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.

3D–3D Computations on Submerged-Driftwood Motions in Water Flows with Large Wood Density around Driftwood Capture Facility

The accumulation of driftwood during heavy rainfall may block river channels and damage structures. It is necessary to mitigate such effects by periodically capturing and removing driftwood from rivers. In this study, the behavior of driftwood in open-channel flows with a relatively large wood density was modeled numerically. The water flow and driftwood motion were solved three-dimensionally, with an Euler-type flow model coupled with a Lagrange-type driftwood motion model. A piece of driftwood was modeled as a set of connected spherical elements in a straight line for easy analysis using a discrete element method. Wood with specific gravity exceeding 1 will travel along a position near the riverbed and will be affected by bed friction. In addition, friction forces for sliding and rolling motions are considerably different. Therefore, in the numerical model, a bed friction term was introduced between the bed and driftwood considering the anisotropy of the friction force. The variation in the drag force of water flow on driftwood was also considered depending on the angle between the driftwood trunkwise direction and flow direction. The model was applied under the same conditions as those used in a laboratory experiment on driftwood behavior around an inlet-type driftwood capture facility. The computational results showed that the proposed model could qualitatively reproduce the driftwood behavior around the capture facility. The secondary flow patterns at the approaching reach and the capture ratio were found to be strongly affected by the turbulence model and the Manning roughness coefficient.

Computing the sediment and ensuing its erosive activities using HEC-RAS to surmise the flooding in Kulfo River in Southern Ethiopia

Purpose The change in sediment transport phenomenon and morphological characteristics of Kulfo River in the southern part of Ethiopia is estimated using one-dimensional hydraulic modelling. The purpose of this study is to predict erosion and sedimentation using hydrological engineering center-river analysis system (HEC-RAS) model. Design/methodology/approach Geometrical survey data of 2005 and 2019 were used to assess the impact of flood depth with 100 years of return per period on the morphology of the river. The bed and bank materials at selected sites of the river were sampled to estimate the grain size using manning roughness coefficient. Discharge and suspended sediment concentration were sampled thrice per day for a stretch of three months to develop a rating curve. Findings HEC-RAS model indicates that flood depth with 100 years return period had a significant inundated area during 2019 in comparison to 2005 demonstrating a temporary change in the morphology of the river. Acker and White method in HEC-RAS was used extensively to calculate the sediment load and subsequently calibrated. In the upper reach of the study area, there was aggradation and degradation, whereas the only degradation was noted in the middle to the downstream reach of the channel. Seasonal flood during peak flow due to a rise in bed level is most prevalent during the aggradation period. Originality/value Comparison of a flood depth inundating the catchment and sediment deposition has been intricately analyzed by using HEC-RAS model.

Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Determining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio “κ” was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program.

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.

One and Two-Dimensional Hydraulic Simulation of a Reach in Al-Gharraf River

One and two-dimensional hydraulic models simulations are important to specify the hydraulic characteristics of unsteady flow in Al-Gharraf River in order to define the locations that facing problems and suggesting the necessary treatments. The reach in the present study is 58200m long and lies between Kut and Hai Cities. Both numerical models were simulated using HEC-RAS software, 5.0.4, with flow rates ranging from 100 to 350 m3/s. Multi-scenarios of gates openings of Hai Regulator were applied. While the openings of Al-Gharraf Head Regulator were ranged between 60cm to fully opened. The suitable manning roughness for the unsteady state was 0.025. The obtained results show that the average velocities for the one-dimensional model were ranged between 0.36 and 0.5 m/s, and the average water surface elevations range between 15.14 m and 17.84 m. While these values ranged between 0.25 and 0.44 m/s and 14.125 and 18.82 m respectively for the two-dimensional model. The simulation results of the two-dimensional model were more accurate than their corresponding one-dimensional model, due to more agreement of these values with measured values, which achieved minimum values of the root mean square error and the determination coefficient.