Manning coefficient of roughness — a case study along Soreq stream, 1971–1981

Azmon, B.

doi:10.1016/0022-1694(92)90186-y

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…the sum of surface and sub‐surface runoff. Then, the flow velocity of the lumped runoff, V (t) (m d −1 ), is calculated following the Manning–Strickler equation (Azmon, ):

V_{((), t)} = 86400 ε_{f} \frac{R h_{((), t)}^{2 / 3} \sqrt{S}}{n}

where 86 400: number of seconds in a day; S : the slope gradient (%); n : Manning's roughness coefficient (s m −1/3 ); ε f : velocity coefficient (0 ≤ ε f ≤ 1); Rh (t) : hydraulic radius of the stream section (m) at time step t . Rh (t) is defined as the ratio of stream section area to the wetted perimeter, assuming that all W (t) contribute to runoff.…”

Section: Methodsmentioning

confidence: 97%

“…the sum of surface and sub-surface runoff. Then, the flow velocity of the lumped runoff, V (t) (m d À1 ), is calculated following the Manning-Strickler equation (Azmon, 1992):…”

Section: Hydrological Modulementioning

confidence: 99%

“…Monthly vegetation cover rate of land-use types measured in the Dong Cao watershed was used as fraction of soil surface with contact cover (Paningbatan et al, 1995;Van Dijk and Bruijnzeel, 2003). Crop coefficients and Manning's roughness coefficients of land-use types were obtained from the literature (Azmon, 1992;Allen et al, 1998;Hessel et al, 2003). Rainfall and potential evapotranspiration are used as the temporally variable input data.…”

Section: Field Measurements and Data Collectionmentioning

confidence: 99%

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Lumped surface and sub-surface runoff for erosion modeling within a small hilly watershed in northern Vietnam

et al. 2013

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Developing models to predict on-site soil erosion and off-site sediment transport at the agricultural watershed scale represent an ongoing challenge in research today. This study attempts to simulate the daily discharge and sediment loss using a distributed model that combines surface and sub-surface runoffs in a small hilly watershed (< 1km2). The semi-quantitative model, Predict and Localize Erosion and Runoff (PLER), integrates theManning-Strickler equation to simulate runoff and the Griffith University Erosion System Template equation to simulate soil detachment, sediment storage and soil loss based on a map resolution of 30m 30m and over a daily time interval. By using a basic input data set and only two calibration coefficients based, respectively, on water velocity and soil detachment, the PLER model is easily applicable to different agricultural scenarios. The results indicate appropriate model performance and a high correlation between measured and predicted data with both Nash-Sutcliffe efficiency (Ef) and correlation coefficient (r2) having values>0.9. With the simple input data needs, PLER model is a useful tool for daily runoff and soil erosion modeling in small hilly watersheds in humid tropical areas

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“…the sum of surface and sub‐surface runoff. Then, the flow velocity of the lumped runoff, V (t) (m d −1 ), is calculated following the Manning–Strickler equation (Azmon, ):

V_{((), t)} = 86400 ε_{f} \frac{R h_{((), t)}^{2 / 3} \sqrt{S}}{n}

Section: Methodsmentioning

confidence: 97%

“…the sum of surface and sub-surface runoff. Then, the flow velocity of the lumped runoff, V (t) (m d À1 ), is calculated following the Manning-Strickler equation (Azmon, 1992):…”

Section: Hydrological Modulementioning

confidence: 99%

Section: Field Measurements and Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

Lumped surface and sub-surface runoff for erosion modeling within a small hilly watershed in northern Vietnam

et al. 2013

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“…In areas outside of the river bed such as in floodplains, the study area image obtained by remote sensing techniques was classified using the controlled classification method to ensure uniformity in the study area. Although many studies have been carried out to determine the roughness coefficients for open channel flow (Azmon, 1992;Mohamoud, 1992;Kaiser et al, 2015), studies for flood plains in dense vegetative parts or urban boundaries are limited. The aim of this study is to propose a new procedure involving remote sensing techniques as well as experimental studies to determine the roughness coefficients for river-stream beds and floodplains outside of the river areas.…”

Section: Each Component Of the Equation Is Explained As Followsmentioning

confidence: 99%

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

Demir

KESKİN

2020

Geofizika (Online)

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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.

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“…Many studies have pointed out that K increases with an increase in hydraulic radius (e.g. Jarrett, 1984;Azmon, 1992;Tarekul Islam & Chowdhury, 2003). Furthermore, K may vary markedly at high stages if the banks are rough and grassy.…”

Section: The Theoretical Background Of the Jones Formulamentioning

confidence: 99%

Modelling stage—discharge relationships affected by hysteresis using the Jones formula and nonlinear regression

Petersen‐Øverleir

2006

Hydrological Sciences Journal

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Gauging stations where the stage-discharge relationship is affected by hysteresis due to unsteady flow represent a challenge in hydrometry. In such situations, the standard hydrometric practice of fitting a single-valued rating curve to the available stage-discharge measurements is inappropriate. As a solution to this problem, this study provides a method based on the Jones formula and nonlinear regression, which requires no further data beyond the available stage-discharge measurements, given that either the stages before and after each measurement are known along with the duration of each measurement, or a stage hydrograph is available. The regression model based on the Jones formula rating curve is developed by applying the monoclinal rising wave approximation and the generalized friction law for uniform flow, along with simplifying assumptions about the hydraulic and geometric properties of the river channel in conjunction with the gauging station. Methods for obtaining the nonlinear least-squares rating-curve estimates, while factoring in approximated uncertainty, are discussed. The broad practical applicability and appropriateness of the method are demonstrated by applying the model to: (a) an accurate, comprehensive and detailed database from a hydropower-generated highly dynamic flow in the Chattahoochee River, Georgia, USA; and (b) data from gauging stations in two large rivers in the USA affected by hysteresis. It is also shown that the model is especially suitable for post-modelling hydraulic and statistical validation and assessment. Key words looped rating curve; hysteresis; Jones formula; nonlinear regression; stage-discharge relationship; streamflow measurement; unsteady channel flow Modélisation des relations hauteur-débit affectées par de l'hystérésis en utilisant la formule de Jones et la régression non-linéaire Résumé Les stations de jaugeage où la relation hauteur-débit est affectée par de l'hystérésis due à de l'écoulement non-stationnaire représentent un défi pour l'hydrométrie. Dans de telles situations, la pratique standard, qui est d'ajuster aux mesures une courbe de tarage bijective entre la hauteur et le débit, n'est plus adaptée. Comme solution à ce problème, cette étude propose une méthode basée sur la formule de Jones et la régression non-linéaire qui n'exige pas de données supplémentaires, vu qu'on dispose soit d'un limnigramme, soit des hauteurs avant et après prise de mesure et de la durée de la mesure. La modélisation par régression de la courbe de tarage par la formule de Jones consiste à appliquer l'approximation de vague levante monoclinale et la loi généralisée de frottement pour l'écoulement uniforme, avec des hypothèses simplificatrices sur les propriétés hydrauliques et géométriques du lit de la rivière associée à la station de jaugeage. Les méthodes d'obtention des courbes de tarage par estimation non-linéaire aux moindres carrées sont discutées, tout en incluant une approximation de l'incertitude. La pertinence et le large champ d'application de la méthode sont démontrés...

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Manning coefficient of roughness — a case study along Soreq stream, 1971–1981

Cited by 13 publications

References 5 publications

Lumped surface and sub-surface runoff for erosion modeling within a small hilly watershed in northern Vietnam

Lumped surface and sub-surface runoff for erosion modeling within a small hilly watershed in northern Vietnam

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

Modelling stage—discharge relationships affected by hysteresis using the Jones formula and nonlinear regression

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