Abstract
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.
Highlights
Reservoir sedimentation is a serious issue in many parts of the world
Together with the increase in world population, non-sustainable development and use of water resources, and the imminent threat associated with climate change, it may cause a crisis in water supply [1,2]
In a scenario where reservoirs are the key infrastructure in mitigating the effects of climate change by their capacity to store and regulate water supply, the expected increase in hydrologic variability will demand more water regulatory capacity [3]
Summary
Reservoir sedimentation is a serious issue in many parts of the world. On average, the annual rate of decrease in the world’s reservoirs’ storage capacity is approximately 1%. Together with the increase in world population, non-sustainable development and use of water resources, and the imminent threat associated with climate change, it may cause a crisis in water supply [1,2]. Water 2018, 10, 1411 storage has been constructed since the Tarbela dam in 1974, is facing a similar situation. The Tarbela dam has lost 40.58% of its storage capacity due to high sediment trap efficiency [4]. The country’s reservoirs’ water holding capacity is sufficient only to supply 30 days’ requirements, and has been decreasing [5]. It is necessary to operate the existing water storage capacities efficiently, and to construct reservoirs so as to trap less sediment. Optimizing reservoir sedimentation will require new techniques for sediment load (SL) estimation, as conventional methods are no longer adequate or reliable
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