Mountain winds are the driving force behind snow accumulation patterns in mountainous catchments, making accurate wind fields a prerequisite to accurate simulations of snow depth for ecological or water resource applications. In this study, we examine the effect that wind fields derived from different coarse data sets and downscaling schemes has on simulations of modeled snow depth at resolutions suitable for basin‐scale modeling (>50 m). Simulations are run over the Tuolumne River Basin, CA for the accumulation season of Water Year 2017 using the distributed snow model, SnowModel. We derived wind fields using observations from either sensor networks, the North American Land Data Assimilation System (12.5 km resolution), or High‐Resolution Rapid Refresh (HRRR, 3 km resolution) data, and downscaled using terrain‐based multipliers (MicroMet), a mass‐conserving flow model (WindNinja), or bilinear interpolation. Two wind fields derived from 3 km HRRR data and downscaled with respect to terrain produced snow depth maps that best matched observations of snow depth from airborne LiDAR. We find that modeling these wind fields at 100 and 50 m resolutions do not produce improvements in simulated snow depth when compared to wind fields modeled at a 150 m resolution due to their inability to represent wind dynamics at these scales. For input to distributed snow models at the basin‐scale, we recommend deriving wind fields from high resolution numerical weather prediction model output and downscaling with respect to terrain. Future studies should compare suspension schemes used in blowing snow models and investigate wind downscaling schemes of complexity between statistical and fluid dynamic models.

中文翻译：

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评估用于流域规模分布雪模型的风场

山风是山区集水区积雪模式背后的驱动力，这使得准确的风场成为为生态或水资源应用准确模拟雪深的前提。在这项研究中，我们研究了来自不同粗略数据集和降尺度方案的风场对模拟雪深模拟的影响，其分辨率适合于盆地尺度模拟（> 50 m）。使用分布式雪模型SnowModel在加州Tuolumne流域进行了模拟，以模拟2017年水年的蓄积季节。我们使用来自传感器网络，北美土地数据同化系统（12.5 km分辨率）或高分辨率快速刷新（HRRR，3 km分辨率）数据的观测值得出风场，并使用基于地形的乘数（MicroMet）进行了缩减，质量保持流模型（WindNinja）或双线性插值。根据3 km HRRR数据得出的两个风场，并根据地形缩小了比例，生成的雪深图与机载LiDAR的雪深观测最匹配。我们发现，与以150 m分辨率建模的风场相比，以100和50 m分辨率建模的这些风场不会在模拟积雪深度方面产生改善，因为它们无法表示这些尺度的风动力。对于流域规模分布的降雪模型的输入，我们建议从高分辨率数值天气预报模型的输出以及相对于地形的缩减中得出风场。