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首页> 外文期刊>Hydrology and Earth System Sciences >Aggregation in environmental systems – Part?1: Seasonal tracer cycles quantify young water fractions, but not mean transit times, in spatially heterogeneous catchments
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Aggregation in environmental systems – Part?1: Seasonal tracer cycles quantify young water fractions, but not mean transit times, in spatially heterogeneous catchments

机译:环境系统中的聚集第1部分:季节性示踪剂循环定量分析了空间异质集水区中的年轻水份,但不是平均穿越时间

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摘要

Environmental heterogeneity is ubiquitous, but environmental systems are often analyzed as if they were homogeneous instead, resulting in aggregation errors that are rarely explored and almost never quantified. Here I use simple benchmark tests to explore this general problem in one specific context: the use of seasonal cycles in chemical or isotopic tracers (such as Clsup?/sup, iδ/isup18/supO, or iδ/isup2/supH) to estimate timescales of storage in catchments. Timescales of catchment storage are typically quantified by the mean transit time, meaning the average time that elapses between parcels of water entering as precipitation and leaving again as streamflow. Longer mean transit times imply greater damping of seasonal tracer cycles. Thus, the amplitudes of tracer cycles in precipitation and streamflow are commonly used to calculate catchment mean transit times. Here I show that these calculations will typically be wrong by several hundred percent, when applied to catchments with realistic degrees of spatial heterogeneity. This aggregation bias arises from the strong nonlinearity in the relationship between tracer cycle amplitude and mean travel time. I propose an alternative storage metric, the young water fraction in streamflow, defined as the fraction of runoff with transit times of less than roughly 0.2?years. I show that this young water fraction (not to be confused with event-based "new water" in hydrograph separations) is accurately predicted by seasonal tracer cycles within a precision of a few percent, across the entire range of mean transit times from almost zero to almost infinity. Importantly, this relationship is also virtually free from aggregation error. That is, seasonal tracer cycles also accurately predict the young water fraction in runoff from highly heterogeneous mixtures of subcatchments with strongly contrasting transit-time distributions. Thus, although tracer cycle amplitudes yield biased and unreliable estimates of catchment mean travel times in heterogeneous catchments, they can be used to reliably estimate the fraction of young water in runoff.
机译:环境异质性无处不在,但是环境系统常常像是同质的一样被分析,从而导致聚集误差很少被发现并且几乎永远无法量化。在这里,我使用简单的基准测试在一个特定的上下文中探讨这一普遍问题:在化学或同位素示踪剂中使用季节性周期(例如Cl ?,δ 18 O或δ 2 H)来估计流域的存储时间尺度。集水区存储的时间尺度通常通过平均渡越时间来量化,平均渡越时间是指以沉淀形式进入而又以溪流形式再次离开的水流之间的平均时间。平均渡越时间越长,意味着季节示踪剂周期的衰减越大。因此,在降水和水流中示踪剂循环的幅度通常用于计算集水区平均渡越时间。在这里,我表明,当将这些计算应用于具有现实程度的空间异质性的集水区时,通常会错误数百%。这种聚集偏差是由示踪剂循环幅度和平均传播时间之间的强非线性所引起的。我提出了另一种存储度量标准,即流中的年轻水份额,定义为渡越时间少于约0.2年的径流份额。我证明了这个年轻的水分量(不要与水位图分离中的基于事件的“新水”相混淆)可以通过季节性示踪剂循环以百分之几的精度进行精确预测,在整个平均渡越时间范围内(几乎从零开始)到几乎无限重要的是,这种关系实际上也没有聚集错误。也就是说,季节性示踪剂循环还可以准确地预测子流域高度异质性混合物中径流中的幼水比例,而其传播时间分布却形成强烈对比。因此,尽管示踪剂循环振幅会产生偏差,并且对非均质集水区中集水区平均行进时间的估算不可靠,但它们可用于可靠地估算径流中年轻水的比例。

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