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Evapotranspiration partitioning and its interannual variability over a winter wheat-summer maize rotation system in the North China Plain
Agricultural and Forest Meteorology ( IF 6.2 ) Pub Date : 2021-09-10 , DOI: 10.1016/j.agrformet.2021.108635
Xingyu Hu 1 , Huimin Lei 1
Affiliation  

Partitioning of evapotranspiration (ET) is important for understanding surface-atmosphere interactions, hydrological cycle, and plant water use strategy. Here, we applied seven widely used eddy covariance (EC)-based methods and 15-year EC measurements in a winter wheat-summer maize rotation cropland in North China Plain to partition ET into soil evaporation (E) and plant transpiration (T). Then the two-stage theory of bare soil evaporation was employed to evaluate these partitioning methods. This innovative evaluation approach is particularly suitable for this kind of ecosystem, requires no direct measurements of ET components, and avoids spatial mismatching between the source areas of reference values and EC-based ET. Combining the two-stage theory and the meta-analysis, we found that among seven partitioning methods, only the Transpiration Estimation Algorithm (TEA) (hereafter, N18 method) utilizing a machine learning approach not only simulated the dynamics of 14-day transpiration fraction of ET (T/ET) quite well, but also yielded reliable 14-day and mean growing season magnitudes of T/ET for both crops. Furthermore, we developed a new partitioning method based on the stomatal slope parameter in the optimality-based unified stomatal optimization (USO) model. The newly developed method showed similar performances with N18 method on partitioning ET of both crops at our site. By using the N18 method and the newly developed method, we revealed that the multi-year mean growing season T/ET (± standard deviation) was 0.72 ± 0.03 and 0.77 ± 0.04 for maize and wheat, respectively. For the interannual variability of ET and its components, only T of maize increased significantly during 2005–2019 at our site. Moreover, it was found that E had the highest interannual variability followed by T and then T/ET for both crops.



中文翻译:

华北平原冬小麦-夏玉米轮作系统蒸散分配及其年际变化

蒸散量 (ET) 的划分对于理解地表-大气相互作用、水文循环和植物用水策略很重要。在这里,我们在华北平原的冬小麦-夏玉米轮作农田中应用了七种广泛使用的基于涡度协方差 (EC) 的方法和 15 年的 EC 测量,将 ET 划分为土壤蒸发 (E) 和植物蒸腾作用 (T)。然后采用裸土蒸发的两阶段理论来评估这些分区方法。这种创新的评价方法特别适用于这种生态系统,不需要直接测量ET分量,避免参考值源区与基于EC的ET之间的空间错配。结合两阶段理论和荟萃分析,我们发现在七种划分方法中,只有利用机器学习方法的蒸腾估计算法 (TEA)(以下称为 N18 方法)不仅很好地模拟了 ET (T/ET) 的 14 天蒸腾分数的动态,而且还产生了可靠的 14 天和平均增长两种作物的 T/ET 季节幅度。此外,我们在基于最优性的统一气孔优化 (USO) 模型中基于气孔斜率参数开发了一种新的划分方法。新开发的方法在我们现场对两种作物的 ET 划分方面表现出与 N18 方法相似的性能。通过使用 N18 方法和新开发的方法,我们发现玉米和小麦的多年平均生长季 T/ET(± 标准差)分别为 0.72±0.03 和 0.77±0.04。对于 ET 及其分量的年际变化,2005-2019 年期间,在我们的站点,只有玉米的 T 显着增加。此外,发现 E 具有最高的年际变异性,其次是 T,然后是两种作物的 T/ET。

更新日期:2021-09-12
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