A National Scale Hybrid Machine Learning Model for Enhanced Groundwater Depth Estimation
- 1Department of Hydrology Geological Survey of Denmark and Greenland Copenhagen Denmark
- 2Forschungszentrum Jülich Institute of Bio‐ and Geosciences Agrosphere (IBG‐3) Jülich Germany
- 3Princeton University High Meadows Environmental Institute Princeton NJ USA
Abstract
Groundwater is one of the most valuable natural resources, yet it is increasingly under pressure from climate change and human activities. Shallow groundwater depth (GWD) is a key indicator for evaluating groundwater availability and flood hazards, yet the measurements are often too sparse to capture its variability both in time and space. In this study, we evaluated hybrid data‐driven machine learning (ML) models that combine the strengths of existing hydrological simulations with the predictive power of ML methods for improved GWD estimation in Denmark. The hybrid model reduced mean bias of hydrological simulations from 1.72 to 0.22 m and decreased the Root Mean Square Error by about 1.64 m at national scale. Furthermore, we demonstrated that increasing the availability of GWD observations over time and space enhances model performance. We also illustrated the flexibility and effectiveness of the hybrid approach for GWD estimation across scales, and results showed that the hybrid model developed with coarse spatial resolutions can be easily used for high‐resolution GWD estimation without significant decrease of accuracy. This study highlights the potential of hybrid modeling as a practical solution for improving groundwater quantification accuracy and shows avenues for higher‐resolution estimates.
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BibTeX
@article{liu2026national,
title = {{A National Scale Hybrid Machine Learning Model for Enhanced Groundwater Depth Estimation}},
author = {Jun Liu and Raphael J. M. Schneider and Lars Troldborg and Yueling Ma and Reed Maxwell and Julian Koch},
journal = {Water Resources Research},
year = {2026},
month = oct,
doi = {10.1029/2026wr043414},
url = {https://doi.org/10.1029/2026wr043414},
note = {CC BY},
}
RIS
TY - JOUR
TI - A National Scale Hybrid Machine Learning Model for Enhanced Groundwater Depth Estimation
AU - Jun Liu
AU - Raphael J. M. Schneider
AU - Lars Troldborg
AU - Yueling Ma
AU - Reed Maxwell
AU - Julian Koch
PY - 2026
DA - 2026/10/28
JO - Water Resources Research
DO - 10.1029/2026wr043414
UR - https://doi.org/10.1029/2026wr043414
AB - Groundwater is one of the most valuable natural resources, yet it is increasingly under pressure from climate change and human activities. Shallow groundwater depth (GWD) is a key indicator for evaluating groundwater availability and flood hazards, yet the measurements are often too sparse to capture its variability both in time and space. In this study, we evaluated hybrid data‐driven machine learning (ML) models that combine the strengths of existing hydrological simulations with the predictive power of ML methods for improved GWD estimation in Denmark. The hybrid model reduced mean bias of hydrological simulations from 1.72 to 0.22 m and decreased the Root Mean Square Error by about 1.64 m at national scale. Furthermore, we demonstrated that increasing the availability of GWD observations over time and space enhances model performance. We also illustrated the flexibility and effectiveness of the hybrid approach for GWD estimation across scales, and results showed that the hybrid model developed with coarse spatial resolutions can be easily used for high‐resolution GWD estimation without significant decrease of accuracy. This study highlights the potential of hybrid modeling as a practical solution for improving groundwater quantification accuracy and shows avenues for higher‐resolution estimates.
SN - 1944-7973
SN - 0043-1397
ER -