Sedimentation Impact on Reservoirs and its Modeling Management Studies

Sedimentation in reservoirs is becoming more problematic as water storage and supply become increasingly endangered with the aging of dams. This is causing severe consequences for water management, flood control, and production of energy. The worldwide loss in reservoir storage capacity due to sedimentation is about to be between 0.5% and 1.0% per annum. The gradual process of sedimentation proceeds with different speeds and that depend on a large number of factors, such as hydrology of the catchments and the characteristics of the river basin. On average sediment will eventually fill a reservoir within 50–200 years. This paper addresses about 3 general strategies of reservoir sedimentation managements and they are (1) Reduction of incoming Sediment yield, (2) Minimization of Sediment deposition and (3) Removal of Sediment from reservoirs. The main management methods associated with minimizing sediment deposition are construction of sediment bypass structures, sediment pass-through (or sluicing) and venting of a sediment-laden density current. The main management methods associated with removing sediment from reservoirs nearing critical storage loss are flushing and dredging. Drawdown flushing has been studied extensively and has been found to work optimally on narrow, gorge-shaped reservoirs where the water can be fully drawn down. Dredging is the most often used sedimentation management technique is also a highly expensive and time-consuming practice, although efficacious when complimented by other methods.

The methods available for sediment estimation are largely empirical, with sediment rating curves being the most widely used. In this study, Artificial Neural Network (ANN) technique based sediment transport system is taken as an example for discussion for Penganga River (a sub-basin of Godavari River) system and a comparison has been made between the results obtained using ANNs and sediment rating curves. The sediment load estimations in the river obtained by ANNs have been found to be significantly superior to the corresponding classical sediment rating curves.