首页> 外文期刊>Agricultural Water Management >Applying segmented Jarvis canopy resistance into Penman-Monteith model improves the accuracy of estimated evapotranspiration in maize for seed production with film-mulching in arid area
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Applying segmented Jarvis canopy resistance into Penman-Monteith model improves the accuracy of estimated evapotranspiration in maize for seed production with film-mulching in arid area

机译:在Penman-Monteith模型中应用分段的Jarvis冠层抗性可提高在干旱地区进行地膜覆盖的玉米估计蒸散量的准确性,以生产种子

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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. (C) 2016 Elsevier B.V. All rights reserved.
机译:作物蒸散量(ET)是灌溉管理的重要基础,通常使用Penman-Monteith模型(P-M模型)进行估算。在P-M模型中,冠层阻力的计算直接影响估算的ET的准确性。于2013年和2014年在中国西北干旱地区进行了玉米地膜覆盖制种的田间试验,通过重新排列的PM模型,使用重新排列的PM模型获得了测得的冠层抗性(r(c)(PM))利用涡度协方差(ETEC)和气象数据观测到的蒸散量。使用BP神经网络方法分析了r(c)(PM)对不同影响因素(Rn净辐射,T气温,VPD蒸气压)的敏感性赤字,θ油水分含量,LAI叶面积指数)来确定Jarvis模型(抗树冠)中的输入因子。因此,在2014年使用Jarvis模型估计的树冠阻力(r(cx)),并根据不同的LAI阈值采用分段方法拟合的参数,将其应用于PM模型中以估计2014年的ET,然后将估计的ET与ETEC进行比较。基于LAI阈值获得最佳分割方法结果表明,r(c)(PM)对不同影响因子的敏感性按R-n,LAI,θ,VPD和T的顺序排列,这被作为Jarvis模型的输入因子。根据LAI阈值拟合Jarvis模型中的参数可以提高r(cx)和估计的ET的准确性,并且使用分段方法使用0.5 m(2)m(-2)的LAI阈值拟合Jarvis模型中的参数可以通过确定模型的确定系数(R-2),均方根误差(RMSE),Akaike信息准则(AIC)和修改后的亲和力指数(d),有效地提高了PM模型在整个生育期的ET估算精度。并观察到ET分别为0.83、0.77 mm d(-1),-26.97和0.83。 (C)2016 Elsevier B.V.保留所有权利。

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