Estimation of maize evapotranspiration in semi-humid regions of northern China using Penman-Monteith model and segmentally optimized Jarvis model.

• The canopy resistance was significantly correlated with R n , while it was correlated with LAI, θ and VPD. • The accuracy of estimated maize canopy resistance was improved by considering LAI threshold segmentation method. • The performance of Differential Evolution Algorithm was better than Genetic Algorithm. Accurate estimation of maize evapotranspiration (ET) is of great significance for the improvement of crop water use efficiency and precision irrigation. The Penman-Monteith model (P-M) has been widely used to simulate crop ET. In the P-M model, the estimation accuracy of canopy resistance (r c) has a direct impact on ET. In this study, based on the eddy covariance system, large-scale lysimeter and meteorological station data from three sites (Yucheng, Yangling and Shangqiu) in semi-humid regions of northern China, the P-M model was applied to obtain canopy resistance (r c-PM) and correlation significances between r c-PM and different impact factors (R n net radiation, T temperature, VPD saturated vapor pressure difference, θ soil moisture content, LAI leaf area index) were analysed. The whole growth period of maize was divided according to different LAI thresholds (0.1, 0.5, 1.0, 1.5, 2.0 and 3.0 m2 m−2). The Genetic Algorithms (GA) and Differential Evolution (DE) algorithms were used to optimize the empirical parameters of the Jarvis model, and the P-M model was applied to estimate ET under different LAI thresholds at the three stations. The correlation significances of r c-PM with different influencing factors followed the order R n > LAI > θ >VPD >T, and it was extremely significant with R n (P < 0.01) and significant with LAI and θ (P < 0.05). The GA and DE algorithm optimization results showed that the calculation accuracy of r c was highest when LAI=0.5 m2 m−2 at Yucheng station, with R2 of 0.80 and 0.81, respectively, and when LAI=1.0 m2 m−2, and the accuracy of r c was highest at Yangling station, with R2 of 0.87 and 0.89, respectively, and when LAI = 1.0 m2 m−2, and the accuracy of r c was highest at Shangqiu station, with R2 of 0.84 and 0.84, respectively. Combined with P-M model to calculate maize ET under different LAI thresholds, the simulation accuracy of ET was best when LAI = 0.5 m2 m−2 at Yucheng station, with averege R2 of 0.85, the order of simulation ET accuracy was: 0.5 >1.0 >1.5 >2.0 >3.0 >0.1 m2 m−2. When LAI = 1.0 m2 m−2, and the accuracy of maize ET was highest at Yangling and Shangqiu stations, with averege R2 of 0.83 and 0.85, respectively, the order of simulation ET accuracy was: 1.0>0.5>1.5>2.0>3.0>0.1 m2 m−2. ET accuracy calculated by the DE optimization algorithm was better than that of the GA optimization algorithm, with R2 of 0.40–0.84 and 0.58–0.86, respectively. This study suggests that the algorithm is of great importance to optimize the empirical parameters of the Jarvis model, of which DE optimization algorithm is recommended to simulate maize ET in semi-humid regions of northern China. [ABSTRACT FROM AUTHOR]