Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots | Evaluation de la méthode du numéro de courbe du Service de la Conservation des Sols à partir de données provenant de parcelles agricoles Evaluación de la metodología de número de curva del Servicio de Conservación de Suelos con datos de parcelas agrícolas 采用小块农田资料评估水土保持曲线数字法 Avaliação da metodologia do número da curva do Serviço de Conservação do Solo utilizando dados de parcelas agrícolas
2017
Lal, Mohan | Mishra, S. K. | Pandey, Ashish | Pandey, R. P. | Meena, P. K. | Chaudhary, Anubhav | Jha, Ranjit Kumar | Shreevastava, Ajit Kumar | Yogendra Kumar,
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.
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