Skillfully forecasting hydrologic outcomes of rain-on-snow (ROS) events is critical for water management and flood mitigation not only in the western U.S. but globally. This study applies methods for a Snowpack Runoff Decision Support System (SR-DSS) to the unimpaired Upper Carson watershed in the eastern Sierra Nevada of California and Nevada by leveraging hourly Natural Resource Conservation Service SNOw TELemetry (SNOTEL) data and compares results to observed soil moisture, streamflow, and an existing operational snowpack-runoff model framework used by the National Oceanic and Atmospheric Administration’s River Forecast Centers. Information provided by the SR-DSS can be disseminated to forecasters in real-time to adjust the SNOW-17 model as conditions change in ways that the model alone might not capture. Our results indicate that SR-DSS can enhance situational awareness by providing detailed snowpack and weather conditions in a time-relevant manner for forecasting and decision-making. We provide case studies to demonstrate how the SR-DSS alone captures the onset of terrestrial water input and how it can help assess the performance of operational models (SNOW-17 and SAC-SMA). The study suggests that the SR-DSS can be a valuable tool for operational hydrologists by helping to refine flood forecasts by identifying specific aspects of models that can be improved or adjusted and enhance decision-making during ROS events by providing additional situational awareness. Further development and testing of the SR-DSS could lead to its adoption in operational forecasting, enhancing the resilience of water management systems in the face of growing extreme precipitation concerns.