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One-dimensional hydrodynamic ediment transport model applicable to steep mountain streams

Author(s): A. N. Papanicolaou; A. Bdour; E. Wicklein

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Keywords: 1-D unsteady sediment transport model; step-pool sequences; steep mountain streams; turbulent bursts

Abstract: A new one-dimensional (1-D) numerical model for calculating flow and sediment transport in steep mountain streams is developed. 3 ST1D, which stands for Steep Stream Sediment Transport 1-D model, is applicable to unsteady flow conditions that occur over transcritical flow stream reaches such as flows over step-pool sequences. 3ST1D consists of two coupled components, the hydrodynamic and the sediment transport. The flow component is addressed here by solving the unsteady form of the Saint-Venant equations. The Total Variation Diminishing Dissipation (TVD)-MacCormack scheme, which is a shock-capturing scheme capable of rendering the solution oscillation free, is employed here to approximate the hydrodynamic solution over transcritical flow stream reaches. The sediment component of the model accounts for multifractional sediment transport and incorporates a series of various incipient motion criteria and frictional formulas applicable to mountain streams. In addition, sediment entrainability is estimated based on a state-of-the art formula that accounts for the bed porosity, turbulent bursting frequency, probability of occurrence of strong episodic turbulent events, and sediment availability in the unit bed area. The model at the end of each time step predicts the flow depth, velocity and shear stress distribution within a cell and calculates changes in bed evolution and grain size distribution. The overall performance of the model is evaluated by comparing its predictions with observations from two flume studies, two field investigations and against the predictions of the quasi-steady model of Lopez and Falcon developed for mountain streams. A sensitivity analysis is performed to assess the effects of cell size and Manning’s roughness coefficient in the predictive ability of the model.

DOI: https://doi.org/10.1080/00221686.2004.9728402

Year: 2004

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