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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
1Le CNRS et l'Université de Pau et des Pays de l'Adour, Laboratoire de Mathématiques et de leurs Applications de Pau, Anglet, France.
Plos One
|June 30, 2023
Summary
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.
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.
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