Despite long-standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium-scale (200–10,000 km²) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre-storm) surface soil moisture (ASM) and the storm-scale runoff coefficient (RC; the fraction of storm-scale precipitation accumulation converted into streamflow). In humid and semi-humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm-scale RC observations (available only in basins that are both lightly regulated and gauged) with high-quality ASM values (available quasi-globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin-wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean-absolute error.

中文翻译：

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利用暴风雨前土壤水分估计来增强未测量水文盆地的地表模型校准

尽管做出了长期努力，水文学家仍然缺乏强大的工具来校准未计量流域内的陆面模型 (LSM) 流量估计。使用来自土壤水分主动被动 4 级土壤水分 (L4_SM) 产品的地表土壤水分估计值、降水观测和 617 个中等规模（200–10,000 km ²) 盆地中，我们测量了前期（即风暴前）地表土壤湿度 (ASM) 与风暴规模径流系数 (RC; 风暴规模降水积累的分数) 之间的时间 (Spearman) 等级相关性转化为流量）。在潮湿和半湿润盆地，这种等级相关性被证明足够强，可以用高质量的 ASM 值（准-从 L4_SM 全局）在流量校准程序中。使用这个原理，我们定义了一种新的、基于 L4_SM 的流域 LSM 流量校准方法，并成功地将其应用于识别与观察到的 RC 产生高等级相关性的 LSM 配置。然而，