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Ann. Geophys., 23, 291-304, 2005
www.ann-geophys.net/23/291/2005/
© European Geosciences Union 2005
Transpiration and CO2 fluxes of a pine forest: modelling the undergrowth effect
V. Rivalland1, J.-Ch. Calvet1, P. Berbigier2, Y. Brunet2, and A. Granier3
1Météo-France/CNRM, 42 Av. Coriolis, F-31057 Toulouse Cedex 1, France
2INRA/Bioclimatologie, Domaine de la Grande Ferrade, F-33883 Villenave d’Ornon Cedex, France
3INRA/Ecologie et Ecophysiologie Forestière, F-54280 Champenoux, France
Abstract. A modelling study is performed in order to quantify the
relative effect of allowing for the physiological properties of an
undergrowth grass sward on total canopy water and carbon fluxes of the
Le-Bray forest (Les-Landes, South-western France). The Le-Bray forest
consists of maritime pine and an herbaceous undergrowth (purple moor-grass),
which is characterised by a low stomatal control of transpiration, in
contrast to maritime pine. A CO2-responsive land surface model is used
that includes responses of woody and herbaceous species to water stress. An
attempt is made to represent the properties of the undergrowth vegetation in
the land surface model Interactions between Soil, Biosphere, and Atmosphere,
CO2-responsive, ISBA-A-gs. The new adjustment allows for a fairly
different environmental response between the forest canopy and the
understory in a simple manner. The model's simulations are compared with
long term (1997 and 1998) micro-meteorological measurements over the Le-Bray
site. The fluxes of energy, water and CO2, are simulated with and
without the improved representation of the undergrowth vegetation, and the
two simulations are compared with the observations. Accounting for the
undergrowth permits one to improve the model's scores. A simple sensitivity
experiment shows the behaviour of the model in response to climate change
conditions, and the understory effect on the water balance and carbon
storage of the forest. Accounting for the distinct characteristics of the
undergrowth has a substantial and positive effect on the model accuracy and
leads to a different response to climate change scenarios.
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