2011
DOI: 10.1111/j.1365-2486.2010.02375.x
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Reconciling the optimal and empirical approaches to modelling stomatal conductance

Abstract: Models of vegetation function are widely used to predict the effects of climate change on carbon, water and nutrient cycles of terrestrial ecosystems, and their feedbacks to climate. Stomatal conductance, the process that governs plant water use and carbon uptake, is fundamental to such models. In this paper, we reconcile two long-standing theories of stomatal conductance. The empirical approach, which is most commonly used in vegetation models, is phenomenological, based on experimental observations of stomat… Show more

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Cited by 1,017 publications
(1,239 citation statements)
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References 84 publications
(119 reference statements)
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“…Mercado et al, 2011), and inclusion of a full representation of a coupled carbon-nitrogen cycle in JULES (Zaehle et al, 2010). Furthermore, it is desirable to test the effects of adding height competition into the vegetation dynamics module of JULES, in order to add ecological succession modelling (Smith et al, 2001;Moorecroft et al, 2001), as well as assess the impacts of improved representation of stomatal conductance (Medlyn et al, 2011;Kala et al, 2015) and plant hydraulics (Sperry et al, 2016) on simulated land carbon and water cycles couplings to climate. The latter could extend as far as testing any hormonal signalling in the hydraulic linkages between soil moisture and stomata response, an effect well known by the physiological community but heretofore never tested in a full large-scale gridded land surface model .…”
Section: Applicationsmentioning
confidence: 99%
“…Mercado et al, 2011), and inclusion of a full representation of a coupled carbon-nitrogen cycle in JULES (Zaehle et al, 2010). Furthermore, it is desirable to test the effects of adding height competition into the vegetation dynamics module of JULES, in order to add ecological succession modelling (Smith et al, 2001;Moorecroft et al, 2001), as well as assess the impacts of improved representation of stomatal conductance (Medlyn et al, 2011;Kala et al, 2015) and plant hydraulics (Sperry et al, 2016) on simulated land carbon and water cycles couplings to climate. The latter could extend as far as testing any hormonal signalling in the hydraulic linkages between soil moisture and stomata response, an effect well known by the physiological community but heretofore never tested in a full large-scale gridded land surface model .…”
Section: Applicationsmentioning
confidence: 99%
“…For instance, the optimal leaf size can be predicted using a mass-energy balance equation (Givnish 1978), leaf nitrogen content and the maximal carboxylation capacity as a function of radiation and temperature (Maire et al 2012b;Prentice et al 2014) as well as stomatal conductance as a function of the vapour pressure deficit (Medlyn et al 2011). As such, the calibration of trait-environment relationship in Maxent and Traitspace models may be derived from this theoretical approach.…”
Section: From Empirical To Predictive Approachmentioning
confidence: 99%
“…However, D is not really independent of ecosystem properties; it is controlled by evapotranspiration and the dynamics of the planetary boundary layer, which in turn are determined by energy exchanges at the land surface as well as by conditions in the free troposphere (Raupach, 1995). Furthermore, stomatal optimization theory (Medlyn et al, 2011) implies a functional relationship between D and c i /c a :…”
Section: Theoretical Derivation Of a (Level 1) Wue Modelmentioning
confidence: 99%
“…There is some evidence that ξ varies among species (Medlyn et al, 2011) and with leaf or soil water potential (Manzoni et al, 2011), but based on an analysis of available data from drying experiments, the response of c i /c a to soil moisture appear to be relatively slight compared with its response to D (Zhou et al, 2012). Accordingly, we do not consider variation in ξ when estimating D. This simplification can be justified on two further grounds.…”
Section: Theoretical Derivation Of a (Level 1) Wue Modelmentioning
confidence: 99%