Dataset: Partitioning ecosystem water fluxes into transpiration, surface evaporation, and canopy-intercepted evaporation using knowledge-guided machine learning (NEON sites)
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KGML_ET_Partitioned: NEON Flux Tower Dataset for ET Partitioning Sadegh Ranjbar1, Einara Zahn2, Danielle Losos1, Sophie Hoffman1, Ojaswee Shrestha3, Elie Bou-Zeid2, Paul C. Stoy1 1Department of Biological Systems Engineering, University of Wisconsin – Madison, Madison, sWI, USA. 2Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA. 3Department of Forest and Wildlife Ecology, University of Wisconsin – Madison, 1630 Linden Drive, Madison, WI 53706, USA. Corresponding author: Sadegh Ranjbar (sranjbar@wisc.edu) Description: High-frequency NEON flux tower dataset for partitioning total evapotranspiration (ET) into transpiration (T), surface evaporation (Es), and canopy-intercepted evaporation (Ei). Designed for use with KG-DecompNet knowledge-guided ML framework. This dataset contains half-hourly measurements from 35 NEON flux tower sites. It includes meteorological and soil variables used as predictors, as well as target ET components derived from the Flux Variance Similarity (FVS) method. The data is formatted for direct ingestion into PyTorch for knowledge-guided ML training, but can also be used in other ML or statistical workflows. File: KGML_ET_Partitioned.csvFormat: CSVNumber of sites: 35Temporal resolution: 30 minutes Columns and Units: Column Description Units Site Id Site identifier - UTC_time Timestamp (UTC) YYYY-MM-DD HH:MM:SS SoilMoisture_NEON Soil volumetric water content m³/m³ AirTemperature_NEON Air temperature °C SolarRadIn_NEON Incoming solar radiation W/m² T_CANOPY_1_1_1 Canopy temperature °C RH_NEON Relative humidity % PAR_NEON Photosynthetically active radiation µmol m⁻² s⁻¹ fluxTemp_NEON Flux tower temperature °C ustar_NEON Friction velocity m/s WindSpeed_NEON Wind speed m/s vpd_NEON Vapor pressure deficit kPa SW_IN_1_1_1 Incoming shortwave radiation W/m² SW_OUT Outgoing shortwave radiation W/m² LW_IN Incoming longwave radiation W/m² LW_OUT Outgoing longwave radiation W/m² T_soil1to4 Soil temperature layers 1–4 °C G_1to5 Ground heat flux W/m² time_since_P Time since last precipitation hours time_since_TD Time since air temperature = dew point hours Tfvs_NEON Transpiration (FVS method) W/m² Efvs_NEON Surface evaporation (FVS method) W/m² ETfvs_NEON Total ET (FVS method) W/m² Tcea_NEON Transpiration (CEA method) W/m² Ecea_NEON Surface evaporation (CEA method) W/m² ETcea_NEON Total ET (CEA method) W/m² kgml T cea KGML predicted transpiration (CEA-based) W/m² kgml Ei cea KGML predicted canopy-intercepted evaporation (CEA-based) W/m² kgml Es cea KGML predicted surface evaporation (CEA-based) W/m² kgml Dep cea KGML predicted residual flux (CEA-based) W/m² kgml T fvs KGML predicted transpiration (FVS-based) W/m² kgml Ei fvs KGML predicted canopy-intercepted evaporation (FVS-based) W/m² kgml Es fvs KGML predicted surface evaporation (FVS-based) W/m² kgml Dep fvs KGML predicted residual flux (FVS-based) W/m² Data Sources Predictor variables: NEON meteorological and soil measurements Some partitioned variables: Derived from conditional eddy accumulation (CEA) and Flux Variance Similarity (FVS) methods (Zahn et al., 2024) KGML outputs from Ranjbar et al., (2025) Timeframe: Multi-year measurements, aligned to half-hourly intervals Usage Download KGML_ET_Partitioned.csv from this repository. Load in Python using Pandas: import pandas as pd df = pd.read_csv('KGML_ET_Partitioned.csv') print(df.head()) Can be used directly with KG-DecompNet, or for other ML/statistical models for ET partitioning. Citation Ranjbar, S. (2025). KGML_ET_Partitioned: High-frequency NEON flux tower dataset for partitioning ET into T, Es, and Ei using knowledge-guided machine learning. License: This dataset is shared under the CC BY 4.0 License (Attribution). Please cite the original work when using this dataset.
