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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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Due to the limitations of past data-based models and the high cost of numerical weather prediction computing, accurately forecasting precipitation proximity remains challenging. A dual encoder-decoder framework is proposed to enhance short-term forecasting and reduce underestimation in extreme precipitation by using spatio-temporal information conversion equations and adaptive weighted gradient loss. Demonstrates better accuracy than existing deep learning methods in precipitation datasets.
Xiyu Li and Le Yu
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We created detailed maps showing where different crops are grown in China and how planting changes over time. By combining existing maps with information learned from satellite images, we produced nationwide maps at 10-meter resolution from 2018 to 2021. The maps agree well with local statistics and clearly reveal shifting crop patterns. These results can support better agricultural planning, farm management, and climate-smart decisions.
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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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Manuscript not accepted for further review
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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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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...
