Numerical analysis of stable brine displacements for evaluation of density-dependent flow theory
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Numerical dispersion of solute transport in an integrated surface–subsurface hydrological model
2021, Advances in Water ResourcesCitation Excerpt :This issue has been investigated in detail in several studies that assess the numerical dispersion arising from different discretization methods (e.g., Radu et al., 2011) or that propose techniques to alleviate this error (e.g., Suciu et al., 2013; Wu et al., 2019; Pathania et al., 2020). Despite its recognized importance, however, numerical dispersion in groundwater solute transport modeling has received little attention in the water resources literature, and it is seldom discussed as a potential source of error in modeling applications (Watson and Barry, 2001; Woods et al., 2003). Recently developed integrated surface–subsurface hydrological models (ISSHMs), such as CATchment HYdrology (CATHY; Camporese et al., 2010), HydroGeoSphere (HGS; Brunner and Simmons 2012), and MIKE SHE (Long et al., 2015), are being increasingly used not only for catchment-scale flow applications but also for coupled simulations of solute transport in the surface–subsurface continuum (e.g., Liggett et al., 2015; Scudeler et al., 2016b; Daneshmand et al., 2019; Gatel et al., 2019).
Effect of nonuniform boundary conditions on steady flow in saturated homogeneous cylindrical soil columns
2009, Advances in Water ResourcesTheoretical analysis of the worthiness of Henry and Elder problems as benchmarks of density-dependent groundwater flow models
2003, Advances in Water ResourcesValidation of classical density-dependent solute transport theory for stable, high-concentration-gradient brine displacements in coarse and medium sands
2002, Advances in Water ResourcesCitation Excerpt :When analysing the results from each displacement experiment, initial guesses for the soil parameters in the four soil regions (regions I–IV) were selected. Values for the soil porosity and spreading parameter (either the longitudinal dispersivity or hydrodynamic dispersion coefficient) in the lowest soil region (region I) were then determined using the optimisation programme to fit a numerical solute breakthrough curve to the experimental breakthrough curve at level L2 [27]. The best-fit values for the porosity and spreading parameter were completely determined by the shape of the solute breakthrough curve at level L2, the distance between the probes at levels L1 and L2, and the separation of the breakthrough curves between level L1 (entered as a boundary condition) and L2.