Development and performance evaluation of SCS-CN based hybrid model

• Published in: Water science and technology Vol. 85; no. 9; pp. 2479 - 2502
• Main Authors: Upreti, Pankaj, Ojha, C S P
• Format: Journal Article
• Language: English
• Published: England IWA Publishing 01.05.2022
• Subjects: Calibration; curve number; Datasets; event-based rainfall-runoff model; hybrid model; Hydrology; Mathematical models; optimization; Parameter identification; Parameters; Performance evaluation; Rain; Retention; Root-mean-square errors; Runoff; Runoff coefficient; scs-cn method; Statistical analysis; Storms; us watersheds; Variables; Water conservation; Watersheds
• DOI: 10.2166/wst.2022.145
• ISSN: 0273-1223

In this study, a hybrid approach has been used to increase the predictive efficiency of the SCS-CN model. A recently proposed Ajmal model (developed after randomized configuration) that ignored initial abstraction and maximum potential retention has been given the conceptual framework of the SCS-CN model and a new outcome-based hybrid model (M ) was formulated. A total of 78 watersheds (7817 events) were used for calibration and the remaining 36 watersheds (3967 events) for validation to develop this hybrid model. The numerical value of hybrid model parameters L , λ and S were calibrated using calibration dataset and a simple non-linear one-parameter model has been developed. The performance of the Ajmal (M ) and hybrid model (M ) was compared with the original SCS-CN method (λ = 0.2 as M and λ = 0.05 as M ). The performance of models was compared by using four statistical error indices i.e. RMSE, NSE, PBIAS, and n(t) and applying ranking and grading system (RGS). The mean RMSE, NSE, PBIAS, and n(t) values were found superior for M (5.60 mm, 0.71, 6.97%, 1.15) model followed by M (5.98 mm, 0.65, 16.52%, 1.01), M (6.27 mm, 0.61, 20%, 0.90) and M (6.98 mm, 0.46, 24.2%, 0.72) model for tested watersheds. The hybrid model (M ) exhibited consistently well performance for all size watersheds. On the basis of the agreement between watershed runoff coefficient (C) and calibrated model parameter (L or CN), R value was found relatively higher for hybrid model (M ) than other models.