IT is unknown how much operational water supply forecasts may benefit from additional snowpack monitoring and how much water supply forecasts accuracy is constrained by snowpack conditions versus…

This $1,228,743.28 Cooperative Agreement award from the Bureau of Reclamation under the Snow Water Supply Forecasting (CFDA 15.078) program aims to improve water supply forecasts by enhancing snow monitoring through emerging technologies like aerial LiDAR surveys. The project will identify locations with unique snowpack dynamics not captured by existing monitoring networks and evaluate how incorporating LiDAR-derived snow data can improve forecasting accuracy compared to relying solely on traditional snow pillow measurements. The University of Oregon and Airborne Snow Observatories Inc. are sub-recipients conducting aerial surveys and modeling work to assess the relative impact of initial snow conditions versus future weather on forecast performance across western U.S. river basins. The results will help water managers identify the most effective ways to leverage new snow monitoring approaches to enhance water supply forecasting and management.

IT is unknown how much operational water supply forecasts may benefit from additional snowpack monitoring and how much water supply forecasts accuracy is constrained by snowpack conditions versus future weather. Addressing this problem would help identify the most effective ways to improve water supply forecasts. This project will address this problem by identifying locations with unique snowpack dynamics not sampled by existing networks and testing the potential for snow water equivalent data from these locations to improve water supply forecasts. Project funds will support acquisition of aerial light detection and ranging snow survey data on multiple dates in the mckenzie river basin (oregon), which currently lacks airborne snow observatories data. The team will utilize new and existing aerial light detection and ranging snow surveys in over 33 basins across the western U.S. To compare snow dynamics from this emerging technology to what is currently monitored at snow pillow sites (e.g., automated system of snowpack). The project will identify hot spots where snow dynamics differ from what is measured at automated system of snowpack and snow water equivalent is substantial. The team will evaluate how including snow data from these hot spots improves water supply forecasts. They will produce forecasts with the state-of-the-art national resources conservation service water supply forecasts system (microwave 4), starting with a baseline model and then adding additional snow water equivalent time series from hot spot locations. The relative importance of initial snow conditions informed by light detection and ranging versus future weather will be summarized to identify when and where there is high potential to improve water supply forecasts through enhanced snow monitoring. The project will focus on basins in three study regions (california, colorado, oregon), which are hydro-climatically distinct.