Robust estimation of lake metabolism by coupling high frequency dissolved oxygen and chlorophyll fluorescence data in a Bayesian framework

Mark Honti, V. Istvánovics, Peter A. Staehr, Ludmila S. Brighenti, Mengyuan Zhu, Guangwei Zhu

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)608-621
Number of pages14
JournalInland Waters
Volume6
Issue number4
DOIs
Publication statusPublished - 2016

Fingerprint

dissolved oxygen
chlorophyll
fluorescence
metabolism
lakes
biomass
lake
time series
primary production
time series analysis
phytoplankton
respiration
learning
disturbance
anthropogenic activities
modeling
methodology
parameter
method

Keywords

  • Bayesian parameter inference
  • Dynamic model
  • Net primary production
  • Photosynthesis
  • Respiration
  • Sequential learning

ASJC Scopus subject areas

  • Aquatic Science
  • Water Science and Technology

Cite this

Robust estimation of lake metabolism by coupling high frequency dissolved oxygen and chlorophyll fluorescence data in a Bayesian framework. / Honti, Mark; Istvánovics, V.; Staehr, Peter A.; Brighenti, Ludmila S.; Zhu, Mengyuan; Zhu, Guangwei.

In: Inland Waters, Vol. 6, No. 4, 2016, p. 608-621.

Research output: Contribution to journalArticle

Honti, Mark ; Istvánovics, V. ; Staehr, Peter A. ; Brighenti, Ludmila S. ; Zhu, Mengyuan ; Zhu, Guangwei. / Robust estimation of lake metabolism by coupling high frequency dissolved oxygen and chlorophyll fluorescence data in a Bayesian framework. In: Inland Waters. 2016 ; Vol. 6, No. 4. pp. 608-621.
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