Pflug et al. (2023) -- Model configuration and outputs

Snow is a vital component of the global land surface energy and water budget. In this study, we investigate the how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use an Observation System Simulation Experiment, specifically investigating how much snow simulated using popular models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24°-by-37° domain in the Western United States and Canada, simulating snow at approximately 250 m resolution and hourly timesteps in water-year 2019. We perform two data assimilation experiments, including: 1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals, and 2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that assimilating synthetic SWE observations improved average SWE biases at peak snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14%, to within 1%. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at peak snowpack were 111 mm, and average SWE biases were on the order of 150%. Here, the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18%) and the SWE mean absolute error (27 mm). Data assimilation also improved estimates of the temporal evolution of both SWE, even in spring snowmelt periods when melting snow and high snow liquid water content block the synthetic SWE retrievals. In fact, in the Upper Colorado River basin, melt-season SWE biases were improved from 63% to within 1%, and the Nash Sutcliffe Efficiency of runoff improved from –2.59 to 0.22. These results demonstrate the value of a snow-focused globally relevant remote sensing platform, and data assimilation for improving the characterization of SWE and associated water availability.

雪是全球陆地表面能量与水分平衡的至关重要的组成部分。在本研究中，我们探讨了如何通过合成孔径雷达遥感平台对雪水当量（SWE）的合成观测，来提升雪层空间时间估计的准确性。我们运用观测系统模拟实验，特别研究了利用流行模型和强迫数据模拟的雪量，通过同化合成SWE观测数据能够得到怎样的改进。研究区域聚焦于美国西部和加拿大一个24°×37°的领域，于2019年水年份以约250米的分辨率和每小时的时间步长模拟雪量。我们进行了两项数据同化实验，包括：1）排除合成观测数据在森林中的模拟，因为森林冠层会阻碍遥感数据提取；2）利用附近无冠层网格单元格的合成观测数据推断森林网格单元格中的雪分布。研究发现，同化合成SWE观测数据，在灌木、草地、农作物、裸地和湿地等土地覆盖类型中，峰值雪层时期的平均SWE偏差从14%降低至1%以内。然而，森林网格单元格中包含了不成比例的SWE体积。在森林中，峰值雪层时的SWE平均绝对误差为111毫米，平均SWE偏差约为150%。在此，使用最近无冠层网格单元格的观测数据来估计森林SWE的数据同化方法，显著改善了这些SWE偏差（18%）和SWE平均绝对误差（27毫米）。数据同化还改善了SWE随时间演化的估计，即使在春季融雪期，融化的雪和高雪液态水含量会阻碍合成SWE的提取。事实上，在上科罗拉多河流域，融雪季节的SWE偏差从63%降低至1%以内，径流的Nash-Sutcliffe效率从-2.59提升至0.22。这些结果证明了以雪为重点的全球相关遥感平台以及数据同化在改善SWE及其相关水资源描述中的价值。