Inferring Watershed-scale Mean Snow Magnitude and Distribution Using Multidecadal Snow Reanalysis Patterns and Snow Pillow Observations
Journal Article
The magnitude and spatial heterogeneity of snow deposition are difficult; to model in mountainous terrain. Here, we investigated how snow patterns; from a 32-year (1985 – 2016) snow reanalysis in the Tuolumne, Kings,; and Sagehen Creek, California Sierra Nevada watersheds could be used to; improve simulations of winter snow deposition. Remotely-sensed; fractional snow-covered area (fSCA) from dates following peak-snowpack; timing were used to identify dates from different years with similar; fSCA, which indicated similar snow accumulation and depletion patterns.; Historic snow accumulation patterns were then used to 1) relate snow; accumulation observed by snow pillows to watershed-scale estimates of; mean snowfall, and 2) estimate 90 m snow deposition. Finally, snow; deposition fields were used to force snow simulations, the accuracy of; which were evaluated versus airborne lidar snow depth observations.; Except for water-year 2015, which had the shallowest snow estimated in; the Sierra Nevada, normalized snow accumulation and depletion patterns; identified from historic dates with spatially correlated fractional; snow-covered area agreed on average, with absolute differences of less; than 10%. Watershed-scale mean winter snowfall inferred from the; relationship between historic snow accumulation patterns and snow pillow; observations had a ±13% interquartile range of biases between 1985 and; 2016. Finally, simulations using 1) historic snow accumulation patterns,; and 2) snow accumulation observed from snow pillows, had snow depth; coefficients of correlations and mean absolute errors that improved by; 70% and 27%, respectively, as compared to simulations using a more; common forcing dataset and downscaling technique. This work demonstrates; the real-time benefits of satellite-era snow reanalyses in mountainous; regions with uncertain snowfall magnitude and spatial heterogeneity.
