On the use of eddy-covariance and optical remote sensing data for biogeochemical modelling Markus Reichstein, Dario Papale Biogeochemical Model-Data-Integration Group, Max-Planck-Institute Jena Laboratory of Forest Ecology, University of Tuscia Carbon Fusion International Workshop Edinburgh, May 2006 Biogeochemical Model-Data Integration Group

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Ecosystem models + provide system understanding + promise inter-/extrapolation capacity + may include historical effects – are simplifications of the world – can’t predict stochastic events Remote sensing + objective/consistent observations + spatially and temporally dense – data quality lower – processes not directly observable, no history, no prediction Ecosystem data + Potentially high quality + often high temporal resolution – data compatibility ? – ‘point’ observations BGC-Model-Data Integration Overview

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Outline Introduction to eddy covariance data Bottom-up perspective of an ‘ideal’ data integration-validation process Problems and obstacles in this process

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein 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

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Observing ecosystem gas exchange: eddy covariance Flux = speed x concentration Photo: Baldocchi

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein + Measures whole ecosystem exchange of CO 2 and H 2 O, … + Non-destructive & continuous + time-scale hourly to interannual + integrates over large area - only on flat sites - relies on turbulent conditions ==> data gaps, stochastic data - source area varying (flux footprint) - only ‚point‘ measurements Does not deliver compartment fluxes, but: NEP = GPP - Reco CO 2, H 2 O Eddy covariance

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Half-hourly eddy covariance data Respiration Carbon uptake Evapotransp.

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Network of ecosystem-level observations >1000 site-years  raw measurements (10 13 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: Raw dataKnowledge  10 8  10 6  10 2 bytes Turb stat. Synth./aggr. Model param.

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Ideal model-data integration cycle (bottom-up) Model (re)formulation (Definition of model structure) Model characterization (Forward runs, consistency check, sensitivity, uncert. analysis) Model parameter estimation (Multiple constraint) Parameter interpretation (Thinking) Generalization (‘up-scaling’) Model validation (against indep. data, by scale or quantity) Model application DATA

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein The bottom-up model PROXEL Canopy Layer 1 Canopy Layer 2 Canopy Layer 3... Canopy Layer n Canopy Solar radiationAir temperature[CO 2 ]Relative humidityWind speed LAI, SAI Leaf physiology Phenology CO 2 H 2 O Soil Layer 1 Soil Layer 2 Soil Layer 3... Soil Layer n Soil Air temperatureWind speed Soil hydraulic parameters Soil thermal parameters Soil respiration parameters Precipitation Water extraction Vapour pressure Root distri- bution CO 2 H2OH2O effective  soil {Quantum use efficiency, electron transport and carboxylation capacities, stomatal conductance}

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein I. Model charaterization / forward model run Reichstein, Tenhunen et al., Global Change Biology, 2002 Drought stressed conditions

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein II. Dual-constraint parameter estimation Reichstein et al. 2003, JGR Target region

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein IIa. Inferred parameter timeseries

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Reichstein et al. 2003, JGR III. Interpretation & Generalization Relative soil water content Relative leaf activity

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein ENFEBFDBFMFSavOshrubCrop RUE [gC / MJ APAR] III. Interpretation and Generalization: Keyp. RUE max inter-PFT variability intra-PFT variability f(species, N, T???)

IV. Validation at larger scale 70°N 29,2° W 11° W23° E 58° E 60°N 50°N 40°N "Les Landes"

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein GCB, in press

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein The problems

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein 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

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Errors in the data

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Error model influence on parameter estimates Const. abs errorsConst. rel. errors Parameter estimate Search strategy I II Simplified after Trudinger et al. (OPTIC)

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein 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 > interannual variability

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Characterization of the random error cf. Richardson et al. (2006)

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein NEE Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec NEE_sigma Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr Jun Jul Aug Sep Oct Nov Dec NEE_sigma [µmol m -2 s -1 ] Quantifying uncertainties

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Error distribution of eddy covariance data

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Distribution of model error against eddy data Chevalier et al. (in rev.)

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein PDF only 10am-3pm and Jun-Sep NEE error

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein More complicated error structures

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Maximizing the likelihood? Bayesian approach Cost function: Trust in dataTrust in apriori model parameters

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Spatial representation problem I Does the tower site represent the grid cell of interest? –0.25-2km km for MODIS/SEAWIFS remote sensing – km for meteorological fields – km for DGVMs, BGCs applied in global context

Aerial photo Spatial heterogeneity... Landsat MODIS 1 km

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein It‘s not always so bad... TM3 coeff. of variation TM 3,4,7MODIS 1,2,7 Dinh et al., subm.

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein 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?

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Day of the year fAPAR [MODIS-RT)  We have to have up-scaling strategies

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein 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)

Carbon Fusion Workshop, Edinburgh May 2006Markus Reichstein Conclusions