Novel Simulation Model for Designing Artificial Recharge Basins to Mitigate Groundwater Salinity

Designing an optimal artificial recharge (AR) system that is both highly efficient and cost-effective is vital for effective groundwater management and salinity control. This research introduces a novel simulation model called the simulation for basin artificial recharge (SBAR), developed to optimize the design and operation of AR basins, specifically focusing on the number, size, and timing of recharge events. The SBAR model simulates key parameters, including recharge volume, effective infiltration, evaporation losses, excess outflow, and the volume of water stored after filtration during operation. This was achieved by utilizing data on inflow rates, infiltration dynamics, evaporation volumes, and the storage capacity of the system. Various management factors were also considered to evaluate the optimized results from the model. The newly developed model and evaluation criteria were applied to a saline groundwater case at an industrial site to assess its applicability and to characterize parameter values at a real-world scale under various scenarios. As a result, the most optimized scenario involved 10 basins, each measuring 200 m by 50 m, with an inflow rate of 0.5 m3/s and a recharge period of 5 days per month during December, January, and February. Under this scenario, groundwater salinity could decrease from approximately 4 0,000 to about 3,000 µS/cm over 5 years. Additional factors such as long-term sustainability, hydrological dynamics, economic viability, and performance metric were also assessed to guide the design. This study demonstrates that optimized AR using a well-established SBAR model can effectively reduce groundwater salinity, supporting sustainable management and rehabilitation of polluted industrial groundwater resources.