Summary
This methodological paper evaluates and compares gap-filling approaches for eddy covariance flux data in challenging managed ecosystems, with particular emphasis on agricultural systems and those recovering from disturbance. Random forest regression is benchmarked against alternative techniques across multiple greenhouse gases and water/energy fluxes. The work extends prior evaluations, which focused on mature minimally-disturbed ecosystems, to more complex agri-ecosystems where data gaps are frequent and flux signals may be weak or variable.
UK applicability
UK agricultural monitoring networks and research institutions relying on eddy covariance measurements would benefit from these gap-filling comparisons, particularly when studying intensive arable systems or grasslands undergoing active management. The methods are directly applicable to UK farming systems and climate monitoring programmes.
Key measures
Gap-filling accuracy and bias across CO₂, H₂O, energy, and CH₄ fluxes; performance relative to gap length; random forest regression versus alternative methods
Outcomes reported
The study evaluated and compared gap-filling techniques for eddy covariance data across carbon dioxide, water, energy, and methane fluxes in actively managed agricultural and recovering ecosystems. Random forest regression performance was assessed against alternative methods across varying gap lengths and flux magnitudes.
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