Evaporation from flux towers
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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). Energy budgeting approach. Latent Heat flux.

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Evaporation from Flux Towers

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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)


Energy budgeting approach

Latent

Heat flux

How do you partition

H and E??

Can directly measure

each of these

variables

Sensible

Heat flux


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


Flux CO2 = w ’ CO2’

Net Ecosystem Exchange

30 minute timescale

Updraft [CO2] > downdraft [CO2]

Flux >0 carbon source

Updraft [CO2] < downdraft [CO2]

Flux < 0 carbon sink


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


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

19992000


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

Perturbation from

The mean

Instantaneous

signal

Time averaged property

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

ρ

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


Eddy covariance

mg

s kg

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


w’

ρv’

J

kg

Latent heat of vaporization

(J kg-1˚C-1)

Mean density of air

QE =

ρ

Lv

Latent Heat

Fluctuation about

the mean of

vertical wind speed

Fluctuation about

the mean of

density of water

vapor in air

mkg

s m2

kg

m3

J

m2s

W

m2

=

=


w’

T’

J

kg ˚C

Specific heat of air at constant pressure

(J kg-1˚C-1)

Mean density of air

QH =

ρ

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

=

=


Instrumentation Requirements


3-D Sonic anemometer

Quantum

sensor

Pyrronometer

IRGA

Net radiometer


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

Advection

Horizontal concentration gradients may also lead to perturbation calculation errors


Issue of energy balance closure


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 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 dominated by

fairly open live-oak savannas


Honey mesquite

Ashe juniper

Worst-case scenario:

Due to overgrazing and fire suppression policies….grasslands are

disappearing as woody species increase


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

May 2004

(TAMU)

Transition site – July 2004

15-20 year old juniper,mesquite

Live Oak-Ashe juniper

woodland – July 2004

(TAMU)


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

> 10 deserts


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)


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

Ca

T

=

Lv

ρv

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


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