Robust Estimation of Lake Metabolism by Coupling High Frequency Dissolved Oxygen and Chlorophyll Fluorescence Data in A Bayesian Framework

Gross primary production (GPP) and community respiration (R) are increasingly calculated from high-frequency measurements of dissolved oxygen (DO) by fitting dynamic metabolic models to the observed DO time series. Because different combinations of metabolic components result in nearly the same DO time series, theoretical problems burden this inverse modeling approach. Bayesian parameter inference could improve identification of processes by including independent knowledge in the estimation procedure. This method, however, requires model development because parameters of existing metabolic models are too abstract to achieve a significant improvement. Because algal biomass is a key determinant of GPP and R, and high-frequency data on phytoplankton biomass are increasingly available, coupling DO and biomass time series within a Bayesian framework has a high potential to support identification of individual metabolic components. We demonstrate this potential in 3 lakes. Phytoplankton data were simulated via a sequential Bayesian learning procedure coupled with an error model that accounted for systematic errors caused by structural deficiencies of the metabolic model. This method provided ecologically coherent, and therefore presumably robust, estimates for biomass-specific metabolic rates and contributes to a better understanding of metabolic responses to natural and anthropogenic disturbances.