Calibration of WetSpa Distributed Hydrological Model using NSGA-II and Black Widow Multi-Objective Optimization Algorithms

Conceptual rainfall-runoff (RR) models are one of the simple and efficient tools in hydrological modeling. These models simulate the flow regime using mathematical equations using input data such as precipitation, evapotranspiration and measured temperature, and basin topographic information. Calibration of RR models, e.g. WetSpa which has been developed in Belgium, is a process in which parameter adjustment are made so as to match the dynamic behavior of the RR model to the observed behavior of the catchment. This research presents an application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Black Widow Optimization (BWO) for multi-objective calibration of WetSpa in Gorganroud river basin, Iran to optimize 11 global parameters of the WetSpa model. The objective functions are Nash–Sutcliffe and logarithmic Nash–Sutcliffe efficiencies in order to improve the model's performance. The WetSpa model then was applied for a period of 1-year flood simulation in the basin and the results were analyzed. Results showed that the evolutionary NSGA-II and BWO algorithms are capable of locating optimal parameter sets in the search space. The measured correlation coefficient in the calibration process was 0.69 and 0.81 for the NSGA-II and BWO algorithms, respectively. Moreover, a sensitivity analysis was conducted on the global parameters in which the surface runoff coefficient was the most sensitive parameter of the model.