This study looks at an alternative ensemble-based approach for a subset of the National Water Model (NWM) domain over a significant portion of Colorado. For the 2017 water year, a real-time cutout of the NWM was ran over the western two thirds of Colorado forced with downscaled National Land Data Assimilation System (NLDAS) forcings to provide real-time model states. Approximately twice a month starting in April, an ensemble of historical NLDAS forcings (2004-2016) was used to run forecasts to the end of the water year from the current real-time states. From these forecasted simulations, a set of ensemble forecasted accumulated flow at a series of Colorado Department of Water Resources (CODWR) observation gage sites were derived for the remainder of the water year. These ensemble streamflow prediction (ESP) forecasts were also generated for additional United States Geological Survey (USGS) gage sites not included with the CODWR sites. Additionally, a long-term retrospective simulation (2004-2016) was run to compare observed streamflow to model simulated streamflow at the gage sites. This was done in order to quantify potential wet/dry biases that occur in the model for these forecast points. This analysis took place for the April 1st – October 1st time frame to focus on the snowmelt period that primarily drives water resources for the state of Colorado. From the retrospective analysis, sets of bias correction factors were derived at each forecast gage point where either adequate daily or hourly observations were available. These bias correction factors were applied to the simulated streamflow as a post-process prior the final forecasted values were disseminated to the CODWR. Additional analysis took place using observed SNOTEL snow water equivalent (SWE) observations, gridded snow states from the National Weather Service (NWS) Snow Data Assimilation System (SNODAS), and gridded snow depth and SWE estimates from the Jet Propulsion Laboratory (JPL) Airborne Snow Observatory (ASO). This additional analysis focused on the snow states in the CO NWM cutout as these errors can result in forecasted streamflow biases given snowmelt-driven hydrology that occurs. This work provides an experimental framework for expanding the ensemble-based approach for the NWM that could be expanded into a more robust operational framework that covers a larger section of the western US in future NWM upgrades.