Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 3
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.
https://doi.org/10.5194/hess-30-503-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 3
Research article
 | 
02 Feb 2026
Research article |  | 02 Feb 2026

Revealing the causes of groundwater level dynamics in seasonally frozen soil zones using interpretable deep learning models

Han Li, Hang Lyu, Boyuan Pang, Xiaosi Su, Weihong Dong, Yuyu Wan, Tiejun Song, and Xiaofang Shen

Viewed

Total article views: 4,959 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,758 1,015 186 4,959 151 223
  • HTML: 3,758
  • PDF: 1,015
  • XML: 186
  • Total: 4,959
  • BibTeX: 151
  • EndNote: 223
Views and downloads (calculated since 05 May 2025)
Cumulative views and downloads (calculated since 05 May 2025)

Viewed (geographical distribution)

Total article views: 4,959 (including HTML, PDF, and XML) Thereof 4,893 with geography defined and 66 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 19 Sep 2026
Download
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.
Read more
Share