dennis baldocchi espm/ecosystem science div. university of california, berkeley
DESCRIPTION
Applications of eddy covariance measurements, Part 1: Lecture on Analyzing and Interpreting CO 2 Flux Measurements. Dennis Baldocchi ESPM/Ecosystem Science Div. University of California, Berkeley. CarboEurope Summer Course, 2006 Namur, Belgium. Outline. Philosophy/Background Processing - PowerPoint PPT PresentationTRANSCRIPT
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
70
80
90
100
110
120
130
140
150
160
DenmarkTennesseeIndianaMichiganOntarioCaliforniaFranceMassachusettsGermanyItalyJapan
Disturbance and Carbon Fluxes
Amiro et al., 2006
Coursolle et al. 2006