Seasonal predictions of spring-summer river flow volume (water supply forecasts, WSFs) are foundational to western US water management. We test a new space-based remote sensing product, spatially and temporally complete (STC) MODSCAG fractional snow-covered area (fSCA), as input for the Natural Resources Conservation Service (NRCS) operational US West-wide WSF system. fSCA data were considered alongside traditional SNOTEL predictors, in both statistical and AI-based NRCS operational hydrologic models, throughout the forecast season, in four test watersheds (Walker, Wind, Piedra, and Gila Rivers in California, Wyoming, Colorado, and New Mexico). Outcomes from over 200 WSF models suggest fSCA-enabled accuracy gains are most consistent and explainable for short-lead, late-season forecasts (roughly 10%–25% improvements, typically), which in operational practice can be challenging as snowlines rise above in situ measurement sites. Gains are roughly proportional to how thoroughly spring-summer runoff is dominated by snowmelt, and how poorly in situ networks monitor late-season snowpack. fSCA also improved accuracy for long-lead, early-season forecasts, which are similarly problematic in WSF practice, but not for WSFs issued around the time of peak snow accumulation, when in situ measurements reasonably characterize mountain snowpack available for upcoming spring-summer snowmelt. The AI-based hydrologic model generally outperformed the statistical model and, in some cases, better-capitalized on satellite remote sensing. Additionally, preliminary analyses suggest reasonable WSF skill in many cases using fSCA as the sole predictor, potentially useful in sparsely monitored regions; and that combining satellite and in situ products in data-driven hydrologic models using genetic algorithm-based predictor selection could help guide new SNOTEL site selection.

中文翻译：

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利用基于下一代卫星遥感的雪数据改进实用机器学习驱动的河流预测系统中的季节性供水预测

春夏河流流量的季节性预测（供水预测，WSF）是美国西部水资源管理的基础。我们测试了一种新的天基遥感产品，时空完整 (STC) MODSCAG 积雪覆盖面积分数 (fSCA)，作为自然资源保护服务 (NRCS) 运营的美国西部 WSF 系统的输入。在整个预测季节，在四个测试流域（加利福尼亚州、怀俄明州、科罗拉多州和新墨西哥州的沃克河、温德河、皮德拉河和希拉河）的统计和基于人工智能的 NRCS 业务水文模型中，fSCA 数据与传统的 SNOTEL 预测因子一起被考虑）。超过 200 个 WSF 模型的结果表明，fSCA 支持的准确度提升对于短周期、季末预测来说是最一致和可解释的（通常提高约 10%–25%），这在操作实践中可能具有挑战性，因为雪线上升到超过现场测量地点。收益大致与春夏径流受融雪支配的程度以及现场网络监测晚季积雪的程度成正比。 fSCA 还提高了长期、早季预报的准确性，这在 WSF 实践中也存在类似问题，但不适用于在积雪高峰期发布的 WSF，此时现场测量合理地描述了即将到来的春夏融雪可用的山地积雪特征。基于人工智能的水文模型总体上优于统计模型，并且在某些情况下更能利用卫星遥感。此外，初步分析表明，在许多情况下，使用 fSCA 作为唯一的预测因子是合理的 WSF 技能，这在监测稀疏的地区可能有用；使用基于遗传算法的预测器选择将卫星和现场产品结合到数据驱动的水文模型中可以帮助指导新的 SNOTEL 选址。