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Understanding links between water-quality variables and nitrate concentration in freshwater streams using high

Claire Kermorvant1, Benoit Liquet1,2,3, Guy Litt4

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High-frequency water quality monitoring reveals consistent relationships between nitrate and other variables across diverse watersheds. This finding enables effective, cost-efficient nitrate management strategies for rivers and streams.

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Ecological Monitoring

Background:

  • Real-time, in-situ sensors provide high-frequency water quality data, generating large datasets for advanced analysis.
  • Understanding nitrate dynamics is crucial for effective watershed management due to its reactivity in aquatic systems.
  • National Ecological Observatory Network (NEON) provides valuable data from diverse U.S. watersheds.

Purpose of the Study:

  • To analyze high-frequency water quality data to understand relationships between nitrate and other key variables.
  • To develop predictive models for nitrate concentration across different environmental and climate zones.
  • To identify cost-effective water quality variables for monitoring nitrate dynamics.

Main Methods:

  • Utilized generalized additive mixed models (GAMMs) to analyze nonlinear relationships between nitrate and variables like conductivity, turbidity, dissolved oxygen, temperature, and elevation.
  • Employed auto-regressive-moving-average (ARIMA) models to account for temporal auto-correlation in the data.
  • Compared variable importance and model performance across three distinct NEON sites.

Main Results:

  • Models explained a high percentage of total deviance (99%) for nitrate concentration at all sites.
  • Despite site-specific differences in variable importance and parameters, the same set of explanatory variables consistently explained the most variation in nitrate.
  • Key water quality variables like conductivity, turbidity, dissolved oxygen, water temperature, and elevation were significant predictors of nitrate concentration.

Conclusions:

  • A unified modeling approach using a consistent set of water quality variables is effective for understanding nitrate dynamics, even in environmentally diverse watersheds.
  • These findings support the selection of cost-effective monitoring variables for comprehensive spatial and temporal nitrate assessment.
  • The study provides a framework for adaptive management of river and stream water quality based on robust nitrate monitoring models.