Sediment Budget and Sediment Trap efficiency of Baglihar Hydroelectric project Reservoir – a calibrated model for prediction of longevity of the Dam

Romesh Kumar,Ahsan Ul Haq,Ghulam M. Bhat,Yudhbir Singh, Javid Ahmed Dar

Journal of The Indian Association of Sedimentologists(2021)

引用 0|浏览0
暂无评分
摘要
The field investigation of the reservoir area of Baglihar Hydropower project shows that the sediment budget to the reservoir is controlled by fragile rock type like shales, sandstones, phyllites and slates, soil characteristics, steep hill slopes, rainfall and landslides. The rocks are highly weathered, fissile and micaceous in nature and very sensitive to water absorption.  The analysed sediments are characterised by dominance of sands, silts and clays with lower values of plasticity (14.3PL), liquidity (23.5 LL), cohesion (118) and shear strength (202 Kpa). The slope wash deposits are highly susceptible to landslides and slope failures and directly contribute to the sediment budget in the reservoir. In addition tributaries of Chenab River also bring sediments in the reservoir from the catchment area. The empirical relationship for estimating the long-term reservoir trap efficiency for large storage based on correlation between the relative reservoir size and trap efficiency was simulated in 3D model which shows that the annual sediment trap efficiency of the Baglihar reservoir is of 0.39%. The extrapolation of the calculated values shows that the total sediment load shall increase by 11% in the next 30 years and 20% in the next 50 years and correspondingly 40% in the next 100 years that shall induce corresponding decrease in the reservoir volume over the time.  By applying flushing schemes, life span of the reservoir can be extended. It is estimated that after 100 years the reservoir shall lose ~35.6% storage volume. On further extrapolation, the trap efficiency will decrease from 25.5% after 30 years to 23% after 100 years. The estimated trap efficiency of Baglihar reservoir is 60%, which is greater than that based on numerical results, showing a significant overestimation.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要