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Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes.

机译:涡流协方差净碳通量缺口填充技术的综合比较。

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We review 15 techniques for estimating missing values of net ecosystem CO< sub>2 exchange (NEE) in eddy covariance time series and evaluate their performance for different artificial gap scenarios based on a set of 10 benchmark datasets from six forested sites in Europe. The goal of gap filling is the reproduction of the NEE time series and hence this present work focuses on estimating missing NEE values, not on editing or the removal of suspect values in these time series due to systematic errors in the measurements (e.g., nighttime flux, advection). The gap filling was examined by generating 50 secondary datasets with artificial gaps (ranging in length from single half-hours to 12 consecutive days) for each benchmark dataset and evaluating the performance with a variety of statistical metrics. The performance of the gap filling varied among sites and depended on the level of aggregation (native half-hourly time step versus daily), long gaps were more difficult to fill than short gaps, and differences among the techniques were more pronounced during the day than at night. The non-linear regression techniques (NLRs), the look-up table (LUT), marginal distribution sampling (MDS), and the semi-parametric model (SPM) generally showed good overall performance. The artificial neural network based techniques (ANNs) were generally, if only slightly, superior to the other techniques. The simple interpolation technique of mean diurnal variation (MDV) showed a moderate but consistent performance. Several sophisticated techniques, the dual unscented Kalman filter (UKF), the multiple imputation method (MIM), the terrestrial biosphere model (BETHY), but also one of the ANNs and one of the NLRs showed high biases which resulted in a low reliability of the annual sums, indicating that additional development might be needed. An uncertainty analysis comparing the estimated random error in the 10 benchmark datasets with the artificial gap residuals suggested that the techniques are already at or very close to the noise limit of the measurements. Based on the techniques and site data examined here, the effect of gap filling on the annual sums of NEE is modest, with most techniques falling within a range of+or-25 g C m-2 year-1.
机译:我们回顾了15种估测涡度协方差时间序列中的净生态系统CO 2 交换(NEE)的缺失值的技术,并基于来自六个森林站点的10个基准数据集,评估了它们在不同人工缺口情景下的性能在欧洲。填补空白的目标是再现NEE时间序列,因此,本工作着重于估计缺失的NEE值,而不是由于测量中的系统误差(例如夜间通量)而在这些时间序列中编辑或删除可疑值。 ,对流)。通过为每个基准数据集生成具有人工间隙(长度从单个半小时到连续12天不等)的50个辅助数据集并使用各种统计指标评估性能来检查间隙填充。空位填充的性能在各个站点之间有所不同,并且取决于聚集的水平(本地半小时时间步长与每天的时间间隔),长空位比短空位更难以填充,并且白天技术之间的差异更加明显。在晚上。非线性回归技术(NLR),查找表(LUT),边际分布采样(MDS)和半参数模型(SPM)通常显示出良好的总体性能。通常,基于人工神经网络的技术(ANN)仅略微优于其他技术。平均日变化(MDV)的简单插值技术显示出中等但一致的性能。几种复杂的技术,如双重无味卡尔曼滤波器(UKF),多重插补方法(MIM),陆地生物圈模型(BETHY),还有一种人工神经网络和一种NLR表现出高偏差,导致可靠性低。年度金额,表明可能需要进行其他开发。将10个基准数据集中的估计随机误差与人工间隙残差进行比较的不确定性分析表明,该技术已经达到或非常接近测量的噪声极限。根据此处检查的技术和站点数据,缺口填充对NEE年度总和的影响不大,大多数技术都在+或-25 g C m-2 year-1范围内。

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