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Research Article Open access CC BY 4.0

Rainfall-runoff Modelling Using MIKE11 NAM Model for Ravishankar Sagar Catchment, Chhattisgarh, India

Gunja Dhruw, Narendra Agrawal, Rahul Kumar Jaiswal

International Journal of Environment and Climate Change · pp. 719–738 · Published 3 May 2024

10.9734/ijecc/2024/v14i44153

Abstract

Estimating rainfall-runoff within a catchment is inherently intricate and crucial for water resource planning via hydrological evaluations. The study focuses on utilizing the MIKE 11 NAM model to simulate rainfall-runoff dynamics within the Ravishankar Sagar Reservoir catchment in the Chhattisgarh state. In order to ensure accurate estimation, data on stream flows from 2004 to 2015 was used for calibration, and from 2016 to 2020 was used for validation. The MIKE 11 NAM model accurately predicted daily runoff and adequately reproduced the hydrological response of the Ravishankar Sagar watershed to rainfall. The calibrated model outputs were good to employ in the water resources management model, specifically for MIKE BASIN. During calibration, the optimal values of the nine NAM model parameters were determined and subsequently employed in the simulation. The reliability of the MIKE 11 NAM model was assessed for the study area using the Coefficient of Determination (R2) and the Nash–Sutcliffe Efficiency Index (EI). The sensitivity analysis helped to determine the most important model parameters. R2 values of 0.730 and 0.704 were obtained from the model's calibration and validation, respectively. With an Efficiency Index of 81%, the model demonstrated its efficiency and ability to forecast runoff for Ravishankar Sagar Reservoir over an extended period. This study helps to manage the water resource and to improve the reservoir operation policy for the reservoir.

MIKE 11 NAM MIKE BASIN simulation Nash–Sutcliffe efficiency index

Cited by 3

Study of multi-source river discharge data assimilation in collaboration with EKF and MIKE11HD

Zhang Yinqin, Li Bin, Nie Yao · Hydrological Sciences Journal · 2025

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