## 3 Results and Discussion
### Consequence of Climate Change on Precipitation  
The SWAT model was calibrated and validated for simulations of streamflow and sediment load over the Nagavali and Vamsadhara watersheds. The sensitivity analysis, parameters, and their fitted values and statistics were found in [PERSON] et al. (2022) [47]. When contrasted with standard model statistics, statistical results for the SWAT model setup for the Nagavali and Vamsadhara watersheds were good [54]. The average annual precipitation for the Nagavali and Vamsadhara watersheds over a period of 40 years (1975-2014) is 1259 mm and 1314 mm, respectively. Table 3 shows the percentage bias between the precipitation predicted by the IMD gridded model and climate models for these watersheds. The climate models exhibited varying predictions, ranging from \(-4.73\%\) to \(1.59\%\) for the Nagavali watershed and \(-2.86\%\) to \(3.71\%\) for the Vamsadhara watershed (Table 3). Four climate models (Ecearth3, MPI-ESM1-2 HR, CanESM5, and INMCM4) slightly underestimate the precipitation, while the ACCESS-CM2 model slightly overestimates it in both watersheds. Table 4 presents the percentage change in precipitation predicted by climate models for the near future (2022-2060) and far future (2061-2100) compared to historical data (1975-2014) in both watersheds. The Cold-Dry model consistently underestimated precipitation across all three scenarios (SSP245, SSP370, and SSP585) in the near future. During the near future, the Warm-Wet model showed the highest overestimation of precipitation under the SSP370 and SSP585 scenarios, while the Cold-Wet model showed the greatest overestimation under the SSP245 scenario. In the far future, the Warm-Wet and Cold-Wet models showed the highest overestimation of precipitation, while the Warm-Dry model showed the lowest overestimation. The highest levels of monthly precipitation patterns were observed in August for both the Nagavali and Vamsadhara watersheds (Figure 4), with intensities of 247.85 mm and 255.6 mm, respectively. However, the INMCM4 and MPI-ESM1-2 HR models showed their peak precipitation in July, while the ACCESS-CM2 and Ecearth3 models showed their peaks in August, and the CanESM5 model showed their peaks in September for both watersheds. Figure 5 shows the future projections of mean monthly precipitation under different scenarios. These projections followed a similar pattern to the historical precipitation data. Figures 4 and 5 indicate that in both watersheds, the majority of rainfall occurs during \begin{table}
\begin{tabular}{l l} \hline \hline
**Model Name** & **Representing Scenario** \\ \hline CanESM5 & Warm-Wet \\ \hline Ecearth3 & Cold-Wet \\ \hline INMCM4 & Average \\ \hline MPI-ESM1-2 HR & Cold-Dry \\ \hline ACCESS-CM2 & Warm-Dry \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RCM models and the corresponding scenarios they represent. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multicolumn{2}{c}{**\% Bias in Precipitation**} \\ \cline{2-3}  & **Nagavali River Basin** & **Vamsadhara River Basin** \\ \hline Ecearth3 & +1.59 & +3.49 \\ MPI-ESM1-2 HR & +0.63 & +2.36 \\ CanESM5 & +0.69 & +3.32 \\ ACCESS-CM2 & \(-4.73\) & \(-2.86\) \\ INMCM4 & +1.29 & +3.71 \\ \hline \hline \end{tabular} Note: “+” sign indicates climate model value is lower than observed IMD value, “\(-\)” sign indicates climate model value higher than observed IMD value.  
\end{table} 
Table 3: Percentage difference between the observed data from the Indian Meteorological Department (IMD) and the historical data simulated from the model. the monsoon season, and approximately 80% of the annual runoff is generated during this period [47; 55]. These findings highlight the importance of implementing watershed management structures in both watersheds.