Longitudinal solute transport in open-channel flow A Numerical Simulation Study on Longitudinal Dispersion, Surface Storage Effects, Transverse Mixing, Uncertainties and Parameter-Transferring Problems

The longitudinal solute transport modeling is critical in river and stream water quality management, control, and the mitigation of hazardous riverine spills. One of the widely used "deadzone" model is the transient storage model (TSM). TSM is a significant improvement over the advection-dispersion model (ADM), but it cannot simulate the breakthrough curve (BTC) immediately after a large pool. Additionally, the calibration (parameterization) method is challenged by the non-identifiability which is common to all inverse modeling, and it seems TSM cannot be easily used as a predictive tool, more of an interpretive tool of solute transport, i.e., is the parameter set calibrated via inverse modeling transferable? Pools are fundamental stream morphology unit in streams with mixed bed materials in pool-riffle or pool-step sequences. Understanding of how a pool impacts the longitudinal solute transport is the first step towards improving current model such as TSM or developing new models.;By introducing a dimensionless group, epsilon = Q/(DtW) (where, Q is the average volumetric flow rate; Dt is an average transverse dispersion coefficient; W is the channel flow width), derived from non-dimensionalization of the governing equations of one of the most rigorous 2-dimensional (2D) (depth-averaged) model, Mike21, this work presents an alternative way of longitudinal solute transport investigation. Using the 2D fully hydrodynamic Mike21, numerical experiments were conducted on hypothetical streams in this dissertation. Simulation study on hypothetical stream with pool reveals that a pool's effects on longitudinal solute transport are manifested by three aspects: boosting longitudinal spreading (concentration peak attenuation), causing a solute plume delay and increasing solute residence time. These effects fade like a "wake" as the solute plume moves downstream. epsilon provides an insight into the physics of longitudinal transport; it outlines a relative transverse mixing intensity of a stream. The internal transport and mixing condition (including the secondary circulations) in a pool together with the pool's dimensions determine the pool's storage effects especially when epsilon >>1. The BTCs downstream from a pool may be "heavy tailed" (i.e., have enormously slow decaying rate) which cannot be modeled by the TSM. Results also suggest that the falling limb of a BTC more accurately characterizes the pool's storage effects because the corresponding solute has more chance to sample the entire storage area.;In a more fundamental perspective, the predictive ability of inverse modeling parameterized model is discussed and conclusion is made about the role of a stream/river system's nonlinearity in determining the predictability; a misleading mis-nomenclature in TSM application is also demonstrated with a numerical experiment.