Stable isotopes reduce parameter uncertainty of an estuarine carbon cycling model

Sri Adiyanti, B.D. Eyre, D.T. Maher, I. Santos, L. Golsby-Smith, P. Mangion, Matthew R. Hipsey

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)

    Abstract

    © 2016 Elsevier Ltd.
    Quantifying estuarine carbon cycling is complex due to the highly-variable environmental conditions associated with the interaction between tides, riverine inflows, meteorological forcing and internal biogeochemical processes. A Markov-Chain Monte Carlo algorithm was utilized to perform unbiased calibration of parameters used by a 1-D isotope-enabled carbon model applied to stable isotope data collected in Caboolture River Estuary, Australia. The parameter posteriors were ported into a 3-D finite-volume isotope-enabled carbon model and run over a range of hydro-meteorological conditions that occurred during a 1.5-year simulation period. The model highlighted the spatio-temporal variations and uncertainties associated with carbon cycling within the estuary, including the shift from being strongly heterotrophic in the upper estuary with a higher water-atmosphere flux of CO2, to a more balanced trophic state in the lower estuary. The approach demonstrates the usefulness of isotope data to constrain model uncertainty and advances our ability to undertake carbon budgeting in coastal environments.
    Original languageEnglish
    Pages (from-to)233-255
    Number of pages23
    JournalEnvironmental Modelling and Software
    Volume79
    Early online date4 Feb 2016
    DOIs
    Publication statusPublished - 1 May 2016

    Fingerprint

    Dive into the research topics of 'Stable isotopes reduce parameter uncertainty of an estuarine carbon cycling model'. Together they form a unique fingerprint.

    Cite this