University of Texas at Austin, Department of Civil, Architectural & Environmental Engineering
Texas is actively pursuing demonstration projects to evaluate whether desalination plants can provide a "drought-proof" water supply. Disposal of the wastewater brine in an ecologically-sound and economically feasible manner is a prerequisite to the success of these projects. In collaboration with QEA, We are investigating how to improve models of the brine transport in a shallow embayment so that better predictions of the brine fate can be made. This will allow engineers to provide better siting of brine discharges to minimize ecological impact.


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Advanced Technology Program,

National Science Foundation

Texas Water Development Board



ATP Award No: 003658-0162-2003; Active dates: 01/01/04 - 12/31/05

TWDB Award No: 2005-001-059; Active Dates: 06/01/05 - 05/31/06

Award amount: $60,000 (ATP); NSF graduate student fellowship (3 years); $50,000 (Texas Water Development Board)

Students supported: Ms. Paula Kulis (MS 2005, PhD 2008); Mr. Cedric David (MS expected 2006)

The basic processes involving of brine underflow are illustrated at right. In the complex bathymetry and stratification conditions of a bay or estuary, it is difficult for computer models to accurately predict the mixing of the brine with the lighter ambient water. Models tend to overpredict the mixing rate, which prevents accurate analysis of the brine plume effects.

Research Objectives: The first objective is to develop an engineering tool to provide project managers and government decision-makers with analyses of the fate of desalination brine introduced into Texas coastal bays and estuaries. This objective supports the Governor’s desalination initiative for a drought-proof water supply to enhance the state’s economic growth (an ATP goal). The second objective is to advance our understanding of how wind-induced turbulence affects brine mixing and dispersal. The overall goal is to quantify the factors that control brine fate so that brine discharges are located to minimize impact on shrimp, oysters and fish.

Goals of ATP – In addition to addressing needs of the desalination initiative as noted above, this project supports ATP goals by making the latest science available to engineers, thereby “enlarging the technology base available to business and industry.” The project also increases “the number and quality of scientists and engineers in Texas” through support for a recent MIT graduate who is working with the PI at UT Austin, and developing a public/private partnership between the PI and QEA, a firm that has recently opened a Texas office.

Motivation and importance: Our motivation is the need to locate brine discharges that minimize water quality impacts at the proposed desalination plants at Brownsville, Corpus Christi, and Freeport. These projects are considering discharge into coastal waters for brine disposal. Present engineering models do not incorporate the latest science and may overestimate the brine dispersal, thereby leading to under-prediction of water quality impacts. Thus, using present engineering techniques will leave the desalination project open to court challenges based on established science. More importantly, poor initial siting of a brine discharge may harm local aquatic life, causing a public relations problem and deterring desalination development.

Effects of brine discharge: The desalination process creates brine with salinities higher than naturally found in Texas coastal waters and in greater volumes than the freshwater produced. Disposal of brine into coastal waters is an economical option for the desalination projects. After discharge, dense brine water flows below the less-dense ambient water to form a stratified cap over the bottom sediment. Typically, it is not the quantity of salt discharged that causes a problem; it is the mixing rate and the brine’s fate prior to complete mixing that determines impacts. If natural forces of plume flow, wind mixing and tidal currents are slow to mix the brine with the overlying water, biogeochemical processes in the sediment may deplete the available dissolved oxygen near the bottom, causing hypoxic (low oxygen) conditions that harm aquatic life. Some Texas bays already experience episodic hypoxia when high evaporation rates combine with weak winds to produce stratified conditions [1, 2], which could be exacerbated by poorly-sited brine discharges. Finding the optimum discharge location requires a tool for computing the fate of brine with varying winds and currents. We will address this need by building a model for brine fate based on the latest science.

Present engineering for brine underflows: Engineers do not have adequate tools for predicting the fate of brine underflows in coastal waters. Using present models, an underflow propagates over varying bathymetry that is modeled either in a “stair-step” configuration or with a “sigma-coordinate” system that stretches and follows the bottom terrain. The stair-step (or Z-level) model is preferred for shallow systems to avoid sigma-coordinate singularities. The Z-level approach, however, over-predicts underflow mixing through an error known as “numerical convective entrainment” [3]. Furthermore, a sufficiently fine model grid that captures the physics of a brine underflow (~10 cm thick) is impractical, so results are distorted by numerical diffusion [4, 6]. Thus, existing engineering models will predict an artificially diffuse brine underflow, which will readily mix with surface waters, thereby predicting artificially rapid oxygen renewal at the bottom. Analysis based on such models will underestimate the brine-affected area, duration, and salinity, which will result in erroneous estimates of water quality impacts for different brine discharge locations.

1. Ritter, C. and P. A. Montagna. 1999. Seasonal hypoxia and models of benthic response in a Texas bay. Estuaries 22:7-20.
2. Morehead, S., C. Simanek, and P.A. Montagna. 2002. GIS database of hypoxia (low oxygen) conditions in Corpus Christi Bay. Final Report to Coastal Coordination Council, Coastal Management Program Grant no. 01-214, UTMSI Tech. Report No. 2002-001. 2 Volumes.
3. Winton, M., R. Hallberg, and A. Gnanadesikan. 1998. Simulation of density driven frictional downslope flow in Z-coordinate ocean models. Journal of Physical Oceanography 28: 2163-2174.
4. Laval, B., B.R. Hodges, and J. Imberger. 2003. Reducing numerical diffusion effects with pycnocline filter. Journal of Hydraulic Engineering 129: (3): 215-224.
5. Hirst, A.C. and T.J. McDougall. 1996. Deep-water properties and surface buoyancy flux as simulated by a z-coordinate model including eddy-induced advection. Journal of Physical Oceanography 26: 1320-1343.
6. Dallimore, C., B.R. Hodges, and J. Imberger. 2003. Coupling an underflow model to a 3D hydrodynamic model. Journal of Hydraulic Engineering 129: (10): 1-10.
7. Beckmann, A., and R. Döscher. 1997. A method for improved representation of dense water spreading over topography in geopotential-coordinate models. Journal of Physical Oceanography 27: 581-591.
8. Hodges, B.R., J. Imberger, A. Saggio, and K. B. Winters. 2000. Modeling basin-scale internal waves in a stratified lake. Limnology and Oceanography 45: (7): 1603-1620.
9. Hodges, B.R. 2000. Numerical techniques in CWR-ELCOM. Centre for Water Research, University of Western Australia. Technical Report WP 1422-BH, 37 pgs.


Present science for brine underflows: Present engineering practice does not reflect the latest advances in numerical modeling. The above model problems have been noted and addressed in recent scientific literature by the PI and others [5, 6, 7], but have not been implemented in engineering models. The PI’s recent results (figure at left) show that a thin saline underflow is diffused by a 3D model, but is correctly simulated when a 2D underflow model is coupled to the 3D model.

Relationship to PI’s work: The PI has five graduate students on modeling projects funded by ONR, TWDB, TWRI, (see current funding) and fellowships from UT-Austin. This project provides a capstone to prior research that developed, tested and validated an underflow model with funding from sources in Australia and Japan. The prior work created a 2D density underflow model coupled to a 3D hydrodynamic model developed by the PI [8, 9]. This successful underflow model is appropriate for academic research, but must be adapted both for practical engineering use and to incorporate effects of wind-mixing.

Industry/academic partnership: The PI has teamed with Quantitative Environmental Analysis (QEA), an engineering firm with extensive experience adapting and applying EFDC, a public-domain 3D model widely used in engineering and supported by the US EPA. QEA has developed an enhanced version of EFDC by upgrading to a more efficient programming language, removing errors in the algorithms, adding new features, and simplifying the input/output requirements. Corrected errors in the model include improper asymmetry in horizontal diffusion coefficients, incorrect horizontal diffusion in mass transport algorithms, and incorrect salinity calculations at open boundaries. QEA found and fixed flaws that inhibited mass conservation and prevented transport of water quality constituents. The result of QEA’s significant efforts is a robust and efficient state-of-the-practice model that is also easier to use and understand. The QEA version of EFDC and the coupled underflow model developed herein are being donated to the public domain as a part of this project.

2006 Ben R. Hodges • last updated July 22, 2005

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