In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 6 ( 2023-6-30), p. e0287640-
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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0287640
DOI:
10.1371/journal.pone.0287640.g001
DOI:
10.1371/journal.pone.0287640.g002
DOI:
10.1371/journal.pone.0287640.g003
DOI:
10.1371/journal.pone.0287640.g004
DOI:
10.1371/journal.pone.0287640.t001
DOI:
10.1371/journal.pone.0287640.t002
DOI:
10.1371/journal.pone.0287640.t003
DOI:
10.1371/journal.pone.0287640.s001
DOI:
10.1371/journal.pone.0287640.s002
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
Permalink