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Data-driven modelling of methane fluxes across a mire complex based on replicated eddy covariance measurements and spatially-resolved driver information

Noumonvi, Koffi Dodji ; Nilsson, Mats B. ; Kljun, Natascha LU orcid ; Ratcliffe, Joshua L. ; Öquist, Mats G. ; Fransson, Johan E.S. and Peichl, Matthias (2026) In Agricultural and Forest Meteorology 378.
Abstract

Northern mires are significant natural sources of atmospheric methane (CH4), yet estimating CH4 emissions remains challenging due to their complex spatio-temporal dynamics. While eddy covariance (EC) measurements provide valuable insights into ecosystem-scale CH4 fluxes (FCH4) over mire areas typically < 0.05 km², the predictability of FCH4 at the mesoscale (∼0.5 – 20 km²) of a mire complex based on single-site EC measurements has not been explored. In this study, we utilized a network of four EC towers and developed a machine learning approach that integrates these EC data with comprehensive spatial information on drivers to predict FCH4 across a boreal mire complex... (More)

Northern mires are significant natural sources of atmospheric methane (CH4), yet estimating CH4 emissions remains challenging due to their complex spatio-temporal dynamics. While eddy covariance (EC) measurements provide valuable insights into ecosystem-scale CH4 fluxes (FCH4) over mire areas typically < 0.05 km², the predictability of FCH4 at the mesoscale (∼0.5 – 20 km²) of a mire complex based on single-site EC measurements has not been explored. In this study, we utilized a network of four EC towers and developed a machine learning approach that integrates these EC data with comprehensive spatial information on drivers to predict FCH4 across a boreal mire complex in Northern Sweden. For this purpose, environmental driver variables were mapped and area-weighted within dynamic EC flux footprints and related to FCH4 in a spatially-explicit random forest model (‘footprint-based model’). For comparison, we also considered a standard random forest model used for gapfilling of FCH4 data that is based on environmental measurements from fixed sensor locations (‘biomet model’). For both models, variable importance analysis revealed NDVI as the strongest predictor of temporal FCH4 patterns, followed by air pressure, soil temperature and water table. Adjusting for site-specific carbon-to-nitrogen (C:N) ratios substantially improved model performance. Both models significantly improved estimates of the mire complex average FCH4 compared to simple extrapolation of single-site measurements, reducing the uncertainty from ∼22 % in 2022 and 32 % in 2023 to <10 % and <25 % for the footprint-based model, and to <11 % and <30 % for the biomet model, respectively. Overall, our findings suggest that the comprehensive spatially-resolved driver information resulted in only marginally improved model performance at our study site. In comparison, the biomet model offers practical advantages through simpler implementation and wider applicability. However, we encourage testing the footprint-based model approach at other more heterogenous sites where it might become superior due to its ability to account for complex site conditions.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Boreal mire complex, Eddy covariance, Footprint analysis, Mesoscale, Methane flux, Random forest, Spatial upscaling
in
Agricultural and Forest Meteorology
volume
378
article number
111002
publisher
Elsevier
external identifiers
  • scopus:105026164349
ISSN
0168-1923
DOI
10.1016/j.agrformet.2025.111002
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025
id
c03e1005-9fc0-4a93-9b94-6511f7bb222c
date added to LUP
2026-01-07 17:13:21
date last changed
2026-01-08 08:51:11
@article{c03e1005-9fc0-4a93-9b94-6511f7bb222c,
  abstract     = {{<p>Northern mires are significant natural sources of atmospheric methane (CH<sub>4</sub>), yet estimating CH<sub>4</sub> emissions remains challenging due to their complex spatio-temporal dynamics. While eddy covariance (EC) measurements provide valuable insights into ecosystem-scale CH<sub>4</sub> fluxes (FCH<sub>4</sub>) over mire areas typically &lt; 0.05 km², the predictability of FCH<sub>4</sub> at the mesoscale (∼0.5 – 20 km²) of a mire complex based on single-site EC measurements has not been explored. In this study, we utilized a network of four EC towers and developed a machine learning approach that integrates these EC data with comprehensive spatial information on drivers to predict FCH<sub>4</sub> across a boreal mire complex in Northern Sweden. For this purpose, environmental driver variables were mapped and area-weighted within dynamic EC flux footprints and related to FCH<sub>4</sub> in a spatially-explicit random forest model (‘footprint-based model’). For comparison, we also considered a standard random forest model used for gapfilling of FCH<sub>4</sub> data that is based on environmental measurements from fixed sensor locations (‘biomet model’). For both models, variable importance analysis revealed NDVI as the strongest predictor of temporal FCH<sub>4</sub> patterns, followed by air pressure, soil temperature and water table. Adjusting for site-specific carbon-to-nitrogen (C:N) ratios substantially improved model performance. Both models significantly improved estimates of the mire complex average FCH<sub>4</sub> compared to simple extrapolation of single-site measurements, reducing the uncertainty from ∼22 % in 2022 and 32 % in 2023 to &lt;10 % and &lt;25 % for the footprint-based model, and to &lt;11 % and &lt;30 % for the biomet model, respectively. Overall, our findings suggest that the comprehensive spatially-resolved driver information resulted in only marginally improved model performance at our study site. In comparison, the biomet model offers practical advantages through simpler implementation and wider applicability. However, we encourage testing the footprint-based model approach at other more heterogenous sites where it might become superior due to its ability to account for complex site conditions.</p>}},
  author       = {{Noumonvi, Koffi Dodji and Nilsson, Mats B. and Kljun, Natascha and Ratcliffe, Joshua L. and Öquist, Mats G. and Fransson, Johan E.S. and Peichl, Matthias}},
  issn         = {{0168-1923}},
  keywords     = {{Boreal mire complex; Eddy covariance; Footprint analysis; Mesoscale; Methane flux; Random forest; Spatial upscaling}},
  language     = {{eng}},
  month        = {{03}},
  publisher    = {{Elsevier}},
  series       = {{Agricultural and Forest Meteorology}},
  title        = {{Data-driven modelling of methane fluxes across a mire complex based on replicated eddy covariance measurements and spatially-resolved driver information}},
  url          = {{http://dx.doi.org/10.1016/j.agrformet.2025.111002}},
  doi          = {{10.1016/j.agrformet.2025.111002}},
  volume       = {{378}},
  year         = {{2026}},
}