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 1 Department of Forest Ecology, University of Helsinki, Finland 2 Physical 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

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: Mäkelä et al. in press, GCB under development, MereGrowth

Daily light use efficiency (LUE) model where β = LUE at optimal conditions Φ k = PAR absorbed by canopy during day k f i, k = modifying factors accounting for suboptimal conditions in day k, f i,k  [0, 1] e k = random error in day k Actual LUE in day k: β f L, k f S, k f D, k f W, k

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

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

Estimation data Sodankylä, Finland, Scots pine, yr, LAI 4.0 Hyytiälä, Finland, Scots pine, 40 yr, LAI 7.0 Norunda, Sweden, Scots pine & Norway spruce, 100 yr, LAI 11.7 Tharandt, Germany, Norway spruce, 140 yr, LAI 22.8 Bray, France, maritime pine, 30 yr, LAI 4.0 Sites Variables GPP k as a function of T k (→ TER k ) and eddy covariance NEE k : ecosystem GPP k Φ k as a constant fraction of above-canopy PAR k : canopy Φ k

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 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 Results

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

Test with independent data NOBS, Manitoba, Canada, black spruce, 160 yr, LAI 10.1 moist, poor site with paludified areas in the vicinity Metolius, Oregon, USA, ponderosa pine, 60 yr, LAI 8.0 dry, sandy site known for measurements of hydraulic limitation Data 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

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

Surprising finding by Annikki M. 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 Measured GPP: eddy covariance GPP, mean of yearly totals Φ TOT : fAPAR times growing season sum of above-canopy PAR, mean of yearly totals Slope ≈ 0.45

A closer look at GPP tot / ( Φ tot ) APAR-weighted mean of the daily product of the modifiers ≈ 1 ≈ 0

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

We are still happy.

Site-specific LUE parameters β vs. foliar nitrogen

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

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

No changes in the degree of happiness.