On the use of eddy-covariance and optical remote sensing data for biogeochemical modelling

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On the use of eddy-covariance and optical remote
sensing data for biogeochemical modelling
Carbon Fusion International Workshop Edinburgh,
May 2006

• Markus Reichstein, Dario Papale
• Biogeochemical Model-Data-Integration Group,
  Max-Planck-Institute Jena
• Laboratory of Forest Ecology, University of Tuscia

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BGC-Model-Data Integration Overview
Ecosystem models
provide system understanding promise
inter-/extrapolation capacity may include
historical effects are simplifications of the
world cant predict stochastic events
Ecosystem data
Remote sensing
Potentially high quality often high temporal
resolution data compatibility ? point
observations
objective/consistent observations spatially
and temporally dense data quality lower
processes not directly observable, no history,
no prediction
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Outline

• Introduction to eddy covariance data
• Bottom-up perspective of an ideal data
  integration-validation process
• Problems and obstacles in this process

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Overview

• General data assimilation example remote sensing
  of a cut!!
• Fluxnet as large data archive
• Particular problems with biospehere eddy error
  and quality discussion, spatial scaling, model
  structure, dynamic parameters
• Proxel example of tracking parameter
• MODIS example of RUE model ? monthly RUE
  structural update, problems with generalisation
• Overview of inverse parameter estimation
  approaches (multiconstraints)
• Future better characterisation of errors,
  spatial scaling, multiple constraints,
  generalization from sites
• Consider pools! Time scales, Error

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Observing ecosystem gas exchange eddy covariance
Flux speed x concentration
Photo Baldocchi
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Eddy covariance
Measures whole ecosystem exchange of CO2 and
H2O, Non-destructive continuous
time-scale hourly to interannual integrates
over large area - only on flat sites - relies on
turbulent conditions gt data gaps, stochastic
data - source area varying (flux footprint) -
only point measurements Does not deliver
compartment fluxes, but NEP GPP - Reco
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Half-hourly eddy covariance data
Evapotransp.
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Network of ecosystem-level observations
gt1000 site-years ? 1012 raw measurements (1013
bytes)

• Network and intercomparison studies
• Harmonised and documented data processing
• Aubinet et al. (2000), Falge et al. (2001), Foken
  et al. (2002), Göckede/Rebmann/Foken (2004)
  general set-up and methodology, quality
  assurance, gap-filling
• Reichstein et al. (2005), Glob. Ch. Biol.
  u-correction, gap-filling, partitioning of NEE
• Papale et al. (in prep), Biogeosciences Quality
  control, eval. uncertainties
• Moffat et al. (in prep) Gap-filling
  inter-comparison
• Online processing tool http//gaia.agraria.unitus
  .it/lab/reichstein/

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Ideal model-data integration cycle (bottom-up)
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The bottom-up model PROXEL
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I. Model charaterization / forward model run
Drought stressed conditions
Reichstein, Tenhunen et al., Global Change
Biology, 2002
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II. Dual-constraint parameter estimation
Reichstein et al. 2003, JGR
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IIa. Inferred parameter timeseries
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III. Interpretation Generalization
Relative leaf activity
Relative soil water content
Reichstein et al. 2003, JGR
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1.8
1.6
III. Interpretation and Generalization Keyp.
RUEmax
1.4
1.2
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RUE gC / MJ APAR
0.8
0.6

• inter-PFT variability
• intra-PFT variability
• f(species, N, T???)

0.4
0.2
0
ENF
EBF
DBF
MF
Sav
Oshrub
Crop
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IV. Validation at larger scale
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GCB, in press
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The problems
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To consider with DA of eddy covariance data

• How is the error structure of the data itself?
• How to address mismatch of scales (point versus
  pixel)?
• Remote sensing
• Meteorological data
• How do perform up-scaling from tower sites?
• Representativity
• Generalization

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Errors in the data
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Error model influence on parameter estimates
Search strategy
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II
Parameter estimate
Const. abs errors
Const. rel. errors
Simplified after Trudinger et al. (OPTIC)
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Errors in eddy covariance data

• Random errors
• 30 for the half-hourly flux, (turbulences !)
• Systematic errors
• can be largely controlled/avoided
• Selective systematic errors
• Conditions where the theory does not apply
• Low turbulent conditions (night-time)
• Advection
• good quality control necessary
• Better few unbiased data, than a lot of biased
  data
• Uncertainties mean NEE gt interannual variability

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Characterization of the random error
cf. Richardson et al. (2006)
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Quantifying uncertainties
NEE
NEE_sigma
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NEE_sigma µmol m-2 s-1
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Error distribution of eddy covariance data
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Distribution of model error against eddy data
Chevalier et al. (in rev.)
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PDF only 10am-3pm and Jun-Sep
NEE error
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More complicated error structures
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Maximizing the likelihood?
Bayesian approach Cost function
Trust in data
Trust in apriori model parameters
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Spatial representation problem I

• Does the tower site represent the grid cell of
  interest?
• 0.25-2km km for MODIS/SEAWIFS remote sensing
• 30-100 km for meteorological fields
• 30-100 km for DGVMs, BGCs applied in global
  context

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Spatial heterogeneity...
1 km
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Its not always so bad...

• TM 3,4,7

• MODIS 1,2,7

• TM3 coeff. of variation

Dinh et al., subm.
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Spatial representation problem II

• Does the network of tower sites represent the
  spatial domain of interest or are there chances
  to generalize with scaling variables?

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fAPAR MODIS-RT)
? We have to have up-scaling strategies
Day of the year
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Conclusions

• Eddy covariance data contains a lot of
  interpretable information on both carbon and
  water cycle
• Inclusion of pools and fluxes for system
  understanding and for linking short and long
  time-scales necessary
• Major challenge within eddy data
• Characterization of the error (random, bias)
• Scale and representativeness problem
• Interpret. Generalization of site specific
  parameters
• Documentation of site dynamics, that may violate
  model structure (e.g., soil water, management)

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Conclusions