Interpreting the Manning Roughness Coefficient in Overland Flow Simulations with Coupled Hydrological-Hydraulic Distributed Models

There is still little experience on the effect of the Manning roughness coefficient in coupled hydrological-hydraulic distributed models based on the solution of the Shallow Water Equations (SWE), where the Manning coefficient affects not only channel flow on the basin hydrographic network but also rainfall-runoff processes on the hillslopes. In this kind of model, roughness takes the role of the concentration time in classic conceptual or aggregated modelling methods, as is the case of the unit hydrograph method. Three different approaches were used to adjust the Manning roughness coefficient in order to fit the results with other methodologies or field observations—by comparing the resulting time of concentration with classic formulas, by comparing the runoff hydrographs obtained with aggregated models, and by comparing the runoff water volumes with observations. A wide dispersion of the roughness coefficients was observed to be generally much higher than the common values used in open channel flow hydraulics.

Еstimating the Chézy roughness coefficient as a characteristic of hydraulic resistance to flow in river channels: a general overview, existing challenges, and ways of their overcoming

This paper deals with results of a systemized overview of the Chézy roughness coefficient calculation problem as one most frequently used empirical characteristics of hydraulic resistance. The overview is given in the context of the formation of reliable empirical data needed to support hydro-engineering calculations and mathematical modelling of open flows in river channels. The problem topicality is because of a large number of practical tasks which need such a pre-research. In many cases, the accuracy of determining empirical hydraulic resistance characteristics can largely affect the accuracy of solving tasks relating to designing hydraulic structures and water management regardless of chosen mathematical models and methods.Rivers are characterized by a significant variety of flow conditions; hydraulic resistance to flows in rivers can thus vary widely determining their flow capacity. Considering the variety of river hydro-morphology and hydrology, the Chézy roughness coefficient often appears to be the most complete characteristic of hydraulic resistance to open flows in river channels comparing with other integral empirical characteristics of hydraulic resistance.At present, there are a large number of empirical and semi-empirical formulas to calculate the Chézy roughness coefficient. The main aim of this study was to analyze and systematize them in the context of providing proper support to the open channel hydraulics tasks. To achieve the aim of the study, a literature review regarding the problem of determining the integral hydraulic resistance characteristics to open flow in river channels was performed, as well as formulas used to calculate the Chézy roughness coefficient in practice were explored and systemized. In total, 43 formulas to calculate the Chézy roughness coefficient, as well as 13 formulas that can be used to estimate the Manning roughness coefficient were analyzed and systematized. Based on all these formulas, about 250 empirical equations can be compiled to calculate the Chézy coefficient depending on hydro-morphological peculiarities of rivers and river channels, hydraulic conditions, formulas application limits, and so on.

Overland Flow Resistance Law under Sparse Stem Vegetation Coverage

To explore the characteristics of overland flow resistance under the condition of sparse vegetative stem coverage and improve the basic theoretical research of overland flow, the resistance characteristics of overland flow were systematically investigated under four slope gradients (S), seven flow discharges (Q), and six degrees of vegetation coverage (Cr). The results show that the Manning roughness coefficient (n) changes with the ratio of water depth to vegetation height (h/hv) while the Reynolds number (Re), Froude number (Fr), and slope (S) are closely related to vegetation coverage. Meanwhile, h/hv, Re, and Cr have strong positive correlations with n, while Fr and S have strong negative correlations with n. Through data regression analysis, a power function relationship between n and hydraulic parameters was observed and sensitivity analysis was performed. It was concluded that the relationship between n and h/hv, Re, Cr, Q, and S shows the same law; in particular, for sparse stem vegetation coverage, Cr is the dominant factor affecting overland flow resistance under zero slope condition, while Cr is no longer the first dominant factor affecting overland flow resistance under non-zero slope condition. In the relationship between n and Fr, Cr has the least effect on overland flow resistance. This indicates that when Manning roughness coefficient is correlated with different hydraulic parameters, the same vegetation coverage has different effects on overland flow resistance. Therefore, it is necessary to study overland flow resistance under the condition of sparse stalk vegetation coverage.

The potential of FFNN and MLP-FFA approaches in prediction of Manning coefficient in ripple and dune bedforms

An accurate prediction of roughness coefficient is of substantial importance for river management. The current study applies two artificial intelligence methods namely; Feed Forward Neural Network (FFNN) and Multilayer Perceptron Firefly Algorithm (MLP-FFA) to predict the Manning roughness coefficient in channels with dune and ripple bedforms. In this regard, based on the flow and sediment particles properties various models were developed and tested using some available experimental data sets. The obtained results showed that the applied methods had high efficiency in the Manning coefficient modeling. It was found that both flow and sediment properties were effective in modeling process. Sensitivity analysis proved the Reynolds number plays a key role in the modeling of channel resistance with dune bedform and Froude number and the ratio of the hydraulic radius to the median grain diameter play key roles in the modeling of channel resistance with ripple bedform. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was performed and obtained results showed an allowable degree of uncertainty for the MLP-FFA model in roughness coefficient modeling.

New Method for Estimating Roughness Coefficient for Debris Flows

Flow resistance is a fundamental control of flow hydraulics in streams and rivers. In this paper, five dimensionless factors affecting the Manning roughness coefficient n and attributed to the external roughness coefficient n1 and the internal roughness coefficient n2 were analyzed comprehensively. And then, dimensionless factors affecting n1 and n2 with precise physical meanings were proposed. With a calculation method for roughness coefficient fitted and analyzed based on observation data from published research papers, the analysis results showed that the external resistance coefficient is closely related to the dimensionless factor D84/R. The correlation between the dimensionless factor (D16/D50) and the internal roughness coefficient n2 was not significant. While the factors H/D50, J, and Sv showed significant correlation. In addition, the expression of external roughness n1 is calibrated based on the observation data of 102 cross-sections listed in previous works, while the internal roughness n2 is calibrated by 20 experimental model tests. Finally, an equation describing the Manning’s roughness coefficient is presented and verified based on 24 groups of observation data from Dongchuan Debris Flow Observation Station (DDFORS) in China. This study is contributing toward a comprehensive model for the Manning coefficient, which provide a scientific reference for the research on disaster prevention and mitigation of debris flow.