Downscaling Snow Deposition Using Historic Snow Depth Patterns: Diagnosing Limitations From Snowfall Biases, Winter Snow Losses, and Interannual Snow Pattern Repeatability

Repeatable snow depth patterns have been identified in many regions between years with similar meteorological characteristics. This suggests that from previous could adjust deposition space as a substitution for unmodeled processes. Here, we tested pattern-based downscaling routine which assumes (a) spatially consistent relationship and depth, (b) interannually repeatable patterns, (c) unbiased mean snowfall. We investigated these assumptions, future avenues improvement, water-year 2014 over the California Tuolumne River Watershed. 6 km snowfall an atmospheric model was downscaled to 25 m resolution using seven different years, compared more common terrain-based method. Snow were influenced not only by accumulation, but also snowmelt, sublimation, density, resulting too heterogeneous. However, simulated homogeneous, less correlated observations (r = 0.27), than simulations simulation season 0.76), or year 0.52). Overall, modeled errors at peak-snowpack timing driven biases methods. In order of most- least-importance, research should focus on bias-correcting coarse-scale estimates, correcting winter losses density spatial variability, identifying historic periods most-similar accumulation.