Application of artificial intelligence (AI) in hydrologic modeling is receiving increasing attention to explore new optimization approaches to the complex nonlinear high-dimensional models. The concept of developing region wise effective rainfall runoff models has always been a subject of interest to hydrologists with regard to its practical importance in water management and planning. The main goal of this research is the investigations on the application and adaptation of tank model to selected arid watersheds in the UAE. Tank model has been selected as for several reasons including its simplicity in presenting the runoff processes, its flexible adaption and its capability to produce real catchment hydrograph if suitable parameters' calibration is attained. The research applies two evolutionary computing (EC) methods to calibrate the parameter of the conceptual rainfall runoff model (i.e., the Tank model). The two global optimization methods (GOM, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are used to optimize the Tank model parameters applied to actual rainfall runoff hydrograph. The performance of two methods have been compared under different scenarios and environments based on two objective functions namely, the root means square error (RMSE) and Nash–Sutcliffe model efficiency coefficient (NSE). The results proved the capability of GOM to calibrate the 4-stage Tank model with 16 parameters, even under insufficient data availability (as in the case of UAE watersheds). Both techniques performed satisfactorily with slight superiority of GA over the PSO. Also, NSE proved that it can be considered a superior criterion, to use as objective function, compared to RMSE. To this end, based on the particular characteristics of arid watersheds in UAE, several model structures (i.e., number of tanks and outlets from each tank) was explored and analyzed. Second, in order to enhance the predictive ability of the model, parameters optimal calibration was performed using the two a