Articles | Volume 18, issue 21
https://doi.org/10.5194/gmd-18-8379-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-8379-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Nov 2025
Development and technical paper |
| 10 Nov 2025
| 10 Nov 2025
Modeling wheat development under extreme weather with WOFOST-EW v1
Jinhui Zheng
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, China
Institute of Carbon Neutrality, Tsinghua University, Beijing, 100084, China
Zhenrong Du
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
Liujun Xiao
College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Xiaomeng Huang
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
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Earth Syst. Sci. Data, 17, 5543–5556, https://doi.org/10.5194/essd-17-5543-2025, https://doi.org/10.5194/essd-17-5543-2025, 2025
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We created the first global maps showing where livestocks have been raised each year from 1961 to 2021. These maps help to see how livestock numbers and locations have changed over time. Using global statistics and satellite data, we built a model to estimate livestock density at a high resolution (5 km). This work supports better decisions in food security, disease control, and environmental protection around the world.
Peng Li, Zhanao Huang, Yongqiang Yu, Xi Wu, Xiaomeng Huang, and Xiaojie Li
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Zihang Lou, Dailiang Peng, Zhou Shi, Hongyan Wang, Ke Liu, Yaqiong Zhang, Xue Yan, Zhongxing Chen, Su Ye, Le Yu, Jinkang Hu, Yulong Lv, Hao Peng, Yizhou Zhang, and Bing Zhang
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Xiaoxuan Liu, Peng Zhu, Shu Liu, Le Yu, Yong Wang, Zhenrong Du, Dailiang Peng, Ece Aksoy, Hui Lu, and Peng Gong
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An increase of 28 % in cropland expansion since 10 000 BCE has led to a 1.2 % enhancement in the global cropping potential, with varying efficiencies across regions. The continuous expansion has altered the support for population growth and has had impacts on climate and biodiversity, highlighting the effects of climate change. It also points out the limitations of previous studies.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
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Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
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Shijun Zheng, Dailiang Peng, Bing Zhang, Yuhao Pan, Le Yu, Yan Wang, Xuxiang Feng, and Changyong Dou
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Lei Lin, Hao Liu, Xiaomeng Huang, Qingjun Fu, and Xinyu Guo
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In this study, the first 30 m resolution terrace map of China was developed through supervised pixel-based classification using multisource, multi-temporal data based on the Google Earth Engine platform. The classification performed well with an overall accuracy of 94 %. The terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and the terrace map will be valuable for studies on soil erosion, carbon cycle, and ecosystem service assessments.
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Short summary
This study integrates the extreme weather index and deep learning algorithms with the World Food Studies Simulation Model (WOFOST), proposing the WOFOST-EW v1. WOFOST-EW significantly improves the simulation of winter wheat growth under extreme weather conditions, providing more accurate predictions of phenology and yield. As extreme weather events become more frequent, WOFOST-EW provides a key tool for agricultural development.
This study integrates the extreme weather index and deep learning algorithms with the World Food...
