Abstract Although the eddy covariance (EC) technique provides direct and continuous measurements of evapotranspiration (ET), separate measurement of evaporation (E) and transpiration (T) at the ecosystem level is not possible. For partitioning ET into E and T, high frequency (10 Hz) time series EC observations collected from Apr 2016 to May 2018 over a rainfed alfalfa (Medicago sativa L.) field in central Oklahoma, USA were analyzed using the open source software Fluxpart. Fluxpart partitions ET by examining the correlation (Rqc) between water vapor (q) and carbon dioxide (c) fluxes as prescribed by the Flux Variance Similarity (FVS) partitioning method. Patterns of Rqc and partitioned E and T were consistent with expected trends associated with vegetation dynamics and short-term transient features (i.e., hay harvesting and rainfall events). The Rqc grew stronger with increasing alfalfa leaf area and exhibited a strong anti-correlation (Rqc close to -1) during peak growth when T and photosynthesis (P) were dominant and co-regulated by the leaf stomata. Consequently, a strong linear relationship (R2 = 0.96) was found between monthly midday average values of Rqc and monthly average Moderate Resolution Imaging Spectroradiometer (MODIS)-derived leaf area index (LAIMOD). Decorrelation of q and c or dominance of non-photosynthetic (e.g., E and respiration, R) fluxes resulted in less negative or positive Rqc values during winter, hay harvest, rainy, and nighttime periods. Growing season (Apr-Oct) average T:ET was approximately 0.82 and 0.77 in 2016 and 2017, respectively. Diurnal cycles and temporal variations of leaf-level water use efficiency (WUE, an input of the FVS method) estimates were consistent with the seasonal dynamics of ecosystem WUE, computed from EC-derived gross primary production (GPP) and EC-measured ET. These results validate the performance of the FVS ET partitioning method using high frequency EC data.

中文翻译：

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使用高频涡流协方差数据对雨养苜蓿田的蒸散量进行基于通量方差相似性的划分

摘要 尽管涡度协方差 (EC) 技术提供了对蒸散量 (ET) 的直接和连续测量，但在生态系统水平上单独测量蒸发量 (E) 和蒸腾量 (T) 是不可能的。为了将 ET 划分为 E 和 T，使用开源软件 Fluxpart 分析了 2016 年 4 月至 2018 年 5 月在美国俄克拉荷马州中部雨育苜蓿 (Medicago sativa L.) 田间收集的高频 (10 Hz) 时间序列 EC 观测值。Fluxpart 通过检查水蒸气 (q) 和二氧化碳 (c) 通量之间的相关性 (Rqc) 来对 ET 进行分区，如通量方差相似性 (FVS) 分区方法所规定的那样。Rqc 和分区 E 和 T 的模式与与植被动态和短期瞬态特征（即干草收获和降雨事件）相关的预期趋势一致。Rqc 随着苜蓿叶面积的增加而变得更强，并且当 T 和光合作用 (P) 占主导地位并由叶气孔共同调节时，在峰值生长期间表现出强烈的反相关性（Rqc 接近 -1）。因此，在 Rqc 的月正午平均值和月平均中分辨率成像光谱仪 (MODIS) 导出的叶面积指数 (LAIMOD) 之间发现了很强的线性关系 (R2 = 0.96)。q 和 c 的去相关或非光合作用（例如 E 和呼吸作用，R）通量的优势导致冬季、干草收获、雨天和夜间期间的负或正 Rqc 值较小。2016 年和 2017 年的生长季节（4 月至 10 月）平均 T:ET 分别约为 0.82 和 0.77。叶级水分利用效率（WUE，FVS 方法的输入）估计值与生态系统 WUE 的季节性动态一致，由 EC 衍生的初级生产总值 (GPP) 和 EC 测量的 ET 计算得出。这些结果验证了使用高频 EC 数据的 FVS ET 分区方法的性能。