Liu et al. (2026) A river-augmented highly parameterized linear inverse method for enhanced water table estimation across China

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-08-18
• Authors: Ailin Liu, Jianying Jiao
• DOI: 10.1016/j.jhydrol.2026.136272

Research Groups

• Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, China Three Gorges University
• College of Civil Engineering and Architecture, China Three Gorges University

Short Summary

The study develops a river-augmented highly parameterized linear inverse method (HPLIMRG) that utilizes river-stage observations to improve the accuracy and reduce the uncertainty of water table mapping in regions where monitoring wells are scarce.

Objective

• To enhance the estimation of water table depth and reduce associated uncertainties by incorporating river-stage information as additional hydraulic-head constraints along river corridors.

Study Configuration

• Spatial Scale: Continental scale (China) and regional scale (High Plains Aquifer), utilizing a 1 km resolution.
• Temporal Scale: Specific snapshots for December 2024 and March 2025.

Methodology and Data

• Models used: HPLIMRG (River-augmented highly parameterized linear inverse method) and HPLIMG (groundwater-only linear inverse method).
• Data sources: In situ well measurements and river-stage observations.

Main Results

• Synthetic Experiments: RMSE was reduced from 0.68 m (HPLIMG) to 0.55 m (HPLIMRG).
• High Plains Aquifer: RMSE decreased from 0.79 m to 0.35 m, and the median standard deviation of the water table dropped from 0.37 m to 0.097 m.
• Continental China Application: RMSE improved from 5.04 m to 4.22 m, while uncertainty was dramatically reduced from 1.29 m to 0.06 m.

Contributions

• The research provides a robust and scalable framework for large-scale water table mapping by effectively converting river networks into a dense, physically constrained observational network, significantly overcoming the limitations imposed by sparse monitoring well distributions.

Funding

• Not mentioned in the provided text.

Citation

@article{Liu2026riveraugmented,
  author = {Liu, Ailin and Jiao, Jianying},
  title = {A river-augmented highly parameterized linear inverse method for enhanced water table estimation across China},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.136272},
  url = {https://doi.org/10.1016/j.jhydrol.2026.136272}
}