A 0.1° monthly potential evapotranspiration dataset based on the optimal models over global vegetation zones.

Zaoying Bi, Shanlei Sun, Qianrong Ma, Yi Liu, Xiaoyuan Li, Jinjian Li, Yibo Liu, Yang Zhou, Botao Zhou, Haishan Chen

Abstract read

In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Zaoying BiState 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.

Shanlei SunState 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. sun.s@nuist.edu.cn.ORCID http://orcid.org/0000-0002-7237-2722

Qianrong MaCollege of Physical Science and Technology, Yangzhou University, Yangzhou, China.

Yi LiuSchool of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia.

Xiaoyuan LiState 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.

Jinjian LiSchool of Atmospheric Sciences/Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu, China.

Yibo LiuJiangsu Key Laboratory of Agricultural Meteorology, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, China.

Yang ZhouState 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.

Botao ZhouState 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.ORCID http://orcid.org/0000-0002-5995-2378

Haishan ChenState 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Choosing effective models for accurately estimating potential evapotranspiration (PET) is essential in various fields, including climatology, ecology, hydrology, and agronomy. However, many currently dominant PET products rely on multiple models with default parameters, which can introduce uncertainties into PET-related research. To address this issue, we derived the parameters for five widely used PET models using observations from 124 eddy covariance sites worldwide. By comparing their performances across various biomes, we identified the calibrated Priestley-Taylor and Milly-Dunne models as the optimal choices, capable of being applied effectively beyond their original observation sites. Using these optimal models, along with four widely-used meteorological datasets and annual land use and land cover data, we generated a monthly PET dataset at a 0.1° resolution during 1992-2022 across global vegetation zones. Finally, we compared the new dataset with the Global Land Evaporation Amsterdam Model V4.2a PET, including daily comparisons for each biome and annual trend comparisons during 1992-2022. This new PET dataset serves as an alternative resource for conducting PET-related research.

Identifiers

PMID41776227

DOI10.1038/s41597-026-06956-3

PMCPMC13065798

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LicenceCC BY-NC-ND

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