Methane emissions from subtropical wetlands: An evaluation of the role of data filtering on annual methane budgets
Methane emissions from subtropical wetlands: An evaluation of the role of data filtering on annual methane budgets
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亚热带湿地的甲烷排放：数据过滤对年度甲烷预算作用的评估
DOI：
10.1016/j.agrformet.2022.108972
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发表时间：
2022
影响因子：
6.2
通讯作者：
Oberbauer, S.F.
中科院分区：
文献类型：
--
作者：
Staudhammer, C.L.;Malone, S.L.;Zhao, J.;Yu, Z.;Starr, G.;Oberbauer, S.F.
涡动相关（EC）方法在甲烷（CH4）通量测量中的广泛采用，增加了连续高频CH4数据的可用性。然而，不可靠的数据经常发生在大气稳定、降雨或仪器故障期间，需要在后续分析之前进行过滤。虽然评估二氧化碳的程序已经成熟，但过滤和填补ch4数据的过程研究较少，因为它们的范围和控制还没有得到很好的理解。此外，出版物往往没有说明数据处理和过滤的程序。我们的主要目标是研究常见过滤阈值的影响，并深入了解过滤产生的差距的大小和时间如何影响ch4预算。我们利用了两个淡水湿地在相同气候条件下不同水期的4年数据。我们采用摩擦速度（U*）和信号强度滤波处理来分离位点特异性效应，并评估滤波对随后通过随机森林（RF）填充间隙的影响。我们还测试了使用“无限制预测模型”（使用所有可能的预测因子而不考虑差距）与“限制预测模型”（使用没有缺失值的空白填充预测因子）的结果对预测数据集的敏感性。根据过滤处理的不同，在研究期间有7 - 50%的ch4数据被去除。使用更高的信号强度阈值会引入更多的小间隙。U*过滤产生了小的间隙（主要是夜间），相应的年度预算估计通常与仅根据信号强度过滤的预算估计不同，但具有更高的不确定性，特别是在长水期站点。无论采用何种过滤方法，使用无限制预测因子的RF模型将2至32天的平均ch4通量确定为主要预测因子，而当预测因子受到限制时，热和潜能是最重要的。虽然过滤对ch4预算的影响可能小于预测变量的选择和预处理，但它可以显著影响不确定性，应该在数据管理协议中考虑。
Widespread adoption of eddy covariance (EC) methods for methane (CH4) flux measurement has led to increased availability of continuous high-frequency CH4data. However, unreliable data frequently occur during periods of atmospheric stability, rain or instrument malfunction, requiring filtering prior to subsequent analyses. While procedures for assessing CO2have matured, processes to filter and gap-fill CH4data are less studied, as their range and controls are not as well-understood. Moreover, publications often fail to describe procedures for data processing and filtering. Our primary objective was to study effects of common filtering thresholds and provide insight on how size and timing of gaps produced by filtering affect CH4budgets. We utilized 4 years of data from two freshwater wetlands under the same climate regime but different hydroperiods. We applied friction velocity (U*) and signal strength filtering treatments to isolate site-specific effects and evaluate impacts of filtering on subsequent gap-filling via Random Forests (RF). We also tested sensitivity of results to predictor datasets with an “unrestricted predictors model” (using all possible predictors regardless of gaps), versus a “restricted predictors model” (using gap-filled predictors with no missing values). Depending on filtering treatment, 7 - 50% of CH4data were removed over the study period. Using higher signal strength thresholds introduced more small gaps. U* filtering created small gaps (mostly nighttime), and corresponding annual budget estimates were generally different from those filtered solely on signal strength but with higher uncertainty, especially at the long-hydroperiod site. Regardless of filtering method, RF models using unrestricted predictors identified 2- to 32-day average CH4flux as primary predictors, whereas heat and latent energy were most important when predictors were restricted. Although filtering may have less impact on CH4budgets than selection and pre-processing of predictor variables, it can significantly impact uncertainty and should be considered in data curation protocols.