Groundwater resource assessment in the Upper Godavari Sub Basin, India: A soft computing and CMIP6 Ensemble Approach.

Groundwater is a critical lifeline for sustaining water resources in Upper Godavari Sub Basin, India's arid regions. However, due to impeding water requirement and demand in these regions along with anthropogenic complexities has raised serious concerns for this vital resource. Along with anthropogenic activities, Climate change also threatens this precious resource due to scarcity of surface water mostly during the summer season of the year. Hence, to comprehend this important issue, the groundwater resource assessment needs to be done for present as well as future scenarios. Therefore, the present study assesses the groundwater resource using a SWAT-MODFLOW model which is a combination of advanced hydrological model with cutting-edge numerical groundwater model. Individual surface and groundwater models are developed in SWAT and MODFLOW respectively and then are linked using the linkages files to get the more enhanced surface and groundwater interaction in the form of recharge, groundwater level and interaction of rivers with sub surface. The surface and groundwater models are calibrated and validated using the streamflow and groundwater level data. The calibrated model thus presents the current scenario of groundwater allocation which is then simulated with different bias corrected climate variables for getting the status of groundwater for future SSPs scenarios. From a range of CMIP6 climate models, the best model is selected based on the statistical index such as NSE, the correlation coefficient, R2, MAE, RMSE, MSE, and NRMSE which was NESM3 in the present case with a highest correlation and R2 with IMD precipitation and temperature dataset. The best selected climate model (NESM3) is then bias corrected using the empirical quantile method. Along with the numerical approach, to map the groundwater level data, soft computing approach using RFR and GBR is also employed to predict the groundwater level data for future scenarios. The optimization of these models was done by the Particle PSO. The study findings in Upper Godavari Sub Basin, India, revealed significant changes in groundwater levels across different seasons, with particularly significant increases observed during the dry season. The study showed that MODFLOW-GBR-PSO is more accurate in predicting groundwater level than MODFLOW-GBR, MODFLOW-RFR-PSO and MODFLOW-RFR. The result also predicted decreased rainfall for the SSP 585 scenario which in turn lead to drop in groundwater level and recharge in the distinct parts of the sub basin. Hence, from the above result a proper mitigation and framework needs to be prepared to counterfort the diminishing groundwater resource for the betterment of environment.

Key words: climate change, hydrological model, SWAT, Nash-Sutcliffe efficiency (NSE), Root mean square error (RMSE), Random Forest regression (RFR) and Gradient Boosting Regression (GBR), Swarm Optimization method (PSO).