DOI: 10.5281/zenodo.15634374
title: Post-processed flux data and visualization tools from FLUXNET2015 sites using the flux-data-qaqc Python package

description: Post-processed flux data and visualization tools from FLUXNET2015 sites using the flux-data-qaqc Python package

This dataset is a post-processed version of the FLUXNET2015 dataset and it includes in situ measurements from 195 eddy covariance flux towers worldwide (Pastorello et al., 2020). The dataset includes daily and monthly aggregated ET, energy balance metrics, and micrometeorological data. Original half-hourly flux data was retrieved from FLUXNET2015 on December 7th 2023. The dataset is oriented towards ET and includes both ET that has been corrected for energy balance closure error as well as the uncorrected values. The dataset has many potential uses including evaluation of regional hydrologic and atmospheric models, energy balance analysis, and others.

Data processing methods

Automated post-processing of half-hourly eddy covariance data that were downloaded was performed using the flux-data-qaqc Python package Volk et al. (2021) using the same methodologies as described in Volk et al. (2023). Those steps are briefly described here. Energy balance components (latent and sensible heat flux, soil heat flux, and net radiation) were subject to limited gap-filling and the FLUXNET2015 quality control (QC) flags were applied to half-hourly data before temporal aggregation to daily timesteps. We filtered out poor quality half-hourly data by using the FLUXNET2015 QC values associated with each variable, specifically those where the values were greater than or equal to one. Energy balance closure corrections were applied using the daily energy balance ratio method described by Pastorello et al. (2020), with minor adjustments used in the OpenET benchmark ET dataset (Volk et al., 2024; Volk et al., 2023; Melton et al., 2022). Other meteorological measurements such as air temperature, precipitation, humidity, etc. are included for most stations depending on availability, and some additional variables were calculated. Interactive graphics of most post-processed data are also included. Station metadata for the flux stations, including instrument height (for wind speed measurements), canopy height, and land cover information, were collected from BADM (Biological, Ancillary, Disturbance, and Metadata) files available on the FLUXNET2015 website. 

In addition to station metadata from FLUXNET BADM, metadata used in the study of Andrade et al. (2025) are included in the metadata file. The “Monthly sample size” columns present the amount of monthly evaporation data available, the number of months with sufficient data for application of the complementary relationship models, and the number of months remaining after applying the filtering and correction steps described in Table 3 of Andrade et al. 2025. The “Main manuscript” column indicates whether the data was included in the main manuscript results or presented only in the Supplementary Material.

Description of the data and file structure

The dataset is in a compressed (zipped) archive titled "flux_ET_dataset", so first it needs to be downloaded and extracted. Once extracted there are four major components within:





A collection of time series files with daily aggregated data (one for each station), these are in the directory named "daily_data_files" and are in CSV format.




A similar collection of time series files for monthly aggregated data in "monthly_data_files".




Interactive graphic files (HTML format) for each station which are in the "graphical_files" directory.




Two additional tables in the root directory, including a metadata file named "station_metadata.xlsx" with site information such as site ID, coordinates, DOI principal investigator information, etc. The other table named "variable_explanation.xlsx" lists all variables that were post-processed in the flux dataset and gives a short description of each as well as their units.



Each data and plot file starts with the station's ID or site ID which are listed in the station_metadata.xlsx file.

Here is a visual of the file structure:

flux_ET_dataset│ README.PDF│ variable_explanation.xlsx│ station_metadata.xlsx│└───daily_data_files│ │ [site ID]_daily_data.csv│ │ ...│ │ └───monthly_data_files│ │ [site ID]_monthly_data.csv│ │ ...│ │ └───graphical_files│ │ [site ID]_plots.html│ │ ...

The variable names in the daily and monthly data files as well as the graphics all follow the same naming scheme which are defined in the variable_explanation.xlsx file. For example, LE stands for latent energy flux and is in units of W/m2.

Sharing/access information

Contact information for each station as well as DOIs and data are included in the "station_metadata.xlsx" file. Data sharing policies follow the same attribution requirements of the original FLUXNET2015, and credit to the original data producers should be acknowledged (e.g., reference FLUXNET2015 DOIs) in addition to citing this dataset.

Code/Software

All files that comprise this dataset were generated using the "flux-data-qaqc" open-source Python package version 0.2.2. The package is hosted on GitHub and PyPI, it also has online documentation including an in-depth user tutorial.

References

Andrade, B. C. de, Huntington, J. L., Volk, J. M., Morton, C., Pearson, C., & Albano, C. M. (2025). Multi-model intercomparison of the complementary relationship of evaporation across global environmental settings. Wiley. https://doi.org/10.22541/essoar.173655402.29591853/v1

Melton, F. S., Huntington, J., Grimm, R., Herring, J., Hall, M., Rollison, D., Erickson, T., Allen, R., Anderson, M., Fisher, J. B., Kilic, A., Senay, G. B., Volk, J., Hain, C., Johnson, L., Ruhoff, A., Blankenau, P., Bromley, M., Carrara, W., ... Anderson, R. G. (2022). OpenET: Filling a critical data gap in water management for the western United States. JAWRA Journal of the American Water Resources Association, 58(6), 971–994. https://doi.org/10.1111/1752-1688.12956

Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., ... Papale, D. (2020). The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data, 7(1), 225. https://doi.org/10.1038/s41597-020-0534-3

Volk, J., Huntington, J., Allen, R., Melton, F., Anderson, M., & Kilic, A. (2021). flux-data-qaqc: A Python package for energy balance closure and post-processing of eddy flux data. Journal of Open Source Software, 6(66), 3418. https://doi.org/10.21105/joss.03418

Volk, J. M., Huntington, J., Melton, F. S., Allen, R., Anderson, M. C., Fisher, J. B., Kilic, A., Senay, G., Halverson, G., Knipper, K., Minor, B., Pearson, C., Wang, T., Yang, Y., Evett, S., French, A. N., Jasoni, R., & Kustas, W. (2023). Development of a benchmark eddy flux evapotranspiration dataset for evaluation of satellite-driven evapotranspiration models over the CONUS. Agricultural and Forest Meteorology, 331, 109307. https://doi.org/10.1016/j.agrformet.2023.109307

Volk, J. M., Huntington, J. L., Melton, F. S., Allen, R., Anderson, M., Fisher, J. B., Kilic, A., Ruhoff, A., Senay, G. B., Minor, B., Morton, C., Ott, T., Johnson, L., Comini de Andrade, B., Carrara, W., Doherty, C. T., Dunkerly, C., Friedrichs, M., Guzman, A., ... Yang, Y. (2024). Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications. Nature Water, 2(2), 193–205. https://doi.org/10.1038/s44221-023-00181-7