Gap-filling carbon dioxide, water, energy, and methane fluxes in challenging ecosystems: comparing between methods, drivers, and gap-lengths

Songyan Zhu, Jon McCalmont, Laura Cardenas, Andrew Cunliffe, Louise Olde, Caroline Signori Müller, Marcy Litvak, Timothy C. Hill

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
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Abstract

Eddy covariance serves as one the most effective techniques for long-term monitoring of ecosystem fluxes, however long-term data integrations rely on complete timeseries, meaning that any gaps due to missing data must be reliably filled. To date, many gap-filling approaches have been proposed and extensively evaluated for mature and/or less actively managed ecosystems. Random forest regression (RFR) has been shown to be stable and perform better
in these systems than alternative approaches, particularly when filling longer gaps. However, the performance of RFR gap filling remains less certain in more challenging ecosystems, e.g., actively managed agri-ecosystems and following recent land-use change due to management disturbances, ecosystems with relatively low fluxes due to low signal to noise ratios, or for trace gases other than carbon dioxide (e.g., methane). In an extension to earlier work on gap filling global carbon dioxide, water, and energy fluxes, we assess the RFR approach for gap filling methane fluxes globally. We then investigate a range of gap-filling methodologies for carbon dioxide, water, energy, and methane fluxes in challenging ecosystems, including European managed pastures, Southeast Asian converted peatlands, and North American drylands.
Our findings indicate that RFR is a competent alternative to existing research standard gap-filling algorithms. The marginal distribution sampling (MDS) is still suggested for filling short (< 12 days) gaps in carbon dioxide fluxes, but RFR is better for filling longer (> 30 days) gaps in carbon dioxide fluxes and also for gap filling other fluxes (e.g. H, LE and CH4). In addition, using RFR with globally available reanalysis environmental drivers is effective when measured drivers are unavailable. Crucially, RFR was able to reliably fill cumulative fluxes for gaps > 3 moths and, unlike other common approaches, key environment-flux responses were preserved in the gap-filled data.
Original languageEnglish
Article number109365
Number of pages14
JournalAgricultural and Forest Meteorology
Volume332
Early online date24 Feb 2023
DOIs
Publication statusPublished - 1 Apr 2023

Bibliographical note

Acknowledgements
The authors thank the FLUXNET-CH4 research groups for providing the CC-BY-4.0 (Tier one) open-access eddy covariance data (https://fluxnet.org/data/fluxnet-ch4-community-product/) and ERA5 (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) for providing meteorology reanalysis data. They also thank the ReddyProc (https://cran.r-project.org/web/packages/R EddyProc/index.html) team, scikit-learn (https://scikit-learn.org/s table/install.html) team, and Xgboost team (https://xgboost.readthedocs.io/en/stable/python/python_api.html) for the packages that help the implementation and validation for gap-filling approaches. SZ and TH would like to acknowledge funding from Shell to support the PhD studentship. Rothamsted thanks BBSRC grants BBS/E/C/000I0320 and BBS/E/C/000J0100. The Eddy Covariance equipment deployed in this work was funded by CIEL (https://www.cielivestock.co.uk/) and the raw data is available on the Farm Platform Portal (https://nwfp.rothamsted.ac.uk/).

Data Availability Statement

Data will be made available on request.

Keywords

  • Eddy covariance
  • Gap-filling
  • managed & low-flux ecosystems
  • ERA5 drivers

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