Majid Niazkar - PhD. Candidate, Department of Civil and Environmental Engineering, School of Engineering,Shiraz University, Shiraz, Iran
Seied Hosein Afzali - Assistant Professor, Department of Civil and Environmental Engineering, School of Engineering,Shiraz University, Shiraz, Iran

Nonlinear Muskingum models improve the accuracy of the lumped hydrologic flood routing methods. In this study, two new improved versions of nonlinear Muskingum model with seven and nine parameters are proposed. The parameters of these models are calibrated using Modified Honey Bee Mating Optimization algorithm. The performances of the new models were compared with fourteen models available in the literature for the most widely used case study in this field. The obtained results show that the new models achieve the best results among the ones considered in the comparison and reduced the best SSQ value of three-parameter Muskingum model by 85%.

Flood routing, nonlinear Muskingum models, Storage function, Parameter estimation,Modified Honey Bee Optimization algorithm