ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Civil Engineering


Civil Engineering

First Advisor

Habib Ahmari


Sediment transport in natural streams is a complex process that can significantly affect the stream’s water quality, ecosystem, and morphology. Suspended sediment concentrations can adversely affect the health of the ecosystem and aquatic species, and the deposited sediment particles can change the morphology and have irreparable environmental impacts. Several numerical models that can be used to manage and mitigate the adverse effects of sediment released into rivers by natural or anthropogenic activities have been developed to estimate the sediment and pollutant transport in waterbodies. However, the goal of the research for this thesis was to develop a Lagrangian stochastic sediment transport model, based on a particle tracking model (PTM), to simulate sediment transport in open channels. The sediment particle displacement was simulated using discretized advection and dispersion; the random walk approach was used to model the stochastic movement of the sediment particles. In advection, the displacement of the particle is based on its linearly interpolated velocity in the flow domain, derived from a 2D hydrodynamic model. In dispersion, using the random walk method, the stochastic movement of the particles is generated in a three-dimensional space. A conditional empirical equation was used to consider the effect of vertical dispersion in the top layers, near the water’s surface. The PTM used the dimensionless mobility number that was developed based on the Shields diagram, to determine whether the particles remain suspended or are deposited onto the streambed. Using laboratory dataset, the ability of the PTM to calculate the sediment concentration of various classes of sediment (very fine, fine, and medium sand) was evaluated, and the results are presented with different dispersion coefficients. A comparison of the particle tracking model and the analytical solution of the advection-dispersion equation showed that the model was acceptably accurate. The result of the sensitivity analysis and validation process showed that the model can be used to simulate sediment transport in open channel flows. The sediment regime in natural streams can be altered by anthropogenic activities such as agriculture, logging, mining, urbanization, bridge and dam constructions, and hydrological alterations. This thesis examines the performance of the PTM at Wilson Creek in McKinney, Texas by utilizing empirical dispersion models that simulate the excess sediment load caused by bridge construction activities. The required hydrodynamic parameters including flow velocity, flow depth, and shear stress were obtained from the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 2 and were coupled with the PTM. A field monitoring program that included collecting suspended solids and surveying depositional areas in the creek, and observing the turbidity, bedload material, and substrate type was conducted during the bridge construction activities to evaluate the performance of the PTM. The PTM outputs included changes in the creek’s sediment regime, suspended sediment concentration and depositional areas. A comparison of the field data and the PTM showed that the model accurately simulated the suspended sediment concentration distribution in the creek and the depositional areas correlated with the field investigations. The use of machine learning-based dispersion models to improve the performance of the PTM was also investigated, and data obtained from previous studies was used to develop ensemble learning-based models to predict the longitudinal and transverse dispersion coefficients in natural streams. Several scenarios for developing prediction models were tested using the grid search cross-validation technique, and the model’s optimal principal parameters (also known as hyperparameters) were presented, using different statistical measures. The dispersion models were coupled with the PTM to investigate the model’s performance, using both empirical and learning-based dispersion coefficients in natural streams. The field data collected at Wilson Creek was used to assess the performance of the model.


Sediment transport, Lagrangian particle tracking, Stochastic approach, Random walk, Construction activity, Sediment erosion


Civil and Environmental Engineering | Civil Engineering | Engineering


Degree granted by The University of Texas at Arlington