Mapping and investigating regional groundwater–surface water dynamics using an integrated hydrologic model of the Kaoping River watershed, Taiwan

Climate change and the increasing reliance of high-tech industries on water resources are impacting the hydrology of the Kaoping River watershed in southern Taiwan. Effective decision-making for water resource management requires a comprehensive understanding of the interactions between groundwater and surface water systems. This study aims to address this need by using the integrated hydrologic model ParFlow-CLM to simulate hourly hydrologic processes on a 250-meter grid, enabling detailed analysis of groundwater–surface water dynamics within the watershed.

A novel aspect of this research is the application of machine learning to estimate the depth to bedrock, which provides critical insights into the subsurface structure. Additionally, a geostatistical approach, Bayesian Maximum Entropy (BME),, is utilized to estimate lithology and hydraulic conductivity, resulting in a refined and detailed hydrogeological framework.

The results reveal key groundwater and surface water interactions and produce detailed maps of the saturation zone. These findings offer insights that can serve as a foundation for informed water resource policy-making and management in the Kaoping River watershed.