Numerical Models of Groundwater Flow and Transport

Holzbecher, Ekkehard; Sorek, S.

doi:10.1002/0470848944.hsa157

Cited by 15 publications

(9 citation statements)

References 22 publications

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“…Numerical stability of the combined flow and biodegradation system simulation was ensured by applying the Peclet and Courant convergence and stability criteria to the model. These criteria affect the time step Δt and the space discretization of the grid Δx in RT3D, respectively, and minimize the numerical errors due to round off and truncation of derivatives that occur when replaced by finite differences [ Holzbecher and Sorek , 2005]. The criteria are Peclet number criterion Courant number criterion where P e is the Peclet stability number (dimensionless), C r is the Courant stability number (dimensionless), is the average linear flow velocity (m/d), Δ t is the time step difference (d), Δ x is the spatial step difference (m) and D is the coefficient of hydrodynamic dispersion (m 2 /d).…”

Section: Model Developmentmentioning

confidence: 99%

“…[21] Numerical stability of the combined flow and biodegradation system simulation was ensured by applying the Peclet and Courant convergence and stability criteria to the model. These criteria affect the time step Dt and the space discretization of the grid Dx in RT3D, respectively, and minimize the numerical errors due to round off and truncation of derivatives that occur when replaced by finite differences [Holzbecher and Sorek, 2005]. The criteria are Peclet number criterion…”

Section: General Substrate Interaction Modulementioning

confidence: 99%

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Modeling benzene plume elongation mechanisms exerted by ethanol using RT3D with a general substrate interaction module

Gómez

Blanc

Rixey

et al. 2008

Water Resources Research

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[1] A mathematical model was developed to evaluate the effect of the common fuel additive ethanol on benzene fate and transport in fuel-contaminated groundwater and to discern the most influential benzene plume elongation mechanisms. The model, developed as a module for the Reactive Transport in 3 Dimensions (RT3D) model, includes commonly considered fate and transport processes (advection, dispersion, adsorption, biodegradation, and depletion of molecular oxygen during biodegradation) and substrate interactions previously not considered (e.g., a decrease in the specific benzene utilization rate due to metabolic flux dilution and/or catabolite repression) as well as microbial population shifts. Benzene plume elongation predictions, based on literature model parameters, were on the order of 40% for a constant source of E10 gasoline (10% vol/vol ethanol), which compares favorably to field observations. For low benzene concentrations (<1 mg/L), oxygen depletion during ethanol degradation was the principal mechanism hindering benzene natural attenuation. For higher benzene concentrations (exerting an oxygen demand higher than the available dissolved oxygen), metabolic flux dilution was the dominant plume elongation process. If oxygen were not limiting, as might be the case in zones undergoing aerobic biostimulation, model simulations showed that microbial growth on ethanol could offset negative substrate interactions and enhance benzene degradation, resulting in shorter plumes than baseline conditions without ethanol.Citation: Gomez, D. E., P. C. de Blanc, W. G. Rixey, P. B. Bedient, and P. J. J. Alvarez (2008), Modeling benzene plume elongation mechanisms exerted by ethanol using RT3D with a general substrate interaction module, Water Resour. Res., 44, W05405,

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Section: Model Developmentmentioning

confidence: 99%

Section: General Substrate Interaction Modulementioning

confidence: 99%

Modeling benzene plume elongation mechanisms exerted by ethanol using RT3D with a general substrate interaction module

Gómez

Blanc

Rixey

et al. 2008

Water Resources Research

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“…The coefficient, which appears in front of the brackets, the parameter combination DÁDt Dx 2 is also known as Neumann-number 3 (abbreviation: Neu), (see Holzbecher and Sorek 2005). It is the given explicit formula for c i,new that is computed in the m-file 'diffusion.m'.…”

Section: A Simple Numerical Modelmentioning

confidence: 99%

Transport Solutions

Holzbecher

2011

Environmental Modeling

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“…Such models simulate the groundwater dynamics in an aquifer by translating its physical, chemical, and biological characteristics into mathematical equations by simplifying assumptions (Holzbecher and Sorek 2006). Such models simulate the groundwater dynamics in an aquifer by translating its physical, chemical, and biological characteristics into mathematical equations by simplifying assumptions (Holzbecher and Sorek 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Groundwater models are commonly used in conjunction with field monitoring to assess the physical processes representing subsurface flow and solute transport. Such models simulate the groundwater dynamics in an aquifer by translating its physical, chemical, and biological characteristics into mathematical equations by simplifying assumptions (Holzbecher and Sorek 2006). These equations require data about aquifer characteristics-such as hydraulic properties, geological borders, boundary conditions, and sources and sinks-that will sufficiently aid in understanding groundwater dynamics (Bakalowicz 2005).…”

Section: Introductionmentioning

confidence: 99%

Data‐Worth Assessment for a Three‐Dimensional Optimal Design in Nonlinear Groundwater Systems

et al. 2018

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Groundwater model predictions are often uncertain due to inherent uncertainties in model input data. Monitored field data are commonly used to assess the performance of a model and reduce its prediction uncertainty. Given the high cost of data collection, it is imperative to identify the minimum number of required observation wells and to define the optimal locations of sampling points in space and depth. This study proposes a design methodology to optimize the number and location of additional observation wells that will effectively measure multiple hydrogeological parameters at different depths. For this purpose, we incorporated Bayesian model averaging and genetic algorithms into a linear data‐worth analysis in order to conduct a three‐dimensional location search for new sampling locations. We evaluated the methodology by applying it along a heterogeneous coastal aquifer with limited hydrogeological data that is experiencing salt water intrusion (SWI). The aim of the model was to identify the best locations for sampling head and salinity data, while reducing uncertainty when predicting multiple variables of SWI. The resulting optimal locations for new observation wells varied with the defined design constraints. The optimal design (OD) depended on the ratio of the start‐up cost of the monitoring program and the installation cost of the first observation well. The proposed methodology can contribute toward reducing the uncertainties associated with predicting multiple variables in a groundwater system.

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Numerical Models of Groundwater Flow and Transport

Cited by 15 publications

References 22 publications

Modeling benzene plume elongation mechanisms exerted by ethanol using RT3D with a general substrate interaction module

Modeling benzene plume elongation mechanisms exerted by ethanol using RT3D with a general substrate interaction module

Transport Solutions

Data‐Worth Assessment for a Three‐Dimensional Optimal Design in Nonlinear Groundwater Systems

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