OCS Study 

MMS 2004-063 

 

Coastal Marine Institute 

High-Resolution Integrated 

Hydrology-Hydrodynamic Model: 

Development and Application 

to Barataria Basin, Louisiana 

Cooperative Agreement

Coastal Marine Institute 

Louisiana State University 

U.S. Department of the Interior

Minerals Management Service 

Gulf of Mexico OCS Region 
OCS Study

 MMS 2004-063 

 

Coastal Marine Institute 

High-Resolution Integrated 

Hydrology-Hydrodynamic Model: 

Development and Application 

to Barataria Basin, Louisiana 

Authors 

Dongho Park 

Masamichi Inoue 

William J. Wiseman, Jr. 

Dubravko Justic 

Gregg Stone 

October 2004 

Prepared under MMS Contract 

14-35-0001-30951-19965 

by 

Coastal Marine Institute 

Louisiana State University 

Baton Rouge, Louisiana 70803 

Published by 

U.S. Department of the Interior Cooperative Agreement 

Minerals Management Service Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University
 iii 

DISCLAIMER

This report was prepared under contract between the Minerals Management Service 

(MMS) and Louisiana State University. This report has been technically reviewed by the MMS 

and approved for publication. Approval does not signify that the contents necessarily reflect the 

view and policies of the Service nor does mention of trade names or commercial products 

constitute endorsement or recommendation for use. It is, however, exempt from review and 

compliance with MMS editorial standards. 

REPORT AVAILABILITY

Extra copies of the report may be obtained from the Public Information Office (MS 5034) 

at the following address: 

U.S. Department of the Interior 

Minerals Management Service 

Public Information Office (MS 5034) 

Gulf of Mexico OCS Region 

1201 Elmwood Park Boulevard 

New Orleans, Louisiana 70123-2394 

Telephone Number: (504) 736-2519 

 1-800-200-GULF 

CITATION

Suggested citation: 

Park, D., M. Inoue, W. J. Wiseman, Jr., D. Justic, and G. Stone. 2004. High-Resolution 

Integrated Hydrology-Hydrodynamic Model: Development and Application to Barataria 

Basin, Louisiana. U. S. Dept. of the Interior, Minerals Management Service, Gulf of Mexico 

OCS Region, New Orleans, LA. OCS Study MMS2004 - 063. 74 pp. 
v

ABSTRACT 

This is the final report of our effort under Coastal Marine Environmental Modeling: Part 

III. Coastal Marine Environmental Modeling targets the development of numerical models of 

estuarine shelf interactions with the ultimate objectives of formulating a coupled hydrodynamicecological model that includes biological and sedimentological components. Under Part II, a 

two-dimensional depth-integrated hydrodynamic model, that includes baroclinic pressure 

gradient, was developed. This model has been applied to various estuaries in Louisiana, namely, 

Terrebonne/Timbalier Bay, Barataria Bay, Chandeleur-Breton Sound and Fourleague Bay. A 

simplified version of the hydrodynamic model has been coupled to a simple ecological model 

that includes suspended sediments, nutrients and phytoplankton, to simulate a spring bloom in 

Fourleague Bay. In this report, development of a high-resolution integrated hydrologyhydrodynamic model of Barataria Basin is presented. First, a hydrology model that explicitly 

accounts for the local hydrological cycle over the surrounding drainage basins for the Barataria 

Basin is developed. Using observed precipitation and estimated evaporation over the 

surrounding drainage basins, the hydrology model provides estimates of local runoff. The 

hydrology model is coupled to a high-resolution (O (100m)) two-dimensional depth-integrated 

hydrodynamic model of the Barataria Basin in order to simulate the hydrodynamic response of 

Barataria Basin to hydrological, tidal, and wind forcing. A flood event during tropical storm 

Allison in June 2001 resulted in significant rise in sea-level heights especially in the upstream 

region of the basin, thus, providing a rare opportunity to test the model. It is shown that the 

integrated model is capable of capturing a significant portion of the observed sea-level variations 

during the flood. The integrated model was also used to simulate a typical dry summer 

condition, namely the 30-day period during the summer of 1999. Despite the relatively crude 

salinity initial condition used (based on only eight observations), the model appears to do 

reasonable job of simulating time evolution of salinity fields inside the bay. The model was then 

used to simulate potential impact of freshwater diversions at Naomi, West Pointe à la Hache, and 

Davis Pond. Those simulation runs suggest that even at reasonable diversion rates, notable 

impacts on water level and salinity should be observable in the multiply connected channels 

through the marsh in the vicinity of operating diversion structures within several days of 

freshwater release, and after 15 days or so even in the downstream regions of the basin. The 

largest impact of diversions should be felt in the mid-bay region where the ambient salinity 

gradients are the steepest. It is notable that the diversion signal propagates at shallow-water 

wave speed, like a tidal bore, due to its mass flux, that is much faster than the diffusion timescale suggested for mixing of two water masses in estuaries (e. g., Elliot and Reid, 1976). It is 

interesting to note that even after day 30, some isolated areas still remain without noticeable 

influence of the diversion. Those observations highlight the need to use high model resolution, 

sufficiently high enough to resolve many of the important complex morphological features of the 

basin in order to achieve reasonable simulation capability for morphologically complex basins 

such as Barataria Basin. With the availability of the integrated hydrology-hydrodynamic model, 

the foundation has been laid toward establishing modeling capability that could be usable for 

realistic management purposes. 
vii

 

TABLE OF CONTENTS 

 PAGE

LIST OF FIGURES ...................................................................................................................... ix 

LIST OF TABLES ...................................................................................................................... xiii 

ACKNOWLEDGMENTS ............................................................................................................xv 

CHAPTER 

1 FORMULATION OF AN INTEGRATED HYDROLOGYHYDRODYNAMIC MODEL .....................................................................................1 

 1.1 Introduction .....................................................................................................1 

 1.2 Hydrology and Freshwater Sources ................................................................4 

 1.3 Modeling Approach.........................................................................................8 

1.4 Freshwater Sources .........................................................................................8 

 1.4.1 Rainfall .................................................................................................8 

 1.4.2 Evaporation ........................................................................................12 

 1.4.3 Freshwater Diversion .........................................................................14 

 1.4.4 Intracoastal Waterway........................................................................15 

 1.4.5 Runoff.................................................................................................15 

1.5 Integration of the Hydrology Model and the 

 Hydrodynamic Model ...................................................................................22 

 1.5.1 Numerical Model Formulation...........................................................22 

 1.5.2 Advection Scheme Used ....................................................................25 

 1.5.3 Model Grid Setup...............................................................................25 

 1.5.4 Forcing Functions...............................................................................26 

1.6 Testing of the Hydrology Model...................................................................26 

1.7 Summary and Conclusions............................................................................31 

 

2 MODEL SIMULATION.............................................................................................35 

 2.1 Simulation of a Typical Summer Condition ...............................................35 

 2.2 Simulation of Water Levels ........................................................................40 

 2.3 Simulation of Salinity .................................................................................51 

 2.4 Impact of Diversions at Naomi and West Pointe à la Hache........................56 

2.5 Impact of Diversion at Davis Pond...............................................................64 

2.6 Summary and Conclusions ...........................................................................64 

 

 3 REFERENCES ..........................................................................................................69 
ix

LIST OF FIGURES 

FIGURE PAGE

1-1 Study area and the model domain used................................................................................2 

1-2 Barataria Basin including the surrounding drainage basins ................................................5 

1-3 Climatological salinity distribution for the Barataria Basin (re-plotted 

from Swensen and Turner (1998)).......................................................................................6 

1-4 Schematic diagram illustrating hydrological cycle and forcing 

functions for hydrodynamics in the Barataria Basin .... .....................................................9 

1-5 Modeling approach for an integrated hydrology-hydrodynamic model............................10 

1-6 Daily precipitation at MSY and GDIL1 in 1999 ...............................................................11 

1-7 Measured evaporation at Houma and estimated evaporation 

rate time series in 1999 from GDIL1 station .....................................................................13 

1-8 Twenty-two watershed management units of Barataria Basin ..........................................18 

1-9 Freshwater sources defined in the hydrology model of the 

Barataria Basin .................................................................................................................19 

1-10 Function defined by ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = − t t

f t

22

exp 5

1

( ) used in this study to 

estimate the shape of a unit hydrograph for various drainage 

basins in the Barataria Basin..............................................................................................23 

1-11 Rainfall measured at MSY during tropical storm Allison in 

 June 2001 ...........................................................................................................................27 

1-12 Sea-level stations, rainfall measurement site, and wind 

measurement site used in the flood simulation during Allison..........................................28 

1-13 Observed wind at GDIL1 for the period 5/25/2001-6/25/2001 .........................................29 
x 

LIST OF FIGURES 

(continued) 

FIGURE PAGE

1-14 Observed and simulated sea-level heights with and without 

 rainfall at three stations (listed in Figure 1-12) during Allison .........................................30 

1-15 Cross correlations between observed and simulated 

 sea-level heights with rainfall at three stations during Allison..........................................32 

2-1 Data stations used in model simulation for the simulation period 

 July 7, 1999 – August 5, 1999 .......................................................................................... 36 

2-2 Measured precipitation at MSY and estimated evaporation 

 using meteorological observations at GDIL1 ....................................................................37 

2-3 Observed wind at GDIL1 for the period 7/7/1999-8/5/1999 .............................................38 

2-4 Coherence squared between water level at S1 and wind component 

 at GDIL1 for the period April 1, 1999 – July 30, 1999; eastward 

 stress and northward stress ................................................................................................39

2-5 Mississippi River stage at Venice and salinity variation in S1 

 (Grand Isle), and their cross-correlation function..............................................................41 

2-6 Salinity distribution used as initial condition for a typical dry 

 summer simulation for the period 7/7/1999-8/5/1999 .......................................................42 

2-7 Freshwater diversion records at Naomi and West Pointe 

 a la Hache during the simulation period ............................................................................43 

2-8 Simulated water level at S1 ...............................................................................................44 

2-9a Simulated water level (m) at 3-hour intervals for the period 

 Hour 501 – Hour 510 .........................................................................................................45 

2-9b Simulated water level (m) at 3-hour intervals for the period 

 Hour 513 – Hour 522 .........................................................................................................46 
xi

LIST OF FIGURES 

(continued) 

FIGURE PAGE

2-10 Selected locations from the mouth of the bay to the uppermost reaches 

 of the basin (AB) and to the top of the Barataria Waterway (AC) ....................................48 

2-11 Simulated water level changes during a tidal cycle starting at Hour 501 

 at locations indicated AB and AC in Figure 2-10..............................................................49 

2-12 Hovmüller diagram: water level vs. time and station location ..........................................50 

2-13a Simulated horizontal salinity (ppt) distribution at Day 5 and 

 Day 10 during the simulation period 7/7/1999 – 8/5/1999................................................52 

2-13b Simulated horizontal salinity (ppt) distribution at Day 15 and 

 Day 20 during the simulation period 7/7/1999 – 8/5/1999................................................53 

2-13c Simulated horizontal salinity (ppt) distribution at Day 25 and 

 Day 30 during the simulation period 7/7/1999 – 8/5/1999................................................54 

2-14 Observed and simulated salinity at eight stations 

 (listed in Figure 2-1) ..........................................................................................................55 

2-15a Simulated salinity illustrating inflow and outflow plumes at 

 the mouth of the Barataria Bay at 2-hour intervals for the 

 period Hour 500 – Hour 510..............................................................................................57 

2-15b Simulated salinity illustrating inflow and outflow plumes at 

 the mouth of the Barataria Bay at 2-hour intervals for the 

 period Hour 512 – Hour 522..............................................................................................58 

2-16 Simulated salinity differences between without and with freshwater 

 diversions (without – with) at Naomi and West Pointe à la Hache 

 at 5-day intervals................................................................................................................60 

2-17 Simulated water level differences (cm) between with and without 

 freshwater diversions (with – without) at Naomi and 

 West Pointe à la Hache during the first day of simulation ................................................61 
xii 

LIST OF FIGURES 

(continued) 

FIGURE PAGE

2-18 Simulated water level differences between (cm) with and without 

 freshwater diversions (with – without) at Naomi and 

 West Pointe à la Hache during the first six days of simulation ..........................................62 

2-19 Simulated water level differences (cm) at 5-day intervals between 

 with and without freshwater diversions (with – without) at Naomi 

 and West Pointe à la Hache with a maximum capped at 2 cm ..........................................63 

2-20 Simulated salinity differences between without and with 

 Davis Pond diversion (without – with) at 5-day intervals .................................................65 
xiii

LIST OF TABLES 

TABLE PAGE

1-1 Freshwater sources in Barataria Basin system.....................................................................7 

1-2 Drainage types and their estimated length and persistence time .......................................21 
 xv

ACKNOWLEDGMENTS 

We would like to thank the Coastal Marine Institute at Louisiana State University that is 

funded by the Minerals Management Service for providing the necessary support for this project. 

We would like to acknowledge assistance provided by many people including Christopher M. 

Swarzenski and Erick M. Swenson in providing field data and many insights into hydrology and 

hydrodynamics of Barataria Basin. Invaluable suggestions and guidance on many 

meteorological problems were provided by S. A. Hsu. We thank the National Oceanic and 

Atmospheric Administration, the U. S. Army Corps of Engineers, the United States Geological 

Survey, and the Louisiana Department of Natural Resources for providing many of the data sets 

used. 
1

CHAPTER 1 

FORMULATION OF AN INTEGRATED 

HYDROLOGY-HYDRODYNAMIC MODEL 

 

1.1 Introduction 

 Barataria Basin is located immediately west of the Mississippi River delta and is bounded 

on the north and east by the Mississippi River, on the west by Bayou Lafourche, and on the south 

by the Gulf of Mexico (Fig. 1-1). The total area of the basin covers approximately 6,300 . 

The basin is an irregularly shaped area bounded on each side by a levee formed by the present 

and a former channel of the Mississippi River. A chain of barrier islands separates the basin 

from the Gulf of Mexico. In the northern half of the basin, several large lakes occupy the sump

position. 

2 km

 During the last several hundred years, in coastal Louisiana, land building processes have 

been greatly reduced, while the erosion rate continues and probably has accelerated due to the 

activities of man (Van Sickle et al., 1976). The primary pattern of land loss in the Louisiana 

coastal zone results from the submergence of coastal marshes and subsequent conversion to open 

water (Turner, 1990). Within the Barataria Basin, wetland loss rates averaged nearly 23.1

per year between 1974 and 1990 (Louisiana Coastal Wetlands Conservation and Restoration 

Task, 1993). This high rate appears to be due to a combination of natural processes such as 

subsidence, sea-level rise, canal dredging, and the river funneling sediments across the 

continental shelf making them unavailable for coastal marshes (Bowman et al. 1995). Of the 

numerous factors contributing to this loss, perhaps the leveeing of the Mississippi River for flood 

control has had the most far-reaching impact. Artificial flood control levees were constructed 

along the Mississippi River and in 1904 Bayou Lafourche was artificially dammed. Ever since 

construction of the flood control levee along the Mississippi River, rainfall had been the main 

source of freshwater to the Barataria Basin. Only a small amount of riverine input, designed to 

mimic a natural crevasse, was introduced into the basin’s wetlands through the recently 

completed siphons at Naomi and West Pointe à la Hache. These have been working at a 

maximum pumping rate of 60 of freshwater at each site. Another diversion site, Davis 

Pond, was recently opened with a maximum design-pumping rate of 300 of freshwater. 

2 km

3 m −1

s

3 m −1

s

 A major consequence of man-made freshwater diversions in estuarine environments such 

as Barataria Basin is its impact on salinity distribution. Salinity gradients, such as those within 

Barataria and Terrebonne estuaries have a significant influence on the distribution of estuarine 

flora and fauna (Melancon et al., 1994). For example, the optimum salinity range for natural 

oyster growth and survival in Louisiana waters is 5 to 15 ppt (Galtsoff, 1964). Consequently, 

most oyster reefs are limited to the mid-regions of estuaries, away from the low salinity waters of 

the upper estuaries and the high salinity waters of the lower estuaries (Melancon et al., 1994). 

Another pressing issue for the state of Louisiana is a potential impact of salinity alteration 

resulting from man-made freshwater diversion in promoting mosquito-borne disease. A recent 
 2 

Figure 1-1. Study area and the model domain used. The southern boundary is open. 
3

field study carried out by a Tulane University graduate student found increasing activities of salt 

marsh mosquitoes, a primary carrier of the West Nile virus, in freshwater areas impacted by the 

Davis Pond diversion (Sunday Advocate, March 23, 2003). 

 Tides in this region are small and diurnal (Marmer, 1954). However, the associated tidal 

currents contribute significantly to the exchange of salt between Barataria Basin and the adjacent 

Gulf of Mexico. Because of their predictability, these tidal flows are particularly important 

during the summer months when wind forcing is characteristically weak. Although the major 

water level fluctuations in Barataria Basin are governed by the diurnal tide at high frequencies, 

the wind controls the time-averaged estuarine water level on time-scales of a few days. In 

shallow water, such as in Louisiana coastal areas, the effect of wind stress usually overpowers 

the effects of the accompanying atmospheric pressure system (Byrne et al., 1976). Strong winds 

from the south “pile up” water along the coast, forcing water into the estuaries and raising water 

level about 0.3~0.5 m above normal. Conversely, winds from the north force water out of the 

estuaries depressing the water levels 0.3~0.5 m below normal (Swenson and Turner, 1998). 

 The summer weather encountered along coastal Louisiana is usually dominated by the 

Bermuda High Pressure system, which puts the Louisiana coast under the influence of synoptic 

winds with southerly and easterly components. The winter, on the other hand, is characterized by 

a greater percentage of winds out of the east and north dominated by cold front passages. 

 Barrett (1971) and Gagliano et al. (1973) described an inverse relation between 

Mississippi River flow and coastal salinities in Louisiana, “Salinity in Louisiana coastal waters is 

seasonally variable, fluctuating primarily with seasonal changes in tide, rainfall, river discharge, 

and evaporation rate” (Barrett, 1971). The salinity signal is highly coherent with Mississippi 

River discharge on time scales of the order of a year (Wiseman and Swenson, 1989). Salt 

wedges and salinity stratification were found to be absent in most of coastal Louisiana's 

estuaries. Increasing salinity has been widely mentioned as a factor contributing to the loss of 

coastal wetlands in Louisiana. Wetland deterioration, channelization and increased tidal 

exchange are thought to be primary factors that could result in increased estuarine salinity 

(Wiseman and Swenson, 1989). Byrne et al. (1976) investigated long-term salinity trends using 

14 to 19 years of data at three stations within Barataria Basin. They found that the trends vary in 

space, but generally, the highest values occurred during September to November, and the spring 

low had a three-month (February to May) lag between Grand Terre and Bayou Barataria. 

Wiseman et al. (1990b) studied salinity trends in Louisiana estuaries and concluded that a 

negative trend in mean salinity was presently occurring at the mouth of Barataria Bay. 

 Even though the study was unpublished due to the request of the Freeport Sulphur 

Company, one of the pioneers of hydraulic model studies for Barataria Bay was von Arx (1950). 

Kjerfve (1973) studied circulation and dynamics in Caminada Bay, using a set of linearized 

differential equations to investigate time-dependent behavior of the water surface; he found that 

the simulated slope vector reproduced measured conditions extremely well, as a function of time. 

Hacker (1973) developed two-dimensional, time-dependent transport equations to predict 
 4 

velocity profiles, tidal fluctuations, and temperature and salinity profiles. He found that the 

hydrodynamic model and energy transport model accurately predicted the dynamics of tidal 

fluctuations and velocity profiles and the time-varying temperature distribution, respectively, in 

the Barataria Bay. Banas (1978) computed salt and momentum balances in Barataria Bay. He 

found that the baroclinic pressure gradient and vertical eddy stress gradient were the dominant 

two forces in the lateral force balance, while the barotropic pressure gradient and Coriolis force 

prevailed in the longitudinal balance. 

1.2 Hydrology and Freshwater Sources 

Climatological salinity distribution in the Barataria Basin presented by Swensen and Turner 

(1998) (re-plotted in Figure 1-3) shows a wide salinity range going from freshwater in the upper 

bay to ~15 ppt in mid-bay and to ~25 ppt at the mouths of the bay. Apparently, freshwater input 

from the surrounding drainage basins contributes significantly, in particular, in the upstream part 

of the basin in order to maintain nearly fresh water salinity there. This is due to the high land to 

water ratio in the upstream compared to the downstream (Figure 1.2). Salinities in the 

downstream part gradually increase toward the open Gulf of Mexico salinities. Consequently, 

freshwater runoff from the surrounding drainage basins appears to be the most dominant fresh 

water source in the long-term climatological salinity distribution (Marmer, 1954; Kjerfve, 1973; 

Light et al., 1973; Butler, 1975; and Van Sickle et al., 1976). 

 Freshwater input into Barataria Basin is contributed largely by the following four sources 

(Table 1-1): 

1) direct rainfall on the open water surface, 

2) runoff of rainfall over the surrounding drainage basins, 

3) man-made freshwater diversions, namely, Naomi Freshwater Diversion, West Pointe à 

la Hache and Davis Pond Freshwater Diversion from the Mississippi River, and 

4) the Gulf Intracoastal Waterway as a conduit of freshwater from the Atchafalaya River, a 

major distributary of the Mississippi River. 

 Annual mean precipitation for the 30-year period 1970-1999 was reported to be 163.9 cm 

at New Orleans International Airport (Garoogian, 2001). Evaporation removes freshwater from 

open water surface. On a long-term (1961-1990) annual basis, precipitation over coastal 

Louisiana exceeds evaporation. In summer, evaporation often exceeds precipitation. Drier thannormal conditions generally result in higher salinities even in the upstream part of the basin (Lee, 

personal communication, 2002). Butler (1975) demonstrated that the low discharge into Lac des 

Allemands in July and August is the result of two different phenomena. Evapotranspiration 

normally exceeds rainfall during the summer, so swamp drainage, the major hydrologic input to 

the lake and bayous, is reduced. Furthermore, during the summer, Ekman convergence due to 

the prevailing southeasterly winds retards the flow of water from Bayou des Allemands. The 

flow is further retarded during the day because of the sea breeze effect. At night the flow 

direction is typically reversed in a gulfward direction due to the land breeze effect. These 

tendencies are, of course, modulated by tides. 
5

Figure 1-2. Barataria Basin including the surrounding drainage basins. 

 
 6 

Figure 1-3. Climatological salinity distribution for the Barataria Basin (re-plotted from 

 Swensen and Turner (1998)). 
7

 

 Table 1-1 

Freshwater Sources in Barataria Basin System

Freshwater Sources Location Amount (m3

 sec ) −1

Direct Rainfall Rainfall on Water 

Surfaces Variable with Rainfall 

Runoff by Rainfall from 64 Known Streams Variable with Rainfall 

 Various Streams 522 Unknown Streams and Drainage Area 

Diversion from Naomi 60 at Maximum

West Point a La Hache 60 at Maximum

Davis Pond 300 at Maximum

Intercoastal Waterway From West of Basin 50 on Average 

 
 8 

Recent observations suggest that freshwater input from the Gulf Intracoastal Waterway 

averages ~60 m3

s

-1 due to sea-level differences between the Barataria Bay and the neighboring 

Atchafalaya Bay that is impacted by the Atchafalaya River (Swarzenski, personal 

communication, 2002). It should be noted, however, that freshwater input from the Gulf 

Intracoastal Waterway could rival those at the man-made diversion sites (Swarzenski, personal 

communication, 2002). 

1.3 Modeling Approach 

 Our previous research efforts to model circulation in Louisiana estuaries included 

applications of a two-dimensional depth-integrated hydrodynamic model to 

Terrebonne/Timbalier Basin (Inoue and Wiseman, 2000), Fourleague Bay (Wiseman and Inoue, 

1993), Barataria Basin (Park, 1998; Inoue et al., 1998) and Chandeleur/Breton Sound (Inoue et 

al., 2001). In all those previous applications, the hydrodynamic model has been carefully tested 

and calibrated using the observed sea-level and/or current data. In order to simulate salinity 

distribution in Louisiana estuaries, an integrated hydrology-hydrodynamic model was developed 

(Park, 2002) to simulate the hydrological cycle depicted in Figure 1-4. Development and 

application of the integrated model for Barataria Basin are presented here. The hydrology model 

that explicitly accounts for freshwater runoff from the surrounding drainage basins is developed 

first. Then, the hydrology model will be coupled to a high-resolution version of the 

hydrodynamic model. The objective is to be able to estimate runoff given estimates of rainfall 

and evaporation, and to simulate hydrodynamics and salinity within the basin in response to 

freshwater input. The integrated model will be forced by observed tides coming from the Gulf of 

Mexico, local wind, rainfall and evaporation over the model domain, salinity estimated at the 

open boundary located offshore of the mouth of the bay. Estimated local precipitation and 

evaporation over the model domain based on actual meteorological observations provide 

hydrological forcing to the hydrological model, which in turn simulates local runoff into the 

hydrodynamic model (Figure 1-5). The integrated hydrology-hydrodynamic model will be used 

to simulate salinity variations within the Barataria Basin for a typical dry summer in Louisiana, 

July 1999. 

1.4 Freshwater Sources 

1.4.1 Rainfall 

Precipitation, as a function of time and space, is highly variable. For example, daily 

precipitation records at New Orleans Moisant Airport (MSY) and the Grand Isle C-MAN 

(GDIL1) station, that are separated by about 80 km, show the correlation coefficient of only 

=2

r 0.34 in 1999 (Figure 1-6) and appears to vary seasonally (Park, 2002). There were only five 

stations where precipitation measurements were made in the vicinity of Barataria Basin. For this 

study, however, only the precipitation record from MSY was used for the following reason. The 

dominant upstream regions which account for 61% of total land are best represented by MSY 
9

Figure 1-4. Schematic diagram illustrating hydrological cycle and forcing 

 functions for hydrodynamics in the Barataria Basin. 
 10 

Figure 1-5. Modeling approach for an integrated hydrology-hydrodynamic model. 
11

0 50 100 150 200 250 300 350

0

1

2

3

4

5

Rainfall (

cm)

Days (1/1 - 12/31, 1999)

GDIL1 Station

0 50 100 150 200 250 300 350

0

1

2

3

4

5

Rainfall (

cm)

MSY Station

Figure 1-6. Daily precipitation at MSY (upper) and GDIL1 (lower) in 1999.
 12 

station. In addition, MSY was the only station where rainfall was measured at hourly intervals, a 

minimum temporal resolution required in the modeling in order to resolve short-duration but 

intense rainfall events that are common in Louisiana in summer. Therefore, uniform spatial 

distribution of precipitation over the entire domain was assumed based on the measurement at 

MSY station.

1.4.2 Evaporation 

 Methods used to estimate the amount of evaporation include: 1) measurement by 

evaporation pan, 2) empirical formulae, 3) water budget methods, 4) mass transfer methods, and 

5) energy budget methods (Raudkivi, 1979). Near the Barataria Basin study area, pan 

measurements are available. Evaporation pan measurement is relatively simple and the results 

are in a reasonably constant ratio to evaporation estimates from large open water surfaces using 

other techniques. The measurements are relatively consistent from region to region. The 

evaporation is measured using a class A Pan at Houma and is only available at daily intervals, 

being too coarse to be comparable to hourly rainfall measurements (Figure 1-7). Therefore, the 

empirical formulae method, well suited for studying the Gulf coastal regions, was used in this 

study. The regional average evaporation is dependent primarily upon the energy available and 

vapor pressure gradients above the evaporating surface. 

Latent heat flux occurs as a result of the transfer of water vapor from the ocean to the 

atmosphere. Latent heat flux due to evaporation, , is estimated as (Roll, 1965) Hl

 (1) 10 ( )U air q sea q E E LtC TL l H = = ρ −

where is the latent heat of vaporization, TL E is the evaporation rate, is the latent heat 

coefficient, 

EC

ρ is the air density, and are the specific humidity for the sea and air, 

respectively, and is wind speed at the 10 m reference height. 

qsea qair

10 U

 

At the sea surface, the specific humidity, , is related to the saturation vapor pressure, 

, through (Hsu, 1988) 

qsea

sea e

) 1 0.62( − = p sea e sea q (2) 

where, [ ( )] Tsea Tsea sea e + = × 7.5 / 237.3 6.1078 10 , p is atmospheric pressure, and is the sea 

surface temperature ( ). Similarly, 

sea T

Co

 ) 1 0.62( − = p air e air q (3) 

where, , and is the dew-point temperature ( ). ⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ +

= × dew T dew T

air e

7.5 / 237.3

6.1078 10 dew T Co

 

Since we are dealing with heat loss from the sea to the air, the following values are used 

in our computations: (Smith et al., 1994); 3 1.12 10− = × E C ρ = 1.2kg −3 m , and
13

0 50 100 150 200 250 300 350

0

0.2

0.4

0.6

0.8

1

1.2

E

vaporation (cm/day)

(a) Measured by Class A Pan at Houma

0 50 100 150 200 250 300 350

0

0.2

0.4

0.6

0.8

1

1.2

Days (Jan. 1 - Dec. 31, 1999)

E

vaporation (cm/day)

(b) Estimated by Empirical Fomulae

Figure 1-7. Measured evaporation at Houma (top) and estimated evaporation 

 rate time series in 1999 from GDIL1 station (bottom). 
 14 

J TL 5 = 2.5 ×10 −1 kg (Hsu, 1988). Note that a latent heat flux of 1W is equivalent to an 

evaporation rate of (Colon, 1963). 

−2

m

cm

3 3.56 10− × −1 day

Hourly measurements of atmospheric pressure, wind speed, sea surface temperature, and 

dew-point temperature available at GDIL1 station were all used to estimate evaporation. In 

1999, which is considered to be a dry year, the total amount of evaporation estimated using the 

GDIL1 data was about 124 cm, which exceeded the total Class A Pan measured by nearly 10 cm. 

Figure 1-7 shows the estimated evaporation time series using GDIL1 station data. Evaporation 

was generally higher than the mean for the year 1999, with small variation, in summer. It was 

generally lower than the mean, but highly variable with several-day time scales, during the other 

seasons. Even though the estimated annual mean, 0.34 cm , is slightly higher than the 

measured mean, 0.32 cm , there are some seasonal differences. It should be noted that the 

measurement value is slightly higher on average during summer and exhibits lower fluctuations 

during the other seasons than the estimated evaporation rate due to several reasons. It has been 

found that cold fronts are the meteorological forcing agents affecting many air-sea interaction 

mechanisms, including evaporation and heat loss to the atmosphere from the Gulf of Mexico 

(e.g., Henry, 1979; Huh et al., 1984; Hsu, 1997). 

−1 day

−1 day

 

1.4.3 Freshwater Diversion 

Diversion structures provide a controlled flow of freshwater and nutrients from the 

Mississippi River into a target area in Barataria Basin. The essential goals of freshwater 

diversions were to manage the productivity of wildlife and fishery resources by controlling 

salinity and to maintain marsh elevation by introducing additional freshwater and sediments to 

the marsh (Roberts et al., 1992). Two freshwater diversions, Naomi and West Pointe à la Hache 

(Figure 1-2), currently were opened along the Mississippi River in 1992. The Naomi project 

area contains approximately 13,000 acres (5,261 ha) of intermediate and brackish marsh. The 

West Pointe à la Hache project area contains approximately 9,300 acres (3,764 ha) of open-water 

and 7,600 acres (3,076 ha) of brackish marsh. Both diversions have a maximum design-pumping 

rate of 60 m3

 s

-1. Another diversion site, Davis Pond, was recently opened in April, 2002 with a 

maximum design-pumping rate of 300 of freshwater to preserve about 33,000 acres 

(13,354 ha) of marsh and benefiting 777,000 acres (31,444 ha) of marshes and bays (Boshart, 

1998). The $119.6 million project, the world’s largest freshwater diversion project, reintroduce 

fresh water, nutrients and sediment to the salt-threatened Barataria estuary, which stretches south

to the Gulf of Mexico. Most of the diversion is expected to occur during the river’s high-water 

season in the first half of the year. But the structure will be closed when storms and tides are 

expected to increase stages in Lakes Cataouatche and Salvador. 

3 m −1

s
15

1.4.4 Intracoastal Waterway 

Water flow in the Barataria Basin is often controlled by man-made navigation and 

drainage canals. Perhaps the best example is the Gulf Intracoastal Waterway. The Gulf

Intracoastal Waterway is a coastal waterway route extending from Apalachee Bay, Florida to the 

Mexican border. As part of the Intracoastal Waterway system, the Gulf Intracoastal Waterway 

provides a practical navigation route along the coast of the Gulf of Mexico. Within Louisiana, 

the Gulf Intracoastal Waterway extends along the coast of the Gulf of Mexico from Lake Borgne 

to the Sabine River, a distance of 432 km. The Gulf Intracoastal Waterway is a conduit of

freshwater and sediments to coastal Louisiana. The amount of freshwater and sediments 

introduced to coastal wetlands depends on the difference in head between the Lower Atchafalaya 

River and surrounding waters. The Lower Atchafalaya River varies in stage from year to year, 

depending upon what happens in the basin of the Mississippi River and Red River (Swarzenski 

et al., 1999). Recent observations suggest that freshwater input into the Barataria Basin from the 

Gulf Intracoastal Waterway averages ~50 m3

s

-1 due to sea-level differences between the 

Barataria Bay and the neighboring Atchafalaya Bay that is impacted by the Atchafalaya River 

(Swarzenski, personal communication, 2002). It should be noted, however, that freshwater input 

from the Gulf Intracoastal Waterway could rival those at the man-made diversion sites, i. e., it 

needs to be included in any hydrological modeling effort to simulate freshwater runoff into the 

Barataria Basin. Swarzenski et al. (1999) found that the relationship with Larose water level and 

discharge in the Gulf Intracoastal Waterway, although positive, was less than linear. Water level 

1.2 m above mean sea level at Larose is critical in terms of transport direction. For this study, 

long-term average flow, 50 , in the Gulf Intracoastal Waterway from west of the Barataria 

Basin was assumed as a constant volume flux. 

3 −1

m s

 

1.4.5 Runoff 

Runoff enters the Barataria Bay estuarine system through a complex series of coastal 

swamps and wetlands, mostly from local precipitation. On a long-term (1961-1990) annual 

basis, precipitation over coastal Louisiana exceeds evaporation, thus, resulting in a net runoff 

into the Barataria Basin. Annual mean precipitation for the 30 years was reported as 160 cm

(Baumann, 1987). In 1999, however, total precipitation was recorded as 114 cm due to the 

prolonged impact of the 1997-1998 El-Nino Southern Oscillation event, while the total amount 

of evaporation estimated using the GDIL1 data was about 124 cm. Hence, net freshwater input 

from the hydrological cycle must dictate the salinity distribution within the bay. Estuarine 

salinity decreases during periods of high runoff as the freshwater-saltwater interface moves down 

the estuary toward the sea, and it reverses when runoff decreases. 

 

There are several previous studies of runoff from land in Barataria Basin. Light et al. 

(1973) developed a hydrologic model to analyze freshwater flow in the Barataria area using the 

watershed management unit method. This model used precipitation, evapotranspiration, and 

physiographic data to calculate annual discharge from Bayous Chevreuil, Boeuf, and des 

Allemands. The investigators also developed a mean annual precipitation map based on a long- 
 16 

term record (1945-1970), and found mean annual rainfall excess values of more than 50.8 cm (20 

inches) in the upper-basin watershed. Similarly, Gagliano et al. (1973) modeled runoff from

land and freshwater inputs to water bodies using the cell method and assuming water losses from

the water surface, both open and vegetation-covered. They computed the mean geographical 

distribution of freshwater flow over the basin. Wax et al. (1978) produced a water budget based 

on climatic conditions to estimate periods of freshwater surplus and deficit for the Barataria 

Basin system. 

Butler (1975) studied the characteristics of freshwater discharge and the drainage area 

near Lac des Allemands. He indicated that the freshwater inflow into Lac des Allemands was 

42~54 sec under average flows and ~80 sec under peak flow conditions. Wiseman 

and Swenson (1989) pro-rated this number to give a total runoff into the basin ~150 sec . 

Muller (1975) estimated that the freshwater input to the Barataria Basin was per tidal 

cycle or 266 sec . Howard (1982) estimated that the total precipitation over Barataria Basin 

was per tidal cycle. Sklar (1983) produced an annual water budget for the upper 

portions of the Barataria Basin system based upon data from 1914 through 1978 and estimated 

that 40 % of the precipitation was available for runoff. His results showed that most of the 

surplus of freshwater occurred in winter, with deficits of freshwater most likely to occur during 

the summer. He also noted that deficits should not be expected to occur regularly, because 

precipitation is usually greater than evaporation. Recently, Swenson and Turner (1998) 

calculated a water budget using the 28-year average (1960~1988) of precipitation and found 

similar results to these of Sklar. For this study, freshwater input by rainfall was estimated simply 

by multiplying total amount of rainfall by the total drainage area. The long-term average rainfall 

is known to be 160 , and the total drainage area is about 4,400 . Therefore, the freshwater 

input into the basin is 4,400 = per tidal cycle or 223 sec , of 

which 25 percent flows to Lac des Allemands, which is comparable to some of the previous 

results. 

3 m −1 3 m −1

3 m −1

6 3 12 ×10 m

3 m −1

6 3 21×10 m

cm 2 km

km 1.6m

2 × −1

year 6 3 1.15 ×10 m 3 m −1

Runoff modeling depends on the records from single point rain gauges, some of which 

provide estimates of rainfall intensities in time steps of one hour or better, while others provide 

daily estimates. In large catchments, models using a daily time step may be adequate for 

applications. In small catchments such as the one under consideration here, a daily time step 

may be longer than the storm response time of the catchment and finer time resolution may be 

required (Beven, 2000). 

The water that contributes to streamflow may reach the stream channel by any of several 

paths from the point where it first reaches the ground as precipitation. The rainfall-produced 

runoff enters the system through a complex series of coastal swamps and wetlands, providing a 

mechanism for the slow release of fresh water over large wetland areas. The primary physical 

characteristics of the drainage basin are its area, shape, elevation, slope, orientation, soil type, 

drainage channel system, water storage capability and vegetal coverage (Raudkivi, 1979). Some

water flows over the soil surface as surface runoff and reaches the stream soon after its 

occurrence as rainfall. Other water infiltrates through the soil surface and flows beneath the 
17

surface to the stream. The groundwater contribution to streamflow cannot fluctuate rapidly 

because of its very low flow velocity. 

 

Because the relation between precipitation and runoff is influenced by various storm and 

basin characteristics, usually many approximate formulas are used to relate rainfall and runoff. 

Since most of the land area for the Barataria Basin is less than 1.5 meters above mean sea level 

(Gagliano et al., 1973) and covered with wetland that is saturated by water, no groundwater flow 

is assumed. Only surface flow is considered significant and is incorporated into the model. In 

many environments, evapotranspiration is as significant a contribution to the water balance as 

stream discharge. Thus, for extended periods of runoff simulation, it will be necessary to 

estimate actual evapotranspiration losses from a catchment area. However, due to the relatively 

small drainage areas considered here, resulting in relatively quick runoff and short periods of 

simulation, transpiration was ignored for this study. As a result, the following simple model was 

adapted to relate rainfall to runoff for any drainage basin, 

Runoff ( hour ) = (Rainfall – Evaporation)(m hour ) 3 m −1 −1 × Area ( ). (4) 2

m

 

Delineation of runoff catchment areas is one of the real challenges in modeling estuarine 

hydrology in South Louisiana. The availability of discharge data is important for the model 

calibration process. Streamflow rates may be determined from stream stage data calibrated using 

measurements of velocity and cross-sectional area. Butler (1975) estimated discharge rate, using 

this method, for two main streams, Bayou Chevreuil and Bayou Boeuf, draining into Lac des 

Allemands. Discharge data are, however, generally available at only a small number of sites in 

any region. Runoff modeling for sites where there are no available discharge data is a difficult 

task. Barataria Basin has numerous known or recognizable, as well as unknown, streams (Table 

1-1) that vary considerably in size and length. It is impossible to install gauges to measure all 

their discharges. Even though most small streams are not immediately discernible on a map, one 

can assume that there should be runoff from all subaerial land. 

In order to estimate the discharge rate from unknown and ungauged streams, the basin 

was divided into twenty-two watershed management units using a pre-existing watershed chart 

(A Digital Map of the State). The area of each watershed management unit was estimated 

(Figure 1-8). Each area segment contains land and water surface. Since the hydrodynamic 

model that was previously developed for shallow water accounts for freshwater input due to 

direct rainfall on the water surface separately, runoff estimates should only account for 

precipitation over land. Therefore, the area of water surface was subtracted from each watershed 

unit area. Based on a minimum two square kilometers of land area, at least 1 stream was 

arbitrarily specified for unknown streams. Known streams were also associated with drainage 

areas (Figure 1-9). Most unknown drainages are smaller than nine square kilometers. The 

length of unknown streams was estimated by assuming that all drainages are semi-circular in 

shape, i. e., length= 2 × area / pi . 

 
 18 

 

727

458

303

103

233

116

178 87

498

90

235

367

93

172

174

428

296

243

670 514

97

152

Figure 1-8. Twenty-two watershed management units of Barataria Basin. 

 The numbers on the map are the area of each subbasin in . 2 km
19

Figure 1-9. Freshwater sources defined in the hydrology model of the Barataria Basin. 
 20 

The persistence time is defined as the time required for surface runoff to flow from the

most remote point in a subbasin or subwatershed to the outlet (Walesh, 1989). All drainages 

were categorized based on their size and estimated persistence time (Table 1-2). The longest 

drainage system, Bayou Chevreuil (26 km), was known to have a 3-day (72 hours) persistence 

time. Other streams’ persistence times were estimated by a linear extrapolation relative to the 

longest stream’s persistence time. 

 Streamflow, at a given location on a watercourse, is represented by a hydrograph. The 

hydrograph produced in a stream is the result of various hydrologic processes that occur during 

and after any precipitation event. This continuous graph displays the properties of streamflow 

with respect to time, normally obtained by means of a continuous recorder that shows stage 

versus time, and then transformed into a discharge hydrograph by applying a rating curve. The 

shape of a hydrograph depends on precipitation pattern and characteristics and basin properties 

(Viessman et al., 1989). A stream’s behavior is greatly affected by the characteristics of its 

watershed. For instance, the steadiness of the stream’s flow at a given point is controlled by the 

area of the watershed upstream of the point. Typically, during a rainfall event, the hydrograph 

of an undisturbed stream rises fairly rapidly, and after reaching a peak value, falls off rather 

gradually. Streams differ from one another in many features besides area, though. 

A complete analysis of the relation between rainfall and runoff, determining the 

characteristic shape of hydrographs for a basin, involves knowledge of the basin’s physical, 

vegetative, and climatic characteristics, all of which affect the quantity of streamflow generated 

in a drainage basin. These parameters are not well known for the Barataria Basin. Another, 

simpler methodology must be employed for estimating the relationship between rainfall and 

runoff for the various sub-basins discharging into the Barataria Bay estuary. 

The unit hydrograph, expected in response to a unit input of rainfall per unit area of 

marsh, may be considered to consist of three general parts: 1) the rising limb or concentration 

curve, 2) the crest segments, and 3) the recession curve or falling limb. Generally, the falling 

limb lasts longer than the rising limb, skewing the curve to the right. In order to estimate the 

total volume runoff entering the estuarine system, such a hydrograph should be multiplied by the 

amount of precipitation and the area of the sub-basin. 

Using an one-sided filter, one can relate rainfall per unit time per unit area, x , to the 

resultant discharge from the sub-basin, y, as follows, 

( ) ( ) ( ) ..... ( ) 1 1 2 2 i j x t j a i a x t i a x t i y t − + + − + − = (5) 

where j is the filter width, and a's are the filter coefficients. By selecting the filter width equal to 

the persistence time of the sub-basin, the effective runoff is limited within the persistence time. 

If water is conserved during runoff process, the following condition has to be satisfied. 
21

Table 1-2 

 

Drainage Types and Their Estimated Drainage Length and Persistence Time

 

Drainage Types 1* 2* 3* 4 5 6 7 8**

Drainage Size (km ) 2 2 3-4 5-8 9-16 17-

32 

33-

64 228 672

Drainage Length (km) 1.1 1.5 2.1 2.8 4.0 5.6 11.5 26.0

Persistence Time (Hour) 3.1 4.2 5.8 7.8 11.0 15.5 32.0 72.0

Number of Streams 373 124 25 32 26 4 1 1

Sub Total Area (km ) 2 746 496 164 424 616 202 228 672

 

 * unknown length of drainages 

 ** known persistence time of drainage
 22 

...... 1 1 2 3 + + + + = j a a a a (6) 

In order to mimic the shape of a typical unit hydrograph response to a typical rainfall 

event, the following filter was adopted, 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = − t t

f t

22

exp 5

1

( ) , (7) 

as shown in Figure 1-10. If the effective filter cutoff is taken as 5 in Figure 1-10, and noting that 

rainfall as well as runoff data are measured at hourly intervals (discrete time steps), one can 

select filter weights at discrete time intervals while maintaining the water conservation 

requirement. Thus, for eight different drainage basin sizes noted previously, appropriate filter 

weights can be selected. In carrying out actual computation, the filter width (i. e., the effective 

filter cutoff) can be set equal to the persistence time for any basin type specified in Table 2. It is 

noted that within Barataria Basin, Bayou Chevreuil and Bayou Boeuf (type 7 and 8, respectively) 

contribute almost 25 percent of freshwater to the entire Barataria Basin system. 

1.5 Integration of the Hydrology Model and the Hydrodynamic Model 

1.5.1 Numerical Model Formulation 

 The hydrology model developed in the previous section was integrated to a highresolution depth-integrated two-dimensional model of estuarine circulation for the 

Barataria Basin. It was assumed that the use of two-dimensional depth-integrated equations for 

conservation of mass and momentum was adequate, considering the typically well-mixed water 

column due to wind and tidal stirring of the system (Inoue et al., 1998). The model used here is 

based on the model initially developed for other neighboring estuaries including TerrebonneTimbalier Basin (Inoue and Wiseman, 2000), Fourleague Bay (Wiseman and Inoue, 1994), as 

well as Barataria Basin (Park, 1998). The description of the numerical model formulation being 

presented here comes from Inoue et al. (2001). 

The model equations for conservation of mass and momentum including baroclinic 

pressure gradient written in Cartesian coordinates in terms of depth-integrated transport (e. g., 

Leendertse, 1967; Elliott and Reid, 1976) are; 

A U x

C

H

V

H

U

H

U

g x gH x fV gH

H

UV

H y

U

t x

U 2

2

2

1

2 2

2

2

1 2

+ + ∇

+

− ∂

∂ − ∂∂ − = − ∂∂

+

∂

∂

+

∂

∂ ⎪⎭

⎪

⎬

⎫

⎪⎩

⎪

⎨

⎧

⎟

⎠

⎞ ⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛

ρ

ζ ρ τ (8) 
23

Figure 1-10. Function defined by ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = − t t

f t

22

exp 5

1

( ) used in this study to estimate the 

 shape of a unit hydrograph for various drainage basins in the Barataria 

 Basin. 

 
 24 

 

 A V y

C

H

V

H

U

H

V

g

y

gH

y

fU gH

H

V

H y

UV

t x

V 2

2

2

1

2 2

2

2

1 2

+ + ∇

+

− ∂

∂ − ∂

∂

+ = − ∂

∂

+

∂

∂

+

∂

∂ ⎪⎭

⎪

⎬

⎫

⎪⎩

⎪

⎨

⎧

⎟

⎠

⎞ ⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛

ρ

ρ τ ζ (9) 

 

 = 0

∂

∂ + ∂∂ + ∂∂

y

V

x

U

t

ζ (10) 

 

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎝

⎛

∂

∂

∂

∂

+

∂

∂

∂

∂

= ∂

∂

+

∂

∂

+

∂

∂

y

y

S

H

x

x

S

H

S D

y

HS

x

HS

t

HS (11) 

 

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎝

⎛

∂

∂

∂

∂

+

∂

∂

∂

∂

= ∂

∂

+

∂

∂

+

∂

∂

y

y

T

H

x

x

T

H

TD

y

HT

x

HT

t

HT (12) 

where ∫

− = ζ

h U udz , ∫

− = ζ

h V vdz , H = h + ζ , ∫

− = ζ

h s z dz

H

S ( )

1 , and ∫

− = ζ

h t z dz

H

T ( )

1

where t denotes time, x, y, and z are Cartesian coordinates, u and v denote velocity components 

in the direction of x and y, respectively, ζ is elevation of the free surface above mean sea level, 

h is the undisturbed depth of the water, f is the Coriolis parameter (assumed to be a constant), g 

is the acceleration due to gravity, x τ and y τ are the x and y components of wind stress, 

respectively, ρ is the density of water, s(z) and t(z) are depth-dependent salinity and 

temperature, respectively, A is the horizontal eddy viscosity, is depth-averaged salinity, T is 

depth-averaged temperature, and are the horizontal eddy diffusivities for and T , 

respectively, and C is the Chezy coefficient which is depth dependent. The bottom roughness is 

represented through Manning’s coefficient, such that the Chezy coefficient is evaluated as 

S

S D TD S

n

 

 6

1

1

H

n C = (13) 

At land boundaries, no normal flow and no-slip boundary conditions are used. Wind 

forcing is assumed to be spatially uniform over the entire model domain. The model equations 

are discretized into a finite-difference formulation on the staggered mesh grid C of Arakawa 

(Mesinger and Arakawa, 1976). The Grammeltvedt C scheme (Grammeltvedt, 1969), which 
25

conserves mass and total energy, is employed. For time integration, the Leapfrog scheme is used 

with an Euler scheme inserted at regular time intervals to eliminate the computational mode due 

to the central time differencing. For numerical stability, the frictional terms are lagged in time. 

1.5.2 Advection Scheme Used 

 One of the novel features of the model is the incorporation of a very accurate advection 

scheme. Traditionally, a simple centered-difference scheme has been used for advective 

transport in hydrodynamic models. This scheme induces so-called numerical dispersion, which 

causes nonphysical spatial oscillation in the tracer field (Hasumi and Suginohara, 1999). These 

oscillations could be damped out by using a large diffusion coefficient. However, as the model 

grid size is reduced to resolve finer and finer spatial scales, the use of large diffusion coefficients 

is no longer justified. Several schemes have been developed that have little numerical dispersion 

and a higher order of accuracy compared with the centered differencing scheme. They include 

the flux corrected transport (FCT) scheme of Boris and Book (1973), the Uniformly Third-Order 

Polynomial Interpolation Algorithm (UTOPIA; Leonard et al., 1993), the Quadratic Upstream

Interpolation for Convective Kinematics (QUICK; Leonard, 1979) and the Multidimensional 

Positive Definite Advection Transport Algorithm (MPDATA; Smolarkiewicz, 1984). 

In this study, advection scheme due to Takacs (1985) is adopted. This scheme is an 

accurate upstream scheme with very little numerical dispersion. In order to apply the Takacs 

scheme to a 2-D depth-integrated advection equation the following procedure is followed. The 

2-D depth-integrated equation for advection is given by 

= 0

∂

∂

+

∂

∂

+

∂

∂

y

VS

x

US

t

HS (14) 

where s is concentration of advected property. In order to apply the Takacs scheme to the above 

equation, it is necessary to account for volume changes into or out of the individual grid cells 

during each time-step, i. e., the following continuity equation needs to be solved at the same

time, 

 

= 0

∂

∂

+

∂

∂

+

∂

∂

y

VH

x

UH

t

H (15) 

1.5.3 Model Grid Setup 

Most of the model bathymetry and morphology was taken from Nautical Charts (scale 

1:80,000), topographic maps (scale 1:100,000) by U.S. Geological Survey, supplemented by 

recent satellite imagery. The morphology was modified using recent satellite images and aerial 

photos, since the most recent nautical charts (published in 1995) have not been changed since 
 26 

1976. The coastal boundaries were approximated in the model as closely as possible. A 

spatially-uniform grid of 100 m was used. 

1.5.4 Forcing Functions 

The model was forced by realistic tides, salinity, water temperature, rainfall, local runoff,

wind and freshwater diversion from the Mississippi River. Historical water level and salinity 

data from 1999 and 2001 collected by the United States Geological Survey (USGS) and the 

Department of Natural Resources, Louisiana (DNR), were obtained for Barataria Basin. 

 At the open boundary located at the southern end of the model domain, sea level height is 

specified. Open boundary conditions for salinity and temperature are set in such a way that for 

outflow (i. e., flowing toward the open boundary) the radiation boundary condition due to 

Carmerlengo and O’Brien (1980) is used, while for inflow (i.e., flowing into the model domain) 

a relaxation toward the specified value at the open boundary is applied. 

 Due to the lack of sea level height measurements near the southern open boundary, the 

water elevation recorder from site S1 was used to estimate sea level height at the southern open 

boundary. In addition, spatially uniform wind forcing based on wind measurements at GDIL1 

(Grand Isle C-MAN Station) was included in the model. 

 An empirical relationship derived for the Barataria Basin (Hsu, 1996, personal 

communication) was used to convert from wind speed to wind stress. The relationships between, 

the wind stress, the shear velocity, and the wind speed at 10 m height, U , for this study area 

are: 

U* 10

 , and U* = 0.037U10 - 0.03 2

ρ aU* τ =

where τ is the wind stress and ρ a is the air density. 

Due to the lack of salinity measurements near the southern open boundary, the salinity 

recorder from site S1 was used to estimate salinity values at the open boundary. 

After initial calibration run, a Manning's n coefficient of 0.02 and a horizontal eddy viscosity 

used of 5 were selected. 2 −1

m s

 

1.6 Testing of the Hydrology Model 

 Rarely, significant impact of local runoff manifests itself on sea-level heights in large 

estuaries such as Barataria Basin. Fortunately, in June 2001during tropical storm Allison, nearly 

60 cm of rainfall fell, almost four-fold the long-term average for the month of July (Figure 1-11). 

Figure 1-12 shows three stations where sea level heights were measured during the storm, while 

Figure 1-13 shows observed wind at GDIL1. During the storm, wind was predominantly 

southerly until Hour 540. Significant impact of local rainfall due to the storm was evident in the 

sea-level records (Figures 1-14). Figure 1-14 also suggests that the predominant southerly wind 

during the storm (Figure 1-13) contributed toward raising sea level heights within the basin. 
27

Figure 1-11. Rainfall measured at MSY during tropical storm Allison in June 2001. 
 28 

Figure 1-12. Sea-level stations, rainfall measurement site, and wind measurement site 

 used in the flood simulation during Allison. 
29

 Figure 1-13. Observed wind at GDIL1 for the period 5/25/2001-6/25/2001. 
 30 

Figure 1-14. Observed (blue) and simulated sea-level heights with (red) and without 

 rainfall (green) at three stations (listed in Figure 1-12) during Allison. 
31

This flood event provided a rare opportunity to test the hydrology model used in this study. The 

observed rainfall measured at MSY was used to simulate the flood event. In coupling the 

hydrology model to the hydrodynamic model, it was assumed that 100 % of local rainfall enters 

the estuary, i.e.; there is no loss of water during the runoff process. This assumption should be 

reasonable considering the fact that during a heavy rainfall event such as the flood of June 2001; 

most of the runoff should enter the basin very quickly. Figure 1-13 shows observed and 

simulateded sea-level heights during the storm. It is apparent that the downstream station (S1) 

does not exhibit significant impact of the flood. However, the two upstream stations do display 

significant impact of the flood due to larger land-to-water surface ratio (Figure 1-2). Especially 

at the uppermost station, S3, runoff due to the flood overwhelms the water level signal after hour 

300. Figure 1-14 shows two cases of simulated sea-level heights, one with rainfall and the other 

without rainfall. It is apparent that rainfall is needed in order to simulate the observed rise in 

sea-level during the storm. Agreement between the observation and the model simulation with 

rainfall included is excellent (Figure 1-15), providing confidence for the hydrology model 

adopted. The observed agreement also provides a justification for adopting 100% coupling 

between the hydrologic model and hydrodynamic model, at least for this flood event. However, 

it is expected that during smaller rainfall events coupling should be less than 100%. 

1.7 Summary and Conclusions 

 A high-resolution (with a model grid resolution of 100 m for the hydrodynamic model), 

integrated hydrology-hydrodynamic model of the Barataria Basin has been developed to simulate 

the local hydrological cycle over the surrounding drainage basin and hydrodynamics within the 

basin. In order to explicit account for runoff from the surrounding drainage basin, 64 known 

streams and 522 unknown streams were identified. Each stream is assigned its own drainage 

subbasin and each drainage subbasin is classified into one of the eight drainage types with its 

distinct persistence time. Runoff characteristic for each subbasin is determined by the shape of 

the unit hydrograph selected for this basin to mimic typical runoff. Runoff from the hydrology 

model is fed into the depth-integrated hydrodynamic model via all the streams specified within 

the model domain. Detailed simulation of runoff from the surrounding drainage basins has never 

been done previously in the Barataria Basin. The hydrodynamic model (Park 2002) is based on 

the model formulation of Inoue et al. (2001) with much higher grid resolution than the one used 

by Park (1998) (also see Inoue et al. (1998)). 

The integrated model is forced by observed tides coming from the Gulf of Mexico, local 

wind, rainfall and evaporation over the model domain, salinity and temperature estimated at the 

open boundary located offshore of the mouth of the bay. Estimated local precipitation and 

evaporation over the model domain based on actual meteorological observations provide 

hydrological forcing to the hydrological model, that in turn simulates local runoff into the 

hydrodynamic model. A novel feature of the hydrodynamic model is its use of a very accurate 

advection scheme, thus, enabling accurate simulation of salinity variations in response to 

changes in various hydrological forcing functions. A flood event that took place during the 

tropical storm Allison in June 2001 resulted in significant sea-level changes especially in the 
 32 

 

Figure 1-15. Cross correlations between observed and simulated sea-level heights with 

 rainfall at three stations during Allison. 
33

upstream region of the basin. The integrated model appears to be able to capture a significant 

portion of the observed sea-level variations during the flood, providing confidence in the 

integration of the hydrology model and the hydrodynamic model, at least in terms of mass flux 

between the hydrology and hydrodynamic models. The next step in the model testing and 

verification is to examine the model’s ability to simulate hydrodynamics and salinity distribution 

within the Barataria Basin. An example of the model simulation is presented in Chapter 2. 
35

CHAPTER 2 

MODEL SIMULATION

2.1 Simulation of a Typical Dry Summer Condition 

 As an example of illustrating the integrated model’s ability to simulate hydrodynamics 

and salinity distribution within the Barataria Basin, a typical dry summer condition was chosen 

to be simulated. This relatively dry season was selected in order to reduce the noise to signal 

ratio that might confound the impacts of freshwater diversions. Specifically, using the observed 

sea level height and salinity estimated at the open boundary, observed wind and precipitation, 

and estimated evaporation as forcing, model simulation was carried out for a 30-day period from

July 7 to August 5, 1999. Figure 2-1 shows the locations of relevant stations. During this 

period, total precipitation was 6.8 cm (Figure 2-2), which is only half of the long-term average of 

15 cm. Evaporation, however, totaled 11.8 cm for the 30 days of the simulation (Figure 2-2). 

 This simulation period represents typical, environmentally fair, summer conditions of 

predominantly northerly or southerly breezes of 3 to 7 m with occasional shifts in wind 

direction (Figure 2-3). It should be noted that the wind directions during the first half of the 

simulation period show primarily southerly breezes except between the 6

−1

s

th and 8th days and that 

the southerly breezes are still modulated by the land-sea breeze system. During the last 12 days, 

however, the directions change by day and night due to the temperature differences between land

and sea. The most pronounced effect of wind forcing on the Gulf of Mexico systems is the 

difference between a northerly and a southerly wind (Swenson and Turner, 1998). 

 In order to delineate relationships between the local wind and water level, approximately 

four months (4/1/1999-7/30/1999) of hourly records of water level and wind data from GDIL1 

were used to estimate the spectral density of water level and wind components. The coherence 

between water level and wind components was estimated by averaging over 30 frequencies, 

giving 60 degrees of freedom with a 95% significance level of 0.098 (Figure 2-4). It was found 

that the coherence between water level and each wind component was frequency dependent. The 

significant energy peak is at very low frequency, roughly less than 0.01 cph. At short sub-tidal 

time scales (a few days), the along-estuary wind stress drives an estuarine-shelf exchange; at 

longer time scales Ekman convergence/divergence driven by alongshore wind stress drives the 

estuarine-shelf exchange (Schroeder and Wiseman, 1986). Kjerfve (1975), based on a summer 

field study, suggested that Louisiana estuaries exchange water with the shelf on time scales 

greater than 1 day in response to Ekman convergence at the coastline driven by the alongshore 

wind stress. 

Salinity studies in coastal areas, including estuaries, are common because of the 

variability of salinity distribution and its effects upon biota and flow regimes (Byrne et al., 

1976). The salinities in Barataria Basin are controlled by rainfall, evaporation, diversions from

the Mississippi River, and Mississippi River discharges, and seasonal changes in tide (Barrett, 

1971). Until two diversion sites at Naomi and West Pointe à la Hache were constructed, the
36 

Figure 2-1. Data stations used in model simulation for the simulation period 

 July 7, 1999 – August 5, 1999. 
37

0 100 200 300 400 500 600 700

0

0.5

1

1.5

2

2.5

cm/hour

Total = 6.8 cm

0 100 200 300 400 500 600 700

0

0.02

0.04

0.06

0.08

0.1

Total = 11.8 cm

cm/hour

Time in Hours with Respect to 7/7/1999 01:00 

Figure 2-2. Measured precipitation at MSY (upper) and estimated evaporation (lower) 

 using meteorological observations at GDIL1. 
38 

 Figure 2-3. Observed wind at GDIL1 for the period 7/7/1999 – 8/5/1999. 
39

0 0.1 0.2 0.3 0.4 0.5

0

0.05

0.1

0.15

0.2

C

oherence Squ

are

d

0.098 (95 %)

0 0.1 0.2 0.3 0.4 0.5

0

0.05

0.1

0.15

0.2

CPH

C

oherence S

quare

d

0.098 (95 %)

 Figure 2-4. Coherence squared between water level at S1 and wind component 

 at GDIL1 for the period April 1, 1999 – July 30, 1999; eastward stress 

 (upper) and northward stress (lower). 
40 

rainfall was found to be the most dominant contributor in the upper basin. The Mississippi River 

discharge coming out of the Mississippi Delta located just east of the region is also an important 

freshwater source in the lower basin. This is obvious in the inverse relationship between 

Mississippi River discharge and Barataria Basin salinity (Figure 2-5). In order to determine the 

relationship between salinities in Barataria Basin and Mississippi River discharge, the phase 

difference was estimated using the Mississippi River water level data at Venice, a surrogate for 

discharge, and salinities at S1 (Figure 2-5). The regression result shows that there is almost a 13-

day phase difference between the two sites. 

 The initial condition for salinity (Figure 2-6) was estimated based on actual salinity 

observations at the eight stations (S1 through S8 shown in Figure 2-1) at Hour 0 on July 7, 1999. 

It should be noted that due to the limited number of observed values used in deriving the salinity 

initial condition, any salinity signal not captured by the eight stations was not accounted for in 

the initial condition used. Using the estimated sea level height and salinity at the open boundary 

based on the observed values at S1, observed precipitation at MSY, and estimated evaporation 

based on meteorological observations at GDIL1, the model was spun up from rest at 0 hour on 

July 7, 1999, and the first 5-day period was considered as the spin-up period. During this 

simulation period actual freshwater diversion rates from the two diversion sites, Naomi and West

Pointe à la Hache, varied with time while averaging 14.6 and 10.8 , respectively 

(Figure 2-7). However, for this model simulation the pumping rates were fixed at zero. 

Therefore, the model simulation is expected to produce slightly different results from 

observations. In addition, the freshwater input from the Gulf Intracoastal Waterway was set to 

50 , a value close to the long-term mean observed (Swarzenski, personal communication, 

2002). The diffusivity was fixed at 5 . 

3 m −1

s 3 m −1s

3 −1

m s

2

m −1s

2.2 Simulation of Water Levels 

 In order to analyze tidal propagation, a series of 3-hour interval data was extracted from

the 30 days of recorded simulation results. The water level on the 501st hour at S1 was selected 

as a beginning frame for the tidal cycle, slack before flood (Figure 2-8). Figures 2-9a and 2-9b 

show salinity distribution at 3-hour intervals starting at Hour 501. Barataria Basin is 

morphologically separated from the Gulf of Mexico by the several islands that block tidal energy 

from propagating into the bay. Figures 2-9a and 2-9b suggest that the tidal regime within the 

basin can be divided into four different regions: (1) the Gulf of Mexico, (2) Barataria Bay and 

Caminada Bay regions, (3) Little Lake and Barataria Waterway regions, and (4) the upper basin 

regions of Lac des Allemands and Lake Salvador. The separations are characterized by a chain 

of islands, narrow canals and bayous, and the Gulf Intracoastal Waterway. During the period 

shown in Figures 2-9a and 2-9b, the tidal range is roughly 40 cm near the mouth of the bay. The 

tidal amplitudes decrease as tides propagate into the upstream regions. There are some

discontinuities at the transitions between Barataria Bay and Barataria Waterway, Little 
41

0 100 200 300 400 500

0.45

0.5

0.55

Phase Difference in Hours

C

orrel

ation C

oefficient (

2

r)

r

2

 = 0.534 at 308th Hour

0 1000 2000 3000 4000 5000 6000 7000 8000

0

0.5

1

1.5

(a) Mississippi River Discharge at Venice

W

ater Level (meter)

0 1000 2000 3000 4000 5000 6000 7000 8000

5

10

15

20

25

30

35

(b) Salinity at Grand Isle (S1) 

Hours (Jan. 1 - Dec . 31, 1999)

S

alinity (

p

pt)

Figure 2-5. Mississippi River stage at Venice (top) and salinity variation 

 in S1 (Grand Isle) (middle), and their cross-correlation function (bottom). 
42 

Figure 2-6. Salinity distribution used as initial condition for a typical dry summer 

 simulation for the period 7/7/1999-8/5/1999. It is based on salinity 

 observations at eight sites (S1 – S8 shown in Figure 2-1) at 0 Hour on 

 7/7/1999. 
43

0 5 10 15 20 25 30

0

10

20

30

40

R

ate (

m3/s)

West Pointe a la Hache (mean 10.8 m

3

/s)

Time in Days (July 7 - August 5, 1999)

Naomi (mean 14.6 m

3

/s)

Figure 2-7. Freshwater diversion records at Naomi and West Pointe a la Hache during 

 the simulation period. 
44 

0 100 200 300 400 500 600 700

-0.4

-0.2

0

0.2

0.4

Time in Hours with Respect to 7/7/1999 01:00

W

ater Le

vel (met

er)

S1 H=501

Day=5 Day=10 Day=15 Day=20 Day=25 Day=30

 Figure 2-8. Simulated water level at S1. Hour 501 is indicated. 
45

Figure 2-9a. Simulated water level (m) at 3-hour intervals for the period Hour 501 – 

 Hour 510. 
46 

Figure 2-9b. Simulated water level (m) at 3-hour intervals for the period Hour 513 – 

 Hour 522. 
47

Lake, and Lake Salvador. It is difficult to distinguish water level changes at upstream sites 

during a tidal cycle due to the smaller tidal range there compared to that in Barataria Bay and the 

Gulf of Mexico. Sometimes wind forcing overwhelms astronomical tidal in the shallow regions. 

The high sea level at hour 501 in the northern lakes is due to the consistent southwesterly wind 

(Figure 2-3). 

Figure 2-10 shows 30 locations selected from the mouth of the bay all the way to the top 

of the basin (AB) and 20 locations selected from the mouth of the bay to the top of the Barataria 

Waterway (AC). Figure 2-11 shows water level changes during a tidal cycle, beginning at hour 

501, for the locations indicated AB (upper) and AC (lower) in figure 2-10. It is clearly seen that 

this basin is made of four different sub-regions as discussed previously. Within Barataria Bay, 

which corresponds to stations 1 through 7, the tidal amplitude is 40-45 cm. There is a sudden 

decrease in amplitude between stations 7 and 9 along transect AB, which lies in the narrow 

channels between Barataria Basin and Little Lake. Between stations 9 and 12 (Little Lake), the 

water level changes about 20 cm during a tidal cycle. Between stations 12 and 14, there is a 

sudden decrease across the narrow shoal between Little Lake and Bayou Perot. Near station 15, 

the water level varies roughly 10 cm during a tidal cycle. As the transect enters a narrow region 

across the Gulf Intracoastal Waterway and proceeds into Lake Salvador, there is a short sudden 

decrease in amplitude at station 17. Above Lake Salvador, the water level changes during a tidal 

cycle are about 7 cm, and then they smoothly decrease into Lac des Allemands. Along the 

transect AC there are similar patterns to those observed in Barataria Bay (between stations 1 and 

7). Between stations 7 and 8, there is a sudden decrease due to the narrowness of the Barataria 

Waterway. Beyond station 8, they smoothly decrease to the top of the Barataria Waterway with 

a small sudden decrease near station 15. It is clearly seen that the water level piled up at hour 

501 and 502 in the top of the Barataria Waterway due to the consistent southwesterly wind. 

It is interesting to estimate tidal propagation speed throughout the basin. One can simply 

apply the well-known shallow wave formula,C = gH as long as the water depth is known. 

Even though the water depth is known, though, it varies with location. Another method to 

estimate tidal propagation speed is by applying the Hovmüller diagram, which is a plot of wave 

amplitude or phase against time and distance. In order to estimate the tidal phase speed for the 

basin, the thirty stations from Figure 2-10 were used. The tidal amplitudes are plotted against 

station and time (Figure 2-12). The blue arrow originating at station 1 near hour 81 indicates the 

propagation of a tidal wave crest and suggests that it takes 16 hours to traverse the system. It is 

easily seen that some sudden changes in phase velocity appear to occur due to the basin’s 

morphological characteristics. For instance, there is a sudden change near station 17, possibly 

due to the Gulf Intracoastal Waterway. Tidal propagation speed estimated from the blue arrow 

in Figure 2-12 is 1.94 m s-1, that can be compared to the speed of ~ 3.83 m s-1 based on the 

shallow-water wave formula with H=1.5 m, suggesting that it is often difficult to estimate actual 

tidal propagation speed by the simple shallow-water wave formula. 

 
48 

Figure 2-10. Selected locations from the mouth of the bay to the uppermost reaches 

 of the basin (AB) and to the top of the Barataria Waterway (AC). 
49

0 5 10 15 20 25 30

-0.2

-0.1

0

0.1

0.2

W

ater Level (met

er)

Stations (AC)

Hour 501

Hour 502

0 5 10 15 20 25 30

-0.2

-0.1

0

0.1

0.2

W

ater Level (met

er)

Stations (AB)

Figure 2-11. Simulated water level changes during a tidal cycle starting at Hour 501 

 at locations indicated AB (upper) and AC (lower) in Figure 2-10. Barataria 

 Bay and Caminada Bay regions extend from Stations 1 to 7 for both AB 

 and AC. Little Lake and Barataria Waterway regions extend from Stations 

 7 to 16 for AB and from Stations 7 to 20 for AC. The upper basin regions 

 of Lac des Allemands and Lake Salvador extend from Stations 16 to 30 for 

 AB. 
50 

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 20 40 60 80 100 120

5

10

15

20

25

30

Time in Hours with Respect to 7/7/1999 09:00

Stations (AB)

meter

 Figure 2-12. Hovmüller diagram: water level vs. time and station location. 

 Blue arrow estimates a characteristic of tidal propagation. 
51

2.3 Simulation of Salinity 

 In order to analyze the horizontal salinity variability during the simulation period, a series 

of data, sampled at 5-day intervals was extracted (Figures 2-13a through 2-13c). All data periods 

selected were near the slack before flood in order to remain consistent relative to tidal phase. On 

day 5, since the stage at the moment of the snapshot was near the beginning of flood for the 

mouth of Barataria Pass, maximum plume excursion from the mouth of the passes was observed. 

Inside the bay, lower salinities extended to the mouth of the basin. It can be seen easily that the 

high salinities are coming from the open boundary. It is notable that there are large gradients in 

the coastal waters due to the time and space variations of salinity imposed along the open 

boundary. 

On day 10, the situation is similar to that of day 5 except for the higher salinity values 

coming from the open boundary. By day 25, very high values of salinity are coming from the 

open boundary and penetrating far into the basin. There are big salinity gradients in the 

northeast region of the Gulf of Mexico in the model domain. On day 30, after several days of 

high salinity values at the open boundary, the high gradients increase at the coast. 

In shallow, bar-built estuaries, it is often assumed that the flow field is vertically 

homogeneous (Wiseman, 1996). When the estuary is shallow, the velocity shear on the bottom 

may be large enough to mix the water column completely and make the estuary vertically 

homogeneous (Dyer, 1973). The concern over the use of a two-dimensional model for this 

modeling study is in its application to deep channels, waterways, or passes, such as the Barataria 

Pass. As mentioned earlier, the Barataria Basin has five passes that connect to the Gulf of 

Mexico. Except for Barataria Pass, all of the other passes are very shallow. Barataria Pass, 

however, is wide (about 1 km) and deep (deeper than 12 m in the middle of the pass) perhaps 

allowing some stratification to be present, i. e., salt wedge might exist along the bottom during 

flood tide. This phenomenon can not be accounted for in the model. 

 

Figure 2-14 shows observed and simulated salinity at eight stations. It is apparent that at all the 

stations simulated salinity can capture low-frequency salinity variability but higher-frequency 

signal is missed. This is due to the fact that the salinity initial condition was based on salinity 

observations at the eight stations, and it completely missed any smaller horizontal-scale 

variability that might have existed at that time. Subsequent tidal advection of the smaller-scale 

salinity variability cannot be simulated by the model as long as the salinity initial condition does 

not contain the signal. As far as the low frequency variability is concerned, the model appears to 

capture large portion of the signal at all the stations. After Day 16, the observed salinity at S1 

shows significant increase apparently due to an intrusion of saltier water from the Gulf of 

Mexico. This salty intrusion propagates upstream reaching S2 after Day 25, and S5 soon after. 

Presently, the model is not capable of capturing the observed increases in salinity at S1 due to the 

present model open boundary set-up where its corresponding salty water has to propagate from 

the open boundary to S1. At station S5, the instrument error resulted in the loss of usable data at 
52 

 Figure 2-13a. Simulated horizontal salinity (ppt) distribution at Day 5 

 and Day 10 during the simulation period 7/71999 – 8/5/1999. 

 All time slices selected were near the slack before flood. 
53

 Figure 2-13b. Simulated horizontal salinity (ppt) distribution at Day 15 

 and Day 20 during the simulation period 7/7/1999 – 8/5/1999. 

 All time slices selected were near the slack before flood. 
54 

 Figure 2-13c. Simulated horizontal salinity (ppt) distribution at Day 25 

 and Day 30 during the simulation period 7/7/1999 – 8/5/1999. 

 All time slices selected were near the slack before flood. 
55

Figure 2-14. Observed (blue) and simulated salinity (red) at eight stations (listed in 

 Figure 2-1).
56 

the beginning of the record until Day 5. At stations further upstream, the overall results look 

better than those downstream due to the relatively short simulation period that did not allow 

upstream propagation of the salty intrusion coming from the Gulf of Mexico. One way to 

improve simulation is to assimilate observed salinity at the mouth of Barataria Pass, thus directly 

forcing the salinity at the mouth of the bay rather than at the offshore open boundary. 

Availability of better salinity initial condition with much higher spatial resolution can certainly 

contribute to improving model simulation. In summary, it is rather encouraging that a 30-day 

model simulation based on the salinity initial condition estimated from only eight stations can 

capture bulk of low-frequency signal. 

In order to describe tidal mixing of salinity and tidal plume characteristics, a series of 2-

hour interval salinity snapshots over a tidal cycle were extracted and zoomed in on at the mouths 

of the basin (Figures 2-15a and 2-15b). The first snapshot, begins at hour 500, shows slack 

before flood. Outflow plumes are easily seen at the three passes. During this stage, the plumes 

reach maximum size. The largest plume, 3 km in diameter, extends from Barataria Pass. There 

are sharp salinity gradients along the frontal zone of the plume. A mushroom-like plume 2 km in 

diameter formed at the mouth of Quatre Bayou Pass. Although the size of the plume is slightly 

smaller than that from Barataria Pass, the salinity gradient is greater than that at Barataria Pass

due to the lower salinity inside the bay. Although the flood has already begun, the scales of 

plume do not change significantly for the next several hours, while their shapes do change. 

However, inflow plumes are noticeable inside the bay by hour 506. At hour 508, the plumes 

from all three passes were clearly retracting into the basin under incoming flood tide and forming 

well-defined inflow plumes inside the bay. It should be noted that high salinity values are 

detectable along the left side of Barataria Pass during the flood, probably due to the alongshore 

salinity gradient immediately offshore. Unlike the outflow plumes, the inflow plumes spread 

much wider. By hour 512, most of the outflow plumes disappeared. At hour 518, inflow plumes 

reached their maximum length of 11 km. Some of this water remained in the bay over the entire 

cycle. After hour 520, the inflow plume began to move back out through the passes. In 

summary, the high resolution of the model allows detailed simulation of exchange and mixing 

processes near the main passes. It appears that mixing and exchange processes at the passes are 

very complex partly due to the highly complex morphological characteristics of the bay, and a 

very high model grid resolution is required in order to correctly simulate mixing and exchange 

processes at those passes. 

2.4 Impact of Diversions at Naomi and West Pointe à la Hache 

 In order to simulate the impact of two smaller freshwater diversion sites, namely, Naomi 

and West Pointe à la Hache, their maximum rate (60 ) was used in the second model 

simulation run for the same period. By adopting the maximum rate, it is 

3 m −1

s

our intention to delineate maximum impact to be expected if those two diversions were operating 

at their peak capacity. 
57

Figure 2-15a. Simulated salinity illustrating inflow and outflow plumes at the mouth 

 of the Barataria Bay at 2-hour intervals for the period Hour 500 – 

 Hour 510. 
58 

Figure 2-15b. Simulated salinity illustrating inflow and outflow plumes at the mouth 

 of the Barataria Bay at 2-hour intervals for the period Hour 512 – 

 Hour 522. 
59

 

To characterize the influence of the freshwater diversions from the Mississippi River, the 

model results with freshwater diversions were subtracted from the results without freshwater 

diversions. Figure 2-16 shows 5-day interval snapshots colored with the maximum difference set 

at 6 ppt. On day 10, Barataria Waterway contained a nearly 1.5 ppt difference. When freshwater 

from West Pointe à la Hache reached Barataria Bay, the impact seemed to accelerate. It is clear 

that high salinity differences are confined to the vicinity of the diversions. It is roughly 

estimated that the impact of freshwater diversion reaches the Gulf of Mexico in around 10 to 15 

days after the freshwater release. After 15 days, freshwater diversion seemed to impact most of 

the downstream region of the Barataria Basin except the eastern most part of Barataria Bay and 

Caminada Bay that appear to be somewhat isolated from the open water of the bay. In the 

middle of Barataria Bay, the difference reached near 2 ppt. By day 25, most of the downstream 

basin was measurably impacted by freshwater diversion. Even after day 30, some isolated 

pockets of unaffected area still remain. It is apparent that in the vicinity of the main passes, 

impact of diversion is reduced due to the strong tidal mixing. 

 Figure 2-17 presents horizontal water level differences between the two cases, with and 

without freshwater diversions, at 4-hour intervals, during the first day of simulation. At hour 4, 

the signal covered the two neighboring diversion sites and traveled over a region almost 5 km in 

diameter. By hour 8, this signal propagated almost 15 km including Lake Salvador. Some signal 

is detectable coming from the Gulf Intracoastal Waterway. At hour 12, the signal covered nearly 

the entire middle basin, about 20 km in diameter. At hour 20, the signal covered almost the 

entire basin, except the lower Barataria Bay and Lac des Allemands, with a value bigger than 0.2 

cm. Figure 2-18 shows the daily water level difference snapshots during the first six days setting 

the maximum at 1 cm. On day 2, the entire basin was under the impact of the diversions except 

the eastern part of Barataria Bay. Some parts of Caminada Bay seemed to be affected by the 

Gulf Intracoastal Waterway. On day 3, the signal reached the Gulf of Mexico. On day 4, the 

water level difference surpassed a value of 1 cm in the upper Barataria Basin while the signal in 

Barataria Bay was rather reduced by the strong tidal dispersion. On day 5, the situation was 

similar to the previous day, but the downstream region exhibited increased impact due to reduced 

tidal energy. On day 6, the impact of the freshwater diversion remained relatively unchanged. It 

is apparent that the water level signal associated with the release of freshwater at the diversion 

sites propagates at the speed of shallow-water wave, somewhat analogous to tidal bore. 

Consequently, depth-integrated salinity, which is simulated in the model, will reflect the 

propagation of water level signal. Those observations suggest that impact of freshwater 

diversion propagates at speeds much faster than diffusion time scale suggested for mixing of two 

different water masses often considered for estuaries (e. g., Elliot and Reid, 1976). 

 Figure 2-19 shows water level difference snapshots every 5 day. On day 10, in the 

upstream, differences seemed to decrease compared to the previous days probably due to the 

draining of the initial pile-up of water. On day 20, the differences were largest of all during the 

30 days. The maximum water level difference between the two cases is 18 cm near the Naomi 

diversion around day 20. In actual situation, because there are numerous connections between 

open waters and the diversions, which are not resolved by our present model grid of 100 m, these 
60 

 Figure 2-16. Simulated salinity differences (ppt) between without and with freshwater 

 diversions (without – with) at Naomi and West Pointe à la Hache at 5-day 

 intervals. 
61

Figure 2-17. Simulated water level differences (cm) between with and without freshwater 

 diversions (with – without) at Naomi and West Pointe à la Hache during the first 

 day of simulation. 
62 

 Figure 2-18. Simulated water level differences between (cm) with and without 

 freshwater diversions (with – without) at Naomi and West Pointe à la 

 Hache during the first six days of simulation. 
63

Figure 2-19. Simulated water level differences (cm) at 5-day intervals between with and 

 without freshwater diversions (with – without) at Naomi and West Pointe à la 

 Hache with a maximum capped at 2 cm. 
64 

differences between the two cases could have been over estimated. On day 25, water level 

differences in the upper basin were similar to those of day 20. However, in Barataria Bay, the 

impact of freshwater decreased, causing significant gradients with neighboring waters to the 

north. On day 30, all impact was reduced except at the diversion sites. 

2.5 Impact of Diversion at Davis Pond 

Another simulation run was made to simulate the impact of Davis Pond diversion on salinity 

distribution in the Barataria Basin the same 30-day period. The model was started from rest on 

July 7, 1999, and the first 5-day period was considered as the spin-up period. The model output 

was then compared to the case without the diversion. During this simulation, the freshwater 

diversion rate from the Davis Pond diversion site was fixed at either zero or 150

corresponding to 50% of the design maximum rate. Differences in salinity between the two 

cases (without Davis Pond diversion – with Davis Pond diversion) at five-day intervals are 

presented in Figure 2-20. On day 10 (HOUR 240), Barataria Waterway contained a nearly 1.5 

ppt difference. When freshwater from Davis Pond reached Barataria Bay, the impact seemed to 

accelerate. It is clearly visible that high salinity difference values concentrate in the mid-bay. It 

is roughly estimated that the impact of freshwater diversion reaches the Gulf of Mexico in 

around 15 days after freshwater release. After about 15 days, the freshwater diversion seemed to 

impact most of the downstream region of the Barataria Basin except the eastern part of Barataria 

Bay and Caminada Bay. In the middle of Barataria Bay, the difference reached near 2 ppt. By 

day 25, most of the downstream basin was measurably impacted by freshwater diversion. Even 

after day 30, though, some isolated areas still remained without noticeable influence of the 

diversion due to morphological sheltering effect, i.e., in order to simulate this sheltering effect, 

detailed representation of morphological features of the basin is required. It is apparent that near 

the mouth and inside the bay, freshwater impact was reduced due to the strong tidal movement. 

Similar impact of freshwater diversion sites at Naomi and West Point a la Hache on salinity 

distribution in Barataria Bay was presented by Park (2002). 

3 m −1

s

2.6 Summary and Conclusions 

 A high-resolution (O (100m)), integrated hydrology-hydrodynamic model of the 

Barataria Basin has been developed to simulate explicitly local hydrological cycle over the 

surrounding drainage basin and hydrodynamics within the basin in response to hydrological, 

tidal and wind forcing. A flood event due to the tropical storm Allison in June 2001 provided a 

rare opportunity to test the model. In the previous chapter, it was shown that the integrated 

model appears to be able to capture a significant portion of the observed sea-level variations 

during the flood. 
65

Figure. 2-20. Simulated salinity differences (ppt) between without and with Davis Pond 

Figure. 2-20. Simulated salinity differences (ppt) between with and without Davis Pond 

 diversion at 5-day intervals. 
66 

In this chapter, the integrated model was used to simulate a typical dry summer condition, 

namely the 30-day period during the summer of 1999. Despite the relatively crude salinity initial 

condition used (based on only eight observations), the model appears to do reasonable job of 

simulating time evolution of salinity fields inside the bay. The model was also used to simulate 

potential impact of freshwater diversions at Naomi, West Pointe à la Hache and at Davis Pond. 

Those simulation runs suggest that even at reasonable diversion rates, notable impacts on water 

level and salinity should be observable in the multiply connected channels through the marsh in 

the vicinity of operating diversion structures within several days of freshwater release, and after 

15 days or so even in the downstream regions of the basin. The largest impact of diversions 

should be felt in the mid-bay region where the ambient salinity gradients are the steepest. It is 

notable that the speed associated with the propagation of diversion signal is much faster than the 

diffusion time-scale suggested for mixing of two water masses in an estuary (e. g., Elliot and 

Reid, 1976). Rather it appears that the signal of diversion propagates at shallow-water wave 

speed, like a tidal bore, due to its mass flux, consequently impacting depth-integrated salinity 

values downstream. It is interesting to note that even after day 30, some isolated areas within the 

bay still remain without noticeable influence of the diversion. Those observations highlight the 

need to use high model resolution, sufficiently high enough to resolve many of the important 

complex morphological features of the basin in order to achieve reasonable simulation capability 

of salinity distribution for morphologically complex basins such as Barataria Basin. Those 

simulation runs suggest a flushing time scale for the entire basin of 20~30 days, implying that 

not only the potential impact from any freshwater diversion but also any outside influence 

coming from the Gulf of Mexico would propagate throughout the basin within this time scale. 

Those model simulation runs suggest that in order to achieve modeling capability usable 

for responsible management purposes, the model should; 1) account for local hydrological cycle 

including evaporation and precipitation, in particular local runoff from the surrounding drainage 

basins; 2) have high resolution in order to simulate very accurately salinity changes in the 

complex network of channels and bayous within the basin; 3) be thoroughly tested and calibrated 

against real observed data to be useful. 

There still remain several important technical issues. One of them concerns the 

importance of three-dimensional effects in such shallow systems especially in deeper ship 

channels where some form of salt wedge might be expected during the flood tides. Another 

important issue is related to the specification of open boundary conditions. In this study, salinity 

values at open boundary were specified based on the estimation from Station S1. One alternative 

is to use data assimilation technique in that the observed salinity values at S1 can be inserted 

near S1. 

Another critical issue is related to the availability of reliable precipitation data needed for 

hydrological input. Presently, in the vicinity the Barataria Basin, there are only two rain gauge 

stations are available with hourly measurements (they are Houma and New Orleans Airport). 

Considering the size of the surrounding drainage basin (6,300 km2

), precipitation estimates 

available only at those two stations won’t be sufficient in order to provide detailed salinity 
67

distribution inside the bay. In the simulation during Tropical Storm Allison, we had to use 

rainfall data at New Orleans Airport to represent for the entire drainage basin. Although the 

results of the simulation were encouraging, considering the limitation of the rainfall estimates, 

actual spatial variability of rainfall during Allison was significant (Waple, et al., 2002). 

Therefore, significant improvement in our ability to estimate rainfall variability over the entire 

domain would be needed in order to achieve the desired goal of establishing simulation 

capability usable for management purposes. One potential way to overcome this problem is to 

utilize real-time rainfall estimation using geosynchronous satellite data recently developed 

(Vicente et al., 1998). The technique called the auto-estimator, uses the Geoestationary 

Operational Environmental Satellite-8 and -9 in the infrared (IR) band to compute real-time 

precipitation amounts based on a power-law regression algorithm. This regression is derived 

from a statistical analysis between surface radar-derived instantaneous rainfall estimates and 

satellite-derived IR cloud-top temperatures collocated in time and space. Although the estimates 

are indirect, the high frequency and high spatial resolution of the measurements, as well as the 

broad area coverage, make them uniquely complementary to rain gauge and radar measurements. 

Verification studies are continuing. A verified and calibrated hydrology-hydrodynamic 

model will be useful for simulation of salinity changes in response to a variety of climactic 

conditions such as drought and flood. Those include impact of climatic changes not only on 

local hydrological cycle but also on the Mississippi River discharge, that would impact the basin 

via forcing coming from the Gulf of Mexico. For management purposes, the integrated model 

will be useful in simulating salinity alterations associated with the introduction of man-made 

freshwater diversions. With the availability of the integrated hydrology-hydrodynamic model, 

the foundation has been laid toward establishing modeling capability that could be usable for 

realistic management purposes. 
69

CHAPTER 3 

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The Department of the Interior Mission

As the Nation's principal conservation agency, the Department of the Interior has responsibility 

for most of our nationally owned public lands and natural resources. This includes fostering 

sound use of our land and water resources; protecting our fish, wildlife, and biological diversity; 

preserving the environmental and cultural values of our national parks and historical places; 

and providing for the enjoyment of life through outdoor recreation. The Department assesses 

our energy and mineral resources and works to ensure that their development is in the best 

interests of all our people by encouraging stewardship and citizen participation in their care. 

The Department also has a major responsibility for American Indian reservation communities 

and for people who live in island territories under U.S. administration. 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) 

primary responsibilities are to manage the mineral resources located on the Nation's Outer 

Continental Shelf (OCS), collect revenue from the Federal OCS and onshore Federal and Indian 

lands, and distribute those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program

administers the OCS competitive leasing program and oversees the safe and environmentally 

sound exploration and production of our Nation's offshore natural gas, oil and other mineral 

resources. The MMS Minerals Revenue Management meets its responsibilities by ensuring the 

efficient, timely and accurate collection and disbursement of revenue from mineral leasing and 

production due to Indian tribes and allottees, States and the U.S. Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially 

affected parties and (2) carrying out its programs with an emphasis on working to enhance the 

quality of life for all Americans by lending MMS assistance and expertise to economic 

development and environmental protection.