On Tracer Breakthrough Curve Dataset Size, Shape, and Statistical Distribution.

Area of Science:

• Hydrology
• Environmental Science
• Data Science

Background:

• Quantitative hydrologic tracing studies aim to generate tracer breakthrough curves (BTCs) for each sampling station.
• High-density datasets from in situ automatic sampling devices can lead to cluttered data and interpretation challenges.
• Dense datasets pose difficulties for solute-transport models, potentially hindering accurate BTC shape reproduction.

Purpose of the Study:

• To address the challenges of analyzing, interpreting, and modeling dense groundwater tracing datasets.
• To investigate methods for managing high-density tracer breakthrough curves (BTCs).
• To improve the application of dense BTC data in solute-transport models.

Main Methods:

• Selective data removal (data decimation) was considered but risks missing key BTC features.
• Signal filtering and smoothing techniques were explored as alternatives to data reduction.
• The application of appropriate probability distributions to describe tracer BTC shapes was examined.

Main Results:

• Data decimation can reduce dataset size but may omit crucial BTC features.
• Signal filtering and smoothing offer ways to manage data density but require careful consideration of potential errors like averaging, outliers, and time shifts.
• Understanding applicable probability distributions can aid in interpreting tracer BTC shapes, including their characteristic long tails.

Conclusions:

• Managing dense tracer breakthrough curve data is crucial for accurate hydrologic interpretation and modeling.
• Techniques like filtering, smoothing, and applying probability distributions offer viable solutions to data density challenges.
• Recognizing BTC shapes through probability distributions enhances understanding of tracer migration processes.