Rainfall-Runoff Modeling Using MIKE 11 NAM Model for Vinayakpur Intercepted Catchment, Chhattisgarh

Abstract

Rainfall-runoff estimation for a catchment is of vital importance in most of the hydrologic analysis for water resources planning. This study envisages the rainfall-runoff modeling using MIKE 11 NAM model in Vinayakpur intercepted catchment in Chhattisgarh state. The model was calibrated using measured stream flow data for the period 2001 to 2004 and then validated from period 2005 to 2007. The calibration and validation procedures were carried out to provide a satisfactory estimation. The simulated runoff occurred maximum in August (1681.63 cumecs) and minimum in April (84.14 cumecs). The outputs of the calibrated model were used in water resources management model viz., MIKE basin as they normally work based on monthly flows with a large time horizon. The optimum values of nine NAM model parameters obtained during calibration procedure were used for simulation. The reliability of MIKE 11 NAM was evaluated based on Nash-Sutcliffe coefficient, correlation coefficient (r 2) and root mean square error (RMSE). The R 2 value of model calibration and validation were observed to be 0.79 and 0.75, respectively.

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

“…The sensitivity analysis of Mike-11 NAM model parameters concluded that the maximum water content in surface storage ( U max ), the maximum water content in the lower zone/root zone ( L max ) and timing constant for base flow ( CK BF ) were the most significant Mike-11 NAM model parameters for daily runoff simulation in the catchment. The sensitivity analysis results were found analogous to a study made in the Vinayakpur catchment of Seonath sub-basin bySingh et al (2014). The rugged terrain upstream of the catchment governs the surface water storage, and dominant agricultural land use in the central part of the catchment affects the runoff generation in the catchment.…”

“…Mike-11 Nedbor Afstromnings model (NAM) is a theory-driven model that simulates the rainfall-runoff process at the catchment scale (Singh et al, 2014). Loliyana et al, (2015) applied the Mike-11 NAM model for Purna catchment in the Tapi basin and found it reasonably good in simulating annual hydrographs at daily time scales along with the prediction of water yield and flooding condition.…”

“…Loliyana et al, (2015) applied the Mike-11 NAM model for Purna catchment in the Tapi basin and found it reasonably good in simulating annual hydrographs at daily time scales along with the prediction of water yield and flooding condition. Singh et al (2014) used the Mike 11 NAM model for rainfall-runoff modelling in Vinayakpur intercepted catchment in the Chhattisgarh state of India and applied maximum and minimum simulated runoff for water resource management. Tiwari et al (2016) used Mike-11 NAM for simulating daily runoff utilizing precipitation, potential evapotranspiration, and observed runoff in the Shipra river basin in Madhya Pradesh and found it sufficiently accurate for future predictions.…”

A Decision Support System for Water Resources Management: The Case Study of Mubuku Irrigation Scheme, Uganda

“…The MIKEHydro Basin model for Sebwe catchment was manually calibrated. The Nedbor-Afstromnings Model (NAM) is a deterministic, conceptual rainfall-runoff, and lumped model, which means that the catchment is analyzed as a single unit with average values of parameters [35]. Owing to the lack of continuous discharge measurements of the Sebwe river, the NAM was adopted for the calibration process.…”

Estimation of discharges of water flows and debris floods in a small watershed

“…Such models are constructed using empirically and/or physically based expressions of hydrological processes, e.g. infiltration, runoff yield, and influx processes (El Alfy, 2016; Jie et al, 2016; Oudin et al, 2006; Singh et al, 2014). However, the uncertainties associated with hydrological modelling should be considered, especially with respect to the inputs, parameters, model structure, and outputs (e.g.…”