Data assimilation techniques for aquatic ecological processes

Manojlovic, Bojana

Data assimilation techniques for aquatic ecological processes

Manojlovic, Bojana

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Publication Type:: Thesis
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Manojlovic, Bojana
dc.date.accessioned	2021-09-28T04:06:04Z
dc.date.available	2021-09-28T04:06:04Z
dc.date.issued	2021
dc.identifier.uri	http://hdl.handle.net/10453/150726
dc.description	University of Technology Sydney. Faculty of Science.	en_US.UTF-8
dc.description.abstract	Data assimilation applications for three aquatic ecological processes are explored through the Particle Marginal Metropolis Hastings technique. Datasets collected from five seagrass meadow sites at Port Phillip Bay, Australia are assimilated with a seagrass growth model for state and parameter estimation and prediction. Predictions are validated and assessed against their ability to accurately predict biomass and sea-floor cover for ongoing monitoring of seagrass meadows. The second application proposes a more flexible alternative to the conventional modelling of seagrass detrital decay using a time-varying decay rate. The proposed approach is tested against an in-situ field study and a laboratory experiment investigating the effects of temperature and nutrients on seagrass in Fagans Bay, Australia. The third application evaluates how combining high density sensor data with a data assimilating microalgae model informs on underlying processes occurring within environmental photo-bioreactors for the production of bio-fuels.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/150726/2/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Data assimilation techniques for aquatic ecological processes	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Data assimilation applications for three aquatic ecological processes are explored through the Particle Marginal Metropolis Hastings technique. Datasets collected from five seagrass meadow sites at Port Phillip Bay, Australia are assimilated with a seagrass growth model for state and parameter estimation and prediction. Predictions are validated and assessed against their ability to accurately predict biomass and sea-floor cover for ongoing monitoring of seagrass meadows. The second application proposes a more flexible alternative to the conventional modelling of seagrass detrital decay using a time-varying decay rate. The proposed approach is tested against an in-situ field study and a laboratory experiment investigating the effects of temperature and nutrients on seagrass in Fagans Bay, Australia. The third application evaluates how combining high density sensor data with a data assimilating microalgae model informs on underlying processes occurring within environmental photo-bioreactors for the production of bio-fuels.

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http://hdl.handle.net/10453/150726