The ‘miniRECgap’ package with GUI-supported R-scripts for simple gap-filling of Eddy Covariance CO₂ flux data: Demonstrating the application of 'miniRECgap' suitable for new users with no prior knowledge of R

The Eddy covariance (EC) is a well-known and widely used technique for investigating ecosystem exchange of greenhouse gases (GHGs) between the biosphere and the atmosphere [1]. Long-term EC datasets frequently contain gaps due to a variety of reasons [2], which have resulted in the development of various gap-filling methods/tools. Despite the availability of various existing gap-filling methods and tools, many users still rely on spreadsheet software for gap-filling via semi-empirical and nonlinear regression computations. Transitioning to R or Python can significantly reduce computation time for large datasets; however, less experienced users of these environments may face challenges in adapting their methods [5]. The development of a user-friendly R-based tool, such as ‘miniRECgap’ [3], that enables effortless application of frequently used semi-empirical approaches [5] is therefore considered beneficial. Further details on the introduction of the ‘miniRECgap’ R-package can be found in Premrov et al. (2024) and Premrov et al. (2025) [4], [5]. One of the main purposes of designing ‘miniRECgap’ was to assist new or less experienced users in applying light- and temperature-response functions (utilised in ‘miniRECgap’) to enable gap-filling in a simple and efficient way, while also serving as a learning tool for those users transitioning from spreadsheets to R [5]. The use of the ‘miniRECgap’ package involves five simple steps and operates via GUI-supported scripts [3], [4], [5], which will be demonstrated. The approach makes the package suitable for all users, including beginners with no prior R experience. To enhance learning, a mini tutorial will be presented on how to use ‘miniRECgap’, showing a practical example of gap-filling Irish peatland ecosystem EC CO₂ flux data from Premrov et al. (2025) [5], aiming to encourage learning and adoption of R-based solutions for EC flux gap-filling tasks.

 

Acknowledgements

The authors are grateful to the Irish Environmental Protection Agency (EPA) for funding the CO2PEAT project (2022-CE-1100) under the EPA Research Programme 2021-2030.

 

References

 [1] Burba, G., Anderson, D., Amen, J., (2007) Eddy Covariance Method: Overview of General Guidelines

[2] Baldocchi, D.D. (2003) Assessing the eddy covariance technique for evaluating carbon dioxide

[3] Premrov, A (2024). ‘miniRECgap’: R-Package for gap-filling of the Missing Eddy Covariance CO2 Flux Measurements Using Selected Classic Nonlinear Environmental Response Functions via Simple user-friendly GUI Supported R Scripts. (v0.1.0). https://github.com/APremrov/miniRECgap; https://doi.org/10.5281/zenodo.13228228; https://doi.org/10.5281/zenodo.13228227

[4] Premrov, A. et al. (2024): Introducing the ’miniRECgap’ package with GUI-supported R-scripts for simple gap-filling of Eddy Covariance CO2 flux data, EGU24-6475, https://doi.org/10.5194/egusphere-egu24-6475, 2024.

[5] Premrov, A. et .al. (2025). Introducing ‘miniRECgap’ R package for simple gap-filling of missing eddy covariance CO2 flux measurements with classic nonlinear environmental response functions via GUI-supported R-scripts (case-study: In-sample gap-filling with ‘miniRECgap’ vs. MDS and an optimised shallow ANN in a ‘challenging’ peatland ecosystem). Environmental Modelling & Software 193 106611. https://doi.org/10.1016/j.envsoft.2025.106611