Understanding the Climate Change and Land Use Impact on Streamflow in the Present and Future under CMIP6 Climate Scenarios for the Parvara Mula Basin, India

Understanding the likely impacts of climate change (CC) and Land Use Land Cover (LULC) on water resources (WR) is critical for a water basin’s mitigation. The present study intends to quantify the impact of (CC) and (LULC) on the streamflow (SF) of the Parvara Mula Basin (PMB) using SWAT. The SWAT model was calibrated and validated using the SWAT Calibration Uncertainty Program (SWAT-CUP) for the two time periods (2003–2007 and 2013–2016) and (2008–2010 and 2017–2018), respectively. To evaluate the model’s performance, statistical matrices such as R2, NSE, PBIAS, and RSR were computed for both the calibrated and validated periods. For both these periods, the calibrated and validated results of the model were found to be very good. In this study, three bias-corrected CMIP6 GCMs (ACCESS-CM2, BCC-CSM2-MR, and CanESM5) under three scenarios (ssp245, ssp370, and ssp585) have been adopted by assuming no change in the existing LULC (2018). The results obtained from the SWAT simulation at the end of the century show that there will be an increase in streamflow (SF) by 44.75% to 53.72%, 45.80% to 77.31%, and 48.51% to 83.12% according to ACCESS-CM2, BCC-CSM2-MR, and CanESM5, respectively. A mean ensemble model was created to determine the net change in streamflow under different scenarios for different future time projections. The results obtained from the mean ensembled model also reveal an increase in the SF for the near future (2020–2040), mid future (2041–2070), and far future (2071–2100) to be 64.19%, 47.33%, and 70.59%, respectively. Finally, based on the obtained results, it was concluded that the CanESM5 model produces better results than the ACCESS-CM2 and BCC-CSM2-MR models. As a result, the streamflow evaluated with this model can be used for the PMB’s future water management strategies. Thus, this study’s findings may be helpful in developing water management strategies and preventing the pessimistic effect of CC in the PMB.