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Applying the Quadrant Method for Pumping-Trace Metal Correlations in Variable Time, Low-Data Systems.

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Area of Science:

  • Hydrogeology
  • Environmental Science
  • Water Resource Management

Background:

  • Growing global freshwater demand necessitates understanding aquifer pumping effects on groundwater chemistry.
  • Existing data collection methods are often inconsistent, hindering long-term correlation studies between pumping and water quality.
  • There is a need for robust statistical methods to analyze the relationship between groundwater pumping rates and water quality parameters.

Purpose of the Study:

  • To develop and evaluate a statistical method for correlating groundwater pumping rates with changes in trace metal concentrations.
  • To compare the effectiveness of the Quadrant method against Kendall's tau correlation for analyzing pumping-quality relationships.
  • To assess the reproducibility of the Quadrant method under varying sample sizes and time intervals.

Main Methods:

  • Utilized the interval-scaled change in mean pumping rate combined with the Quadrant method.
  • Examined correlations between pumping rates and trace metal concentrations in groundwater samples.
  • Compared the Quadrant method with Kendall's tau correlation, including sample size reduction analysis.

Main Results:

  • Correlations between pumping rates and groundwater chemistry vary geographically and are well-site specific.
  • The Quadrant method and Kendall's tau produced similar results with large sample sizes and short time intervals.
  • The Quadrant method demonstrated higher reproducibility with very small sample sizes and large time intervals compared to Kendall's tau.

Conclusions:

  • The Quadrant method is a viable alternative for investigating pumping effects on groundwater quality, especially when Kendall's tau yields non-significant correlations.
  • The method's performance is influenced by factors such as sample size and the time interval between samples.
  • This research contributes to better predictive models for groundwater quality management under increasing pumping stresses.