HR: 09:00h
AN: B31E-05    [Abstracts]
TI: Semi-Empirical Modelling Of Biotic And Abiotic Factors Controlling Ecosystem Respiration Across Eddy-Covariance Sites
AU: * Migliavacca, M
EM: mmiglia@bgc-jena.mpg.de
AF: Remote Sensing of Environmental Dynamics Lab. – DISAT-UNIMIB, P.zza della Scienza, 1, Milan, 20126, Italy
AU: * Migliavacca, M
EM: mmiglia@bgc-jena.mpg.de
AF: Model Data Integration Group – Max Planck Institute for Biogeochemistry, Hans-Knöll- Strasse 10, Jena, 07745, Germany
AU: Reichstein, M
EM: mreichstein@bgc-jena.mpg.de
AF: Model Data Integration Group – Max Planck Institute for Biogeochemistry, Hans-Knöll- Strasse 10, Jena, 07745, Germany
AU: Richardson, A D
AF: University of New Hampshire, Complex Systems Research Center, Morse Hall, 39 College Road, Durham, NH 03824, United States
AU: Lasslop, G
AF: Model Data Integration Group – Max Planck Institute for Biogeochemistry, Hans-Knöll- Strasse 10, Jena, 07745, Germany
AB: In this study we analyse the ecosystem respiration (RECO) data from 83 eddy covariance sites belonging to the FLUXNET network. The aim is to develop a semi-empirical model able to explain the temporal variability of RECO and the site-to-site variability within each plant-functional-type (PFT) and the variability between different PFT (e.g evergreen needleleaf, grasslands, croplands..). At site level daily RECO data, derived from eddy covariance measurements, were described with a semi- empirical model that has been developed for soil respiration by Reichstein et al. (2003), which uses air temperature and precipitation as predictors of respiration. While the model generally gave a good description of the data, a residual analysis showed that productivity had an additional effect on RECO since model residuals were correlated with gross primary production (GPP). We analysed different functional responses of the GPP to the RECO and the best results, in terms of variance explained by the model, modelling efficiency and standard error of parameters estimates, were obtained with a simple linear model. Even though the model was able to explain the temporal variability of RECO for all sites, the high level of variability of model parameters estimates within each PFT was not easily generalizable into a single model parameterization. For all PFTs we found a linear relationship between the reference respiration (R0), and maximum leaf area index (LAIMAX). Considering the LAIMAX as possible factor explaining the intersite variability within each PFT, we included it into the model (TPGPP&LAI Model) as an additional predictor possibly accounting for spatial variability of RECO. Finally, model parameters for each PFT were estimated. The new extended model showed higher modelling efficiencies ranging from 0.51 to 0.86, indicating that both abiotic factors, recent GPP and the general site productivity (indicated by LAIMAX) influence RECO. Additional variance might be explained by site history such as disturbance. The TPGPP&LAI Model could be used for up-scaling ecosystem respiration from flux sites to continental and global scale, linking for example the model into the MODIS GPP/NPP data stream.
UR: http://www.disat.unimib.it/telerilevamento/
DE: 0400 BIOGEOSCIENCES
DE: 0430 Computational methods and data processing
DE: 0466 Modeling
SC: Biogeosciences [B]
MN: 2007 Fall Meeting