A multiscale approach to water quality variables in a river ecosystem

Abstract Monitoring select ecosystem variables across time and interpreting the collected data are essential components of ecosystem assessment supporting management. Increasingly affordable sensors and computational capacity have made very large dataset assembly more common. However, these datasets...

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Main Authors: El‐Amine Mimouni, Jeffrey J. Ridal, Joseph D. Skufca, Michael R. Twiss
Format: Article
Language:English
Published: Wiley 2020-02-01
Series:Ecosphere
Subjects:
Online Access:https://doi.org/10.1002/ecs2.3014
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spelling doaj-7fa0ad99bb7c4859972b4218126770e52020-11-25T02:20:14ZengWileyEcosphere2150-89252020-02-01112n/an/a10.1002/ecs2.3014A multiscale approach to water quality variables in a river ecosystemEl‐Amine Mimouni0Jeffrey J. Ridal1Joseph D. Skufca2Michael R. Twiss3River Institute Cornwall Ontario CanadaRiver Institute Cornwall Ontario CanadaDepartment of Mathematics Clarkson University Potsdam New York 13699 USADepartment of Biology Clarkson University Potsdam New York 13699 USAAbstract Monitoring select ecosystem variables across time and interpreting the collected data are essential components of ecosystem assessment supporting management. Increasingly affordable sensors and computational capacity have made very large dataset assembly more common. However, these datasets initiate analytical challenges by their size and theoretical challenges due to the scale of the processes they encompass. Multiscale assessment of high temporal resolution water quality sensor data (temperature, in vivo chlorophyll a, colored dissolved organic matter) collected year‐round was conducted for the Upper St. Lawrence River. Using numerical methods that directly integrate the concept of scale, we show that consideration of scale‐dependent processes can lead to increased predictive power and a clearer understanding of ecosystem function. These results suggest that multiscale methods are not only an alternative way of approaching long‐term data assessment, but also a necessity in order to avoid spurious interpretation. Consequently, the concept of scale as described here can be consistently integrated into long‐term data studies to assist in the interpretation of high‐resolution data that help describe natural phenomena in aquatic systems.https://doi.org/10.1002/ecs2.3014limnologymodelingriversscaletemporal scalewater quality
collection DOAJ
language English
format Article
sources DOAJ
author El‐Amine Mimouni
Jeffrey J. Ridal
Joseph D. Skufca
Michael R. Twiss
spellingShingle El‐Amine Mimouni
Jeffrey J. Ridal
Joseph D. Skufca
Michael R. Twiss
A multiscale approach to water quality variables in a river ecosystem
Ecosphere
limnology
modeling
rivers
scale
temporal scale
water quality
author_facet El‐Amine Mimouni
Jeffrey J. Ridal
Joseph D. Skufca
Michael R. Twiss
author_sort El‐Amine Mimouni
title A multiscale approach to water quality variables in a river ecosystem
title_short A multiscale approach to water quality variables in a river ecosystem
title_full A multiscale approach to water quality variables in a river ecosystem
title_fullStr A multiscale approach to water quality variables in a river ecosystem
title_full_unstemmed A multiscale approach to water quality variables in a river ecosystem
title_sort multiscale approach to water quality variables in a river ecosystem
publisher Wiley
series Ecosphere
issn 2150-8925
publishDate 2020-02-01
description Abstract Monitoring select ecosystem variables across time and interpreting the collected data are essential components of ecosystem assessment supporting management. Increasingly affordable sensors and computational capacity have made very large dataset assembly more common. However, these datasets initiate analytical challenges by their size and theoretical challenges due to the scale of the processes they encompass. Multiscale assessment of high temporal resolution water quality sensor data (temperature, in vivo chlorophyll a, colored dissolved organic matter) collected year‐round was conducted for the Upper St. Lawrence River. Using numerical methods that directly integrate the concept of scale, we show that consideration of scale‐dependent processes can lead to increased predictive power and a clearer understanding of ecosystem function. These results suggest that multiscale methods are not only an alternative way of approaching long‐term data assessment, but also a necessity in order to avoid spurious interpretation. Consequently, the concept of scale as described here can be consistently integrated into long‐term data studies to assist in the interpretation of high‐resolution data that help describe natural phenomena in aquatic systems.
topic limnology
modeling
rivers
scale
temporal scale
water quality
url https://doi.org/10.1002/ecs2.3014
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