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<p>Although the eddy covariance (EC) technique has been widely adopted to measure the CO<sub>2</sub> and water vapor exchanges between ecosystems and the atmosphere, challenges remain for its applications in mountainous regions. Ideally, the EC technique requires homogeneous canopies and flat underlying surfaces. Over complex terrain, heterogeneity of source/sink areas across wind directions complicates data analysis, particularly quality control (filtering) and gap-filling of nocturnal CO<sub>2</sub> flux data. In this study, we evaluated the performance of nocturnal CO<sub>2</sub> flux filtering in mountainous forests (comparing cases with or without wind direction sector partitioning) and assessed different gap-filling methods for the analyzed data that were measured from the Qingyuan-Ker Towers in Northeast China from January 2020 to December 2022. Results showed that surface roughness varied across wind direction sectors, leading to reduced accuracy in nocturnal CO<sub>2</sub> fluxes filtering if the conventional friction velocity (<i>u</i>*) threshold method (a single threshold for all wind sectors) was applied. Alternative to the conventional method, identifying the <i>u</i>* threshold (<i>u</i><Stack> <sub><i>c</i></sub> <sup>*</sup> </Stack>) based on wind sectors could significantly improve the accuracy of nighttime CO<sub>2</sub> flux filtering. The gaps in the CO<sub>2</sub> flux series of forest ecosystems over complex terrain are filled using four methods: mean diurnal course (MDC), look-up table (LUT), marginal distribution sampling (MDS), and random forest (RF). Biases in the filled nocturnal net ecosystem carbon exchange (NEE) were substantially reduced by 16%, in comparison to the adopted conventional method, following a treatment of data filtering based on wind-sector-specific <i>u</i><Stack> <sub><i>c</i></sub> <sup>*</sup> </Stack> values. In general, the accuracy of all four gap-filling methods decreased with increasing gap length; however, the RF method consistently outperforms the others, yielding more robust and reliable estimates of annual NEE. Through extensive analyses in this study, we recommend identifying <i>u</i><Stack> <sub><i>c</i></sub> <sup>*</sup> </Stack> values for canopies in different directions surrounding a flux tower, with a moving window and a careful selection of gap-filling methods when processing EC data from a mountainous forest. These findings advance methodologies in handling flux data from forest ecosystems over complex terrains.</p>

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Filtering and gap-filling strategies in eddy covariance CO2 flux series of mountainous forests: Comparison based on measurements from Qingyuan-Ker Towers

  • 期刊论文

  • 发表日期：

    2025年12月17日

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  • Tongtong Li,
  • Tian Gao,
  • Dexiong Teng,
  • Zhi Chen,
  • Bai Yang,
  • Xingchang Wang,
  • Changming Zhao,
  • Jinsong Zhang,
  • Hui Huang,
  • Chao Guan,
  • Jiabing Wu,
  • Fengyuan Yu,
  • Jinxin Zhang,
  • Yirong Sun,
  • Shuangtian Li,
  • Xinhua Zhou,
  • Jiaojun Zhu

摘要

Although the eddy covariance (EC) technique has been widely adopted to measure the CO2 and water vapor exchanges between ecosystems and the atmosphere, challenges remain for its applications in mountainous regions. Ideally, the EC technique requires homogeneous canopies and flat underlying surfaces. Over complex terrain, heterogeneity of source/sink areas across wind directions complicates data analysis, particularly quality control (filtering) and gap-filling of nocturnal CO2 flux data. In this study, we evaluated the performance of nocturnal CO2 flux filtering in mountainous forests (comparing cases with or without wind direction sector partitioning) and assessed different gap-filling methods for the analyzed data that were measured from the Qingyuan-Ker Towers in Northeast China from January 2020 to December 2022. Results showed that surface roughness varied across wind direction sectors, leading to reduced accuracy in nocturnal CO2 fluxes filtering if the conventional friction velocity (u*) threshold method (a single threshold for all wind sectors) was applied. Alternative to the conventional method, identifying the u* threshold (u c * ) based on wind sectors could significantly improve the accuracy of nighttime CO2 flux filtering. The gaps in the CO2 flux series of forest ecosystems over complex terrain are filled using four methods: mean diurnal course (MDC), look-up table (LUT), marginal distribution sampling (MDS), and random forest (RF). Biases in the filled nocturnal net ecosystem carbon exchange (NEE) were substantially reduced by 16%, in comparison to the adopted conventional method, following a treatment of data filtering based on wind-sector-specific u c * values. In general, the accuracy of all four gap-filling methods decreased with increasing gap length; however, the RF method consistently outperforms the others, yielding more robust and reliable estimates of annual NEE. Through extensive analyses in this study, we recommend identifying u c * values for canopies in different directions surrounding a flux tower, with a moving window and a careful selection of gap-filling methods when processing EC data from a mountainous forest. These findings advance methodologies in handling flux data from forest ecosystems over complex terrains.