On the Ia–S relation of the SCS-CN method | Zendy

Manoj Jain | Zendy; Sumeet Mishra | Zendy; Suresh Babu | Zendy; Krishnaveni Venugopal | Zendy

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On the Ia–S relation of the SCS-CN method

Author(s) -

Manoj Jain,

Sumeet Mishra,

Suresh Babu,

Krishnaveni Venugopal

Publication year - 2006

Publication title -

hydrology research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.665

H-Index - 48

eISSN - 1996-9694

pISSN - 0029-1277

DOI - 10.2166/nh.2006.011

Subject(s) - runoff curve number , surface runoff , relation (database) , storm , mean squared error , set (abstract data type) , abstraction , calibration , hydrology (agriculture) , field (mathematics) , field trial , runoff model , data set , mathematics , environmental science , soil science , computer science , meteorology , statistics , data mining , geology , geography , agronomy , geotechnical engineering , ecology , philosophy , epistemology , pure mathematics , biology , programming language

The initial abstraction ( I a) versus maximum potential retention ( S ) relation in the Soil Conservation Service Curve Number (SCS-CN) methodology was revisited, and a new non-linear relation incorporating storm rainfall ( P ) and S was proposed and tested on a large set of storm rainfall-runoff events derived from the water database of United States Department of Agriculture-Agriculture Research Service (USDA-ARS). Employing root mean square error (RMSE), the performance of both the existing and proposed models was evaluated using the complete database, and for model calibration and validation, data were split into two groups: based on ordered rainfall ( P -based) and runoff ( Q -based). A specific formulation of the proposed model I a= λS ( P /( P + S )) α with λ =0.3 and α =1.5 was found to generally perform better than the existing I a=0.2 S , and therefore was recommended for field applications. When evaluated using the observed I a data, the proposed version performed significantly better than the existing one.

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