1. PARTITIONING ET INTO E AND T USING CHAMBERS C. A. Garcia, D. I. Stannard, B. J. Andraski, M.J. Johnson

2. OUTLINE Importance in hydrologic studies Discrete measurements of E and T Component-scale fluxes Landscape-scale fluxes Comparisons with eddy-covariance ET Continuous estimates of E and T

3. IMPORTANCE OF CHAMBERS IN HYDROLOGIC STUDIES Limited Fetch Determine factors controlling ET Heterogeneous settings Soil-plant-atmosphere interactions Spatial variability of ET fluxes Determine contaminant fluxes to atmosphere Determine relative rates of water use

4. Limited Fetch Turf Grass at a city park Bare soil over a leach field

5. Factors Controlling ET

6. DISCRETE MEASUREMENTS Component- and landscape-scale estimates Two Case Studies Amargosa Desert Research Site (ADRS) Arid site in southern Nevada Measurements of plants and bare soil made quarterly (August 2003–January 2006) Walnut Gulch (WG), Arizona Semi-arid site in southeastern Arizona Three days of plant and bare-soil measurement (August 1, 8, and 9, 1990)

7. Slope of Vapor Density Curve Slope computed using a 5–10 point regression

8. One-Layer, Multi-Component Model Component-scale λ T Component-scale λ E λET ls – landscape-scale latent heat flux in W/m 2 Fc – fractional cover of plant species (i) or bare soil (s) λET i – chamber latent-heat flux in W/m 2, combined plant and soil Rc – relative crown cover λET s – chamber latent-heat flux in W/m 2, bare soil only (Stannard, 1988)

9. Fractional Cover of Plants and Bare Soil ADRS Two perpendicular 400-m transects 4 measured species 6–10% plant cover Dominant species is 80% of plant cover WG Five parallel 30.5-m transects 5 measured species 26% plant cover Dominant species is 40% of plant cover

10. Relative Crown Cover Rc ranges from 20 – 70% cover at ADRS 15 – 40% cover at WG Rc – relative crown cover H – camera height h – major axis of plant crown Rc’ – ratio of plant crown cover to chamber area (Stannard, 1988)

11. ADRS Fluxes

12. WG Fluxes

13. Discrete Component-Scale Fluxes Bare soil fluxes were lowest as a result of drier surface soils ADRS vegetation Wolfberry was greatest in spring Creosote bush was greatest during the summer, fall, and winter WG vegetation Desert zinnia and tarbush fluxes were greatest Upper canopy negatively correlated with shallow soil-water content Lower canopy correlated with air temperature and relative humidity Leaves closer to the ground undergo greater temperature changes Saturation-vapor pressure increases with increasing temperature

14. Discrete Landscape-Scale Fluxes Bare-soil importance substantially increased as a result of Fc ADRS – soil contribution was greater than each plant WG – soil contribution was greater than 4 of 5 plants E and T partitioning ADRS – 60% E to 40% T on 5/2/2005; – 70%E to 30% T over all periods WG – 15% E to 85% T over three days measured

15. Landscape-scale ET Chamber vs. Eddy-Covariance (EC) ADRSWG

16. ADRS Over all periods, chamber ET was 7% less than EC No clear trend relating chamber ET to EC ET Temperature Antecedent moisture Season WG Over 3 days, chamber ET ~30% greater than EC Difference likely due to high bias in chamber ET Mismatch of internal air and external wind speed Chamber heating during measurement Landscape-scale ET Chamber vs. EC

17. CONTINUOUS ET ESTIMATES Partitioning continuous ET into E and T Continuous ET: measured (eddy-covariance station) E: estimated (Priestley-Taylor Model) Periodic chamber measurements from bare soil Continuous micrometeorological data Soil water content Net radiation Ground heat flux Air temperature T: estimated from daily ET − E

18. (Modified by Davies and Allen, 1973) λE – actual latent heat flux, W/m 2 α’ – Priestley-Taylor coefficient S – slope of saturation vapor pressure temperature curve, g/m 3 /K γ – psychrometric constant R n – net radiation, W/m 2 G – ground heat flux, W/m 2 Priestley-Taylor Model α′ = f(θ), 0 < θ < θ ns α ′ = 1.26, θ ≥ θ ns

19. Priestley-Taylor Calibration Dataset follows a linear, segmented model α’ = 5.07 θ – 0.03, 0 < θ < θ ns α’ = 1.26, θ ≥ θ ns

20. Continuous ET Partitioned into E and T 75%E to 25%T

21. ET Partitioning Priestley-Taylor Approach vs. Lysimeters Weighing lysimeters commonly used as direct measure of water balance Measure ET from vegetated lysimeter Measure E from bare soil lysimeter (devoid of roots)

22. Lysimeter Case Study at the Nevada Test Site (NTS) 75% E to 25% T 85% E to 15% T E-to-T partitioning at NTS was within 10–15 percent

23. Lysimeter Case Study Major difference in partitioning was experimental design ADRS – E measured from plant-interspace areas NTS – E measured from bare soil devoid of roots In Mojave Desert Shrub roots extend laterally up to 4 m Soil-water extraction beneath canopies is similar to interspace areas

24. CONCLUSIONS Chambers help quantify contributions to ET in mixed communities Chamber estimates of landscape-scale ET ADRS – 7% less than eddy-covariance ET WG – 30% greater than eddy-covariance ET ET partitioning into E and T ADRS – 70% E to 30% T (discrete) and 75% E to 25% T (cont.) WG 15% E to 85% T ADRS - ongoing numerical modeling shows 72%E to 28%T for cumulative ET