Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, India.
Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, Gujarat, India.
Environ Monit Assess. 2024 Jul 17;196(8):743. doi: 10.1007/s10661-024-12883-w.
This research bears significant implications for river management, flood forecasting, and ecosystem preservation in the Lower Narmada Basin. A more precise estimation of Manning's Roughness Coefficeint (n) will enhance the accuracy of hydraulic models and facilitate informed decision-making regarding flood risk management, water resource allocation, and environmental conservation efforts. Ultimately, this study aspires to contribute to the sustainable management of perennial river systems in India and beyond by offering a robust methodology for optimizing Manning's n tailored to the complex hydrological dynamics of the Lower Narmada Basin. Through a synthesis of empirical evidence and computational modelling, it seeks to empower stakeholders with actionable insights toward preserving and enhancing these invaluable natural resources. Using the new HEC-RAS v 6.0, a one-dimensional hydrodynamic model was developed to predict overbank discharge at different points along the basin. The study analyzes water levels, stream discharges, and river stage, optimizing Manning's n and required flood risk management. The model predicted a strong output agreement with R, NSE, and RMSE for the 2020 event as 0.83, 0.81, and 0.36, respectively, with an optimum Manning's n of 0.03. The lower Narmada Basin part near the coastal zone (validation point) appears inundated frequently. The paper aims to provide insights into optimizing Manning's coefficient, which can ultimately lead to better water flow predictions and more efficient water management in the region.
本研究对纳尔默达河流域下游的河流管理、洪水预测和生态系统保护具有重要意义。更精确地估算曼宁粗糙系数（n）将提高水力模型的准确性，并有助于在洪水风险管理、水资源分配和环境保护工作方面做出明智决策。最终，本研究旨在通过提供一种针对纳尔默达河流域下游复杂水文动态优化曼宁 n 的稳健方法，为印度乃至其他地区的常年河流系统可持续管理做出贡献。通过实证证据和计算模型的综合分析，本研究旨在为利益相关者提供可行的见解，以保护和增强这些宝贵的自然资源。本研究使用新的 HEC-RAS v 6.0 开发了一个一维水动力模型，以预测流域不同点的漫滩流量。该研究分析了水位、河流流量和河流水位，优化了曼宁 n 和所需的洪水风险管理。该模型对 2020 年事件的预测结果与 R、NSE 和 RMSE 的输出一致性很强，分别为 0.83、0.81 和 0.36，最优曼宁 n 为 0.03。靠近沿海地区（验证点）的纳尔默达河流域下游部分经常被淹没。本文旨在深入了解优化曼宁系数的方法，这最终可以实现该地区更准确的水流预测和更高效的水资源管理。
