Articles | Volume 30, issue 3
https://doi.org/10.5194/hess-30-503-2026
© 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-30-503-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Revealing the causes of groundwater level dynamics in seasonally frozen soil zones using interpretable deep learning models
Han Li
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130026, China
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130026, China
Institute of Water Resources and Environment, Jilin University, Changchun,130021, China
Hang Lyu
CORRESPONDING AUTHOR
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130026, China
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130026, China
Institute of Water Resources and Environment, Jilin University, Changchun,130021, China
Boyuan Pang
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130026, China
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130026, China
Institute of Water Resources and Environment, Jilin University, Changchun,130021, China
Xiaosi Su
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130026, China
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130026, China
Institute of Water Resources and Environment, Jilin University, Changchun,130021, China
Weihong Dong
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130026, China
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130026, China
Institute of Water Resources and Environment, Jilin University, Changchun,130021, China
Yuyu Wan
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130026, China
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130026, China
Institute of Water Resources and Environment, Jilin University, Changchun,130021, China
Tiejun Song
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130026, China
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130026, China
Institute of Water Resources and Environment, Jilin University, Changchun,130021, China
Xiaofang Shen
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130026, China
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130026, China
Institute of Water Resources and Environment, Jilin University, Changchun,130021, China
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Han Cao, Jinlong Qian, Huanliang Chen, Chunwei Liu, Shuai Gao, Minghui Lyu, Weihong Dong, and Caiping Hu
Hydrol. Earth Syst. Sci., 29, 5213–5231, https://doi.org/10.5194/hess-29-5213-2025, https://doi.org/10.5194/hess-29-5213-2025, 2025
Short summary
Short summary
This study presents a multi-method framework assessing managed aquifer recharge (MAR) and extraction effects on groundwater dynamics in a typical temperate semi-arid fissured karst system, Jinan's Baotu Spring area. Results show MAR and extraction significantly influence karst groundwater levels and quality. The complementary techniques enhance the quantitative research accuracy and provide practical references for MAR in karst regions with similar hydrogeological conditions.
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Short summary
Groundwater level dynamics under freeze-thaw conditions remain unclear. We use interpretable deep learning to simulate water table changes and identify seasonal drivers in seasonally frozen regions. During freeze-thaw, changes in soil water potential cause two-way exchange between soil water and groundwater, while rainfall, runoff, and irrigation dominate in other periods. These insights inform groundwater modeling and management in cold regions.
Groundwater level dynamics under freeze-thaw conditions remain unclear. We use interpretable...