scholarly journals Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling

2015 ◽  
Vol 7 (1) ◽  
pp. 836-864 ◽  
Author(s):  
Joseph Mtamba ◽  
Rogier van der Velde ◽  
Preksedis Ndomba ◽  
Vekerdy Zoltán ◽  
Felix Mtalo
Keyword(s):  
Hydraulic Modeling ◽  
Landsat Tm ◽  
Roughness Coefficient ◽  
Manning’S Roughness Coefficient

scholarly journals Manning’s roughness coefficient for the Doce River

RBRH ◽  
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.

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

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.

Manning's roughness coefficient for ecological subsurface channel with modules

2019 ◽  
Vol 18 (3) ◽  
pp. 349-361 ◽  
Author(s):  
Reza Mohammadpour ◽  
Muhammad Kashfy Zainalfikry ◽  
Nor Azazi Zakaria ◽  
Aminuddin Ab. Ghani ◽  
Ngai Weng Chan
Keyword(s):  
Roughness Coefficient ◽  
Manning’S Roughness Coefficient

scholarly journals Applying micro-genetic algorithm in the one-dimensional unsteady hydraulic model for parameter optimization

2013 ◽  
Vol 16 (4) ◽  
pp. 772-783 ◽  
Author(s):  
Tsun-Hua Yang ◽  
Yu-Chi Wang ◽  
Shun-Chung Tsung ◽  
Wen-Dar Guo
Keyword(s):  
Evaluation Criteria ◽  
Hydraulic Modeling ◽  
Hydraulic Model ◽  
Roughness Coefficient ◽  
Limited Information ◽  
Good Tool ◽  
Physical Conditions ◽  
The One ◽  
Flow Quantity ◽  
River Meandering

Selection of an appropriate value for Manning's roughness coefficient could significantly impact the accuracy of a hydraulic model. However, it is highly variable and depends on flow circumstances, such as water stage and flow quantity; a stream's geomorphology, such as the fluvial process and river meandering; and physical conditions, such as the channel surface roughness and irregularities. Nevertheless, choosing proper roughness coefficients is not easy, especially with limited information and time in a practical application. Even it is done for a specific event it may not apply to another event due to its time- and site-dependency. This study proposes a Visual Basic (VB)-based system, which integrates the HEC-RAS modeling tool and the μGA to efficiently search for Manning's roughness coefficients. The matching coefficients will thereafter improve the accuracy of hydraulic modeling. Two events in the Yilan River Basin were applied to test the feasibility of the system and four evaluation criteria were used to evaluate the system performance. The results showed that μGA efficiently converged and the hydraulic model showed good agreement in comparison with the measured data. The system can be used as a good tool for finding onsite Manning's roughness coefficients in hydraulic modeling when detailed information is not available.

Manning’s N Value of Bridge-in-Backpack

2014 ◽  
Vol 638-640 ◽  
pp. 965-968
Author(s):  
Jing Ma ◽  
Ling Qiang Yang
Keyword(s):  
Water Flow ◽  
Roughness Coefficient ◽  
N Value ◽  
New Type ◽  
Manning’S Roughness Coefficient ◽  
Manning’S N ◽  
Manning's N

Bridge-in-a-Backpack is a new type bridge. this study will investigate the interaction of flow under the bridge with the tubes and decking, and recommend Manning’s roughness coefficient for water flow under the composite backbridge system.

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