reducing uncertainty in nee estimates from flux measurements d. hollinger, l. mahrt, j. sun, and...
TRANSCRIPT
Reducing uncertainty in NEE estimates from flux measurements
D. Hollinger, L. Mahrt, J. Sun,
and G.G. Katul
Ameriflux Meeting, Boulder CO., October 20, 2005
Organizing Framework
Uncertainty in flux measurements (random and systematic errors) over sampling intervals needed to average out turbulence.
Gap-filling missing data when averaging over extended time scales (relevant to impact of carbon allocation on ecosystem properties).
Linking measured fluxes to biological sources and sinks (main issues – stable flows; topography).
All recent reviews concerning measurements and
modeling of surface-atmosphere mass, energy, and
momentum exchange expressed the need to confront
the problem of turbulent flows within plant canopies
on non-flat terrain.
Background
Field Studies [Mainly forests, stable flows, mild topography]
Aubinet et al. (2003; 2005);Yi et al. (2004);Staebler and Fitzjarrald, (2004); Feigenwinter et al., (2004);Fokken et al., (2005);
Laboratory Studies: [steep topography]
Finnigan and Brunet (1995)
Previous Studies
Results from Recent Field Experiments
CO2 advection study at the Niwot Ridge AmeriFlux site by Yi et al. (2004) suggested that:
1) Both longitudinal and vertical advective fluxes are
important and often larger than the turbulent flux.
2) They often act in opposite direction
Feigenwinter et al1. (2004)
“The opposite sign of horizontal and vertical advection supports the idea that the two fluxes will cancel out each other in the long-term carbon balance”.
“The mean advective fluxes at night have magnitudes comparable to the daily NEE”.
1Feigenwinter et al., 2004, Boundary-Layer Meteorology.
Aubinet et al1. (2005)
“The advective fluxes strongly influence the nocturnal CO2 balance, with the exception of almost flat and highly homogeneous sites”.
Storage - significant “only during periods of both low turbulence and low advection”.
“All sites where advection occurs show the onset of a boundary layer characterized by a downslope flow, negative vertical velocities and negative vertical CO2 concentration gradients during nighttime”.
1Aubinet et al., 2004, Boundary-Layer Meteorology.
Hill Properties:Four hill modulesHill Height (H) = 0.08 mHill Half Length (L) = 0.8 m
Canopy PropertiesCanopy Height = 0.1 mRod diameter = 0.004 mRod density = 1000 rods/m2
Flow Properties:Water Depth = 0.6 mBulk Re > 1.5 x 105
Polytechnic of Turin (IT) Flume Experiments
Velocity MeasurementsSampling Frequency = 300 Hz
Sampling Period = 300 sLaser Doppler Anemometer
Displaced Coordinates
Coordinate Systems
Mean Velocity (m/s)
Turbulent Stress (m2/s2)
0
z
W
x
U
),(1
cd hzFz
wu
x
P
z
UW
x
UU H
Model Formulation: 2-D Mean Flow
Fluid Continuity:
Mean Momentum Equation:
Two equations with two unknowns – after appropriate parameterization
Produced by the Hill
CanopyDrag
Finnigan and Belcher (2004)Analytical Model
2' 'U U
u w lz z
Closure for Reynolds StressConstant mixing length insidecanopy:
d d bF C aU U
Linearized Adv.:bb
UU U UU W U W
x z x z
Closure for Linearized Drag:
2 U Uu w l
z z
Mixing Length Model
Linearized Advective Term
Linearized Drag Force
Deep Inside the Canopy
AdvectionDrag
Turbulent Stress Pressure Gradient
MeanMomentumBalance
4 2o
u w u wS
u w
w
u
EJECTIONS
SWEEPS
Ejection-Sweep Cycle
Canopy Surface
Smooth Surface (no canopy)
Advective fluxes are opposite in sign
They are often larger than Photosynthesis (Sc)
Advective terms are (individually) of the same order of magnitude as photosynthesis, consistent with field experiments to date. Note that the model does not consider atmospheric stability.
The effects of advective terms on CO2 fluxes at a particular point can be as large as 100%. Both advective terms must be considered in any flux-correction treatment due to topography.
Conclusions
~1 km
(b): Tower relief mapTumbarumba, AU
(a): SLICERData from Duke Forest
(c): Eucalyptus vegetationTumbarumba, AU
Gap-Filling
What new information is being added in the Gap-filling?
How much are the distributional and spectral properties altered by gap-filling?
Distributional and Autocorrelation Properties
fBm process with Hurst exponent=1/3
Shannon-Entropy
1
log( )m
i ii
E p p
p=Empirical probability density function OR Energy distribution (e.g. from spectral analysis)
Maximum Entropy:1
; ( ) log( )ip Max E mm
Entropy = Information Content (Shannon, 1948)
Wavelet-Based Spectra
Haar wavelet, localizedin time domain – can remove gaps fromspectral calculations.
Schimel & others – use Entropy measures for assessing New information injected by gap-filling.
PP = Pine PlantationOF = Old Field
HW = Hardwood Forest
Duke Forest Ameriflux Sites
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
OF PP HW OF PP HW
Shan
non
Ent
ropy
__
_
raw
gapfilled
SpectraProbability
Entropy, Gap filling, ET
Entropy, Gap filling, Daytime NEE
Daytime
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
OF PP HW OF PP HW
Sh
ann
on E
ntr
opy
___
raw
gapfilled
SpectraProbability
Night-time NEE
Nighttime
00.10.20.30.40.50.60.70.80.9
OF PP HW OF PP HW
Sh
ann
on E
ntr
opy
___
raw
gapfilled
SpectraProbability
Remarks
If after gap-filling, the
log( )raw gapfilledE E
Em
is large (>20%), the ‘long-term’ estimates of NEE are going to be sensitive to gap-filling and are likely to have significant artificial correlation with the gap-filling drivers.
Extra References
Katul et al., 2001, Advances in Water Resources , 24, 1119.
Katul et al., 2001, Geophysical Research Letters, 28, 3305.
Katul et al., 2001, Physics of Fluids, 13, 241.
Wesson et al., 2003, Boundary-Layer Meteorology, 106, 507.
Mahrt et al., 1999, Journal of the Atmospheric Sciences, 48, 472.