A hybrid physics–ML framework for integrating groundwater dynamics into land surface modeling: a ParFlow-based demonstration in headwater watersheds of the Pearl River Basin, China

Three-dimensional groundwater dynamics play a critical role in regulating land–atmosphere interactions, yet resolving three-dimensional subsurface flow processes at large scales remains computationally prohibitive. Here we present a hybrid coupling framework that enables the integration of three-dimensional groundwater processes into land surface modeling at substantially reduced computational cost. The framework replaces the physics-based groundwater solver with a deep learning surrogate while preserving the original coupling interface, providing a practical pathway for incorporating groundwater dynamics into Earth system simulations. A key feature of the framework is an error-control strategy based on a free-drainage lower bound, which approximates the treatment of subsurface processes in conventional land surface models where groundwater feedback is largely neglected. The hybrid solution is considered acceptable when its deviation remains below the free-drainage bound and, when specified, a user-defined threshold, allowing the fidelity criterion to be tailored to specific applications. The framework is demonstrated over three headwater watersheds covering approximately 34 000 km 2 in the Pearl River Basin, China, using the coupled CoLM/ParFlow model as the physics-based reference. The hybrid system achieves an 18.24× speedup while maintaining strong agreement with the physics-based reference. Over the 8040 h evaluation period (hours 721–8760 of water year 2021), the median water table depth error is within 0.5 m and the domain-averaged latent heat flux reaches a Kling–Gupta efficiency of 0.959. This study demonstrates the feasibility of hybrid surrogate–physics coupling for representing groundwater processes and provides a flexible foundation for multi-timescale, on-demand simulations. Although the trained surrogate is specific to the present study domain, the framework may be extended to other hydroclimatic settings through domain-specific retraining or fine-tuning and integrated into Earth system modeling frameworks.