An innovative approach to minimizing uncertainty in sediment load boundary conditions for modelling sedimentation in reservoirs

A number of significant investigations have advanced our understanding of the parameters influencing reservoir sedimentation. However, a reliable modelling of sediment deposits and delta formation in reservoirs is still a challenging problem due to many uncertainties in the modelling process. Modelling performance can be improved by adjusting the uncertainty caused by sediment load boundary conditions. In our study, we diminished the uncertainty factor by setting more precise sediment load boundary conditions reconstructed using wavelet artificial neural networks for a morphodynamic model. The model was calibrated for hydrodynamics using a backward error propagation method. The proposed approach was applied to the Tarbela Reservoir located on the Indus River, in northern Pakistan. The results showed that the hydrodynamic calibration with coefficient of determination (R²) = 0.969 and Nash-Sutcliffe Efficiency (NSE) = 0.966 also facilitated good calibration in morphodynamic calculations with R² = 0.97 and NSE = 0.96. The model was validated for the sediment deposits in the reservoir with R² = 0.96 and NSE = 0.95. Due to desynchronization between the glacier melts and monsoon rain caused by warmer climate and subsequent decrease of 17% in sediment supply to the Tarbela dam, our modelling results showed a slight decrease in the sediment delta for the near future (until 2030). Based on the results, we conclude that our overall state-of-the-art modelling offers a significant improvement in computational time and accuracy, and could be used to estimate hydrodynamic and morphodynamic parameters more precisely for different events and poorly gauged rivers elsewhere in the world. The modelling concept could also be used for predicting sedimentation in the reservoirs under sediment load variability scenarios.