1. Evaporation from Flux Towers  S = P – D - ET Change in water content of volume of soil precipitation drainage By Dr Marcy Litvak Dept of Biological Sciences University of Texas at Austin (now at the University of New Mexico)

2. Energy budgeting approach Can directly measure each of these variables How do you partition H and E?? Sensible Heat flux Latent Heat flux

3. Eddy Covariance Directly measure how much CO 2 or H 2 O vapor blows in or out of a site in wind gusts. Net Ecosystem Production Integrated measure of ecosystem fluxes Link changes in [CO 2 ] or [H 2 O] in the air above a canopy with the upward or downward movement of that air

4. Net Ecosystem Exchange Flux CO 2 = w ’ CO 2 ’ 30 minute timescale Updraft [CO2] > downdraft [CO2] Flux >0 carbon source Updraft [CO2] < downdraft [CO2] Flux < 0 carbon sink

5. 0 200 400 600 800 1000 146.0146.5147.0147.5148.0 May 26, 2000May 27, 2000 Sunlight CO 2 Exchange CO 2 Exchange (  mol m -2 s -1 ) Sunlight (Wm -2 ) The net CO 2 flux is calculated for each half hour from the measurements of vertical wind and CO 2 concentration. A positive flux indicates a net loss of CO 2 from the surface (respiration) and a negative flux indicates the net uptake of CO 2 (photosynthesis) -20 -15 -10 -5 0 5 12 AM12PM12AM12PM12AM

6. CO 2 Exchange (  mol m -2 s -1 ) Annual C accumulation (Tons C ha -1 ) 19992000 A years worth of half-hour data can be summed to determine how much Carbon the ecosystem gained or lost 0 1 2 3 4 5

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 signal Instantaneous Perturbation from The mean All atmospheric entities show short-period fluctuations about their long term mean value Time averaged property

9. 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

10. Eddy Covariance

11. Eddy covariance Velocity of air being moved upwards or downwards m s -1 Fluctuation of entity about it’s mean g kg air -1 Density of air kg air m -3 F = ρ w’x’ Average vertical flux of entity over 30 minute period 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. = g m -2 s -1 m g s kg kg m 3 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

13. 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 kg s m 2 kg m 3 J kg Jm2sJm2s = Wm2Wm2 = Q E = ρ w’w’ ρv’ρv’ LvLv Latent Heat

14. 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 ◦Csm ◦Cs kg m 3 J kg ˚C Jm2sJm2s = Wm2Wm2 = Q H = ρ w’w’ T’T’ CpCp Sensible Heat

15. Instrumentation Requirements

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

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 = 0.61 Average = 0.71

26. Wind Speed (m/s)

27. Net Radiation (W/m 2 )

28. Sensible Heat Flux (W/m 2 )

29. Latent Heat Flux (W/m 2 )

30. Evaporation (mm/day) Average = 3.15 mm/day

31. Ground Heat Flux

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 Collaboration with: James Heilman, Kevin McInnes, James Kjelgaard, Texas A&M Melba Crawford, Roberto Gutierrez, Amy Neuenschwander, UT Freeman Ranch - Texas State University Marcy Litvak Section of Integrative Biology University of Texas, Austin

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 Ashe juniper Honey mesquite Worst-case scenario:

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. Grassland TAMU Woodland TAMU Transition UT Study site

40. 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 Experimental design

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. 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

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 (t 2 - t 1 ) / ( e 2 –e 1 ) Psychrometer Constant F(T,P) air temperatures measured at two points at different heights above the land surface vapor pressures measured at the same two points

50. Average values of the air-temperature differences (t 2 - t 1 ) and vapor-pressure differences (e 2 - e 1 ), taken every 30 seconds for a 30-minute period are used to determine . Bowen Ratio  = Q H Q E = TT ρvρv CaCa LvLv Specific heat capacity 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