DOI: 10.5281/zenodo.17253672
title: Dataset: Partitioning ecosystem water fluxes into transpiration, surface evaporation, and canopy-intercepted evaporation using knowledge-guided machine learning (NEON sites)

description: 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.