Effect of Sediment Load Boundary Conditions in Predicting Sediment Delta of Tarbela Reservoir in Pakistan
Setting precise sediment load boundary conditions plays a central role in robust modeling
of sedimentation in reservoirs. In the presented study, we modeled sediment transport in Tarbela
Reservoir using sediment rating curves (SRC) and wavelet artificial neural networks (WA-ANNs) for
setting sediment load boundary conditions in the HEC-RAS 1D numerical model. The reconstruction
performance of SRC for finding the missing sediment sampling data was at R2 = 0.655 and NSE = 0.635.
The same performance using WA-ANNs was at R2 = 0.771 and NSE = 0.771. As the WA-ANNs
have better ability to model non-linear sediment transport behavior in the Upper Indus River,
the reconstructed missing suspended sediment load data were more accurate. Therefore, using more
accurately-reconstructed sediment load boundary conditions in HEC-RAS, the model was better
morphodynamically calibrated with R2 = 0.980 and NSE = 0.979. Using SRC-based sediment
load boundary conditions, the HEC-RAS model was calibrated with R2 = 0.959 and NSE = 0.943.
Both models validated the delta movement in the Tarbela Reservoir with R2 = 0.968, NSE = 0.959 and
R2 = 0.950, NSE = 0.893 usingWA-ANN and SRC estimates, respectively. Unlike SRC,WA-ANN-based
boundary conditions provided stable simulations in HEC-RAS. In addition, WA-ANN-predicted
sediment load also suggested a decrease in supply of sediment significantly to the Tarbela Reservoir
in the future due to intra-annual shifting of flows from summer to pre- and post-winter. Therefore,
our future predictions also suggested the stability of the sediment delta. As the WA-ANN-based
sediment load boundary conditions precisely represented the physics of sediment transport,
the modeling concept could very likely be used to study bed level changes in reservoirs/rivers
elsewhere in the world.