Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots

Lal, Mohan; Mishra, Sumeet; Pandey, Ashish; Pandey, Rajendra; Meena, Pramod Kumar; Chaudhary, Anubhav; Jha, Ranjit Kumar; Shreevastava, Ajit Kumar; Kumar, Yogendra

doi:10.1007/s10040-016-1460-5

Cited by 66 publications

(58 citation statements)

References 70 publications

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“…Nevertheless, this criterion has not been re‐evaluated since it was published, including the validity of the value (1 in.). Furthermore, in Lal et al () is suggested a value equal to 15 mm and this threshold is acknowledged as an arbitrary criterion.…”

Section: Selection Of Representative Rainfall–run‐off Events As a Soumentioning

confidence: 99%

Sources of uncertainty in the NRCS CN model: Recognition and solutions

Durán‐Barroso

González

Valdés

2017

Hydrological Processes

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The Natural Resources Conservation Service Curve Number model is one of the most recognizable procedures in the field of rainfall-run-off estimation. It has been widely applied for different purposes in hydrological models. In spite of its widespread use, some uncertainties have not even clarified and must be examined for its proper application. Particularly, choosing the most representative rainfall-run-off events, and the coefficient λ that relates the parameters of the model (curve number CN and initial abstraction I a ). In this research, an advanced analysis is developed to evaluate the influence of λ for a set of representative watersheds of the Agricultural Research Service of the United Stated Department of Agriculture. They are characterized by different soil properties, land uses, and climatic conditions. Finally, 2 novel methodologies for the selection of the most representative rainfall-run-off events and for the adaptation of coefficient λ are included, based on the pattern of rainfall distribution.

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Section: Selection Of Representative Rainfall–run‐off Events As a Soumentioning

confidence: 99%

Sources of uncertainty in the NRCS CN model: Recognition and solutions

Durán‐Barroso

González

Valdés

2017

Hydrological Processes

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“…Existe uma necessidade de incorporação do tempo no desenvolvimento da metodologia CN, pois, há a necessidade de incorporação da intensidade de infiltração e de precipitação. No estudo desenvolvido por Lal et al (2016) em parcelas experimentais, encontraram que a capacidade de infiltração é a principal variável explicativa para a formação do escoamento superficial, apresentando uma relação inversa entre a capacidade de infiltração e o valor CN.…”

Section: Histórico E Procedimentos Do Método Curve Number (Scs-cn)unclassified

“…O Método SCS-CN foi desenvolvido inicialmente para determinar o escoamento superficial e a capacidade de armazenamento de água do solo em pequenas bacias hidrográficas agrícolas. Desenvolvido nas condições edafoclimáticas dos EUA, atualmente é utilizado em diversas zonas climáticas terrestres para o conhecimento do escoamento superficial e da retenção de água no solo em microbacias hidrográficas (AJMAL et al, 2015;GRILLONE, 2010GRILLONE, , 2012DE VILLARÁN SAN JUAN, 2006;HAWKINS et al, 2009HAWKINS et al, , 2010LAL et al, 2016;WOODWARD et al, 2002). Atualmente, os procedimentos para a aplicação da metodologia encontram-se disponíveis no NEH-4, distribuídos em capítulos, que abordam aspectos hidrológicos e de aplicação da metodologia.…”

Section: Parâmetros Do Método Scs-cnunclassified

“…Em estudo realizado em parcela experimental de uso da terra agrícola a partir de dados de precipitação e escoamento superficial , Lal et al (2016), determinaram os índices de abstração inicial, e os resultados para os dados naturais variaram de 0 a 0,208. Ordenando os dados de precipitação e de escoamento superficial em ordem decrescente, os índices de abstração inicial oscilaram de 0 a 0,659.…”

Section: Calhasunclassified

“…A média dos dados naturais foram de 0,030 e a média dos dados ordenados foram de 0,108, inferiores ao índice adotado pelo NRCS. Lal et al (2016), afirmam não haver correlação entre a capacidade de armazenamento de água do solo e a abstração inicial. Lal et al (2016) Segundo D'asaro e Grillone (2010), aplicando-se métodos de estimativa da abstração inicial e dos índices de abstração inicial a partir de dados de precipitação e de escoamento superficial em bacias hidrográficas da Sicília, verificou-se que a mediana foi de λ=0 para dados naturais e de λ=0,035 para dados ordenados.…”

Section: Calhasunclassified

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Abstração inicial e valores CN em diferentes usos da terra no Cerrado com utilização de um simulador de chuva

Silva¹

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Q-escoamento superficial; Ia-abstração inicial; Ssi-capacidade de armazenamento de água do solo do simulador de chuva; λsi-índice de abstração inicial com Ssi; CNsi-valor CN; Sp-q-capacidade de armazenamento de água do solo com Ia=0,2‧S; λp-q-índice de abstração inicial com Sp-q; CNp-q-valor CN com Ia=0,2‧S; Sλ-capacidade de armazenamento de água do solo com o índice de abstração inicial obtido com o simulador de chuva; CALHA 6A

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Revised runoff curve number for runoff prediction in the Loess Plateau of China

Shi

Wang

et al. 2021

Hydrological Processes

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The soil conservation service (now Natural Resources Conservation Service) Curve Number (SCS-CN), one of the most commonly used methods for surface runoff prediction. The runoff calculated by this method was very sensitive to CN values. In this study, CN values were calculated by both arithmetic mean (CN_C) and least square fit method (CN_F) using observed rainfall-runoff data from 43 sites in the Loess Plateau region, which are considerably different from the CN 2 values obtained from the USDA-SCS handbook table (CN_T). The results showed that using CN_C instead of CN_T for each watershed produce little improvement, while replacing CN_T with CN_F improves the performance of the original SCS-CN method, but still performs poorly in most study sites. This is mainly due to the SCS-CN method using a constant CN value and discounting of the temporal variation in rainfall-runoff process. Therefore, three factors-soil moisture, rainfall depth and intensity-affecting the surface runoff variability are considered to reflect the variation of CN in each watershed, and a new CN value was developed. The reliability of the proposed method was tested with data from 38 watersheds, and then applied to the remaining five typical watersheds using the optimized parameters. The results indicated that the proposed method, which boosted the model efficiencies to 81.83% and 74.23% during calibration and validation cases, respectively, performed better than the original SCS-CN and the Shi and Wang (2020b) method, a modified SCS-CN method based on tabulated CN value. Thus, the proposed method incorporating the influence of the temporal variability of soil moisture, rainfall depth, and intensity factors suggests an accurate runoff prediction for general applications under different hydrological and climatic conditions on the Loess Plateau region.

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Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots

Cited by 66 publications

References 70 publications

Sources of uncertainty in the NRCS CN model: Recognition and solutions

Sources of uncertainty in the NRCS CN model: Recognition and solutions

Abstração inicial e valores CN em diferentes usos da terra no Cerrado com utilização de um simulador de chuva

Revised runoff curve number for runoff prediction in the Loess Plateau of China

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