1 / 18

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.

odele
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related