Author/Authors :
Ji S. نويسنده Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China. , Tong L. نويسنده Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China , Kang S. نويسنده Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China. , Li F. نويسنده College of Agriculture, Guangxi University, Nanning, Guangxi 530005, China. , Lu H. نويسنده Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China. , Du T. نويسنده Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China , Li S. نويسنده Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China. , Ding R. نويسنده Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China
Abstract :
Accurate estimation of leaf stomatal conductance (gs) is important in predicting carbon and water cycles of terrestrial ecosystem. To estimate gs on field-grown soybean and maize under water-stressed condition accurately, a modified optimal stomatal conductance (OSCM) model was established based on the relationship between marginal water cost of carbon gain and soil water content by introducing a water stress factor (f(?v)). f(?v) had same form with that in Jarvis and Ball-Berry-Leuning (BBL) models. The OSCM model was evaluated and compared with the original optimal stomatal conductance (OSC), Jarvis and BBL models by comparing observed and estimated gs of three-year data on soybean and four-year data on maize in an arid region of northwest China. Results show that the OSCM and OSC models were more steady and accurate than the Jarvis and BBL models for estimating gs on soybean and maize at the different years. Moreover, the OSCM model performed better than the OSC model because of considering the effect of water stress. Compared with the OSC, Jarvis and BBL models, the OSCM model improved the accuracy of estimating gs on soybean and maize on average 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 and water-stressed conditions, the OSCM model also performed better than the OSC, Jarvis and BBL models. Under water-stressed condition, only the OSCM model is recommended due to its high accuracy, conservative and accessible parameter, which can provide a more accurate and convenient tool in predicting water and carbon fluxes of terrestrial ecosystem in the arid area.
