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

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

doi:10.5194/hess-30-503-2026

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

Data sets

Long-term series of daily snow depth dataset in China (1979-2024) Tao Che et al. https://doi.org/10.11888/Geogra.tpdc.270194

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

Model code and software

oreopie/hydro-interpretive-dl: Updated implementation for European catchments (v0.3.0) Shijie Jiang https://doi.org/10.5281/zenodo.4686106

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.