Numerical modeling of tides in the Great Bay Estuarine System: dynamical balance and spring–neap residual modulation

Mclaughlin, John W.; Bilgili, Ata; Lynch, Daniel R.

doi:10.1016/s0272-7714(02)00355-4

Cited by 24 publications

(10 citation statements)

References 8 publications

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“…where N is the number of measurements and n is the index of the given data point. Similarly, the normalized root mean square error (NRMSE) between the field and model data was calculated using Equation 2 (McLaughlin et al 2003):…”

Section: Water Surface Elevation Statistical Analysismentioning

confidence: 99%

Lower Columbia River Adaptive Hydraulics (AdH) model : development, water surface elevation validation, and sea level rise analysis

Pevey¹,

Savant²,

Moritz³

et al. 2020

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The U.S. Army Engineer Research and Development Center (ERDC) solves the nation's toughest engineering and environmental challenges. ERDC develops innovative solutions in civil and military engineering, geospatial sciences, water resources, and environmental sciences for the Army, the Department of Defense, civilian agencies, and our nation's public good. Find out more at www.erdc.usace.army.mil. To search for other technical reports published by ERDC, visit the ERDC online library at http://acwc.sdp.sirsi.net/client/default.

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Section: Water Surface Elevation Statistical Analysismentioning

confidence: 99%

Lower Columbia River Adaptive Hydraulics (AdH) model : development, water surface elevation validation, and sea level rise analysis

Pevey¹,

Savant²,

Moritz³

et al. 2020

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“…The governing equations of the original model depended only upon this balance of forces. To accommodate deeper channels and meteorological forcing, the model has since evolved to include local acceleration, wind stress, and the opportunity to include a depth dependent bottom friction coefficient in the governing equations (McLaughlin et al 2003). When the water elevation in a given element reaches below a threshold level of 0.5 m, local acceleration is ignored, significantly speeding the computation where grid spacing is smallest.…”

Section: Model Descriptionmentioning

confidence: 99%

“…where H is total depth of the water column, Q = HV is the volumetric flux, V is the depth averaged velocity, g is gravitational acceleration, ζ is surface elevation relative to mean sea level, C d is the bottom drag coefficient, and W is the kinematic wind stress (see also McLaughlin et al, 2003). These equations may be rearranged to eliminate Q and produce a nonlinear diffusion equation.…”

Section: Model Descriptionmentioning

confidence: 99%

Estimation of Residence Time in a Shallow Back Barrier Lagoon, Hog Island Bay, Virginia, USA

Fugate

Friedrichs

Bilgili

2006

Estuarine and Coastal Modeling (2005)

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Hog Island Bay, Virginia, is a shallow back barrier lagoon that is subject to seasonal inputs of inorganic nitrogen and related episodes of hypoxia. Numerical simulations were carried out to estimate the importance of physical flushing times relative to biochemical turnover times known to be a few days or less within the system. A 2D vertically averaged finite element hydrodynamic model, which was designed to accommodate regular flooding and dewatering of shallow flats and marshes, was coupled with a particle tracking model to estimate median lagoon residence time and the spatial distribution of local residence time in the lagoon. The model was forced with observed tidal elevations and winds from the end of the growing season when hypoxia tends to occur. The median residence time estimated by numerical modeling is on the order of weeks (358 hours), and variations in tidal stage, tidal range and wind produced deviations in median residence time on the order of days. Residence times near the inlets were very short, while those near the mainland were long, showing that (i) horizontal mixing in the Bay is insufficient to successfully apply integral methods to obtain residence times, and (ii) residence times near the mainland are long compared to timescales of biologically driven chemical transformations.

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“…The non-linear system of governing equations of the model is solved iteratively at each time step. The reader is referred to [4] for the standard governing equations and detailed solution schemes. Lagrangian particle tracking is performed by advecting particles in the simulated Eulerian velocity field utilizing a 4 th order Runge-Kutta (RK) scheme at the end of each hydrodynamic model time step.…”

Section: Numerical Modelmentioning

confidence: 99%

Simulating the effects of dredging on the pollutant particles originating from a wastewater treatment facility in a tidal river

Bilgili¹,

Proehl²,

Swift³

2006

WIT Transactions on Ecology and the Environment, Vol 88

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A 2-D hydrodynamic finite element model with a Lagrangian particle module is used to investigate the effects of dredging on the horizontal dilution of pollutant particles originating from a Wastewater Treatment Facility (WWTF) in a tidal river. The model is driven by the semi-diurnal (M 2 ) tidal component and includes the effect of flooding and drying of mud flats. The particle tracking method consists of tidal advection plus a horizontal random walk model of subgrid scale turbulent processes. Our approach is to perform continuous pollutant particle releases from the outfall, simulating three different scenarios: a basecase representing the present conditions and two different dredged channel / outfall location configurations. Lagrangian particle concentrations are simulated on finite elements and dilution improvement ratios are presented for both scenarios. Results show that although no particles leave the river in a single M 2 cycle, flushing takes place in longer time scales. Simulated dilution maps show that relocation of the WWTF outfall into the dredged main channel is required for increased dilution performance. The addition of a pool at the head of the river also improves dilution by adding to the tidal volume. Case oriented short-term investigations of coastal hydrodynamic problems suitable for Lagrangian particle methods should be encouraged to improve our knowledge of estuarine processes and how we model them while providing solutions to the management community in time and budget constrained decision making.

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Numerical modeling of tides in the Great Bay Estuarine System: dynamical balance and spring–neap residual modulation

Cited by 24 publications

References 8 publications

Lower Columbia River Adaptive Hydraulics (AdH) model : development, water surface elevation validation, and sea level rise analysis

Lower Columbia River Adaptive Hydraulics (AdH) model : development, water surface elevation validation, and sea level rise analysis

Estimation of Residence Time in a Shallow Back Barrier Lagoon, Hog Island Bay, Virginia, USA

Simulating the effects of dredging on the pollutant particles originating from a wastewater treatment facility in a tidal river

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