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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Online Access: | https://doi.org/10.1002/ecs2.3014 |
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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 |
work_keys_str_mv |
AT elaminemimouni amultiscaleapproachtowaterqualityvariablesinariverecosystem AT jeffreyjridal amultiscaleapproachtowaterqualityvariablesinariverecosystem AT josephdskufca amultiscaleapproachtowaterqualityvariablesinariverecosystem AT michaelrtwiss amultiscaleapproachtowaterqualityvariablesinariverecosystem AT elaminemimouni multiscaleapproachtowaterqualityvariablesinariverecosystem AT jeffreyjridal multiscaleapproachtowaterqualityvariablesinariverecosystem AT josephdskufca multiscaleapproachtowaterqualityvariablesinariverecosystem AT michaelrtwiss multiscaleapproachtowaterqualityvariablesinariverecosystem |
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