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Open AccessArticle

Effect of Sediment Load Boundary Conditions in Predicting Sediment Delta of Tarbela Reservoir in Pakistan

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College of Earth & Environmental Sciences (CEES), University of the Punjab, Punjab 54590, Pakistan
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CEN, Institute of Geography, University of Hamburg, 20148 Hamburg, Germany
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Department of Space Science, Institute of Space Technology, Islamabad 44000, Pakistan
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Department of Agriculture Engineering, BZ University, Multan 60800, Pakistan
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Key Laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
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University of Chinese Academy of Sciences, Beijing 100049, China
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Chair of Hydraulic and Water Resources Engineering, Technical University of Munich, 80333 Munich, Germany
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Author to whom correspondence should be addressed.
Water 2019, 11(8), 1716; https://doi.org/10.3390/w11081716
Received: 16 June 2019 / Revised: 10 August 2019 / Accepted: 13 August 2019 / Published: 18 August 2019
(This article belongs to the Special Issue Modeling of Soil Erosion and Sediment Transport)
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 using WA-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. View Full-Text
Keywords: Upper Indus Basin (UIB); Tarbela Reservoir; Besham Qila; sediment modeling; uncertainty; wavelet transform analysis-artificial neural network (WA-ANN); sediment rating curve (SRC); HEC-RAS Upper Indus Basin (UIB); Tarbela Reservoir; Besham Qila; sediment modeling; uncertainty; wavelet transform analysis-artificial neural network (WA-ANN); sediment rating curve (SRC); HEC-RAS
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MDPI and ACS Style

Tarar, Z.R.; Ahmad, S.R.; Ahmad, I.; Hasson, S.; Khan, Z.M.; Washakh, R.M.A.; Ateeq-Ur-Rehman, S.; Bui, M.D. Effect of Sediment Load Boundary Conditions in Predicting Sediment Delta of Tarbela Reservoir in Pakistan. Water 2019, 11, 1716.

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