Geospatial datasets are very much useful in carrying out hydrological modeling in distributed and semi-distributed models. Through modeling approach, this study is performed to study the impact of climate change on reservoir inflows in the upper Krishna sub-basin, India using the Coupled Model Intercomparison Project (CMIP5) climate dataset for the hydrological year (2021–2055) considering the base period as 1985–2020. Bias corrected Centre National de Recherches Meterolgiques (CNRM5) with Representative Concentration Pathway (RCP4.5) climate data are used in this study. Variable Infiltration Capacity (VIC) hydrological model is used for the study because of its ability to simulate hydrological processes by incorporating storage structures. The water storage structures in terms of Major reservoirs are incorporated using the VIC-Reservoir Representation module. The cascade effect of reservoirs is captured by considering the upstream reservoir's operational strategies and hydraulic particulars. The model performance in terms of R2, Nash Sutcliffe Efficiency (NSE) and Kling Gupta Efficiency (KGE) during the calibration period is found to be 0.91, 0.74, 0.74 and 0.95, 0.84, 0.86 during the validation period respectively in the baseline period. The future study period is divided into near (2021–2032), mid (2033–2044) and far (2045–2055) decade. The projected runoff coefficient varies 0.15–0.54 and the Evapotranspiration coefficient lies 0.32–0.72. It is found that the reservoir Almatti and Narayanpur receives a maximum inflow of 1926 m3/s and 2057 m3/s against the Long Term Average of 1577 m3/s and 2319 m3/s in the mid and far decadal periods respectively.