Methods for Predicting the Values of Manning’s Roughness Coefficient: State of the Art
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Abstract
Manning's roughness coefficient (n) is crucial for reliable hydraulic analysis of natural and artificial open channels. This paper presents a state-of-the-art work on principles of flow resistance mechanisms of natural channels and n estimation approaches. Furthermore, it reviews classical empirically driven approaches, such as Strickler, Cowan, Meyer–Peter and Müller, Limerinos, and Henderson, as well as tabulated, photographic, and storage-based techniques. The latter is a consideration in terms of formulations that factor into vegetation effects, bed material characteristics, bedform-induced resistance, and composite channel roughness. Newer advances in entropy theory, statistical approaches, and data-driven methods are also introduced. The study describes both the advantages and disadvantages of specific methods and shows that no way to predict Manning’s n can be applied universally under different flow conditions and channel types. Instead, the precise estimation has to be based on empirical field observations, analytical construction, and engineering judgment. The observations support the use of an integrated and hybrid approach and can be supplemented with high-resolution measurements and standardized databases for the purpose of minimizing prediction uncertainty for the roughness estimation. This review contributes to enhancing the knowledge and application base of researchers and practitioners in river hydraulics, channel design, and flood modeling.
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