Advancing Snow Observation Systems to Improve Operational Streamflow Prediction Capabilities

In situ snow monitoring infrastructure exhibits limitations for operational hydrologic models in complex topography because of the spatial heterogeneity of snow distribution. The focus of the project is prototyping a robust, low-cost, low-power snow monitoring networks for collecting near real-time, physiographically representative water and energy balance observations in montane headwater catchments. The sensor networks will complement existing snow monitoring infrastructure (e.g., SNOTEL), expand edge computing capabilities, and use terrain features and machine learning (ML) to optimize catchment sensing locations with respect to aspect, slope, vegetation, and elevation. Site instrumentation will expand on current sensing capabilities to capture key snow-energy-balance processes, including snow depth, short- and long-wave radiation, soil moisture, and ML-enabled rain-on-snow (ROS) detection. Using the increase in catchment observations and new sensing capabilities, the project will benchmark SWE modeling improvements by comparing physically-based models calibrated using the sensing network compared to models calibrated with existing snow-sensing infrastructure, using UAV-derived snow observations and manual snow surveys for validation. Broader impacts of the project will support an operational pathway to advance snow monitoring networks, provide new datasets to train and calibrate models, and enhance snow state assimilation into operational hydrological models (e.g., NextGen) to advance national-scale water resources management capacity.