J.-C. Calvet Agricultural and Forest Meteorology 103 2000 229–247 243
Fig. 11. Comparison between measurements boxes and diamonds performed over the MUREX fallow and simulations line of the ISBA–A–g
s
model Calvet et al., 1998, accounting for the offensive strategy described in Fig. 8 the same numerical values are used. A Leaf area index and B extractable soil water content.
during this period. A possible explanation may be that the planimetric method employed to estimate LAI
fails when many leaves become senescent, because of the difficulty to make the difference between ‘green’
leaf-parts and senescent ones.
7. Discussion
In this study, two types of data are employed: cham- ber or cuvette measurements at the leaf scale, and
micrometeorological field measurements characteris- ing a vegetation canopy. One may wonder whether
chamber data are of equal value to field data, in terms of accuracy and representativeness. Although the em-
ployed datasets represent the current state of the art, both chamber and field data are subjected to measure-
ments uncertainties. As shown in this study, both A–g
s
and Jarvis parameters may depend on the interaction between roots and the soil substrate. This effect may
also be at stake during soil depletion through interac- tions between soil moisture, soil resistance to rooting,
and hormonal control of rooting. The field-adaptation which is often observed e.g. Tardieu and Simonneau,
1998 may be due to this kind of interaction. However, since obstacles to rooting may also occur in the field,
there is no fundamental difference between chamber and field data.
Documenting diversity of local observations may seem contradictory with the objective of improving
more general SVAT models. However, it must be un- derlined that the meteorological question considered
in this study is not whether a Jarvis or A–g
s
model can be parameterised for any given situation, but whether
the parameters of these models can be estimated us- ing guidelines gained from observable features of the
soil–plant–atmosphere system. Figs. 4–8 give a first response to this question by showing the effect of soil
moisture on the parameters of commonly used param- eterisations. Of course, there may be an uncertainty
in parameter selection. However, the employed A–g
s
model Jacobs et al., 1996 is a simplified parameter- isation using a small number of parameters. Among
these parameters, few are expected to be strongly driven by soil water stress. As shown in Appendix
A, the basic variables employed to describe net as- similation and stomatal conductance are maximum
photosynthesis A
m
Eq. A.1 and intercellular CO
2
concentration C
i
Eq. A.2. These variables depend mainly on three parameters: g
m
, D
max
, and f . As
244 J.-C. Calvet Agricultural and Forest Meteorology 103 2000 229–247
Table 6 Comparison of the g
m
–D
max
and g
m
–f analyses in terms of average RMS error on the stomatal conductance g
′ s
of the micrometeorological measurements of MUREX Calvet et al., 1999 and the Soybean field Olioso et al., 1996
Variables RMS error on MUREX g
′ s
mm s
− 1
RMS error on the soybean field g
′ s
mm s
− 1
g
m
–D
max
1.2 0.4
g
m
–f 1.3
0.7
shown in Appendix A, the f parameter represents
a potential value of the C
i
C
s
ratio. In this study, it was assumed that g
m
and D
max
may vary according to soil moisture, while f
is prescribed Table 1. Also, tests were made assuming stress-dependent g
m
and f , with a constant value D
max
= 45 g kg
− 1
. Sim- ilar to the g
m
–D
max
analysis presented in Sections 4 and 5, good correlations were obtained between the
unstressed g
m
and f , and two opposite behaviours
were observed in conditions of stress not shown. However, the g
m
–D
max
analysis is more efficient to describe the observations as shown by Table 6.
In this study, two types of parameterisations are considered: Jarvis and A–g
s
. The Jarvis model is clearly a phenomenological approach which does not
pretend to describe the mechanism of the stomatal re- sponse. Multiplying maximum stomata conductance
by an empirical soil water function is perfectly consis- tent with this approach Noilhan and Planton, 1989.
On the other hand, the A–g
s
model describes the in- teraction between carbon uptake, transpiration and
stomatal aperture in a more mechanistic way. Calvet et al. 1998 employed a phenomenological approach
to describe soil water stress in an A–g
s
model: the un- stressed mesophyll conductance g
∗ m
is multiplied by the normalised extractable soil water. Although there
is a contradiction between incorporating soil water stress via a phenomenological approach in an A–g
s
model, which is mechanistic by nature, this pragmatic solution was chosen because the employed A–g
s
model Jacobs et al., 1996 did not include a represen- tation of soil water stress, and because it was the most
obvious way to do so. Nonetheless, this first order rep- resentation of water stress was very efficient to simu-
late the water balance of the six sites studied by Calvet et al. 1998. Here, an attempt is made to investigate
whether a more mechanistic description of the stressed parameters would be useful. The fact that the soil wa-
ter stress effect presented in Figs. 4–7 is all the more apparent since the model is more mechanistic the
A–g
s
approach seems to be superior to the T
s
-based Jarvis one, and the T
s
-based Jarvis to the T
a
-based one confirms that it is possible to go further into the com-
prehension of the phenomena by using available data. Regarding more mechanistic descriptions of stress,
physiologists have often proposed to use a plant hydraulic model to make a connection to soil water
potential e.g. Tardieu and Simonneau, 1998. Param- eterisations of chemical signalling from roots to leaves
were coupled to this kind of models. Nevertheless, the use of soil water potential ψ is controversial. A
number of past and recent findings indicate that the apparently more mechanistic approach of using a
plant hydraulic model to make a connection to ψ may not be adequate. SVAT models’ intercomparison made
by meteorologists e.g. Mahfouf et al., 1996; Chen et al., 1997 show that models using the notion of soil
ψ do not surpass those employing simple parameter- isations based on bulk soil moisture. In some cases,
these simple models outdo ψ models Calvet et al., 1999. During the past decade, physiologists have
presented results confirming this view. Although ψ models accounting for hormonal drought signal were
developed, the role of water relations and hormonal signals in the plant response to drought are still un-
clear e.g. Socias et al., 1997. In particular, the xylem sap hormonal or pH response to soil water stress may
occur before any change in soil or leaf ψ is detected, and in many cases soil water content is a much more
sensitive indicator of soil dryness effects Turner et al., 1985; Schulze, 1986; Garnier and Berger, 1987; Kopka
et al., 1997; Wilkinson et al., 1998; Ali et al., 1999.
8. Conclusion
