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Outline

Eddy covariance - estimating ecosystem fluxes of carbon and water Asko Noormets, Jiquan Chen Dept. Earth, Ecological and Environmental Sciences The University of Toledo Ge Sun, Steve McNulty Southern Global Change Program, USDA FS. Outline. Background - brief theory and history

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Outline

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  1. Eddy covariance - estimating ecosystem fluxes of carbon and waterAsko Noormets, Jiquan ChenDept. Earth, Ecological and Environmental SciencesThe University of ToledoGe Sun, Steve McNultySouthern Global Change Program, USDA FS

  2. Outline • Background - brief theory and history • What does it look like? • Carbon flux in a managed landscape • Significance

  3. How do they co-vary GEP + R = NEE ? day day & night Principle of operation • 3-D wind speed and direction • CO2 and H2O concentration

  4. Co-variance

  5. Theory

  6. History(Baldocchi 2003 GCB) Theoretical framework - Sir Osborne Reynolds (1895) First application - Scrase (1930),momentum transfer, analog instruments Also later, post WW II experiments, mostly focused on measuring the turbulent structure of atmospheric boundary layer rather than CO2 exchange. First measurements of CO2 flux -Desjardins & Lemon (1974) First measurements over forests -Baumgartner (1969), Denmead (1969), Jarvis (1976) First application of open-path IRGA-s and sonic anemometers for CO2 flux measurements -Anderson et al. (1984), Anderson & Verma (1986), Ohtaki (1984), Desjardins (1985) First yearlong study - Wofsy et al. (1993), started in 1990 Flux measurement networks - Fluxnet, Ameriflux, CarboEuroflux etc. Current questions - sources of interannual variation, winter fluxes, separating contribution from layers, separating soil heterotrophic and autotrophic R.

  7. NEE – net ecosystem exchange of carbon R – ecosystem respiration GEP – gross ecosystem productivity Ecosystem C fluxes Fc (g CO2 m-2 d-1) Date

  8. Seasonal course - NEE, R, GEP NEE – net ecosystem exchange R – ecosystem respiration GEP – gross ecosystem productivity

  9. Decomposing NEE

  10. PAR (mol m-2 d-1) PAR (mol m-2 d-1) GEP (g CO2 m-2 d-1) Age effect on light response of GEP Mixed hardwood 3 years & 63 years Red pine 8 years & 65 years

  11. Resid. (µmol CO2 m-2 s-1) Resid. (µmol CO2 m-2 s-1) VPD (kPa) Hs (W m-2 d-1) Regression of residuals of AQ response

  12. Significance • Narrows error margins for ecosystem C balance estimates (“missing carbon”). • Sensitivity analysis of fluxes to environmental drivers. Differences in magnitude and seasonality. • Due to being a spatially integrative technique (footprint), it can be linked to RS, and used to model exchange in larger scales. • Quantitation of anthropogenic effects.

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