River sediment dynamics have been significantly altered by dam constructions, with the Three Gorges Dam (TGD) on the Yangtze River being a prominent example. This study aims to quantitatively assess the attenuating impact of TGD operations on downstream sediment discharge using artificial neural network (ANN) models. Seven ANN models were developed for stations along the middle and lower Yangtze River, trained on measured water and sediment data. The models effectively captured the relationships between upstream inputs and downstream outputs. Results revealed that variations in sediment discharge at each station were predominantly influenced by changes in sediment input from its immediate upstream section, rather than by changes in water discharge. This suggests that the TGD's impact on downstream sediment transport was primarily due to its sediment trapping efficiency, rather than alterations in the hydrological regime. The influence diminished gradually farther downstream, attributed to tributary contributions and sediment transport dynamics. ANNs, adept at handling input uncertainties, underscore the importance of boundary conditions; the integration of data-driven and physics-based models illuminates sediment dynamics in regulated rivers. This approach provides insight into dam management implications and sediment transport processes.

  • Quantify TGD impact on downstream sediment with ANNs.

  • Sediment discharge changes are driven by upstream input variations.

  • Tributary contributions and sediment dynamics modulate TGD impact downstream.

  • Data-driven models robust to input uncertainties, and emphasize boundary conditions.

  • Integrating data and physics-based models improves regulated river understanding.

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