Eddy covariance - estimating ecosystem fluxes of

carbon and water

Asko Noormets, Jiquan ChenDept. Earth, Ecological and Environmental Sciences

The University of Toledo

Ge Sun, Steve McNultySouthern Global Change Program, USDA FS

Outline

1. Background - brief theory and history

2. What does it look like?

3. Carbon flux in a managed landscape

4. Significance

Principle of operation

• 3-D wind speed and direction

• CO2 and H2O concentrationHow do they co-vary

GEP + R = NEE ?

day day & night

Co-variance

-0.6

-0.3

0

0.3

0.6

12:21:45 12:21:46 12:21:48 12:21:50 12:21:52 12:21:53 12:21:55 12:21:57 12:21:58 12:22:00 12:22:02

u(z)

, m

s-1

583

583.75

584.5

585.25

586

[CO

2], mg m

-3

Theory

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.

Ecosystem C fluxes

-20

-10

0

10

4/24 6/13 8/2 9/21 11/10 12/30 2/18 4/9

Date

Fc

(g C

O2 m

-2 d

-1)

NEE – net ecosystem exchange of carbonR – ecosystem respirationGEP – gross ecosystem productivity

Seasonal course - NEE, R, GEP

-250

-200

-150

-100

-50

0

50

100

150May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

NE

E (

g C

m-2

mo

-1)

MHWMRPPBCCYMP

0

50

100

150

200

250

May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

R (

g C

m-2

mo

-1)

-500

-450

-400

-350

-300

-250

-200

-150

-100

-50

0May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

GE

P (

g C

m-2

mo

-1)

NEE – net ecosystem exchangeR – ecosystem respirationGEP – gross ecosystem productivity

Decomposing NEE

0

0.5

1

1.5

May Jun Jul Aug Sep

SRR

/R

-1

-0.5

0

0.5

1May Jun Jul Aug Sep

NEE

/GEP

MHWMRPPBCCYMP

-2

-1.5

-1

-0.5

0May Jun Jul Aug Sep

R/G

EP

Age effect on light response of GEP

-12

-6

0

60 20 40 60 80

-12

-6

0

60 20 40 60 80

PAR (mol m-2 d-1)PAR (mol m-2 d-1)

GE

P (

g C

O2 m

-2 d

-1)

Mixed hardwood3 years & 63 years

Red pine8 years & 65 years

Regression of residuals of AQ response

-2

-1

0

1

2

3

0 1 2 3

MHWMRPPBCCYMP

VPD (kPa)Res

id. (

µm

ol C

O2 m

-2 s

-1)

-4

-2

0

2

4

6

8

10

-100 0 100 200 300 400

MHWMRPPBCCYMP

Hs (W m-2 d-1)

Res

id. (

µm

ol C

O2 m

-2 s

-1)

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.