EVAonline: An open-source web platform for global reference evapotranspiration estimation via multi-source data fusion

This dataset provides the complete validation results and reproducibility package for EVAonline, an open-source system designed for high-accuracy reference evapotranspiration (ETo) estimation using multi-source data fusion. This version updates the deposit with fully referenced data sources, an expanded four-source validation, robustness (sensitivity) analyses, and software test/reproducibility metadata. 📦 What's Included 186,286 daily ETo observations spanning 30 years (1991–2020) across 17 Brazilian cities (MATOPIBA region: Maranhão, Tocantins, Piauí, Bahia — plus Piracicaba/SP as an out-of-region control site). 17 cities × 10,958 days = 186,286 daily values. 1,304,002 daily meteorological records per reanalysis source (7 variables × 17 cities × 10,958 days) for both NASA POWER and Open-Meteo, with 0.00% missing data. Four ETo sources compared against the reference: Reference (ground truth): Xavier et al. BR-DWGD (0.1° ≈ 10 km resolution). NASA POWER ETo, computed in-house with the FAO-56 Penman-Monteith engine (underlying reanalysis: MERRA-2, ~0.5° × 0.625°). Open-Meteo ETo, computed in-house with FAO-56 PM (underlying reanalysis: ERA5-Land, ~9 km). Open-Meteo API ETo, pre-computed by the Open-Meteo service (ERA5-Land), used as an independent benchmark. EVAonline — Adaptive Kalman Fusion product ⭐. 📂 Repository Structure 📁 data/ — raw inputs, intermediate files, descriptive statistics, per-source ETo, per-city validations, the four-source comparison, sensitivity analyses, and final figures. 📁 notebooks/ — demonstration notebooks for each provider (NASA POWER, Open-Meteo Archive/Forecast, MET Norway, NWS Forecast, NWS Stations) plus utility examples. 📁 scripts/ — API adapters (NASA POWER, Open-Meteo, MET Norway, NWS, OpenTopoData), the Kalman fusion core, the FAO-56 Penman-Monteith engine, and the sequential pipeline (scripts 1–7) to reproduce every manuscript result, including the two sensitivity scripts. 📁 validation/ — supplementary, unit-tested validation analyses reported in the article: temporal-split + leave-one-site-out cross-validation, site-wise metrics (median/IQR/min–max across the 17 sites), a component ablation (variable/component importance), and the error-boxplot figure. A single tested metrics.py provides one definition of KGE/NSE/R²/MAE/RMSE/PBIAS. 📄 Root files — complete_validation_analysis.ipynb (main entry point, all 17 cities), tutorial_full_pipeline.ipynb (step-by-step single-city guide using Piracicaba/SP), environment.yml and requirements.txt (Python 3.12 environment). 🗄️ Data Sources & References All data sources used to build and validate EVAonline are documented below with their original scientific and data citations. Full bibliographic entries are provided in the References section. Source Role Native resolution Underlying dataset Primary citation Xavier BR-DWGD Reference / ground truth 0.1° (~10 km) Brazilian Daily Weather Gridded Data Xavier et al. (2022), Int. J. Climatol. — doi:10.1002/joc.7731 NASA POWER Global reanalysis (validation) ~0.5° × 0.625° MERRA-2 NASA LaRC POWER Project; Gelaro et al. (2017) — doi:10.1175/JCLI-D-16-0758.1 Open-Meteo Archive High-res reanalysis (validation + benchmark) 0.1° (~9 km) ERA5-Land / ERA5 Zippenfenig (2023) — doi:10.5281/zenodo.7970649; Muñoz-Sabater et al. (2021) — doi:10.5194/essd-13-4349-2021 Open-Meteo Forecast Global forecast (platform) 0.1° (~9 km) ECMWF IFS / ERA5 Zippenfenig (2023) — doi:10.5281/zenodo.7970649 MET Norway Regional forecast (platform, Europe) ~1 km Locationforecast 2.0 MET Norway (NLOD / CC BY 4.0) — api.met.no NWS (USA) Regional forecast/stations (platform) Station/grid NWS/NOAA API National Weather Service (NOAA), public domain — weather.gov Open Topo Data Station elevation ~30 m SRTM / ASTER GDEM v3 Nisbet (2019); Farr et al. (2007) — doi:10.1029/2005RG000183; NASA/METI ASTER GDEM v3 — doi:10.5067/ASTER/ASTGTM.003 Data attribution required by providers (following each source's official referencing/licensing page): NASA POWER (https://power.larc.nasa.gov/docs/referencing/): include both references. Project reference: "The data was obtained from National Aeronautics and Space Administration (NASA) Langley Research Center's Prediction Of Worldwide Energy Resources (POWER) project funded through the NASA Earth Science Division." Data reference: "The data was obtained from the POWER Project's Daily API (data version v10.2.1) on 2025/11/20." Data under CC BY 4.0. Open-Meteo (https://open-meteo.com/en/licence): cite the API software as Zippenfenig (2023), doi:10.5281/zenodo.7970649 (CC BY 4.0); underlying data from Copernicus C3S ERA5-Land (CC BY 4.0). MET Norway (https://www.met.no/en/free-meteorological-data/Licensing-and-crediting): licensed under NLOD and CC BY 4.0; credit "Based on data from MET Norway". NWS / NOAA (https://www.weather.gov/disclaimer): information produced by the U.S. National Weather Service is in the public domain (U.S. Government work). Elevation: point queries served by the Open Topo Data API (Nisbet, 2019) over the SRTM (Farr et al., 2007) and ASTER GDEM v3 (NASA/METI, 2019) digital elevation models (~30 m). Xavier BR-DWGD: Brazilian daily weather gridded product; cite Xavier et al. (2022). 🔬 Methodology Notes Input vs. output distinction: nasa_power_raw and open_meteo_raw contain raw meteorological variables (T2M_MAX, T2M_MIN, T2M, RH2M, WS2M, ALLSKY_SFC_SW_DWN, PRECTOTCORR) used as inputs to EVAonline's FAO-56 PM engine. eto_open_meteo contains ETo values pre-calculated by the Open-Meteo service, used for independent benchmarking (source OPENMETEO_API). Reference evapotranspiration (FAO-56 Penman-Monteith): ETo is computed with the standard FAO-56 PM equation (Allen et al., 1998). Wind-speed handling is critical: NASA POWER provides native 2 m wind, whereas Open-Meteo provides 10 m wind that must be converted to 2 m using FAO-56 Eq. 47 (omitting this conversion causes ~15% ETo overestimation). Station elevation is fetched from OpenTopoData. Adaptive Kalman Fusion: EVAonline combines NASA POWER and Open-Meteo estimates through an adaptive Kalman filter (Kalman, 1960) with (i) seasonal process noise derived from Xavier monthly variability, (ii) measurement noise reflecting relative source uncertainty (R_NASA ≈ 0.3, R_Open-Meteo ≈ 0.4), and (iii) bias correction anchored to Xavier BR-DWGD climatology, producing a fused ETo plus per-day uncertainty. Validation scope: focuses on the mathematical core (Kalman filter, weighted fusion, FAO-56 engine) — not the full web platform (FastAPI, Celery, Redis). Climatological rigor: the 30-year period (1991–2020) follows WMO guidelines for climate normals. Evaluation metrics: coefficient of determination (R²), Kling-Gupta Efficiency (KGE; Gupta et al., 2009), Nash-Sutcliffe Efficiency (NSE; Nash & Sutcliffe, 1970), mean absolute error (MAE), root mean squared error (RMSE), and percent bias (PBIAS; Moriasi et al., 2007). 📈 Scientific Performance Mean performance across all 17 cities, evaluated against the Xavier BR-DWGD reference (n = 10,958 days per city): Metric EVAonline (Fusion) Open-Meteo API (ERA5-Land) NASA POWER (FAO-56) Open-Meteo (FAO-56) R² 0.694 0.649 0.740 0.636 KGE 0.814 0.584 0.411 0.432 NSE 0.676 0.216 −0.363 −0.547 MAE (mm/d) 0.423 0.690 0.845 0.859 RMSE (mm/d) 0.566 0.860 1.117 1.097 PBIAS (%) +0.71 +8.27 +15.78 +13.02 Key improvements of EVAonline over individual global sources: MAE reduction of 38.7–50.8% (0.423 mm/d vs. 0.690–0.859 mm/d). Bias reduction of 91.4–95.5% (PBIAS +0.71% vs. +8.27% to +15.78%). Highest KGE (0.814) — 39% higher than the best individual source (Open-Meteo API) and up to 98% higher than NASA POWER. Only source with positive NSE across all cities, and the most consistent performance (smallest inter-city metric spread: KGE SD = 0.05 vs. 0.19–0.41 for individual sources). Robustness / sensitivity analyses (data/analysis_results/): Fusion weights (sensitivity_weights.csv): perturbing the source weights by ±20% changes mean RMSE and MAE by less than ~4%, confirming the fusion is not over-tuned. Kalman parameters (sensitivity_kalman_params.csv): sweeping the process/measurement-noise ratio, base measurement variance, and clipping factors leaves KGE within ~0.75–0.85, confirming stability of the filter configuration. 🧪 Reproducibility & Testing Deterministic pipeline: scripts 1→7 regenerate every table, figure, and CSV in this deposit from the raw inputs; the two notebooks reproduce the single-city tutorial and the full 17-city study end-to-end. Environment: pinned Python 3.12 environment via environment.yml (conda) and requirements.txt (pip). Automated software tests: the EVAonline codebase is covered by a pytest suite (unit + integration) with 86.5% coverage (as reported in the associated article) across the core modules (FAO-56 engine, Kalman ensemble, API adapters, preprocessing); coverage reports (coverage.xml) are version-controlled in the source repository. The supplementary validation/ package adds its own unit tests for the metric definitions. Data integrity: primary datasets carry MD5 checksums in the Zenodo deposit; raw datasets contain 0.00% missing values over 1991–2020. 📚 References Data sources Xavier, A. C., Scanlon, B. R., King, C. W., & Alves, A. I. (2022). New improved Brazilian daily weather gridded data (1961–2020). International Journal of Climatology, 42(16), 8390–8404. https://doi.org/10.1002/joc.7731 Xavier, A. C., King, C. W., & Scanlon, B. R. (2016). Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology, 36(6), 2644–2659. https://doi.org/10.1002/joc.4518 Gelaro, R., McCarty, W., Suárez, M. J., et al. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1 NASA Langley Research Center, Prediction Of Worldwide Energy Resources (POWER) project, funded through the NASA Earth Science Division. Daily API (data version v10.2.1), accessed 2025/11/20. https://power.larc.nasa.gov/ (referencing guide: https://power.larc.nasa.gov/docs/referencing/) Zippenfenig, P. (2023). Open-Meteo.com Weather API (Computer software). Zenodo. https://doi.org/10.5281/zenodo.7970649 (CC BY 4.0; https://open-meteo.com/en/licence) Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., et al. (2021). ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349–4383. https://doi.org/10.5194/essd-13-4349-2021 Hersbach, H., Bell, B., Berrisford, P., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803 Norwegian Meteorological Institute (MET Norway) (2026). Locationforecast API — Free meteorological data. https://api.met.no/ (NLOD / CC BY 4.0; https://www.met.no/en/free-meteorological-data/Licensing-and-crediting) National Weather Service (NOAA), U.S. Department of Commerce (2026). National Weather Service API. https://www.weather.gov/documentation/services-web-api (public domain; https://www.weather.gov/disclaimer) Nisbet, A. (2019). Open Topo Data: An elevation API. https://www.opentopodata.org/ (accessed 2026-08-03) Farr, T. G., Rosen, P. A., Caro, E., et al. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2), RG2004. https://doi.org/10.1029/2005RG000183 NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/ASTER/ASTGTM.003 Methods & metrics Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration — Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45. https://doi.org/10.1115/1.3662552 Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1–2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003 Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6 Moriasi, D. N., Arnold, J. G., Van Liew, M. W., et al. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153 📄 License and Citation Data license: Creative Commons Attribution 4.0 International (CC BY 4.0). Software license: GNU AGPL-3.0 (see GitHub repository). Recommended citation: Soares, A.S.M.C., Ribeiro, V.P., Duarte, S.N., Balestieri, J.A.P., Padovani, C.R., Bordignon, Á.J.Z., Maciel, C.D., & Marques, P.A.A. EVAonline: An open-source web platform for global reference evapotranspiration estimation via multi-source data fusion (1.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.19339254 Associated article: Soares, A.S.M.C., Ribeiro, V.P., Duarte, S.N., Balestieri, J.A.P., Padovani, C.R., Bordignon, Á.J.Z., Maciel, C.D., & Marques, P.A.A. (2026). EVAonline: An open-source web platform for global reference evapotranspiration estimation via multi-source data fusion. Environmental Modelling & Software, 204, 107113. https://doi.org/10.1016/j.envsoft.2026.107113 🔗 Links Live repository: https://github.com/angela-cunha-soares/EVAONLINE Associated article (DOI): https://doi.org/10.1016/j.envsoft.2026.107113 Reference data (BR-DWGD): https://doi.org/10.1002/joc.7731 NASA POWER: https://power.larc.nasa.gov/ Open-Meteo: https://open-meteo.com/ · https://doi.org/10.5281/zenodo.7970649 ERA5-Land: https://doi.org/10.5194/essd-13-4349-2021