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.

Water supply reliability in the Truckee River basin stands to substantially benefit from forecast-informed reservoir operations(FIRO), especially given expected increases in rain: snow ratios and a transition to earlier runoff under warming climate that current infrastructure and operational rules were not designed for. However, in its position on the lee side of the Sierra Nevada mountains (CA/NV, USA), several unique forecast uncertainties exist that must be considered to mitigate against increased flood risk potential. Both water supply and floods are strongly linked to wintertime atmospheric rivers (AR) but despite improvements in forecasting these events at long lead times, the timing and amount of spillover precipitation onto the lee side remains a key uncertainty. In addition, storm runoff volumes in this basin are highly sensitive to rain-snow elevation, which is also difficult to forecast. Finally, antecedent snowpack and soil conditions have the potential to modulate runoff volumes but factors controlling the strength of these modulations are incompletely understood and monitored. In this study, we assess streamflow forecast skill in the Truckee River to provide a preliminary understanding of potential forecast-related challenges and opportunities for FIRO. To accomplish this, we used an archive of readily available short-range Hydrologic Ensemble Forecast System winter (Oct-Apr) streamflow forecasts for water years 2015-2020 and compared these to observed3-day flows at lead times of 0 to 15 days. We subset the data into AR days, non-AR days and top 10% flow days examined the variance explained between the ensemble median and observed 3-day flows as a function of lead time. We also examined how the observed 3-day flows rank in relation to the ensemble members for each day. We found that forecast accuracy improves considerably starting at a 7-day lead time but tends to be lower for high-flow and AR events relative to non-ARs. We also found the ensembles to have a slight bias toward underprediction and tendency toward under-dispersion (i.e. observed flows were sometimes outside the ensemble range) with this being the case for AR and high flow days for some but not all sites.