Efficient and Systematic Calibration of Manning’s Roughness Coefficients in River Networks: An Integrated Workflow Using Orthogonal Experiments and Successive Approximation

Hydrodynamic models of river networks are commonly used for flood disaster simulation, and the accuracy of model parameter settings directly affects the reliability of simulation results. Among these, Manning’s roughness coefficient is the core parameter for calibrating one-dimensional(1D) hydrodynamic models, as it is the most sensitive and frequently adjusted parameter. Taking the Yunxi Area of Huai’an City as a case study, this paper proposes an integrated workflow using orthogonal experiments and successive approximation for calibrating Manning’s roughness coefficients in river networks. In this workflow, 13 river reaches (from six major rivers) serve as experimental factors. The Manning’s roughness coefficients for the main channel and floodplains are assigned different values as experimental levels. Model performance is evaluated using the Nash–Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). A multi-factor and multi-level orthogonal table L27(313) of main channel or floodplains roughness is alternately selected to design 27 sets of experiments. Through HEC-RAS simulation and orthogonal analysis, the roughness coefficients of the main channel and floodplains are alternately screened and successively approximated to the target values. Finally, the roughness coefficients of the main channel and floodplains for each river reach meeting the accuracy requirements are obtained, with corresponding values of NSE = 0.93 and RMSE = 0.04 m. The results show that orthogonal experimental design significantly reduces the number of simulation tests and effectively saves computational time and costs, while the successive approximation strategy addresses the complexity of solving problems with multiple decision variables. Additionally, the experimental factors consider variations in cross-section types and hydraulic conditions along the river by setting roughness coefficients in segments. The orthogonal experimental design ensures the relevance, simultaneity, and systematic nature of parameter adjustments across all river reaches, significantly enhancing the rationality and reliability of the model parameter calibration.