Multiple gap-filling for eddy covariance datasets. Lucas-Moffat, A. M, Schrader, F., Herbst, M., & Brümmer, C. Agricultural and Forest Meteorology, 325:109114, 2022.
Multiple gap-filling for eddy covariance datasets [link]Paper  doi  abstract   bibtex   
With novel developments in technology, eddy covariance flux measurements have become feasible for a variety of trace gases. While the statistical properties and gap-filling strategies have been well examined for carbon dioxide, these are much less understood for other gases. Here, we propose a universal methodology deploying multiple gap-filling techniques and artificial gap scenarios to evaluate the techniques' performances, infer the statistical flux properties, and fill the real gaps in eddy covariance datasets of any trace gas. The methodology was implemented in a gap-filling framework with techniques spanning from simple and diurnal interpolations, look-up tables, artificial neural networks, to an inferential model. For the new scheme of half-hourly and daily artificial gaps, each additional gap was superimposed one at a time (thus keeping the disturbance to a minimum) for the whole dataset and the scenarios were resampled by bootstrapping. The gap-filled sums were then estimated from the ensemble of well-performing gap-filling techniques. The gap-filling framework was applied to campaign data of three different trace gases (51 days of ammonia, 79 days of total reactive nitrogen, and 89 days of methane flux measurements). The aggregated fluxes are stated as ensemble ranges of multiple techniques plus the techniques' uncertainties. Additionally, the framework was used to gap-fill a full year of carbon dioxide flux measurements yielding similar performances as previously reported. Based on a review of gap-filling comparison studies and on our findings, we suggest reconsidering the standard procedure of using one gap-filling technique for multi-site studies. Deploying multiple gap-filling techniques and providing ensemble results of gap-filled sums will help to minimize the influence of a single technique and thus lead to a more robust flux aggregation. Furthermore, the estimated overall uncertainty will be more realistic by accounting for the ensemble range of multiple techniques.
@Article{LUCASMOFFAT2022109114,
  author   = {Lucas-Moffat, Antje M and Schrader, Frederik and Herbst, Mathias and Br{\"{u}}mmer, Christian},
  journal  = {Agricultural and Forest Meteorology},
  title    = {{Multiple gap-filling for eddy covariance datasets}},
  year     = {2022},
  issn     = {0168-1923},
  pages    = {109114},
  volume   = {325},
  abstract = {With novel developments in technology, eddy covariance flux measurements have become feasible for a variety of trace gases. While the statistical properties and gap-filling strategies have been well examined for carbon dioxide, these are much less understood for other gases. Here, we propose a universal methodology deploying multiple gap-filling techniques and artificial gap scenarios to evaluate the techniques' performances, infer the statistical flux properties, and fill the real gaps in eddy covariance datasets of any trace gas. The methodology was implemented in a gap-filling framework with techniques spanning from simple and diurnal interpolations, look-up tables, artificial neural networks, to an inferential model. For the new scheme of half-hourly and daily artificial gaps, each additional gap was superimposed one at a time (thus keeping the disturbance to a minimum) for the whole dataset and the scenarios were resampled by bootstrapping. The gap-filled sums were then estimated from the ensemble of well-performing gap-filling techniques. The gap-filling framework was applied to campaign data of three different trace gases (51 days of ammonia, 79 days of total reactive nitrogen, and 89 days of methane flux measurements). The aggregated fluxes are stated as ensemble ranges of multiple techniques plus the techniques' uncertainties. Additionally, the framework was used to gap-fill a full year of carbon dioxide flux measurements yielding similar performances as previously reported. Based on a review of gap-filling comparison studies and on our findings, we suggest reconsidering the standard procedure of using one gap-filling technique for multi-site studies. Deploying multiple gap-filling techniques and providing ensemble results of gap-filled sums will help to minimize the influence of a single technique and thus lead to a more robust flux aggregation. Furthermore, the estimated overall uncertainty will be more realistic by accounting for the ensemble range of multiple techniques.},
  doi      = {https://doi.org/10.1016/j.agrformet.2022.109114},
  keywords = {artificial gap scenarios, bootstrapping, ensemble results, multiple gap-filling, trace gases,Eddy covariance fluxes},
  url      = {https://www.sciencedirect.com/science/article/pii/S016819232200301X},
}
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