Adjoint-method-based estimation of Manning roughness coefficient in an overland flow model

2015 ◽  
Author(s):  
Van Tri Nguyen ◽  
Didier Georges ◽  
Gildas Besancon
Keyword(s):  
Adjoint Method ◽  
Flow Model ◽  
Overland Flow ◽  
Roughness Coefficient ◽  
Manning Roughness ◽  
Overland Flow Model ◽  
Manning Roughness Coefficient

scholarly journals Overland Flow Resistance Law under Sparse Stem Vegetation Coverage

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1657
Author(s):  
Jingzhou Zhang ◽  
Shengtang Zhang ◽  
Si Chen ◽  
Ming Liu ◽  
Xuefeng Xu ◽  
...  
Keyword(s):  
Flow Resistance ◽  
Overland Flow ◽  
Vegetation Coverage ◽  
Dominant Factor ◽  
Roughness Coefficient ◽  
Hydraulic Parameters ◽  
Slope Condition ◽  
Manning Roughness ◽  
The Relationship ◽  
Manning Roughness Coefficient

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.

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.

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

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3433
Author(s):  
Marcos Sanz-Ramos ◽  
Ernest Bladé ◽  
Fabián González-Escalona ◽  
Gonzalo Olivares ◽  
José Luis Aragón-Hernández
Keyword(s):  
Channel Flow ◽  
Overland Flow ◽  
Roughness Coefficient ◽  
Runoff Water ◽  
Distributed Models ◽  
Water Volumes ◽  
Manning Roughness ◽  
Runoff Hydrographs ◽  
Manning Roughness Coefficient

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.

Algebraic Solution of the Horton-Izzard Turbulent Overland Flow Model of the Rising Hydrograph

1977 ◽  
Vol 8 (4) ◽  
pp. 249-256 ◽  
Author(s):  
Mohammad Akram Gill
Keyword(s):  
Differential Equation ◽  
Turbulent Flow ◽  
Flow Model ◽  
Overland Flow ◽  
Flow Equation ◽  
Algebraic Solution ◽  
Mixed Flow ◽  
Overland Flow Model

In the differential equation of the overland turbulent flow which was first postulated by Horton, Eq.(6), the value of c equals 5/3. For this value of c, the flow equation could not be integrated algebraically. Horton solved the equation for c = 2 and believed that his solution was valid for mixed flow. The flow equation with c = 5/3 is solved algebraically herein. It is shown elsewhere (Gill 1976) that the flow equation can indeed be integrated for any rational value of c.

scholarly journals Modelling Pluvial Flooding in Urban Areas Coupling the Models Iber and SWMM

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2647
Author(s):  
Esteban Sañudo ◽  
Luis Cea ◽  
Jerónimo Puertas
Keyword(s):  
Urban Areas ◽  
Flow Model ◽  
Overland Flow ◽  
Drainage System ◽  
Urban Drainage ◽  
Dynamic Link Library ◽  
Storm Water Management Model ◽  
Urban Street ◽  
Sewer Network ◽  
Overland Flow Model

Dual urban drainage models allow users to simulate pluvial urban flooding by analysing the interaction between the sewer network (minor drainage system) and the overland flow (major drainage system). This work presents a free distribution dual drainage model linking the models Iber and Storm Water Management Model (SWMM), which are a 2D overland flow model and a 1D sewer network model, respectively. The linking methodology consists in a step by step calling process from Iber to a Dynamic-link Library (DLL) that contains the functions in which the SWMM code is split. The work involves the validation of the model in a simplified urban street, in a full-scale urban drainage physical model and in a real urban settlement. The three study cases have been carefully chosen to show and validate the main capabilities of the model. Therefore, the model is developed as a tool that considers the main hydrological and hydraulic processes during a rainfall event in an urban basin, allowing the user to plan, evaluate and design new or existing urban drainage systems in a realistic way.

scholarly journals Experimental study on determining Manning roughness coefficient during slope runoff process

2020 ◽  
Vol 39 (4) ◽  
pp. 651-659
Author(s):  
Yashan CHENG ◽  
Zhonggen WANG ◽  
Jun LI ◽  
Zhen HUANG ◽  
Xiangyu YE ◽  
...  
Keyword(s):  
Experimental Study ◽  
Roughness Coefficient ◽  
Manning Roughness ◽  
Slope Runoff ◽  
Manning Roughness Coefficient

Uncertainty in Rating-Curves Due to Manning Roughness Coefficient

2019 ◽  
Vol 33 (15) ◽  
pp. 5153-5167 ◽  
Author(s):  
Sajjad M. Vatanchi ◽  
Mahmoud F. Maghrebi
Keyword(s):  
Roughness Coefficient ◽  
Rating Curves ◽  
Manning Roughness ◽  
Manning Roughness Coefficient
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