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

Loading...
Thumbnail Image
File version

Version of Record (VoR)

Author(s)
Kermorvant, C
Liquet, B
Litt, G
Mengersen, K
Peterson, EE
Hyndman, RJ
Jones, JB
Leigh, C
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)

Larsen, Stefano

Date
2023
Size
File type(s)
Location
Abstract

Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data from in-situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive–moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly.

Journal Title

PLoS ONE

Conference Title
Book Title
Edition
Volume

18

Issue

6

Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

© 2023 Kermorvant et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Item Access Status
Note
Access the data
Related item(s)
Subject

Freshwater ecology

Environmental assessment and monitoring

Surface water quality processes and contaminated sediment assessment

Persistent link to this record
Citation

Kermorvant, C; Liquet, B; Litt, G; Mengersen, K; Peterson, EE; Hyndman, RJ; Jones, JB; Leigh, C, Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data, PLoS ONE, 2023, 18 (6), pp. e0287640

Collections