Data assimilation techniques for aquatic ecological processes

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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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