A modified optimal stomatal conductance model under water-stressed condition

Accurate estimation of leaf stomatal conductance (gs) is important in predicting carbon andwater cycles of terrestrial ecosystem. To estimate gs on field-grown soybean and maize underwater-stressed condition accurately, a modified optimal stomatal conductance (OSCM) modelwas established based on the relationship between marginal water cost of carbon gain and soilwater content by introducing a water stress factor (f(θv)). f(θv) had same form with that inJarvis and Ball-Berry-Leuning (BBL) models. The OSCM model was evaluated and comparedwith the original optimal stomatal conductance (OSC), Jarvis and BBL models by comparingobserved and estimated gs of three-year data on soybean and four-year data on maize in anarid region of northwest China. Results show that the OSCM and OSC models were moresteady and accurate than the Jarvis and BBL models for estimating gs on soybean and maize atthe different years. Moreover, the OSCM model performed better than the OSC modelbecause of considering the effect of water stress. Compared with the OSC, Jarvis and BBLmodels, the OSCM model improved the accuracy of estimating gs on soybean and maize onaverage by 7%, 25% and 35% and reduced the RMSE by 19%, 56% and 43%, respectively.As for estimating diurnal change of gs on soybean and maize under both well-watered andwater-stressed conditions, the OSCM model also performed better than the OSC, Jarvis andBBL models. Under water-stressed condition, only the OSCM model is recommended due toits high accuracy, conservative and accessible parameter, which can provide a more accurateand convenient tool in predicting water and carbon fluxes of terrestrial ecosystem in the aridarea.