Manning’s N Value of Bridge-in-Backpack

The prediction of arid region flash floods (magnitude and frequency) is essential to ensure the safety of human life and infrastructures and is commonly based on hydrological models. Traditionally, catchment characteristics are extracted using point-based measurements. A considerable improvement of point-based observations is offered by remote sensing technologies, which enables the determination of continuous spatial hydrological parameters and variables, such as surface roughness, which significantly influence runoff velocity and depth. Hydrological models commonly express the surface roughness using Manning’s roughness coefficient (n) as a key variable. The objectives were thus to determine surface roughness by exploiting a new high spatial resolution spaceborne synthetic aperture radar (SAR) technology and to examine the correlation between radar backscatter and Manning’s roughness coefficient in an arid environment. A very strong correlation (R2 = 0.97) was found between the constellation of small satellites for Mediterranean basin observation (COSMO)-SkyMed SAR backscatter and surface roughness. The results of this research demonstrate the feasibility of using an X-band spaceborne sensor with high spatial resolution for the evaluation of surface roughness in flat arid environments. The innovative method proposed to evaluate Manning’s n roughness coefficient in arid environments with sparse vegetation cover using radar backscatter may lead to improvements in the performance of hydrological models.

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Field Determination of Manning's n Value for Shrubs and Woody Vegetation

Engineering Approaches to Ecosystem Restoration ◽

10.1061/40382(1998)7 ◽

1998 ◽

Cited By ~ 4

Author(s):

Gary E. Freeman ◽

Ronald R. Copeland ◽

William Rahmeyer ◽

David L. Derrick

Keyword(s):

Woody Vegetation ◽

N Value ◽

Manning’S N ◽

Manning's N

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Hydraulic and Hydrologic Modeling of Steep Channel of Putih River, Magelang District, Central Java Province, Indonesia

Journal of the Civil Engineering Forum ◽

10.22146/jcef.26507 ◽

2017 ◽

Vol 3 (3) ◽

pp. 125

Author(s):

Adi Putri Anisa Widowati

Keyword(s):

Rainfall Intensity ◽

Infiltration Rate ◽

Hydraulic Modeling ◽

Rainfall Runoff ◽

N Value ◽

Watershed Response ◽

Central Java ◽

Manning’S N ◽

Manning's N ◽

Central Java Province

Hydrologic and hydraulic modeling are important to be conducted to examine the watershed response based on a rainfall input, especially over disaster-prone watershed such as Putih River watershed in Magelang, Central Java Province. A GIS-based grid-based distributed rainfall-runoff model was used to simulate the rainfall-runoff transformation. A two-dimensional hydrodynamic flow modeling was then carried out to simulate the flood processes on the stream and floodplain area. A sensitivity analysis was conducted on infiltration rate, Manning’s n value, and rainfall intensity. Infiltration rate, Manning’s n value, and rainfall intensity give considerable effects to the resulted flow hydrographs. The modeling results show that the results of hydrologic-hydraulic modeling is in good agreement with the observed results.

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The Manning’s Roughness Coefficient Calibration Method to Improve Flood Hazard Analysis in the Absence of River Bathymetric Data: Application to the Urban Historical Zamora City Centre in Spain

Applied Sciences ◽

10.3390/app11199267 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9267

Author(s):

Julio Garrote ◽

Miguel González-Jiménez ◽

Carolina Guardiola-Albert ◽

Andrés Díez-Herrero

Keyword(s):

Urban Areas ◽

Flood Hazard ◽

Flood Damage ◽

City Centre ◽

Average Error ◽

Accurate Estimation ◽

Roughness Coefficient ◽

Flow Depth ◽

Manning’S N ◽

Manning's N

The accurate estimation of flood risk depends on, among other factors, a correct delineation of the floodable area and its associated hydrodynamic parameters. This characterization becomes fundamental in the flood hazard analyses that are carried out in urban areas. To achieve this objective, it is necessary to have a correct characterization of the topography, both inside the riverbed (bathymetry) and outside it. Outside the riverbed, the LiDAR data led to an important improvement, but not so inside the riverbed. To overcome these deficiencies, different models with simplified bathymetry or modified inflow hydrographs were used. Here, we present a model that is based upon the calibration of the Manning’s n value inside the riverbed. The use of abnormally low Manning’s n values made it possible to reproduce both the extent of the flooded area and the flow depth value within it (outside the riverbed) in an acceptable manner. The reduction in the average error in the flow depth value from 50–75 cm (models without bathymetry and “natural” Manning’s n values) to only about 10 cm (models without bathymetry and “calibrated” Manning’s n values), was propagated towards a reduction in the estimation of direct flood damage, which fell from 25–30% to about 5%.

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Calibration method for Manning's roughness coefficient for a river flume model

Water Science & Technology Water Supply ◽

10.2166/ws.2020.235 ◽

2020 ◽

Vol 20 (8) ◽

pp. 3597-3603

Author(s):

Yujian Li ◽

Yixin Geng ◽

Liang Mao

Keyword(s):

Numerical Simulation ◽

Error Analysis ◽

Water Surface ◽

Calibration Method ◽

Empirical Method ◽

Tarim River ◽

Roughness Coefficient ◽

Flume Experiments ◽

N Value ◽

Manning’S Roughness Coefficient

Abstract This paper takes the Tarim River as an example to study the selection of Manning's roughness coefficient (n) in numerical simulation and presents a new method for calibrating Manning's roughness coefficient of a flume model. The measured topographic data and hydraulic data obtained from the flume experiments are taken as the initial boundary conditions in flow simulation, and n value for a flume model of the Qiman reach of Tarim River is calibrated by using a CCHE2D model. The consistency between the simulated water surface and the measured water surface with different n value is compared by using the error analysis method. Manning's n value for a flume model which meets the minimum error requirements is determined. The relative error between n value obtained by the empirical method and n value obtained by the numerical simulation method is analyzed. The result shows that the calibration method of n value for a flume model by using the CCHE2D model and error analysis presented in this paper is feasible and reliable.

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Gene expression programming to predict Manning’s n in meandering flows

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2016-0569 ◽

2018 ◽

Vol 45 (4) ◽

pp. 304-313 ◽

Cited By ~ 6

Author(s):

Arpan Pradhan ◽

Kishanjit K. Khatua

Keyword(s):

Gene Expression ◽

Field Study ◽

Discharge Capacity ◽

Gene Expression Programming ◽

Roughness Coefficient ◽

Compound Channel ◽

Factors Affecting ◽

Manning’S N ◽

Bed Material ◽

Manning's N

Accurate prediction of Manning’s roughness coefficient is essential for the computation of conveyance capacity in open channels. There are various factors affecting the roughness coefficient in a meandering compound channel and not just the bed material. The factors, geometric as well as hydraulic, are investigated and incorporated in the prediction of Manning’s n. In this study, a new and accurate technique, gene expression programming (GEP) is used to estimate Manning’s n. The estimated value of Manning’s n is used in the evaluation of the conveyance capacity of meandering compound channels. Existing methods on conveyance estimation are assessed to carry out a comparison between them and the proposed GEP model. Results show that the discharge capacity computed by the new model provides far better results than the traditional models. The developed GEP model is validated with three individual sections of a natural river, signifying that the model can be applied to field study of rivers, within the stated range of parameters.

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Automated calibration of a two-dimensional overland flow model by estimating Manning's roughness coefficient using genetic algorithm

Journal of Hydroinformatics ◽

10.2166/hydro.2017.110 ◽

2017 ◽

Vol 20 (2) ◽

pp. 440-456

Author(s):

J. Drisya ◽

D. Sathish Kumar

Keyword(s):

Genetic Algorithm ◽

Flow Model ◽

Overland Flow ◽

Fitness Function ◽

Roughness Coefficient ◽

Model Parameters ◽

Automatic Data ◽

Automated Calibration ◽

Manning’S Roughness Coefficient ◽

Overland Flow Model

Abstract Calibration is an important phase in the hydrological modelling process. In this study, an automated calibration framework is developed for estimating Manning's roughness coefficient. The calibration process is formulated as an optimization problem and solved using a genetic algorithm (GA). A heuristic search procedure using GA is developed by including runoff simulation process and evaluating the fitness function by comparing the experimental results. The model is calibrated and validated using datasets of Watershed Experimentation System. A loosely coupled architecture is followed with an interface program to enable automatic data transfer between overland flow model and GA. Single objective GA optimization with minimizing percentage bias, root mean square error and maximizing Nash–Sutcliffe efficiency is integrated with the model scheme. Trade-offs are observed between the different objectives and no single set of the parameter is able to optimize all objectives simultaneously. Hence, multi-objective GA using pooled and balanced aggregated function statistic are used along with the model. The results indicate that the solutions on the Pareto-front are equally good with respect to one objective, but may not be suitable regarding other objectives. The present technique can be applied to calibrate the hydrological model parameters.

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Manning’s roughness coefficient for the Doce River

RBRH ◽

10.1590/2318-0331.231820180013 ◽

2018 ◽

Vol 23 (0) ◽

Author(s):

Emmanuel Kennedy da Costa Teixeira ◽

Márcia Maria Lara Pinto Coelho ◽

Eber José de Andrade Pinto ◽

Jéssica Guimarães Diniz ◽

Aloysio Portugal Maia Saliba

Keyword(s):

River Flow ◽

Hydraulic Modeling ◽

Roughness Coefficient ◽

Local Approach ◽

Water Courses ◽

Time Variations ◽

The Subject ◽

Manning’S Roughness Coefficient ◽

Doce River ◽

Water Depths

ABSTRACT The Manning’s roughness coefficient is used for various hydraulic modeling. However, the decision on what value to adopt is a complex task, especially when dealing with natural water courses due to the various factors that affect this coefficient. For this reason, most of the studies carried out on the subject adopt a local approach, such as this proposal for the Doce River. Due to the regional importance of this river in Brazil, the objective of this article was to estimate the roughness coefficient of Manning along the river, in order to aid in hydraulic simulations, as well as to discuss the uncertainties and variations associated with this value. For this purpose, information on flow rates and water depths were collected at river flow stations along the river. With this information, the coefficients were calculated using the Manning equation, using the software Canal, and their space-time variations were observed. In addition, it was observed that the uncertainties in flow and depth measurements affect the value of the Manning coefficient in the case studied.

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