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Journal of Environmental Science and Health, Part A
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A MODELING STUDY OF WATER QUALITY IN MAIN CHANNEL AND
ESTUARINE WETLAND
Wen-Cheng Liu a; Ming-Hsi Hsu b; Albert Y. Kuo c
a Hydrotech Research Institute, National Taiwan University, Taipei, Taiwan b Department of
Agricultural Engineering and Hydrotech Research Institute, National Taiwan University, Taipei,
Taiwan c School of Marine Science/Virginia Institute of Marine Science, The College of William and
Mary, Gloucester Point, Virginia, U.S.A. Online publication date: 31 May 2001
To cite this Article Liu, Wen-Cheng, Hsu, Ming-Hsi and Kuo, Albert Y.(2001) 'A MODELING STUDY OF WATER
QUALITY IN MAIN CHANNEL AND ESTUARINE WETLAND', Journal of Environmental Science and Health, Part A, 36: 5, 641 — 660
To link to this Article: DOI: 10.1081/ESE-100103751
URL: http://dx.doi.org/10.1081/ESE-100103751
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A MODELING STUDY OF
WATER QUALITY IN MAIN CHANNEL
AND ESTUARINE WETLAND
Wen-Cheng Liu,1,* Ming-Hsi Hsu,2 andAlbert Y. Kuo3
1
Hydrotech Research Institute, National Taiwan University, No. 158 Chou-Shan Road, Taipei 10617, Taiwan
2
Department of Agricultural Engineering and Hydrotech Research Institute, National Taiwan University,
Taipei 10617, Taiwan
3
School of Marine Science/Virginia Institute of Marine Science, The College of William and Mary, Gloucester Point,
Virginia 23062
ABSTRACT
A simple computation framework is applied to include estuarine wetland and their interaction with main channels in estuarine modeling. The concept and the model implementation of the scheme are explained using a vertical two-dimensional model of estuarine hydrodynamics and water quality. The model was applied to the Tanshui River estuary and Kuan-Du wetland. The model is calibrated and verified by the available measured data. Simulations are also conducted for various upstream freshwater discharges to predict water quality in the main channel and estuarine wetland. The results show that the inclusion of estuarine wetland in a water-quality model not only provides a framework for computing water-quality conditions but also accounts for the interaction between wetland and main channel. The model provides a useful tool for environmental planning, protection and proposed wetland restoration works.
641
Copyright#2001 by Marcel Dekker, Inc. www.dekker.com
* Corresponding author. E-mail: wcliu@hy.ntu.edu.tw
Key Words: Estuarine wetland; Water quality; Numerical model; Side storage area; Main channel; Calibration and verification.
INTRODUCTION
Wetlands are among the most important ecosystems on Earth. In the great scheme of things, the swampy environment of the carboniferous period and preserved many of the fossil fuels on which we now depend. In more recent biological and human time periods, wetlands are valuable as sources, sinks, and transformers of a multitude of chemical, biological, and genetic materials. Although the value of wetlands for fish and wildlife protection has been known for several decades, some of the other benefits have been identified more recently. Wetlands are sometimes described as ‘‘the kidneys of the landscape’’ because of the functions as the downstream receivers of waste from both natural and human resources. They have been found to cleanse polluted waters, prevent floods, protect shorelines, and recharge groundwater aquifers. Wetlands have also been called ‘‘biological super-markets’’ for the extensive food chain and rich biodiversity they support. They play major roles in the landscape for providing unique habitats for a wide variety of flora and fauna (1). Unfortunately, many wetlands have been converted to agricultural fields, altered for port development, or filled for industrial, com-mercial and residual development (1, 2). Those wetland areas which still remain are faced with deteriorating water quality and subsequent changes in ecosystem function and structure (3, 4). The current interest in the con-struction and restoration of wetlands had led to the need to understand the physical, chemical, and biological processes that control such ecosystems.
The total area of the natural wetlands in Taiwan is 11896 ha, that include the coastal land 1356 ha and the inner wetlands 540 ha. The major part area of the coastal wetland in Taiwan are scattered in western coastal area where are rivers and ocean cross. There are five types of the coastal wetlands: 1. The swamplands of mangroves. 2. The sticky mass of earth. 3. The grass wetlands. 4. The coastal sandy area. 5. The lake in the coastal area. The ecological functions are: high productivity, high species diversity and as the middle station to rest for the birds. The protective func-tions are: to protect the coastal area, to prevent the flooding and to preserve the waters, to supply the recreational and research area. Before, the wetlands have been neglected and have changed to be as the industry, agricultural and fishing area. At present, the environmental protection issues have been very important. Due to the large areas of wetlands have been developed and the interruptions by human beings, so the most emergent and important issues is to consider how to conserve the coastal wetlands in Taiwan (5).
Wetlands such as salt marshes and mangrove swamps are continually exchanging tidal waters with adjacent estuaries. The water and material exchanges between the main body of estuary and its fringing wetland exert
great influence on the ecological landscape of the wetland. Therefore, hydro-dynamic and water quality conditions in the adjacent main body of estuary are majors factors to be considered in planning the protection or restoration of estuarine wetland.
In this paper, we propose a simple computational framework to include mangrove wetland and their interaction with adjacent main channels in estuarine modeling. The scheme, which treats mangrove wetland as tempo-rary side storage areas, accounts for the water and material exchanges between mangrove wetland and main channels as the tide rises and falls, and for the biogeochemical processes affecting non-conservative substances such as water quality parameters in mangrove wetland. A vertical two-dimensional numerical model of estuarine hydrodynamics and water quality described in Hsu et al. (6, 7) and Liu (8) was used to explain the concept and the model implementation of the proposed method. The model is first calibrated and verified by the existing available observational data. Then, water quality simulations in the main channel and mangrove wetland are presents under various hydrological conditions.
STUDY AREA
The Tanshui River is the largest tidal river in Taiwan. The entire river system has a drainage area of 2726 km2, and a total channel length of 327.6 km. It consists of three major tributaries: the Tahan Stream, Hsintien Stream and Keelung River (Figure 1). The downstream reaches of all three
Figure 1. Mapof the Tanshui River estuarine system.
tributaries are affected by tide. The Kuan-Du estuarine wetland is situated at the confluence of the Keelung River and the Tanshui River, lying on an alluvial fan which accumulates deposition of suspended materials, nutrients, and biological debris flushing from all three tributaries. The mean tidal ranges at the Tanshui river mouth and at the location near the Kuan-Du wetland are 222 cm and 226 cm, respectively. The mean discharges at the tidal limits of the three major tributaries are 62.1 m3/s, 72.7 m3/s and 26.1 m3/s for the Tahan Stream, Hsintien Stream and Keelung River, respectively. Tidal seawater, which can intrude into the upper estuary approximately 25 km from the river mouth, can mix well with the river water during high tide, but mixing is only partial during low tide (9). The mean annual river discharge and suspended particulate matter transport over the last 40 years have been approximately 7044 106m3/year m3/year and 11.45 106 tons/year, respectively (10).
Six million people, over a quarter of Taiwan’s entire population, reside in the catchment area of the Tanshui River system. The river system receives untreated domestic discharge and both treated and untreated industrial effluent from its tributaries; thus, it is heavily polluted by nutrients and organic materials. It is estimated that approximately 1790 Ml/day of domestically treated and untreated, mostly untreated, sewage is input to the Tanshui River system (11). Because it receives the sewage discharge and waste effluence from industries, the upper estuary is suboxic and gradually becomes oxic in the lower estuary where the tidal seawater intrudes (12).
Because of its vast area and topographic effects, the Kuan-Du wetland forms a complicated environment of estuarine wetland, coastal wetland and inland wetland. It is the most important landscape among the twelve remain-ing estuarine wetlands in Taiwan. A dike of 3.5 m in height was constructed in 1968 to carve-out 85% of the area for development. The dike was designed to protect against flood of five-year return period (13). The wetland outside the dike is filled with mangroves that can tolerate higher salinity and form the typical tidal salt marsh ecosystem. The salt marsh has high species diversity and commensurate food sources and habitat types for wildlife and is very sensitive to human impact.
MODEL DESCRIPTION
The original version of the model was developed by Park and Kuo (14, 15) and then was refined and expanded to handle tributaries and the mainstem of an estuarine system and applied to the Tanshui River estuarine system. The vertical two-dimensional finite difference model, consisting of linked hydrodynamic model is based on the principles of conservation of volume, momentum, and conservative substance (such as salt). The
water-quality model is based on the principles of conservation of eight inter-linked water-quality state variables: dissolved oxygen, chlorophyll a, carbo-naceous biochemical oxygen demand, organic nitrogen, ammonia nitrogen, nitrite-nitrate nitrogen, organic phosphorus, and phosphate (ortho) phos-phorus. A full description of the model can be found in Park and Kuo (14, 15) and Liu (8).
Governing Equations
The governing equations, which include terms to account for the exchange between side storage area (wetland) and main channels, are solved for the main channel only. They are
the laterally integrated continuity equation
@ðuBÞ
@x þ
@ðwBÞ
@z ¼Qo ð1Þ
the laterally integrated momentum balance equation
@ðuBÞ @t þ @ðuBuÞ @x þ @ðuBwÞ @z ¼ B r @p @x þ @ @x AxB @u @x þ @ @z AzB @u @z þMo ð2Þ
the laterally integrated mass balance equation for a water-quality state variable @ðcBÞ @t þ @ðcBuÞ @x þ @ðcBwÞ @z ¼ @ @x KxB @c @x þ @ @z KzB @c @z þCoþB SeþB Si ð3Þ
where x ¼ distance seaward along river axis; z ¼ distance upward in vertical direction; t ¼ time; u and w ¼ laterally averaged velocities in the x and z directions, respectively; c ¼ laterally averaged concentration of a water-quality state variable; B ¼ river width; p ¼ pressure; r ¼ density; Ax and
Az¼turbulent viscosities in the x and z directions, respectively; Kx and
Kz¼turbulent diffusivities in the x and z directions, respectively; Qo, Mo
and Co¼source or sink of water, momentum and a water-quality state
variable, respectively, including exchange with side storage area (mangrove wetland). The last two-terms of equation 3, Se and Si, represent the external
and internal sources (or sinks), respectively, of a water-quality state variable, the latter being due primarily to biogeochemical reaction processes. The processes included in the water-quality model and their formulations are presented in Park and Kuo (14). Equations 1 to 3 are integrated over a vertical layer thickness of the main channel and then solved by a two-time level, finite difference scheme with spatially staggered grid. The method of solution is detailed in Park and Kuo (14, 15) and Liu (8).
Treatment of the Wetland
The coupling framework proposed by Kuo and Park (16) is refined and adopted to the model for the Tanshui River system. In the estuarine model, the Kuan-Du wetland may be treated as temporary storage area with spatially constant water depth, function as a sink or source of momentum and mass to the main channel as the tide rises or falls, respectively. The water level in the wetland is assumed to rise and fall instantly with the main chan-nel, and the inundated area is assumed to increase linearly with water level as it rises above a certain level. Since the volume exchange between the wet-land and the main channel is determined by the temporal variation of water surface level in the wetland, and the water depth in the wetland is small, the momentum and mass exchanges are assumed to occur only at the toplayer of the vertical two-dimensional model. At the rising tide, the wetland serves as a sink for both momentum and mass.
On the rising tide, the wetland acts as a sink for water-quality variables as the water and water-borne materials leave the main channel. That is, equation 3 needs to include a sink term. Co, which may be expressed as
Co¼ C1BSA t 1 h1þZ0 if Z ¼ Z0Z > 0 ð4Þ
Where BBA¼equivalent width of wetland; Z ¼ position of the free surface
above mean sea level; t ¼ time increment; h1¼thickness of main channel
surface layer at mean sea level; Z0¼position of the free surface above
mean sea level at new time step; C1¼mass concentration in the main
channel surface layer. The mass concentration in wetland also changes because of the mixing between the incoming water and the water in wet-land. On the rising tide, the mass concentration in wetland for the non-conservative substances like water-quality variables are affected by the biogeochemical processes (Figure 2) as well as exchange with the main channel. The change in water-quality variables within the wetland over t can be estimated from
C0SA¼ CSAðhSAþZÞ þ C1Z ðhSAþZ00Þ þ ðSe;SAþSi;SAÞt if Z ¼ Z00Z > 0 ð5Þ
Where CSA¼mass concentration in the wetland; hSA¼water depth in the
wetland at mean sea level; Se,SAand Si,SA¼the external and internal sources
(or sinks), respectively, in the wetland.
On the falling tide, the wetland acts as a source of mass concentration as the water enters into the main channel. The source term, Co, in equation 3
may be expressed as Co¼ CSA BSAZ t 1 h1þZ0 if Z ¼ Z0Z<0 ð6Þ
Note that Z<0 in equation 6 so that Co>0. On the rising, the mass
con-centration in the wetland is determined solely by the biochemical processes. That is,
C00SA¼CSAþ ðSe;SAþSi;SAÞt
if Z ¼ Z0Z<0 ð7Þ
MODEL CALIBRATION AND VERIFICATION
Calibration and verification are far more difficult for the water quality model than for the hydrodynamic model, due to the large number of water quality state variables and biochemical reaction coefficients involved. Since the model predictions will change depending upon the selection of the values
Figure 2. A schematic diagram showing (a) plan view (b) cross-sectional view of main chan-nel and side storage area of an estuary.
of biochemical coefficients, the water quality model should employ consistent coefficient values for different simulation runs. That is, the coefficient values should be transferable for the model predictions to compare with indepen-dent sets of field observations.
Field data, including long-term time-series result of water quality variables at different stations in the river collected by Taiwan EPA from 1994 to 1996 were used to calibrate and verify of the water quality model. They include the environmental conditions such as water temperature, down-stream boundary conditions, waste loadings including non-point and point sources.
The model was conducted for one-year simulation. The daily averages of the model results are compared with the values from field observations of different stations. Figure 3 and 4 show the comparison of results computed and measured at different stations for calibration in 1994 and 1995, respec-tively. Figure 5 presents the model verification in 1996. The results show very high nutrient concentrations including nitrogen and phosphorus, rendering the dissolved oxygen deficiency in the river. Figures show that model results and field measurements were in generally good agreement. Discrepancies were often attributable to observance of the consistency principle between calibration and verification rather than to failure to curve-fit the model results to the field data; some differences, however, did exist between the model results and the field measurements. The changes of variations in model results were generally smaller than those in the field data, because the model calculated the lateral average concentrations while the field data were point measurements, and also because of the random variability inherent to natural system.
Figure 3 to Figure 5 provide a qualitative comparison of model predic-tions and field observapredic-tions. This tradition assessment of model accuracy, the perceived agreement between predictions and observations, depends upon the viewpoint and experience of assessors. In order to render the evaluation of models less subjective, quantitative assessment of model accuracy are desirable. No single measure of set of measures is universally applicable for this purpose. The selection of appropriate measures is dependent upon the quantity and quality of the field data used and upon the nature of the model predictions. In the present study scatterplots, the root-mean-square error (RMS) and mean absolute error (E) are used.
RMS ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 n Xn i¼1 ðPiOiÞ2 s ð8Þ E ¼1 n Xn i¼1 PiOi ð9Þ
Where Piis the ithprediction (daily average), Oiis the ithobservation and n is
the number of observations.
Figure 3. Model calibration results at Hwa-Chung Bridge (Hsintien Stream) in 1994 (a) dissolved oxygen (b) CBOD (c) ammonium nitrogen (d) total nitrogen (e) total phosphorus.
Scatterplots for point-by-point comparison of predictions and observa-tions at Kuan-Du Bridge are presented in Figure 6. A solid, diagonal line indicates the one-to-one correspondence. Magnitude of water quality parameters can range from zero (limit value) to an unbounded value at the higher end. The mean absolute errors of the differences between computed and measured data and the root-mean-square errors are presented in Table 1.
Figure 4. Model calibration results at Chrong-Yang Bridge (Tanshui River) in 1995 (a) dissolved oxygen (b) CBOD (c) ammonium nitrogen (d) total phosphorus.
The table shows quantitative assessments for model calibration and verification at the Kuan-Du Bridge (Tanshui River), Chrong-Yang Bridge (Tanshui River), Pa-Ling Bridge (Keelung River) and Hwa-Chung Bridge (Hsintien Stream).
Figure 5. Model verification results at Pa-Ling Bridge (Keelung River) (a)–(d) and Kuan-Du Bridge (Tanshui River) (e)–(h) in 1996.
(continued)
MODEL RESULTS AND DISCUSSIONS
The calibrated and verified model was employed to investigate the water quality in main channel and Kuan-Du wetland under mean fresh-water discharges and Q75 low flows at the upstream boundaries. Upstream
Figure 5. Continued.
Figure 6. Scatterplots, RMS errors, and mean absolute errors for model calibration and verification at Kuan-Du Bridge (Tanshui River).
(continued)
boundary conditions were specified with daily freshwater discharges at Cheng-Ling Bridge (Tahan Stream), Hsui-Lang Bridge (Hsintien Stream), and Wu-Tu Station (Keelung River). The mean discharges at the tidal limit of the three major tributaries are 62.1 m3/s, 72.7 m3/s, and 26.1 m3/s, for the Tahan Stream, Hsintien Stream, and Keelung River, respectively. The Q75
flows at upstream boundaries are 8.15 m3/s, 20.2 m3/s, and 3.61 m3/s, for the Tahan Stream, Hsintien Stream, and Keelung River, respectively. For the water quality conditions at the upstream and downstream boundaries, historical data of water quality field measurements was collected and analyzed. The same values are specified for the boundaries under mean freshwater discharges and Q75 flows. A nine-constituent tide was used for
the model application when a synthetic tide was employed to specify the downstream boundary condition. Nine constituents are M2 (12.42 hr), S2
(12 hr), N2 (12.9 hr), K1 (23.93 hr), Sa (8765.32 hr), O1 (25.82 hr), K2
(11.97 hr), P1 (24.07 hr), and M4 (6.21 hr). Amplitudes and phases of nine
tidal constituents were specified to generate surface elevation as the down-stream boundary condition for model simulation.
Figure 6. Continued.
Figure 7 presents the model results in concentration in the Keelung River off the Kuan-Du wetland. The dissolved oxygen and chlorophyll concentrations in the main channel are noticeably different with nitrogen concentration. The dissolved oxygen and chlorophyll concentrations under mean flow conditions are higher than that under Q75low flows, and nutrients
under mean flow conditions are lower than that under Q75low flows, because
the pollutant should be diluted during high freshwater discharge. Figure 8 shows the concentration in the Kuan-Du wetland. Maximum dissolved oxygen concentration in Kuan-Du wetland compared to the concentration in the channel shows mass exchanges between wetland and main channel, result in high dissolved oxygen in the Kuan-Du wetland. The model, there-fore, cannot simulate the condition in wetland correctly without proper simulation of main channel conditions. Many modeling efforts that study biochemical processes in shallow waters have not included the main channel conditions as a boundary condition. The dependency of wetland conditions on main channel conditions limit the general applicability of such models for management purposes. For example, explicit modeling of main channel conditions and of exchange between wetland and main channel is essential to project the potential impacts on the water-quality conditions in wetland of any changes in nutrient loadings into the system.
Table 1. Quantitative Assessment for Calibration and Verification of Water Quality Model
Station State variable (mg/l) Number of observations RMS errors (mg/l) Mean absolute errors (mg/l) Dissolved oxygen 69 0.33 0.28 CBOD 69 1.46 0.86
Kuan-Du Ammonia nitrogen 69 0.53 0.35 Bridge Total nitrogen 23 0.59 0.46 Total phosphorus 69 0.067 0.053 Dissolved oxygen 69 0.73 0.51
CBOD 69 1.06 0.72
Chrong-Yang Ammonia nitrogen 69 0.79 0.58 Bridge Total nitrogen 23 0.52 0.41 Total phosphorus 69 0.089 0.062 Dissolved oxygen 69 0.99 0.70
CBOD 69 2.80 0.57
Pa-Ling Ammonia nitrogen 69 1.15 0.78 Bridge Total nitrogen 23 1.20 0.77 Total phosphorus 69 0.12 0.08 Dissolved oxygen 69 0.68 0.42
CBOD 69 1.27 0.84
Hwa-Chung Ammonia nitrogen 69 0.61 0.37 Bridge Total nitrogen 23 0.45 0.29 Total phosphorus 69 0.092 0.067
Figure 7. Model prediction in the Keelung River off the Kuan-Du wetland for mean and Q75
CONCLUSIONS
A vertical two-dimensional estuarine model for the Tanshui River system is developed and used to simulation water quality of the Kuan-Du wetland and main channel. A simple computational framework to include mangrove wetland and their interaction with main channels in estuarine
Figure 8. Model prediction of the concentration in the Kuan-Du wetland for mean and Q75
flow conditions.
(continued)
modeling is presented. The proposed scheme treats wetland as temporary storage areas. It account for the water and material exchange between wetland and main channels as the tide rises and falls, and the biogeochemical processes for nonconservative substances such as water-quality variables in wetland. The water quality model has been calibrated and verified using field data from 1994 to 1996. Considering the random variability inherent in natural systems and the goal of consistency in calibrated coefficients, the agreement between predictions and field observations is more than satisfac-tory. In general, the agreement between predictions and observations depends upon both quality and quantity of input data, and the nature and number of observations.
The calibrated and verified model is further used to simulation water quality in the main channel and mangrove wetland under various hydro-logical conditions. The dissolved oxygen and chlorophyll concentrations under mean flow conditions are higher than that under Q75 low flows, and
nutrients under mean flow conditions are lower than that under Q75 low
flows, because the pollutant should be diluted during high freshwater dis-charge. Maximum dissolved oxygen concentration in Kuan-Du wetland com-pared to the concentration in the channel shows mass exchanges between wetland and main channel, result in high dissolved oxygen in the Kuan-Du wetland. The difficulty is especially so because most of the monitoring pro-grams in estuaries have been forced mainly on the main channels, and thus little field data are available for water-quality processes and variables in the wetland, which may be quite different from those in main channel. Further field surveys for estuarine modeling, therefore, should be designed to include measurement of water-quality condition in the wetland. However, the model provides the useful tool to predict water-quality conditions in the wetland, using with a simple computational scheme.
Figure 8. Continued.
ACKNOWLEDGMENTS
The study is supported, in part, by National Science Council, R.O.C. under grant No. 89-2211-E-002-145. The financial support is highly appreciated. The authors also thank the Taiwan EPA for providing the prototype data.
REFERENCES
1. Mitsch, W. J.; Grosselink, J. G. Wetland, 2nd Ed.; Van Nostrand Rheihold: New York, 1993; 722pp.
2. Willans, T. H. Changes in Marsh Area along the Canadian Shore of Lake Ontario. J. Great Lakes Res. 1982, 8(3), 570–577.
3. Mudroch, A. AStudy of Selected Great Lakes Coastal Marshes, NWRI Scientific Series No. 122. National Water Research Institute: Burlington, Ontario, 1981.
4. Bacchus, H. M. An Ecological Study of Phytoplankton in Cootes Paradise. Master Science Thesis, McMaster University: Hamilton, Ontario, 1974. 5. Zheng, C. C.; Wang, Y. N. The Introduction of the Coastal Wetland in Taiwan
and the Discussion of the Problems. Journal of Experiment for National Taiwan University 1998, 12(3), 213–221. (in Chinese)
6. Hsu, M. H.; Kuo, A. Y.; Kuo, J. T.; Liu, W. C. Study of Tidal Characteristics Estuarine Circulation and Salinity Distribution in Tanshui River System (I), (II), Technical Report No. 239 and 273; Hydrotech Research Institute, National Taiwan University: Taipei, Taiwan, 1996, 1997; 245pp. (in Chinese) 7. Hsu, M. H.; Kuo, A. Y.; Kuo, J. T.; Liu, W. C. Procedure to Calibrate and
Verify Numerical Models of Estuarine Hydrodynamics. Journal of Hydraulic Engineering, ASCE 1999, 125(2), 166–182.
8. Liu, W. C. Modeling Study on Dynamic Transport of Hydrodynamic and Water Quality in Tidal Estuarine System. Ph. D Thesis, Graduate Institute of Agriculture Engineering, National Taiwan University: Taipei, Taiwan, 1998; 272pp. (in Chinese)
9. Liu, W. C.; Hsu, M. H.; Kuo, A. Y.; Kuo, J. T. The Influence of River Discharge on Salinity Intrusion in the Tanshui Estuary, Taiwan. Journal of Coastal Research 2000. (tentatively accepted)
10. Hydrological Year Book of Taiwan, Water Resources Planning Commission: Taipei, Taiwan, 1996. (in Chinese)
11. Wu, S. C. The Treatment of Domestic Sewage by Soil Taiwan, EPA Report: Taipei, Taiwan, 1997. (in Chinese)
12. Chen, Y. C.; Hung, T. C. The Behavior and Mobilization of Trace Metals in the Tanshui River. Journal of the Environmental Protection Society of the Republic of China 1998, 11, 21–31. (in Chinese)
13. Hsu, M. H.; Kuo, A. Y.; Kuo, J. T.; Liu, W. C. Modeling Estuarine
Hydrodynamics and Salinity for Wetland Restoration. Journal of
Environmental Science and Health, Part A 1998, 33(5), 891–921.
14. Park, K.; Kuo, A. Y. AVertical Two-Dimensional Model of Estuarine Hydrodynamics and Water Quality, Spec. Rep. App. Mar. Sci. and Ocean Eng. 321, Virginia Institute of Marine Science: Gloucester Point, VA, 1993; 47pp.
15. Park, K.; Kuo, A. Y. Numerical Modeling of Advection and Diffusion Transport in the Rappahannock Estuary, Virginia, Proceedings of the Third International Conference on Estuarine and Coastal Modeling, ASCE, NY; Spaulding, M. L., Bedford, K. W., Blumberg, A. F., Cheng, R. T., Swanson. J. C., Eds; 1994; 461–474.
16. Kuo, A. Y.; Park, K. A Framework for Coupling Shoals and Shallow Embayments with Main Channels in Numerical Modeling of Coastal Plain Estuaries. Estuaries 1995, 18(2), 341–350.
Received August 29, 2000
