Articles | Volume 21, issue 19
https://doi.org/10.5194/bg-21-4285-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-4285-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method
Xin Chen
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
Tiexi Chen
CORRESPONDING AUTHOR
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
Qinghai Provincial Key Laboratory of Plateau Climate Change and Corresponding Ecological and Environmental Effects, Qinghai University of Science and Technology, Xining 810016, China
School of Geographical Sciences, Qinghai Normal University, Xining 810008, Qinghai, China
Xiaodong Li
Qinghai Institute of Meteorological Sciences, Xining 810008, Qinghai, China
Yuanfang Chai
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Shengjie Zhou
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
Renjie Guo
Faculty of Geographical Science, Beijing Normal University, Beijing, China
Jie Dai
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
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This preprint is open for discussion and under review for Biogeosciences (BG).
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This research evaluates how global vegetation models simulate crop growth in eastern China’s double-cropping farmlands. Satellite observations reveal two clear growth peaks each year, while models reproduce only one. This mismatch limits our understanding of regional greening and carbon cycling. Incorporating realistic farming practices into global models is essential for more accurate assessments of agriculture and climate interactions.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
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Currently people are very concerned about vegetation changes and their driving factors, including natural and anthropogenic drivers. In this study, a general browning trend is found in Syria during 2001–2018, indicated by the vegetation index. We found that land management caused by social unrest is the main cause of this browning phenomenon. The mechanism initially reported here highlights the importance of land management impacts at the regional scale.
Jiao Lu, Guojie Wang, Tiexi Chen, Shijie Li, Daniel Fiifi Tawia Hagan, Giri Kattel, Jian Peng, Tong Jiang, and Buda Su
Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, https://doi.org/10.5194/essd-13-5879-2021, 2021
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This study has combined three existing land evaporation (ET) products to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25° to define the global proxy ET with lower uncertainties. The merged product is the best at capturing dynamics over different locations and times among all data sets. The merged product performed well over a range of vegetation cover scenarios and also captured the trend of land evaporation over different areas well.
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Short summary
We provide an ensemble-model-based GPP dataset (ERF_GPP) that explains 85.1 % of the monthly variation in GPP across 170 sites, which is higher than other GPP estimate models. In addition, ERF_GPP improves the phenomenon of “high-value underestimation and low-value overestimation” in GPP estimation to some extent. Overall, ERF_GPP provides a more reliable estimate of global GPP and will facilitate further development of carbon cycle research.
We provide an ensemble-model-based GPP dataset (ERF_GPP) that explains 85.1 % of the monthly...
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