Operational Water Forecast Ability of the HRRR-iSnobal Combination: An Evaluation to Adapt into Production Environments
Operational water-resource forecasters, such as the Colorado Basin River Forecast Center (CBRFC) in the Western United States, currently rely on historical records to calibrate the temperature-index models used for snowmelt runoff predictions. This data dependence is increasingly challenged, with global and regional climatological factors changing the seasonal snowpack dynamics in mountain watersheds. To evaluate and improve the CBRFC modeling options, this work ran the physically based snow energy balance iSnobal model, forced with outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model across 4 years in a Colorado River Basin forecast region. Compared to in situ, remotely sensed, and the current operational CBRFC model data, the HRRR-iSnobal combination showed well-reconstructed snow depth patterns and magnitudes until peak accumulation. Once snowmelt set in, HRRR-iSnobal showed slower simulated snowmelt relative to observations, depleting snow on average up to 34 d later. The melting period is a critical component for water forecasting. Based on the results, there is a need for revised forcing data input preparation (shortwave radiation) required by iSnobal, which is a recommended future improvement to the model. Nevertheless, the presented performance and architecture make HRRR-iSnobal a promising combination for the CBRFC production needs, where there is a demonstrated change to the seasonal snow in the mountain ranges around the Colorado River Basin. The long-term goal is to introduce the HRRR-iSnobal combination in day-to-day CBRFC operations, and this work created the foundation to expand and evaluate larger CBRFC domains.
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Meyer, Joachim; Horel, John; Kormos, Patrick; Hedrick, Andrew; Trujillo, Ernesto; and McKenzie Skiles, S.. (2023). "Operational Water Forecast Ability of the HRRR-iSnobal Combination: An Evaluation to Adapt into Production Environments". Geoscientific Model Development, 16(1), 233-250. https://doi.org/10.5194/gmd-16-233-2023