Articles | Volume 18, issue 6
https://doi.org/10.5194/essd-18-4097-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-4097-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A high-resolution gridded dataset of water footprints for China's major food crops from 2001 to 2020
En Hua
College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
Ling Huang
College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
Liangliang Zhang
College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
Quanbo Zhao
College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
Yingxuan Wang
College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
Bowei Wu
College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
Sien Li
College of Water Resources and Civil Engineering, China Agricultural University, Beijing, 100083, China
Yaokui Cui
School of Earth and Space Sciences, Peking University, Beijing, 100871, China
Yubao Wang
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
Xuhui Wang
CORRESPONDING AUTHOR
College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
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Understanding how water moves from land to the atmosphere is essential for studying climate and water resources. However, ground observations in China are often incomplete and short. We created the first seamless half-hourly dataset covering China from 2000 to 2024 by carefully filling data gaps and extending observations in time. The dataset closely matches measurements and provides a reliable foundation for climate, water, and environmental research.
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Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
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Changming Li, Ziwei Liu, Wencong Yang, Zhuoyi Tu, Juntai Han, Sien Li, and Hanbo Yang
Earth Syst. Sci. Data, 16, 1811–1846, https://doi.org/10.5194/essd-16-1811-2024, https://doi.org/10.5194/essd-16-1811-2024, 2024
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Using a collocation-based approach, we developed a reliable global land evapotranspiration product (CAMELE) by merging multi-source datasets. The CAMELE product outperformed individual input datasets and showed satisfactory performance compared to reference data. It also demonstrated superiority for different plant functional types. Our study provides a promising solution for data fusion. The CAMELE dataset allows for detailed research and a better understanding of land–atmosphere interactions.
Changming Li, Hanbo Yang, Wencong Yang, Ziwei Liu, Yao Jia, Sien Li, and Dawen Yang
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Revised manuscript not accepted
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A long-term (1980–2020) global ET product is generated based on a collocation-based merging method. The produced Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data (CAMELE) performed well over different vegetation coverage against in-situ data. For global comparison, the spatial distribution of multi-year average and annual variation were in consistent with inputs.The CAMELE products is freely available at https://doi.org/10.5281/zenodo.6283239 (Li et al., 2021).
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The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
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Short summary
Accurately quantifying the blue and green water footprints of crops is essential for addressing unsustainable agricultural water use. However, long-term, high spatiotemporal resolution data have been lacking. Hence, multi-source remote sensing was integrated with high-resolution crop distribution and phenology within a detailed water footprint framework. ChinaCropWF provides daily, 1-km resolution blue and green water footprints for China's five major food crops from 2001 to 2020.
Accurately quantifying the blue and green water footprints of crops is essential for addressing...
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