Advancing CONUS-scale Operational Snow Modeling Capabilities

The need for accurate characterization of snow water equivalent (SWE) and subsequent melt timing and rate are essential inputs for water management efforts in mountainous watersheds and downstream regions. With the Next Generation water resources modeling framework (NexGen), there is an opportunity to integrate distributed, process-based modeling and novel machine learning (ML) into large-scale hydrological operations to advance the representation of snow conditions in heterogeneous montane environments. The project aims to advance large-scale snow accumulation and melt modeling by explicitly representing key surface-atmosphere interactions driving snow energy and mass balance. Concurrent with advancing physically based modeling efforts, the project will develop novel data assimilation methods and mature ML methods for estimating real-time catchment-scale SWE based on the intrinsic relationships between in-situ observations and landscape characteristics. The CONUS-scale nature of the project will extend to snow environments across the nation (e.g., Sierra Nevada, CA, Rockies, CO, and Green Mountains, VT), where we will build on existing CIROH projects to create a standardized workflow for evaluating snow and hydrological modeling prediction benefits from including more physically explicit representations of snow. We anticipate the research will advance the quality of snow model outputs for use in large-scale operational hydrological models by improving the realism of snow-to-streamflow model coupling and integration techniques, creating a standardized testbed supporting a research-to-operations (R2O) transition in snow-dominated catchments, and provide a foundation for a cold regions processes Summer Institute.