Stable isotopes reduce parameter uncertainty of an estuarine carbon cycling model

Abstract

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.