Actual Evapotranspiration Dataset of Mongolia Plateau from 2001 to 2020 Based on SFE-NP Model
Year: 2026
Type: article
Abstract: Evapotranspiration (ET) refers to the total water vapor flux transported by vegetation and surface soil to the atmosphere. It is not only an important component of water and heat regulation, but also has an impact on plant productivity, water cycle, and other processes. Understanding the spatiotemporal distribution characteristics of ET in the Mongolian Plateau is of great significance for water resource regulation, as it is characterized by drought and uneven distribution of rainwater resources. This study calculated the spatiotemporal distribution characteristics of actual ET in the Mongolian Plateau based on the SFE-NP model, and generated a surface ET dataset with a spatial resolution of 1km and a temporal resolution of monthly from 2001 to 2020 in the Mongolian Plateau region. The correlation coefficient R, determination coefficient R2, and root mean square error RMSE between this dataset and the ERA5_Land ET dataset were 0.78, 0.62, and 0.97, respectively, indicating that this dataset has high accuracy and reliable estimation results. This dataset can provide data support for research and applications such as surface water resource allocation and livestock regulation in the Mongolian Plateau, which helps to finely grasp the distribution pattern of water resource consumption in the Mongolian Plateau.
Source: Data
DOI: 10.3390/data11010020
Authors: Yuhui Su, Juanle Wang, Baomin Han
Institutions: Shandong University of Technology, Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, State Key Laboratory of Resources and Environmental Information System, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
Award: G1546816848
OpenAlex ID: W7123775683
