Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-3147-2023
© Author(s) 2023. 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-15-3147-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
Jingya Han
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Jiaojiao Gou
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Haiyan Zheng
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Qi Zhang
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Xiaoying Guo
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
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Jinlong Hu, Chiyuan Miao, Jiajia Su, Qi Zhang, Jiaojiao Gou, and Qiaohong Sun
Earth Syst. Sci. Data, 17, 3987–4004, https://doi.org/10.5194/essd-17-3987-2025, https://doi.org/10.5194/essd-17-3987-2025, 2025
Short summary
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We developed a high-precision daily precipitation dataset for the Chinese mainland called CHM_PRE V2. Using data from 3746 rain gauges, 11 precipitation-related variables, and advanced machine learning methods, we created a daily precipitation dataset spanning 1960–2023 with unprecedented accuracy. Compared to existing datasets, it better captures rainfall events while reducing false alarms. This work provides a reliable tool for studying water resources, climate change, and disaster management.
Qi Zhang, Chiyuan Miao, Jiajia Su, Jiaojiao Gou, Jinlong Hu, Xi Zhao, and Ye Xu
Earth Syst. Sci. Data, 17, 837–853, https://doi.org/10.5194/essd-17-837-2025, https://doi.org/10.5194/essd-17-837-2025, 2025
Short summary
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Our study introduces CHM_Drought, an advanced meteorological drought dataset covering mainland China, offering detailed insights from 1961 to 2022 at a spatial resolution of 0.1°. This dataset incorporates six key drought indices, including multi-scale versions, facilitating early detection and monitoring of droughts. Through the provision of consistent and reliable data, CHM_Drought enhances our understanding of drought patterns, aiding in effective water management and agricultural planning.
Yi Wu, Chiyuan Miao, Yiying Wang, Qi Zhang, Jiachen Ji, and Yuanfang Chai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-600, https://doi.org/10.5194/essd-2024-600, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
Our study introduces BMA-ET, a novel multi-dataset fusion product. Spanning 1980 to 2020 with spatial resolution of 0.5° and 1°, BMA-ET uses Bayesian model averaging (BMA) to combine thirty ET datasets. A key innovation is its dynamic weighting scheme, which adjusts for different vegetation types and non-common coverage years among ET datasets. BMA-ET provides a comprehensive resource for understanding global ET patterns and trends, addressing the limitation of prior ET fusion efforts.
Ting Su, Chiyuan Miao, Qingyun Duan, Jiaojiao Gou, Xiaoying Guo, and Xi Zhao
Hydrol. Earth Syst. Sci., 27, 1477–1492, https://doi.org/10.5194/hess-27-1477-2023, https://doi.org/10.5194/hess-27-1477-2023, 2023
Short summary
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The Three-River Source Region (TRSR) plays an extremely important role in water resources security and ecological and environmental protection in China and even all of Southeast Asia. This study used the variable infiltration capacity (VIC) land surface hydrologic model linked with the degree-day factor algorithm to simulate the runoff change in the TRSR. These results will help to guide current and future regulation and management of water resources in the TRSR.
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Short summary
Constructing a high-quality, long-term daily precipitation dataset is essential to current hydrometeorology research. This study aims to construct a long-term daily precipitation dataset with different spatial resolutions based on 2839 gauge observations. The constructed precipitation dataset shows reliable quality compared with the other available precipitation products and is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling.
Constructing a high-quality, long-term daily precipitation dataset is essential to current...
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