Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India

Abstract

Runoff estimation is of immense importance in hydrological analysis for water resource planning and management. The developing countries cannot afford to establish a large number of gauging sites due to huge initial and operating expenditures. Hydrological modelling is an alternative solution to simulate the catchment response to extreme events under climate change for taking preventive measures. The hydrological models have their own leads and constraints, so because of limited hydrological data availability of the catchment, wavelet neural network (WNN), artificial neural network, adaptive neuro-fuzzy inference system, and Mike-11 Nedbor Afstromnings models were used in this study. These models were calibrated and validated using daily rainfall and runoff observations taken at Hamp Pandariya gauging station on Hamp river in the Chhattisgarh state of India. A comparative study of these models was carried out to investigate their performance, efficiency, and suitability for daily runoff simulation in Hamp Pandariya catchment and found suitable in simulating the hydrological response of the catchment and predicting runoff with a high degree of accuracy. The performance of these models was evaluated and compared with the aid of multiple goodness of fit criteria including coefficient of determinations (r 2 ), Nash-Sutcliffe model efficiency index (NS), root mean square error, and water balance for model calibration and validation. These parameters indicated good agreement between observed and simulated runoff in terms of time to peak, discharge rate, daily and accumulated runoff volume, and shape of the hydrograph. The WNN was found the most appropriate model for future application due to Nash-Sutcliffe efficiency (NS) of 97% and 98% in calibration and validation, respectively, and the coefficient of determination as 99% both in calibration and validation.