Articles | Volume 30, issue 13
https://doi.org/10.5194/hess-30-4175-2026
© 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/hess-30-4175-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Enhanced understanding of dominant drivers of Water Yield change across China through the improved coupled carbon and water model
Huilan Shen
Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China
Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China
Changming Li
Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China
School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, 510641, China
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Due to shortcomings such as extensive data gaps and limited observation durations in current ground-based latent heat flux (LE) datasets, we developed a novel gap-filling and prolongation framework for ground-based LE observations, establishing a benchmark dataset for global evapotranspiration (ET) estimation from 2000 to 2022 across 64 sites at various timescales. This comprehensive dataset can strongly support ET modeling, water–carbon cycle monitoring, and long-term climate change analysis.
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The determination of the coefficient α in the Priestley–Taylor equation is empirical. Based on an atmospheric boundary layer model, we derived a physically clear and parameter-free expression to investigate the behavior of α. We showed that the temperature dominates changes in α and emphasized that the variation of α with temperature should be considered for long-term hydrological predictions. Our works advance and promote the most classical models in the field.
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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.
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Hydrol. Earth Syst. Sci., 26, 6427–6441, https://doi.org/10.5194/hess-26-6427-2022, https://doi.org/10.5194/hess-26-6427-2022, 2022
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We produced a daily 0.1° dataset of precipitation, soil moisture, and snow water equivalent in 1981–2017 across China via reconstructions. The dataset used global background data and local on-site data as forcing input and satellite-based data as reconstruction benchmarks. This long-term high-resolution national hydrological dataset is valuable for national investigations of hydrological processes.
Changming Li, Hanbo Yang, Wencong Yang, Ziwei Liu, Yao Jia, Sien Li, and Dawen Yang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-456, https://doi.org/10.5194/essd-2021-456, 2022
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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Short summary
Climate change, rising [CO2], and vegetation dynamics are reshaping global water cycle, but their impacts remain unclear. We improved the coupled carbon and water model to analyze China’s water yield (WY) changes (1982–2017). Our results showed that climate change was the dominant driver nationally, vegetation/ [CO2] most affected in 400-1600 mm precipitation zones. Projections indicate [CO2] may outweigh vegetation effects on WY by 2100. This work informs sustainable water management.
Climate change, rising [CO2], and vegetation dynamics are reshaping global water cycle, but...