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

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. 139:382-398

Mäkelä et al. in press, GCB

under development, MereGrowth

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

,effffGPP kkW,kD,kS,kL,kk β

Daily LUE model: modifiers

Light:

Temperature (state of acclimation):

1,S

minSf kSmax

k

S

111-kk1-kk TX,XT1

XX τ

0,XmaxS kk 0X

1

1f

kkL

γ

Daily LUE model: modifiers

VPD:

Soil water (relative extractable water):

kDkD eDf κ

1

kW

W11Wf

υ

k

α

υkWα e1Wf kW

1,θθ

θθminWk

PWPFC

PWPk

Estimation data

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

Sites

Variables

GPPk as a function of Tk (→ TERk) and eddy covariance NEEk : ecosystem GPPk

Φk as a constant fraction of above-canopy PARk : canopy Φk

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, 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

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

ΦTOT: fAPAR times growing season sum of above-canopy PAR, mean of yearly totals

Slope ≈ 0.45

A closer look at GPPtot / ( Φtot) β̂

a);fff(WMb

β̂

efffΦ

β̂

β

β̂

)efffΦ β(

β̂

GPP

β̂

GPP41.0

DSL

kk

kk

kk

kk,Dk,Sk,Lk

kk

kkk,Dk,Sk,Lk

kk

kk

tot

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

Site Location Elevation

(m) Dominant species

Age (a)

Hdom

(m)

N (ha-1)

Years of data

Brasschaat, Belgium

51°18'N 4°31'E

16 Pinus sylvestris, Quercus robur

75 19 350 1997-1998, 2000-2002, 2004

Sorø, Denmark

55°29' N 11°36' E

40 Fagus sylvatica, Picea abies, Larix decidua

82 28 330 2005-2006

Zotino, Russia

60°45'N, 89°23'E

90 Pinus sylvestris 200 18 450 1999-2000

Wind River, Washington, USA

45°49'N, -121°57'W

371

Pseudotsuga menziesii, Tsuga heterophylla, Thuja plicata, Taxus brevifolia, Abies amabilis

500 52 430 1999-2004

Teshio, Hokkaido, Japan

45°03'N, 142°06'E

70

Quercus crispula, Betula ermanii, B. platyphylla, Abies sachalinensis, Picea jezoensis

165 24 2002

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

Site Location Elevation

(m) Dominant species

Age (a)

Hdom

(m)

N (ha-1)

Years of data

Abisko River Delta, Sweden

68°21'N 18°47'E

376 Betula pubescens 2005

Renon, Italy

46°35'N 11°26'E

1730 Picea abies, Pinus cembra, Larix decidua

0-180 31 270 1999, 2001-2005

Tumbarumba, New South Wales, Australia

35°39'S 148°09'E

1200 Eucalyptus delegatensis, E. dalrymplean

2001-2005

Hyytiälä, Finland

61°51' N 24°18' E

170 Pinus sylvestris 42 14 1450 1997-2000, 2004-2005

No changes in the degree of happiness.