FLUXNET: Measuring CO2 and Water Vapor Fluxes
Across a Global Network
Dennis BaldocchiESPM/Ecosystem Science Div.
University of California, Berkeley
IndoFlux, Chennai, India, July 2006
FLUXNET: From Sea to Shining Sea379 Sites, circa 2006
Global distribution of Flux Towers with Respect to Climate
Evolution of FLUXNET
• Measure Annual Cycle of NEE– Micromet issues of Detrending, Transfer Functions, Flux
Sampling and Measurements, Gap-filling, Error Assessment• Measure and Interpret Intra-annual Variation of NEE
– Flux partitioning (GPP & Reco); assessment of metadata,e.g. Vcmax, soil respiration, LAI, biomass inventories.
• Measure and Interpret Inter-annual variations of NEE• Measure NEE over multiple Land-Use Classes
– crops, grasslands, deciduous and evergreen broadleaf and conifer forests
– Disturbance: logging, biodiversity and fire• Manipulative Studies
– Nitrogen and H2O additions• Measure NEE over Representative Areas
– Scaling Flux Information of Footprint to MODIS pixel
Successes
• Mountains of data from a spectrum of canopy roughness conditions, functional types and climate spaces have been collected
• A Model for Data Sharing– FLUXNET Web Site, a venue for distributing Primary, Value-added and
Meta-Data products• Value-Added Products have been produced
– Development of Gap-Filling Techniques– Production of Gap-Filled Daily and Annual Sums
• Data for Validating and Improving SVAT models used for weather, climate, biogeochemistry and ecosystem dynamics
• Collaboration & Synthesis through Workshops and Hosting Visitors– Building a Collaborative, Cooperative, Multi-Disciplinary & International
Community of Researchers– Characterizing Annual C Fluxes– Environmental Controls on NEE
• Training New and Next Generation of Scientists, Postdocs, Students
‘Failures’/’Un-resolved’ Issues
• Not Measuring Night-time Fluxes Well• Not Measuring Fluxes over Complex terrain
and during Advection Well• ImPerfect U* correction
– New Gu Algorithm• ImPerfect Flux Partitioning
– Works Better on Longer Time Scales• ImPerfect Energy Balance Closure
– Could be ‘red-herring’• Need Better Outreach and Training• Needs Expansion into other Regions
– India– Africa
Visions with a Flux Measurement Network
• Processes– Canopy-Scale Response Functions
• Emergent Processes– Flux Partitioning, NEP=GPP-Reco
• Acclimation• Time
– Daily/Seasonal Dynamics– Pulses, Lags, Switches– Intra- + Interannual Variability– Stand Age/Disturbance
• Space– Climate/Structure/Function– Coherence/Gradients– Upscaling with Remote Sensing
• New Directions
Probability Statistics of NEE
Col 4 vs Col 5
NEE (gC m-2 y-1)
-1500 -1000 -500 0 500 1000
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16Published data (275 site years)Median: -229 gC m-2 y-1
Light and Photosynthesis:
Emergent Processes at Leaf and Canopy Scales
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
model: clumped leaves
PPFD (mol m-2 s-1)
0 500 1000 1500 2000
F c (
mol
m-2
s-1)
-40
-30
-20
-10
0
10measured
(a)
0 500 1000 1500 2000-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
absorbed PAR (µmol m-2 s-1)
WHEAT
F c (mg
m-2 s-1 )
Volcanoes, Aerosols + NEE
CO2 Flux and Diffuse Radiation
Niyogi et al., GRL 2004
Photosynthesis-Respiration
Processed by Falge
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
b[1] 1.18r ² 0.893
Mean Summer Temperature (C)5 10 15 20 25 30
Tem
pera
ture
Opt
imum
fo
r Can
opy
CO 2 upt
ake
(C)
5
10
15
20
25
30
35
b[0] 3.192b[1] 0.923r ² 0.830
Analysis of E. Falge
NEE: Acclimation with TemperatureNEE: Acclimation with Temperature
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
Temporal Dynamics of C Fluxes
• Hour• Day• Month• Season• Year• Multiple Years • Pulses
• Lags• Switches
Complicating Dynamical Factors
• Switches/Pulses– Rain– Phenology/Length of
Season– Frost/Freezing
• Emergent Processes– Clouds & LUE
• Acclimation• Lags • Stand Age/Disturbance
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)(+)
Decadal Plus Time Series of NEE:Flux version of the Keeling’s Mauna Loa Graph
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
Data of Wofsy, Munger, Goulden et al.
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
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
Lag Effects Due to Drought/Heat Stress
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
Temperate Deciduous Forests
Day, Tsoil >Tair
70 80 90 100 110 120 130 140 150 160
Day
NE
E=0
70
80
90
100
110
120
130
140
150
160
DenmarkTennesseeIndianaMichiganOntarioCaliforniaFranceMassachusettsGermanyItalyJapan
Spatial Variations in C Fluxes
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
Tower vs Satellite NDVI
0.00 0.25 0.50 0.75 1.000.00
0.25
0.50
0.75
1.00
NDVIMODIS
ND
VI TO
WE
R
conifers deciduous
crops grassland
Falk et al., to be submitted
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
Heinsch et al. IEEE 2006
Heinsch et al. IEEE 2006
Global MODIS Test
Limits to Landscape Classification by Functional Type
• Stand Age/Disturbance• Biodiversity• Fire• Logging• Insects/Pathogens• Management/Plantations• Kyoto Forests
Effects of Stand Age:After Logging
Law et al. 2003 Global Change Biology
Biodiversity and Evaporation
Temperate/Boreal Broadleaved ForestsSummer Growing Season
Number of Dominant Tree Species (> 5% of area or biomass survey)
1 2 3 4 5 6 7 8
E/
Eeq
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
Baldocchi, 2004: Data from Black, Schmid, Wofsy, Baldocchi, Fuentes
Value of Flux Networks
• Documenting Change in Ecosystem Metabolism– Network acts as ‘canary in the mine’
• Produces Large and Long Data Sets– Reduced Sampling Error– Robust Dataset for Model Development
• Study Spectra of Time Scales– Capture Pulses and Lags
• Study Gradient of Climates, Structure and Function• Field of Dreams: ‘Build it and they will Come’
– Better Integrated Research Studies
