Abstract
The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (P) and runoff (Q) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed P and Q datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall–runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (λ) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median λ values were, respectively, 0.030 and 0 for the natural rainfall–runoff dataset and 0.108 and 0 for the ordered rainfall–runoff dataset. Runoff estimation was very sensitive to λ and it improved consistently as λ changed from 0.2 to 0.03.
Résumé
La méthode du numéro de courbe du Service de la Conservation des Sols (SCS-CN), aussi connue sous le nom de la méthode du numéro de courbe du service de la Conservation des Ressources Naturelles (NRCS-CN), est populaire pour le calcul du volume d’eau de ruissellement direct pour un événement pluvieux donné. La performance de la méthode SCS-CN, basée sur un grand ensemble de données de pluies (P) et de ruissellement (Q) de bassins versants des Etats-Unis d’Amérique, est évaluée en utilisant un grand ensemble de données d’événements naturels pluvieux à partir de 27 parcelles agricoles en Inde. Dans l’ensemble, le CN estimé à partir des tables (chapitre 4) du manuel national d’ingénierie ne correspond pas aux valeurs dérivées des ensembles de données observées P et Q. Par conséquent, la prévision du ruissellement en utilisant les valeurs antérieures de CN était de mauvaise qualité pour 22 (sur 24) parcelles. Cependant, la comparaison était un peu mieux pour les valeurs les plus élevées de CN, en accord avec l’idée générale que la méthode existante SCS-CN donne de meilleurs résultats pour les événements avec des précipitations et ruissellement élevées (CN élevées). La capacité d’infiltration (fc) était la principale variable explicative de la production du ruissellement (ou CN) dans les parcelles d’étude car il montrait la relation inverse attendue entre le CN et la fc. L’optimisation graphique de données a abouti à des valeurs initiales de coefficient d’abstraction (λ) comprises entre 0 et 0.659 pour l’ensemble de données classées et entre 0 et 0.208 pour l’ensemble naturel des données (avec 0 comme la valeur la plus fréquente). Les valeurs moyennes et médianes λ étaient, respectivement, 0.030 et 0 pour l’ensemble de données de pluie–ruissellement naturel et 0.108 et 0 pour l’ensemble des données ordonnées de pluie–ruissellement. L’estimation du ruissellement était très sensible à λ et est amélioré de manière constante quand λ variait entre 0.2 et 0.03.
Resumen
El método del número de curva del Servicio de Conservación de Suelos (SCS-CN), también conocido como el método del número de curva del Servicio de Conservación de Recursos Naturales (NRCS-CN), es popular para calcular el volumen de escorrentía superficial directa para un evento de lluvia dada. El rendimiento del método SCS-CN, basado datos de grandes precipitaciones (P) y la escorrentía (Q) de las cuencas de los Estados Unidos, se evalúa usando un gran conjunto de datos de eventos de tormentas naturales a partir de 27 parcelas agrícolas en la India. En general, el CN calcula a partir de tablas del Manual Nacional de Ingeniería (Capítulo 4) que no se corresponden con las derivadas de los conjuntos de datos observados P y Q. Como resultado, la predicción de la escorrentía usando las anteriores CN era pobre para los datos de 22 (de 24) parcelas. Sin embargo, la coincidencia era un poco mejor para los valores más altos de CN, consistente con la noción general de que el actual método SCS-CN tiene un mejor rendimiento para eventos de alta precipitación–escorrentía (CN altos). La capacidad de infiltración (fc) fue la principal variable explicativa de la producción de escorrentía (o CN) en las parcelas de estudio, ya que exhibió la relación inversa esperada entre NC y fc. La optimización de la trama de datos dio valores iniciales del coeficiente de abstracción (λ) de 0 a 0.659 para el conjunto de datos ordenados y de 0 a 0.208 para el conjunto de datos naturales (siendo 0 el valor más frecuente). Los valores de la media y la mediana de λ fueron, respectivamente, 0.030 y 0 para el conjunto de datos de lluvia–escorrentía natural y 0.108 y 0 para el conjunto de datos de lluvia–escorrentía ordenados. La estimación de la escorrentía era muy sensible a λ y mejoró consistentemente con λ cambiado desde 0.2 a 0.03.
摘要
水土保持曲线数字法也称为自然资源保持曲线数字法,在计算某一特定降雨事件直接地表径流量中非常流行。根据美国流域大的降雨(P)和径流(Q)数据集,采用印度27块农田天然暴雨事件的大的数据集对水土保持曲线数字法的性能进行了评估。总的来说,国家工程手册(第四章)表中的曲线数字估算值与来源于观测的P和Q数据集并不匹配。结果是,采用以前的曲线数字对(24块中的)22块农田资料进行的径流预测结果很差。然而,对于较高的曲线数值匹配稍微好一点,符合总的认识,即现有的水土保持曲线数字法对于较高的降雨-径流(高曲线数字)事件表现较好。渗入能力(fc)在研究农田径流的产生中是主要的解释变量,因为其展现了所预料的曲线数字和fc之间的相反关系。农田资料最优化产生了初始损失系数(λ),有序数据集为0到0.659,自然数据集为0到0.208(0作为最频遇值)。天然降雨–径流数据集的λ平均值和λ中间值分别为0.030和0,有序降雨–径流数据集的λ平均值和λ中间值分别为0.108和0。径流估算结果对于λ来说非常敏感,随着λ从0.2变为0.03,径流估算结果不断改进。
Resumo
O método do número da curva do Serviço de Conservação de Solo (NC–SCS), também conhecido como o método do numero da curva do Serviço de Conservação de Recursos Naturais (NC-SCRN) é popular por computar o volume do escoamento superficial direto para um dado evento de precipitação. O desempenho do método NC-SCS, baseado em dados de precipitação (P) e escoamento (Q) em bacias hidrográficas nos Estados Unidos, foi avaliado utilizando um grande conjunto de dados sobre 27 eventos de precipitação em parcelas agrícolas na Índia. No geral, as estimativas do NC das tabelas do Manual Nacional de Engenharia (Capítulo 4) não coincidem com os valores obtidos através do conjunto de dados de P e Q observados. Como resultado, a previsão de escoamento superficial utilizando os NC antigos foi insuficiente para os dados de 22 (de 24) parcelas. Entretanto, a similaridade foi um pouco melhor para altos valores de NC, coerente com a noção geral de que o método NC-SCS existente apresenta um melhor desempenho em eventos com grande volume de precipitação-escoamento. A capacidade de infiltração (fc) foi a principal variável explicativa para a geração do escoamento superficial (ou NC) nas parcelas estudadas uma vez que mostrou a relação inversa à esperada entre o NC e a fc. A optimização dos dados das parcelas rendeu valores do coeficiente da abstração inicial (λ) de 0 a 0.659 para o conjunto de dados ordenados e de 0 a 0.208 para o conjunto de dados natural (com 0 sendo o valor mais frequente). Os valores médios e medianos de λ foram, respectivamente, 0.030 e 0 para o conjunto de dados de precipitação–escoamento natural e 0.108 e 0 para o conjunto de dados de precipitação–escoamento ordenado. A estimativa do escoamento superficial foi muito sensível a λ e melhorou consistentemente assim que λ mudou de 0.2 para 0.03.
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Acknowledgements
This research was funded by the Indian National Committee on Surface Water (INCSW) (formerly Indian National Committee on Hydrology (INCOH)), and Ministry of Water Resources, Govt. of India, New Delhi, under the Research and Development project on “Experimental Verification of SCS Runoff Curve Numbers for Selected Soils and Land Uses”.
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Appendix: Notation
Appendix: Notation
- I a :
-
Initial abstraction (mm)
- Rcm :
-
Mean runoff coefficient of plot
- Rc:
-
Event runoff coefficient
- λ :
-
Initial abstraction coefficient
- P :
-
Rainfall (mm)
- Q :
-
Observed runoff (mm)
- Q m :
-
Mean observed runoff of plot (mm)
- Q c :
-
Predicted runoff (mm)
- CN0.20 :
-
Curve number associated with λ = 0.20
- CN0.030 :
-
Curve number associated with λ = 0.030
- NEH:
-
National engineering handbook
- NSE:
-
Nash-Sutcliffe efficiency coefficient
- CNHT :
-
Curve number derived from NEH-4 tables
- S :
-
Maximum potential retention (mm)
- θ :
-
Previous day soil moisture (%)
- HSG:
-
Hydrologic soil group
- P 5 :
-
5-day antecedent rainfall (mm)
- fc:
-
Infiltration capacity (mm/hr)
- CNLSn :
-
Curve number derived from P–Q data set using least square method (λ = 0.2) for natural data series
- CNLSo :
-
Curve number derived from P–Q data set using least square method (λ = 0.2) for ordered data series
- CNLSDn :
-
Curve number derived from P–Q data set using least square method (optimized λ) for natural data series
- CNLSDo :
-
Curve number derived from P–Q data set using least square method (optimized λ) for ordered data series
- CNHT0.20 :
-
NEH-4 tables CN associated with λ = 0.20
- CNHT0.030 :
-
NEH-4 tables CN associated with λ = 0.03
- I :
-
Rainfall threshold for runoff generation (mm)
- n :
-
Number of event (or observation)
- n t :
-
Statistic used for performance evaluation
- r :
-
Statistic used for showing the improvement in NSE
- R 2 :
-
Coefficient of determination
- RMSE:
-
Root mean square error (mm)
- PBIAS:
-
Percent bias (%)
- SD:
-
Standard deviation (mm)
- SPSS:
-
Statistical Package for the Social Sciences
- SE:
-
Standard error of estimate (mm)
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Lal, M., Mishra, S.K., Pandey, A. et al. Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots. Hydrogeol J 25, 151–167 (2017). https://doi.org/10.1007/s10040-016-1460-5
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DOI: https://doi.org/10.1007/s10040-016-1460-5