Articles | Volume 30, issue 17
https://doi.org/10.5194/hess-30-5647-2026
© 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-30-5647-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Sep 2026
Research article |
| 08 Sep 2026
| 08 Sep 2026
Hydrochemistry and modeling nitrate concentration in farmland groundwater under different hydrological seasons by integrating hybrid quantum-classical ML, virtual sample generation and AlphaEarth Foundation
Junjie Xu
College of Life Sciences, Hebei University, Baoding, Hebei, 071000, China
Key Laboratory of Land Water Cycle and Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Xin Wei
College of Ecology and Environment, Institute of Disaster Prevention Science and Technology, Sanhe, Hebei, 065201, China
College of Life Sciences, Hebei University, Baoding, Hebei, 071000, China
Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education of China, Beijing Normal University, Beijing, 100875, China
Lihu Yang
Key Laboratory of Land Water Cycle and Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Yuanzheng Zhai
College of Water Sciences, Beijing Normal University, 100875, Beijing, China
Cuicui Lv
Xiong'an Institute of Innovation, Xiong'an, 071899, China
Xianfang Song
College of Life Sciences, Hebei University, Baoding, Hebei, 071000, China
Related authors
Junjie Xu, Yilei Yu, Lihu Yang, and Xin Wei
EGUsphere, https://doi.org/10.5194/egusphere-2026-2673, https://doi.org/10.5194/egusphere-2026-2673, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
As the climate warms, knowing how the land responds and where temperatures are heading is vital for adaptation. Using eighteen years of satellite and ground observations across China and a new artificial intelligence model that learns how neighboring provinces affect each other, we found that warming quickly greens vegetation but slowly dries deep soils in the north. The model predicts provincial temperatures accurately, even in fragile regions, showing soil heat is key to climate prediction.
Junjie Xu, Yilei Yu, Lihu Yang, and Xin Wei
EGUsphere, https://doi.org/10.5194/egusphere-2026-2673, https://doi.org/10.5194/egusphere-2026-2673, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
As the climate warms, knowing how the land responds and where temperatures are heading is vital for adaptation. Using eighteen years of satellite and ground observations across China and a new artificial intelligence model that learns how neighboring provinces affect each other, we found that warming quickly greens vegetation but slowly dries deep soils in the north. The model predicts provincial temperatures accurately, even in fragile regions, showing soil heat is key to climate prediction.
Yue Li, Ying Ma, Xianfang Song, Qian Zhang, and Lixin Wang
Hydrol. Earth Syst. Sci., 27, 3405–3425, https://doi.org/10.5194/hess-27-3405-2023, https://doi.org/10.5194/hess-27-3405-2023, 2023
Short summary
Short summary
We proposed an iteration method in combination with the MixSIAR model and water isotopes to quantify the river water contribution (RWC) to riparian deep-rooted trees nearby a losing river. River water can indirectly contribute by 20.3 % to water uptake of riparian trees. River recharged riparian groundwater rapidly with a short groundwater residence time (no more than 0.28 d). The RWC to riparian trees was negatively correlated with the water table depth and leaf δ13C in linear functions.
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Short summary
Nitrate from farms and villages often seeps into groundwater, threatening water safety. We studied a farming area in northern China across dry, wet, and normal seasons to track and predict nitrate. Nitrate peaked in the dry season due to evaporation, and manure plus household wastewater supplied three quarters of it. Combining artificial data samples with advanced learning methods, we predicted nitrate accurately. This helps officials find pollution hotspots cheaply and protect rural water.
Nitrate from farms and villages often seeps into groundwater, threatening water safety. We...
