1. 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

2. Outline 1.Background - brief theory and history 2.What does it look like? 3.Carbon flux in a managed landscape 4.Significance

3. Principle of operation 3-D wind speed and direction CO 2 and H 2 O concentration How do they co-vary GEP + R=NEE? day day & night

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 CO 2 exchange. First measurements of CO 2 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 CO 2 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.

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

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

13. Decomposing NEE

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

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

16. 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.