Evaporation from Flux Towers
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Transcript of Evaporation from Flux Towers
Evaporation from Flux Towers
S = P – D - ET
Change in water content of volume of soil precipitation
drainage
By Dr Marcy LitvakDept of Biological Sciences
University of Texas at Austin(now at the University of New Mexico)
Energy budgeting approach
Can directly measure each of these
variables
How do you partition H and E??
SensibleHeat flux
LatentHeat flux
Eddy Covariance
Directly measure how much CO2
or H2O vapor blows in or out of a site in wind gusts.
Net Ecosystem Production
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
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146.0 146.5 147.0 147.5 148.0
May 26, 2000 May 27, 2000
Sunlight
CO2 Exchange
CO
2 E
xch
ang
e (
mo
l m-2 s
-1)
Su
nlig
ht
(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)
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12 AM 12PM 12AM 12PM 12AM
CO
2 E
xcha
nge
(m
ol m
-2 s
-1)
Ann
ual C
acc
umul
atio
n (
Ton
s C
ha-1
)
1999 2000
• A years worth of half-hour data can be summed to determine how much Carbon the ecosystem gained or lost
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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
Mean
Fluctuation
Instantaneous signal
InstantaneousPerturbation from
The mean
All atmospheric entities show short-period fluctuations about their long term mean value
Time averaged property
Turbulent mixing
Propterties carried by eddies:Mass, density ρVertical velocity w
Volumetric content
)'( = )'( )'(
1) Expand2) 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
Velocity of air being moved upwards or downwardsm s-1
Fluctuation of entity about it’s meang kg air-1
Density of air kg air m-3
F = ρw’ x’
Average vertical flux of entity over 30 minute period
At any given instant, multiply velocity of airbeing moved upwards or downwards at a speed of m s-1, by the fluctuation of the entitiyabout its mean
= g m-2 s-1m g s kg
kgm3
Result:vertical speed of transfer of entity measured in m s-1 and at a concentration of g per kg of air
Eddy covariance
g of entity transferred vertically, per square meter of surface area per second
Fluctuation about the mean of
vertical wind speed
Fluctuation about the mean of
density of water vapor in air
Mean density of air
Latent heat of vaporization(J kg-1 ˚C-1)
m kgs m2
kg m3
J kg
J m2s
= W m2
=
QE = ρ w’ρv’Lv
Latent Heat
Fluctuation about the mean of
vertical wind speed
Fluctuation about the mean of
air temperature
Mean density of air
Specific heat of air at constant pressure(J kg-1 ˚C-1)
m ◦Cs
kg m3
J kg ˚C
J m2s
= W m2
=
QH = ρ w’ T’Cp
Sensible Heat
Instrumentation Requirements
IRGA
3-D Sonic anemometer
Net radiometerPyrronometer
Quantumsensor
Instrumentation Requirements
Challenges of operating eddy flux systems in remote locations!
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
AdvectionHorizontal concentration gradients may also lead to perturbation calculation errors
H +
Le
(W m
-2)
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1000
y = 0.93x - 4.24r2 = 0.85n = 4304
a)
Rnet - G (W m-2)
-200 0 200 400 600 800 1000
H +
Le
(W m
-2)
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0
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400
600
800
1000
y = 0.94x - 7.09r2 = 0.86n = 3310
b)
Issue of energy balance closure
Impact of encroachment of Ashe juniper and Honey mesquite on carbon and water cycling in central Texas savannas
Collaboration with:James Heilman, Kevin McInnes, James Kjelgaard, Texas A&MMelba Crawford, Roberto Gutierrez, Amy Neuenschwander, UTFreeman Ranch - Texas State University
Marcy LitvakSection of Integrative Biology
University of Texas, Austin
Figure 1. Location and geographical extent of Edwards Plateau
Extensive areas of Edwards Plateau historically were dominated by fairly open live-oak savannas
Due to overgrazing and fire suppression policies….grasslands are disappearing as woody species increase
Ashe juniperHoney mesquite
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
GrasslandTAMU
WoodlandTAMU
TransitionUT
Study site
3 stages of woody encroachmentOpen 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 fluxesleaf-level gas exchange, sap-flow, bole-respiration rates, herbaceous NEE
-soil carbon, soil microclimate, soil respiration rates
-Ecosystem structure biomass, LAI, species composition
Experimental design
open grasslandMay 2004
(TAMU) Transition site – July 200415-20 year old juniper,mesquite
Live Oak-Ashe juniper woodland – July 2004
(TAMU)
Cumulative NEE for three land covers - Freeman Ranch
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Day of Year - 2004
Cum
ulat
ive
NE
E (g
C m
-2)
grassland
transition
forest
Cumulative ET for three land covers at Freeman Ranch
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Day of Year - 2004
Cum
ulat
ive
ET (m
m d
ay-1
) grassland
transition
forest
H/ (E)
Bowen Ratio
Energy balance approach to estimating convective fluxesSeeks to partition energy available into sensible and latent heat terms
Typical values:
0.1- 0.3 tropical rainforests; soil wet year-round0.4 – 0.8 temperate forests and grasslands2-6 semi-arid regions; extremely dry soils> 10 deserts
Bowen RatioBowen (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 )
PsychrometerConstant
F(T,P)
air temperatures measured at two points at different
heights above the land surface
vapor pressures measured at the same two points
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 .
Bowen RatioBowen Ratio
= QH
QE
= Tρv
Ca
Lv
Specific heat capacity
Latent heatOf 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