## 5 Conclusions  
A SWAT model was built, and its performance was evaluated in terms of, R\({}^{2}\) NSE, PBIAS, KGE, and RSR, which revealed that there was a strong correlation between simulated and observed streamflow for the baseline period at a monthly time step. The overall objective of this study was to assess the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Objective Function** & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**NSE**} & \multicolumn{2}{c}{**PBIAS**} & \multicolumn{2}{c}{**KGE**} & \multicolumn{2}{c}{**RSR**} \\ \hline  & **Cal** & **Val** & **Cal** & **Val** & **Cal** & **Val** & **Cal** & **Val** & **Cal** & **Val** \\ \hline Tianr & 0.83 & 0.82 & 0.80 & 0.79 & 8.4 & 13.9 & 0.85 & 0.80 & 0.43 & 0.46 \\ Qianjiang & 0.88 & 0.85 & 0.86 & 0.83 & 9.04 & 14.62 & 0.87 & 0.82 & 0.37 & 0.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance indices for Tianr and Qianjiang Hydrological Stations. future climate change projections on streamflow and water yield over the Upper Xijiang River Basin. The key conclusions from this study are: (a) Statistical performance indices, along with P-factor and R-factor, achieved ideal values in the calibration (1991-1997) and validation (1998-2001) periods, so the SWAT model performed well during these periods. LULC ESA (2005) was considered as a reference scenario for future streamflow projection, based on its best match and most acceptable parameter indices. (b) In this study, it was concluded that a set of five global climate models have similar patterns in pre-monsoon discharge trends, followed by abrupt peaks during monsoon and average peaks post-monsoon. February, March, and April had the lowest peaks recorded (below 1000 cm\({}^{3}\)/s). Future projections (2020-2050) predicted decreases of 8.1%, 8.2%, 11.45%, and 13.5% in streamflow under RCP-2.6, RCP-4.5, RCP-6.0, and RCP-8.5, respectively. The change in streamflow in the January-April period was higher, as compared to that of the wet-season of May-September; thus, dry climate corresponded to a decrease in streamflow. The largest decrease was observed in January-April, likely due to the declining trend of precipitation in this period. (c) The total water yield from 1991-2001 was estimated at 1310.86 mm, comprising 58.55 mm of surface runoff, 1211.45 mm of base flow (groundwater flow + tile flow), and 13.50 mm of lateral flow, with the remainder being transmission losses. Water yield for the future period (2020-2050) is likely to decrease by 30% in water yield under the average of all future scenarios; a decrease of 40% is likely to be more prominent under RCP-4.5. Thus, the water balance components of the Upper Xijiang River Basin are expected to change significantly due to the projected climate change that, in turn, would seriously affect water resources and streamflow patterns in the future. Further studies should be conducted under different modified LULC and climate change scenarios for estimating these future projections. There are many other dynamic factors such as crop management practices, water infrastructures, and land management within the catchment that can affect the future projections. Furthermore, attention is required for selecting parameters for calibration and validation of hydrological models. To tackle the impacts of climate change, the availability of adaptation options is vital. The results of this study can be useful for developing climate change adaptation plans, measures, and reforms in the agriculture and water sectors, by keeping the future projections in mind for the policy makers. [PERSON] came up with the idea, performed the analysis, designed the study, and wrote the paper; [PERSON] and T.M. helped in providing technical support and supervision; [PERSON] and [PERSON] helped in collecting the data and partial analyses; A.K. and A.S. helped with language editing and figures production. [PERSON] helped in review and proof-reading. All authors have read and agreed to the published version of the manuscript.