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The Global Network of Isotopes in Rivers (GNIR): integration of water isotopes in watershed observation and riverine research

机译:全球河流同位素网络(GNIR):流域观测和河流研究中水同位素的整合

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摘要

We introduce a new online global database of riverine water stable isotopes (Global Network of Isotopes in Rivers, GNIR) and evaluate its longer-term data holdings. Overall, 218 GNIR river stations were clustered into three different groups based on the seasonal variation in their isotopic composition, which was closely coupled to precipitation and snowmelt water runoff regimes. Sinusoidal fit functions revealed phases within each grouping and deviations from the sinusoidal functions revealed important river alterations or hydrological processes in these watersheds. The seasonal isotopic amplitude of delta O-18 in rivers averaged 2.5 parts per thousand, and did not increase as a function of latitude, like it does for global precipitation. Low seasonal isotopic amplitudes in rivers suggest the prevalence of mixing and storage such as occurs via lakes, reservoirs, and groundwater. The application of a catchment-constrained regionalized cluster-based water isotope prediction model (CC-RCWIP) allowed for direct comparison between the expected isotopic compositions for the upstream catchment precipitation with the measured isotopic composition of river discharge at observation stations. The catchment-constrained model revealed a strong global isotopic correlation between average rainfall and river discharge (R-2 = 0.88) and the study demonstrated that the seasonal isotopic composition and variation of river water can be predicted. Deviations in data from model-predicted values suggest there are important natural or anthropogenic catchment processes like evaporation, damming, and water storage in the upstream catchment.
机译:我们引入了一个新的在线河流水稳定同位素全球数据库(河流中的同位素全球网络,GNIR),并评估了其长期数据持有量。总体而言,根据其同位素组成的季节性变化,将218个GNIR河站分为三个不同的组,这与降水和融雪水径流状况密切相关。正弦拟合函数揭示了每个组中的阶段,与正弦函数的偏离揭示了这些流域中重要的河流变化或水文过程。河流中O-18三角洲的季节性同位素振幅平均为千分之2.5,并且没有像全球降水那样随纬度增加。河流中较低的季节性同位素振幅表明混合和存储的普遍性,例如通过湖泊,水库和地下水发生的混合和存储。集水区受限的基于区域集簇的水同位素预测模型(CC-RCWIP)的应用可以直接比较上游集水区降水的预期同位素组成与观测站测得的河流排放同位素组成。流域约束模型揭示了平均降雨量与河流流量之间的强全球同位素相关性(R-2 = 0.88),该研究表明可以预测季节同位素组成和河水变化。数据与模型预测值的差异表明上游流域存在重要的自然或人为流域过程,如蒸发,筑坝和蓄水。

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