Bi et al. (2026) A 0.1° monthly potential evapotranspiration dataset based on the optimal models over global vegetation zones

Identification

• Journal: Scientific Data
• Year: 2026
• Date: 2026-03-03
• Authors: Zaoying Bi, Shanlei Sun, Qianrong Ma, Yi Liu, Xiaoyuan Li, Jinjian Li, Yibo Liu, Yang Zhou, Botao Zhou, Haishan Chen
• DOI: 10.1038/s41597-026-06956-3

Research Groups

• State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
• College of Physical Science and Technology, Yangzhou University, Yangzhou, China
• School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia
• School of Atmospheric Sciences/Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu, China
• Jiangsu Key Laboratory of Agricultural Meteorology, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, China

Short Summary

This study developed a global 0.1° monthly potential evapotranspiration (PET) dataset for 1992–2022 by calibrating and selecting optimal PET models (Priestley-Taylor and Milly-Dunne) using observations from 124 eddy covariance sites, aiming to reduce uncertainties in existing PET products.

Objective

• To generate a high-resolution, monthly potential evapotranspiration (PET) dataset for global vegetation zones by identifying and applying optimal PET models calibrated with extensive observational data, thereby reducing uncertainties associated with default model parameters.

Study Configuration

• Spatial Scale: Global vegetation zones, 0.1° spatial resolution.
• Temporal Scale: Monthly, from 1992 to 2022.

Methodology and Data

• Models used: Five widely used PET models were evaluated; calibrated Priestley-Taylor and Milly-Dunne models were selected as optimal.
• Data sources:
  • Observations from 124 eddy covariance sites worldwide (FLUXNET community: AmeriFlux, AsiaFlux, OzFlux, FLUXNET2015, LaThuile; Integrated Carbon Observation System (ICOS)).
  • Four widely-used meteorological datasets (ERA-5, TerraClimate, MSWX-Past, MERRA-2).
  • Annual land use and land cover data (CCI-LC).

Main Results

• The calibrated Priestley-Taylor and Milly-Dunne models were identified as the optimal choices for estimating PET across various biomes, demonstrating effective applicability beyond their original observation sites.
• A new monthly PET dataset was generated at a 0.1° resolution for global vegetation zones covering the period 1992–2022.
• The new dataset was compared with the Global Land Evaporation Amsterdam Model V4.2a PET, including daily comparisons for each biome and annual trend comparisons during 1992–2022.

Contributions

• Provides a new, high-resolution (0.1°), long-term (1992–2022) monthly global PET dataset based on optimally calibrated models.
• Addresses uncertainties in existing PET products by deriving parameters for widely used models using extensive eddy covariance observations.
• Identifies the calibrated Priestley-Taylor and Milly-Dunne models as robust for global PET estimation across diverse biomes.
• Offers an alternative and potentially more accurate resource for PET-related research in climatology, ecology, hydrology, and agronomy.

Funding

• National Key Research and Development Program of China (Grant NO. 2022YFF0801603)
• National Natural Science Foundation of China (Grant NO. 42075189)
• Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant NO. KYCX24_1431)

Citation

@article{Bi202601,
  author = {Bi, Zaoying and Sun, Shanlei and Ma, Qianrong and Liu, Yi and Li, Xiaoyuan and Li, Jinjian and Liu, Yibo and Zhou, Yang and Zhou, Botao and Chen, Haishan},
  title = {A 0.1° monthly potential evapotranspiration dataset based on the optimal models over global vegetation zones},
  journal = {Scientific Data},
  year = {2026},
  doi = {10.1038/s41597-026-06956-3},
  url = {https://doi.org/10.1038/s41597-026-06956-3}
}