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

中文翻译：

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使用历史积雪深度模式降低积雪量：诊断降雪偏差、冬季积雪损失和年际积雪模式重复性的局限性

在具有相似气象特征的年份之间的许多地区已经确定了可重复的雪深模式。这表明前几年的积雪模式可以调整空间中的积雪，以替代未建模的积雪过程。在这里，我们测试了基于模式的雪沉积降尺度程序，该程序假设 (a) 雪沉积和雪深之间存在空间一致的关系，(b) 年际可重复的雪模式，以及 (c) 无偏平均降雪量。我们在 2014 年加利福尼亚图奥勒米河流域的水年调查了这些假设和未来的改进途径。使用来自七个不同年份的雪深模式，将来自大气模型的 6 公里降雪缩小到 25 m 分辨率，并与更常见的基于地形的降雪方法进行比较。雪深模式不仅受积雪的影响，还受融雪、雪升华和雪密度的影响，导致基于模式的积雪降尺度在空间上过于不均匀。然而，使用基于地形的降尺度模拟的雪深在空间上过于均匀，与观测的空间相关性较低（r  = 0.27)，而不是使用模拟季节 ( r  = 0.76) 或不同年份 ( r  = 0.52) 的雪深模式进行基于模式的降尺度模拟。总体而言，与不同的降尺度方法相比，峰值积雪时间的模拟雪深误差更多地是由大气模型降雪偏差驱动的。从最重要到最不重要的顺序，未来的研究应侧重于对粗尺度降雪估计进行偏差校正，针对冬季降雪损失和雪密度空间变异性校正积雪模式，并确定最相似积雪的历史时期。