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dc.contributor.authorBaldry, Kimberlee
dc.contributor.authorHardman-Mountford, Nick
dc.contributor.authorGreenwood, Jim
dc.date.accessioned2017-11-29T11:13:55Z
dc.date.available2017-11-29T11:13:55Z
dc.date.issued2017-06-01
dc.identifier.citationBaldry 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.
dc.identifier.issn1810-6285
dc.identifier.doi10.5194/bg-2017-221
dc.identifier.urihttp://hdl.handle.net/10754/626232
dc.description.abstractOwing 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.
dc.description.sponsorshipThank you to the CSIRO Vacation Program for supporting this research and also SGS Australia for the receipt of the Brian Doran Scholarship for Physical Chemistry during the period of research. We thank Francois Dufois for producing Figure 6. We acknowledge the immense effort of Australia’s Integrated Marine Observing System (IMOS) in collecting the observations used for this study, especially the ocean carbon monitoring team under the leadership of Bronte Tilbrook, and the continued effort of NASA and the Barcelona Expert Center to provide EO data products for open use.
dc.publisherCopernicus GmbH
dc.relation.urlhttps://www.biogeosciences-discuss.net/bg-2017-221/
dc.rightsThis work is distributed under the Creative Commons Attribution 3.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/
dc.titleEstimating total alkalinity for coastal ocean acidification monitoring at regional to continental scales in Australian coastal waters
dc.typeArticle
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.identifier.journalBiogeosciences Discussions
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCSIRO Oceans & Atmosphere, Floreat, WA 6913, Australia
dc.contributor.institutionUniversity of Western Australia, Crawley, WA 6009, Australia
kaust.personBaldry, Kimberlee
refterms.dateFOA2018-06-14T02:20:58Z


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