Study on the Application Method of Aquifer Depth Distribution Patterns as Model Input on the Performance of a Physically Based Distributed Hydrologic Model

Groundwater discharge is critical for maintaining river flow during dry seasons, especially in lowland areas. Despite its significance, groundwater resources have often been overlooked highlighting the need for comprehensive studies amidst growing pressure to develop new water resources. This study focuses on the Soyang River Basin, South Korea, including its ungauged northern regions, the nearby DMZ (Demilitarized Zone), using the physically based Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model. A three-year simulation was conducted to examine variable aquifer depth distribution patterns by assuming an inverse relationship between surface elevation and aquifer bottom depth. Three case studies (i.e., equal distribution, linear regression, and logarithmic regression) were evaluated and compared. The method to identity optimal aquifer depth distributions to enhance groundwater simulation accuracy in regions with significant topographical variation was incorporated. Groundwater levels at six monitoring sites showed that altitude-based variable aquifer depths outperformed the equal distribution case. The results showed strong agreement between simulated and observed values, particularly in the linear regression case with an R-squared statistic of 0.858 and Nash–Sutcliffe Efficiency index of 0.789, indicating that linear regression-based aquifer depth estimation can significantly improves long-term runoff modeling and groundwater simulation accuracy. The logarithmic regression case had the lowest relative peak error in peak flow. These findings highlight the importance of adjusting aquifer depth distributions in physically based hydrologic models to better reflect real-world conditions. Overall, this study contributes to advance groundwater modeling by integrating variable aquifer depth distributions into a physically based hydrologic model for large scale watersheds.