Morphological Model for Erosion Prediction of India’s Largest Braided River Using MIKE 21C Model

Abstract

The Brahmaputra River has a dynamic, highly braided channel pattern with frequent river bar formation, making it morphologically very dynamic, especially during the monsoon season with high discharge and sediment load. To understand how the river changes over time, this study focused on two stretches: Palasbari-Gumi and Dibrugarh. Using 2D morphological models (MIKE-21C), the study aimed to predict erosion patterns, plan protective measures, and assess morphological changes over short-term (1 year), medium-term (3 year), and long-term (5 year) periods. Model runs were conducted to predict design variables across these river reaches, encompassing different hydrological scenarios and development-planning scenarios. The coarse sand fraction yielded mean annual sediment load predictions of 257 Mt/year for the 2021 hydrological year and 314 Mt/year under bankfull discharge conditions in the Palasbari-Gumi reach. In the Dibrugarh reach, the corresponding values were 78 Mt/year and 100 Mt/year. Notably, historical records indicate an annual sediment load of 400 Mt/year in the Brahmaputra River. The model results were compared to measurements from Acoustic Doppler Current Profilers (ADCP), showing good accuracy for flow velocities, flood levels, and sediment loads. Discrepancies in peak model velocities compared to ADCP measurements remain consistently below 9% across the majority of recorded data points. The predicted flood levels for the bankfull discharge condition exhibited an outstanding accuracy, reaching nearly 91% at the Palasbari-Gumi site and a notable 95% at the Dibrugarh site. This study has presented a valuable methodology for enhancing the strategic planning and implementation of river training endeavours, particularly within the dynamic and highly braided channels of rivers such as the Brahmaputra River. The approach leverages predictive models to predict morphological changes over a 2–3 years timeframe, contributing to improved river management.