A National Scale Hybrid Model for Enhanced Groundwater Depth Estimation

Groundwater is an essential part of the hydrological system and is increasingly affected by climate variability and human pressures. Spatial and temporal variation of groundwater depth (GWD), defined as the depth to the saturated zone below ground surface, is a key variable for assessing groundwater–surface interactions and for evaluating risks to infrastructure, land use, droughts and flooding. In-situ measurements of GWD are often too sparse to capture its variability both in time and space and modelling becomes a necessity for consistent assessment.

In this study, we evaluated hybrid machine learning (ML) models that combine the strengths of existing hydrological simulations from Physically Based Models (PBM) with the predictive power of ML methods for improved GWD estimation in Denmark. The hybrid model reduced mean bias of PBM GWD estimates from 1.65 m to 0.21 m and decreased the Root Mean Square Error (RMSE) by about 1.5 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 effectively used for high-resolution GWD estimation while maintaining a satisfactory level of accuracy. Specifically, the mean RMSE is reduced from 2.66 m for the model trained and applied at 500 m to only 2.30 m for the model trained at 500 m but applied at 10 m. Similarly, for the model trained at 100 m but applied at 10 m for prediction the RMSE is reduced from 2.35 m to 2.28 m.

This study highlights the potential of hybrid modeling as a practical solution for improving groundwater quantification accuracy and shows avenues for higher-resolution estimates.