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

A - Papers appearing in refereed journals

Zhu, S., McCalmont, J., Cardenas, L. M., Cunliffe, A. M., Olde, L., Signori-Muller, C., Litvak, M. E. and Hill, T. 2023. Gap-filling carbon dioxide, water, energy, and methane fluxes in challenging ecosystems - Comparing between methods, drivers, and gap-lengths. Agricultural and Forest Meteorology. 332, p. 109365. https://doi.org/10.1016/j.agrformet.2023.109365

AuthorsZhu, S., McCalmont, J., Cardenas, L. M., Cunliffe, A. M., Olde, L., Signori-Muller, C., Litvak, M. E. and Hill, T.
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. sensible heat, latent energy and methane). 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.

KeywordsEddy covariance; Gap-filling; Managed & low-flux ecosystems; ERA5 drivers
Year of Publication2023
JournalAgricultural and Forest Meteorology
Journal citation332, p. 109365
Digital Object Identifier (DOI)https://doi.org/10.1016/j.agrformet.2023.109365
Web address (URL)https://www.sciencedirect.com/science/article/pii/S016819232300059X?via%3Dihub
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
CIEL
Funder project or codeS2N - Soil to Nutrition - Work package 2 (WP2) - Adaptive management systems for improved efficiency and nutritional quality
The North Wyke Farm Platform- National Capability [2017-22]
Publisher's version
Output statusPublished
Publication dates
Online24 Feb 2023
Publication process dates
Accepted07 Feb 2023
ISSN0168-1923
PublisherElsevier

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