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

Abstract

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.

Land Use and Land Cover Change Detection Using the Random Forest Approach: The Case of The Upper Blue Nile River Basin, Ethiopia

“…The Kappa coefficient measures how much less error is produced by a classification procedure when compared to an entirely random classification. [ 82,83,84,85 ] indicated that a kappa value of < 40% is poor, 40%–55% is fair, 55%–70% is good, 70%–85% very good, and >85% excellent. Therefore, a confusion matrix has been computed to obtain the kappa index.…”

Impact of Climate Change and Land Use Land Cover Change on Stream Flow of the Teesta River Basin under CMIP6 Climate Scenarios

“…Global studies, exemplified by (Gaur & Singh, 2023;Mohseni et al, 2023), highlight the significant influence of LULC changes on basin flow patterns, while (Jiménez-Navarro et al, 2021;Mohammed et al, 2018;Mohammed, Saiful Islam, et al, 2017;Mohseni et al, 2023;Mollel et al, 2023;Sun et al, 2022;Ullah et al, 2023;Yalcin, 2023) explore the impact of climate change on flow across diverse basins. Recent research combining climate change and LULC effects reveals a notable impact on river basin flow, predicting decreased dry period flow and increased wet season flow (Bal et al, 2021;Fields et al, 2021;Mohseni et al, 2023). Although specific studies for the Teesta basin are lacking, research in the broader Indian sub-continent, like that conducted by (Mohseni et al, 2023), underscores the substantial impact of LULC and climate change on future flow, indicating a significant increase in peak flow.…”

“…Recent research combining climate change and LULC effects reveals a notable impact on river basin flow, predicting decreased dry period flow and increased wet season flow (Bal et al, 2021;Fields et al, 2021;Mohseni et al, 2023). Although specific studies for the Teesta basin are lacking, research in the broader Indian sub-continent, like that conducted by (Mohseni et al, 2023), underscores the substantial impact of LULC and climate change on future flow, indicating a significant increase in peak flow.…”

Assessment of the Impact of Climate Change on Streamflow and Sediment in the Nagavali and Vamsadhara Watersheds in India

“…In recent years, the importance of streamflow forecasting has gained significant recognition [14,15]. On the other hand, streamflow prediction plays a vital role in long-term water resources planning and management [16][17][18]. Streamflow predictions are based on climate models and scenarios, which provide insights into potential changes in streamflow patterns under different climate change scenarios [11,19,20].…”

“…Recent studies have used the SWAT model to evaluate the consequences of changing the climate on streamflow and sediment load [22,29,30]. For example, Mohseni et al (2023) [18] used the SWAT model to evaluate the impact of climate change and land use change on streamflow over the Parvara Mula Basin, India. The findings of the study indicate an increase in streamflow for future periods.…”