Improved SCS-CN model incorporating storm intensity for runoff estimation
2022
Sharma, Nand Kishore | Mishra, Surendra Kumar | Pandey, Ashish | Verma, Ravindra Kumar | Verma, Sangeeta
The Soil Conservation Service Curve Number (SCS-CN) methodology is the most globally recognized and practiced empirical model for estimation of direct surface runoff from rainfall events, largely due to its simplicity, ease of use, and accounting major runoff producing watershed characteristics. This method (designated as M1) and its explicit form (designated as M2) was originally developed for runoff estimation in small agriculture watersheds of US, now it is also applicable for other land uses. Like other hydrological or hydro-climatic methods, it also has some limitations. Therefore, this paper aims to account for one of the critical limitations, viz., and storm duration/intensity and develop an improved SCS-CN model (designated as M3 for general form and M4 for a specific form) for more accurate runoff estimation. The Generalized Reduced Gradient (GRG) non-linear method is also used in this study to optimize the SCS-CN-improved model’s parameters. Furthermore, sensitivity analysis is also carried out of the M3 model both analytically and numerically. Sensitivity results show that P is the most sensitive variable, whereas r is the least sensitive. Finally, all models (M1 through M4) are applied to the rainfall runoff dataset derived from 45 watersheds of the USDA-ARS. Furthermore, the performance evaluation of all models based on Root Mean Square Error (RMSE), Nash Sutcliffe efficiency (NSE) (%), Mean absolute error (MAE), and RMSE-observations standard deviation ratio (RSR) revealed the M3 to have performed quite better than all other models in almost all 45 studied watersheds. Overall, based on performance measures, the models’ performance from best to worst can be ranked as M3 > M1 > M4 > M2.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par Fundamental Library of Latvia University of Life Sciences and Technologies
Découvrez la collection de ce fournisseur de données dans AGRIS