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摘要
Crop evapotranspiration (ET) is an important basis for irrigation management, and is often estimated using the Penman-Monteith model (P-M model). In the P-M model, the calculation of canopy resistance directly affects the accuracy of estimated ET. The field experiments about maize for seed production with film-mulching were conducted in 2013 and 2014 in the arid region of northwest China, and the measured canopy resistance (r(c)(PM)) was obtained by the re-arranged P-M model using the observed evapotranspiration by eddy covariance (ETEC) and meteorological data in 2013. The BP neural network method was used to analyze the sensitivity of r(c)(PM) to different affecting factors (R-n net radiation, T air temperature, VPD vapor pressure deficit, theta oil moisture content, LAI leaf area index) to determine the input factors in Jarvis model of canopy resistance (Jarvis model). Thus the estimated canopy resistance (r(cx)) in 2014 using the Jarvis model with the parameters fitted by the segmented method according to different LAI thresholds was applied into P-M model to estimate ET in 2014, and then the estimated ET was compared with ETEC. to obtain the best segmented method based on LAI threshold. Results showed that the sensitivity of r(c)(PM) to different affecting factors was in the order of R-n, LAI, theta, VPD and T, which were taken as the input factors of Jarvis model. Fitting the parameters in Jarvis model according to LAI thresholds can improve the accuracies of r(cx) and estimated ET, and the parameters in Jarvis model fitted by the segmented method using the LAI threshold of 0.5 m(2) m(-2) can effectively improve the accuracy of ET estimation by P-M model in the whole growing period, with the determination coefficient (R-2), root mean square error (RMSE), Akaike information criterion (AIC) and modified affinity index (d) between the estimated and observed ET of 0.83, 0.77 mm d(-1), -26.97 and 0.83, respectively.
