Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters
Owing to a lack of resources, tools, and knowledge, the natural variability and distribution of Total Alkalinity (TA) has been poorly characterised in coastal waters globally, yet variability is known to be high in coastal regions due to the complex interactions of oceanographic, biotic, and terrest...
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ftkingabdullahun:oai:repository.kaust.edu.sa:10754/626232 2023-12-31T10:21:31+01:00 Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters Baldry, Kimberlee Hardman-Mountford, Nick Greenwood, Jim Marine Science Program Red Sea Research Center (RSRC) CSIRO Oceans & Atmosphere, Floreat, WA 6913, Australia University of Western Australia, Crawley, WA 6009, Australia 2017-06-01 application/pdf http://hdl.handle.net/10754/626232 https://doi.org/10.5194/bg-2017-221 unknown Copernicus GmbH https://www.biogeosciences-discuss.net/bg-2017-221/ Baldry K, Hardman-Mountford N, Greenwood J (2017) Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters. Biogeosciences Discussions: 1–23. Available: http://dx.doi.org/10.5194/bg-2017-221. doi:10.5194/bg-2017-221 1810-6285 Biogeosciences Discussions http://hdl.handle.net/10754/626232 This work is distributed under the Creative Commons Attribution 3.0 License. https://creativecommons.org/licenses/by/3.0/ Article 2017 ftkingabdullahun https://doi.org/10.5194/bg-2017-221 2023-12-02T20:18:37Z Owing to a lack of resources, tools, and knowledge, the natural variability and distribution of Total Alkalinity (TA) has been poorly characterised in coastal waters globally, yet variability is known to be high in coastal regions due to the complex interactions of oceanographic, biotic, and terrestrially-influenced processes. This is a particularly challenging task for the vast Australian coastline, however, it is also this vastness that demands attention in the face of ocean acidification (OA). Australian coastal waters have high biodiversity and endemism, and are home to large areas of coral reef, including the Great Barrier Reef, the largest coral reef system in the world. Ocean acidification threatens calcifying marine organisms by hindering calcification rates, threatening the structural integrity of coral reefs and other ecosystems. Tracking the progression of OA in different coastal regions requires accurate knowledge of the variability in TA. Thus, estimation methods that can capture this variability at synoptic scales are needed. Multiple linear regression is a promising approach in this regard. Here, we compare a range of both simple and multiple linear regression models to the estimation of coastal TA from a range of variables, including salinity, temperature, chlorophyll-a concentration and nitrate concentration. We find that regionally parameterised models capture local variability better than more general coastal or open ocean parameterised models. The strongest contribution to model improvement came through incorporating temperature as an input variable as well as salinity. Further improvements were achieved through the incorporation of either nitrate or chlorophyll-a, with the combination of temperature, salinity, and nitrate constituting the minimum model in most cases. These results provide an approach that can be applied to satellite Earth observation and autonomous in situ platforms to improve synoptic scale estimation of TA in coastal waters. Thank you to the CSIRO Vacation Program for ... Article in Journal/Newspaper Ocean acidification King Abdullah University of Science and Technology: KAUST Repository |
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King Abdullah University of Science and Technology: KAUST Repository |
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ftkingabdullahun |
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Owing to a lack of resources, tools, and knowledge, the natural variability and distribution of Total Alkalinity (TA) has been poorly characterised in coastal waters globally, yet variability is known to be high in coastal regions due to the complex interactions of oceanographic, biotic, and terrestrially-influenced processes. This is a particularly challenging task for the vast Australian coastline, however, it is also this vastness that demands attention in the face of ocean acidification (OA). Australian coastal waters have high biodiversity and endemism, and are home to large areas of coral reef, including the Great Barrier Reef, the largest coral reef system in the world. Ocean acidification threatens calcifying marine organisms by hindering calcification rates, threatening the structural integrity of coral reefs and other ecosystems. Tracking the progression of OA in different coastal regions requires accurate knowledge of the variability in TA. Thus, estimation methods that can capture this variability at synoptic scales are needed. Multiple linear regression is a promising approach in this regard. Here, we compare a range of both simple and multiple linear regression models to the estimation of coastal TA from a range of variables, including salinity, temperature, chlorophyll-a concentration and nitrate concentration. We find that regionally parameterised models capture local variability better than more general coastal or open ocean parameterised models. The strongest contribution to model improvement came through incorporating temperature as an input variable as well as salinity. Further improvements were achieved through the incorporation of either nitrate or chlorophyll-a, with the combination of temperature, salinity, and nitrate constituting the minimum model in most cases. These results provide an approach that can be applied to satellite Earth observation and autonomous in situ platforms to improve synoptic scale estimation of TA in coastal waters. Thank you to the CSIRO Vacation Program for ... |
author2 |
Marine Science Program Red Sea Research Center (RSRC) CSIRO Oceans & Atmosphere, Floreat, WA 6913, Australia University of Western Australia, Crawley, WA 6009, Australia |
format |
Article in Journal/Newspaper |
author |
Baldry, Kimberlee Hardman-Mountford, Nick Greenwood, Jim |
spellingShingle |
Baldry, Kimberlee Hardman-Mountford, Nick Greenwood, Jim Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters |
author_facet |
Baldry, Kimberlee Hardman-Mountford, Nick Greenwood, Jim |
author_sort |
Baldry, Kimberlee |
title |
Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters |
title_short |
Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters |
title_full |
Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters |
title_fullStr |
Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters |
title_full_unstemmed |
Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters |
title_sort |
estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in australian coastal waters |
publisher |
Copernicus GmbH |
publishDate |
2017 |
url |
http://hdl.handle.net/10754/626232 https://doi.org/10.5194/bg-2017-221 |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_relation |
https://www.biogeosciences-discuss.net/bg-2017-221/ Baldry K, Hardman-Mountford N, Greenwood J (2017) Estimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters. Biogeosciences Discussions: 1–23. Available: http://dx.doi.org/10.5194/bg-2017-221. doi:10.5194/bg-2017-221 1810-6285 Biogeosciences Discussions http://hdl.handle.net/10754/626232 |
op_rights |
This work is distributed under the Creative Commons Attribution 3.0 License. https://creativecommons.org/licenses/by/3.0/ |
op_doi |
https://doi.org/10.5194/bg-2017-221 |
_version_ |
1786832322420539392 |