1. Flux Networks – Measurements and Analysis
Bev Law
Oregon State University

2. Outline Flux networks Measurements made at flux sites
Data analysis issues
Processes influencing fluxes

3. AmeriFlux sites http://public.ornl.gov/ameriflux
12 cluster sites in different management or veg types near main tower
60 AmerFlux sites total
AmeriFlux sites

4. Questions
How are temporal and spatial variability in CO2, and H2O exchange influenced by ecosystem processes in response to climate variability, land-use, management, disturbance history?
What is the relative effect of these factors?
What is the spatial and temporal variation in CO2 in continental atmospheric boundary layer, and how does this vary with topography, climate, and vegetation?

5. Common Measurements Soil Flux densities Storage flux Vegetation
CO2 efflux
Heat flux
Tsoil
soil moisture
Bulk density, texture, carbon
Vegetation
Aboveground biomass, tree dimensions
Litterfall/mass
LAI
Physiology (foliage respiration, A-Ci)
Flux densities
CO2, H2O
Sensible heat (H)
Latent heat (lE)
Momentum flux
Storage flux
CO2 profile
Meteorology
Rnet, PAR, Tair, RH, PPT

6. FLUXNET
2001
157 Towers

7. Distribution of Towers by Global Precip and Temp
La Selva Site
Wind River Site
Alaskan
Sites

8. Selected ancillary data in database*
Vegetation type
Species 94
Tower height 95
Stand age 72
Annual Temp/PPT sites
LAI at 70 sites
NPP at 27 sites
Growing season 24 sites
Soil C/N sites
Leaf N sites
*based on 172 flux tower sites, May 2002

9. Fluxnet Website
ORNL DAAC

10. Flux-Model-MODIS Comparison
* Fluxes: 17 tower flux sites
Micrometeorology posted near real-time
Gap-filled flux estimates posted annually
* Models: open to all modelers
Common site characteristics–model driver data
* MODIS: 8 products for 25 sites (8-day composites)
Tsurface, Veg Index, LAI, fPAR, Photosynthesis (8-day and annual), Surface Reflectance, BRDF
Includes EOS Core Validation Sites, Flux Tower Sites, BigFoot Sites, LAINet Sites
Format is 7 km x 7 km cutouts in ISIN projection in ASCII files

11. Eddy covariance – Fundamental assumptions
Steady-state conditions: dc/dt=0
Horizontal homogeneity: udc/dx=0; dFx/dx=0
Constant flux layer, dFz/dz=0
1) Steady state: CO2 concentration doesn’t vary significantly with time
2) Horizontal homogeneity: surface is horizontally uniform and level, resulting in no advection (ie flux is same at two different locations above the system of interest) x = horizontal distance
3) The mean vertical turbulent covariance (F) is constant with height in a well-adjusted internal boundary layer (the layer of air that is adjacent to and affected by the underlying surface) and equals the molecular, gradient diffusion-flux at the surface (z = height)
Stephen McMillan

12. Where to Make Flux Measurements?
Horizontally Homogeneous + Extensive Vegetation
(fetch ~100 x ht)
‘Flat’ Terrain
Fully-developed Internal Boundary Layer (away from persistent circulations)
Above Roughness Sublayer:
(~1.5x to 2x ht; 3-4 x canopy ht for widely scattered trees to avoid wake effects)
1 Need large fetch of uniform vegetation to generate internal boundary layer where fluxes are constant with height
X = fetch is about 100 x canopy height (4 m tall canopy should have 400 m fetch)
Fetch = z[ln(z/z0) – 1 + zo/z] / k^2 ln(0.90)
Zo = roughness length ~ 10% canopy height (e.g. 0.4 m for 4 m tall canopy)
Z = measurement height (e.g. 2 x canopy ht = 8 m)
K = Von Karman’s constant (0.4)
Ex. 4 m tall trees (dense) measure at z = 8 m ht, fetch ~460 m, or roughly 100xcanopy ht
Actual estimate of fetch to ht ratio is function of atmospheric stability and surface roughness (transition from rought to smooth surf requires greater fetch-to-ht ratio)
2 Flat terrain to avoid cold air drainage, flux divergence and convergence
3 Should measure above roughness sublayer (e.g. for a 4 m tall canopy, 6- 8 m measurement height)
Ht + 1.5Lt (Lt = transverse element length scale, or about canopy diameter in closed canopy)
e.g. 4 m tall canopy, 2 m diameter crown, = 4+3= 7 m meas ht
Hanan: Krueger

13. Eddy Covariance Method
Simplified, eddy flux is mean covariance of the fluctuations in vertical wind velocity and CO2 concentration from the means over some averaging period (typically ½ h)

14. - Net gain from atmosphere
Flux Calculation
common time scale for flux calculation is about 1/2 hour (have significant statistics and remove non-stationary trends)
W’ = fluctuation from 30 min mean
W’c’ = negative is net uptake by ecosystem, positive is net loss to atmosphere
+ Net loss to atmosphere
- Net gain from atmosphere
January 2, 2019

15. Data Processing Mean Removal Coordinate Rotation
Density fluctuations cause nonzero mean vertical velocity - WPL term
1) Mean removal – to calculate fluctuations, w’ and c’
2) Coordinate rotation computations so that vertical (w) and lateral (u) velocity components = 0, to proceed to compute turbulent flux perpendicular to streamline
3) To make sure that the high frequency eddies that contribute to turbulent flux are being detected, the appropriate frequency response of instrument and sampling rate need to be determined. The sensor should respond to eddies as small as (z-d)/(2 pi fc). Or, given a known frequency response, you can compute appropriate ht for measurements for given wind conditions. High frequencies are associated with nighttime, stable conditions, and co-spectra can be examined to determine if high frequency eddies are being missed.
4) With proper experimental design, this error is often <10%, and these corrections can be ignored.
5) Density corrections – assumption of horizontal homogeneity causes mean vertical velocity = 0, but this isn’t always true. Exchange of heat and water vapor over a uniform surface leads to fluctuations in the density of dry air, which causes nonzero wbar. WPL term is function of sensible heat flux and absolute temp. For closed path sensor, air is drawn through long tube to analyzer and temp fluctuations are dampened, so correction for heat flux need not be applied in the density correction.

16. Coordinate Rotation
Coordinate rotation computations so that vertical (w) and stream-wise (u) velocity components = 0, to proceed to compute turbulent flux perpendicular to streamline

17. Closed vs Open-path IRGA
Closed-path: water absorption
and adsorption on tube walls can cause underestimate of water vapor flux by ~20%
Open-path: WPL term can be as large as flux in regions when sensible heat flux is large (e.g. arid). Data loss during wet periods

18. Corrections for Density Fluctuations
___
Fc = w’rc’+7.386·10-3lE H (mol m-2 s-1)
Webb-Pearman-Leuning equation is a function of:
latent heat flux density
sensible heat flux density
amount of scalar material in the atmosphere
air temperature
Correction applied to open-path sensor. Tube attenuation of temperature on closed-path sensor allows modification of correction – the velocity temperature covariance w’T’ is dropped.

19. Effect of WPL term
CO2 flux densities in a young pine stand. Indicates respiration (+ flux)
when WPL term applied, but significant photosynthesis when only use
the covariance (w’c’)

20. Factors Affecting Flux Covariances
Storage (Fc + Fstor)
Advection
Flux Divergence
Insufficient Turbulence
Limited Energy Balance Closure
When the thermal stratification of the atmosphere is stable or turbulent mixing is weak,
material leaving the soil may not reach the reference height h. Under such
conditions the storage term becomes non-zero, so it must be added to the eddy covariance
measurement if we expect to obtain a measure of material flowing into and out of the soil
and vegetation.

21. Advection? Gradient in fluxes due to:
-changes in source/sink of surface
-changes in flow due to roughness or topography
Fluxes measured in irrigated potato field in an arid region: LE measured at distance x from edge of field / LE at 800 m increases with distance from edge. Advection of sensible heat over the irrigated field, and high VPD occurred to about 300 m in from the edge. A negative feedback was that stomatal conductance and LAI were reduced near the edge, such that LE was lower near the edge. Flux divergence of LE near surface in relation to the 4 m measurement ht became small at about 300 m from edge. There wasn’t a sharp gradient of Fc near the edge, but there was variation in Fc with LAI within the field.
Raupach (1991) suggests that interfield advection can be ignored if the horizontal scale of landscape patches is on the same scale as that of the convective boundary layer. Furthermore, the advection of dry air over actively transpiring vegetation may not necessarily increase evaporation if negative feedback causes stomatal
closure, as stomatal closure would offset any potential increase in evaporation due to the advection of hot, dry air (Philip, 1987; Kroon and deBruin, 1993; Baldocchi and Rao, 1996).

22. Flux divergence
Measurements of fluxes at different heights above and within canopy

23. Spatial variation in CO2

24. Flow Distortion over Non-level Terrain?
Vertical velocity / horizontal velocity as a function of wind direction shows flow distortion due to complex terrain (should be zero, not + and -). Rotating these data to zero may not be valid. (Baldocchi et al., 2000).
Impact of terrain on vertical velocity w is a function of wind direction
as wind blows up, across or down a gentle slope.

25. Insufficient Turbulence? Tall Forest, Rough Terrain
Friction velocity (u*; m/s)
Under low wind speeds, CO2 seems to drain out of the control volume
and not pass the imaginary line marking the canopy atmosphere interface

26. Rn = Rs.dn - Rs.up + Rl.dn - Rl.up (available energy)
Energy Balance
Rn = H + LE + G + S
Rn = Rs.dn - Rs.up + Rl.dn - Rl.up (available energy) G H LE Rn S
latent heat and sensible heat flux are primary carrier for energy transfer between earth’s surface and the atmosphere. Without energy transfer the earth would not be suitable to live, intense heating at the earth surface
water vapor and carbon dioxide are efficient absorbers of infrared radiation => heating of the atmosphere: without these absorbers the atmosphere would be about 30 K colder
January 2, 2019

27. Energy Balance Closure? Tall Forest
Slope = 0.93, thus lack of closure is about 7%
Across Fluxnet sites: Energy balance ratio, the ratio of turbulent energy fluxes to available energy indicates a general lack of closure across virtually all sites, with a mean imbalance on the order of 20%. The imbalance was prevalent in all measured vegetation types and in climates ranging from Mediterranean to temperate and arctic. There were no clear differences between sites using open and closed path infrared gas analyzers. At a majority of sites closure improved with turbulence intensity (friction velocity), but lack of total closure was still prevalent under most conditions.
FLUXNET: Mean imbalance ~20% - generally improved with u*

28. Ecosystem Processes
Know from leaf level and soil process studies that the water and CO2 linkage is important.
Thus, we have been measuring component fluxes, including sap flux, foliage respiration, and soil CO2 effluxes.

29. Flux components NEE = NEP = Pg + Ra + Rh = Pg + Re GEP = |NEE – Re|
during daytime
GEP = gross ecosystem production
NEE = net ecosystem exchange CO2
Re = ecosystem respiration

30. Monthly GEP and lE – deciduous broadleaf forests
Fluxnet data showed a strong correlation between monthly photosynthesis (estimated from the flux data), and water vapor exchange within functional groups. This makes sense from a leaf-level perspective, and CO2 and H2O linkages are defined in models (e.g. Farquhar + PM).
(Law et al. AFM. In press)

31. Weekly H2O and CO2 flux at young and old pine sites

32. Drought Stress in Young Trees

33. Inter-annual Variation in Soil CO2 Efflux
Annual efflux
(g C m-2 y-1)
Young
99’ (68)
00’ (80)
01’ (38)
Old
99’ (32)
00’ (45)
01’ (68)
2001 driest year
Y site generally shows decrease in flux earlier in year than O site. Transpiration rates and predawn water potential suggest that drought stress was greater in the younger trees with less developed root systems (hydraulic redistribution was also less evident at Y site). Thus, we think the decrease in soil fluxes as drought progresses is driven primarily by a physiological response, whereby decreased photosynthesis leads to less allocation to roots, lower root respiration, and respiration of associated microbes.
The inter-annual data allow us to examine these responses in more detail. In the extremely dry year, the O site finally showed drought stress (although not as much as Y trees), and this is apparent in the soil fluxes. Ie., water became an important variable for predicting soil fluxes at the O site in the driest year.

34. GEP / lE (slope = water-use efficiency)
Old forest more water use efficient at high LE (e.g. mid-summer, warmer conditions) – more water stress in young trees

35. Net gain (sink) NEP (g C m-2 y-1) Net loss (source)
Example from Florida slash pine, to demonstrate how measurements along disturbance gradients can be used to evaluate models through stand development, and how we can use biological & EC measurements and modeling to examine effects of management and disturbance on net C uptake.
Solid line is model ensemble mean and interannual standard deviation (dotted line) for simulated NEE following harvest. Estimates of NEE from chronosequence data are shown, with standard errors estimated from individual plot data in Gholz and Fisher (1982). Eddy flux measurements of NEE are also shown, for measurements made at three different stand ages: immediately following clearcut; in a mid-rotation aged stand; and in a rotation-aged stand. (-) = loss to atmosphere, (+) = sink
EC data show more loss in first years after harvest than model predicts, and greater net uptake near end of rotation (~30 y) than biometric and model estimates.
(Thornton et al. 2001)

36. Landscape Heterogeneity
500 m
Footprint
Wind direction
Tower
Big elipse is estimate for stable stratification, smaller footprint for unstable stratification

37. Changes in NEP with age (a) and the age structure of a hypothetical landscape (b) determine the cumulative NEP of the landscape (c)
(a)
(b)
(c)
Jiquan Chen, Univ of Toledo

38. Inter-annual Variation in Fluxes
Long-term measurements are useful to assess the effect of interannual variation in climate on fluxes

39. Cumulative uptake of carbon dioxide (g C m-2)
2000
Cumulative
total
Annual
total
Temperate deciduous forest
Boreal coniferous forest
1500
Growing
season
1000
Cumulative uptake of carbon dioxide (g C m-2)
Dormant
season
500
Figure 5
1992
1993
1994
1995
1996
1997
1998
1999
2000
Year

40. Spatial Coherence of Seasonal Flux Anomalies
Differences between
1998 and
Similar pattern at
several flux sites
(WI, MA, Manitoba)
The spring 98 warm
period and a later
cloudy period appear
at all 3 sites.
(K. Davis et al.)
Air Temp
[CO2]
NEE

41. References
Aubinet, M. et al Estimates of the annual net carbon and water exchange of European forests: the EUROFLUX methodology. Advances of Ecol. Res
Baldocchi, D.D. et al Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology 69:
Kaimal, J.C., J.J. Finnigan Atmospheric Boundary Layer Flows: Their Structure and Measurement. Oxford University Press, Oxford, UK. 289 pp.
Law, B.E.et al Carbon storage and fluxes in ponderosa pine forests at different developmental stages. Global Change Biol. 7:
Raupach, MR, Finnigan, JJ The influence of topography on meteorological variables and surface-atmosphere interactions. 190:
Schmid, HP, Lloyd, CR Spatial representativeness and the location bias of flux footprints over inhomogeneous areas. Agric. For. Meteorol 93,
Wilson, K.B., et al Surface energy partitioning between latent and sensible heat flux at FLUXNET sites. Agric. For. Meteorol. In press.

42. More references (on Fluxnet & Metolius)
Bowling, D.R., N.G. McDowell, B.J. Bond, B.E. Law, J. Ehleringer C content of
ecosystem respiration is linked to precipitation and vapor pressure deficit.
Oecologia 121:
Irvine, J., Law, B.E., Seasonal soil CO2 effluxes in young and old ponderosa pine
forests. Global Change Biology. In press.
Law, B.E., E. Falge, D.D. Baldocchi, et al Carbon dioxide and water vapor exchange
of terrestrial vegetation in response to environment. Agric. For. Meteorol. In press.
Law, B.E., F.M. Kelliher, D.D. Baldocchi, P.M. Anthoni, J. Irvine Spatial and
temporal variation in respiration in a young ponderosa pine forest during a summer drought.
Agric. For. Meteorol. 110:27-43.
Thornton, P., B.E. Law, and D.S. Ellsworth, H. Gholz, A. Goldstein, D. Hollinger, K.T.
Paw U. Modeling the effects of disturbance history and climate on carbon and water budgets
in evergreen needleleaf forests. Agric. For. Meteorol. In press.

43. Acknowledgements
Dennis Baldocchi
Dick Olson
DOE, NASA