dennis baldocchi espm/ecosystem science div. university of california, berkeley

Applications of eddy covariance measurements, Part 1: Lecture on Analyzing and Interpreting CO2 Flux Measurements

Dennis BaldocchiESPM/Ecosystem Science Div.

University of California, Berkeley

CarboEurope Summer Course, 2006Namur, Belgium

Outline

• Philosophy/Background• Processing• Time Series Analysis

– Diurnal– Seasonal– Interannual

• Flux Partitioning– Canopy photosynthesis– Ecosystem Respiration

• Processes– Photosynthesis

• f(T,PAR, LAI, soil moisture)– Respiration

• f(photosynthesis, soil C &N, T, soil moisture, growth)

– Functional Type– Disturbance

• Space– Cross-Site Analyzes

Philosophy/Background

• Philosophy– What, How, Why, Will be?

• BioPhysical Processes– Meteorology/Microclimate

• Light, temperature, wind, humidity, pressure– Vegetation

• Structure (height, leaf area index, leaf size)• Physiology (photosynthetic capacity, stomatal conductance)

– Soil• Roots• Microbes• Abiotic conditions (soil moisture, temperature, chemistry, texture)

• Spatial-Temporal Variability– Spatial

• Vertical (canopy) and Horizontal (footprint, landscape, functional type, disturbance)

– Temporal• Dynamics• Diurnal• Seasonal• Inter-annual

microbesrootswoodleafnetstoragec RRRRPNEEFF

What a Tower Sees

Schulze, 2006 Biogeosciences

What the Atmosphere Sees

Eddy Covariance

F w c ' '

Mean

Fluctuation

F h F S z dzz z B

h

( ) ( ) ( ) z00

Cloud

Cloud

Reality

Real-time Sampling

Sample instruments at 10 to 20 Hz, depending on height of sensors and wind speed. fsample = 2 times fcutoff (f=nz/U)

Store real-time data on hard disk

Process and Compute Means, Variances and Covariances, Skewness and Kurtosis.

Compute 30 or 60 minute averages of statistical quantities.

Document data and procedures.

Diagnose instrument and system performance

Look for Spikes and Off-Scale Signals

Post Processing, hourly data

Compute Means, Covariances, Variances, Skewness and Kurtosis using Reynolds averaging

Merge turbulence and meteorological data

Apply calibration coefficients and gas law corrections to compute unit-correct flux densities and statistics

Apply transfer functions and frequency corrections

Compute Storage and Advective fluxes

Compute power spectra and co-spectra; examine instrument response and interference effects

From the Field to your Dissertation

Post Processing, daily data

Apply QA/QC and eliminate bad data

Fill gaps using gap filling methods

Correct nighttime data using such corrections as with well-mixed friction velocity, or check against independent measurements, such as soil respiration chambers

Compute daily integrals

Think and Read

Time Series Analysis: Raw Data

Vaira Grassland 2001

Day/Hour

0 50 100 150 200 250 300 350

Fc (

mol

m-2

s-1

)

-25

-20

-15

-10

-5

0

5

10

15

CO2 Exchange (mol m-2 s-1)Grassland, 2002

Day

0 50 100 150 200 250 300 350

Hou

r

0

4

8

12

16

20

24

-15 -10 -5 0

Time Series: FingerPrint

0 400 800 1200 1600 2000 2400

F c ( m

ol m

-2 s

-1)

-20

-15

-10

-5

0

5

10

15Temperate Deciduous Forest

D215

Time Series: Diurnal Pattern

Temperate ForestDays 150 to 250, 1997

time (hours)

0 4 8 12 16 20 24

CO

2 Fl

ux D

ensi

ty m

ol m-2

s-1

-25

-20

-15

-10

-5

0

5

10

Ne: measured (-4.84 gC m-2 day-1)Ne: computed (-5.09 gC m-2 day-1)

neemdtst.spw2/10/99

Fwpl+Storage: measured (-5.96 gC m-2 day-1)Fwpl: measured (-6.12 gC m-2 day-1)

Time Series: Mean Diurnal Pattern

a r a

z

a B

h

w c zctdz w c S z dz

r

' ' ( ) ' ' ( ) ( )

z z0 0

0

Vaira, D296-366, 2000

u* (m s-1)

0.0 0.1 0.2 0.3 0.4

Fc (

mol

m-2

s-1

)

-2

0

2

4

6

8

10

u* (m s-1)

0.0 0.2 0.4 0.6 0.8 1.0

F co2 (

mol

m-2

s-1

)

0

1

2

3

4

5

6

7

8

r ² 0.325Canopy Respiration8 to 13 C

Wheat, Columbia River Valley, Oregon

Night time Biased Respiration

DAY

0 50 100 150 200 250 300 350

CO

2 Sto

rage

(g

C m

-2 d

ay-1

)

-1.50-1.25-1.00-0.75-0.50-0.250.000.250.500.751.001.251.50

Deciduous Forest1995

ah ctdt

z0 CO2 Storage ‘Flux’

1995 Growing SeasonBroad-leaved Temperate Forest

Time (hours)

0 4 8 12 16 20 24

NE

(m

ol m

-2 s

-1)

-10

-5

0

5

10

15

20

25

30

D141-154: well-wateredStress factor > 1

D239-252: droughtStress factor = 0.72

D197-210: moderate soil moisture deficitStress factor = 0.84

D225-238: soil moisture deficitstress factor = 0.77D168-182: well-watered

stress factor > 1

Deciduous Broadleaved Forests

Temperate Broadleaved Deciduous Forest

Day

0 50 100 150 200 250 300 350

NE

E (g

C m

-2 d

-1)

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

LAI=0GPP=0;Litterfall (+)Reco=f(litterfall)(+)

snow:

Tsoil(+)

GPP=0; Reco(+)

no snow

Tsoil (-)

Reco (-)

GPP=f(LAI, Vcm

ax )

late spring

early spring

Drought:(-)GPP(-); Re(-)

Clouds:PAR(-) GPP=f(PAR)(+)

Evergreen Conifer ForestsTemperate/Boreal Ecosystems

Day

0 50 100 150 200 250 300 350

NE

E (g

C m

-2 d

-1)

-4

-3

-2

-1

0

1

2

Maritim

e/Humid Clim

atesMaritime/Humid Climates

GPP=f(PAR)

Drought, (-)Reco(-), GPP(-)

VPD(++)GPP(-)

frostGPP(-)

Boreal ClimateGPP=0

Tsoil(-)Reco(-)

Tmin > 0 oCGPP>0

Mediterranean Evergreen Forest

Day

0 50 100 150 200 250 300 350

NE

E (g

C m

-2 d

-1)

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

Sprin

g/Su

mm

er D

roug

ht

(-); G

PP(-)

, Rec

o(-)

rain eventsReco(++)

Autumn RainsGPP(+), Reco (+)

Grasslands

Day

0 50 100 150 200 250 300 350

NE

E (g

C m

-2 d

-1)

-6

-4

-2

0

2

4

Mediterranean GrasslandTemperate C4 grassland

Data sources: Valentini et al. 1996; Baldocchi + Xu, unpublished; Verma +Suyker

Late

sprin

g rain

s

GPP(+)

Spring/Summer Drought(-)GPP(-); Reco(-)

GPP > 0;AM Frost:GPP(-)

Tmin > 0 oCGPP =f(LAI) (+)

Rain PulseReco(++)GPP=0

Autumn Rains:T(-), (++)GPP(+), Reco(-)

snow covereddormant grassGPP=0, Reco > 0

Agricultural Crop

Day

0 50 100 150 200 250 300 350

NE

E (g

C m

-2 d

-1)

-4

-3

-2

-1

0

1

2

3

Planting Datetime when GPP>0

Tsoil(+), Residual Matter(+)Reco(+)GPP=0

Grain FillingReco(++)

Late SnowTsoil (-)Reco(-)

Harvest

Winter fallowGPP=0Reco=f(Tsoil,)

GPP=f(aPAR)

Reco <<G

PP

snow meltGPP > 0

Northern Wetlands/Tundra

Day

140 160 180 200 220 240 260

NE

E (g

C m

-2 d

-1)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

water table(-)Reco(+)NEE > 0

Snow CoverTsoil < 0GPP=0Reco ~> 0

Tair(+)Reco (++); GPP(+)Reco > GPP

snow meltGPP > 0 Tmin < 0; Senescence

GPP~0

S A A k B kkk

N

kk

N

( ) cos sin 0

1 12

0

)('' dScc cc

Fourier Transforms

frequency (cycles per hour)0.00001 0.0001 0.001 0.01 0.1 1 10

S F wpl(f)

/F2

0.0001

0.001

0.01

0.1

1

101997Temperate Forest 1 day

12 hours

3-6 days

17.5 days

24 days

173 days

Time Series: Spectral Analysis

Baldocchi et al., 2001 AgForMet

Stoy et al. 2005 Tree Physiol

Harvard Forest

Year

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

CO

2 Fl

ux D

ensi

ty (g

C m

-2 d

-1)

-12

-8

-4

0

4

8 NEERecoGEE

Time Series: Interannual Variability

Data of Wofsy, Munger, Goulden, Harvard Univ

2002 2003 2004 2005

NE

E[g

C m

-2 w

eek-

1 ]

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

HainichLeinefelde

Knohl et al Max Planck, Jena

Intern-annual Lag Effects Due to Drought/Heat Stress

Processes

• Canopy Photosynthesis– Light– Temperature– Soil Moisture– Functional Type

• Ecosystem Respiration– Temperature– Soil Moisture– Photosynthesis

From E. Falge

Concepts:NEE and Environmental Drivers

Pulses, Switches and Lags are Important too!

• They are Features of Complex Dynamical Systems

• Biosphere is a Complex Dynamical System – Constituent Processes are Non-linear and Experience Non-

Gaussian Forcing– Possess Scale-Emergent Properties– Experiences Variability Across a Spectrum of Time and Space

Scales– Solutions are sensitive to initial conditions– Solutions are path dependent– Chaos or Self-Organization can Arise

Light and Photosynthesis:Leaves, Canopies and Emerging Processes

D208 Oak leaf, forest floorTleaf: 25o CCO2 : 360 ppm

Qpar (mol m-2 s-1)

0 200 400 600 800 1000 1200 1400 1600 1800

A ( m

ol m-2 s-1 )

0

2

4

6

8

10

12datamodel

0 250 500 750 1000 1250 1500 1750 2000-0.75

-0.50

-0.25

0.00

0.25

Qa (µmol m-2 s-1)

F c/L

AI (

mg

m-2 le

af a

rea

s-1)

CORN

WHEAT

CO2 uptake-Light Response Curve: Crops

Linear Function and High r2 (~0.90)

0 500 1000 1500 2000-2.0

-1.5

-1.0

-0.5

0.0

0.5

PAR (µmol m-2 s-1)

Temperate Broadleaved ForestSpring 1995, well-watered

F c (m

g m

-2 s-

1 )

Function is Non-Linear and Low r2 (~0.50)

CO2 uptake-Light Response Curve: Forest

CO2 flux vs Sunlight at different LAI

F C (

mol

m-2

s-1) -20

-15

-10

-5

0

5

F C (

mol

m-2

s-1)

-20

-15

-10

-5

0

5

Qp (mol m-2s-1)

0 500 1000 1500 2000-25

-20

-15

-10

-5

0

5

0 500 1000 1500 2000-25

-20

-15

-10

-5

0

5

(a) DOY025-040, LAI=1.0(b) DOY071-080, LAI=1.8

(c) DOY096-105, LAI=2.4

(d) DOY130-140 end of senesence

Xu and Baldocchi, 2003, AgForMet

PAR (mol m-2 s-1)

0 500 1000 1500 2000

P c ( m

ol m

-1 s

-1)

-10

0

10

20

30

40

50

crop canopyVcmax = 100 mol m-2 s-1

LAI=5

LAI=3

LAI=1

Use Theory to Interpret Complex Field Data Patterns

PAR (mol m-2 s-1)

0 500 1000 1500 2000

P c(m

ol m

-1 s

-1)

-10

0

10

20

30

40

50

crop canopyLAI = 5

Vcmax = 100 mol m-2 s-1

Vcmax = 50

Vcmax = 25

Deciduous forest

model: clumped leaves

PPFD (mol m-2 s-1)

0 500 1000 1500 2000 2500

F c (m

ol m-2

s-1 )

-40

-30

-20

-10

0

10

measured

(b)

0 500 1000 1500 2000 2500

F c (

mol

m-2 s-1 ) -40

-30

-20

-10

0

10

(a)

model: spherical leaves

Leuning et al. 1995, PCE

Ac vs Qp: Daily Sums Become Linear!?

Role of Averaging Period:Hourly vs Daily

Evergreen needlefeaf forest

Gro

ss C

O2

flux

(m

ol m

-2 s-1

)

-10

0

10

20

30

40BlodgettNiwotHowland

Deciduous broadleaf forest

Gro

ss C

O2

flux

(m

ol m

-2 s-1

)

-10

0

10

20

30

40

50

Harvard forestMMSF

Grassland and savanna

PAR (mol m-2 s-1)0 500 1000 1500 2000

Gro

ss C

O2

flux

(m

ol m

-2 s-1

)

-10

0

10

20

30

40 Tonzi LethbridgeViara

Evergreen needlefeaf forest

Gro

ss C

O2

flux

(mm

ol m

-2 da

y-1)

0

500

1000

1500

BlodgettNiwotHowland

Deciduous broadleaf forest

Gro

ss C

O2

flux

(mm

ol m

-2 da

y-1)

0

500

1000

1500

2000

HarvardMMSF

Grassland and savanna

PAR (mol m-2 day-1)0 10 20 30 40 50 60 70

Gro

ss C

O2

flux

(mm

ol m

-2 da

y-1)

0

500

1000

1500

TonziLethbridgeViara

Half hour data Daily data

Sims et al. AgForMet, 2005

8 day means

Dai

ly g

ross

CO

2 flu

x

(mm

ol m

-2 d

ay-1

)0

200

400

600

800

1000

1200

1400

16008 day means

Dai

ly n

et C

O2 f

lux

(mm

ol m

-2 d

ay-1

)

-400

-200

0

200

400

600

800

r2 = 0.92

8 day means

Dai

ly g

ross

LU

E

0.00

0.01

0.02

0.03

r2 = 0.65

Single clear days

AM net CO2 flux (mmol m-2 hr-1)

0 20 40 60 80 100 120

Dai

ly n

et C

O2 f

lux

(mm

ol m

-2 d

ay-1

)

-400

-200

0

200

400

600

800Single clear days

AM gross CO2 flux (mmol m-2 hr-1)

0 20 40 60 80 100 120 140

Dai

ly g

ross

CO

2 flu

x

(mm

ol m

-2 d

ay-1

)

0

200

400

600

800

1000

1200

1400

1600

r2 = 0.88

Single clear days

AM gross LUE

0.00 0.01 0.02 0.03

Dai

ly g

ross

LU

E

0.00

0.01

0.02

0.03

r2 = 0.73

r2 = 0.64

r2 = 0.56

Evergreen needleleaf forestDeciduous broadleaf forestGrassland and woody savanna

a b c

d e f

Sims et al 2005, AgForMet

Role of Averaging Period:Snap Shot vs Daily Integral

PPFD (mol m-2 s-1)0 500 1000 1500 2000

NEE

(m

ol m

-2 s

-1)

-40

-35

-30

-25

-20

-15

-10

-5

0

5

10Sunny daysdiffuse/total <= 0.3

Cloudy daysdiffuse/total >= 0.7

Temperate Broad-leaved ForestSpring 1995 (days 130 to 170)

Canopy Light Response Curves: Effect of Diffuse Light

CO2 Flux and Diffuse Radiation

Niyogi et al., GRL 2004

C Fluxes and Remote Sensing: NPP and NDVI of a Grassland

Xu, Gilmanov, Baldocchi

Rahman et al 2005 GRL

GPP RH/Ca (mol m-2s-1)

0.00 0.01 0.02 0.03 0.04 0.05 0.06

g c (m

ol m

-2s-1

)

0.0

0.2

0.4

0.6

0.8

Linking Water and Carbon:Potential to assess Gc with Remote Sensing

Xu + DDB

-120 0 120 240

-0.2

0.0

0.2

0.4

-4

0

4

LSW

I

MODIS - LSWI

gC

day

-1

Daily NEE

-0.2 0.0 0.2 0.4

-5

0

5

daily

NE

E

LSWI

R2 =0.74

Land Surface Water Index (LSWI) plotted with daily NEE for 2004/2005

PRI and NEE

-120 -60 0 60 120 180

-0.10

-0.08

-0.06-4

-2

0

2

4

6

PR

I

DOY after 1/1/2005

PRI

gC

day

-1

14 day NEE

Land Surface Water Index LSWI = (ρ860 - ρ1640)/(ρ860 + ρ1640)

PRI = (531 - 570) / (531 +570)

Falk, Baldocchi, Ma

Partitioning Carbon Fluxes

NEP GPP Reco

Law and Ryan, 2005, Biogeochemistry

Kuzyakov, 2006

De-Convolving Soil Respiration

From E. Falge

Deconstructing NEP:Flux Partitioning into Reco and GPP

Xu and Baldocchi

Reco from Fc/PAR curve (mol m-2s-1)

0 1 2 3 4 5 6

Ave

rage

d R

nigh

t ( m

ol m

-2s-1

)

0

1

2

3

4

5

6

Respiration from Light response (g C m-2)

0 300 600 900 1200 1500R

espi

ratio

n fr

om N

ight

time

flux

(g C

m-2

)0

300

600

900

1200

1500

f(x) = 16.51 + 0.98 x r2 = 0.98

SH

NB

GU

SWh

BVNO

Falge et al

0 10 20 30

Rec

o ( m

ol m

-2s-1

)

-2

0

2

4

6

8

10

Soil temperature (oC)0 10 20 30

Rec

o ( m

ol m

-2s-1

)

-2

0

2

4

6

8

10

DOY347-365Q10=2.51

DOY180-230, Q10=2.11

DOY129-139,Q10=2.2

(a) (b)

Ecosystem Respiration

Xu + Baldocchi, AgForMet 2003

Is Q10 Conservative?

Environmental Controls on Respiration

Soil volumetric water content (m3 m-3)

0.0 0.1 0.2 0.3 0.4

Rec

o/Rre

f

0.0

0.5

1.0

1.5

2.0Fast growth period data

Rain pulse

Xu + Baldocchi, AgForMet 2003

Rains Pulse do not have Equal Impacts

R eco

(g C

m-2

d-1)

2

4

6

8

10

PPT

(mm

d-1

)

10

20

30

40

50

60

DOY270 285 300 315

v (cm

3 cm-3

)

0.0

0.1

0.2

0.3

v (c

m3 c

m-3

)

0.0

0.1

0.2

0.3

Reco

PPT

Xu, Baldocchi Agri For Meteorol , 2004

Rain Pulses: Heterotrophic Respiration

Days After Rain Pulse

-10 -5 0 5 10 15 20 25 30

C E

fflux

(gC

m-2

d-1

)

0

1

2

3

4

5

8 mm 12.7 mm61 mm12 mm3 mm

Respiration time Constant & ppt

Amount of ppt (mm)

0 20 40 60

Tim

e C

onst

ant t

(d)

0

2

4

6

8

10

senesced grassland

oak savanna understory

0 20 40 60

Tota

l C re

spira

ted

(gC

/m2 )

0

20

40

60

80

senesced grassland

oak savanna understory

Xu + DDB

Soil tempreture (oC)

30 35 40 45 500.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

14:50h

6hFo=0.037e0.0525T, Q10=1.69, R2=0.95

Tonzi Open areas

Soil temperature (oC)25 30 35

1.1

1.2

1.3

1.4

1.5

1.6

1.7

Under treesDOY 211

Fu=0.337e0.0479T, Q10=1.61, R2=0.80

20h

6h

12:50h12h

16h

Tonzi Under trees

10h

24h

Tang, Baldocchi, Xu, Global Change Biology, 2005

Respiration and Photosynthesis

Lags and Leads in Ps and Resp: Diurnal

June

Time (hour)

0 4 8 12 16 20 24

Flux

Den

sity

-10

-8

-6

-4

-2

0

6

7

soil respirationcanopy photosynthesis

Tang et al, Global Change Biology 2005.

Cross-Site Analyses

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180-90

-75

-60

-45

-30

-15

0

15

30

45

60

75

90

0123456789101112

deFries G lobal 1x1 degree land cover m ap

What is Wrong with this Picture?

Valentini et al., 2000, Nature

FLUXNET DataForests

Latitude

-10 0 10 20 30 40 50 60

NE

E (g

C m

-2 y

-1)

-1000

-800

-600

-400

-200

0

200

Longitudinal Gradients across Continents in T and ppt Break the Relationship

FLUXNETForests

Latitude

25 30 35 40 45 50 55 60 65

NE

E (g

C m

-2 y

-1)

-1500

-1000

-500

0

500

1000

EuropeNorth America

North America + Europe

Reco (gC m-2 yr-1)

0 200 400 600 800 1000 1200 1400 1600

GP

P (g

C m

-2 y

r-1)

0

200

400

600

800

1000

1200

1400

1600

Falge et al., 2002

Law et al 2002 AgForMet

Mean Summer Temperature (C)5 10 15 20 25 30

Tem

pera

ture

Opt

imum

fo

r Can

opy

CO2 u

ptak

e (C

)

5

10

15

20

25

30

35

b[0] 3.192b[1] 0.923r ² 0.830

Temperature Acclimation

Falge et al; Baldocchi et al.

Respiration: Temperature and acclimation

Analyst: Enquist et al. 2003, Nature

Atkin

Spatial Gradients:NEE and Length of Growing Season

Broad-Leaved Forests

Length of Growing Season

100 150 200 250

NEE

(gC

m-2

yr-1

)

-800

-700

-600

-500

-400

-300

-200

-100

0

100Japan

DenmarkItaly

Massachusetts, USA

Belgium

Tennessee, USAPrince Albert, CANADA

Ontario

Indiana, USA

Michigan, USA

Re vs GPP

Interannual Variability in NEE

d GPP/dt

-300 -200 -100 0 100 200 300

d R

eco/

dt

-300

-200

-100

0

100

200

300

Soroe, DenmarkBeech Forest1997

day

0 50 100 150 200 250 300 350-10

-5

0

5

10

15

20

NEE, gC m-2 d-1

Tair, recursive filter, oC

Tsoil, oC

Data of Pilegaard et al.

Soil Temperature: An Objective Indicator of Phenology??

Data of: ddb, Wofsy, Pilegaard, Curtis, Black, Fuentes, Valentini, Knohl, Yamamoto. Granier, SchmidBaldocchi et al. Int J. Biomet, in press

Soil Temperature: An Objective Measure of Phenology, part 2

Mean Air Temperature, C

4 6 8 10 12 14 16 18

Day

of NEE

= 0

60

80

100

120

140

160

Coefficients:b[0]: 169.3b[1]: -4.84r ²: 0.691

Temperate Deciduous Forests

Day, Tsoil >Tair

70 80 90 100 110 120 130 140 150 160

Day

NEE=

0

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Disturbance and Carbon Fluxes

Amiro et al., 2006

Coursolle et al. 2006