CONUS Gridded Reference Evapotranspiration Bias Correction: Inputs, Station Validation, and Outputs (gridMET/OpenET)

This Zenodo record archives the complete input data (Data/) and the full set of generated outputs (Plots/) associated with the GitHub repository Open-ET/gridMET-bias-correction. Changes from v2.0 v2.1 fixes some plot and map label issues in v2.0. Therefore, Data.zip and bias_correction_surfaces.zip are exactly the same as in v2.0. Only Plots.zip and the software repository have been updated. What this record is for To view the full study results without running any code: download and extract the Plots/ archive. All figures and intermediate plot products produced by the repository workflows are included. To reproduce (re-create) the Plots/ outputs from scratch: you must set up the GitHub repository environment, extract this record’s Data/ archive into the repository Data/ directory, and run the analysis scripts provided in the repository. Contents Data/ (inputs): All datasets required by the analysis scripts (station time series, metadata, supporting layers, and intermediate inputs used by the workflows). bias_correction_surfaces/ (outputs): Geotiffs of monthly ETo and ETr bias correction surfaces as developed and presenting in the related reseach article (#1). Plots/ (outputs): All figures and intermediate plot products generated by executing the repository scripts. This is provided so users can inspect results directly without executing the code. gridMET-bias-correction-1.0.1(source code): Python Source code of the code base that is used to reproduce all outputs from the inputs provided. This is the release of Open-ET/gridMET-bias-correction repository that was generated at the time of the first version of this record. Reproducing the outputs (optional) To regenerate the contents of Plots/: Clone the GitHub repository: https://github.com/Open-ET/gridMET-bias-correction Create the software environment as described in the repository documentation. Extract this record’s Data/ archive into the repository so the path is: <repo_root>/Data/… Run the analysis scripts in gridMET-bias-correction/gridmetbias/ to recreate the figures and other plot outputs. Related manuscripts This dataset supports two manuscripts: Volk, J. M., Dunkerly, C., Majumdar, S., Huntington, J. L., Minor, B. A., Kim, Y., Morton, C. G., ReVelle, P., Kilic, A., Melton, F., Allen, R. G., Pearson, C., Purdy, A. J., & Caldwell, T. G. (2026). Assessing and correcting bias in gridded reference evapotranspiration over agricultural lands across the contiguous United States. Agricultural Water Management, 333, 110647. https://doi.org/10.1016/j.agwat.2026.110647 Dunkerly, C., Volk, J. M., Majumdar, S., Huntington, J. L., Allen, R. G., Pearson, C., Kim, Y., Morton, C. G., Minor, B. A., ReVelle, P., Kilic, A., Melton, F., Purdy, A. J., & Caldwell, T. G. (2026). A Benchmark Dataset of Agricultural Weather Stations over the Contiguous United States for Evapotranspiration Applications. Accepted in Nature Scientific Data. https://doi.org/10.1038/s41597-026-07819-7. Preprint: https://doi.org/10.31223/X56T9Z. Related software GitHub repository: https://github.com/Open-ET/gridMET-bias-correction Acknowledgments We gratefully acknowledge funding from the S. D. Bechtel, Jr. Foundation; Walton Family Foundation; Lyda Hill Philanthropies; U.S. Bureau of Reclamation; United States Geological Survey (USGS) Water Resources Research Institute (grant G22AC00584-00); National Aeronautics and Space Administration (NASA) Applied Science Program (grants NNX17AF53G and NNX12AD05A); USGS-NASA Landsat Science Team (grant number 140G0118C0007); USGS Cooperative Ecosystem Studies Units (CESU) (grant G23AC00568); NASA Western Water Applications Office (grant 1669431, 80NSSC23K0836); California State University Agricultural Research Institute (grant number 21-01-106); Desert Research Institute Maki Endowment; and the Windward Fund. We also thank all the weather station network data providers for providing the critical in situ data and the open-source software (Python, QGIS) and open-access data communities (e.g., Google Earth Engine, awesome-gee-community-catalog) for their public resources. Essential computational support was provided by the OpenET consortium, its funding partners, and Google Earth Engine. Finally, we thank our colleagues and families for their unwavering support. The opinions and findings presented here are solely those of the authors and do not necessarily reflect the views of the funding agencies.