Assessment of the Soil Conservation Service–Curve Number method performance in a tropical Oxisol watershed

Alves, Geovane Junqueira; Mello, Carlos Rogério de; Beskow, Samuel; Junqueira, José A.; Nearing, M. A.

doi:10.2489/jswc.74.5.500

Cited by 10 publications

(18 citation statements)

References 40 publications

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“…Several studies that applied M1 to estimate the CN parameter in watersheds reported that its values obtained from the standard tables differed significantly from those empirically computed from observed data (Kowalik and Walega, 2015). In this sense, authors from different regions of the world have investigated and reported better estimates of CN values for watersheds using methods based on the calibration of observed data (D'Asaro et al, 2014;Ajmal and Kim, 2014;Alves et al, 2019).…”

Section: Cn Values Obtained From M1 M2 M3 and M4 Methodsmentioning

confidence: 99%

“…Knowledge of the hydrological cycle is essential for the adequate management of water resources. In this sense, direct surface runoff (DSR) is one of the most important components of the hydrological cycle, especially when considering practical issues involving hydrological engineering (Alves et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Ponce and Hawkins (1996) reported that λ depends on the relief, vegetation, and climatological characteristics of the watershed. Due to the direct influence of λ on the estimates of ER, the definition of its value has been discussed by several authors (Alves et al, 2019;Mishra and Singh., 2006;Valle Junior et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…It is known that the SCS/CN model requires the CN parameter, which can be calculated for each rainfall-runoff event by using data on ER and Ia, as in the study by Alves et al (2019). However, when there are limitations or absence of observed data, CN is determined based on the tabulated CN values included in the method documentation for various soil/land use complexes, without considering the characteristics of the rainfall event.…”

Section: Introductionmentioning

confidence: 99%

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Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

et al. 2021

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The Soil Conservation Service Curve Number Model is a conceptual model intended for estimating effective rainfall (ER). This model is grounded in a parameter – referred to as Curve Number (CN), which is determined from information on the characteristics of the watershed. The Standard Method (M1) for determining the CN is based on soil and land-use tables; however, some authors have proposed alternative methodologies for defining the CN value from monitored rainfall-runoff events, such as those described by Hawkins (1993) (M2), Soulis and Valiantzas (2012) (M3), and Soulis and Valiantzas (2013) (M4). The objective of this study was to evaluate the impact of using these methods for determination of the CN parameter on the estimation of ER, taking as reference forty rainfall-runoff events monitored between 2015 and 2018 in the Cadeia River Watershed, which has characteristics of the Pampa biome. The different methods assessed for definition of the CN parameter resulted in contrasting performances with respect to the estimation of ER for CRW, as the following findings: i) M1 gave ER values with little reliability, mainly due to the classification of antecedent moisture content classes; ii) M3 provided the best results in determining ER, followed by M2; and iii) the ER values estimated according to M4 differed from those observed, mainly for events with lower rainfall depths.

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Section: Cn Values Obtained From M1 M2 M3 and M4 Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

et al. 2021

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“…Several models for estimating direct runoff have been developed (Alves et al 2019;Mello et al 2016;Mohammad and Adamowski 2015;Soomro et al 2019;Yu 2012). The SCS-CN method is one of the most popular for rainwater flow modeling applied to the design of hydraulic and hydrological simulation in different regions (Alves et al 2019;Mello et al 2016;Yu 2012;Banasik et al 2014;D'Asaro et al 2014;Kim and Shin 2019;Mishra et al 2012;Suresh Babu and Mishra 2012;Thong et al 2019).Because of its simplicity, it was the first tool used to provide input information for practical mechanics designed to reduce soil erosion and sediment transport (Jena et al 2012). The methodology is based on concepts of surface runoff produced in each precipitation event associated with runoff coefficients.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of rainfall–runoff parameters models to estimate flows

et al. 2021

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Determinate the runoff of a watershed is a challenge due to the complexity of representing all “inlets” and “outlets” involved in a rainfall–runoff model. Therefore, methodologies applied for this purpose should have a good representation of the variables that most influence in this process. One of the models used to calculate the design flow is the (USDA in Urban Hydrology for Small. Technical release, no 55 (TR-55). Soil Conservation Service. Washigton, DC, http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Urban+Hydrology+for+Small+watersheds#1, 1986), which considers the analysis changes in soil coverage, time of concentration (tc), and recurrence period (T). In this way, this study sought to evaluate the hydrological behavior of a watershed with an increase in soil waterproofing. These modifications were correlated with the variation of runoff coefficients (CN), modifications of the periods of recurrence indicated by the literature, and different equations of the time of concentration. Its application was carried out in the Ribeirão do Suru watershed, Santana de Parnaíba, SP, Brazil. The CN {75; 80; 85; 90} increased 3.14, 5.61, 10.90 and 15.85%, respectively. In the most critical situation, runoff was 15.85% higher in estimated CN. The variation of precipitation as a function of T (2, 5, 10, 25, 50, 100 and 500) and application of 11 time of concentration methods designed 132 hydrographs and flow values that were statistically treated in T of Student and in the Analysis of Variance (ANOVA). Except for Bransby Willians associated Cinematic Method, Dooge with Johnstone and CTH with Tsuchyia, the pairs showed degrees of correlation below 59%. The greatest correlation was observed in Jonhstone with Dooge (90%), followed by the Kinematic Method with the Soil Conservation Service Method (83%) and with Dodge (74%). As a result, it was possible to demonstrate the behavior of the SCS parameters to minimize subjectivities and revealing how each parameter impacts the flow of the watershed. Finally, the sensitivity attributed to T was the highest among the three analyzed.

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Assessment of Spatial and Temporal Soil Water Storage Using a Distributed Hydrological Model

Andrade

Viola

Beskow

et al. 2020

Water Resour Manage

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Assessment of the Soil Conservation Service–Curve Number method performance in a tropical Oxisol watershed

Cited by 10 publications

References 40 publications

Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

Sensitivity analysis of rainfall–runoff parameters models to estimate flows

Assessment of Spatial and Temporal Soil Water Storage Using a Distributed Hydrological Model

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