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Technical Brief

Calibration and Validation of Hydrodynamics and Salinity Transport Model for Sabine Lake Water System PUBLIC ACCESS

[+] Author and Article Information
Hairui Wang

Department of Mathematical Sciences,
McNeese State University,
Lake Charles, LA 70609

Ning Zhang

Department of Chemical, Civil, and
Mechanical Engineering,
McNeese State University,
Lake Charles, LA 70609
e-mail address: nzhang@mcneese.edu

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received February 4, 2019; final manuscript received May 14, 2019; published online June 20, 2019. Assoc. Editor: Shawn Aram.

J. Fluids Eng 141(10), 104502 (Jun 20, 2019) (6 pages) Paper No: FE-19-1087; doi: 10.1115/1.4043804 History: Received February 04, 2019; Revised May 14, 2019

In this study, a hydrodynamic and a salinity transport models were developed for simulations of Sabine Lake water system located on the Texas-Louisiana border. The target simulation area includes several major water bodies, such as Sabine Lake, Sabine River, Sabine Pass, Sabine Neches Canal (Ship Channel), and part of Gulf Intracoastal Waterway (GIWW) and Sabine River Diversion Canal (SRDC). The SRDC supplies fresh water to the area industry, mainly petrochemical. High salinity in SRDC could significantly affect the daily production of the industry. Two-dimensional (2D) depth-averaged shallow water equation set and 2D depth-averaged salinity transport equation were used for developing the hydrodynamic and salinity transport numerical models in order to carry out the simulation. The major purposes of this study are to calibrate and validate hydrodynamic and salinity transport models in order to assess and predict the salinity in SRDC under severe weather conditions such as hurricane storm surges in future study. Measurement data from National Oceanic and Atmospheric Administration (NOAA) and United States Geological Survey (USGS) were used to calibrate the boundary conditions as well as to validate the model. Boundary conditions were calibrated at locations in Sabine Pass and in the north edge of the lake by using water–surface elevation data. Hydrodynamic model was validated at the USGS location using water–surface elevation data. Then, the simulation estimations of water surface level and salinity were compared at three locations, and the results show the accuracy of the validated model. Parallel computing was conducted in this study as well, and computational efficiency was compared.

FIGURES IN THIS ARTICLE
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Sabine Lake is a 90,000-acre saltwater estuary on the Texas-Louisiana border. It is 14 miles (23 km) long and 7 miles (11 km) wide, and is formed by the confluence of the Neches and Sabine rivers [1]. The Neches River begins in Van Zandt County and flows 416 miles (669 km) through east Texas to its mouth on Sabine Lake near the Rainbow Bridge [2]. The Gulf Intracoastal Waterway (GIWW) is a major east–west navigable inland waterway running approximately 1050 miles (1690 km) from Carrabelle, FL, to Brownsville, TX and connects to Sabine River [3]. The Sabine River is 510 miles (820 km) long. In its lower course, it forms part of the boundary between the two states and empties into Sabine Lake [4]. The Sabine River Diversion Canal (SRDC) is 35 miles (56 km) long, with about 4.5 miles (7.2 km) of underground pipe, and begins on the Old Sabine River 2.5 miles (4.0 km) north of Niblett's Bluff and runs to east [4]. The SRDC system is to divert surface water from the Sabine River to supply agricultural, municipal, and industrial water needs in Southwest Louisiana, mainly the petrochemical industry in Lake Charles area.

Hydrodynamic and sediment/salinity transport modeling research was conducted for the Calcasieu Lake and nearby area for various purposes in several previous studies [58]. Huang et al. employed the coupled two-dimensional (2D) hydrodynamic and sediment transport model for the Altai flood to investigate its propagation process and morphologic evolution [9]. Gallerano et al. proposed a finite volume 3D model for the simulation of wave–structure interaction and hydrodynamic phenomena [10]. Huang et al. applied a hydrodynamic model in a case study of Hurricane Dennis to investigate salinity variations in Apalachicola bay [11]. In this study, hydrodynamic and salinity transport models were implemented for the Sabine Lake and nearby area including Sabine River, Neches River, SRDC, and GIWW of the target research domain.

Salinity can affect soil, water quality, agricultural production, and life of creatures. High salinity in SRDC could significantly affect the daily production of the industry. Water of low salinity is an important requirement of the petrochemical industries. Salinity transport modeling has been widely applied to various applications previously [1214]. Xing et al. developed and applied a three-dimensional (3D) semi-implicit finite volume numerical model to study tidal circulation and salinity stratification in the region of Oujiang River Estuary, China [12]. Bauer et al. applied coupled flow and salinity transport modeling in semi-arid environments of Shashe River Valley in Botswana [13]. Johnson et al. developed a three-dimensional numerical hydrodynamic model of Chesapeake Bay in Maryland to compute water surface level, salinity, and temperature [14]. In this study, a salinity transport model was implemented in the target area, and simulation shows that there is no salinity in SRDC in normal condition which is true. However, severe weather conditions such as hurricane storm surges can make a negative impact on SRDC water quality and cause flooding in the nearby area. These outcomes can affect people's life and cause huge damage. The research of next step will focus on the salt water intrusion in SRDC.

In this study, the hydrodynamic and salinity transport models were calibrated and validated, and the salinity in SRDC was monitored for the case of the normal tidal condition. The result shows that saline water does not reach north of Sabine Lake under the normal tidal condition. Storm surges will be simulated in future to see threats on SRDC water quality.

The governing equations include flow phase and salinity phase equations.

Flow Phase Equations.

Two-dimensional depth-averaged shallow water equation set was applied to solve the flow velocity and water surface elevation [5,6]. There are three equations in the equation set Display Formula

(1)ut+uux+vuy+gzx=γu+fvvt+uvx+vvy+gzy=γvfuzt+[(h+z)u]x+[(h+z)v]y=0

In the equation, u and v are the flow velocity components in x and y direction, z is the water surface elevation, and h is the land elevation. γ is the bottom friction coefficient, and f is Coriolis frequency.

Salinity Transport Model.

Two-dimensional depth-averaged salinity transport equation is used for calculating salinity as below [15,16]: Display Formula

(2)(SH)t+(uSH)x+(vSH)y=x(kHSy)+y(kHSy)+(QE)S

In the equation, S is the depth-averaged salinity, H is the total water depth, k is the salt diffusion coefficient in water, and Q and E are precipitation and evaporation, respectively. Precipitation and evaporation are both set to zero in this study.

In order to solve the above depth-averaged shallow water equations, structured grid meshes with staggered arrangements were used. The salinity S and water surface elevation z are located at the center of each cell, the flow velocity u is in the middle of the left and right cell faces, and v is in the middle of the top and bottom cell faces.

For the water phase equations, an implicit algorithm [17] was used to solve the unknown variables. The performance of the method is further improved by combining an alternating direction implicit technique [5]. A method of Eulerian–Lagrangian type was used for calculating the convection terms in Eq. (1), which was along with a blended first- and second-order upwind approximation [18] for the simulations in the study. This numerical scheme is unconditionally stable, and the restriction on the time-step is thus only from the consistency requirement [5,17]. The time-step size for the computing is set to 10 s, and that of measured water surface level data from United States Geological Survey (USGS) and National Oceanic and Atmospheric Administration (NOAA) are 15 and 6 min, respectively.

For the salinity phase, a second-order explicit method of central difference in space and Adam–Bashforth in time was used. From previous observations, the stability restriction of the salinity phase is not significantly worse than the flow phase scheme. Since the explicit method was used in salinity phase, a parallel computing scheme was applied to the salinity equation only. The governing equations were solved using an in-house code and OpenMP was used for parallel computing in this study.

Water–surface elevation, river flow, and salinity were specified for boundary conditions during a period of Jan. 1–20, 2018. The time-step size for the computing is set to 10 s. Measured water surface level data of Gulf of Mexico at south boundary, Sabine River at north boundary, and Neches River at west boundary were specified, and data updated every 6 min for Gulf of Mexico and Neches River and 15 min for Sabine River. Salinity data were specified at Gulf of Mexico for south boundary with time interval of 1 h. Velocity data were set at 0.18 m/s from east to west for GIWW on the east boundary based on a previous study [16].

Parallel Computing.

In order to accelerate the simulation process, parallel computing was conducted during the research. The calculation part of the program includes mainly two modules, water phase and salinity phase. Only salinity phase was paralleled due to its explicit algorithm used in the model. The paralleled program runs on a server with multiple central processing units and memory shared by using OpenMP.

Runtime is defined as the time that elapses from the moment that a computation starts to the moment of the execution finished. Speedup is defined as the ratio of the runtime of sequential algorithm to the time taken by the parallel algorithm to solve the problem [19]. In this study, the speedup is the ratio of runtime of program without paralleled to the program with salinity module paralleled. Number of threads 2, 4, 8, and 16 were tested to compare with the serial program and efficiency results of different number of threads are shown in Fig. 1. It can be seen that parallel computing with 2 thread is 1.6 times the speed of serial computing, which shows that parallel computing is a good way to improve the efficiency. However, when increasing thread number to 4, 8, and 16, the efficiency is decreasing in the range from 1.6 to 1.5, which could be due to the excessive time on message passing and the use of the barrier synchronization process. Two threads have the best results for this parallel computing with only salinity module paralleled.

The target simulation area shown in Fig. 2 ranges from Sabine River near Deweyville, TX as the north boundary to the Gulf of Mexico as the south boundary, and from Neches River near Beaumont, TX as the west boundary to part of GIWW, and SRDC as the east boundary. The entire area includes several major water bodies, such as Sabine Lake, Neches River, Sabine River, Sabine Pass, Sabine Neches Canal (Ship Channel), and part of GIWW and SRDC.

Measured water surface elevations are at five different locations/sites, Sabine River on the north boundary, Neches River on the west side of the west boundary, station rainbow bridge (TCOON) on the Neches River near the Sabine Lake, and USGS and Sabine Pass on the Sabine Pass near the Gulf of Mexico. Gulf of Mexico salinity data were used. These measured data are from USGS [20] and NOAA [21] for the period of Jan. 1–20, 2018, and velocity data at GIWW on the east boundary are from a previous published study [16]. Canal Point was selected as the sample location in the SRDC for comparing the salinity under different storm surge conditions.

The measured data of water–surface elevations of Sabine River, Neches River, and Sabine Pass and salinity of the Gulf of Mexico were used as boundary conditions. Water–surface elevation data at Sabine Pass and TCOON were used to calibrate the boundary conditions, and water–surface elevation data at USGS were used for validating the model. Part of GIWW is on the east boundary of the research domain. However, there is no measured flow velocity data of GIWW available for this area. The velocity value of 0.18 m/s directing from east to west was set for the boundary condition. The value is from a previous study that used numerical tests of different flow velocities of GIWW to decide the best value [16].

The grid-size independence test has been performed to determine the proper resolution for the research. There are three levels of grid sizes: namely, coarse, normal, and fine with grid size of each level is 180 m × 160 m, 90 m × 80 m, and 45 m × 40 m, respectively. Figure 3 is the comparison of the time histories of water surface elevation at the location USGS inside the Sabine Pass. The reason this location was selected as the representations is that it is the key point within the area of interests and was also used for model validation. Figure 3 shows that the normal and fine grid results are much closer to each other than the coarse grid result. There is little difference between the normal and fine grid results, which indicates the grid convergence has been reached. The coarse grid result has a big difference compared to the results from normal and fine grid resolutions. The reason for this difference is that there could be only one grid to cover the width of the narrow water area, which is not enough to resolve the flow. However, due to the limitation of the computer power, the normal grid was selected for this study. We believe that the normal grid results can get sufficient accuracy for water surface elevation and salinity computing.

The measured data of water–surface elevation at Sabine River, Neches River, and Sabine Pass were used as the boundary conditions. However, the site locations of Neches River and Sabine Pass are near the boundary but not exactly on the boundary, so the simulation results of water–surface elevation at Sabine Pass and TCOON were compared to measurement data to calibrate the boundary conditions.

The south boundary is in the Gulf of Mexico; however, no measurement data of water surface level are available there. So the measurement data of the nearest site (Sabine Pass) were used for the south boundary condition with calibration. The simulation result of water–surface elevation at Sabine Pass was compared to the measurement data at the same location. The process continued until the two results matched, then a calibrated south boundary condition was obtained. The calibrated result of water surface level at Sabine Pass is shown in Fig. 4. The black solid line is the measurement data of water–surface elevation and the red dash dot line is the simulation result, and the simulation result matched measurement data very well during a 20-day period from Jan. 1–20, 2018.

Neches River is on the west boundary, and the water–surface elevation measurement site is several miles away from the west boundary. The measurement data of water–surface elevation were used as the west boundary condition with calibration. The simulation result of water surface level at TCOON was compared to the measurement data at the same location until they matched, then the calibrated Neches River water-level boundary condition was obtained. The calibrated result of TCOON is shown in Fig. 5. The black solid line is the measurement data of water surface level at TCOON and the red dash dot line is the simulation result at TCOON, and they matched very well during the whole period from January 1st to January 20th.

The results of Figs. 4 and 5 both show that the simulation results match with the measurement data of water surface level at the same location. The hydrodynamic model was calibrated well for the boundary conditions.

After boundary conditions were well calibrated at two locations Sabine Pass and TCOON for water surface elevation, in order to prove the accuracy of the flow phase simulation, a comparison between simulation results and measurement data on site USGS was conducted. The site USGS is near location Sabine Pass and between Sabine Pass and TCOON, which is suitable for validating hydrodynamic model.

The measured data of water–surface elevation at the location USGS were used for validating hydrodynamic model after boundary conditions were calibrated, and the result is shown in Fig. 6. The black solid line is the measurement data of water surface level at USGS and the red dash dot line is the simulation result at USGS, and the simulation result matches measurement data well in the same 20-day period from January 1st to January 20th, which proves the accuracy of the model and the model is ready for applications.

The model accuracy on flow phase was validated successfully. Due to the lack of measurement salinity data in the target area, the salinity transport phase has not been validated yet. However, the salinity transport is dictated by the flow, the accuracy of salinity transport model should be reasonable if the flow model is accurate. It is worth mentioning that the same salinity model was applied to simulate a nearby similar water body, Calcasieu Lake [17], and the salinity results were validated in that study. Therefore, the accuracy of this salinity transport model is expected to be satisfactory for simulating this Sabine Lake system.

In this section, results under normal tidal condition were simulated to show the water surface elevation and salinity distribution in the research domain including SRDC. Normal condition is the condition without any storm surge. The simulation results of water–surface elevation and salinity at three locations (Sabine Pass, USGS, and TCOON) under normal condition are shown in Figs. 7 and 8, respectively. Sabine Pass is the green dashed line, USGS is the red solid line, and TCOON is the blue dash dot line in the plots.

Figure 7 compares the time histories of water–surface elevation at the three locations during the 20-day period. It can be seen that Sabine Pass has the largest range of water–surface elevation from −0.8 m to 0.7 m, USGS has a range from −0.7 m to 0.6 m, and TCOON has the smallest range approximately from −0.5 m to 0.5 m. The range of water level at Sabine Pass and USGS is quite similar and fluctuation of them is almost the same, but the fluctuation at TCOON was delayed compared to Sabine Pass and USGS because of the flow direction and the location difference. Sabine Pass and USGS sites are in the Sabine Pass near the Gulf of Mexico, and the Sabine Pass site is slightly south to the USGS site, leading to a slightly larger range of water level at the Sabine Pass site. The TCOON site is near influx of Neches River to Sabine Lake and is significantly north to the Sabine Pass and USGS sites, so the range of water surface level there is smaller and fluctuation is delayed compared to the other two sites.

Figure 8 compares the time histories of salinity at the same three locations as in Fig. 7. It can be seen that Sabine Pass has the largest salinity peak around 30 ppt, USGS has a peak about 28 ppt, and TCOON has the smallest peak approximately 10 ppt. The salinity estimation at Sabine Pass is larger than that at USGS with similar fluctuation patterns. The salinity estimation at USGS shows a very small delay comparing to the one at Sabine Pass due to the location difference. However, the salinity estimation at TCOON is very different comparing to the other two sites, which shows the significant changes of salinity between the south and north ends of Sabine Lake. The Sabine Pass and USGS sites are near the Gulf of Mexico, while the TCOON site is near influx of Neches River to Sabine Lake, relatively far north of the Gulf of Mexico. Neches River carries less saline water, which also reduces the salinity in the nearby area. Figure 8 shows a reasonable salinity pattern in the system, which is an evidence of reasonable accuracy of the salinity transport model.

The results shown in Figs. 7 and 8 reveal the hydrodynamic and salinity characteristics in the southern part of the research area, the Sabine Lake area. It is worth mentioning that the salinity in the area north of Sabine Lake is very low under normal tidal condition.

Figure 9 shows water–surface elevation contours for high tide and low tide conditions. During the high tide, the water surface level is around 0.5 m. During the low tide, the water level decreases and it can be observed that the water surface level is around 0.1 m. Figure 10 shows salinity contours for same high tide and low tide conditions in Fig. 9. During high tide, the salinity concentration has higher value and distribution covers more areas in the Sabine Lake than the low tide. For the normal condition, no matter high tide or low tide, salinity distributions can be found only at the south part of Sabine Lake and at Neches River through the Ship Channel.

Hydrodynamic model was implemented with calibration and validation using measured water–surface elevation data from USGS and NOAA and salinity transport model was calibrated using measured salinity data from NOAA in this study. Then, the simulation results of water–surface elevation and salinity were compared at three locations, and results show the accuracy of the validated model.

This research project was funded by the Louisiana Board of Regents Support Fund—ITRS subprogram, and the Citgo Endowed Professorship to McNeese State University. Special thanks to Dr. R. Spall at Utah State University for his computer program which was incorporated into our modeling package.

  • Louisiana Board of Regents Support Fund—ITRS subprogram (Funder ID: 10.13039/100006952).

Texas Parks and Wildlife Department, 1987, “ An Analysis of Texas Waterways,” A Report on the Physical Characteristics of Rivers, Streams, and Bayous in Texas, The Texas Parks and Wildlife Department, Austin, TX, Report. https://tpwd.texas.gov/publications/pwdpubs/pwd_rp_t3200_1047/08_e_tx_pine_red_sabine.phtml#sabine
Roger Omohundro, J. , 1984, “ Early History of the Sabine and Neches Rivers,” Texas Gulf Historical and Biographical Record 20, Texas State Historical Association, Austin, TX, Report. https://tshaonline.org/handbook/online/articles/rnn04. line372
Alperin, L. M. , 1983, “ History of the Gulf Intracoastal Waterway,” Institute of Water Resources, Report No. Navigation History NWS-83-9.
Geographic Names Information System, 2011, to “U.S. Board on Geographic Names,” accessed Jan. 15, 2019, https://www.usgs.gov/core-science-systems/ngp/board-on-geographic-names
Zhang, N. , Zheng, Z. C. , and Yadagiri, S. , 2011, “ A Hydrodynamic Simulation for the Circulation and Transport in Coastal Watersheds,” Comput. Fluids, 47(1), pp. 178–188. [CrossRef]
Zhang, N. , Kee, D. , and Li, P. X. , 2013, “ Investigation of the Impacts of Gulf Sediments on Calcasieu Ship Channel and Surrounding Water Systems,” Comput. Fluids, 77, pp. 125–133. [CrossRef]
Zhang, N. , Li, P. , and He, A. , 2014, “ Coupling of 1-D and 2D Hydrodynamic Models Using an Immersed-Boundary Method,” ASME J. Fluids Eng., 136(4), p. 040907. https://www.researchgate.net/publication/266352682_Coupling_1D-2D_Shallow_Water_models_for_Simulating_Floods_due_to_Overtopping_and_Breaching_of_Leeves
Yadav, P. K. , Thapa, S. , Han, X. , Richmond, C. , and Zhang, N. , 2015, “ Investigation of the Effects of Wetland Vegetation on Coastal Flood Reduction Using Hydrodynamic Simulation,” ASME Paper No. AJKFluids2015-3044.
Huang, W. , Cao, Z. , Yue, Z. , Pender, G. , and Zhou, J. , 2012, “ Coupled Modelling of Flood Due to Natural Landslide Dam Breach,” Proc. ICE Water Manage., 165(10), pp. 525–542.
Gallerano, F. , Cannata, G. , Lasaponara, F. , and Petrelli, G. , 2017, “ A New Three-Dimensional Finite Volume Non-Hydrostatic Shock-Capturing Model for Free Surface Flow,” J. Hydrodyn., 29(4), pp. 552–566. [CrossRef]
Huang, W. , Hagen, S. , and Bacopoulos, P. , 2014, “ Hydrodynamic Modeling of Hurricane Dennis Impact on Estuarine Salinity Variation in Apalachicola Bay,” J. Coastal Res., 30(2), pp. 389–398. [CrossRef]
Xing, Y. , Ai, C. F. , and Jin, S. , 2013, “ A Three-Dimensional Hydrodynamic and Salinity Transport Model of Estuarine Circulation With an Application to a Macrotidal Estuary,” Appl. Ocean Res., 39, pp. 53–71. [CrossRef]
Bauer, P. , Held, R. , Zimmermann, S. , Linn, F. , and Kinzelbach, W. , 2006, “ Coupled Flow and Salinity Transport Modelling in Semi-Arid Environments: The Shashe River Valley, Botswana,” J. Hydrol., 316(1–4), pp. 163–183. [CrossRef]
Johnson, B. H. , Kim, K. W. , Heath, R. E. , Hsieh, B. B. , and Butler, H. L. , 1993, “ Validation of Three-Dimensional Hydrodynamic Model of Chesapeake Bay,” J. Hydraul. Eng., 119(1), pp. 2–20. [CrossRef]
Li, H. H. , Reed, C. W. , and Brown, M. E. , 2012, “ Salinity Calculations in the Coastal Modeling System,” Engineer Research and Development Center, Coastal and Hydraulics Lab, Vicksburg, MS, Report No. ERDC/CHL-CHETN-IV-80.
Zhang, N. , Han, X. , and Wang, W. , 2018, “ A Comprehensive Hydrodynamics-Salinity-pH Model for Analyzing the Effects of Freshwater Withdrawals in Calcasieu Lake and Surrounding Water Systems,” ASME J. Fluids Eng., 141(5), p. 051103. [CrossRef]
Casulli, V. , 1990, “ Semi-Implicit Finite Difference Methods for the Two-Dimensional Shallow Water Equations,” J. Comput. Phys., 86(1), pp. 56–74. [CrossRef]
Spaulding, M. L. , 1974, “ Laterally Integrated Numerical Water Quality Model for an Estuary,” ASME J. Fluids Eng., 96(2), pp. 103–111. [CrossRef]
Hennessy, J. L. , and David, A. P. , 2012, Computer Architecture: A Quantitative Approach, Morgan Kaufmann, Waltham, MA, pp. 46–47.
USGS, 2019, “National Water Information System,” United States Geological Survey, accessed Jan. 15, 2019, https://waterdata.usgs.gov/nwis/uv
Tides & Currents, 2019, “NGOFS–Sabine Neches,” Tides & Currents, accessed Jan. 15, 2019, https://tidesandcurrents.noaa.gov/ofs/ngofs/ngofs_sabine.html
Copyright © 2019 by ASME
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References

Texas Parks and Wildlife Department, 1987, “ An Analysis of Texas Waterways,” A Report on the Physical Characteristics of Rivers, Streams, and Bayous in Texas, The Texas Parks and Wildlife Department, Austin, TX, Report. https://tpwd.texas.gov/publications/pwdpubs/pwd_rp_t3200_1047/08_e_tx_pine_red_sabine.phtml#sabine
Roger Omohundro, J. , 1984, “ Early History of the Sabine and Neches Rivers,” Texas Gulf Historical and Biographical Record 20, Texas State Historical Association, Austin, TX, Report. https://tshaonline.org/handbook/online/articles/rnn04. line372
Alperin, L. M. , 1983, “ History of the Gulf Intracoastal Waterway,” Institute of Water Resources, Report No. Navigation History NWS-83-9.
Geographic Names Information System, 2011, to “U.S. Board on Geographic Names,” accessed Jan. 15, 2019, https://www.usgs.gov/core-science-systems/ngp/board-on-geographic-names
Zhang, N. , Zheng, Z. C. , and Yadagiri, S. , 2011, “ A Hydrodynamic Simulation for the Circulation and Transport in Coastal Watersheds,” Comput. Fluids, 47(1), pp. 178–188. [CrossRef]
Zhang, N. , Kee, D. , and Li, P. X. , 2013, “ Investigation of the Impacts of Gulf Sediments on Calcasieu Ship Channel and Surrounding Water Systems,” Comput. Fluids, 77, pp. 125–133. [CrossRef]
Zhang, N. , Li, P. , and He, A. , 2014, “ Coupling of 1-D and 2D Hydrodynamic Models Using an Immersed-Boundary Method,” ASME J. Fluids Eng., 136(4), p. 040907. https://www.researchgate.net/publication/266352682_Coupling_1D-2D_Shallow_Water_models_for_Simulating_Floods_due_to_Overtopping_and_Breaching_of_Leeves
Yadav, P. K. , Thapa, S. , Han, X. , Richmond, C. , and Zhang, N. , 2015, “ Investigation of the Effects of Wetland Vegetation on Coastal Flood Reduction Using Hydrodynamic Simulation,” ASME Paper No. AJKFluids2015-3044.
Huang, W. , Cao, Z. , Yue, Z. , Pender, G. , and Zhou, J. , 2012, “ Coupled Modelling of Flood Due to Natural Landslide Dam Breach,” Proc. ICE Water Manage., 165(10), pp. 525–542.
Gallerano, F. , Cannata, G. , Lasaponara, F. , and Petrelli, G. , 2017, “ A New Three-Dimensional Finite Volume Non-Hydrostatic Shock-Capturing Model for Free Surface Flow,” J. Hydrodyn., 29(4), pp. 552–566. [CrossRef]
Huang, W. , Hagen, S. , and Bacopoulos, P. , 2014, “ Hydrodynamic Modeling of Hurricane Dennis Impact on Estuarine Salinity Variation in Apalachicola Bay,” J. Coastal Res., 30(2), pp. 389–398. [CrossRef]
Xing, Y. , Ai, C. F. , and Jin, S. , 2013, “ A Three-Dimensional Hydrodynamic and Salinity Transport Model of Estuarine Circulation With an Application to a Macrotidal Estuary,” Appl. Ocean Res., 39, pp. 53–71. [CrossRef]
Bauer, P. , Held, R. , Zimmermann, S. , Linn, F. , and Kinzelbach, W. , 2006, “ Coupled Flow and Salinity Transport Modelling in Semi-Arid Environments: The Shashe River Valley, Botswana,” J. Hydrol., 316(1–4), pp. 163–183. [CrossRef]
Johnson, B. H. , Kim, K. W. , Heath, R. E. , Hsieh, B. B. , and Butler, H. L. , 1993, “ Validation of Three-Dimensional Hydrodynamic Model of Chesapeake Bay,” J. Hydraul. Eng., 119(1), pp. 2–20. [CrossRef]
Li, H. H. , Reed, C. W. , and Brown, M. E. , 2012, “ Salinity Calculations in the Coastal Modeling System,” Engineer Research and Development Center, Coastal and Hydraulics Lab, Vicksburg, MS, Report No. ERDC/CHL-CHETN-IV-80.
Zhang, N. , Han, X. , and Wang, W. , 2018, “ A Comprehensive Hydrodynamics-Salinity-pH Model for Analyzing the Effects of Freshwater Withdrawals in Calcasieu Lake and Surrounding Water Systems,” ASME J. Fluids Eng., 141(5), p. 051103. [CrossRef]
Casulli, V. , 1990, “ Semi-Implicit Finite Difference Methods for the Two-Dimensional Shallow Water Equations,” J. Comput. Phys., 86(1), pp. 56–74. [CrossRef]
Spaulding, M. L. , 1974, “ Laterally Integrated Numerical Water Quality Model for an Estuary,” ASME J. Fluids Eng., 96(2), pp. 103–111. [CrossRef]
Hennessy, J. L. , and David, A. P. , 2012, Computer Architecture: A Quantitative Approach, Morgan Kaufmann, Waltham, MA, pp. 46–47.
USGS, 2019, “National Water Information System,” United States Geological Survey, accessed Jan. 15, 2019, https://waterdata.usgs.gov/nwis/uv
Tides & Currents, 2019, “NGOFS–Sabine Neches,” Tides & Currents, accessed Jan. 15, 2019, https://tidesandcurrents.noaa.gov/ofs/ngofs/ngofs_sabine.html

Figures

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Fig. 1

Speed up by using parallel computing

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Fig. 2

Two-dimensional view topography of Sabine Lake research domain

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Fig. 3

Comparison of water–surface-elevation estimations at site USGS of different grid sizes for grid-independence study

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Fig. 4

Comparison of water–surface-elevation histories between measured data and simulation result at Sabine Pass

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Fig. 5

Comparison of water–surface-elevation histories between measured data and simulation result at TCOON

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Fig. 6

Comparison of water–surface-elevation histories between measured data and simulation result at USGS

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Fig. 7

Comparison of water–surface elevation estimations (simulation results) at Sabine Pass, USGS, and TCOON

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Fig. 8

Comparison of salinity estimations (simulation results) at Sabine Pass, USGS, and TCOON

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Fig. 9

Water surface level (m) contour comparison for (a) high tide and (b) low tide

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Fig. 10

Salinity concentration (ppt) contour comparison for (a) high tide and (b) low tide

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