Understanding the distribution of snow‐water equivalent (SWE) is crucial for the prediction of water resources in the western United States. Backward running SWE reconstructions use satellite‐observed, binary snow presence imagery to reconstruct SWE mass estimates. This approach relies on the connection between snow timing and peak SWE, yet few studies have directly examined this relationship. Here, we investigate the strength and spatiotemporal variation in this relationship across the western United States and Alaska. Within the SNOTEL network (n = 611 sites), we find that most variance in peak SWE is explained (median R² = 0.64, σ = 0.18) by, in order of explanatory skill, the timing of snow disappearance, onset, and cover duration, with variation in skill primarily related to climate conditions—like winter storm size and storm frequency—rather than topographical setting. We expand this analysis with a diagnostic model of peak SWE driven by remotely sensed snow timing indices applied to five hydrologically important regions in the western United States and Alaska. Uncertainties arising between blending point and 500 m grid‐scale observations were found to influence model SWE bias, but a robust correlation (median R = 0.88) with observations persisted across all tested thresholds. Overall, this supports the viability of snow timing information for quantifying spatial patterns of peak SWE (mean R² = 0.76, percent bias = 3.6%) over the past two decades. These findings carry important implications for the development of SWE reanalysis products and for the evaluation of climate and hydrologic models.

中文翻译：

---

研究美国西部和阿拉斯加的峰值雪水当量与降雪时间指标之间的关系

了解雪水当量（SWE）的分布对于预测美国西部的水资源至关重要。向后运行的SWE重建使用卫星观测的二元雪存在图像来重建SWE质量估计。这种方法依赖于下雪时间和峰值SWE之间的联系，但是很少有研究直接检查这种关系。在这里，我们调查了美国西部和阿拉斯加之间这种关系的强度和时空变化。在SNOTEL网络（n  = 611个站点）内，我们发现峰值SWE的最大方差得到了解释（中位数R ²  = 0.64，σ = 0.18），按照解释性技能的顺序，是降雪的时间，开始时间和覆盖持续时间，技能的变化主要与气候条件（例如冬季风暴大小和风暴频率）有关，而不是与地形设置有关。我们通过对美国西部和阿拉斯加的五个重要水文地区应用由遥感降雪定时指数驱动的峰值SWE诊断模型来扩展该分析。发现融合点和500 m网格尺度观测值之间产生的不确定性会影响模型SWE偏差，但 与观测值的稳健相关性（中位数R = 0.88）在所有测试阈值上均持续存在。总体而言，这支持了降雪时间信息的可行性，以量化峰值SWE的空间模式（平均值R ² =过去的20年中的偏差= 0.76，偏差百分比= 3.6％）。这些发现对SWE再分析产品的开发以及气候和水文模型的评估具有重要意义。