Carbon & Water Exchange of an Oak-Grass Savanna
EcosystemDennis Baldocchi
ESPMUC Berkeley
Oak-Savanna Model System for Studying Ecosystem Ecology
• Structure/Function– Oak and grasses provide contrasting life forms, woody/herbaceous,
perennial/annual– The Canopy is open and heterogeneous, giving us a opportunity to
test the applicability of ecosystem and biogeophysical models, mostly developed for ideal and closed canopies
• Environmental Biology– The Mediterranean climate provides distinct wet/ cool and dry/hot
seasons to examine the ecosystem response (photosynthesis, transpiration, respiration, stomatal conductance) to a spectrum of soil moisture and temperature conditions
• Global Change– The Mediterranean climate experiences great extremes in inter-
annual variability in rainfall; we experience a wider range in pptover a few years than long-term predicted changes.
Oak-Grass Savanna: A Two Layer System
Summer:Trees green; grass dead
Spring:Trees green;grass green
Winter:Trees deciduous; grass green
Water and the Environment: Biogeophysical-Ecohydrological View
Water
SurfaceConductance
Transpiration/Evaporation
AvailableEnergy
Photosynthesis/Respiration
LAI
Carbon
NutrientsLitterSoil Moisture
PBL ht
Sensible Heat
Goals of Research
• Quantify the Biophysical Controls on Ecosystem Metabolism (carbon gains and losses) and Water Balance of Oak Woodlands
• Quantify net annual budgets and inter-annual variability of carbon, water and energy exchange of oak woodland and annual grassland
• Produce predictive and mechanistic ability to quantify future conditions, e.g. global warming, elevated CO2 and ozone, perturbed water supply, and land use change, in order to manage rangelands
Kueppers et al 2005 PNAS
Land use in Northern CA
From Joe McFadden
Precipitation ~500 - 700 mm/y
Mean Temperature and Precipitation
Camp Pardee, CA
Climate Trends:Pardee, CA
1940 1950 1960 1970 1980 1990 2000 2010
Mea
n te
mpe
ratu
re (o C
)
14.5
15.0
15.5
16.0
16.5
17.0
17.5
18.0
1940 1950 1960 1970 1980 1990 2000 2010
Prec
ipita
tion
(mm
/yea
r)
0
200
400
600
800
1000
1200
Temperature Increased by about 1.25 C over 50 Years
Inferred Trends in Phenology;leaf-out about 10 days earlier over 50 years
Estimate of onset of photosynthesis for blue oak woodland
Year
1940 1950 1960 1970 1980 1990 2000 2010
Day
NEE
= 0
82
84
86
88
90
92
94
96
98Coefficients:b[0] 303.01b[1] -0.108r ? 0.331
Experimental Methods
• Eddy Covariance– above the stand (20 m tower)– below the stand (2 m tower)
• Micrometeorology • Sap flow (heat pulse)• Soil respiration chambers• Leaf Physiology (A-Ci curves)
Eddy Covariance
F w c= ' '
Mean
Fluctuation
IKONOS: Savanna & Fetch
IKONOS:Grassland
Results and Discussion
http://www.terrysteinke.com/pixpages/etchingpages/valleyoak.html
Dynamics of Canopy Structure
Annual Grassland, Vaira Ranch
Day of Year
-100 -50 0 50 100 150 200
Leaf
Are
a In
dex
0.0
0.5
1.0
1.5
2.0
2.5
3.0
2001-20022002-20032003-20042004-2005
Grass Understory, Tonzi Ranch
Day of Year
-100 -50 0 50 100 150 200
Leaf
Are
a In
dex
0.0
0.5
1.0
1.5
2.0
2.5
3.0
2001-20022002-20032003-20042004-2005
Canopy Structure: Tonzi Ranch
– Blue oak (Quercus douglasii)– LAI=0.90– Height 7.1 +/- 3.05 m– Diameter at breast height
26.6 +/- 0.11 cm– Understory: annual C3
grasses• Brachypodium distachyon,
Hypochaeris glabra, Bromus madritensis
Canopy Structure:Laser Altimeter Data
20 40 60 80 100 120 140 160 180
20
40
60
80
100
120
140
160
180
200
0.14835 to 5.7046 5.7046 to 12.581 12.581 to 24.942 24.942 to 40.76 40.76 to 399.8
Trees and Leaf Area Index
[N]ppt/Eeq
0.1 1 10 100
LAI
0.1
1
10
various functional types:Baldocchi and Meyers (1998)savanna:Eamus et al. 2001Oak Savanna, CA
b[0]: -0.68b[1]: 0.84r ?: 0.69
MeteorologySavanna, Overstory
(Tonzi Ranch)
Year2002 2003 2004 2005 2006
Tair
(o C)
-20
-10
0
10
20
30
40
50
Prec
ipita
tion
(mm
)
0
20
40
60
80
100
120
mean TairMin TairMax Tairprecipitation(mm)
Soil Moisture
Savanna, Overstory(Tonzi Ranch)
Year2002 2003 2004 2005 2006
Soi
l Moi
stur
e (c
m3
cm-3
)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0 cm20 cm50 cm
Leaf Physiology
How Does Stomatal Conductance Respond to Drought?
• H0: Ball-Berry Coefficient Varies– Sala & Tenhunen– Damesin, Rambal,
Joffre
• H1: Ball-Berry Coefficient is constant– Vcmax varies and
stomata gradually close to keep Ci/Ca ~ 0.7
– Joe Berry, SIB II– Gabriel Cornic
0ss
k Arhg gC
= +
Physiological Capacity:Seasonal Dynamics
DOY
90 120 150 180 210 240 270
Dar
k R
espi
ratio
n ( μ
mol
m-2
s-1)
0
2
4
6
8
10
Normalized Vcmax to 25oC Blue Oak leafTonzi Ranch 2001
DOY
90 120 150 180 210 240 270
V cmax
( μm
ol m
-2s-1
)
0
20
40
60
80
100
Xu and Baldocchi, 2003 Tree Physiology
Predawn Ψ and Stomatal conductance
DOY
100 150 200 250 300Stom
atal
con
duct
ance
(mol
m-2
s-1)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
DOY
100 150 200 250 300 350
Ψpd
(MPa
)
-8
-6
-4
-2
0
Quercus douglasii
PreDawn Water Potential (MPa)
-7 -6 -5 -4 -3 -2 -1 0
V cmax
( μm
ol m
-2 s
-1)
0
20
40
60
80
100
120
140
Vcmax varies with time and scales with Pre-Dawn Water Potential
DOY100 150 200 250 300 350
Vcm
ax
0
20
40
60
80
100
120
140
Quercus alba (Wilson et al)Quercus douglasii (Xu and Baldocchi)
High Vcmax must be Achieved in Seasonal lyDroughted Ecosystems to attain Positive Carbon
Balance
Stomatal ConductanceTonzi oak leaf, tree #92, Li-Cor 6400 measurement, 2001
ARH/Ca (mol m-2s-1)
0.00 0.01 0.02 0.03 0.04 0.05
g s (m
ol m
-2s-1
)
0.0
0.1
0.2
0.3
0.4
0.5b[0] 0.0063b[1] 8.88r ? 0.88
Ball-Berry Coef is constant with Pre-dawn water potential down to –70 bars
Xu and Baldocchi2003, Tree Physiol
Tree Leaf Age (Days)
0 50 100 150 200 250
Ci/C
a
0.5
0.6
0.7
0.8
Day of Year
0 50 100 150 200 250
N (%
)
1.0
1.5
2.0
2.5
3.0
3.5
4.0
From Leaf Isotope and Nutrition Measurements,Ci/Ca is relatively constant, near 0.7, and N is high early in
the season
Ma et al in prep
gs
0.0 0.1 0.2 0.3 0.4 0.5
Ci/C
a
0.4
0.6
0.8
1.0
Gas Exchange Data suggest Ci/Ca decreases with Drought
‘Isotope derived Ci/Ca are cumulative, Not the same as gas exchange’ Kevin Tu
Ecosystem Ecology
Switches, Pulses and Lags are Evident in Annual Time Series of Trace Gas Exchange
Vaira Grassland 2001
Day/Hour
0 50 100 150 200 250 300 350
Fc (μ
mol
m-2
s-1
)
-25
-20
-15
-10
-5
0
5
10
15
Xu and Baldocchi, AgForestMet, 2004
Complicating Dynamical Factors
G rass lands
D ay
0 50 100 150 200 250 300 350
NE
E (g
C m
-2 d
-1)
-6
-4
-2
0
2
4
M ed ite rran ean G rass lan dT em p era te C 4 g rass lan d
D a ta s o u rc e s : V a len tin i e t a l. 1 99 6 ; B a ld o c c h i + X u , u n p u b lis h e d ; V e rm a + S u yk e r
Late s
pring ra
ins
GPP(+)
S p rin g /S u m m er D ro u g h tθ (-)G P P (-); R eco(-)
G P P > 0 ;AM F ro s t:G P P (-)
T m in > 0 oCG P P = f(L AI) (+ )
R a in P u lseR eco(++ )G P P =0
Au tu m n R a in s :T (-), θ (++ )G P P (+), R eco(-)
sn o w co v eredd o rm an t g rassG P P =0 , R eco > 0
• Pulses• Switches• Lags
Daily Carbon Fluxes
Savanna, Overstory(Tonzi Ranch)
Day2002 2003 2004 2005 2006
NE
E (g
C m
-2 d
ay-1
)
-8
-6
-4
-2
0
2
4
6
8
10
12
NEERecoGPP
Annual C balance:NEE = GPP + Reco
Oak Savanna
Day
0 50 100 150 200 250 300 350
NEE
(gC
m-2
d-1
)
-8
-6
-4
-2
0
2
4
6
8
10
2002: -140 gC m-2 y-1
2003: -622004: -722005: -128
Day
0 50 100 150 200 250 300 350
GPP
(gC
m-2
d-1
)
0
2
4
6
8
10
12
2002: 840gC m-2 y-1
2003: 8902004: 8312005: 1059
Day
0 50 100 150 200 250 300 350
Rec
o (g
C m
-2 d
-1)
0
2
4
6
8
2002: 657 gC m-2 y-1
2003: 8012004: 6872005: 919
Ione, CA
Hydrological Year
00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08
NE
E (g
C m
-2 y
-1)
-200
-100
0
100
200
300
oak savanna annual grassland
Oak Woodlands are Risk Adverse, they Experience less inter-annual variation in NEE
than Grasslands
John Battle's biometric NPP = 235 gC m-2 yr-1.
NPP = GPPtree - Ra_tree - Rh = 299 gC m-2 yr-1
NPP=NEP+Rh=97+186=283 gC m-2 yr-1.
Carbon Fluxes Scale with Spring Rainfall
Open Grassland
PPT3-6 (mm)
0 50 100 150 200 250 300
Annu
al F
lux
(gC
m-2
)
-200
0
200
400
600
800
1000
1200
Savanna
PPT3-6 (mm)
0 50 100 150 200 250 300
GPP RecoNEE
Ma et al, 2007 AgForMet
Interannual Variability in NEE
d GPP/dt
-400 -300 -200 -100 0 100 200 300 400
d R
eco/
dt
-400
-300
-200
-100
0
100
200
300
400
California Savanna and Annual Grassland
dGPP/dt
-600 -400 -200 0 200 400 600 800
dRec
o/dt
-400
-200
0
200
400
600
TreesAnnual GrasslandWoodland understoryOak-grass savanna
b[0] 28.21b[1] 0.605r ?0.878
Interannual Variability inGPP and Reco scale with oneanother
Grassland CO2 flux vs Sunlight at different LAI
F C ( μ
mol
m-2
s-1) -20
-15
-10
-5
0
5
F C ( μ
mol
m-2
s-1)
-20
-15
-10
-5
0
5
Qp (μmol 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 + Baldocchi, AgForMet 2004
0 10 20 30
R eco (
μmol
m-2
s-1)
-2
0
2
4
6
8
10
Soil temperature (oC)0 10 20 30
R eco (
μmol
m-2
s-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 2004
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 2004
274 276 278 280 282 284 286 288
-1
0
1
2
3
4
5
6
7
8
9
DOY
NEE
[ μm
ol m
-2 s
-1]
Vaira 2008
Sustained and Elevated Respiration after Fall Rain
Impact of rain pulse on ecosystem respiration: Fast response
Day
150 200 250 300 350
Fc
(μm
ol m
-2 s
-1)
0
1
2
3
4
5
6
understoryopen grassland
Baldocchi et al, JGR, Biogeosciences, 2006
Quantifying the impact of rain pulses on respiration: Assessing the Decay Time constant
Day after rain (d)-5 0 5 10 15 20
Rec
o (gC
m-2
d-1)
0
2
4
6
8
10
0 2 4 6 8 10 12 140
1
2
3d214 2003 understory
(τ, Max/e)
Amount of the rain (mm)0 10 20 30 40 50 60
Tim
e co
nsta
nt (d
)0
2
4
6
8
10
Understoryb0=1.43b1=0.097r2=0.95
Grasslandb0=0.88b1=0.07r ? 0.98
Xu, Baldocchi, Tang, 2004 Global Biogeochem Cycles R b b t
eco = +−
0 1 exp( )τ
Respiration Enhancement Depends on Initial Condition
R e c o p u l s e - b a c k g r o u n d v s b a c k g r o u n d . d a t a f r o m g r a s s l a n d a n d s a v a n n a
R e c o _ b a c k g r o u n d ( g C / m 2 d )
0 . 0 0 . 5 1 . 0 1 . 5 2 . 0 2 . 5 3 . 0
Peak
of p
ulse
-bac
kgro
ud (g
C/m
2s)
0
2
4
6
8
Xu, Baldocchi, Tang Global Biogeochem Cycles 2004
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.
Soil Resp Lags Ps by about 5 to 6 hours
June, Rsoil-Ps lag
lag (30 min intervals)
-30 -20 -10 0 10 20 30
lag
corre
latio
n
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
Tang et al, Global Change Biology 2005.
•Photosynthesis Switches Partitioning between dominance by Roots vs Microbes
Stimulation of Autotrophic is much delayed after onset of photosynthesis
2003, Nocturnal CO2 Efflux (0-5, 19-24)
Day
0 50 100 150 200 250 300 350
F unde
rsto
ry-F
gras
slan
d (μ m
ol m
-2 s
-1)
-6
-4
-2
0
2
4
6Oak Savanna, 2003
Day
0 50 100 150 200 250 300 350 400
Can
opy
Phot
osyn
thes
is (g
C m
-2 d-1
)
-10
-8
-6
-4
-2
0
2
Baldocchi et al, JGR, Biogeosciences, 2006
Remote Sensing of NPP
LED sensor intercomparison and calibration
Falk, Ma, Ruiz, Hehn, Baldocchi . in prep
Vaira 2006-2007
DOY
30 60 90 120 150 180 210 240 270 300 330 360 390 420 450 480 510 540
ND
VI (a
vera
ge 1
000
to 1
500)
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0G
PP (g
C m
-2 d
ay-1
)
0
2
4
6
8
10
12
NDVIGPP
-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 = (ρ531 - ρ570) / (ρ531 + ρ570)
Falk, Ma, Ruiz, Hehn, Baldocchi . in prep
Jingfeng Xiao and D Baldocchi
area-averaged fluxes of NEE and GPP were -150 and 932 gCm-2 y-1
net and gross carbon fluxes equal -8.6 and 53.8 TgC y-1
Water and Evaporation
Annual ET and Interannual Variation
Vaira 2004
Day
0 50 100 150 200 250 300 350
E (m
m d
-1)
0
1
2
3
4
5
2001: 301 mm 2002: 292 mm 2003: 353 mm 2004 : 284 mm
Oak Savanna
Day
0 50 100 150 200 250 300 350
ET
(mm
d-1
)
0
1
2
3
4
5
2002: 389 mm2003: 422 mm2004: 340 mm2005: 484 mm
Savanna Soil Stores about 80 mm water and uses that much extra to sustain a sparse woodland, over a grassland
Grassland
θ, weighted by roots (cm3 cm-3)
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40
λ E/λ
Eeq
0.00
0.25
0.50
0.75
1.00
1.25
summer rain
ET and Soil Water Deficits:Root-Weighted Soil Moisture
Baldocchi et al., 2004 AgForMet
Oak Savanna
θ weighted by roots (cm3 cm-3)
0.00 0.05 0.10 0.15 0.20 0.25 0.30
λE/λ
Eeq
0.0
0.2
0.4
0.6
0.8
1.0
Gs (mm s-1)
0 2 4 6 8 10 12 14 16
LE/L
Eeq
0.0
0.2
0.4
0.6
0.8
1.0
Savanna WoodlandAnnual Grassland
Monthly Averages
Landscape DifferencesOn Short Time Scales, Grass ET > Forest ET
Ryu, Baldocchi, Ma and Hehn, JGR-Atmos, submitted
156 158 160 162 164 166158
158.02
158.04
158.06
158.08
158.1
158.12
158.14
158.16
158.18
158.2
DOY
grou
nd w
ater
ele
vatio
n [m
]groundwater elevation at Tonzi
G. Miller, Y. Rubin, D. Baldocchi unpublished data
Oak Trees Tap Ground Water
California Savanna
Hydrological Year
02_03 03_04 04_05 05_06 06_07
Eva
pora
tion
(mm
y-1
)
240
260
280
300
320
340
360
380
400
420
440
Oak WoodlandAnnual Grassland
Role of Land Use on ET:On Annual Time Scale, Forest ET > Grass ET
Ryu, Baldocchi, Ma and Hehn, JGR-Atmos, submitted
Photosynthesis >Respiration
DOY100 150 200 250 300 350
cmax
0
20
40
60
80
100
120
140
Quercus alba (Wilson et al)Quercus douglasii (Xu and Baldocchi)
CO2
Ps Capacity must be Great,For Short Period to Facilitatehigh rates of photosynthesis
Leaf N and Leaf Thicknessmust be adequate tosupport Ps Machinery
At Ecosystem scale LeafArea is limited enabling theSparse Canopy to Reduce
ET, too
20 40 60 80 100 120 140 160 180
20
40
60
80
100
120
140
160
180
200
Broadleaved, Deciduous Trees
Specific Leaf Area (m2 g-1)
60 80 100 120 140 160 180 200
A(
l)
0
50
100
150
200
250
300
Quercus douglasii
data of Reich et al and Xu and Baldocchi
θ v (%)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.05 m0.50 m
Savanna, 2005
Day
0 50 100 150 200 250 300 350
Wt
Fl
0
100
200
300
400
500
600
700
ET ppt
E T (m m d-1
)
0 1 2 3 4
GP
P (g
C m
2d
)
0
2
4
6
8
10
Synthesis/Conclusions
Role of Land Management on Water and Energy Exchange and Climate
Case Study:Savanna Woodland vs Grassland
Case Study:
Energetics of a Grassland and Oak Savanna
Measurements and Model
2006
Day
0 50 100 150 200 250 300 350
Ener
gy F
lux
Den
sity
(MJ
m-2
d-1
)
0
5
10
15
20
25
30
35
Solar RadiationNet Radiation, GrasslandNet Radiation, Savanna
1. Savanna absorbs much more Radiation (3.18 GJ m-2 y-1) than the Grassland (2.28 GJ m-2 y-1) ; ΔRn: 28.4 W m-2
Available Energy Drives Heat Exchange and Evaporation
u*, oak woodland, daily average
0.0 0.2 0.4 0.6 0.8 1.0
u*, g
rass
land
, dai
ly a
vera
ge
0.0
0.1
0.2
0.3
0.4
0.5
2002
4b. Savanna injects more Sensible Heat into the atmosphere because it has more Available Energy and it is Aerodynamically Rougher
4a. U* of tall, rough Savanna > short, smooth Grassland
2006
Day
0 50 100 150 200 250 300 350
Sen
sibl
e H
eat F
lux
Den
sity
(MJ
m-2
d-1)
0
2
4
6
8
10
12
14GrasslandSavanna
2006
Day
0 50 100 150 200 250 300 350
Air
Tem
pera
ture
(C)
0
10
20
30
40GrasslandSavanna
5. Mean Potential Temperature differences are relatively small (0.84 C; grass: 290.72 vs savanna: 291.56 K); despite large differences in Energy Fluxes--albeit the Darker vegetation is Warmer
Compare to Greenhouse Sensitivity ~2-4 K/(4 W m-
2)
2006, Ione, CA
Potential Temperature, Grassland
275 280 285 290 295 300 305 310 315
Pote
ntia
l Tem
pera
ture
, Oak
Sav
anna
275
280
285
290
295
300
305
310
315
b[0] -2.67b[1] 1.012r ? 0.953
Vapor Pressure
LongwaveEnergy
ShortwaveEnergy
Sensible HeatLatent Heat
PBL Height
Time 1
Time 2
Time 3
Temperature
Conceptual Diagram of PBL Interactions
Time (hrs)
6 8 10 12 14 16 18
pbl h
t (m
)
0
500
1000
1500
2000
2500
3000
Time (hrs)
6 8 10 12 14 16 18e a (
Pa)
0
500
1000
1500
2000
2500
3000
H and LE: Analytical/Quadratic version of Penman-Monteith Equati
T im e
6 8 10 12 14 16 18 20
T su
rfac
e
280
290
300
310
albedo = 0 .3; R c=2560 s /malbedo = 0 .15; R c = 320 s /m
ET-PBL O ak-G rass Savanna Land U se
T im e
6 8 10 12 14 16 18 20
Rne
t (W
m-2
)
0
200
400
600
800
albedo = 0.3; R c=2560 s /malbedo = 0.15; R c = 320 s /m
•The Energetics of afforestation/deforestation is complicated
•Forests have a low albedo, are darker and absorb more energy
•But, Ironically the darkerforest maybe cooler (Tsfc) than a bright grassland due to evaporative cooling
And Smaller Temperature Difference, like field measurements, if weconsider PBL, Rc, Ra and albedo….!!
Summer Conditions
Time (hours)
4 6 8 10 12 14 16 18 20
Tair
(K)
286
288
290
292
294
296
298
grass, albedo = 0.30; Rc = 2560 s/m; Ra = 40 s/msavanna, albedo=0.15; Rc = 320 s/m; Ra= 10 s/m
u* savanna = 2 u* grassland
0200
400600
800
0.10.2
0.30.4
0.5288
289
290
291
292
293
RsAlbedo
T air
289.5
290
290.5
291
291.5
292
292.5
Tair can vary by 3 C by changing albedo and Rs
Biometeorology Team
Funding: US DOE/TCP; NASA; WESTGEC; Kearney; Ca Ag Expt Station
Conclusions
• Savanna woodlands need about 80 mm more water to function than nearby grasslands
• Year to year variability in Carbon Uptake is due to length of wet season.
• Photosynthesis and Respiration are tightly linked