Comparison of peak discharge and runoff characteristic estimates from the rational method to field observations for small basins in central Virginia

Hayes, Donald C.; Young, Richard L.

doi:10.3133/sir20055254

Cited by 28 publications

(24 citation statements)

References 17 publications

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“…6), a range typical for small watersheds (Hayes and Young 2005). Also, the model performances exhibit largest variations for watersheds located in the states of Arizona, Idaho, Ohio and Oklahoma (Fig.…”

Section: Model Performancementioning

confidence: 95%

“…Usually, t c is defined as the time required for a drop of water to travel from the most hydraulically distant point in a watershed to its outlet (Wigham 1970). A variety of formulas for t c have been developed (Papadakis andKazan 1987, Viessman andLewis 2002), but those formulas could give inconsistent values (Hayes and Young 2005) for a given watershed. If a watershed is gauged, its t c can be visually estimated as the time difference from the end of excess precipitation on a hyetograph to the inflection point on the recession portion of the resulting flow hydrograph.…”

Section: Description Of the Rational Equationmentioning

confidence: 99%

“…In practice, the rainfall-runoff process is most commonly modelled using either the Rational Equation (Mulvaney 1851, Turazza 1880, Kuichling 1889 or the US Department of Agriculture (USDA) Soil Conservation Service (SCS; presently Natural Resources Conservation Service or NRCS) curve number (CN) method (USDA-NRCS 2004), because these two methods are simple, easy to understand, and applicable for gauged as well as ungauged watersheds (Wigham 1970, Sharma and Singh 1992, Kurothe et al 2001, Sahu et al 2007. The Rational Equation is often recommended for estimating design peak runoff from impervious areas (Guo 2001) and small (<40 ha) rural watersheds (Hewlett andHibbert 1967, Hayes andYoung, 2005), and is rarely used to predict direct runoff depth or volume (Debo and Reese 2003). The tabulated values (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Development of a robust runoff-prediction model by fusing the Rational Equation and a modified SCS-CN method

Wang

Liu

Yang

2012

Hydrological Sciences Journal

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The objective of this study is to develop a Modified Rational Equation (MoRE) that combines the advantages of the Rational Equation (e.g. simplicity and global acceptance) and those of the standard US Department of Agriculture (USDA) Soil Conservation Service (SCS) curve number (CN) method (e.g. easy parameterization and extensive verification across the world). Herein, the hypothesis is that the MoRE is more accurate, consistent and robust than the SCS-CN method and its improved versions in predicting runoff in watersheds with limited data. The MoRE was designed to have a simple structure that is described by four intrinsic parameters: CN, permanent wilting point, field capacity and saturation soil moisture, and does not include initial abstraction as a variable. An evaluation of 77 USDA small agricultural watersheds indicated that CN of the MoRE has different physical meanings from CN of the SCS-CN method. The MoRE (mean Nash-Sutcliffe coefficient, E > 0.73) performed better than the SCS-CN (mean E < 0.32) and the four improved models (mean E < 0.56) in reproducing the runoff of the study watersheds. Performance of all six models varied greatly between watersheds, as well as between events, but was independent of watershed drainage area. However, the model performances tend to be better for watersheds and/or events with a runoff-to-rainfall ratio of between 0.1 and 0.3 than for those with a ratio outside this range. The MoRE has the most consistent and robust performance. Elaboration d'une modèle robuste d'estimation du ruissellement fusionnant la méthode rationnelle et une méthode du SCS-CN modifiéeRésumé L'objectif de cette étude est de développer une méthode rationnelle modifiée (MoRE), qui combine les avantages de la méthode rationnelle (par exemple la simplicité et l'acceptation mondiale) et ceux de la méthode standard du "curve number" (CN) du Soil Conservation Service (SCS) du Département américain de l'agriculture (USDA) (par exemple le paramétrage aisé et une vérification approfondie à travers le monde). Ici, l'hypothése est que la méthode MoRE est plus précise, cohérente et robuste que la méthode SCS-CN et que ses versions améliorées pour prévoir le ruissellement dans les bassins versants avec des données limitées. La méthode MoRE a été conçue pour avoir une structure simple qui est décrite par quatre paramètres intrinsèques: CN, le point de flétrissement permanent, la capacité au champ et l'humidité du sol à saturation, et qui ne comprend pas comme variable le prélèvement initial. Une évaluation sur 77 petits bassins versants agricoles de l'USDA a indiqué que le paramètre CN de la méthode MoRE a des significations physiques différentes du CN de la méthode SCS-CN. La méthode MoRE (avec un critère de Nash-Sutcliffe moyen, E, supérieur à 0,73) obtient de meilleurs résultats que la méthode SCS-CN (moyenne E inférieure 0,32) et que les quatre modèles améliorés (moyenne E inférieure à 0,56) pour la reproduction de l'écoulement des bassins versants d'étude. La performance des six modèles varie grandement en...

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Section: Model Performancementioning

confidence: 95%

Section: Description Of the Rational Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a robust runoff-prediction model by fusing the Rational Equation and a modified SCS-CN method

Wang

Liu

Yang

2012

Hydrological Sciences Journal

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“…The main aim of the design is to obtain the pipe diameter sizes considering peak discharges. These discharges are estimated using a linear translatory transport method or rational method [ Brutsaert , ; Dooge , ; Hayes and Young , ]. Peak discharge

Q_{j}

using rational method for a subarea j is estimated for using information about area

A_{j}

, runoff coefficient

C_{j}

and design storm intensity

i_{j}

.…”

Section: Optimization Model Formulation For Stormsewer Designmentioning

confidence: 99%

Climate change‐sensitive hydrologic design under uncertain future precipitation extremes

Teegavarapu

2013

Water Resources Research

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[1] Precipitation as an important component of hydrologic cycle bears a significant influence on hydrologic design and water resources management. The uncertainties associated with future climate change coupled with limitations of climate change models and uncertainties in projections, our inability to quantify these introduce additional complexities in hydrologic design using future precipitation extremes. A new optimal compromise hydrologic design of a stormsewer system using a fuzzy mixed integer nonlinear mathematical programming (MINLP) model with discrete, binary variables and logical constraints is developed and evaluated in this study. Preferences of hydrologists toward expected uncertain future changes in precipitation extremes are modeled using linear, nonlinear, triangular and Gaussian fuzzy membership functions. Methods for deriving membership functions based on multimodel multiple scenario GCM-based simulations are also suggested. Incorporation of triangular functions in optimization formulation required the use of binary variables. A hypothetical hydrologic design example with realistic parameter values is used to obtain compromise elements of stormsewer system. Results from the study suggest a compromise design is possible based on the preferences and a balance between over and under design of stormsewer infrastructure is achievable. The nature of membership functions reflecting the preferences attached to future extremes influence the optimal solutions obtained resulting in changes in cost-based performance measures.

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“…This is because it is a simple equation and all the parameters involved can be easily obtained. The method is based on the assumptions that the rainfall intensity and storm duration is uniform over the study area, that the storm duration must be equal to the time of concentration of the catchment, and that the runoff coefficient must be constant during the storm [9]. The runoff coefficient C j is a dimensionless parameter that represents the percentage of rainfall appearing as runoff.…”

Section: Rational Methodsmentioning

confidence: 99%

Estimating Peak Flow-Discharge During Extreme Rainfall Events for the Gao-Ping River, Taiwan

CHEN

Yang

Ching

2016

WIT Transactions on State-of-the-Art in Science and Engineering

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Typhoon Morakot struck central and southern Taiwan on August 8, 2009. The high intensity and accumulation of rainfall induced floods, landslides and debris flows. During Typhoon Morakot, the flood level was beyond the design limit of many river embankments in southern Taiwan. It is, therefore, desirable to estimate the peak flow-discharge for similar extreme rainfall events in future. In this study, the destructive flood in the Gao-ping river in southern Taiwan, caused by Typhoon Morakot, was selected as an area for case analysis. First, hydrological and geomorphological data for historical typhoons together with rainfall data were collected and analyzed. Next, the rational method, combined with a number of equations, was proposed to estimate peak flow-discharges in the river produced by extreme rainfall events. The estimated peak flow-discharges of historical events were then compared with the observation data acquired from stream gauge stations and previous studies. The results showed that the proposed method is able to estimate the flow-discharge in the Gao-ping river with reasonable accuracy if the flow rate of interest is greater than 5000 cms or the return period of interest is more than 100 years. It could, therefore, be used to determine the peak flow-discharge for extreme rainfall events.

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Comparison of peak discharge and runoff characteristic estimates from the rational method to field observations for small basins in central Virginia

Cited by 28 publications

References 17 publications

Development of a robust runoff-prediction model by fusing the Rational Equation and a modified SCS-CN method

Development of a robust runoff-prediction model by fusing the Rational Equation and a modified SCS-CN method

Climate change‐sensitive hydrologic design under uncertain future precipitation extremes

Estimating Peak Flow-Discharge During Extreme Rainfall Events for the Gao-Ping River, Taiwan

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