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Evaporation from Flux Towers

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Title: Evaporation from Flux Towers


1
Evaporation from Flux Towers
?S P D - ET
drainage
Change in water content of volume of soil
precipitation
By Dr Marcy Litvak Dept of Biological
Sciences University of Texas at Austin (now at
the University of New Mexico)
2
Energy budgeting approach
Latent Heat flux
How do you partition H and ?E??
Can directly measure each of these variables
Sensible Heat flux
3
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
4
Net Ecosystem Exchange
30 minute timescale
Updraft CO2 gt downdraft CO2
Flux gt0 carbon source
Updraft CO2 lt downdraft CO2
Flux lt 0 carbon sink
5
1000
Sunlight
800
600
Sunlight (Wm-2)
  • The net CO2 flux is calculated for each half hour
    from the measurements of vertical wind and CO2
    concentration.
  • A positive flux indicates a net loss of CO2 from
    the surface (respiration) and a negative flux
    indicates the net uptake of CO2 (photosynthesis)

400
200
0
146.0
146.5
147.0
147.5
148.0
5
0
-5
CO2 Exchange (mmol m-2 s-1)
-10
-15
CO2 Exchange
-20
12 AM
12PM
12AM
12PM
12AM
May 26, 2000
May 27, 2000
6
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

5
4
Annual C accumulation (Tons C ha-1)
3
2
1
0
1999 2000
7
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

8
Basic Theory
Instantaneous Perturbation from The mean
Instantaneous signal
Time averaged property
All atmospheric entities show short-period
fluctuations about their long term mean value
9
Turbulent mixing
Propterties carried by eddies Mass, density
? 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
10
Eddy Covariance
11
Eddy covariance
Average vertical flux of entity over 30 minute
period
Fluctuation of entity about its mean g kg air-1
Density of air kg air m-3
?
F
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
12
Eddy covariance
m g s kg
kg m3
g m-2 s-1
Resultvertical 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
13
Latent heat of vaporization (J kg-1 C-1)
Mean density of air
QE
?
Lv
Latent Heat
Fluctuation about the mean of vertical wind
speed
Fluctuation about the mean of density of water
vapor in air
m kg s m2
kg m3
J m2s
W m2


14
Specific heat of air at constant pressure (J kg-1
C-1)
Mean density of air
QH
?
Cp
Sensible Heat
Fluctuation about the mean of vertical wind
speed
Fluctuation about the mean of air temperature
m ?C s
kg m3
J m2s
W m2


15
Instrumentation Requirements
16
3-D Sonic anemometer
Quantum sensor
Pyrronometer
IRGA
Net radiometer
17
Instrumentation Requirements
18
Challenges of operating eddy flux systems in
remote locations!
19
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

20
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.

21
Stationiarity
Advection
Horizontal concentration gradients may also lead
to perturbation calculation errors
22
Air Temperature in Degrees F and C
23
Air Temperature at 1m and 10m
24
Vapor Pressure and Saturated Vapor Pressure (kPa)
25
Relative Humidity at 1m and 10m
Average 0.71
Average 0.61
26
Wind Speed (m/s)
27
Net Radiation (W/m2)
28
Sensible Heat Flux (W/m2)
29
Latent Heat Flux (W/m2)
30
Evaporation (mm/day)
Average 3.15 mm/day
31
Ground Heat Flux
32
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33
Issue of energy balance closure
34
Impact of encroachment of Ashe juniper and Honey
mesquite 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 AM Melba Crawford,
Roberto Gutierrez, Amy Neuenschwander, UT Freeman
Ranch - Texas State University
35
Figure 1. Location and geographical extent of
Edwards Plateau
36
Extensive areas of Edwards Plateau historically
were dominated by fairly open live-oak
savannas
37
Due to overgrazing and fire suppression
policies.grasslands are disappearing as woody
species increase
38
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
39
Study site
40
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

41
open grassland May 2004
(TAMU)
Transition site July 2004 15-20 year old
juniper,mesquite
Live Oak-Ashe juniper woodland July 2004
(TAMU)
42
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43
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44
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45
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48
H/ (?E)
Bowen Ratio
Energy balance approach to estimating convective
fluxes Seeks to partition energy available into
sensible and latent heat terms
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 gt 10 deserts
49
Bowen Ratio
Bowen (1926)
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
air temperatures measured at two points at
different heights above the land surface
Psychrometer Constant F(T,P)
50
Bowen Ratio
Bowen 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 ? .
Specific heat capacity
  • QH
  • QE

?T
Ca

Lv
??v
Latent heat Of vaporization
51
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

52
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
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