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Physical and Biological Processes that Controls W t V E h b t V t tiWater Vapor Exchange between Vegetation
and the Atmosphere
Dennis BaldocchiDepartment of Environmental Science, Policy and Management
University of California, Berkeley
Contributions from the Biometeorology Lab
Youngryel RyuJ h Fi hJosh FisherSiyan Ma
Xingyuan ChenGretchen MillerM tthi F lkMatthias Falk
Kevin Tu
Outline
• Processes, Supply vs Demand, Short and Long Time scalesShort– Short
• Energy• Meteorology
– Long• Leaf area indexLeaf area index• Nutrition• Plant Functional type
– Short to Long• Surface Conductance• Soil Moisture
• Time– Day/Night– Seasonal– Interannual
• Space– Land Use– PBL/Landscape– Globe
Water and the Environment: Bi h i l E h d l i l ViBiogeophysical-Ecohydrological View
PBL ht
Transpiration/Evaporation
AvailableEnergy
LAI Sensible Heat
Water
SurfaceConductance
p
Photosynthesis/RespirationRespiration
Carbon
NutrientsLitterSoil Moisture
Processes and Linkages:R l f Ti d S S lRoles of Time and Space Scales
continent
Weather modelEcosystemDynamics
Biogeo-chemistry
landscape
region/biomeWeather model Dynamics
λE HR
Species,Functional
Type
ppt, Ta, P, ea,u,Rg,L Ac Gc
canopy
landscape
Biophyscial Model
λE, HRn
Leaf Area, N/C,Ps capacity
Biophysical model
, g, c, c
secon
day
year
centurnds
ys rs ries
Penman Monteith Equationq
λρ
γ γE
s R S C G D
s Gn p H
H=
− + ⋅ ⋅ ⋅
+ +
( )
Function of:
γ γsGs
+ +
•Available Energy (Rn-S)•Vapor Pressure Deficit (D)•Aerodynamic Conductance (Gh)•Surface Conductance (Gs)Surface Conductance (Gs)
Eco-hydrology:y gyET, Functional Type, Physiological Capacity and Drought
?
λEeq
?
λE/λ
?
θ?
Effects of Functional Types and Rsfc on Normalized Evaporation
1.75
2.00
wheatcorn
E eq
1.25
1.50jack pineoak-savanna
λE/ λ
E
0.75
1.00
0 00
0.25
0.50
Rcanopy (s m-1)
10 100 1000 100000.00
Rc is a f(LAI, N, soil moisture, Ps Pathway)
Stomatal Conductance Scales with N, via Photosynthesis
Stomatal Conductance Scales with Photosynthesis
80
Photosynthetic Capacity Scales with Nitrogen
Stomatal Conductance scales with Nitrogen
mol
m-2 s
-1)
0.10
0.12
0.14
0.16
0.18
0.20
oak, varying lightCa: 360 ppmTa: 25 C
mol
m-2
s-1
)
40
60after Schulze et al (1994)
al c
ondu
ctan
ce (
mm
s-1)
6
8
10
12
14
A ( mol m-2 s-1)
0 1 2 3 4 5 6 7 8
g s (
mm
0.00
0.02
0.04
0.06
0.08
V cm
ax (µ
m
0
20
leaf nitrogen (mg g-`1)
5 10 15 20 25 30 35 40
max
imum
sto
mat
a
0
2
4
6
A (µmol m s )
Na (g m-2)
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Wilson et al. 2001, Tree PhysiologySchulze et al 1994. Annual Rev Ecology
Integrated Stomatal Conductance Scales with Photosynthetic Capacity and LAI
0.018
LAI =1
0.012
0.014
0.016LAI =1LAI =2LAI =3LAI - 4LAI =5
Gs (
m s
-1)
0.006
0.008
0.010
0 000
0.002
0.004
Vcmax (µmol m-2 s-1)
20 40 60 80 100 1200.000
CANVEG Computations
Effects of Leaf Area and Photosynthetic Capacity on Normalized Evaporation:Well Watered ConditionsWell-Watered Conditions
1.3 Priestley-Taylor= 1 26
1.1
1.2= 1.26
QE/
QE,
eq
0.9
1.0
0.7
0.8
Vcmax*LAI
0 20 40 60 80 100 120 140 160 180 2000.6
Vcmax LAI
Canveg Model, Baldocchi and Meyers, 1998 AgForMet
Effects of Leaf Area and Photosynthetic Capacity on Normalized Evaporation:
Watered Deficits
Boreal Forest
1 3
Watered-Deficits
1.2
1.3
k=8.0
k=10
k=7.0
QE,
eq 1.0
1.1
QE/
Q
0.8
0.9
0 6
0.7
Vcmax*LAI
0 20 40 60 80 100 120 140 160 180 2000.6
LAI and Ps Capacity also affects Soil vs Total Evaporation
0.30
0.20
0.25Q
E,so
il/QE
0.15
0.05
0.10
LAI * Vcmax
0 20 40 60 80 100 120 140 160 180 2000.00
cmax
Canopy Surface Conductance does not equal the Canopy Stomatal Conductance
Crop: Rsoil = 1500 s m-1
2.00crop Rsoil = 500 s m
-1
1 50
1.75G
can/
int g
s dl
1.25
1.50
0.75
1.00
Vc max (µmol m-2 s-1)
20 40 60 80 100 1200.50
Be Careful about using Gcan to compute isotopic discrimination
Forest Biodiversity is Negatively Correlated with Normalized Evaporation
Temperate/Boreal Broadleaved ForestsSummer Growing Season
1.2
1.0
1.1
λ E/λ
Eeq
0.8
0.9
0.6
0.7
Number of Dominant Tree Species (> 5% of area or biomass survey)
0 1 2 3 4 5 6 7 80.5
Baldocchi, 2005 In: Forest Diversity and Function: Temperate and Boreal Systems.
Use Appropriate and Root-Weighted Soil Moisture ( ) ( )
( )0
z
z
z dP z
dP z
θθ =
∫
∫ ( )0
dP z∫
Soil Moisture, arthimetic average
Soil Moisture root weighted
Chen, Baldocchi et al, in prep.
Soil Moisture, root-weighted
ET and Soil Water Deficits:a d So ate e c tsRoot-Weighted Soil Moisture
Grassland1.0
eq
1.00
1.25
summer rain
Oak Savanna
eq
0.6
0.8
λ E/λ
Ee
0.25
0.50
0.75
λE/λ
Ee
0.2
0.4
θ, weighted by roots (cm3 cm-3)
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.400.00
θ weighted by roots (cm3 cm-3)
0.00 0.05 0.10 0.15 0.20 0.25 0.300.0
Baldocchi et al., 2004 AgForMet
ET and Soil Water Deficits:W t P t ti lWater Potential
p ( )
1.25annual grassland
0.8
1.0
predawn water potentialsoil water potential
oak savanna
λE/λ
Eeq
0.50
0.75
1.00
λ E/λ
Eeq
0.4
0.6
-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.00.00
0.25
-5 -4 -3 -2 -1 00.0
0.2
soil water potential (MPa)soil water potential (MPa)
5 3 0
Root-Weighted Soil Moisture Matches Pre-Dawn Water Potential
ET of Annual Grass responds to water deficits differently than
Baldocchi et al., 2004 AgForMet
Trees
Leaf Area Index scales with Water Balance Deficits
10
various functional types:Baldocchi and Meyers (1998)savanna:Eamus et al. 2001Oak Savanna, CA
LAI
1 b[0]: -0.68b[1]: 0.84r ² : 0.69
[N]ppt/Eeq
0.1 1 10 1000.1
Nocturnal Transpiration from Blue Oak
Fisher et al. 2006, Tree Physiology
Seasonal and Annual Time Scales
Annual Grassland, 2004
14
) 10
12
14
Actual LEPotential LE
λE (M
J m
-2 d
-1
6
8
λ
2
4
Day
0 50 100 150 200 250 300 3500
Potential and Actual Evaporation are Decoupled in Semi-Arid System
Interannual Variation ET
Vaira 2004
5
2001: 301 mm
Oak Savanna
5
2002: 389 mm
mm
d-1
) 3
42002: 292 mm
2003: 353 mm 2004 : 284 mm
mm
d-1
) 3
42003: 422 mm2004: 340 mm2005: 484 mm
E (m
1
2 ET
(m
1
2
Day
0 50 100 150 200 250 300 3500
Day
0 50 100 150 200 250 300 3500
Annual ET of Annual Grassland varies with Hydrological Growing Seasonyd o og ca G o g Seaso
Ryu, Baldocchi, Ma and Hehn, JGR-Atmos, submitted
Growing Season Length and ET, Temperate Forest
5
6
1996 St t d121
Oak Ridge, TN
4
5
Are
a In
dex
3
4
5 1996, Starts d1211997, Starts d108
(mm
d-1
) 3
4
1996: 492 mm 1997: 519 mm
Leaf
A
1
2
E
1
2
Day
0 50 100 150 200 250 300 3500
Day
0 50 100 150 200 250 300 3500
Year with Longer Growing Season (13 days)Year with Longer Growing Season (13 days) Evaporated More (27 mm).
Other Climate Factors could have confounded results, b t R (5 43 5 41 GJ 2) d T (14 5 14 9 C)but Rg (5.43 vs 5.41 GJ m-2) and Tair (14.5 vs 14.9 C)
were similar and rainfall was ample (1682 vs 1435 mm)
Wilson and Baldocchi, 2000, AgForMet
Year to Year differences in ET is partly due to differences in Growing Season Length
CANOAK
690
700
Slope: -1.68 mm/day
Temperate Deciduous ForestOak Ridge, TN
7
Leaf out: D90
ET
(mm
y-1
)
650
660
670
680p y
(mm
d-1
)
3
4
5
6 D100D110D120D130
E
620
630
640
650
ET
0
1
2
3
Date of Leaf-Out
80 90 100 110 120 130 140620
Day
0 50 100 150 200 250 300 3500
Field data show that ET decreases by 2.07 mm for each day the start of the growing season is delayed
ET: Spatial ScaleET: Spatial Scale
Landscape DifferencesOn Short Time Scales, Grass ET > Forest ET
1.0
Monthly Averages
O S o t e Sca es, G ass o est
0.8
LE/L
Eeq
0.6
L
0.2
0.4
Savanna Woodland
0 2 4 6 8 10 12 14 160.0
Savanna WoodlandAnnual Grassland
Gs (mm s-1)
Ryu, Baldocchi, Ma and Hehn, JGR-Atmos, submitted
Role of Land Use on ET:On Annual Time Scale Forest ET > Grass ET
California Savanna
440
On Annual Time Scale, Forest ET > Grass ET
) 380
400
420
440or
atio
n (m
m y
-1
320
340
360
380
Eva
po
260
280
300
320
Oak WoodlandAnnual Grassland
Hydrological Year
02_03 03_04 04_05 05_06 06_07240
260
Ryu, Baldocchi et al, JGR-Atmos, submitted
•Savanna Uses More Water than Grassland•Savanna Soil holds about 78 mm more Water•Annual ET Decreases with Rg•Rg is negatively correlated with Rain and Clouds•System is Water not Radiation Limited
Different Land Use
440
460
(mm
y-1
)
360
380
400
420
Eva
pora
tion
300
320
340
360
17.4 17.6 17.8 18.0 18.2 18.4240
260
280 Oak Savanna WoodlandAnnual Grassland
(MJ m-2 d-1)
Ryu, Baldocchi, Ma, Hehn, JGR-Atmos, submitted
Stand Age also affects differences between ET of forest vs grassland
Plynlimon, Wales
o o est s g ass a d
) 700
800
900
grassland conifer forest
ratio
n (m
m y
-1)
500
600
700
Evap
or
300
400
500
Year
1970 1975 1980 1985 1990 1995 2000 2005 2010200
Marc and Robinson, 2007 HESS
Assessing Spatial g pAverages with Subgrid
Variabilityy
E Es A s A C Dg D gp a a[ ]
' ' ( ' ')( ( / '))
λρ
=+ + +
+ + +
A C D( )
s g g g ra s a s( ( / '))γ γ+ + +
E EsA r C Dg r
s g g rAs s A p a Dga D ga
a s gr ga rs
[ ]( )
( ( / ))λ
σ σ ρ σ σγ γ σ σ
=+ + +
+ + +
Sub-Grid Variability:MODIS d IKONOSMODIS and IKONOS
0.1
Oak/grass savannagrassland
σ2N
DVI
0.01
1 10 100 10000.001
∆x
Use Power Law scaling to Estimate small scale Variance
Baldocchi et al 2005 TellusE f x f x f x
xx[ ( )] ( ) ( ) ( )= + ∂
∂12
2
22σ
Errors in ET ScalingErrors in ET Scaling
E f x f x f xx
x[ ( )] ( ) ( ) ( )= + ∂∂
12
2
22σ
x∂2
Baldocchi et al 2005 Tellus
Linking Water and Carbon:Potential to assess Gc with Remote Sensing
0.8
(mol
m-2
s-1 )
0.4
0.6
Annual Grassland
-2 -1
0.00 0.01 0.02 0.03 0.04 0.05 0.06
Gc
0.0
0.2Regr. Coef:b[0]: 0.117b[1]: 9.908r ²: 0.793
Ac rh/Ca (mol m2s
1)
s-1 ) 0.20
0.25
0.30
oak savanna
Gc (
mol
m-2
s
0.05
0.10
0.15
b[0]: 8.53e-3b[1]: 8.97b[2]: 736.0r ²: 0.812
Xu + DDB, 2003 AgForMetAc rh/Ca (mol m
-2 s-1)
0.000 0.002 0.004 0.006 0.008 0.010 0.012 0.0140.00
Gc Exhibits Scale ‘Invariance’
30
40FLUXNET Data
y"
0
10
20
30"B
all-B
erry
40Stomatal Conductance SurveyDennis Baldocchi; UC Berkeleyy
BV98
BV99
BV97
BV00
WBW
96LW
98W
BW97
HV99
DU99
BL98
WR9
9AB
97LW
97CP
97NO
97SO
98SO
98BO
97GU
97NW
00W
BW95
PF99
NB96
0
10
20
30 Dennis Baldocchi; UC Berkeley
Ball-
Berr
y
P. ba
nksia
na
P. m
arian
a
Euca
lyptus
gran
dis
Euca
lyptus
gran
dis
Euca
lyptus
gran
dis
Andro
pogo
n gera
rdii
P. ab
iesP.
abies
Acer
rubrum
P. ba
nksia
na
Tritic
um ae
stivu
m
Bothr
iocho
loa ca
ucas
ia
Trips
acum
dacty
loide
s
Pinus
flexil
is
Zea m
ays
Goss
ypium
hirsu
tum
Querc
us ile
x
Euca
lyptus
gran
dis
Euca
lyptus
gran
dis
Euca
lyptus
gran
dis
Euca
lyptus
gran
dis
Euca
lyptus
gran
dis
Acer
sacc
harum
Acer
sacc
harum
Fagu
s
Pistac
ia len
tiscu
sBe
tula
Picea
Dacty
lis.gl
omera
ta
Polyg
onum
vivip
arum
Rhina
nthus
. Alec
torolo
ph
P. vi
viparu
m
Vacc
inium
mytr
tillus
Planta
nus o
rienta
lisPr
unus
0
R
Processed by M. Falk
Fisher et al Global ET Model
Total Latent heat flux ics LELELE ++
fwet 10RHflux
Canopy Transpiration ( ) ncTgwet Rfff γα +∆
∆−1
fgfAPARf
IPAR
APAR
ff
11 bSAVIm +
Soil Evaporation( ) ( )GRfff ns −+∆
∆−+
γα)1( wetSMwet
fIPARfcfT
22 bNDVIm +
IPARf
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛ −−
2
expλ
opta TT
Intercepted Evaporation ncRf γα +∆
∆wet
T
fSMTopt
⎠⎝
VPDRH
{ }VPDaAPARa RHTfT maxat
VPDRH A measure for downscaling ETRH A measure for downscaling ET with Drought???
Fisher, Tu and Baldocchi, RSE in press
Global ET, 1989, ISLSCP
ET (mm/y) reference
613 Fisher et al
286 Mu et al. 2007
467 Van den Hurk et al 2003
649 Boslilovich 2006
560 Jackson et al 2003
Fisher et al, in press
Global ET pdf, 1989, ISLSCP
Fisher et al.
ConclusionsConclusions• Biophysical data and theory help explain powerful ideas p y y p p p
of Budyko and Monteith that provide framework for upscaling and global synthesis of ET– ET scales with canopy conductance, which scales with LAI and py ,
Ps capacity, which scales with precipitation and N
Be CarefulBe Careful
• Short-Term Differences in Potential and Actual ET may not hold at Annual Time ScalesET may not hold at Annual Time Scales– Grass has greater potential for ET than Savanna
• Sub-Grid Variability in surface properties can produce huge errors in upscaled ET at the 1 km Modis Pixel Scale
Present and Past Biometeorology ET Team
Funding: US DOE/TCP; NASA; WESTGEC; Kearney; Ca Ag Expt Station
FluxNET WUEFluxNET WUE6000
FLUXNET 20075000
P (g
C m
-2 y
-1)
3000
4000
GP
P
1000
2000
LE (mm y-1)
0 200 400 600 800 1000 1200 14000
LE (mm y )
Global ET JulyJuly
Fisher et al, in press
Budyko Curve, Fluxnet data
1.4
1.0
1.2
Ea/
ppt
0.6
0.8
0.2
0.4
Rn/(λ ppt)
0 1 2 3 40.0
Photosynthesis > 200yRespiration
CO2
Limited Lear Area andSparse Canopy Reduce ET,
too60
80
100
120
140
160
180
140
Quercus alba (Wilson et al)Quercus douglasii (Xu and Baldocchi)
20 40 60 80 100 120 140 160 180
20
40
Sh t i
Vcm
ax
20
40
60
80
100
120
Ps Capacity must be Great,For Short Periodθ v
(%)
0.1
0.2
0.3
0.4
0.5
0.6
0.05 m0.50 m
Short growing season with available moisture
DOY100 150 200 250 300 350
0
Broadleaved, Deciduous Trees
250
300
0.0
Savanna, 2005
600
700
StomatalClosure so
Evaporation >Precipitation
Leaf N and Leaf Thicknessmust support Ps
Machinery
60 80 100 120 140 160 180 200
Am
ass
(nm
ol g
-1 s
-1)
0
50
100
150
200
Quercus douglasii
0 50 100 150 200 250 300 350
Wat
er F
lux
0
100
200
300
400
500
ET ppt
Specific Leaf Area (m2 g-1)
60 80 100 120 140 160 180 200
data of Reich et al and Xu and Baldocchi
Day
Interannual Variation ET:Grassland
Vaira 2004
5
4
2001: 301 mm 2002: 292 mm 2003: 353 mm 2004 : 284 mm
mm
d-1
) 3
E (m
1
2
0 50 100 150 200 250 300 3500
1
Day
0 50 100 150 200 250 300 350
Inter annual Water BalanceOak Savanna
5
2002 389
4
2002: 389 mm2003: 422 mm2004: 340 mm2005: 484 mm
ET
(mm
d-1
)
2
3
E
1
Day
0 50 100 150 200 250 300 3500
Day
Savanna, 20041.0
RH
vpd
0 4
0.6
0.8
0.0
0.2
0.4VPDRH
Day
0 50 100 150 200 250 300 350
0 4
0.5
A measure for downscaling ET
met
ric S
oil M
oist
ure
0.2
0.3
0.4
with Drought???
0 50 100 150 200 250 300 350
Vol
um
0.0
0.1 surface0.20 m0.50 m
Day
Fisher et al. Remote Sensing Environment, in press