Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

Dataset · Thu Jan 01 00:00:00 EST 2026

DOI:https://doi.org/10.15485/3017661· OSTI ID:3017661

Chen, Han ^[1]; Good, Stephen ^[1]; Caylor, Kelly ^[2]; Fiorella, Richard ^[3]; Wang, Lixin ^[4]

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1. Oregon State University
2. University of California Santa Barbara
3. Los Alamos National Laboratory
4. Indiana University Indianapolis

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

• Research Organization: Improving ESS Approaches to Evapotranspiration Partitioning Through Data Fusion
• Sponsoring Organization: ESS-DIVE; U.S. DOE > Office of Science > Biological and Environmental Research (BER)
• DOE Contract Number: AC02-05CH11231
• OSTI ID: 3017661
• Country of Publication: United States
• Language: English

Related Subjects

EARTH SCIENCE > ATMOSPHERE
EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR PROCESSES > EVAPOTRANSPIRATION
EARTH SCIENCE > BIOSPHERE > VEGETATION
EARTH SCIENCE > LAND SURFACE
ESS-DIVE CSV File Formatting Guidelines Reporting Format
ESS-DIVE File Level Metadata Reporting Format
transpiration_evapotranspiration_ratio
transpiration_flux