Title of article :
Evapotranspiraton estimation based on scaling up from leaf stomatal conductance to canopy conductance
Author/Authors :
Baozhong Zhang، نويسنده , , Yu Liu، نويسنده , , Di Xu، نويسنده , , Jiabing Cai، نويسنده , , Fusheng Li، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Abstract :
Evapotranspiraton (ET) estimation based on scaling up from leaf stomatal conductance (gs) to canopy conductance (gc) is important in improving effective use and evaluation of agricultural water resources. Taking a summer maize field in north China as an example, after the response of gs to main environmental factors was analyzed based on the measured value, the Jarvis model for gs was established and calibrated. Then the weighted integration model (WI model) was established on the basis of weighted model (W model) after considering the difference of intercept diffuse radiation by shaded leaves in different canopy heights and nonlinear relationship between gs and the photosynthetically active radiation (PAR) to improve gc estimation for shaded leaves using integration equation. Meanwhile the estimation accuracy of W and WI models for gc was compared, and then field ET was estimated using the Penman–Monteith equation. Results indicate that the variation of gs was similar to that of PAR and the Jarvis model could better express the response of gs to PAR, vapour pressure deficit and air temperature. Compared to the W model, WI model could effectively improve the estimation accuracy of gc, with the relative error of 4.4%. Penman–Monteith equation overestimated λET by 9.4% using the estimated gc by the W model, but underestimated λET by 2.3% using the estimated gc by the WI model. Therefore, Penman–Monteith equation can estimate maize field ET using the estimated gc by WI model in the region.
Keywords :
Scaling up , Canopy conductance (gc) , Leaf stomatal conductance (gs) , Maize , Evapotranspiraton (ET) , Photosynthetically active radiation (PAR)
Journal title :
Agricultural and Forest Meteorology
Journal title :
Agricultural and Forest Meteorology