Putman Annie L, Longley Patrick C, McDonnell Morgan C, Reddy James, Katoski Michelle, Miller Olivia L, Brooks J Renée
Utah Water Science Center, US Geological Survey, Salt Lake City, Utah, USA.
Colorado Water Science Center, US Geological Survey, Grand Junction, Colorado, USA.
Hydrol Earth Syst Sci. 2024 Jul 4;28(13):2895-2918. doi: 10.5194/hess-28-2895-2024.
The National Water Model (NWM) provides critical analyses and projections of streamflow that support water management decisions. However, the NWM performs poorly in lower-elevation rivers of the western United States (US). The accuracy of the NWM depends on the fidelity of the model inputs and the representation and calibration of model processes and water sources. To evaluate the NWM performance in the western US, we compared observations of river water isotope ratios ( and expressed in notation) to NWM-flux-estimated (model) river reach isotope ratios. The modeled estimates were calculated from long-term (2000-2019) mean summer (June, July, and August) NWM hydrologic fluxes and gridded isotope ratios using a mass balance approach. The observational dataset comprised 4503 in-stream water isotope observations in 877 reaches across 5 basins. A simple regression between observed and modeled isotope ratios explained 57.9 % ( ) and 67.1 % ( ) of variance, although observations were 0.5 ( ) and 4.8 ( ) higher, on average, than mass balance estimates. The unexplained variance suggest that the NWM does not include all relevant water fluxes to rivers. To infer possible missing water fluxes, we evaluated patterns in observation-model differences using ( ) and ( ). We detected evidence of evaporation in observations but not model estimates (negative and positive ) at lower-elevation, higher-stream-order, arid sites. The catchment actual-evaporation-to-precipitation ratio, the fraction of streamflow estimated to be derived from agricultural irrigation, and whether a site was reservoir-affected were all significant predictors of in a linear mixed-effects model, with up to 15.2 % of variance explained by fixed effects. This finding is supported by seasonal patterns, groundwater levels, and isotope ratios, and it suggests the importance of including irrigation return flows to rivers, especially in lower-elevation, higher-stream-order, arid rivers of the western US.
国家水模型(NWM)提供了对河流量的关键分析和预测,以支持水资源管理决策。然而,NWM在美国西部低海拔河流中表现不佳。NWM的准确性取决于模型输入的保真度以及模型过程和水源的表示与校准。为了评估NWM在美国西部的性能,我们将河水同位素比率(以δ符号表示)的观测值与NWM通量估算的(模型)河段同位素比率进行了比较。模型估算值是使用质量平衡方法,根据长期(2000 - 2019年)夏季(6月、7月和8月)的NWM水文通量和网格化同位素比率计算得出的。观测数据集包括5个流域877个河段的4503个河流水同位素观测值。观测值与模型估算同位素比率之间的简单回归解释了57.9%(δ18O)和67.1%(δ2H)的方差,尽管观测值平均比质量平衡估算值分别高0.5‰(δ18O)和4.8‰(δ2H)。未解释的方差表明NWM没有包括所有与河流相关的水流通量。为了推断可能缺失的水流通量,我们使用δ18O和δ2H评估了观测值与模型差异的模式。我们在低海拔、高河流阶数、干旱站点的观测值中检测到了蒸发的证据,但在模型估算值中未检测到(δ18O为负,δ2H为正)。在一个线性混合效应模型中,集水区实际蒸发与降水比率、估计源自农业灌溉的河流量比例以及一个站点是否受水库影响,都是δ2H的显著预测因子,固定效应解释了高达15.2%的方差。这一发现得到了季节模式、地下水位和同位素比率的支持,它表明将灌溉回归水流纳入河流模型的重要性,特别是在美国西部低海拔、高河流阶数、干旱的河流中。