from PERSIANN precipitation data implies that the higher mean monthly water yield concentrates over the upper part of the basin. The mean monthly water yield consistently decreases from the upper reaches towards downstream and up to the outlet of the basin, reflecting a moderate to very low mean monthly water yield from the middle and lower parts of the basin. Further, inter-annual variability of water yield obtained from PERSIANN precipitation datasets showed that higher water yield was associated with the wet period, and lower water yield was associated with the dry period. The seasonal water yield from CHIRPS precipitation showed higher water yield during monsoons (July and August, with water yield amounts of 290 mm/month and 200 mm/month, respectively) (Figure 10). The water yield obtained from CHIRPS precipitation data for the pre-monsoon and post-monsoon seasons depicted very low water yield in the basin. The spatial variability of mean monthly water yield derived from CHIRPS precipitation data showed that the upper part of the basin, especially the southeast portion, has a higher mean monthly water yield value (40-60 mm/month). In contrast, the middle and lower portions of the basin have almost zero to negative water yield. The pattern of inter-annual variability of the water yield derived from the CHIRPS precipitation datasets was almost similar to the pattern derived from PERSIANN precipitation datasets. CHIRPS precipitation datasets also showed higher water yield during the wet period and lower water yield during the dry period. For a better understanding, we have assessed the water yields for the entire basin and every individual year of the study period. Both precipitation datasets have shown lower yearly water yield in the middle and lower portions of the basin during the dry period (Figure 11).