Water resources numerical models are dependent upon various input hydrologic field data. As models become increasingly complex and model simulation times expand, it is critical to understand the inherent value in using different input datasets available. One important category of model input is precipitation data. For hydrologic models, the precipitation data inputs are perhaps the most critical. Common precipitation model input includes either rain gauge or remotely-sensed data such next-generation radar–based (NEXRAD) data. NEXRAD data provides a higher level of spatial resolution than point rain gauge coverage, but is subject to more extensive data pre and post processing along with additional computational requirements. This study first documents the development and initial calibration of a HEC-HMS model of a sub-tropical watershed in the Upper St. Johns River Basin in Florida, USA. Then, the study compares calibration performance of the same HEC-HMS model using either rain gauge or NEXRAD precipitation inputs. The results are further discretized by comparing key calibration statistics such as Nash-Sutcliffe Efficiency for different spatial scale and at different rainfall return frequencies. The study revealed that at larger spatial scale, the calibration performance of the model was about the same for the two different precipitation datasets while the study showed some benefit of NEXRAD for smaller watersheds. Similarly, the study showed that for smaller return frequency precipitation events, NEXRAD data was superior.