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

Limited FetchLimited Fetch

Turf Grass at a city park Bare soil over a leach field

Factors Controlling ETFactors Controlling ET

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)

ADRS FluxesADRS Fluxes

WG FluxesWG Fluxes

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

Continuous ET Continuous ET Partitioned into E and TPartitioned into E and T

75%E to 25%T

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