This paper explores the performance of the analysis‐and‐assimilation configuration of the National Water Model (NWM) v1.0 in Iowa. The NWM assimilates streamflow observations from the United States Geological Survey (USGS), which increases the performance but also limits the available data for model evaluation. In this study, Iowa Flood Center Bridge Sensors (IFCBS) data provided an independent nonassimilated dataset for evaluation analyses. The authors compared NWM outputs for the period between May 2016 and April 2017, with two datasets: USGS streamflow and velocity observations; Stage and streamflow data from IFCBS. The distribution of Spearman rank correlation (r_s), Nash–Sutcliffe efficiency (E), and Kling–Gupta efficiency (KGE) provided quantification of model performance. We found the performance was linked with the spatial scale of the basins. Analysis at USGS gauges showed the strongest performance in large (>10,000 km²) basins (r_s = 0.9, E = 0.9, KGE = 0.8), with some decrease at small (<1,000 km²) basins (r_s = 0.6, E = −0.25, KGE = −0.2). Analysis with independent IFCBS observations was used to report performance at large basins (r_s = 0.6, KGE = 0.1) and small basins (r_s = 0.2, KGE = −0.4). Data assimilation improves simulations at downstream basins. We found differences in the characterization of the model and observed data flow velocity distributions. The authors recommend checking the connection of USGS gauges and NHDPlus reaches for selected locations where performance is weak.

中文翻译：

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使用独立观察法在爱荷华州建立国家水模型的绩效

本文探讨了爱荷华州国家水模型（NWM）v1.0的分析和同化配置的性能。NWM吸收了美国地质调查局（USGS）的流量观测结果，这不仅提高了性能，而且限制了用于模型评估的可用数据。在这项研究中，爱荷华州洪水中心桥梁传感器（IFCBS）数据提供了独立的非同化数据集进行评估分析。作者将2016年5月至2017年4月期间的NWM输出与两个数据集进行了比较：USGS流量和速度观测；来自IFCBS的舞台和流数据。Spearman等级相关性（r _s）的分布，纳什-萨特克利夫效率（E），而Kling–Gupta效率（KGE）提供了模型性能的量化。我们发现性能与盆地的空间尺度有关。USGS仪表盘的分析显示，在大型（> 10,000 km ²）盆地（r _s  = 0.9，E  = 0.9，KGE = 0.8）中表现最强，而在小型（<1,000 km ²）盆地（r _s  = 0.6，E  = -0.25，KGE = -0.2）。使用独立的IFCBS观测值进行分析，以报告大型盆地（r _s  = 0.6，KGE = 0.1）和小型盆地（r _{s）的性能。} = 0.2，KGE = -0.4）。数据同化改善了下游盆地的模拟。我们发现模型的表征和观察到的数据流速分布之间存在差异。作者建议在性能较弱的选定位置检查USGS压力表和NHDPlus伸臂的连接。