Evaporation from Flux Towers
S = P – D - ET
Change in water
content of volume of soil
drainage
precipitation
By Dr Marcy Litvak
Dept of Biological Sciences
University of Texas at Austin
(now at the University of New Mexico)
Energy budgeting approach
Latent
Heat flux
Can directly measure
Sensible
each of these
Heat flux
variables
How do you partition
H and E??
Net Ecosystem Production
Eddy Covariance
Directly measure how much CO2
or H2O vapor blows in or out of a
site in wind gusts.
Integrated measure of
ecosystem fluxes
Link changes in [CO2] or
[H2O] in the air above a
canopy with the upward or
downward movement of
that air
Net Ecosystem Exchange
Flux
CO2
= w ’ CO2’
30 minute timescale
Updraft [CO2] > downdraft [CO2]
Flux >0 carbon source
Updraft [CO2] < downdraft [CO2]
Flux < 0 carbon sink
1000
800
600
400
• The net CO2 flux is
calculated for each half
hour from the
measurements of vertical
wind and CO2
concentration.
146.5
147.0
147.5
148.0
• A positive flux indicates a
net loss of CO2 from the
surface (respiration) and a
negative flux indicates the
net uptake of CO2
(photosynthesis)
-10
-5
0
5
146.0
-15
-20
CO2 Exchange (mmol m-2 s-1)
0
200
Sunlight (Wm-2)
Sunlight
CO2 Exchange
12 AM
12PM
May 26, 2000
12AM
12PM
12AM
May 27, 2000
CO2 Exchange (mmol m-2 s-1)
• A years worth of half-hour
data can be summed to
determine how much Carbon
the ecosystem gained or lost
Annual C accumulation
(Tons C ha-1)
5
4
3
2
1
0
1999
2000
ET -Eddy covariance method
• Measurement of vertical transfer of water
vapor driven by convective motion
• Directly measure flux by sensing
properties of eddies as they pass through
a measurement level on an instantaneous
basis
• Statistical tool
Basic Theory
Instantaneous
signal
Time averaged property
Instantaneous
Perturbation from
The mean
Fluctuation
Mean
All atmospheric entities show short-period fluctuations about their long term mean value
Turbulent mixing
Propterties carried by eddies:
Mass, density ρ
Vertical velocity w
Volumetric content 
= (    ' ) (   ' )(    ' )
1) Expand
2) Simplify:
a) remove all terms with single primed entity
b) remove terms with fluctuations
c) remove terms containing mean vertical velocity
Eddy Covariance
Eddy covariance
Average vertical flux of entity over
30 minute period
Fluctuation of entity about it’s mean
g kg air-1
Density of air
kg air m-3
F = ρ w’ x’
Velocity of air being moved upwards or downwards
m s-1
At any given instant, multiply velocity of air
being moved upwards or downwards at a
speed of m s-1, by the fluctuation of the entitiy
about its mean
Eddy covariance
m g kg
s kg m3
= g m-2 s-1
Result:vertical speed of transfer of entity measured in m s-1
and at a concentration of g per kg of air
g of entity transferred vertically, per square meter of surface area per second
Latent heat of vaporization
(J kg-1 ˚C-1)
Mean density of air
QE = ρ Lv w’ ρv’
Latent Heat
Fluctuation about
the mean of
vertical wind speed
J
kg
Fluctuation about
the mean of
density of water
vapor in air
kg m kg = J
W
=
m3 s m2
m2s
m2
Specific heat of air at constant pressure
(J kg-1 ˚C-1)
Mean density of air
QH = ρ Cp w’ T’
Sensible Heat
Fluctuation about
the mean of
vertical wind speed
Fluctuation about
the mean of
air temperature
kg m ◦C = J
J
W
=
kg ˚C m3 s
m2s
m2
Instrumentation
Requirements
3-D Sonic anemometer
Pyrronometer
Net radiometer
Quantum
sensor
IRGA
Instrumentation
Requirements
Challenges of operating eddy flux systems in remote location
Advantages of eddy covariance
• Inherently averages small-scale variability of fluxes over a surface
area that increaes with measurement height
• Measurements are continuous and in high temporal resolution
• Fluxes are determined without disturbing the surface being
monitored
• Great tool to look at ecosystem physiology
Disadvantages
•
•
•
•
•
Need turbulence!
Gap filling issues
Relatively Expensive
Stationarity issues
Open-path IRGA issues
•
The eddy covariance method is most accurate when the
atmospheric conditions (wind, temperature, humidity,
CO2) are steady, the underlying vegetation is
homogeneous and it is situated on flat terrain for an
extended distance upwind.
Stationiarity
Advection
Horizontal concentration gradients may also lead to perturbation calculation errors
Issue of energy balance closure
1000
a)
-2
H + Le (W m )
800
600
y = 0.93x - 4.24
r2 = 0.85
n = 4304
400
200
0
-200
1000
-2
H + Le (W m )
800
b) y = 0.94x - 7.09
r2 = 0.86
n = 3310
600
400
200
0
-200
-200
0
200
400
600
-2
Rnet - G (W m )
800
1000
Impact of encroachment of Ashe juniper and Honey mesqui
on carbon and water cycling in central Texas savannas
Marcy Litvak
Section of Integrative Biology
University of Texas, Austin
Collaboration with:
James Heilman, Kevin McInnes, James Kjelgaard, Texas
A&M
Melba Crawford, Roberto Gutierrez, Amy
Neuenschwander, UT
Freeman Ranch - Texas State University
Figure 1. Location and geographical extent of Edwards
Plateau
Extensive areas of Edwards Plateau historically were dominate
fairly open live-oak savannas
Due to overgrazing and fire suppression policies….grasslands
disappearing as woody species increase
Honey mesquite
Ashe juniper
Worst-case scenario:
Research Objectives
• Determine sink strength for carbon associated with woody
encroachment and analyze the variables that determine gains/losses of
carbon from key central Texas ecosystems
• Determine change in ET, energy balance and potential groundwater
recharge associated with woody encroachment
• Provide objective data for validation of land surface process models
(CLM2 – Liang Yang, UT) related to growth, primary production, water
cycling, hydrology
• Aid in regional scale modeling efforts
Carbon/water
tradeoff
Transition
UT
Woodland
TAMU
Grassland
TAMU
Study site
Experimental design
3 stages of woody encroachment
Open grassland, transition site, closed canopy woodland
-NEE carbon, water, energy: open-path eddy covariance
(net radiation, solar radiation (incoming, upwelling), PAR, air temperature,
relative humidity, precipitation)
-physiological measures of ecosystem component fluxes
leaf-level gas exchange, sap-flow, bole-respiration rates, herbaceous NEE
-soil carbon, soil microclimate, soil respiration rates
-Ecosystem structure
biomass, LAI, species composition
open grassland (TAMU)
May 2004
Transition site – July 2004
15-20 year old juniper,mesquit
Live Oak-Ashe juniper
woodland – July 2004
(TAMU)
Cumulative NEE for three land covers - Freeman
Ranch
600
grassland
500
transition
400
forest
300
200
100
0
-100
250
300
350
400
Cumulative ET for three land covers at Freeman
Ranch
Day of Year - 2004
350
-1
200
Cumulative ET (mm day )
-2
Cumulative NEE (g C m )
700
grassland
300
transition
250
forest
200
150
100
50
0
200
250
300
Day of Year - 2004
350
400
Bowen Ratio
Energy balance approach to estimating convective fluxes
Seeks to partition energy available into sensible and latent heat terms
H/ (E)
Typical values:
0.1- 0.3 tropical rainforests; soil wet year-round
0.4 – 0.8 temperate forests and grasslands
2-6 semi-arid regions; extremely dry soils
> 10 deserts
Bowen (1926)
Bowen Ratio
B can be approximated as a
function of vertical
differences of temperature
and vapor pressure in the air,
or ,
B = g (t2- t1 ) / ( e2 –e1 )
vapor pressures measured
at the same two points
Psychrometer
Constant
F(T,P)
air temperatures measured
at two points at different
heights above the land surface
Bowen
BowenRatio
Ratio
Average values of the air-temperature differences (t2 - t1)
and vapor-pressure differences (e2 - e1),
taken every 30 seconds
for a 30-minute period
are used to determine  .
= QH
QE
Specific heat
capacity
=
T Ca
ρv Lv
Latent heat
Of vaporization
Bowen Ratio
The energy budget can then be solved for LE:
LE = ( Rn –G – W) / ( 1+ )
Uses gradients of heat and water to partition
available energy into SH and LE
Assumptions:
•One-dimensional heat and vapor flow, only vertical
•No transfer to/from measurement area from adjacent area
•No significant heat storage in plant canopy
•2 fluxes originate from same point on land surface
•Atmosphere equally able to transfer heat and water vapor,
so turbulence need not be considered
Needs large tract of uniform vegetation
Sensors to measure air
temperature and humidity
Determine average differentials
for 15-minutes, then switch sensors,
and determine average differentials
for another 15 minutes to avoid sensor bias