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.
