This reports summarizes work and key findings to date from the Upper RIO Grande Basin SNOwfall Measurement and streamFLOW (RIO-SNO-FLOW) Forecasting Improvement Project conducted from Jan. 1, 2014 through Dec. 31, 2015. The project area was centered over the upper mainstem Rio Grande and Conejos River basins in southern Colorado. This report is organized into 7 chapters that detail the major elements of the project including; a Project Description, NOAA Gap-filling Radar, NASA Airborne Snow Observatory, In-Situ Ground Observations, Distributed Hydrologic Modeling, and Community Engagement. While several follow-on activities are still in progress, a number of conclusions and recommendations have emerged from the RIO-SNO-FLOW project. These major conclusions and recommendations are as follows:
- NOAA experimental gap-filling radar observations greatly improved the spatial and temporal distribution of precipitation over the Conejos River Basin in comparison to the existing operational National Weather Service (NWS) radar network.
- Local radar adds value by reducing forcing-related biases in model-simulated runoff and providing more information in areas not currently monitored by SNOTEL stations.
- More/better ground-based snowpack monitoring is needed at elevations above 11,000 ft. and in areas with greater/persistent snowpack.
- Snowpack remote sensing platforms from the NASA Airborne Snow Observatory provide valuable sources of quantitative spatially distributed, high-resolution information on snow depth, snow water equivalent and snow albedo to uniquely constrain the modeling and assess WRF-Hydro and SNODAS simulations.
- In-situ meteorological measurements identified significant biases in operational meteorological forcing datasets that need to be addressed through improved observation and/or assimilation and bias correction methodologies.
- Physics-based, high-resolution (<1 km) hydrologic modeling with the soon-to-be operational community WRF-Hydro modeling system showed reasonable simulation skill in snowpack conditions and in seasonal runoff accumulation when compared against available data.
- Probabilistic streamflow forecasts from the National Weather Service synthesized within the Colorado Water Conservation Board (CWCB)-funded Rio Grande Decision Support Tool, developed by Riverside Technologies, Inc., provided useful, skillful probabilistic water supply forecast information compared with single value, regression-based forecasts.
