Abstract A tracer breakthrough curve (BTC) for each sampling station is the ultimate goal of every quantitative hydrologic tracing study, and dataset size can critically affect the BTC. Groundwater-tracing data obtained using in situ automatic sampling or detection devices may result in very high-density data sets. Data-dense tracer BTCs obtained using in situ devices and stored in dataloggers can result in visually cluttered overlapping data points. The relatively large amounts of data detected by high-frequency settings available on in situ devices and stored in dataloggers ensure that important tracer BTC features, such as data peaks, are not missed. Alternatively, such dense datasets can also be difficult to interpret. Even more difficult, is the application of such dense data sets in solute-transport models that may not be able to adequately reproduce tracer BTC shapes due to the overwhelming mass of data. One solution to the difficulties associated with analyzing, interpreting, and modeling dense data sets is the selective removal of blocks of the data from the total dataset. Although it is possible to arrange to skip blocks of tracer BTC data in a periodic sense (data decimation) so as to lessen the size and density of the dataset, skipping or deleting blocks of data also may result in missing the important features that the high-frequency detection setting efforts were intended to detect. Rather than removing, reducing, or reformulating data overlap, signal filtering and smoothing may be utilized but smoothing errors (e.g., averaging errors, outliers, and potential time shifts) need to be considered. Appropriate probability distributions to tracer BTCs may be used to describe typical tracer BTC shapes, which usually include long tails. Recognizing appropriate probability distributions applicable to tracer BTCs can help in understanding some aspects of the tracer migration.

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关于示踪突破曲线数据集大小、形状和统计分布

摘要 每个采样站的示踪剂突破曲线（BTC）是每个定量水文示踪研究的最终目标，数据集大小对BTC具有重要影响。使用原位自动采样或检测设备获得的地下水追踪数据可能会产生非常高密度的数据集。使用原位设备获得并存储在数据记录器中的数据密集示踪剂 BTC 可能会导致视觉上混乱的重叠数据点。通过原位设备上可用的高频设置检测到的相对大量数据并存储在数据记录器中，确保不会遗漏重要的示踪 BTC 特征，例如数据峰值。或者，这种密集的数据集也可能难以解释。更难的是，是在溶质传输模型中应用如此密集的数据集，由于大量的数据，这些模型可能无法充分再现示踪剂 BTC 的形状。与分析、解释和建模密集数据集相关的困难的一种解决方案是从整个数据集中选择性地删除数据块。虽然可以安排周期性地跳过跟踪 BTC 数据块（数据抽取）以减小数据集的大小和密度，但跳过或删除数据块也可能导致丢失高-频率检测设置工作旨在检测。可以使用信号过滤和平滑而不是去除、减少或重新形成数据重叠，但是平滑误差（例如，平均误差、异常值、和潜在的时间变化）需要考虑。示踪 BTC 的适当概率分布可用于描述典型的示踪 BTC 形状，通常包括长尾。识别适用于示踪 BTC 的适当概率分布有助于理解示踪迁移的某些方面。