Performance of a new dynamic model for predicting diurnal time courses of stomatal conductance at the leaf level

Abstract

Under natural conditions, plants are subjected to continuous changes of irradiance that drive variations of stomatal conductance to water vapour (g(s)). We propose a dynamic model to predict the temporal response of g(s) at the leaf level using an asymmetric sigmoid function with a unique parameter describing time constants for increasing and decreasing g(s). The model parameters were adjusted to observed data using Approximate Bayesian Computation. We tested the model performance for (1) instant changes of irradiance; or (2) continuous and controlled variations of irradiance simulating diurnal time courses. Compared with the two mostly used steady-state models, our dynamic model described daily time courses of g(s) with a higher accuracy. In particular, it was able to describe the hysteresis of g(s) responses to increasing/decreasing irradiance and the resulting rapid variations of intrinsic water-use efficiency. Compared to the mechanistic model of temporal responses of g(s) by Kirschbaum, Gross & Pearcy, for which time constants were estimated with a large variance, our model estimated time constants with a higher precision. It is expected to improve predictions of water loss and water-use efficiency in higher scale models by using a small number of parameters.