partitioning et into e and t using chambers c. a. garcia, d. i. stannard, b. j. andraski, m.j....
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PARTITIONING ET PARTITIONING ET INTO E AND T INTO E AND T
USING CHAMBERSUSING CHAMBERS
C. A. Garcia, D. I. Stannard, B. J. Andraski, M.J. Johnson
OUTLINEOUTLINE
• 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
IMPORTANCE OF CHAMBERS IN IMPORTANCE OF CHAMBERS IN HYDROLOGIC STUDIES 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
DISCRETE MEASUREMENTSDISCRETE 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)
Slope of Vapor Density CurveSlope of Vapor Density Curve
AMVC
E Slope computed using a 5–10 point regression
One-Layer, Multi-Component ModelOne-Layer, Multi-Component Model
Component-scale λT Component-scale λE
λETls – landscape-scale latent heat flux in W/m2
Fc – fractional cover of plant species (i) or bare soil (s)λETi – chamber latent-heat flux in W/m2, combined plant and soilRc – relative crown coverλETs – chamber latent-heat flux in W/m2, bare soil only
sEsFc
n
ii
Rc
iRcsE
iET
iFc
lsET
1
1
(Stannard, 1988)
Fractional Cover of Plants and Bare SoilFractional 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
Relative Crown CoverRelative Crown Cover
• Rc ranges from• 20 – 70% cover at ADRS
• 15 – 40% cover at WG
'Rc
H
hHRc
2
Rc – relative crown coverH – camera heighth – major axis of plant crownRc’ – ratio of plant crown cover to chamber area
(Stannard, 1988)
Discrete Component-Scale FluxesDiscrete 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
Discrete Landscape-Scale FluxesDiscrete 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
Landscape-scale ETLandscape-scale ETChamber vs. Eddy-Covariance (EC)Chamber vs. Eddy-Covariance (EC)
ADRS WG
• 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 ETLandscape-scale ETChamber vs. ECChamber vs. EC
CONTINUOUS ET ESTIMATESCONTINUOUS 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
(Modified by Davies and Allen, 1973)
λE – actual latent heat flux, W/m2
α’ – Priestley-Taylor coefficientS – slope of saturation vapor pressure temperature curve, g/m3/Kγ – psychrometric constantRn – net radiation, W/m2
G – ground heat flux, W/m2
Priestley-Taylor ModelPriestley-Taylor Model
GRγS
Sα'λE n
α′ = f(θ), 0 < θ < θns
α′ = 1.26, θ ≥ θns
Priestley-Taylor CalibrationPriestley-Taylor Calibration
Dataset follows a linear, segmented model
α’ = 5.07 θ – 0.03, 0 < θ < θns
α’ = 1.26, θ ≥ θns
ET PartitioningET PartitioningPriestley-Taylor Approach Priestley-Taylor Approach
vs. vs. Lysimeters 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)
Lysimeter Case Study at the Lysimeter Case Study at the Nevada Test Site (NTS)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
Lysimeter Case StudyLysimeter 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
CONCLUSIONSCONCLUSIONS
• 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