Li et al. (2026) Fine‐Scale Characterization of Groundwater Recharge Efficacy Under Ecological Water Replenishment: An AI‐Enhanced Learning Framework Benchmarked Against Traditional Geostatistics

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Geophysical Research Letters
• Year: 2026
• Date: 2026-03-25
• Authors: Wei Li, Chao Lü, Jingsi Zhu, Yu Liu, Chengcheng Wu, Qin Liu, Bo Liu, Longcang Shu
• DOI: 10.1029/2025gl121538

Research Groups

Not specified in the abstract.

Short Summary

This study reconstructs high-resolution (250 m) groundwater level dynamics in the Yongding River basin using LightGBM and multi-source data, demonstrating that Ecological Water Replenishment (EWR) drives groundwater recovery but with diminishing marginal returns, while outperforming traditional interpolation methods.

Objective

• To reconstruct high-resolution (250 m) groundwater level dynamics in the Yongding River basin, Beijing.
• To quantify the feature importance influencing groundwater levels.
• To elucidate the spatiotemporal impacts of Ecological Water Replenishment (EWR) on groundwater recovery.

Study Configuration

• Spatial Scale: Yongding River basin, Beijing; 250 m resolution for groundwater level dynamics.
• Temporal Scale: Not explicitly specified, but implies dynamic changes over time.

Methodology and Data

• Models used: Light Gradient Boosting Machine (LightGBM); compared against traditional Kriging interpolation.
• Data sources: Multi-source datasets, including dynamic hydrological variables and static geographic factors.

Main Results

• LightGBM simulations significantly outperform traditional Kriging interpolation for reconstructing groundwater levels.
• Dynamic hydrological variables dominate model construction, while static geographic factors are crucial for maintaining spatial consistency, mitigating limitations of purely data-driven approaches.
• Ecological Water Replenishment (EWR) drives groundwater recovery.
• The efficiency of EWR exhibits distinct diminishing marginal returns.

Contributions

• Provides a high-resolution (250 m) reconstruction of groundwater level dynamics using LightGBM, demonstrating its superior performance over traditional methods.
• Elucidates the spatiotemporal impacts of EWR, including the finding of diminishing marginal returns in its efficiency.
• Offers scientific insights to optimize water allocation strategies in water-scarce regions.
• Integrates static geographic factors to maintain spatial consistency, addressing limitations of purely data-driven models.

Funding

Not specified in the abstract.

Citation

@article{Li2026FineScale,
  author = {Li, Wei and Lü, Chao and Zhu, Jingsi and Liu, Yu and Wu, Chengcheng and Liu, Qin and Liu, Bo and Shu, Longcang},
  title = {Fine‐Scale Characterization of Groundwater Recharge Efficacy Under Ecological Water Replenishment: An AI‐Enhanced Learning Framework Benchmarked Against Traditional Geostatistics},
  journal = {Geophysical Research Letters},
  year = {2026},
  doi = {10.1029/2025gl121538},
  url = {https://doi.org/10.1029/2025gl121538}
}