A modified optimal stomatal conductance model under water-stressed condition

Abstract

Accurate estimation of leaf stomatal conductance (gs) is important in predicting carbon and<br />water cycles of terrestrial ecosystem. To estimate gs on field-grown soybean and maize under<br />water-stressed condition accurately, a modified optimal stomatal conductance (OSCM) model<br />was established based on the relationship between marginal water cost of carbon gain and soil<br />water content by introducing a water stress factor (f(θv)). f(θv) had same form with that in<br />Jarvis and Ball-Berry-Leuning (BBL) models. The OSCM model was evaluated and compared<br />with the original optimal stomatal conductance (OSC), Jarvis and BBL models by comparing<br />observed and estimated gs of three-year data on soybean and four-year data on maize in an<br />arid region of northwest China. Results show that the OSCM and OSC models were more<br />steady and accurate than the Jarvis and BBL models for estimating gs on soybean and maize at<br />the different years. Moreover, the OSCM model performed better than the OSC model<br />because of considering the effect of water stress. Compared with the OSC, Jarvis and BBL<br />models, the OSCM model improved the accuracy of estimating gs on soybean and maize on<br />average by 7%, 25% and 35% and reduced the RMSE by 19%, 56% and 43%, respectively.<br />As for estimating diurnal change of gs on soybean and maize under both well-watered and<br />water-stressed conditions, the OSCM model also performed better than the OSC, Jarvis and<br />BBL models. Under water-stressed condition, only the OSCM model is recommended due to<br />its high accuracy, conservative and accessible parameter, which can provide a more accurate<br />and convenient tool in predicting water and carbon fluxes of terrestrial ecosystem in the arid<br />area.