<p>A hydrologic model traditionally calibrated with only gaged streamflow data often face challenges in accurately simulating other hydrological variables. Reliable streamflow simulations do not guarantee accurate simulations of other hydrological components. To overcome these challenges, we employed the SWAT model and calibrated it using five different calibration schemes: gaged streamflow (SF) alone and combinations of gaged streamflow with remote-sensing-derived soil moisture (SM) and evapotranspiration (ET). These schemes included SF (single variable), SF + SM, SF + ET, SM + ET, and SF + ET + SM (multivariable). Traditional performance evaluation indices, such as Nash-Sutcliffe Efficiency, often fail to assess internal hydrological processes comprehensively. Therefore, a Repeated Measure Design (RMD) based on the concept of stability measure was employed in each calibration scheme to better account for these complexities. We evaluated the model’s performance at the sub-basin scale by focusing on the stability of simulated outputs such as surface runoff (SURQ), SM, and ET, and assessed the stability of most sensitive parameters: CN₂ (Curve Number) and ALPHA_BNK (Bank Storage Factor). Our findings revealed that calibration schemes incorporating remote-sensing data, alongside streamflow, such as SF + ET, SF + SM, and SF + ET + SM, exhibited greater stability compared to single-variable SF calibration scheme. Specifically, the SF + ET + SM and SF + ET schemes demonstrated more stability in simulating ET, while the SF + ET + SM and SF + SM schemes provided more stable simulations of SURQ and SM. Incorporating satellite-based data into the calibration process enhances the stability of hydrological modelling, leading to more effective water resource assessment, management, optimized water allocation, and improved flood control.</p>