Snow prediction reliability in upland watersheds is crucial due to climate change affecting snowmelt timing and river flows. The Soil and Water Assessment Tool (SWAT) can predict streamflow from rain and snowmelt. By calibrating SWAT models with snow data, researchers found that streamflow predictions were not greatly impacted by different calibration methods. However, using remote-sensing-based snow data improved snow water-equivalent (SWE) predictions. Default snow parameters were best for high flows, while watershed and lapse rate parameters affected flow predictions more than SWE predictions. This study suggests using remote-sensing-based SWE data for model calibration in snow-influenced watersheds to improve surface water supply predictions for agriculture in changing climates.