A new method for dividing flood period in the variable-parameter Muskingum models

• Published in: Hydrology Research 2022-01, Vol.53 (1), p.241-257
• Main Authors: Akbari, Reyhaneh, Hessami-Kermani, Masoud-Reza
• Format: Article
• Language: English
• Subjects: Accuracy; Case studies; Flood hydrographs; Flood routing; Floods; Genetic algorithms; Hydrographs; Hydrology; Mathematical models; Methods; Model accuracy; Modelling; Morphology; muskingum model; Objective function; Optimization algorithms; Parameter estimation; Parameters; particle swarm optimization-genetic algorithm; random selection; river routing; Uncertainty; Uncertainty analysis
• ISSN: 0029-1277; 1998-9563; 2224-7955
• DOI: 10.2166/nh.2021.192

The Muskingum routing model is favored by water engineers owing to its simplicity and accuracy. A large amount of research is done to improve the accuracy of the model. One way to do so is to consider variable hydrological parameters during the flood routing period. In this study, the random selection (RS) method was proposed to divide the flood period of the nonlinear Muskingum model into three sub-periods. The proposed method was based on RS of members in each sub-region. It was applied to rout three flood hydrographs, and the objective function was the sum of squared errors. Comparing the results from the three variable-parameter nonlinear Muskingum model with those from the variable-parameter nonlinear Muskingum models in previous studies, the proposed model optimized the objective function in these hydrographs up to 61%. The uncertainty analysis of Muskingum parameters for Wilson's hydrograph was performed by the fuzzy alpha cut method, and it was found that the uncertainty of the parameter x is greater than the uncertainty of the parameters k and m.