AustralieFrV1, PascalFrancis, Checkpoint, bibRecord, 001D32

Assessing parameter variability in a photosynthesis model within and between plant functional types using global Fluxnet eddy covariance data

Identifieur interne : 001D32 ( PascalFrancis/Checkpoint ); précédent : 001D31; suivant : 001D33

Assessing parameter variability in a photosynthesis model within and between plant functional types using global Fluxnet eddy covariance data

Auteurs : M. Groenendijk [Pays-Bas] ; A. J. Dolman [Pays-Bas] ; M. K. Van Der Molen [Pays-Bas] ; R. Leuning [Australie] ; A. Arneth [Suède] ; N. Delpierre [France] ; J. H. C. Gash [Pays-Bas, Royaume-Uni] ; A. Lindroth [Suède] ; A. D. Richardson [États-Unis] ; H. Verbeeck [Belgique] ; G. Wohlfahrt [Autriche]

Source :

Agricultural and forest meteorology : (Print) [ 0168-1923 ] ; 2011.

RBID : Pascal:11-0032158

Descripteurs français

Pascal (Inist)
- Paramètre, Variabilité, Photosynthèse, Modélisation, Utilisation, Méthode covariance turbulence, Donnée, Transpiration, Biométéorologie, Modèle, Mathématiques appliquées, <<>>.

English descriptors

KwdEn :
- Applied mathematics, Biometeorology, Data, Eddy covariance method, Modeling, Models, Parameter, Photosynthesis, Transpiration, Use, Variability.

Abstract

The vegetation component in climate models has advanced since the late 1960s from a uniform prescription of surface parameters to plant functional types (PFTs). PFTs are used in global land-surface models to provide parameter values for every model grid cell. With a simple photosynthesis model we derive parameters for all site years within the Fluxnet eddy covariance data set. We compare the model parameters within and between PFTs and statistically group the sites. Fluxnet data is used to validate the photosynthesis model parameter variation within a PFT classification. Our major result is that model parameters appear more variable than assumed in PFTs. Simulated fluxes are of higher quality when model parameters of individual sites or site years are used. A simplification with less variation in model parameters results in poorer simulations. This indicates that a PFT classification introduces uncertainty in the variation of the photosynthesis and transpiration fluxes. Statistically derived groups of sites with comparable model parameters do not share common vegetation types or climates. A simple PFT classification does not reflect the real photosynthesis and transpiration variation. Although site year parameters give the best predictions, the parameters are generally too specific to be used in a global study. The site year parameters can be further used to explore the possibilities of alternative classification schemes.

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">The vegetation component in climate models has advanced since the late 1960s from a uniform prescription of surface parameters to plant functional types (PFTs). PFTs are used in global land-surface models to provide parameter values for every model grid cell. With a simple photosynthesis model we derive parameters for all site years within the Fluxnet eddy covariance data set. We compare the model parameters within and between PFTs and statistically group the sites. Fluxnet data is used to validate the photosynthesis model parameter variation within a PFT classification. Our major result is that model parameters appear more variable than assumed in PFTs. Simulated fluxes are of higher quality when model parameters of individual sites or site years are used. A simplification with less variation in model parameters results in poorer simulations. This indicates that a PFT classification introduces uncertainty in the variation of the photosynthesis and transpiration fluxes. Statistically derived groups of sites with comparable model parameters do not share common vegetation types or climates. A simple PFT classification does not reflect the real photosynthesis and transpiration variation. Although site year parameters give the best predictions, the parameters are generally too specific to be used in a global study. The site year parameters can be further used to explore the possibilities of alternative classification schemes.</div>
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<sZ>9 aut.</sZ>
</fA14>
<fA14 i1="07"><s1>Laboratory of Plant Ecology, Ghent University, Coupure Links 653</s1>
<s2>9000 Ghent</s2>
<s3>BEL</s3>
<sZ>10 aut.</sZ>
</fA14>
<fA14 i1="08"><s1>Institut für Ökologie, Universität Innsbruck</s1>
<s3>AUT</s3>
<sZ>11 aut.</sZ>
</fA14>
<fA20><s1>22-38</s1>
</fA20>
<fA21><s1>2011</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA43 i1="01"><s1>INIST</s1>
<s2>11784</s2>
<s5>354000194934860030</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2011 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>2 p.3/4</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>11-0032158</s0>
</fA47>
<fA60><s1>P</s1>
<s3>PR</s3>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Agricultural and forest meteorology : (Print)</s0>
</fA64>
<fA66 i1="01"><s0>GBR</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>The vegetation component in climate models has advanced since the late 1960s from a uniform prescription of surface parameters to plant functional types (PFTs). PFTs are used in global land-surface models to provide parameter values for every model grid cell. With a simple photosynthesis model we derive parameters for all site years within the Fluxnet eddy covariance data set. We compare the model parameters within and between PFTs and statistically group the sites. Fluxnet data is used to validate the photosynthesis model parameter variation within a PFT classification. Our major result is that model parameters appear more variable than assumed in PFTs. Simulated fluxes are of higher quality when model parameters of individual sites or site years are used. A simplification with less variation in model parameters results in poorer simulations. This indicates that a PFT classification introduces uncertainty in the variation of the photosynthesis and transpiration fluxes. Statistically derived groups of sites with comparable model parameters do not share common vegetation types or climates. A simple PFT classification does not reflect the real photosynthesis and transpiration variation. Although site year parameters give the best predictions, the parameters are generally too specific to be used in a global study. The site year parameters can be further used to explore the possibilities of alternative classification schemes.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>002A32C03</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Paramètre</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Parameter</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Parámetro</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Variabilité</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Variability</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Variabilidad</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Photosynthèse</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Photosynthesis</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Fotosíntesis</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Modeling</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Modelización</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Utilisation</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Use</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Uso</s0>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Méthode covariance turbulence</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Eddy covariance method</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Método covariancia turbulencia</s0>
<s5>06</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Donnée</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Data</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Dato</s0>
<s5>07</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Transpiration</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Transpiration</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Transpiración</s0>
<s5>08</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Biométéorologie</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Biometeorology</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Biometeorología</s0>
<s5>09</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Modèle</s0>
<s5>28</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Models</s0>
<s5>28</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Modelo</s0>
<s5>28</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Mathématiques appliquées</s0>
<s5>29</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Applied mathematics</s0>
<s5>29</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Matemáticas aplicadas</s0>
<s5>29</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0><<>></s0>
<s4>INC</s4>
<s5>68</s5>
</fC03>
<fC07 i1="01" i2="X" l="FRE"><s0>Méthode statistique</s0>
<s5>33</s5>
</fC07>
<fC07 i1="01" i2="X" l="ENG"><s0>Statistical method</s0>
<s5>33</s5>
</fC07>
<fC07 i1="01" i2="X" l="SPA"><s0>Método estadístico</s0>
<s5>33</s5>
</fC07>
<fN21><s1>024</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
<affiliations><list><country><li>Australie</li>
<li>Autriche</li>
<li>Belgique</li>
<li>France</li>
<li>Pays-Bas</li>
<li>Royaume-Uni</li>
<li>Suède</li>
<li>États-Unis</li>
</country>
<region><li>Massachusetts</li>
<li>Province de Flandre-Orientale</li>
<li>Île-de-France</li>
</region>
<settlement><li>Cambridge (Massachusetts)</li>
<li>Gand</li>
<li>Orsay</li>
</settlement>
<orgName><li>Université Harvard</li>
<li>Université Paris-Sud</li>
<li>Université de Gand</li>
</orgName>
</list>
<tree><country name="Pays-Bas"><noRegion><name sortKey="Groenendijk, M" sort="Groenendijk, M" uniqKey="Groenendijk M" first="M." last="Groenendijk">M. Groenendijk</name>
</noRegion>
<name sortKey="Dolman, A J" sort="Dolman, A J" uniqKey="Dolman A" first="A. J." last="Dolman">A. J. Dolman</name>
<name sortKey="Gash, J H C" sort="Gash, J H C" uniqKey="Gash J" first="J. H. C." last="Gash">J. H. C. Gash</name>
<name sortKey="Van Der Molen, M K" sort="Van Der Molen, M K" uniqKey="Van Der Molen M" first="M. K." last="Van Der Molen">M. K. Van Der Molen</name>
</country>
<country name="Australie"><noRegion><name sortKey="Leuning, R" sort="Leuning, R" uniqKey="Leuning R" first="R." last="Leuning">R. Leuning</name>
</noRegion>
</country>
<country name="Suède"><noRegion><name sortKey="Arneth, A" sort="Arneth, A" uniqKey="Arneth A" first="A." last="Arneth">A. Arneth</name>
</noRegion>
<name sortKey="Lindroth, A" sort="Lindroth, A" uniqKey="Lindroth A" first="A." last="Lindroth">A. Lindroth</name>
</country>
<country name="France"><region name="Île-de-France"><name sortKey="Delpierre, N" sort="Delpierre, N" uniqKey="Delpierre N" first="N." last="Delpierre">N. Delpierre</name>
</region>
</country>
<country name="Royaume-Uni"><noRegion><name sortKey="Gash, J H C" sort="Gash, J H C" uniqKey="Gash J" first="J. H. C." last="Gash">J. H. C. Gash</name>
</noRegion>
</country>
<country name="États-Unis"><region name="Massachusetts"><name sortKey="Richardson, A D" sort="Richardson, A D" uniqKey="Richardson A" first="A. D." last="Richardson">A. D. Richardson</name>
</region>
</country>
<country name="Belgique"><region name="Province de Flandre-Orientale"><name sortKey="Verbeeck, H" sort="Verbeeck, H" uniqKey="Verbeeck H" first="H." last="Verbeeck">H. Verbeeck</name>
</region>
</country>
<country name="Autriche"><noRegion><name sortKey="Wohlfahrt, G" sort="Wohlfahrt, G" uniqKey="Wohlfahrt G" first="G." last="Wohlfahrt">G. Wohlfahrt</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>

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	Serveur d'exploration sur les relations entre la France et l'Australie
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Assessing parameter variability in a photosynthesis model within and between plant functional types using global Fluxnet eddy covariance data

Assessing parameter variability in a photosynthesis model within and between plant functional types using global Fluxnet eddy covariance data

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