Velocity profiles assessment in natural channels during high floods

Moramarco, Tommaso; Saltalippi, Carla; Singh, Vijay P.

doi:10.2166/nh.2011.064

Cited by 35 publications

(26 citation statements)

References 12 publications

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“…In this context, to carry out new velocity measurements is feasible. In fact, on the one hand, the sampling of umax only is not costly, considering that umax occurs in the upper portion of the flow area [7] and, on the other hand, the hydraulic radius, RH, can be easily computed if the geometry of river section is known. Therefore, in order to verify the possibility of assessing Φ from α values, a backwards-testing procedure is considered.…”

Section: Estimation Of φ On the Basis Of A Historical Sample Of Convementioning

confidence: 99%

“…Entropy theory [4] shows that umax is strictly linked to the mean flow velocity, um, through a linear entropic relationship based on a single parameter, M, which is a characteristic of the gauged river site in particular [5,6], and also of the river in general [7]. Therefore by sampling umax, the information on um (and hence on discharge) can be inferred also for high flows in situations where the flow area is known.…”

Section: Introductionmentioning

confidence: 99%

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Conventional Point-Velocity Records and Surface Velocity Observations for Estimating High Flow Discharge

Corato¹,

Ammari

Moramarco

2014

Entropy

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Flow velocity measurements using point-velocity meters are normally obtained by sampling one, two or three velocity points per vertical profile. During high floods their use is inhibited due to the difficulty of sampling in lower portions of the flow area. Nevertheless, the application of standard methods allows estimation of a parameter, α, which depends on the energy slope and the Manning roughness coefficient. During high floods, monitoring of velocity can be accomplished by sampling the maximum velocity, umax, only, which can be used to estimate the mean flow velocity, um, by applying the linear entropy relationship depending on the parameter, M, estimated on the basis of historical observed pairs (um, umax). In this context, this work attempts to analyze if a correlation between α and M holds, so that the monitoring for high flows can be addressed by exploiting information from standard methods. A methodology is proposed to estimate M from α, by coupling the "historical" information derived by standard methods, and "new" information from the measurement of umax surmised at later times. Results from four gauged river sites of different hydraulic and geometric characteristics have shown the robust estimation of M based on α.

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Section: Estimation Of φ On the Basis Of A Historical Sample Of Convementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Conventional Point-Velocity Records and Surface Velocity Observations for Estimating High Flow Discharge

Corato¹,

Ammari

Moramarco

2014

Entropy

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“…Indeed, based on field data it is shown in several papers that the surface velocities can be properly approximated through functions such as a parabola or an ellipse. Moramarco et al [27,35] justified the validity of these equations for fitting the surface velocities through the Chezy's formula. Specifically, in this study the following functions are used: (a) parabolic function type 1: two parabolas are drawn, the vertex of both coinciding with the only point of measurement and passing through the left and right banks; (b) parabolic function type 2: two parabolas are drawn, with their vertices on the banks and each passing through the point of measurement of the maximum velocity; (c) elliptic function: two branches of an ellipse are drawn, both passing through the point of measurement of the maximum velocity and through the banks; (d) cubic function: two cubic parabolas are drawn, with their vertex on the banks and both passing through the only available point of measurement.…”

Section: Methodsmentioning

confidence: 98%

“…Equation (6) thus has validity on the y-axis, but different authors [25][26][27][28] have shown that it is reasonable in practical applications to extend this profile to the generic vertical of a cross-section without having to rely on Equation (5) for the assigned z.…”

Section: The Entropy Methodsmentioning

confidence: 99%

Three Methods for Estimating the Entropy Parameter M Based on a Decreasing Number of Velocity Measurements in a River Cross-Section

Farina

Alvisi

Franchini

et al. 2014

Entropy

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Abstract:The theoretical development and practical application of three new methods for estimating the entropy parameter M used within the framework of the entropy method proposed by Chiu in the 1980s as a valid alternative to the velocity-area method for measuring the discharge in a river is here illustrated. The first method is based on reproducing the cumulative velocity distribution function associated with a flood event and requires measurements regarding the entire cross-section, whereas, in the second and third method, the estimate of M is based on reproducing the cross-sectional mean velocity U by following two different procedures. Both of them rely on the entropy parameter M alone and look for that value of M that brings two different estimates of U , obtained by using two different M-dependent-approaches, as close as possible. From an operational viewpoint, the acquisition of velocity data becomes increasingly simplified going from the first to the third approach, which uses only one surface velocity measurement. The procedures proposed are applied in a case study based on the Ponte Nuovo hydrometric station on the Tiber River in central Italy.

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“…O primeiro tipo são os modelos que usam medidas de velocidades ou vazões. Eles requerem menos informações de batimetria, mas necessitam de informações de vazões e da seção para ajustar os parâmetros do modelo (CORATO et al, 2011;MORAMARCO et al, 2011). Informações de velocidades podem ser empregadas para este fim, mas deve-se considerar um perfil de velocidades para estimar a vazão (CHOO et al, 2013(CHOO et al, , 2014.…”

Section: Introductionunclassified

Uso de modelo hidrodinâmico para determinação da vazão a partir de medições de nível

Oliveira

Pereira

Soares

et al. 2016

RBRH

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Este é um artigo publicado em acesso aberto (Open Access) sob a licença Creative Commons Attribution, que permite uso, distribuição e reprodução em qualquer meio, sem restrições desde que o trabalho original seja corretamente citado. RESUMOO uso de modelos hidrodinâmicos para a determinação das vazões em cursos d'água tem crescido nos últimos anos devido aos problemas relativos à utilização do método da curva-chave para monitoramento das vazões. Este trabalho tem por objetivo desenvolver um método para determinação das vazões em canais abertos utilizando algoritmos evolucionários multiobjetivos (NSGA-II) e medições de nível da superfície livre associadas a um modelo hidrodinâmico. Dados obtidos de ensaios em um canal experimental de laboratório foram utilizados para calibração e validação do modelo. O algoritmo de otimização NSGA-II apresentou excelentes resultados, obtendo-se coeficientes de Nash-Sutcliffe superiores a 0,99 tanto para a calibração quanto para a validação do modelo hidrodinâmico. As vazões calculadas pelo modelo hidrodinâmico apresentaram uma variação de até 150% para um mesmo nível quando comparadas às vazões estimadas pela curva-chave, o que demonstrou que o uso da curva-chave para medição de vazão em regime variável apresenta erros elevados. Palavras-chave:Otimização multiobjetivo; Equações de Saint-Venant; Escoamento não permanente; NSGA-II; Curva-chave. ABSTRACTThe use of hydrodynamic models to determine stream discharges has been grown in recent years due to the problems regarding the use of rating curve method for discharge estimating. This study aims to develop a method for determining discharges in open channels using multiobjective evolutionary algorithms (NSGA-II) and free surface level measurements associated with a hydrodynamic model. Data obtained from experiments on a laboratory experimental channel have been used for model calibration and validation. NSGA-II optimization algorithm presented excellent results and Nash-Sutcliffe coefficients greater than 0.99 have been obtained for both calibration and validation of the hydrodynamic model. Discharges calculated by the hydrodynamic model showed variations of up to 150% for the same level when compared to the discharges estimated by the rating curve, which showed that the use of rating curve for discharge measurements during transient flows results in high errors.

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Velocity profiles assessment in natural channels during high floods

Cited by 35 publications

References 12 publications

Conventional Point-Velocity Records and Surface Velocity Observations for Estimating High Flow Discharge

Conventional Point-Velocity Records and Surface Velocity Observations for Estimating High Flow Discharge

Three Methods for Estimating the Entropy Parameter M Based on a Decreasing Number of Velocity Measurements in a River Cross-Section

Uso de modelo hidrodinâmico para determinação da vazão a partir de medições de nível

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