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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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slide1

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

photosynthesis
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

slide3

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

slide4

Daily LUE model: modifiers

Light:

Temperature (state of acclimation):

slide5

Daily LUE model: modifiers

VPD:

Soil water (relative extractable water):

slide6

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

slide7

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

slide8

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

slide9

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

slide10

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
slide11

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
slide12

A closer look at GPPtot / ( Φtot)

≈ 0

≈ 1

APAR-weighted mean of the daily product of the modifiers

slide13

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

slide16

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

slide17

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

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