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A. Mäkelä 1 , M. Pulkkinen 1 , P. Kolari 1 , F. Lagergren 2 , P. Berbigier 3 ,

An empirical model of stand GPP with LUE approach: analysis of eddy covariance data at several contrasting sites. A. Mäkelä 1 , M. Pulkkinen 1 , P. Kolari 1 , F. Lagergren 2 , P. Berbigier 3 , A. Lindroth 2 , D. Loustau 3 , E. Nikinmaa 1 , T.Vesala 4 & P. Hari 1.

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A. Mäkelä 1 , M. Pulkkinen 1 , P. Kolari 1 , F. Lagergren 2 , P. Berbigier 3 ,

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  1. An empirical model of stand GPP with LUE approach: analysis of eddy covariance data at several contrasting sites A. Mäkelä1, M. Pulkkinen1, P. Kolari1, F. Lagergren2, P. Berbigier3, A. Lindroth2, D. Loustau3, E. Nikinmaa1, T.Vesala4 & P. Hari1 1 Department of Forest Ecology, University of Helsinki, Finland 2Physical Geography and Ecosystems Analysis, Geobiosphere Center, Lund University, Sweden 3 INRA EPHYSE, France 4 Division of Atmospheric Sciences, Department of Physical Sciences, University of Helsinki, Finland

  2. Photosynthesis • SPP – a detailed process model using half-hourly weather data • Empirical model – daily weather data: APAR, T, VPD • Super Simple Model – annual GPP Mäkelä et al. 2006, Agric. For. Meteor. 139:382-398 Mäkelä et al. in press, GCB under development, MereGrowth

  3. Daily light use efficiency (LUE) model where β = LUE at optimal conditions Φk = PAR absorbed by canopy during day k fi, k = modifying factors accounting for suboptimal conditions in day k, fi,k [0, 1] ek = random error in day k Actual LUE in day k: β fL, k fS, k fD, k fW, k

  4. Daily LUE model: modifiers Light: Temperature (state of acclimation):

  5. Daily LUE model: modifiers VPD: Soil water (relative extractable water):

  6. Estimation data Sites • Sodankylä, Finland, 2001-2002 • Scots pine, 50-80 yr, LAI 4.0 • Hyytiälä, Finland, 2001-2003 • Scots pine, 40 yr, LAI 7.0 • Norunda, Sweden, 1995-2002 • Scots pine & Norway spruce, 100 yr, LAI 11.7 • Tharandt, Germany, 2001-2003 • Norway spruce, 140 yr, LAI 22.8 • Bray, France, 2001-2002 • maritime pine, 30 yr, LAI 4.0 Variables GPPk as a function of Tk (→ TERk) and eddy covariance NEEk: ecosystem GPPk Φkas a constant fraction of above-canopy PARk : canopy Φk

  7. Parameter estimation • For each year in each site → site-year-specific models • Over all the years in each site → site-specific models • Over all the years and sites → whole-data model • Over all the years and sites with a separate LUE parameter β • for each site → varying-LUE model Results Soil water modifier improved the fit significantly only in very few site-year combinations → the following results are from the models with light, temperature and VPD modifiers

  8. Parameter estimates are correlated within each site as well as across sites: a "global" parameter set could perhaps be found

  9. Test with independent data Data • NOBS, Manitoba, Canada, 2000-2002 • black spruce, 160 yr, LAI 10.1 • moist, poor site with paludified areas in the vicinity • Metolius, Oregon, USA, 2002-2004 • ponderosa pine, 60 yr, LAI 8.0 • dry, sandy site known for measurements of hydraulic limitation Test Compare the measured daily GPP to the GPP predicted with (i) the whole-data model (ii) the varying-LUE model with a re-estimated LUE parameter β

  10. Discussion & Conclusions (but presentation continues) • A simple model with APAR, temperature and VPD as input could explain a major part of the day-to-day variation in the GPP of boreal and temperate coniferous canopies • The maximum LUE was found to vary between sites • influential factors omitted or mis-represented in the model: • foliar nitrogen, ground floor vegetation, estimation of APAR • Some between-years variation in the GPP remained uncaptured in each site • year-to-year variation in LAI • estimation of GPP from eddy covariance NEE • Against expectation, soil water was not an important explanatory factor • soil water effect possibly embedded in the VPD effect

  11. Surprising finding by Annikki M. Measured GPP: eddy covariance GPP, mean of yearly totals Slope ≈ 0.45 ΦTOT: fAPAR times growing season sum of above-canopy PAR, mean of yearly totals • Estimates of site-specific LUE parameters β: • for the European sites taken from the fitting of the variable-LUE model • for the Ameriflux sites estimated with linear regression

  12. A closer look at GPPtot / ( Φtot) ≈ 0 ≈ 1 APAR-weighted mean of the daily product of the modifiers

  13. Additional eddy flux data At the moment 5 sites, 18 site-years These additional data & original estimation and test data make altogether 42 site-years

  14. We are still happy.

  15. Site-specific LUE parameters β vs. foliar nitrogen

  16. Potential usage of the ”super-simple” model: determine site-specific LUE from eddy covariance measurements and predict the future growing-season GPP with predicted growing season APAR

  17. Even more eddy flux data Still 3 more sites to be included in the analysis (as well as 6 more years in Hyytiälä), 17 site-years All the data will finally make altogether 59 site-years

  18. No changes in the degree of happiness.

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