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
Rn -G – W =H + lE (1)
where
Rn is the net solar radiation, in W/m2,
G is the amount of energy passing through the soil or involved with change in temperature of the surface layer of soil, in W/m2,
W is the amount of energy involved with change in temperature of water standing on the land surface, in W/m2,
H is the sensible heat flux (heat transported by convection), in W/m2,
l is the latent heat of vaporization of water, in J/g, E is the evaporation rate of water in g/m2-s, and
the product lE is the latent heat flux, or heat involved in vaporization or condensation of water, in W/m2.
In equation 1, each term on the left of the equals sign is measured, and the left side of the equation represents the total amount of energy available for latent heat and sensible heat (available energy). The sum of H and lE is the turbulent flux.
Net radiation (Rn) is measured directly by the net radiometers,
Soil heat flux (G) can be measured at all vegetated sites, by a heat-flux plate buried 5 centimeters (cm) below the land surface and the change in heat stored in the soil profile above the plate.
Calculation of the soil heat storage required measurement of average soil temperature in the soil column above the heat-flux plate, and also measurement of the moisture content of the soil in the same column.
The change in soil heat storage for each 30-minute computation interval is given by the following equation (Campbell Scientific, 1990):
D S = DTsC s d ¤ D t (2) where
DS is the change in energy in the soil above the heat-flux plate, in W/m2,
10,000 is a conversion factor between cm-2 and m-2,
DTs is the difference in average soil temperature for the 30-minute time interval, in oC,
Cs is the volumetric heat capacity of the soil, in J/oC-cm3,
d is the thickness of the soil layer (5 cm), and
Dt is the time interval (1,800), in seconds.
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
Flux CO2 = w ’ CO2’
30 minute timescale
Updraft [CO2] > downdraft [CO2]
Flux >0 carbon source
Updraft [CO2] < downdraft [CO2]
Flux < 0 carbon sink

5. 1000
Sunlight
800
Sunlight (Wm-2)
600
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 5
146.0
146.5
147.0
147.5
148.0 -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

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 longer term mean value
Overbar indicates time-averaged property
Prime signifies instantaneous deviation from the mean, positive indicates above the mean
All atmospheric entities show short-period fluctuations about their long term mean value

9. Propterties carried by eddies:
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 mixes air from above downwards, and from below upwards
Concentration of entity is greater below, as it is mixed upwards causes a positive perturbation =

10. Eddy Covariance

11. Eddy covariance F = ρ w’ x’ 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’
Evaluate the vertical flux of any entity as follows.
W’= w- mean w, but mean w= 0, w’=w, velocity of air being moved upwards at speed of m s-1
At any given instant, multiply velocity of air being moved upwards at a speed of m s-1, by the fluctuation of the entity about its mean (g kg air-1)
You will get the vertical speed of transfer of this entity in units of m s-1 x g kg-1 , which means we are transfering the entity at a vertical speed measured in m s-1 and at a concentration of g per kg of air.
g of entity transferred vertically, per square metre of surface area per second.
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 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
At any given instant, multiply velocity of air being moved upwards at a speed of m s-1, by the fluctuation of the entity about its mean (g kg air-1)
You will get the vertical speed of transfer of this entity in units of m s-1 x g kg-1 , which means we are transfering the entity at a vertical speed measured in m s-1 and at a concentration of g per kg of air.
g of entity transferred vertically, per square metre of surface area per second.
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 = ρ Lv w’
ρv’
Latent Heat
Fluctuation about
the mean of
vertical wind speed
Fluctuation about
the mean of
density of water
vapor in air
Latent heat flux
Covariance of w’pv’ calculated over 30 minute period, tells us on average whether updrafts or downdrafts contain on average, more water vapor and by how much. During day, evaporation will cause updrafts to contain, on average more water vapor than downdrafts.
Multiply by air density to convert to unit volume
Multiply by latent heat find the energy required for evaporation J kg kg m3
m kg
s m2 = J
m2s W m2 =

14. 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
Sensible heat flux
Covariance of w’T’ calculated over 30 minute period, tells us on average whether updrafts or downdrafts are on average, warmer and by how much. During day, surface heating will cause updrafts to be, on average warmer than average temperature, and downdrafts to be, on average, colder than average temperature.
Multiply by air density to convert to unit volume
Multiply by specific heat of air to find the energy required for the change in temperature J
kg ˚C kg m3
m ◦C s = J
m2s W m2 =

15. Instrumentation Requirements
The eddy correlation technique is very simple in theory, but quite difficult to put into practice. A major problem has been availability of sufficiently fast instruments to measure the fluctuating signals. Unless an instrument has a sufficiently fast response and logging time to resolve the fastest eddies contributing to the turbulent flux, this portion of the flux will not be recorded. Another effect of slow logging is that the faster eddies may fold around the
Ultrasonic Doppler anemometers are normally used to measure the three components of wind. These instruments are easily fast enough to resolve turbulence under most practical conditions, - they have maximum response and reporting frequencies up to around 100 Hz. The problem with such anemometers tends to be designing logging systems fast enough to log the data.
The potential difficulty in designing working Eddy Covariance systems is often in finding the right instrument to detect the variable of interest to be correlated with the vertical wind fluctuations (the scalar variable). Some instruments simply are not fast enough, for example the DMPS (see section 7), and many chemiluminescent analysers. Also, measuring certain gases is fundamentally problematic, as some ("sticky") gases can adhere to the inside of inlet lines before reaching the analyser. This damps the signal, giving the same problem as a poor frequency response instrument (although without the frequency folding).
The final factor affecting response time of eddy covariance systems is the size of the sensor. Eddies smaller than the size of the transducer array of the anemometer cannot be detected effectively. This places an upper frequency limit on response according to Taylor’s hypothesis (see section 4.1). The effect of this limit has not been observed in measurements referred this work.
The frequency required for the Eddy Covariance method depends upon the mean eddy size at the sensors. This is a function of surface roughness length z0 (see section 4.2.4) and measurement height z. Specifically, larger values of z0 give larger mean eddy sizes and ease the frequency requirements for the system. Equally, larger values of z (larger measurement heights) give larger mean eddy sizes, having the same effect.

16. 3-D Sonic anemometer
Quantum
sensor
Pyrronometer
IRGA
Net radiometer

17. Instrumentation Requirements
The fetch describes the terrain upwind of the measurement location. It is an important concept as the rate of deposition to a surface depends upon the nature of the surface. The footprint is defined as the area upwind of the measurement site contributing to the measured flux. It is possible to estimate the footprint of an Eddy Covariance measurement using the roughness length and an assumed logarithmic wind profile. However it is simpler and more practical to ensure that the part of the fetch of interest is large enough that the whole of the footprint must fall within it.

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
Stationarity
One of the requirements for a valid Eddy Covariance flux measurement is that the mean particle concentration ( ) should not change significantly over the averaging time used to determine the mean. This is called the Stationarity requirement. It is clear that if there is a marked trend of increasing concentration over this period, the earlier perturbation parts are underestimated and the later ones overestimated. The converse is true for trends of decreasing concentration. Figure 3 shows a hypothetical increasing concentration situation.
Advection
 Horizontal concentration gradients may also lead to perturbation calculation errors, which would propagate into the flux calculation, again causing errors. An example of such an effect can be seen at approximately 13:09 GMT in figure 4. Unless horizontal concentration gradients are explicitly measured at the same time as the flux, there is no effective way of correcting for this (Nemitz, 1998). Figure 5 shows an advection effect more clearly than figure 4. In figure 5, there is no concurrent trend as such in the data.
Horizontal concentration gradients may also lead to perturbation calculation errors, which would propagate into the flux calculation, again causing errors.
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

33. Issue of energy balance closure

34. Section of Integrative Biology University of Texas, Austin
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
I’m going to tell you today about two of the systems I’ve been working on :
Work I started as a postdoc with Mike Goulden at UC-Irvine
Work I started last year here in Texas -
Both of the systems I work on play potentially large, yet unknown roles in the global carbon cycle, so I wanted to take a minute and review the C cycle briefly.
Collaboration with:
James Heilman, Kevin McInnes, James Kjelgaard, Texas A&M
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
Historically open live-oak savannas that have undergone dramatic change in land cover over the last two hundred years due to overgrazing and fire suppression policies that have allowed woody species that were always present but held in check by fairly regular summer burns, to just come in and proliferate.

37. Due to overgrazing and fire suppression policies….grasslands are
disappearing as woody species increase
Ashe juniper
Honey mesquite
Worst-case scenario:

38. Carbon/water tradeoff
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. Grassland
TAMU
Woodland
Transition UT
Study site

40. 3 stages of woody encroachment
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)

48. Bowen Ratio
Energy balance approach to estimating convective fluxes
Seeks to partition energy available into sensible and latent heat terms
H/ (E)
Typical values:
tropical rainforests; soil wet year-round
0.4 – 0.8 temperate forests and grasslands
2-6 semi-arid regions; extremely dry soils
> 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 = = QH QE T Ca ρv Lv
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
ρv Lv
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
The technique consists of determining average differentials of air temperature and vapor pressure between the higher and lower sensors for a 15-minute period from data measurements that are taken every 30 seconds, reversing the sensor positions, and determining average differentials for another 15-minute period. By averaging the two differentials for the consecutive 15-minute periods, an unbiased 30-minute average differential is obtained. The averaging technique may be demonstrated as follows for two sensors, referred to as the right-hand sensor and the left-hand sensor.