The mountains across the western United States are among the many regions around the world that are experiencing a shift in snow extent and snowmelt timing due to changing global climatological and local factors, such as precipitation phase changes and dust on snow. Snow is an essential hydrological resource, and accurate meltwater supply predictions by local forecast centers are essential for water-resource planning. The Colorado Basin River Forecast Center (CBRFC) is currently using a temperature-based index melt model to predict timing and volume. By not representing the physical processes that control snowmelt, primarily the balance between incoming and outgoing solar and longwave radiation, errors can result when snow conditions are outside the historical model calibration period. To supplement the operational forecasting by the CBRFC, we evaluate the use of a physically-based snow-energy balance model (Automated Water Supply Model/iSnobal). We ran the model over the East River Watershed, Colorado, to compare the model input data to local instrumentation sites and modeled snowmelt timing and magnitude to the operational results by the CBRFC. This assessment will inform the next phase of the study: assimilating remote sensing data into iSnobal to improve the snowmelt prediction accuracy for operational use by CBRFC over the larger Gunnison River Basin.