Developing a new lumped monthly water balance model for estimating groundwater level and runoff volume

In this study, a water balance model, namely, the RAK model, is developed, which links a tank groundwater model and a surface water balance model. The RAK model optimizes the parameters of the two linked models simultaneously by minimizing errors in the groundwater table and surface outflow estimations. It is expected that by this model, more accurate estimates of water exchanges between surface and groundwater sources can be obtained compared to previously developed monthly water balance models. A Genetic Algorithm (GA) was used to optimize the parameters of the proposed model. To evaluate the efficiency of the proposed model, it was calibrated and validated for the Ghorve-Deh-Gholan basin located in northwestern Iran. The results of this study indicated the potential of the proposed approach for rapid water balance modeling of basins, specifically those located in areas with limited data availability.