The Effects of Land Cover, Climate, and Urbanization on Groundwater Resources in 
Dauphin Island 
 
by 
 
Katherine S. Petty 
 
 
 
 
A thesis submitted to the Graduate Faculty of 
Auburn University 
in partial fulfillment of the 
requirements for the degree of 
Master of Science 
 
Auburn, Alabama 
December 12, 2011 
 
 
 
 
 
 
 
 
Approved by 
 
Prabhakar Clement, Co-Chair, Arthur H. Feagin Professor of Civil Engineering 
Latif Kalin, Co-Chair, Associate Professor of Forestry and Wildlife Science 
Xing Fang, Associate Professor of Civil Engineering
 
 
 
 
 
 
ii 
 
Abstract 
 
 
 The effects of land cover change, climate change, and population growth on the 
groundwater resources of a barrier island were explored in this study.  The relationship between 
land cover and groundwater recharge was studied for seven locations in the Southeast.  
SEAWAT was used to develop a detailed groundwater model for managing water resources in 
Dauphin Island, Alabama.  Various scenarios were simulated to assess the sensitivity of the 
groundwater aquifer to parameters such as sea level rise, increased pumping rates, and decreases 
in recharge due to climate change or land cover change.  A heuristic approach was used to 
estimate sustainable pumping levels for the Dauphin Island aquifer as a function of the annual 
groundwater recharge. 
 Based on the model predictions from the Dauphin Island groundwater model, it is 
expected that decreasing recharge due to climate change would have the greatest effect on the 
island?s groundwater resources.  Land cover change, sea level rise, as well as increased water 
demand due to expected population growth did not have as large of an effect on the aquifer.  
Some of the scenarios simulated indicated a definite risk of lateral saltwater intrusion occurring 
in the aquifer.  This information is useful for introducing water management practices on the 
island. 
  
 
 
   
 iii  
Acknowledgments 
 
 
This research was funded by the Center for Forest Sustainability at Auburn University, 
AL.  This work would not have been possible without the guidance of my advisors Dr. Clement 
and Dr. Kalin.  I am also grateful to my third committee member, Dr. Fang, for his time and 
willingness to be a part of this process.  Ruoyu Wang kindly provided assistance by providing 
valuable recharge data used this research.  Vaile Feemster from the Dauphin Island Sewer and 
Water Authority provided well pumping data.  Dan O?Donnell was very helpful providing 
information on his work relating to Dauphin Island.  My officemates have been an invaluable 
resource to me, both in my research and as friends.  The unconditional support and 
encouragement from my parents and grandparents has gotten me to the point I am at today.  I am 
also thankful for the patience and support of my husband, Ben.  I am grateful to God for giving 
me the ability and desire to accomplish all that I have been able to. 
  
   
 iv  
Table of Contents 
 
 
Abstract???????????????????????????????????ii  
Acknowledgements??????????????????????????????iii  
List of Tables????????????????????????????????..vi  
List of Figures????????????????????????????????vii  
1. Introduction ................................................................................................................................1 
2. Literature survey ........................................................................................................................3 
2.1 Groundwater concepts for managing island aquifers ........................................................3 
2.2 Groundwater recharge .......................................................................................................6 
2.3 Density-dependent numerical modeling .........................................................................12 
2.4 Additional factors affecting groundwater resources in islands .......................................15 
3. Recharge and land cover estimation for the southeastern United States .................................18 
3.1 Background .....................................................................................................................18 
3.2 Research objectives .........................................................................................................19 
3.3 Recharge estimation ........................................................................................................20 
3.4 Quantify LU/LC effects on Recharge .............................................................................28 
3.5 Land cover analysis and curve number calculations .......................................................31 
4. Geography and ground water issues of Dauphin Island ..........................................................40 
4.1 Location, size, and morphology ......................................................................................40
   
 v  
4.2 Climate and tides.............................................................................................................41 
4.3 Soil types .........................................................................................................................42 
4.4 Geology ...........................................................................................................................43 
4.5 Land Use/Land Cover .....................................................................................................46 
4.6 Water issues ....................................................................................................................47 
5. Sensitivity of Dauphin Island?s Water-Table aquifer to changing factors ..............................54 
5.1 Background .....................................................................................................................54 
5.2 Research objectives .........................................................................................................54 
5.3 Input data, methods, and study methodology .................................................................55 
5.4 Results .............................................................................................................................78 
5.5 Discussions .....................................................................................................................90 
6. Sustainable yield study for Dauphin Island .............................................................................92 
6.1 Background .....................................................................................................................92 
6.2 Input Data and Study Methodology ................................................................................92 
6.3 Results .............................................................................................................................94 
6.4 Discussions .....................................................................................................................97 
7. Conclusions and Recommendations ........................................................................................98 
8. References ..............................................................................................................................100 
9. Appendix ................................................................................................................................108 
9.1 Additional Data .............................................................................................................108 
 
 
   
 vi  
List of Tables 
 
Table 3-1.  Sy values used for recharge estimations ......................................................................23 
Table 3-2.  Recharge values using the RISE method.....................................................................27 
Table 3-3.  Values used to determine AMC  .................................................................................34 
Table 3-4.  NCDC weather stations used for each groundwater well............................................35 
Table 3-5.  Calculated average CN values for seven sites  ............................................................36 
Table 4-1.  LC/LU by percentage for Dauphin Island in 2001; data from NLCD  .......................47 
Table 4-2.  Well depth, screened interval, and location; data from DIWSA  ................................51 
Table 5-1.  Top and bottom layer elevations  ................................................................................62 
Table 5-2.  Hydraulic conductivity values used for Dauphin Island  ............................................62 
Table 5-3.  Well depth, screened interval, and location; data from DIWSA  ................................63 
Table 5-4.  Parameter values used for surface water bodies  .........................................................63 
Table 5-5.  Summary of scenarios simulated  ................................................................................71 
Table 5-6.  Land Use/Land Cover change scenarios simulated in this study  ...............................73 
Table 5-7.  Volume of freshwater in aquifer after scenario simulation  ........................................89 
  
 
   
 vii  
List of Figures 
 
 
Figure 2-1.   Cross-Sectional view of a circular oceanic island [Chesnaux, 2008] .........................5 
Figure  2-2.  Measurement of recharge spike [from USGS Groundwater Information, 2008] ........9 
Figure 3-1.   Map of well locations used in this study  ..................................................................18 
Figure 3-2.   Example of a hydrograph from the Baldwin County, AL well  ................................21 
Figure 3-3.   Bin averaged MRC for Baldwin County, AL  ..........................................................25 
Figure 3-4.   Bin averaged MRC for Montgomery ........................................................................25 
Figure 3-5.   Comparison of recharge methods in Minnesota [Delin et al., 2006]  .......................26 
Figure 3-6.   Comparison of recharge methods in Baldwin Co, AL  .............................................27 
Figure 3-7.   Land Cover/Land Use sites for FL Site #3, Baldwin, Covington, and Montgomery,      
AL  ............................................................................................................................33 
Figure 3-8.   Recharge versus continuing abstractions for seven sites  .........................................36 
Figure 3-9.   Cumulative infiltration vs. cumulative recharge for seven sites  ..............................37 
Figure 3-10. Cumulative infiltration vs. cumulative recharge, Dec-April for five sites  ...............38 
Figure 4-1.   Map of Mobile Bay and Dauphin Island  ..................................................................40 
Figure 4-2.   Monthly temperatures for Dauphin Island in 2008  ..................................................41 
Figure 4-3.   Monthly precipitation for Dauphin Island from January 1995-December2005; data 
from NCDC  ..............................................................................................................42 
Figure 4-4.   Soil types on Dauphin Island, USDA NRCS Soil Survey Database  .......................43 
Figure 4-5.   Details of the layering of the aquifers beneath Dauphin Island [O?Donnell, 2005 ] 45 
Figure 4-6.   Land cover data from the Multi-Resolution Land Characteristics Consortium 
[MRLC] ....................................................................................................................46 
Figure 4-7.   Well locations on Dauphin Island (well size is exaggerated). Blue color indicates 
discharge towards the ocean  ....................................................................................50
 
   
 viii  
Figure 5-1.   Comparison of precipitation and recharge for Dauphin Island, January 2000 - Dec 
2004...............................................................................................................60 
Figure 5-2.   Locations of Alligator Lake and Oleander Pond on Dauphin Island  .......................61 
Figure 5-3.   Steady state head distribution, April 2, 1985, from Kidd [1998]  .............................68 
Figure 5-4.   Steady state head distribution, April 2, 1985, from model developed in this study  68 
Figure 5-5.   Head distribution after transient simulation from May 22-June 15, 1985, from Kidd 
[1998]  .......................................................................................................................69 
Figure 5-6.   Head distribution after transient simulation from May 22-June 15, 1985, from 
model developed in this study ..................................................................................69 
Figure 5-7.   Head distribution after pumping simulation in 1988, from Kidd .............................70 
Figure 5-8.   Head distribution after pumping simulation in 1988, from model developed in this 
study  .........................................................................................................................70 
Figure 5-9.   Recharge used for six scenarios as obtained from SWAT???? ? ????.72  
Figure 5-10. Changes in temperature and precipitation predicted by 4GCMs and 3 emission 
scenarios [Wang, 2011]  ...........................................................................................74 
Figure 5-11. Dauphin Island population; data obtained from the United States Census Bureau  .76 
Figure 5-12. Predicted head profiles at Row 40 under various scenarios  .....................................78 
Figure 5-13. Cross-sectional view of freshwater lens beneath Dauphin Island  ............................78 
Figure 5-14. Location of crosscut taken at Row 40  ......................................................................79 
Figure 5-15. Comparison of bottom interface using two recharge methods  .................................80 
Figure 5-16. Comparison of bottom interface position using scenarios 1 and 5  ..........................81 
Figure 5-17. Comparison of bottom interface position for all scenarios  ......................................81 
Figure 5-18. Saltwater-freshwater interface movement in Scenario 1.1.Red indicates saltwater, 
aqua indicates freshwater  .........................................................................................82 
Figure 5-19. Saltwater-freshwater interface movement in Scenario 1.2. Red indicates saltwater, 
aqua indicates freshwater  .........................................................................................83 
Figure 5-20. Saltwater-freshwater interface movement in Scenario 2. Red indicates saltwater, 
aqua indicates freshwater  .........................................................................................83
 
   
 ix  
Figure 5-21. Saltwater-freshwater interface movement in Scenario 3. Red indicates saltwater, 
aqua indicates freshwater  .........................................................................................83 
Figure 5-22. Saltwater-freshwater interface movement in Scenario 4. Red indicates saltwater, 
aqua indicates freshwater  .........................................................................................84 
Figure 5-23. Saltwater-freshwater interface movement in Scenario 5. Red indicates saltwater, 
aqua indicates freshwater ..........................................................................................84 
Figure 5-24. Saltwater-freshwater interface movement in Scenario 6. Red indicates saltwater, 
aqua indicates freshwater  .........................................................................................84 
Figure 5-25. Saltwater-freshwater interface sectional cut for Scenario 1.1  ..................................85 
Figure 5-26. Saltwater-freshwater interface sectional cut for Scenario 1.2  ..................................85 
Figure 5-27. Saltwater-freshwater interface sectional cut for Scenario 2  .....................................85 
Figure 5-28. Saltwater-freshwater interface sectional cut for Scenario 3  .....................................86 
Figure 5-29. Saltwater-freshwater interface sectional cut for Scenario 4  .....................................86 
Figure 5-30. Saltwater-freshwater interface sectional cut for Scenario 5  .....................................86 
Figure 5-31. Saltwater-freshwater interface sectional cut for Scenario 6  .....................................86 
Figure 5-32. Cross Section of Dauphin Island illustrating lateral intrusion (crosscut taken at Col 
91)  ............................................................................................................................87 
Figure 5-33. Location of crosscut taken at Column 91  .................................................................88 
Figure 6-1.   Concentration at monitoring well, detected concentration of 1.09 lbs/ft3 at 124 
months  ......................................................................................................................94 
Figure 6-2.   Isochlor at 124 mo, showing conc. of 1.09 lbs/ft3 reaching the monitoring well  ....94 
Figure 6-3.   Concentration at monitoring well, detected concentration of 1.09 lbs/ft3 at 73 mo  .95 
Figure 6-4.   Isochlor 73 months, showing concentration of 1.09 lbs/ft3reaching the monitoring 
well  ...........................................................................................................................95 
 
 1 
1. Introduction 
Dauphin Island is a small barrier island located between the Mississippi Sound and the Gulf 
of Mexico about 4 miles off the coast of Mobile County, Alabama [Chandler, 1983].  The 
residents of Dauphin Island obtain their water from a shallow lens of freshwater located in the 
island?s unconfined aquifer.  According to the United States Census Bureau, the population of 
Dauphin Island has been steadily increasing for the past 20 years.  Due to the ever-growing 
desire of Americans to live on the coast, it is reasonable to assume that this trend will continue.  
Because of this, there is a need to understand the capacity, limitations, and characteristics of such 
shallow coastal aquifers, and understand the impacts of changing climatic factors and hydrologic 
parameters on these highly vulnerable water resource systems. 
This research thesis consists of four sections.  The first section investigates recharge issues in 
the Southeast United States.  Several recharge estimation methods were explored.  The first 
research question addressed in this section was which recharge estimation method gave 
consistent results for our sites in the Southeast and should be used in the rest of the study?  The 
second research question was can a relationship be found between land cover type and amount of 
water recharged into the aquifer? 
The second section of this thesis provides background information about Dauphin Island.  It 
contains information on geology, soil, land use, and water problems of the island.  It is intended 
to introduce the reader to Dauphin Island hydrogeology and present the water issues faced by the 
island.  
The third section specifically focuses on the groundwater resources of Dauphin Island and 
the effects of changing factors on the island?s water-table aquifer. The factors that were 
examined in this section were the effects of land cover/land use change, climate change, and 
 2 
increasing population on the groundwater resources.  The first research question considered is 
whether changing the parameters based upon scenarios mimicking land-cover/land-use change, 
climate change, and population change have a significant impact on the groundwater resources.  
The second research question was if they did have a significant impact, which factors was the 
aquifer most sensitive to. 
The fourth section focused on assessing what percentage of Dauphin Island?s annual recharge 
could be withdrawn from the wells without significantly impacting the aquifer.  This study was 
done because all of the scenarios modeled in the third study were hypothetical.  Since it is 
impossible to predict what the actual future recharge situation will be, it is important to know 
what percentage of recharge can be pumped in order to make management decisions.  The goal 
was to estimate what percentage of the annual recharge on the island could be pumped without 
saltwater contaminating any of the wells on the island. 
 
 
 3 
2. Literature survey 
 
This chapter briefly introduces concepts and surveys relevant literature on several topics, 
including groundwater aquifers and their importance in barrier islands, estimation techniques for 
groundwater recharge, numerical modeling of groundwater, and other environmental factors that 
affect groundwater resources. 
 
2.1 Groundwater concepts for managing island aquifers 
Increasing populations, increasing economic and industrial activities, and increasing 
developments and urban sprawl around the world have significantly amplified demands on water 
resources around the world.  Depletion of surface water is becoming more evident in many areas, 
putting an increased stress on groundwater sources.  Additionally, some areas don?t have 
naturally occurring surface water reservoirs or any considerable river systems.  Because of this, 
the demand for groundwater resources has become increasingly more substantial.  Fortunately, 
the amount of available freshwater in the form of groundwater is much higher than the amount 
available as surface water, but usage of groundwater must be carefully managed [Fetter, 2001]. 
The existence of groundwater occurs when water is stored in the void spaces of soil, 
fractured rock, or any other substance that makes up the underlying substrate.  Groundwater can 
occur in unconfined and confined aquifers.  Unconfined aquifers have no confining layer 
between the surface and the saturation zone.  Confined groundwater is overlain by a confining 
unit with a significantly lower hydraulic conductivity than that of the aquifer itself, and prevents 
the flow of water through the confining strata [Fetter, 2001]. 
On small, barrier islands, the proportion of water used by humans coming from 
groundwater is very high.  Barrier islands are significantly smaller than continental landmasses.  
 4 
This means that there are no large watersheds feeding water to river systems.  Additionally, 
because of storms, tides, and sediment budget deficits, the morphology of barrier islands changes 
almost constantly.  Further, with sea level rise, there may be observable effects on the 
morphology of the island [Morton, 2008].  Therefore, because of their relatively small size and 
changing geomorphology, it is unlikely that there would be any well-established, major river 
channels in these systems.  Without any major river systems, reservoirs cannot be used to 
provide a source of water for human consumption.  Because of this, groundwater is extremely 
important in barrier islands.  Most of groundwater pumped from barrier islands comes from 
island aquifer lens systems, a relatively shallow unconfined layer of water that is exploitable for 
human use.  Typically, these systems are precipitation derived freshwater lenses that overly 
denser saltwater. 
Chesnaux [2008] performed a detailed study of unconfined island aquifers.  He specifically 
developed analytical solutions for groundwater travel times in islands bounded by freshwater as 
well as by seawater.  Figure 2-4 illustrates the cross sectional view of an island aquifer system, 
showing the lens of exploitable freshwater [Chesnaux, 2008]. 
 
 
 5 
 
 
Figure 2-1.  Cross-Sectional view of a circular oceanic island [Chesnaux 2008]. 
 
As shown in the figure, the sole source of input to the system is precipitation derived 
recharge.  Water is lost from the system via groundwater discharge occurring radially towards 
the saline ocean.  When the groundwater is pumped this is also a loss to the system [Chesnaux, 
2008].  Withdrawals of water from these systems have serious consequences that must be 
considered.  If withdrawal rates from island aquifers are larger than recharge rates from 
precipitation, saltwater intrusion will occur since the aquifer is in direct hydraulic contact with 
the ocean. 
Saltwater intrusion occurs because when water is pumped from island aquifers the inland 
water level is reduced and the higher density salt water flows in due to the head gradient, 
creating a saltwater wedge.  As pumping continues, saltwater intrusion moves further inland and 
eventually has the potential to contaminate the groundwater resources [Fetter, 2001].  In coastal 
aquifers, intrusion can occur in a variety of modes.  As already discussed, saltwater can intrude 
 6 
upward from deeper, saline zones, but intrusion can also occur laterally from the ocean as well as 
downward from coastal waters [Barlow and Reichard, 2010]. 
The extent of the saltwater intrusion depends on factors such as rate of groundwater 
withdrawls, distance between the pumping wells, geological properties of the aquifer, and the 
hydraulic properties of the aquifer [Barlow and Reichard, 2010]. 
 
2.2 Groundwater recharge  
Groundwater recharge is the process in which surface water reaches the water-table in the 
aquifer?s phreatic zone [Martinez-Santos, 2010].  Groundwater recharge can occur in a variety of 
ways.  The two most common vehicles for recharge are deep seepage recharge occurring 
between aquifer units and by infiltration recharge from precipitation. 
As previously discussed, recharge is an integral part of the water budget for a shallow, 
freshwater aquifer.  Understanding and quantifying recharge is extremely important from an 
aquifer planning and management standpoint so that sustainable abstraction levels can be 
estimated for the aquifer. The rate and quantity of groundwater recharge directly affects the 
quantity of freshwater resources contained in the aquifer, and the amount that can be safely 
withdrawn.  Shallow, precipitation driven aquifers are considerably sensitive to recharge rate.  
Additionally, recharge estimates are important from a hydrogeological standpoint [Martinez-
Santos, 2010].  In order to accurately understand and model a specific aquifer system, there must 
be a known estimate for recharge. 
Additionally, being able to quantify recharge is also useful if saltwater intrusion occurs in 
the aquifer.  Under natural equilibrium conditions, high inland groundwater levels and flow of 
fresh water to the sea impede inland movement of saltwater into aquifer systems, and the 
 7 
position of the boundary is a function of the amount of freshwater discharge [Fetter, 2001].  
However, when aquifers are over exploited the salt water wedge advances into the aquifer and 
saltwater intrusion occurs. The effect of recharge intensity and duration on saltwater intrusion 
was studied by Mahesha and Nagaraja using a one-dimensional finite element model [1995].  
They found that a relationship can be developed between interface motion of the saltwater wedge 
and the intensity and duration of recharge. 
There are at least three basic ways to obtain recharge at a certain location.  The first, and 
perhaps most obvious, is to measure it directly.  This would include the use of expensive field 
equipments.  A potential drawback to direct measurement is the cost of equipment.  Also, it is 
known that this method is largely site specific [Sophocleous, 1991]. 
A second way to estimate recharge is using the hydrologic continuity equation as the 
foundation.  The equation is 
s IQt? ??? , where         (2-1) 
st???  change in storage per time  [L3/t]; I? inflow  [L3/t]; and Q? outflow in [L3/t]. 
This equation suggests that the change in the storage volume is quantified using the 
difference between the inflow and outflow of a hydrologic system [Bedient and Huber, 1992]. 
This concept can also be applied to small basins by defining the terms that constitute the 
inflow and the outflow.  By doing this, the following water balance equation can be derived: 
S P R G E T I? ? ? ? ? ?, where        (2-2) 
S??change in storage in a specified time period; P? precipitation;R? surface runoff; G?
groundwater flow [recharge]; ET? evapotranspiration, and I = interception [Bedient and Huber, 
1992]. 
 8 
The main problem with this method of recharge estimation is that while the input term, 
precipitation, can be easily measured, many of the output terms are not easily measurable.  Most 
of the output terms either have to be measured with expensive equipment or estimated using 
empirical relationships that are not always accurate for the given circumstance or site location.  
For these reasons, this method is not always easy to apply or realistic. 
The third method, which was the basis for the simpler method used later in this research, 
is called the Water-Table Fluctuation (WTF) method.  This method requires the input of 
groundwater level data as well as an estimation of the specific yield of the aquifer.  Specific 
yield, Sy, is a property of rock or soil that indicates the ratio of the volume that the soil will yield 
due to gravity drainage to the total soil volume [Fetter, 2001]. 
By measuring the fluctuations in groundwater level, the groundwater recharge can be 
estimated.  Each positive fluctuation in the groundwater level indicates recharge into the aquifer.  
By measuring the change in groundwater level and multiplying the change by the specific yield 
of the system, the value of groundwater recharge is found for that site.  Mathematically, recharge 
is calculated using the following equation: 
( ) ( )j j yR t H t S? ? ?, where        (2-3) 
()jRt? recharge from 0t to jt [L]; H??the peak water level rise during the recharge period [L]; 
and yS? Specific yield [dimensionless]. 
 
 
 
 9 
In order to get an accurate H? , the height of the increase must be measured from where 
the antecedent recession curve would be extrapolated had the recharge spike not occurred.  This 
is illustrated in Figure 2-2: 
 
 
 
Figure 2-2.  Measurement of recharge spike [from USGS Groundwater Information, 2008]. 
 
There have been several studies done attempting to estimate groundwater recharge using 
the methods described above.  For example, Delin et al. [2006] used four local and basin scale 
methods to compare recharge estimations in Minnesota [Delin et al., 2006].  The local scale 
estimates were done using an Unsaturated Zone Water Balance (UZWB), the WTF method, and 
groundwater age dating.  The results of the study showed that the UZWB method gave 
inconsistent results when compared to other methods.  The study also found that the WTF 
method was the easiest to apply.  Additionally, their research illustrated that regionalized 
recharge estimates compared well to local and basin scale estimates [Delin et al., 2006]. 
 10 
Crosbie et al. [2005] also used the WTF method, but combined it with a time series 
approach to estimate recharge.  Using the time series approach, long term water-table and 
precipitation records were examined and effects due to evapotranspiration, atmospheric tides, the 
Lisse effect, which occurs when air is trapped by infiltration, and varying specific yields values 
were removed [Crosbie et al., 2005]. 
Recharge was estimated by Samper and Pisani [2009] using a combination of the soil 
water balance and a groundwater flow model for And?jar alluvial aquifer in Spain.  The soil 
water balance alone gave too large of values for recharge estimates. The combined method 
overcame common problems that are often encountered when recharge estimation is attempted 
by soil water balance or groundwater flow models alone [Samper and Pisani, 2009]. 
Two recharge estimation methods were also combined by Sophocleous [1990] in an 
attempt to quantify groundwater recharge in the Kansas Prairies.  Sophocleous combined the soil 
water balance and the WTF method to obtain his ?hybrid water-fluctuation method.?  For each 
storm event, the recharge amount was calculated using the hydrologic budget.  This amount was 
divided by the measured water-table rise in the groundwater record for the corresponding event, 
and the estimate of storativity was obtained.  After this was done for several events, the average 
storativity was found, and this value was applied to specific water-table rises to find groundwater 
recharge values [Sophocleous, 1990]. 
In a study completed by Mart?nez-Santos and Andreu [2010] results from lumped and 
distributed approaches to estimate recharge were compared for the Vent?s Aquifer in Spain.  
Lumped models assume the system can be expressed using a combination of transfer functions, 
and the physics of recharge are rarely considered.  Distributed models use detailed data records 
to establish a relationship and provide spatial information.  Both models obtained similar results, 
 11 
although the results from the lumped model agreed better with the available field data [Mart?nez-
Santos and Andreu, 2010]. 
Another problem that is often encountered in recharge estimation is difficulty in 
measuring recharge for data poor areas.  For example, some areas may not have groundwater 
monitoring stations, so applying some of the previously discussed methods would be difficult.  
Crosbie et al [2010] attempted to overcome such problems in their study of almost 200 sites in 
Australia.  They estimated recharge at 172 data rich sites in an attempt to obtain empirical 
relationships that could relate recharge to national datasets and characteristics such as vegetation, 
climate, and surface materials.  This way the relationships could also be applied for data poor 
areas.  The study found that the relationships were most sensitive to vegetation and soil type 
[Crosbie et al, 2010]. 
While hydrologic modeling was briefly mentioned earlier, a specific modeling tool to 
estimate recharge that should be mentioned in depth, as it was used in this research project, is the 
Soil Water Assessment Tool (SWAT).  SWAT is a hydrologic continuous time model that was 
developed to assess the effects of land management practices and climate on complex watersheds 
[Arnold, 2005].  SWAT uses many input parameters and uses precipitation as the driver.  One of 
the outputs that can be obtained from the model is groundwater recharge for the watershed. 
SWAT has been used in many instances to estimate groundwater recharge.  For example, 
Arnold et al. used it to estimate recharge in the upper Mississippi River Basin [Arnold et al., 
2000].  It was also used to quantify recharge in the Liverpool Plains of Australia by Sun and 
Cornish [2005].  The specifics behind the SWAT procedures used in this research will be 
discussed in later chapters. 
 
 12 
2.3 Density-dependent numerical modeling 
As previously discussed, over exploitation of aquifers is currently stressing these systems 
and causing distortion in the natural recharge-discharge equilibrium.  Groundwater modeling has 
become a powerful tool to visualize current groundwater flow conditions as well as predict 
potential impact of future hypothetical scenarios.  This aids in establishing long-term planning 
practices for the aquifer.  Groundwater flow models solve the general groundwater flow 
equation, and are capable of providing visualization of either two or three dimensional flow in 
aquifers. Many of these models are based on the popular MODFLOW groundwater model 
[Harbaugh, 2000].  MODFLOW operates by using a finite difference solution scheme to solve 
the three dimensional groundwater flow differential equation. 
In order to simulate the interaction of saltwater and freshwater as well as the occurrence of 
saltwater intrusion, a density-dependent groundwater flow model can be used [Lin et al., 2009].   
SEAWAT was developed by combining MODFLOW and MT3DMS [Zheng, 1990] into one 
program and making modifications to account for saltwater-freshwater density variations.  By 
doing this, a finite difference numerical model which is capable of solving the coupled flow and 
solute transport equations was obtained [Guo and Langevin, 2002].  SEAWAT can use either an 
implicit or explicit solution scheme.  When solved implicitly, SEAWAT uses MODFLOW to 
solve the flow field for each time step, and then MT3D to solve the concentration field.  This 
concentration is used to update the density field, which is used by MODFLOW as the relative 
density difference term.  This is repeated a number of times within the same time step until the 
difference in density is smaller than the user-defined value [Rao et al., 2004].  When solved 
explicitly, the flow and transport equations are solved alternately and repeated until the allotted 
amount of stress periods are complete [Guo and Langevin, 2002]. 
 13 
The SEAWAT modeling approach was validated by Goswami and Clement [2007] by 
comparing laboratory data for both steady state and transient experiments to results obtained by 
modeling done in SEAWAT [Goswami and Clement, 2007].  Previous to this, the benchmark for 
validating saltwater intrusion models was the steady state Henry solution [Henry, 1964]. 
Many coastal aquifer studies have utilized SEAWAT to simulate the freshwater-saltwater 
interface.  For example, SEAWAT was used by Larabi et al. [2008] to model the groundwater 
quantity and quality contained in the Rmel Coast aquifer in Morocco [Larabi et al., 2008].  
Pravena and Aris [2010] used SEAWAT to model the aquifer underlying Manukan Island in 
Malaysia.  They modeled six scenarios representing possible human pressures and climate 
change [Praveena and Aris, 2009].  SEAWAT was used by Lin et al. [2008] to model the degree 
of saltwater intrusion in the Gulf coast aquifers of Alabama [Lin et al., 2008].  The study done by 
Lin et al. included a 40 year predictive simulation run, which illustrated a large amount of 
saltwater intrusion potential if groundwater pumping goes beyond the 1996 level.  The paper 
suggested a need for better groundwater development and management strategies for the Gulf 
Coast, especially for the deep, confined aquifer systems. 
An extensive modeling study using SEAWAT was done by Masterson [2004] to model the 
complex groundwater system of Cape Cod, Massachusetts.  The aquifer system at Cape Cod 
consists of four distinct lenses.  Increasing development and demand on the groundwater system 
had raised serious concerns for the sustainability of the system.  Using a complex groundwater 
model, the current groundwater situation was simulated, as well as future groundwater levels 
with predicted pumping rates [Masterson, 2004]. 
SEAWAT has also been used as a tool in a more unconventional manner to quantify aquifer 
parameters.  For example, Cecan et al. [2008] used it to analyze pumping test data in order to 
 14 
find horizontal hydraulic conductivity and vertical anisotropy in Cape Cod, Massachusetts.  The 
results of the study showed that classical methods such as the Hantush-Jacob method and 
numerical models that do not account for density difference do not predict horizontal hydraulic 
conductivity and vertical anisotropy values as accurately as SEAWAT [Cecan et al., 2008]. 
Rao et al. [2004] utilized SEAWAT in an unusual and interesting way. They used 
SEAWAT to model the saltwater intrusion dynamics in a hypothetical coastal aquifer, but then 
also explored if the SEAWAT model could be replaced by a trained artificial neural network.  
An artificial neural network (ANN) is a computational tool that attempts to mimic the structure 
and/or function of the biological neural network.  Because of the computational burden that 
corresponds with complex groundwater models, ANN was used to replace the model.  In this 
study, the ANN was improved by data training sets from repeated runs of SEAWAT.  Once this 
was done, the ANN was able to produce results very similar to the results obtained from 
SEAWAT [Rao et al., 2004]. 
Other density dependent groundwater flow models have been used to model groundwater 
flow in coastal aquifer systems.  Joscon et al. [2001] used the SWIG2D to find the depth to the 
saltwater interface in the Northern Guam Lens Aquifer [Joscon et al., 2001].  The region of the 
Biscayne Aquifer underlying Hallandale, Florida was modeled by Anderson et al. [1988] using 
the program SWICHA [Anderson et al., 1988].  Sherif and Singh [1999] used 2D-FED to model 
the effects of climate change on two coastal aquifers, one in Egypt and one in India [Sherif and 
Singh, 1999]. 
 
 15 
2.4 Additional factors affecting groundwater resources in islands 
While increased demand due to increasing population and pumping rates can cause large 
stresses on an aquifer, there are other confounding factors that can affect the quality and quantity 
of groundwater resources.  Some of these factors are land cover/land use change and climate 
change.  Climate change includes scenarios such as changing precipitation patterns, increase in 
hurricanes and other large storm events, and sea level rise. 
Studies have been done that have illustrated the significant effects of land use on 
groundwater recharge.  By monitoring water level measurements from two monitoring wells for 
122 days, Zhang and Schilling [2005] were able to observe the effects of land cover on the 
water-table, evapotranspiration, soil moisture, and groundwater recharge.  The two wells were on 
either side of Walnut Creek, in Iowa.  One of the wells was located in grassy field and the other 
well was located in bare ground.  The water level data showed significant variations in water 
level between the two sites.  Because of increased ET at the grass covered well, much less 
groundwater recharge reached the water-table.  They also found that soil moisture was also less 
in the grass covered site due to ET [Zhang and Schilling, 2005]. 
Since there is often an obvious relationship between land use and recharge, scientists have 
attempted to estimate recharge using land cover data.  Cherkauer and Sajjad [2005] outlined a 
method to estimate recharge which uses ground-surface information instead of long-term 
groundwater monitoring data.  They used the topography, hydrogeology, and land cover of the 
site to estimate recharge.  The method obtained a conservative approximation for recharge, but 
recommended that the estimate should be refined with other methods [Cherkauer and Sajjad, 
2005].  Similarly, Ranjan et al. [2005] estimated recharge based on land use and climatic factors.  
They then used the estimated recharge amounts as inputs into a numerical groundwater model. 
 16 
Researchers have not only studied the effect of land use/land cover on groundwater 
resources, but they have also studied the effect of land use/land cover change on aquifer systems.  
Scanlon et al. [2005] completed a study on the Southwestern United States to test their 
hypothesis that the land use/land cover (LU/LC) change of a natural rangeland into an 
agricultural ecosystem will affect the groundwater recharge and chloride mass balance.  By 
examining three types of LU/LC they were able to detect significant differences in mean chloride 
concentrations as well as mean matric potential.  Information gained from this study and similar 
studies suggest that groundwater resources can be somewhat managed through modification of 
LU/LC [Scanlon et al., 2005]. 
Another factor that has the potential to significantly affect groundwater resources is climate 
change.  Since the mid-twentieth century carbon dioxide levels in the atmosphere have been 
steadily rising.  If this phenomenon continues, many researchers believe that the global and local 
climate characteristics will be significantly altered [Ranjan et al., 2006].  This trend has been 
termed climate change, and would likely have large effects on the hydrologic cycle around the 
world.  Increased atmospheric carbon dioxide levels would lead to an increased ?greenhouse 
effect,? in which solar radiation is trapped by the increased gases.  This results in increased 
temperatures, which in turn affects evapotranspiration, precipitation, and soil moisture. 
While increased temperatures would likely lead to an overall global increase in 
precipitation, it will lead to both increases and decreases on the local scale, depending on the 
location and topography of the region [Ranjan et al., 2006].  There have been numerous studies 
done which assess the impact of climate change and decreased precipitation on fresh 
groundwater resources.  Ranjan et al. [2006] used the high and low emissions scenarios from the 
Hadley Centre climate model to predict the change in climate that should be input into their 
 17 
groundwater model.  Among the five locations modeled, which were located around the globe, 
all but one showed increasing losses of fresh groundwater resources. 
Drought due to climate change could not only cause a decrease in groundwater recharge, 
but also a decrease in water levels in surface reservoirs that would force more of a demand onto 
groundwater.  This situation was studied by Mollema et al. [2010] for Terceira Island in 
Portugal.  The water demands of the island are currently met by rain fed springs, but with 
increased droughts they may need to begin to exploit the freshwater lens that underlies the island.  
The study was devoted to understanding the size, characteristics, and limitations of the lens, so 
that it could be exploited if necessary.  
Another effect of climate change is sea-level rise.  Sea level rise is caused by changes in 
atmospheric pressure, expansion of ocean water, and the melting of ice sheets and glaciers 
[Sherif and Singh, 1999].  The effects of sea level rise on saltwater intrusion have been studied 
by Webb and Howard [2011], Lo?iciga et al. [2011], and Chang et al. [2011].  Webb and 
Howard [2011] found that the hydraulic properties of the aquifer played a large role in rate of 
intrusion.  Lo?iciga et al [2011] found that groundwater pumping had a much larger effect on 
saltwater intrusion than sea level rise.  Chang et al. [2011] found that sea level rise does not have 
a long-term impact on confined aquifers.  While the sea level rise will initially cause saltwater 
intrusion, a reversal effect will drive the wedge back out over time [Chang et al., 2011]. 
 
  
 18 
3. Recharge and land cover estimation for the southeastern United States 
This section discusses the process used to relate land cover type to groundwater recharge in 
the Southeastern U.S.  It discusses how land cover type and groundwater recharge values were 
quantified as well as the methods used to find a relationship between the two factors. 
3.1 Background  
In order to obtain a relationship between groundwater recharge and land cover type, seven 
sites were examined in the Southeast region.  These sites were mostly located in coastal Alabama 
and Florida, although two were located more inland than the others (Figure 3-1).  They are all 
located in un-consolidated and semi-consolidated shallow unconfined aquifers.    The regional 
aquifers that the well sites are located in are the Southeastern Coastal Plains aquifer, Coastal 
Lowland aquifer, and the Floridian Sand and Gravel Surficial aquifer.  The aquifers were all 
unconfined with similar soil types, and the aquifer characteristics of the various regional aquifers 
are similar.  Therefore, after a small adjustment to specific yield values based on the site?s soil 
characteristics, we can assume differences in recharge are due to land cover differences.  
The sites labeled later in the research as FL1, FL2, and FL4 are in the 
Gonzalez/Ensley/Pace area of Florida.  Site FL3 is located in Pensacola, FL.  The site labeled as 
Covington was located in Covington County, AL, near Opp, AL.  The site labeled Baldwin was 
located in Baldwin County, AL, near Fairhope, AL.  The site labeled Montgomery was located in 
Montgomery, AL. 
 19 
 
Figure 3-1.  Map of well locations used in this study. 
 
3.2 Research objectives 
The primary research objective of this section was to determine whether a relationship 
could be derived between the groundwater recharge values and the land cover characteristics for 
the seven sites in the Southeast.  A significant relationship between the two factors would 
indicate that land cover and land use is an important aspect in relation to groundwater resources 
and the management of these resources.  In an effort to obtain this relationship between 
groundwater recharge and land cover characteristics, both continuing abstractions, which is the 
amount of water taken into the soil once ponding begins,  and infiltration were examined for the 
seven sites. 
This effort is valuable because whether or not a relationship can be derived between 
groundwater recharge and LU/LC for the particular sites chosen is an interesting issue that is 
worth investigating.  If a relationship is found, the same concepts and methods could be later 
 20 
applied to find recharge in areas where there is no groundwater elevation data, but there is land 
cover data.  Specifically, the same methodology could be applied for Dauphin Island.  As already 
discussed, groundwater recharge is an important input for groundwater modeling.  Dauphin 
Island does not have publically available groundwater data from non-pumping wells, therefore; a 
relationship between land cover and recharge would enable us to use the island?s land cover data 
to calculate recharge. 
Research methodology for this chapter is divided into three distinct parts.  The first step 
was estimating groundwater recharge for the seven sites.  This was done using daily groundwater 
level data from seven USGS groundwater monitoring wells.  The recharge was calculated for a 
year-long time period and summed to obtain an annual cumulative recharge value.  A year-long 
time period was used to eliminate the effects of differences in recharge rates due to seasonal 
factors, such as changing evapotranspiration patterns in different seasons.  The second section 
describes how the Curve Number (CN) was used to relate recharge to land-cover and the third 
part describes how the CN was calculated for each site. 
3.3 Recharge estimation 
This section discusses the methods examined for recharge estimation at the seven sites.  
The results from the various methods are shown for a few of the sites in order to illustrate the 
methods and then one method was selected as the best method for this study. 
3.3.1 Methods and input data  
The Water-Table Fluctuation (WTF) method, which was previously discussed, was the 
method used to estimate cumulative recharge for the year-long time periods for each site.  Since 
the WTF method requires the peak water table rise during the recharge period, or H? , as input to 
calculate recharge, multiple methods were used and compared to generate H? values. 
 21 
 Perhaps the simplest method to measure groundwater recharge is the graphical method.  
Using a hydrograph for a given site, which has groundwater elevation vs. time, the graphical 
method can be completed manually.  An example hydrograph for Baldwin County, AL is shown 
in Figure 3-2.  For each hydrograph spike, the height of the increase was measured from the 
location the antecedent recession curve would be extrapolated had the spike not occurred.  Prior 
to extrapolating the curves, it is useful to examine the entire data set in order to get an estimate 
for recession rates [USGS, 2008].  The measured heights were multiplied by the specific yield 
values for each site.  The recharge amounts were found, and Table 3-1 lists the values used for 
specific yield in these calculations.  The specific yield values were obtained by examining the 
soil type at each of the seven sites.  By summing the spikes for the year-long time period, the 
cumulative recharge was found.  The graphical method is prone to subjectivity when performed 
manually as each person would likely draw the recession curve differently. 
 
Figure 3-2.  Example of a hydrograph from the Baldwin County, AL well. 
 
 22 
The second method of recharge estimation that was used was the Master Recession Curve 
(MRC) approach to the WTF method.  Developing a MRC is a similar idea to the graphical 
method, but instead of manually extrapolating the hydrograph recession beneath each positive 
fluctuation a MRC is developed to calculate the antecedent recession curve.  A MRC is a water-
table recession hydrograph that is unique to the evaluated site.  For a specific site it represents 
the average behavior of a declining water-table [Heppner and Nimmo, 2005].  A MRC can be 
developed using MATLAB [Heppner and Nimmo, 2005] or in Excel. 
For this project, Excel was used to find the MRC for the various sites and the general 
method is described in Heppner and Nimmo [2005].  In this method time and water level data for 
the desired site is required.  This data is used to calculate the water-table fluctuation rate for each 
time step.  The water table fluctuation rate is the change in water table elevation divided by the 
change in time for each time step.  The water-table fluctuation rate is plotted on the y-axis 
against the water-table elevation on the x-axis.   
This method assumes that at a certain water-table elevation, there will be a characteristic 
water-table decline rate.  Due to this, we would expect a linear relationship when the data is 
plotted.  However, due to various factors, this may not always be the case.  The type of MRC 
that can be created is selected based on the trend in the plotted data.  In addition to a linear 
method, power and bin averaged may also be used [Heppner and Nimmo, 2005].  The bin 
averaged method was used for this research, so it will be discussed more in depth. 
The bin-averaged method is best suited for large data sets that may have an irregular 
pattern when initially plotted [Heppner and Nimmo, 2005].  After inspecting the range of 
elevation values from the lowest to highest observed water-table elevations, the user decides on 
an appropriate number of bins, which are ranges of elevation, based on the elevation range.  The 
 23 
total range is then divided into equally spaced bins of elevation range.  As the elevation levels 
are placed into the appropriate elevation bins, the corresponding decline rates are placed in the 
bins [Heppner and Nimmo, 2005].  Each elevation and decline rate bin is averaged and plotted.  
Once these values are plotted the relationship usually appears more linear and a trend line 
equation can be obtained that is specific for the particular site and time period. 
The equation obtained represents the MRC.  For example, a hypothetical MRC may be 
represented by the equation y mx b??.  Given a dataset that contains time as well as 
groundwater elevation values, the equation can then be used to find total recharge.  For each time 
step, the groundwater level in from the previous time step is used as the x-value, and the y-value 
is the predicted groundwater level from the Master Recession Curve.  By subtracting the 
predicted value from the actual value, H? is obtained.  This H? is subsequently multiplied by 
the specific yield of the aquifer to obtain the recharge. 
The third method used to obtain H? and thus cumulative recharge, mimicked the RISE 
program developed by Rutledge [2007].  This program calculates the daily rise in a given 
observation well by calculating the amount of water level increase from the previous day.  The 
value for that day is set to zero if the difference is negative, but it is considered groundwater 
recharge if the difference is positive [Delin et al., 2006]. Daily recharge values are evaluated 
using this program and the positive recharge values are summed to obtain a cumulative annual 
rise in the aquifer (cumulative H? ). This estimate was multiplied by the specific yield of the 
subsurface material to obtain the cumulative annual recharge. 
Specific yield values were varied based on the soil material of the site.  Soil data was 
obtained from the SSURGO database and imported into ArcMap program as a shapefile.  The 
well locations were marked on their latitude and longitude.  Using the soil types of the region, a 
 24 
specific yield value was assigned to each site.  The values that were used for specific yield were 
based on values from Fetter [2001], Nachabe [2002], and estimations based on these ranges and 
soil types.  The specific yield was used to multiply H? to obtain the daily and cumulative annual 
recharge for each site.  Table 3-1 shows the values specific yield values of each site: 
Table 3-1.  Sy values used for recharge estimations. 
Location Soil type Sy 
FL1 Sand, Loamy sand, Sandy loam 0.13 
FL2 Sandy loam, Loamy sand 0.13 
FL3 Sandy loam 0.13 
FL4 Sandy loam, Loamy sand 0.13 
Covington Loamy sand, Loamy fine sand 0.12 
MGM Fine sandy loam 0.12 
Baldwin Loamy fine sand, Loamy alluvial 0.15 
 
Since the RISE approach does not take into account the hydrograph recession that would 
have occurred if recharge had not taken place, this method generally underestimates actual 
recharge.  While this is not ideal, it is acceptable for our study because all of the sites will be 
underestimated so it will not skew the relationship found between the recharge values and land 
cover.  It does, however, slightly underestimate groundwater recharge when the estimate is used 
for future predictions.  This is actually ideal, because it will result in a more conservative 
estimation, which is usually desired when dealing with groundwater resources management.  For 
this research, this method was done using MS Excel instead of the actual RISE program but an 
identical protocol was followed to obtain the annual recharge estimates for each of the seven 
sites. 
3.3.2 Results 
The results for bin-averaged MRC obtained from Baldwin, AL and Montgomery, AL are 
shown in Figures 3-3 and 3-4.  The Baldwin County MRC had a relatively linear relationship, 
 25 
while the Montgomery MRC did not.  The y-axis, labeled ?rate of decline,? is the change in 
water table elevation divided by the change in time for each time step. 
 
 
Figure 3-3.  Bin averaged MRC for Baldwin County, AL. 
 
 
Figure 3-4.  Bin averaged MRC for Montgomery, AL. 
 
y  =  0 . 0 2 3 6 x  +  0 . 0 1 2 1
R ?  =  0 . 8 6 4 2
0 . 0 0
0 . 0 1
0 . 0 2
0 . 0 3
0 . 0 4
0 . 0 5
0 . 0 6
0 . 0 7
0 . 0 8
0 . 8 0 1 . 0 0 1 . 2 0 1 . 4 0 1 . 6 0 1 . 8 0 2 . 0 0 2 . 2 0
R
a
t
e
 of
 
D
e
c
li
ne
 
(f
t
/
da
y
)
G r ou ndw a t e r  E l e v a t i on  ( f t  be l ow  da t um )
B in  A v e r a g e d MR C  f or  B a ld w in  C o
y  =  0 . 0 0 0 7 x  +  0 . 1 3 2
R ?  =  0 . 0 0 3 7
0 . 0 0
0 . 0 2
0 . 0 4
0 . 0 6
0 . 0 8
0 . 1 0
0 . 1 2
0 . 1 4
0 . 1 6
0 . 1 8
0 . 2 0
8 9 10 11 12 13 14 15
R
a
t
e
 of
 
D
e
c
li
ne
 
(f
t
/
da
y
)
G r ound w a t e r  E l e v a t i on ( f e e t  be l ow  da t um )
B in  A v e r a g e d MR C  f or  Mont g om e r y
 26 
Figure 3-5 is from Delin et al. [2006] in which the graphical, MRC, and RISE approaches 
were used to calculate percent of water recharged in a Minnesota site.  Delin?s figure is shown as 
a comparison to Figure 3-6, which we obtained by using the same methods for recharge 
estimation described in Delin?s study and applying them to Baldwin County, AL.  As shown, the 
Baldwin County estimates that were obtained have larger values for percentage recharged, but 
this would be expected due to site characteristics.  The important aspect to note in this 
comparison is that the main trends are similar, with the MRC estimate being generally the 
largest, followed by the graphical and RISE approach respectively. 
 
Figure 3-5.  Comparison of recharge methods in Minnesota [Delin et al, 2006]. 
 
 27 
 
Figure 3-6.  Comparison of recharge methods in Baldwin Co, AL. 
 
Table 3-2 summarizes the values obtained for cumulative recharge at each site using the 
RISE method. 
Table 3-2.  Recharge values using the RISE method. 
Site Location Dates Evaluated 
Rise Recharge 
Estimate [ft] 
Precip 
[ft] 
FL1 7/1/1981-6/30/1982 1.75 3.79 
FL2 10/1/1983-9/30/1984 3.60 4.94 
FL3 9/7/1983-9/6/1984 1.21 5.36 
FL4 1/15/1980-1/14/1981 2.10 4.05 
Covington Co 5/1/2007-4/30/2008 3.53 4.56 
Montgomery Co 1/27/1990-1/26/1991 1.81 4.13 
Baldwin Co 11/1/2007-10/31/2008 1.63 6.28 
 
3.3.3 Observations 
The graphical method was deemed inefficient and subjective because different results are 
obtained by different users of the method, hence the results of this method will not be used 
further in this study.  For some of the sites evaluated in this study, the line of best fit generated 
using the MRC method had a high R2 value (Figure 3-2) and the relationship would have been 
0 . 0 0
0 . 1 0
0 . 2 0
0 . 3 0
0 . 4 0
0 . 5 0
0 . 6 0
0 . 7 0
0 . 8 0
0 . 9 0
60 110 160 210 260 310 360 410 460
R
e
c
ha
r
g
e
/
pr
e
c
ipi
t
a
t
ion,
 
pe
r
c
e
nt
M e a s ur e m e nt  i nt e r v a l ,  da y s
B a l d w i n  C ou n t y  R e cha r g e
M R C  A p p r o a c h
R I S E  p r o g r a m  a p p r o a c h
G r a p h ic a l a p p r o a c h
 28 
acceptable for use in estimating recharge.  For other sites, however, the relationship had a 
relatively low R2 value (Figure 3-3) and the relationship was deemed insignificant and not 
acceptable for use in this study.  This is most likely due to other factors causing a difference in 
recharge rate, such as antecedent moisture conditions, the Lisse Effect, and heterogeneities in the 
subsurface material.  Due to this, the RISE method of recharge estimation was employed in order 
to quantify the amount of annual recharge at the seven sites, and the RISE estimates were used in 
the following sections of this chapter. 
3.4 Quantify LU/LC effects on Recharge 
This section discusses the method used in an attempt to relate LU/LC to the amount of 
water recharged into the aquifer at each of the seven sites. 
3.4.1 Study methodology and input data 
The Soil Conservation Service Curve-Number (SCS-CN) method was developed by the 
United States Department of Agriculture-Soil Conservation Service (USDA-SCS) in the 1950?s.  
The SCS-CN method can be used to predict flood-flow volumes for ungauged watersheds for 
runoff generating rainfall events [Lyon et al., 2004].  Accumulated rainfall (P) and accumulated 
runoff (Q) are important variables in the SCS-CN method.  Therefore, the general form of the 
SCS-CN equation is as follows: 
SIP IPQ a a????
2)(
        (3-1)
 
where Q is runoff [in],P is the event precipitation [in]; aI is the initial abstractions [in]; and S is 
the potential maximum retention after runoff begins [in].  This equation is only valid if P>Ia.  If 
this precipitation is not greater than the initial abstractions, Q=0.  The CN is used to calculate 
both S and Ia [Michel et al., 2005].  The equations for both of these variables will be outlined 
 29 
below.  The CN is a function of LU/LC, Hydrologic Soil Group (HSG), and the Antecedent 
Moisture Condition (AMC) of the soil. 
Once the appropriate adjusted CN was found for each day in the precipitation record, it 
was used along with daily precipitation records for the area to calculate the continuing 
abstractions, Fa, for events that qualify.  As will be illustrated in the following calculations, Fa is 
calculated using both CN and precipitation, so it takes into account land cover type as well as 
precipitation events.  This makes it a sensible value to use in developing a relationship between 
recharge and LU/LC type. 
The value of initial abstractions, or aI , is calculated to determine which events qualify as 
large enough to generate continuing abstractions.  aI includes interception by vegetation and 
water that ponds on the surface [Lecture Notes, Kalin, 2010].  In order to obtain aI , the adjusted 
CN value was used to first calculate S using equation 3-2.  The method and calculations for 
obtaining the CN will be described in Section 3.5: 
101000 ?? CNS          (3-2) 
where S is the potential maximum retention after runoff begins [in]; and CN is the curve number 
[dimensionless]. 
With S , the value of the initial abstraction of water during a rainfall event could be 
calculated using the following equation: 
SIa *2.0?          (3-3) 
 
The calculated aI values were used to determine which rainfall events were large enough 
to generate continuing abstractions.  As already stated, for each rainfall event, the total amount of 
 30 
precipitation must be larger than or equal to S*2.0  in order for the event to generate continuing 
abstractions.  The values of aI  and S were subsequently used to calculate the value of runoff, or
,Q using equation 3-1 that was presented above. 
Using the obtained values the continuing abstractions can be calculated through equation 
3-4: 
QIPF aa ??? .        (3-4) 
The sum of the aF values for the annual period for each site were plotted against the 
recharge values for the respective sites on the same scatter plot (Figure 3-7) to seek a relationship 
between recharge and aF  .  If a relationship is found between recharge and aF , this relationship 
could be related back to land cover type as described above.  With a relationship between the two 
factors, recharge could be calculated using the average CN of a particular site. 
As an alternative, a relationship between infiltrating depth and recharge was also sought 
to see if a relationship between LU/LC and recharge could be developed.  First interception 
depth was calculated using the following equation [Bras, 1990] : 
* nI a b P??          (3-5) 
where I is interception [in]; ,,aband n are empirical values that vary with vegetation type; and 
P is the amount of precipitation [in]. 
Interception is the amount of rainfall that is intercepted by vegetation before it is able to 
reach the ground [Fetter, 2001].  Once interception was calculated, infiltration was estimated by 
subtracting Q and interception from total precipitation for each rain event. 
The values that were calculated for each site were plotted against the recharge values for the 
respective sites on the same scatter plot to find a relationship between recharge and land cover 
 31 
(Figure 3-8).   Additionally, the data points from a few of the sites seemed less reliable and these 
locations were removed from the analysis. The relationship was observed for the time period of 
December to April (Figure 3-9).  This time period was examined because it can be assumed that 
the least evapotranspiration would be occurring during this time period, causing the infiltration 
values to be greater.  The sites that were not included in this last plot were the Montgomery, AL 
site and the Baldwin County, AL site since Montgomery is more inland than the others and the 
Baldwin County site could have been tidally influenced as it is very close to the coast.   
3.5 Land cover analysis and curve number calculations 
This section details the method used to utilize land cover/land use data in order to calculate 
the curve number for a given land area.  Average curve numbers were obtained for each of the 
seven study sites.  As previously explained, the SCS-CN method has been widely used for years 
as a tool to calculate the volume of surface runoff for rain events, reflecting factors such as 
LU/LC effects [Mishra and Singh, 2011]. 
  
 32 
3.5.1 Study methodology and input data 
After an annual recharge estimate was found using the RISE estimation method for the 
seven sites, the next step was to quantify land cover type for each site.  Land cover data was 
obtained from the National Land Cover Database (NLCD).  Using the latitude and longitude of 
the well locations, data was downloaded for the region surrounding the well.  Landcover data 
downloaded from NLCD was imported into ArcMap and cropped.  An ellipsoid shaped boundary 
with 100 meters from the well to the side of the ellipse and 200 meters to the top of the ellipse 
was applied around the well.  The orientation of the ellipse was decided by the direction of flow 
on the surface, as it was assumed that the shallow subsurface flow approximately mimics the 
direction of the surface flow. 
The land cover map was cropped and a measured grid was applied with grid cells of 20 m 
x 20 m (Figure 3-6).  Each grid cell was assigned a land cover type that occupied the majority of 
each cell. 
 
 
 
 
 
 
 33 
 
 
Figure 3-7.  Land Cover/Land Use sites for FL Site #3, Baldwin, Covington, and Montgomery, 
AL. 
 
A curve number was assigned to each grid based on land cover type.  The curve number, 
CN, is an empirical value that is used in predicting runoff or infiltration and is a function of 
hydrologic soil group, land cover type, land cover treatment, and hydrologic condition such as 
antecedent moisture condition [USDA SCS TR-55 Manual, 1986]. 
The curve number for each land cover type depends on the hydrologic soil group of the 
soil in area.  The hydrologic soil group indicates the soil?s tendency for infiltration or runoff.  
The hydrologic soil group for each soil type was obtained from the NRCS Web Soil Survey 
 34 
(websoilsurvey.nrcs.usda.gov/). Using the hydrologic soil group, a CN value was assigned for 
each grid.  
Next, the distance between the monitoring well and the center of each cell was calculated.  
The inverse of the distances were used to weight the CN of each cell, and an average CN was 
obtained for each well site using the weighted CN values.   
In order to accurately use the average CN, the antecedent moisture condition (AMC) had 
to be taken into account.  The average CN that was calculated in the previous step is for normal 
conditions (CN2) and a CN1 can be found for a dry AMC, while conversely a CN3 is used for a 
wet AMC.  Table 2-2 outlines the antecedent moisture values that dictate which CN value should 
be used [Chow et al., 2005]: 
Table 3-3.  Values used to determine AMC. 
AMC Total 5-day antecedent precipitation [in] Dormant Season Growing Season 
I < 0.5 < 1.4 
II 0.5 to 1.1 1.4 to 2.1 
III > 1.1 > 2.1 
 
Daily precipitation data was obtained from the National Climatic Data Center (NCDC) 
for the same year-long time period for each site in which the recharge measurements were made.  
Table 3-4 lists the appropriate weather station ID used for each of the wells, and the approximate 
distance from the weather station to the groundwater well. 
 
 
 
 
 
 
 
 
 
 35 
Table 3-4.  Approximate distance from well to weather station 
 
Site Name Well ID Weather Station 
Distance: well to station 
[mi] 
FL 1 USGS303610087165001 Pensacola Reg Airport 13 
FL 2 USGS303558087155501 Pensacola Reg Airport 13 
FL 3 USGS30283008711390 Pensacola Reg Airport 1.25 
FL 4 UGSG303614087190901 Pensacola Reg Airport 13 
Covington Co USGS311319086153601 Andalusia, AL 18 
MGM Co USGS322047086214301 MGM Airport 6 
Baldwin Co USGS302416087505501 Fairhope, AL 12 
 
Using this data, the AMC for each day in the record was calculated by summing the five 
previous day?s precipitation amounts.  Based on this sum for each day, the CN number was 
calculated accordingly using equations 3-6 and 3-7 for wet and dry conditions respectively 
[Chow et al., 2005]: 
 
)(058.010 2.4 221 d r yCNCNCN ??        (3-6) 
23
2
23 ( ) .1 0 0 .1 3CNC N w e tCN? ?
       (3-7) 
 
3.5.2 Results  
The Table 3-5 shows the average calculated values of CN.  As illustrated, the values that 
were calculated vary widely from site to site, depending on the land cover type.  For example, 
the site labeled FL3, which has a very large CN is located in the Pensacola Regional Airport.  In 
contrast, the site labeled FL1 is located in an area that is heavily forested.   
 
 
 36 
 
Table 3-5.  Calculated average CN values. 
Site Location Calculated Avg CN 
FL1 50 
FL2 59 
FL3 93 
FL4 76 
Covington Co 84 
Montgomery Co 77 
Baldwin Co 78 
 
Figure 3-8 is the scatter plot of the seven site?s cumulative recharge values plotted against 
each site?s cumulative continuing abstraction values. 
 
Figure 3-8.  Recharge versus continuing abstractions. 
 
 
 
 
 
y  =  - 0 . 3 2 6 5 x  +  3 0 . 9 2 9
R ?  =  0 . 0 0 9 7
0
5
10
15
20
25
30
35
40
45
50
0 5 10 15 20
R
e
c
ha
r
g
e
 (i
n)
C on t i nui ng  A bs t r a c t i on s ,  Fa  ( i n)
C on t i n u i n g  A b s t r a ct i on s  v s .  R e cha r g e
 37 
Figure 3-9 shows the scatter plot of cumulative recharge and cumulative infiltration for 
each site. 
 
Figure 3-9.  Cumulative Infiltration vs. Cumulative recharge  
Figure 3-10 shows the scatter plot of cumulative recharge plotted against cumulative 
infiltration at five of the sites for December to April.  As stated earlier, these calculations were 
done using only data from December to April, as the least evapotranspiration occurs during these 
months, and the sites of Baldwin County, AL and Montgomery, AL were not used in an effort to 
obtain a better relationship.  As stated previously, these sites were left out since Montgomery is 
more inland than the other sites and the Baldwin County site could have been tidally influenced.   
y  =  0 . 9 9 7 x  - 8 . 4 1 6 4
R ?  =  0 . 2 1 3
0
5
10
15
20
25
30
35
40
45
50
0 10 20 30 40 50
R
e
c
ha
r
g
e
 (i
n)
I nf i l t r a t i on  ( i n)
I n f i l t r a t i on  v s .  R e cha r g e
 38 
 
Figure 3-10  Cumulative Infiltration vs. Cumulative Recharge for December-April  
 
3.5.3 Discussions 
As illustrated in the figures, no significant linear relationship was found between aF and 
recharge or the infiltration values and recharge.  The seasonal investigation of infiltration and 
recharge did not yield any significant results either.  This is likely due to a number of 
confounding factors.  Some factors that may have contributed to an unclear relationship between 
land use and recharge could have been due to some of the water-tables being deeper than others, 
unpredictable heterogeneities in the soil profiles causing flow impediment or changing specific 
yield values, or the Lisse effect, which occurs when air is trapped by recharging water causing 
the water level to be at a higher level than it would appear if only the recharged water height was 
taken into account.  Many of the recharge values calculated using the RISE method seemed 
larger than would be expected.  For example, there was an instance that the recharge value 
exceeded the infiltration value for the same site, which is physically impossible.  This would 
suggest that there were factors influencing the recharge estimate.  Also, it is possible that the area 
y  =  1 . 1 1 3 4 x  - 0 . 4 5 8 3
R ?  =  0 . 3 2 6
0
5
10
15
20
25
0 5 10 15 20
R
e
c
ha
r
g
e
 (i
n)
I nf i l t r a t i on  ( i n)
I n f i l t r a t i on  v s .  R e cha r g e :  D e c - A p r i l
 39 
of land taken into account for the CN calculations was too large, although the weighting scheme 
should have taken this into account. 
Due to the fact that there was no clear relationship found between land cover and recharge in 
this study, this method was not used to calculate average annual recharge on Dauphin Island with 
land cover data.  Instead, recharge estimates were obtained by directly applying the Soil Water 
Assessment Tool (SWAT) which will be explained in further detail in Chapter 5.  
 40 
4. Geography and ground water issues of Dauphin Island 
This chapter provides necessary background information on Dauphin Island and also 
summarizes the island?s current water issues. 
4.1 Location, size, and morphology 
Dauphin Island is a small barrier island located between the Mississippi Sound and the Gulf 
of Mexico about 4 miles off the coast of Mobile County, Alabama (Figure 4-1).  The island is 
shaped like an oval on the east end, with a thin strip of land coming off the oval to make up the 
west end (Figure 4-1).  The east end oval is about 1.5 miles wide at its widest and 3 miles long.  
The thin strip of land that makes up the west end is about 12 miles long and 0.5 miles wide.  In 
total, Dauphin Island is about 6.3 square miles in size.  Elevation on the island mostly varies 
between 5 to 15 feet but can reach up to 40 feet at the locations of the island?s sand dunes 
[Chandler, 1983].   
 
Figure 4-1.  Map of Mobile Bay and Dauphin Island. 
 41 
Since Dauphin Island is a barrier island its morphology is constantly changing as a result of 
coastal winds, tides, and currents.  The shoreline of the island has greatly varied throughout 
history, mostly due to longshore drift and hurricanes.  Longshore drift, which is the process of 
sediment transport along the coast, is likely responsible for the formation of the long,thin sandy-
spit extension on the west end of the island.  Vegetation on this island is important as it prevents 
erosion and promotes accretion in some areas.  Additionally, the marsh areas are nutrient 
processing sites [Chandler, 1983]. 
4.2 Climate and tides 
The climate of Dauphin Island is significantly influenced by the Gulf of Mexico and is warm 
to subtropical.  Temperature variations on Dauphin Island in 2008 are shown in Figure 4-2.  
Monthly average temperature variations are less than 20?F from the annual mean [Chandler, 
1983].  The annual average maximum temperature is 74.8 ?F and the average minimum 
temperature is 63.1 ?F. 
 
Figure 4-2.  Monthly temperatures for Dauphin Island in 2008. 
 
 
 
 42 
The island receives on average 163 cm (62.4 inches) of rain per year.  The precipitation 
record for Jan 1995-Dec 2005 is shown in Figure 4-3.   
 
Figure 4-3.  Monthly Precipitation for Dauphin Island from January 1995-December2005, data 
from NCDC 
 
Because of its location in the Gulf of Mexico the island has been hit numerous times by 
hurricanes, including Hurricane Katrina, which brought extensive damage to large portions of the 
island.  The tides on Dauphin Island are diurnal, with a high tide and low tide occurring once a 
day, and average about 1.5 feet [Chandler, 1983]. 
4.3 Soil types  
As illustrated in Figure 4-4, the majority of the soil types on the island are sands or loams, 
both of which have a relatively high permeability.  The Hydrologic Soil Group (HSG) of a land 
area indicates the ?minimum rate of infiltration obtained for bare soil after prolonged wetting? 
[USDA SCS TR-55 Manual, 1986].  Dauphin Island has an interesting HSG condition.  Dauphin 
Island is generally classified as A/D because of the relatively high water-table on the island.  
Although the soil types on the island would generally be classified as A, the high water-table on 
0
200
400
600
800
1000
1200
O c t - 95 M a r - 97 J u l - 98 De c - 99 A p r - 01 Se p - 02 J a n - 04 M a y - 05
P
r
e
c
ip 
(m
m
)
M on t h
Da u p h i n  I s l a n d  P r e c i p i t a t i o n - J a n  1 9 9 5  t o  De c  
2005
 43 
the island creates some drainage problems.  Therefore, the HSG is A/D indicating that drained 
soil is type A and undrained soil is type D [USDA SCS TR-55 Manual, 1986]. 
 
Figure 4-4.  Soil types on Dauphin Island from USDA NRCS Soil Survey Database. 
 
4.4 Geology 
Dauphin Island is located in the Coastal Lowland Aquifer system.  There are three distinct 
hydrogeologic units underlying Dauphin Island.  These units are the Pleistocene-Holocene 
interval, shallow Miocene Siliclastic interval, and the deep Miocene Siliclastic interval.  These 
units are considered hydraulically separate for a number of reasons: they outcrop in entirely 
different locations, they are separated by confining clay layers, and water quality parameters 
such as concentration of certain dissolved solids are different [O?Donnell, 2005]. 
As already noted, Dauphin Island?s main source of water resources is the shallow aquifer 
underlying the barrier island.  While the island does possess two relatively small reservoirs, they 
do not have the capacity to meet the freshwater demands of the island.  The island?s shallow 
29% 
20% 20% 
17% 
6% 
5% 
2% 1% 
Soil Types on Dauphin Island 
Osier Loamy Sand
Fripp Sand
Psamments
Pactolus Loamy Sand
Axis Mucky Sandy Clay Loam
Beaches
Grady Loam
Duckston Sand
 44 
aquifer consists of a thin layer of Holocene sand, which is underlain by a Pleistocene unit known 
as the Gulfport Formation.  The aquifer is approximately 28-42 feet thick with a thick layer of 
marine clay at the base.  The water in Dauphin Island?s aquifer is generally low in chloride 
content except near the island?s coast.  Recharge to the aquifer occurs through rainfall.  
Freshwater is lost from the aquifer due to seepage to surface water, evapotranspiration and 
pumping [Kidd and Moody, 1987]. 
As discussed, the top unit visible from the ground level is the Water-Table Aquifer.  This 
aquifer extends about 42 feet below ground level and is composed of fine to coarse grain sand 
[O?Donnell, 2005].  A layer of clay underlies this aquifer and separates it from the Shallow Sand 
Aquifer, which occurs at about 70 feet below ground level and is composed of very fine to very 
coarse grain quartzose sand [O?Donnell, 2005].  The top surficial aquifer is hydraulically 
separate from the other aquifers located below it. 
There are three layers of the Shallow Sand Aquifer, each of which has a clay layer 
underneath.   Another clay layer underlies the Shallow Sand Aquifer.  Beneath this clay layer is 
the Deep Sand Aquifer, which begins at about 441 feet below the surface. 
The Water-Table Aquifer and the Shallow Sand Aquifer are the only units deemed potential 
sources of freshwater without rigorous treatment.  The water pumped from the Deep Sand 
Aquifer has a chloride concentration too high to be used for human consumption [O?Donnell, 
2005].  There are eight shallow wells drilled into the Water-Table Aquifer and two wells tapping 
the Shallow Sand Aquifer.  There has also recently been a well drilled into the Deep-Sand 
Aquifer.  Water pumped from this well will be treated by reverse osmosis before it is distributed 
to the island?s customers [O?Donnell, 2005].  In this research, only the Water-Table Aquifer and 
its wells will be examined.  In Figure 4-5 shown below, the Water-Table Aquifer is shaded. 
 45 
 
 
 
Figure 4-5.  Details of the layering of the aquifers beneath Dauphin Island O?Donnell, 2005 ]. 
  
 46 
4.5 Land Use/Land Cover 
Land Cover/Land Use (LC/LU) data was obtained from the National Land Cover Database 
for the year 2001.  As illustrated in Figure 4-5 and Table 4-1 the island is mostly in various 
stages of development, with some forests and wetland areas.  As previously discussed, the sandy 
soil types on the island generally allow for quick infiltration.  However, due to the large amount 
of residential development on the island and high water table, there is the potential for 
considerable runoff in large rain events, resulting in a relatively high average curve number. 
 
 
 
Figure 4-6.  Land cover data from the Multi-Resolution Land Characteristics Consortium  
 
 47 
 
 
 
 
 
Table 4-1.  LC/LU by percentage for Dauphin Island in 2001, data obtained from NLCD. 
NLCD 2001 Area [ha] Percent [%] 
Water [WATR] 7.19 1.60 
Residential-Low Density [URLD] 158.09 35.25 
Residential-Medium Density [URMD] 113.89 25.39 
Residential-High Density [URHD] 13.76 3.07 
Wetlands-Forested [WETF] 14.69 3.28 
Wetlands-Non-Forested [WETN] 8.19 1.83 
Southwestern US [Arid] Range [SWRN] 10.32 2.30 
Range-Grasses [RNGE] 6.62 1.48 
Forest-Evergreen [FRSE]  112.86 25.16 
Range-Brush [RNGB] 2.79 0.62 
Industrial [UIDU] 0.13 0.03 
 
4.6 Water issues 
This section discusses the island?s water demands, current system in place to meet these 
demands, and current issues as well as potential future problems. 
4.6.1 Freshwater-saltwater interaction and the hydrologic cycle 
It is important to understand the dynamics of a freshwater lens in order to fully understand 
Dauphin Island?s freshwater situation.  Barrier Islands often form a freshwater lens because of 
the unique saltwater-freshwater relationship.  Since freshwater is less dense than saltwater, the 
freshwater ?floats? on the salt water due to the density differences, water-table elevation, layers 
of low permeability, and the slow rate of diffusion to surrounding salt water.  The lens that forms 
is generally irregularly shaped and influenced by landforms.  The subsurface flow of freshwater 
 48 
towards the saline water results in the formation of a stable, wedge shaped surface that defines 
the lens.  Evapotranspiration and pumping losses may cause the lens shape to change. 
Dauphin Island is surrounded by brackish water on the Mississippi Sound side and saline 
water on the Gulf of Mexico side. The lens is recharged solely by precipitation. The precipitation 
that does not reach the freshwater lens is lost to the atmosphere as evaporation and transpiration 
or lost to the ocean as runoff.  Losses from the system occur as diffusion to the surrounding 
saltwater and losses from pumping [Chandler, 1983]. 
4.6.2 Water demands 
Dauphin Island is mainly a residential community with a fairly small amount of 
commercial development.  According to the 2000 Census, there were about 1300 permanent 
residents on the island, with the total population soaring temporarily during vacation and holiday 
periods.  Because of the ever-growing desire of Americans to live on the coast, it is reasonable to 
assume that the number of permanent residents has grown since then, and will continue to grow 
well into the future. 
While many of the residents of the nearby Fort Morgan Peninsula have switched to 
municipal water supply from Gulf Shores Water Authority, groundwater is still heavily used on 
Dauphin Island itself [Liu et al, 2008]. Tourism is very important to the economy of Dauphin 
Island, and the hotels and tourist attractions rely on groundwater.  Because of the large influx of 
people at certain times of the year there are peaks in water demand throughout the year.  These 
demand peaks may not coincide with peaks in precipitation, and may occur in months with 
historically little rainfall, such as May and June. 
 49 
4.6.3 Current water supply system 
Dauphin Island?s surficial aquifer is about 50 to 100 feet thick, and is composed of 
alluvial, low terrace and coastal deposits.  The aquifer is elliptical in shape oriented east west 
along Dauphin Island, and consists of fine to coarse grain sands [Chandler, 1983].   
Previous to the drilling of the shallow water-table aquifers, the island was obtaining all of 
their freshwater from deeper wells that were extracting water from the aquifers underlying the 
surficial sand aquifer.  The water obtained from the deeper wells was extremely high in chlorides 
and iron, so test drills were installed in the water-table aquifer in order to assess the capacity of 
the shallow aquifer to meet the island?s freshwater needs [Caldwell, 1996]. 
Through a variety of tests, it was determined that the shallow aquifer was capable of 
providing sufficient freshwater for the island.  Up to this point, however, the residents on the 
island had been using septic tanks to deal with their sewage.  When it was decided that the 
shallow sand aquifer was going to be exploited, an island wide sewer system was installed to 
eliminate the need for septic tanks.  Once the sewer system was in place, the shallow wells were 
drilled [Caldwell, 1996]. 
The first four shallow wells were installed and came on line in 1990.  These were wells 
#10, #20, #30, and #40.  In 1992, four more shallow wells were added to this supply when they 
installed well #50, #60, #70, #80 (see Figure 4-7 and Table 4-2).  The water system supply on the 
island contains approximately 55 miles of water mains.  The wells extended about 35 feet into 
the ground.  When the four additional wells were added in 1992, the total pumping for the eight 
shallow wells and one deep well was 520 gallons per minute or about 700,000 gallons per day 
[Caldwell,1996]. 
 50 
Because of a few instances in which the demand of water exceeded the production from 
the eight shallow wells and single deep well, alternative options had to be considered.  One of 
the options to obtain additional freshwater included installing two additional shallow wells in the 
Water-Table aquifer.  The addition of two more shallow wells would bring the total number of 
shallow wells to the number that the Unites States Geological Survey had deemed the maximum 
for the island.  Another option was to pipe water about 18 miles from the main land.  The option 
that was ultimately chosen was to build a reverse osmosis plant that could treat high levels of 
chloride.  This would allow another deep well that was not in use to be brought back online.  The 
final inspection and approval of this plant was completed at the end of 1996.  The reverse 
osmosis plant was successful in removing the large majority of chlorides in the water, thus 
making it potable and safe for public consumption [Caldwell, 1996]. 
In addition to the construction of the reverse osmosis plant, another deep well is currently 
under construction and should be coming on line soon.  This well extends to a depth of 547 feet.  
With this deep well, the pumping capacity for the island will be nearly doubled [McElroy, 2010].  
Figure 4-7 shows the location of the island?s water-table pumping wells. 
 
 51 
 
Figure 4-7.  Well locations on Dauphin Island (well size is exaggerated). Blue color indicates 
discharge towards the ocean. 
 
 
 
 
 
 
Table 4-2.  Well depth, screened interval, and location; data obtained from DIWSA. 
Well ID # Well Depth [ft below sea level] Screened Interval [ft below sea level] Lat/Long 
10 30.0 18.00-28.00 30.25380/88.11003 
20 32.5 20.50-30.50 30.25156/88.10153 
30 34.5 22.50-32.50 30.25124/88.9686 
40 33.0 21.00-31.00 30.25168/88.10746 
50 40.0 23.65-33.65 30.24913/88.10757 
60 40.0 24.75-34.75 30.24940/88.10377 
70 40.0 26.10-36.10 30.24767/88.09223 
80 40.0 26.65-36.65 30.24676/88.09223 
 
 52 
4.6.4 Current and future water issues  
Due to increasing stresses on the island?s groundwater system, concerns about the quantity 
and quality of groundwater resources on the island have been raised.  There are multiple factors 
that already have or have the possibility to affect the groundwater resources on the island.  One 
of these factors is increasing population which increases the daily demand for freshwater.  The 
DIWSA has reported several high capacity weekends in which the water demands put a much 
larger than normal stress on the system [Caldwell, 1996].  Additionally, increasing population on 
the island would likely lead to increasing development on the island resulting in more 
impervious surfaces.  Additional impervious surfaces would increase runoff and decrease 
groundwater recharge. 
Another factor is climate change which has the possibility of altering precipitation 
patterns, evapotranspiration patterns, and increasing sea level rise.  Climate change could also 
increase the number of large storm events such as hurricanes.  Large storms and hurricanes can 
be devastating for small barrier islands.  For example, Hurricane Katrina caused a 1.9 km breach 
in the island landform after the storm.  There was approximately 2 m of overwash flow during 
the storm [Fritz et al, 2007]. 
 In addition to issues with water demand, Dauphin Island has multiple water quality 
problems such as high iron, manganese, chloride, dissolved solids content, color, and turbidity.  
Iron content increases and manganese content decreases with hydrogeologic depth [Chandler, 
1983]. 
While some of these problems are already being addressed by the addition of the new deep 
well, it is important to fully understand the dynamics of the Water-Table Aquifer.  The Water-
Table Aquifer is the easiest and most economically feasible aquifer to exploit.  There are already 
 53 
eight wells installed in the aquifer as well as an extensive water piping system.  Although 
Dauphin Island may switch to deep wells for part of their water in the near future, it is still 
important to understand the capacity, limitations, and characteristics of the shallow aquifer, as 
well as how it responds to certain changing climate factors and the sensitivity to hydrologic 
parameters. 
 54 
5. Sensitivity of Dauphin Island?s Water-Table aquifer to changing factors 
This section will discuss the details of the groundwater model and sensitivity analysis.  The 
sensitivity analysis was performed to detect which factors most affected the island?s groundwater 
resources. 
5.1 Background 
The coastal regions of Baldwin County are experiencing steadily increasing development as 
well as an increasing economy [Murgulet and Tick, 2008].  As previously discussed, this 
phenomenon is specifically of concern in Dauphin Island because their entire water supply is 
derived from the freshwater lens underlying the island.  Because of the possibility of decreasing 
supply of freshwater and the threat of saltwater intrusion, groundwater modeling was performed 
to predict the impact of various anthropogenic factors.  Using the information gained in this 
section, it is possible to develop better management practices that could be integrated on the 
island to preserve the island?s valuable freshwater resources. 
5.2 Research objectives 
There were multiple objectives to this section of research.  The first objective was to create 
an accurate groundwater model using SEAWAT to simulate the steady-state and transient 
groundwater situations at Dauphin Island.  The second objective was to determine the effects of 
different factors on the island?s freshwater resources.  In order to do this, some of the parameters 
and inputs in the model were altered to model future scenarios (from the time period of 2010-
2030).  For example, recharge patterns were changed due to land cover change or climate change 
to determine which parameters have the most effect on the position of the saltwater-freshwater 
boundary and the total volume of usable freshwater in the Water-Table Aquifer. 
 55 
5.3 Input data, methods, and study methodology 
This section will discuss the data used in the Dauphin Island groundwater models, as well 
as the methods used in the development of the model.  Additionally, the different scenarios that 
were simulated to detect the effects of the changing factors will be introduced.  
5.3.1 Input data 
The MODFLOW family code, SEAWAT, was used to simulate the Dauphin Island 
aquifer and the freshwater/saltwater interactions occurring for a variety of scenarios.  As 
previously discussed, SEAWAT was developed by combining MODFLOW and MT3DMS into 
one program and making modifications to account for saltwater-freshwater density variations 
[Guo and Langevin, 2002]. SEAWAT operates by solving the flow and solute transport 
equations.  The groundwater flow equation is as follows [Langevin and Guo, 2006]: 
() sp hCq q S t C t?? ? ? ?? ? ?? ? ? ? ? ?? ? ?   (5-1) 
where ? = density of saline groundwater; C? concentration of dissolved salts in groundwater; 
h? fresh water equivalent hydraulic head; t? time variable; ??the gradient operator in 
Cartesian three-dimensional coordinates. 
The solute transport equation is as follows [Diersch, 2006, Langevin and Guo, 2006]: 
? ? ? ? 1Ns skkqC D C v C C Rt ? ?? ? ? ? ?? ? ? ? ? ?? ?      [5-2] 
where D? hydrodynamic dispersion tensor;v = fluid velocity; Cs = solute concentration of water 
entering from sources or sinks, and Rk= the rate of solute production or decay in reaction of k of 
N different reactions.  
All scenario simulations were modeled with the same parameters and values for the time 
period 1990-2010.  The only values that differed before 2010 between scenarios were the 
 56 
recharge values.  This will be explained below.  The following list details which values were 
used for each parameter as well as the reasoning behind the values used.   
? Number of Columns:  The number of columns used in the model was 126. 
? Number of Rows:  The number of rows used in the model was 78. 
? Grid Size:  The cells used in the model are 164 feet on each side.  Usually a convergence test 
is done to determine the appropriate grid size, but this grid size had already been used in a 
successful Dauphin Island finite difference model [Kidd, 1988]. 
? Number of Layers:  Fifteen layers were used for the model.  The top ten layers represent the 
sandy water-table aquifer.  Layers eleven through fifteen represent the underlying confining 
clay unit. 
? Layer Elevations:  The sandy water-table aquifer was estimated to extend to approximately 
42 feet below mean sea level.  This value was obtained from records of subsurface lithology 
taken when a well on the island was being drilled [O?Donnell, 2005].  While the bottom 
elevation of this layer likely varies slightly throughout the island, it was assumed to be flat 
for this simulation.  The elevations for the ten layers representing the water-table aquifer 
were obtained by dividing 42 by the number of layers.  The clay, confining layer was 
estimated to go to 70 feet below mean sea level, so this was used as the bottom elevation of 
this layer.  This value was also obtained from the drilling records mentioned previously.  The 
clay unit was split into 5 layers.  They are not all of equal size, as the first two layers in the 
clay unit are thinner than the last three layers.  This was done because in preliminary 
simulations the bottom freshwater-saltwater interface fluctuated around the region of the last 
layer of sandy unit, and the top two layers of the clay unit.  By making the top two layers of 
 57 
the clay unit thinner, a greater resolution of the bottom interface position could be obtained.  
Table 5-1 lists the layer elevations. 
? Hydraulic Conductivity:  The hydraulic conductivity values used in the model for the water-
table aquifer were obtained from ranges presented in O?Donnell?s report titled ?Dauphin 
Island Water and Sewer Authority?s Public Water Supply Wells? which were obtained from 
Kidd?s USGS report on Dauphin Island titled ?Hydrogeology and water-supply potential of 
the water-table aquifer on Dauphin Island?.  Two well tests were done in order to estimate 
the hydraulic conductivity in the water-table aquifer.  The first test that was done was a 48 
hour aquifer test performed with multiple observation wells around the island [O?Donnell, 
2005].  The range obtained from this study was 45-55 ft/day.  The second well test was a 26 
day test, and the range that this test produced was 56-59 ft/day [Kidd, 1988].  The values for 
the water-table aquifer were changed slightly during the calibration process, but they 
remained within the ranges found in the literature.  It should also be noted that the hydraulic 
conductivity values were not varied spatially within the same layer, as it was assumed that 
the value was homogeneous in each layer.  This is acceptable, because aquifer tests reported 
in Kidd?s report indicate that the aquifer properties are uniform across the island [Kidd, 
1988].  The hydraulic conductivity values used for the clay confining layer were based off of 
the generally accepted range for clay, which is 10-9 to 10-6 cm/s [Fetter, 2001].  Table 5-2 
shows the hydraulic conductivity values used for each layer in the model. 
? Storage/Porosity:  Inputs required for the SEAWAT model are Ss, Sy, and Porosity (n).   
O?Donnell?s report gives a range of specific yield values from the previously mentioned well 
tests.  This range was 0.03-0.15 [O?Donnell, 2005].  The value used for Sy was 0.1.  The 
 58 
value used for Ss was 0.002.  The value used in this model for porosity was 0.3, which is a 
generally accepted porosity value for sand. 
? Well Locations:  The well locations for the shallow wells were obtained from the latitude and 
longitude given by the Dauphin Island Water and Sewer Authority (DIWSA).  The latitude 
and longitude values were used to mark the well locations in ArcMap.  This figure was then 
used to place the wells within the Groundwater Vistas interface by importing a bitmap that 
could be seen under the finite difference grid.  Table 4-3 lists data for each well, including 
location.  Since the wells were not all at a constant depth below sea level, they were located 
in different layers of the model based upon their depth.  Wells #10 and #20 were placed in 
layer 7.  Wells #30 and #40 were placed in layer 8.  The rest of the wells were placed in layer 
9 of the groundwater model. 
? Well Pumping:  Pumping rates were also obtained from DIWSA.  The DIWSA provided 4 
years of pumping data, from September 2000 - August 2002 and January 2009 - December 
2010, but were unable to provide the rest.  To overcome this, the missing years were 
estimated using the years that were provided.  Appendix 9-6 lists the Dauphin Island well 
pumping rates as provided by the DIWSA. 
In regards to assigning pumping rates to the simulation time it is easiest to view the 
simulation period as broken up into 6 time periods.  The first time period is from 1990 to 
1994.  This is the time period in which only the first four shallow wells (#10, 20, 30, and 40) 
were on line and pumping.  The pumping rates used for this time period were each wells 
average pumping rate as calculated from the DIWSA data.  In 1994, the other four wells 
(#50, 60, 70, and 80) came on line and began pumping.  For the time period between 1994 
and August 2000 (which is the last month before the DIWSA data begins), the same averages 
 59 
that were previously used for 1990-1994 were also used.  The first set of pumping data 
provided by the DIWSA starts in September 2000.  From September 2000 to August 2002, 
the known pumping values that were provided were used.  In order to reduce the number of 
stress periods in the model, which in turn reduces computational time, similar consecutive 
pumping rates were grouped together and averaged.  Using this method for September 2000-
August 2002, the number of stress periods for this time span was reduced from 24 to 8 stress 
periods.  For the time period between September 2002 and September 2008, the average 
values used from 1990 to 2000 were used here as well.  Pumping data was provided from the 
DIWSA for the months between October 2008 and December 2010.  Similar consecutive 
pumping rates were grouped for this time periods as well in order to reduce the number of 
stress periods.  This reduced the number of stress periods for this period from 27 to 19.  For 
the years after 2010, which are deemed the ?future years? for this study, average values from 
the previous years of pumping were also used.  In some scenarios, such as an increased 
pumping scenario, different pumping schemes were used and this will be discussed in a later 
section.   
? Recharge:  The SEAWAT model allows the user to define recharge that can vary both with 
time and space.  For this study, the recharge was assumed to be spatially constant since the 
island is small, but it did vary temporally. 
As previously mentioned, the recharge assessments described earlier did not yield usable 
relationships between land cover and recharge that could be applied to Dauphin Island to 
estimate recharge.  Due to this fact, recharge estimates to be used as inputs to the model had 
to be obtained by another method. SWAT was used to estimate recharge for Dauphin Island 
[Wang, 2011].  SWAT was chosen as the tool to estimate recharge because SWAT had 
 60 
already been successfully calibrated and applied to several watersheds close to Dauphin 
Island [Wang, 2011, Singh, 2010].  
Two different SWAT methods were used to calculate the retention parameter within SWAT.  
The more traditional method calculates the retention parameter based on the soil profile 
water content.  The retention parameter is then used to find the curve number.  An alternate 
method uses accumulated plant evapotranspiration to calculate the daily curve number.  The 
daily curve numbers from each method are used to calculate infiltration.  As would be 
expected, differing curve numbers produce differing infiltration values.  Recharge is directly 
derived from these infiltration values.  Equation 5-3 is used within SWAT to calculate 
recharge [Arnold et al., 1996]: 
, , 1( 1 e x p [ 1 / ] ) e x p [ 1 / ]r c h r g i g w s e e p g w r c h r g iw w w?? ?? ? ? ? ? ? ?    (5-3) 
where ,rchrgiw = the amount of water entering the aquifer on day i [mm], gw? = the delay time 
or drainage time of overlying geologic units [days], sepw = the total amount of water exiting 
the bottom of the soil profile on day i [mm], ,1rchrgiw ? = the amount of recharge entering the 
aquifer on day i-1 [mm].   
Figure 5-1 shows both precipitation and recharge estimated using the plant 
evapotranspiration method. 
 61 
 
Figure 5-1 Comparison of precipitation and recharge on Dauphin Island,  Jan 2000-Dec 2004 
 
 
? Dispersivity:  The model uses longitudinal dispersivity, transverse dispersivity, and vertical 
dispersivity as input parameters to model the effect of dispersion in the model.  Dispersion is 
the process in which a solute in groundwater mixes with uncontaminated water and thus 
decreases in concentration.  This is caused by the differences in velocity in pore travel and 
difference in flow rate through different strata [Fetter, 2001].  In order to calculate 
dispersion, dispersivity is multiplied by flow velocity. 
For this model, dispersivity values were set to zero, which is a common practice in field 
models [Masterson, 2004].  There are multiple reasons for using this approach.  It was 
assumed that the flow system in Dauphin Island was an advection dominated flow system 
because of the large recharge rates used and the relatively high hydraulic conductivities of 
the sandy layer.  It was also assumed that any possible effects from dispersion were taken 
into account by numerical dispersion.  
0
10
20
30
40
50
60
70
80
90
100
0
50
100
150
200
250
300
350
400
450
J a n - 00 M a y - 01 S e p - 02 Fe b - 04
P
r
e
c
ip
it
a
t
io
n
 (m
m
/
m
o
n
t
h
)
M ont h
P r e c i p i t a t i o n  a n d  r e c h a r ge  f r o m  Ja n  2 0 0 0 - D e c  2 0 0 4
P r e c ip it a t io n
R e c h a r g e
R
e
c
h
a
r
g
e
 (m
m
/
m
o
n
t
h
)
 62 
As the discretization yielded grid cells that were 164 feet by 164 feet, numerical dispersion 
was certainly an issue.  This reasoning was adapted from prior work done on Cape Cod, 
Massachusetts [Masterson, 2004]. 
? Surface Water Bodies:  In cells that contained surface water bodies such as ponds, the 
hydraulic properties were changed so that they would be simulated appropriately.  The two 
notable surface water bodies are Alligator Lake and Oleander Pond, which are shown in 
Figure 5-2, and are located on the southeastern shore of the island.  Table 5-4 shows the 
values K, porosity, Sy, and Ss used at these nodes.   
? Time Step Size:  The time step size used for the model was one month.  This time step size 
was chosen because the pumping and recharge data that was obtained was all in the monthly 
time scale.   
? Density of Saltwater:  The density of saltwater used was 2.18 lbs/ft3. 
 
Figure 5-2.  Locations of Alligator Lake and Oleander Pond on Dauphin Island. 
 
 
 
 
 
 63 
Table 5-1.  Top and bottom layer elevations. 
Layer Top Elevation [ft] Bottom Elevation [ft] 
1 0.0 -4.2 
2 -4.2 -8.4 
3 -8.4 -12.6 
4 -12.6 -16.8 
5 -16.8 -21.0 
6 -21.0 -25.2 
7 -25.2 -29.4 
8 -29.4 -33.6 
9 -33.6 -37.8 
10 -37.8 -42.0 
11 -42.0 -45.5 
12 -45.5 -49.0 
13 -49.0 -56.0 
14 -56.0 -63.0 
15 -63.0 -70.0 
 
 
Table 5-2.  Hydraulic conductivity values used for Dauphin Island. 
Layer Kx [ft/month] Ky [ft/month] Kz [ft/month] 
1 1370 1200 30 
2 1370 1200 30 
3 1370 1200 30 
4 1370 1200 30 
5 1370 1200 30 
6 1370 1200 30 
7 1370 1200 30 
8 1370 1200 30 
9 1370 1200 30 
10 1370 1200 30 
11 0.00262 0.00262 0.000262 
12 0.00262 0.00262 0.000262 
13 0.00262 0.00262 0.000262 
14 0.00262 0.00262 0.000262 
15 0.00262 0.00262 0.000262 
 
 64 
 
Table 5-3.  Well depth, screened interval, and location; data obtained from DIWSA. 
Well ID # Well Depth [ft below sea level] Screened Interval [ft below sea level] Lat/Long 
10 30.0 18.00-28.00 30.25380/88.11003 
20 32.5 20.50-30.50 30.25156/88.10153 
30 34.5 22.50-32.50 30.25124/88.9686 
40 33.0 21.00-31.00 30.25168/88.10746 
50 40.0 23.65-33.65 30.24913/88.10757 
60 40.0 24.75-34.75 30.24940/88.10377 
70 40.0 26.10-36.10 30.24767/88.09223 
80 40.0 26.65-36.65 30.24676/88.09223 
 
Table 5-4.  Parameter values used for surface water bodies. 
Parameter Value Unit 
K 50000 ft/day 
Porosity 0.1 dimensionless 
Sy 1 dimensionless 
Ss 1 Feet-1 
 
In order to refine the finite difference grid to delineate the island, discharge regions, no 
flow boundaries, etc, numerous modeling steps were used.  First, a map of the island was 
obtained from the National Land Use Consortium in a format that could be imported into 
ArcMap and retain its spatial information.  This was beneficial because the information 
regarding latitude, longitude, and distances across the island remained with the image while it 
was being altered in ArcMap.  Once the map was in ArcMap, it was cropped and gridded to mark 
the distances so that scale would be known. 
The map developed in ArcMap was then imported into Groundwater Vistas as a bitmap.  
This way, the map was overlain with the finite difference grid.  Using the map, the island could 
be delineated using a constant head boundary at the shoreline around the island.  The constant 
 65 
head boundary condition grid extended offshore on the first three layers in order to mimic 
aquifer discharge into the surrounding surface water.  The width of the discharge area was largest 
on the first layer, and decreased in width in both the second and third layer.  The protocol used in 
the Cape Cod Study [Masterson, 2004] was closely followed with respect to demarcating the 
offshore discharge.  Past this discharge area, island was surrounded by no flow boundary cells.  
The no flow boundaries around the island were intended to terminate lateral groundwater flow 
past the island-lens boundary. 
5.3.2 Steady-state and transient models 
With all of the inputs listed above a general steady state and transient models were 
developed to simulate the density dependent flow and saltwater-freshwater interactions using 
SEAWAT.  Before differing scenarios could be simulated, it was necessary to develop an 
accurate steady state model.  Steady state is reached in a groundwater flow model when 
equilibrium has been achieved and the head distribution stops changing.  This occurs when the 
inflows to the system equal the outflows [Fetter, 2001]. 
A steady state model is necessary because the output head files and concentration files 
that are created at the final time in which the model reaches steady state are important input files 
for the transient simulation.  In the transient model, the matrix of head values is used as the 
initial heads for the first time step.  Additionally, the initial concentration values in the transient 
model are the concentration values at the last time step of the steady state model. 
Once the steady state model was developed, the general transient model was developed 
for the years 1990-2010 using the recharge and pumping data described above.  The benefit of 
transient simulations is the ability to vary input values over time and be able to view the 
simulation results at various time steps, instead of just at the final equilibrium state. 
 66 
5.3.3 Calibration 
Once the initial Dauphin Island steady state and transient models had been developed 
based on data obtained and past studies, they were tested using the model data available in 
Kidd?s USGS report.  The study done by Kidd [1988] used data from 40 test wells to develop a 
two-dimensional finite difference groundwater flow model for the island?s water-table aquifer.  
This was done in order to assess the freshwater resources in the shallow aquifer and determine if 
it would be a feasible source of freshwater for the island at the time.  The model was calibrated 
for both high and low water-table conditions.  Using the data collected from the monitoring wells 
and the groundwater model that was developed, Kidd estimated that the water-table aquifer could 
produce up to 0.6 million gallons per day without lateral encroachment of seawater. 
Three figures in Kidd?s report were used to validate the model. The model was calibrated 
to reach approximately the same head contours found in Kidd?s figures as shown in the top layer 
of the aquifer.  The recharge values used in the calibration were given in the Kidd report. 
There were multiple steps taken in order to calibrate the model.  As previously described, 
a steady state and a transient groundwater model for Dauphin Island was built based on data 
collected from previous studies and literature that outlined the hydrologic properties of the 
island.  Since many of the properties were given in ranges, such as hydraulic conductivity or 
porosity, our models were built with values in the middle of the given ranges as a starting point.  
With these models, several steady state and transient simulations were done to mimic situations 
presented in the Kidd report.  Using the results, specifically the water-table elevation contour 
lines, the results from our models and Kidd?s results were compared visually.  The results of the 
Kidd model, which are accepted as valid, were compared to the results of the study model to 
validate the latter, a validation method known as comparison testing [Balci, 1998]. 
 67 
After comparison, certain model parameters were adjusted in order to better match Kidd?s 
figures based on the response of the aquifer and prior knowledge of unconfined aquifer behavior.  
It is important to note, however, that the changes always remained within the published range of 
values for the hydrologic properties on the island.  This process was continued until the contour 
lines from our models and Kidd?s results matched fairly well upon visual inspection.  Visual 
inspection was done to ensure the contour shapes were similar between ours and Kidd?s, the 
same maximum and minimum head values were obtained, and that the same contour lines 
surrounded the pumping wells during pumping.  The final steady state and transient model that 
remained after the tweaking process were the models used in the remainder of this Dauphin 
Island study. 
Matching these figures was a valuable effort.  The validity of a groundwater model 
depends on how accurately the model is able to predict field conditions [Wang and Anderson, 
1982].  Since the Kidd study used data from 40 monitoring wells on the island, and the 
simulations were being run for the same time period, Kidd was able to compare the observed 
groundwater monitoring data to the results obtained from the numerical model.  For example, in 
the steady state simulations, sixty percent of the observed groundwater levels were within 0.5 
feet of the simulated levels.  Additionally, all of the observed groundwater levels were within 1 
foot of the simulated values [Kidd, 1988].  Therefore, these figures provided an indispensable 
point of reference to calibrate and develop the initial steady state and transient models.   
Figures 5-3 and 5-4 show the figure from the Kidd report and from our study, 
respectively.  The figures depict the groundwater flow situation using the water-table altitude at 
steady state in the year 1985. 
 68 
Figures 5-5 and 5-6 show the figure from the Kidd report and from our study, 
respectively.  The figures were obtained after a transient simulation of a time period.  During the 
beginning of the simulation, the recharge was 15 inches/year, but the simulation time period 
ended with about two months of no recharge.  Due to this, the water-table levels were lower than 
in the previous figure. 
Figures 5-7 and 5-8 show the figure from the Kidd report and from our study, 
respectively.  The figures were matched for a steady state simulation that included pumping.  In 
this simulation, there are four wells on the island pumping a total of 0.3 MGD.  The recharge 
value used for this simulation was also 15 inches/year. 
  
  
 69 
 
 
 
Figure 5-3.  Steady State Head Distribution, April 2, 1985, from Kidd [1998] 
 
 
Figure 5-4.  Steady State Head Distribution, April 2, 1985, from model developed in this study 
 
 
 
 70 
 
Figure 5-5.  Head Distribution after Transient Simulation from May 22-June 15, 1985, from 
Kidd [1998] 
 
 
Figure 5-6.  Head Distribution after Transient Simulation from May 22-June 15, 1985, from 
model developed in this study  
 
 
 71 
 
 
Figure 5-7.  Head Distribution after Pumping Simulation in 1988, from Kidd  
 
 
Figure 5-8.  Head Distribution after Pumping Simulation in 1988, from model developed in this 
study  
 
 
 72 
5.3.4 Scenario simulations 
Six scenarios were simulated for Dauphin Island in an attempt to both visualize and 
quantify the effects of different factors on the unconfined aquifer.  All of the scenarios were 
modeled for the same time period, 1990-2030.  The simulation start time of 1990 was chosen 
because that was when the first four shallow wells came on-line.  The end year of 2030 was 
chosen because that gave about a 20-year prediction into the future. 
In all of the scenarios, the values used as inputs were similar to those described before, 
and they remained constant.  Additionally, they all had the same initial head values that were 
obtained from the steady state simulation described above.  Therefore, each model run should be 
identical until the year 2010.  After the year 2010, the input data was modified to simulate 
various scenarios.  The following section describes in detail each of the scenario simulated in this 
study.  Table 5-5 summarizes the factors examined in each scenario, although they will each be 
described extensively in the following section. 
Table 5-5 Summary of Scenarios Simulated 
Simulation Description 
Scenario 1 Base-case 
Scenario 2 Land-cover change 
Scenario 3 Land-cover change+dry climate change 
Scenario 4 Land-cover change+wet climate change 
Scenario 5 Land-cover change+dry climate change+sea level rise 
Scenario 6 Land-cover change+dry climate change+increased pumping 
 
 
 
 
 73 
 
Figure 5-9.  Summary of Recharge Values used for six scenarios as obtained from SWAT 
 
Scenario 1:Base Case-  The first scenario simulated was the general, no-change scenario 
modeling the groundwater situation with base-line inputs for the time period 1990-2010 and then 
no changes from the current condition were applied for the time period of 2011-2030.  This 
means future pumping values were the same average pumping values used in previous years.  
Also, the recharge patterns were assumed to remain approximately constant, so an average 
recharge value based on the data from 1990-2010 was assigned.  The Scenario 1 model was run 
twice (and the two runs were labeled 1.1 and 1.2).  The first simulation was run with the SWAT 
recharge estimates that were obtained using the soil moisture method and the second simulation 
was run with the recharge estimations based on the plant evapotranspiration method. Since the 
recharge values for the two regimes varied, this gave a good range for the simulation results. 
While it is highly unlikely that the situation on Dauphin Island would not change from 
2010 into the future, this scenario was still an important base-case scenario.  The results of the 
simulations provide a reference datum to compare other scenarios against.   
 
 74 
Scenario 2: LU/LC change- The second scenario was developed to explore the effects of land 
cover change induced by urban development.  By altering the land cover scenario we are able to 
simulate the change in groundwater resources on the island with a hypothetical land cover 
change that could possibly occur in the future.  The scenario modeled is an extreme case which 
illustrates what would likely be the largest possible change in land cover.  The following table 
illustrates the changes that were simulated: 
Table 5-6.  Land Use/Land Cover change scenarios simulated in this study. 
NLCD 2001 LU/LC Future LU/LC Area [ha] Percent [%] 
Water [WATR] Water [WATR] 7.19 1.60 
Residential-Low Density [URLD] Residential-Medium Density [URMD] 158.09 35.25 
Residential-Medium Density [URMD] Residential-High Density [URHD] 113.89 25.39 
Residential-High Density [URHD] Residential-High Density [URHD] 13.76 3.07 
Wetlands-Forested [WETF] Wetlands-Forested [WETF] 14.69 3.28 
Wetlands-Non-Forested [WETN] Wetlands-Non-Forested [WETN] 8.19 1.83 
SW US [Arid] Range [SWRN] SW US [Arid] Range [SWRN] 10.32 2.30 
Range-Grasses [RNGE] Range-Grasses [RNGE] 6.62 1.48 
Forest-Evergreen [FRSE] 50% Forest-Evergreen [FRSE]  56.43 12.58 
Forest-Evergreen [FRSE] 50% Residential-Low Density [URLD] 56.43 12.58 
Range-Brush [RNGB] Range-Brush [RNGB] 2.79 0.62 
Industrial [UIDU] Industrial [UIDU] 0.13 0.03 
 
For this scenario, the recharge values used for the years of 2010-2030 were obtained 
using Global Climate Models (GCMs) to predict the future recharge patterns based on changing 
precipitation patterns.  Both dry and wet future climate scenarios were used to calculate the 
recharge for the future time period between 2010-2030.  Using a combination of four GCMs and 
three greenhouse gas emission scenarios, 12 temperature estimates and 12 precipitation estimates 
were obtained for each month in the time period [Wang, 2011].  Figure 5-10 shows the change in 
mean temperature and precipitation as predicted from the GCMs and greenhouse gas emission 
scenarios.  A dry future climate was modeled by choosing the highest temperature values and 
 75 
lowest precipitation values for each month. Those values were then used as input to SWAT to 
calculate recharge.  In order to mimic a wet future climate, the estimates that had the lowest 
temperature values and the highest precipitation values for each month were chosen to run 
SWAT [Wang, 2011].  The dry climate change scenario was used for Scenario 3.  The wet 
climate change scenario was used for Scenario 4.  The average of the dry climate change and wet 
climate change recharge estimates were used for Scenario 2, since we just wanted to examine the 
effect of land cover change.   
 
Figure 5-10 Changes in temperature and precipitation predicted by 4GCMs and 3 emission 
scenarios [Wang, 2011] 
 
The recharge values used for the future years can be found in the Appendix in Table 9-3.  
It should be noted that the recharge values used for the years 2011-2030 were actually some 
extreme values simulated by SWAT for later years.  We used them to study worst-case 
conditions.  
 
Scenario 3: LU/LC and climate (dry) change- The third scenario that was developed to 
simulate both effects of LC/LU change as well as a dry climate change scenario.  The dry climate 
 76 
change scenario mentioned previously was used for the recharge estimates for this simulation.  
The recharge values assumed for future years can be found in the Appendix Table 9-4. 
 
Scenario 4: LU/LC and climate (wet) change- The fourth scenario that modeled was identical 
to the third scenario that was modeled, but instead of using the dry climate change scenario, the 
wet climate change scenario was used.  The same time shift that was previously mentioned for 
the recharge predictions was also applied for this scenario, and all other scenarios in which the 
climate change recharge values were used. 
 
Scenario 5: LC/LU change, climate (dry) change, sea-level rise-  The fifth scenario simulated 
was the dry climate change scenario discussed earlier, land cover/land use change, with the 
addition of sea level rise.   
The rate of sea level rise used was 9.09 mm/year, or 0.36 inches/year.  This value was 
estimated by Rahmstorf [2007] using a semi-empirical approach which connected global sea 
level rise to global mean surface temperature [Rahmstorf, 2007].  This is a relatively extreme 
value when compared to other values that have been suggested.  Since our simulation time for 
sea level rise is 20 years, we can estimate a cumulative sea level rise of 7 inches.  This value was 
used as the elevation at the head boundaries surrounding the island.  Since the model was set to 
not use the same head values for the beginning and end of the simulation, the model is able to 
mimic a rising sea level.  It should be noted that this is a somewhat idealized boundary condition 
since hypothetical vertical rise was assumed and inundation was ignored.   
 
 77 
Scenario 6: LC/LU change, climate (dry) change, increased pumping-  The sixth scenario 
that was simulated combined the dry climate change scenario and land use/land cover change, 
with the addition of increased pumping in the years of 2010-2030. The dry climate change as 
well as the land cover change scenarios has already been described.  The increased pumping 
scenario depicts the groundwater situation with an increase in pumping due to a growing 
population on the island and increasing demand for freshwater.  As seen in Figure 5-11, the 
population of Dauphin Island has been steadily increasing over the past 20 years. 
 
Figure 5-11.  Dauphin Island population; data obtained from the United States Census Bureau. 
 
If it is assumed that water demand will be directly reflected by the rate of population 
growth, then an approximation for the increase in water demand in the time period from 2010 to 
2030 can be made.  From the population data shown above, a percentage increase per year was 
calculated.  It was estimated that the population increased about 2.5% every year.  Using this as a 
guide, the pumping values were also increased 2.5% every year for each well starting in the year 
2011.  The exception to this was Well #40, as it is out of service and was assumed to remain 
 78 
offline for the remainder of the simulation.  The pumping values used for the years of 2011-2030 
can be found in the Appendix Table 9-7. 
While it may be extreme to assume that water demand so closely mirrors population 
growth, it will give us a conservative estimate for the demand on freshwater resources.  This is 
beneficial because it best motivates more conservative water management and preservation 
practices on the island. 
5.4 Results 
The results of the scenario simulations will be presented in four formats.  The first format 
will be the water-table elevation above sea level at the end of each simulation time.  The second 
format will be plots of the location of the bottom of the saltwater-freshwater interface over time 
from 1990-2030.  This will reveal whether or not upward intrusion occurs in any of the 
scenarios.  The third format is the location of the saltwater-freshwater interface along the 
perimeter of the aquifer after each simulation in order to detect lateral intrusion.  The fourth way 
will be the cumulative amount of freshwater stored in the water-table aquifer in the year 2030 at 
the end of each simulation. 
5.4.1 Water-table elevations 
The simulated groundwater elevation profile was plotted for Row 40 (Figure 5-14) of the 
model after each scenario was run.  As shown in Figure 5-12 below, the head profiles all share a 
similar shape due to the well positioning but the height differs.  The highest head profile was 
obtained from the Scenario 5, which was the sea level rise simulation.  This is due to the ?lifting 
effect.?  The increase in sea level ?lifts? by acting as a wedge beneath the freshwater lifting the 
lens from below.  It does not necessarily indicate anything about the quantity of freshwater in the 
 79 
aquifer.  The smallest profile was obtained from Scenario 6, which was the increased pumping 
simulation. 
 
Figure 5-12.  Predicted head Profiles at Row 40 under various scenarios. 
 
5.4.2 Saltwater-freshwater interface 
In order assess the position of the bottom freshwater-saltwater interface, a point in the grid 
was chosen and the bottom interface was found for this point at each time step.  The interface 
was assumed to occur at 50% of the saltwater concentration.  As shown in Figure 5-13, the lens 
was relatively flat along the bottom, so the crosscut was taken towards the middle in Row 40. 
 
Figure 5-13.  Cross-sectional view of freshwater lens beneath Dauphin Island. 
0
1
2
3
4
5
6
7
8
9
0 5000 10000 15000 20000 25000
H
e
a
d 
A
bo
v
e
 S
e
a
 
L
e
v
e
l 
(
f
t
)
D i s t a nc e  ( f t )
H e a d  E l e v a t i o n  A b o v e  S e a  L e v e l  i n  2 0 3 0
Sc e n a r io  1
Sc e n a r io  2
Sc e n a r io  3
Sc e n a r io  4
Sc e n a r io  5
Sc e n a r io  6
 80 
 
 
Figure 5-14.  Location of Crosscut taken at Row 40 
 
 
Figure 5-15 shows the comparison of the freshwater-saltwater interface using the soil 
moisture method for recharge estimation and using the plant evapotranspiration method for 
recharge estimation.  As illustrated in the figure, the soil moisture method for estimating 
recharge push the interface down slightly as compared to the plant ET method.  This is because 
the soil moisture method gives larger recharge values.  Eventually, the interfaces match up at the 
depth where they hit the clay confining layer. 
 81 
 
Figure 5-15.  Comparison of interface using two recharge methods. 
 
Figure 5-16 illustrates a comparison of both the soil moisture and plant ET based 
scenarios (the differences between the soil moisture and plant ET methods were described in 
Section 5.3.1) as well as Scenario 5, which includes LU/LC change and sea level rise.  
Interestingly, the interface position for the plant ET method for recharge estimation almost 
exactly matches the interface position for the sea level rise scenario. 
 
- 43
- 41
- 39
- 37
- 35
- 33
- 31
- 29
- 27
1990 2000 2010 2020 2030
E
le
v
a
t
ion
 
B
e
low
 S
e
a
 
L
e
v
e
l 
(f
t
)
T i m e  ( y e a r s )
In t e r fa c e  P o s i t i o n  fo r  T w o  R e c h a r ge  M e t h o d s
So il M o is t u r e
P la n t  E T
 82 
 
Figure 5-16.  Comparison of interface position using scenarios 1 and 5. 
 
Figure 5-17 displays the interface positions for each scenario.  It should be noted that the 
recharge pattern used for the beginning of Scenarios 2, 3, 4, 5, and 6 was the plant ET recharge 
estimation method. 
 
Figure 5-17.  Comparison of interface position for all scenarios. 
- 43
- 41
- 39
- 37
- 35
- 33
- 31
- 29
- 27
- 25
1990 1995 2000 2005 2010
E
le
v
a
t
io
n
 
B
e
lo
w
 S
e
a
 
L
e
v
e
l 
(
f
t
)
T i m e  ( y e a r s )
C o m p a r i s o n  o f  S ce n a r i o  1  a n d  5
S o il M o is t u r e  R
P la n t  E T  R
S L R  S c e n a r io  ( 5 )
- 43
- 41
- 39
- 37
- 35
- 33
- 31
- 29
- 27
1990 2000 2010 2020 2030
E
le
v
a
t
ion
 
B
e
low
 S
e
a
 
L
e
v
e
l 
(f
t
)
T i m e  ( y e a r s )
I n t e r f a ce  P o s i t i o n s
Sc e n a r io  1  ( So il M o is t u r e )
Sc e n a r io  1  ( P la n t  E T )
Sc e n a r io  3
Sc e n a r io  4
Sc e n a r io  5
Sc e n a r io  6
 83 
 
As shown in Figure 5-17, the bottom interface position is extremely insensitive to the 
changing factors on the island.  Almost all of the interface positions are identical, especially after 
the model has been run for a long time.  This response indicates no upward intrusion is occurring 
and is expected because of the characteristics of the aquifer, such as the confining layer below 
the shallow water-table, as well as the relatively large recharge inputs.  Due to this response, it 
could be anticipated that lateral intrusion was occurring instead of upward intrusion.  
5.4.3 Lateral movement of the saltwater-freshwater interface  
The following figures are plan views of the island.  In each comparison pair, the first 
figure is a plan cut at layer 9 at the beginning of the transient simulation [i.e., at steady state 
conditions] and the second figure is the same layer 9 cut at the end of the simulation period, 
2030.  The contour lines displayed are the head contours.  The red indicates saltwater, the aqua 
indicates freshwater, and the colors in between indicate the mixing-interface.  The 50% isochlor 
is located in this mixing interface.  The figures illustrate significant intrusion occurring in some 
of the scenarios.  Layer 9 is examined because that is the layer where most of the intrusion is 
occurring.   
 
Figure 5-18.  Saltwater-freshwater interface movement in Scenario 1.1.Red indicates saltwater, 
aqua indicates freshwater. 
 84 
 
Figure 5-19.  Saltwater-freshwater interface movement in Scenario 1.2. Red indicates saltwater, 
aqua indicates freshwater. 
 
 
Figure 5-20.  Saltwater-freshwater interface movement in Scenario 2. Red indicates saltwater, 
aqua indicates freshwater. 
 
Figure 5-21.  Saltwater-freshwater interface movement in Scenario 3. Red indicates saltwater, 
aqua indicates freshwater. 
 85 
 
Figure 5-22.  Saltwater-freshwater interface movement in Scenario 4. Red indicates saltwater, 
aqua indicates freshwater. 
 
 
 
Figure 5-23.  Saltwater-freshwater interface movement in Scenario 5. Red indicates saltwater, 
aqua indicates freshwater. 
 
Figure 5-24.  Saltwater-freshwater interface movement in Scenario 6. Red indicates saltwater, 
aqua indicates freshwater. 
 
 
 86 
The following figures (Figures 5-25 through 5-31) are sectional cuts through the island at 
Column 91.  The contour lines that are displayed are concentration contours, and they provide 
useful points of reference to compare the different concentration contours and intrusion 
occurring in the different scenarios. 
 
Figure 5-25.  Saltwater-freshwater interface sectional cut for Scenario 1.1. 
 
Figure 5-26.  Saltwater-freshwater interface sectional cut for Scenario 1.2. 
 
Figure 5-27.  Saltwater-freshwater interface sectional cut for Scenario 2. 
 87 
 
Figure 5-28.  Saltwater-freshwater interface sectional cut for Scenario 3. 
 
Figure 5-29.  Saltwater-freshwater interface sectional cut for Scenario 4. 
 
 
Figure 5-30.  Saltwater-freshwater interface sectional cut for Scenario 5. 
 
Figure 5-31.  Saltwater-freshwater interface sectional cut for Scenario 6. 
 
 88 
Figure 5-32 shows the same interface sectional cut view as seen in Figures 5-25 through 
5-31 but all of the scenarios are displayed at once for the purpose of comparison.  The values 
displayed in Figure 5-32 are the 50% isochlor positions for all of the scenarios.  The saltwater 
intrusion is seen beginning to occur in some of the scenarios starting at around -27.5 below mean 
sea level.  Figure 5-33 shows where the crosscut was taken in order to construct Figure 5-32. 
 
Figure 5-32.  Cross Section of DI illustrating lateral intrusion (crosscut taken at Col 91) 
 
- 47
- 42
- 37
- 32
- 27
- 22
- 17
- 12
- 7
- 2
4000 5000 6000 7000 8000 9000 10000 11000
E
le
v
a
t
io
n
 
B
e
lo
w
 S
e
a
 
L
e
v
e
l 
(
f
t
)
D i s t a nc e  ( f t )
C r o s s  S e c t i o n  o f  D I  I l l u s t r a t i n g L a t e r a l  I n t r u s i o n
Sc e n  1 . 1
Sc e n  1 . 2
Sc e n  2
Sc e n  3
Sc e n  4
Sc e n  5
Sc e n  6
 89 
 
Figure 5-33. Location of crosscut taken at Column 91 
5.4.4 Volume of freshwater contained in the water-table aquifer 
Another method to express the effects of the different factors in each scenario is to 
quantify the results of each run by calculating the amount of freshwater contained in the aquifer 
at the end of each simulation.  Freshwater was defined using the same protocol used in defining 
the saltwater-freshwater interface used in the earlier study.  Water with a saltwater concentration 
less than 1.09 lbs/ft3 was considered freshwater.  This value was chosen because it is 50% of the 
concentration of pure saltwater.  In reality, the secondary standard for drinking water is much 
lower than this 50% value.  The standard set by the EPA is 250 mg/L of chloride [EPA Drinking 
Water Contaminants, 2011].  This is equivalent to about 0.016 lbs/ft3. 
A short code was written in Visual Basic to calculate the number of total grid cells 
containing freshwater at the end of each scenario run.  Since the size of the grid cells was known, 
the volume of the total number of grid cells containing freshwater could be calculated.  Then, the 
volume of freshwater was calculated by multiplying the volume of cells that contained water 
 90 
with a saltwater concentration less than 1.09 lbs/ft3 by the porosity of the medium, which was 
0.3.  Table 5-7 shows the volume calculations. 
Table 5-7. Volume of Freshwater in Aquifer after Scenarios are Simulated. 
Scenario # Freshwater Grid Cells  Volume of Cells [ft3] 
Volume of 
Freshwater [ft3] % of Vol of 1.1 
1.1 29004 3276384653 9.83E+08   
1.2 28325 3199682640 9.60E+08 100.00 
2 27840 3144895488 9.43E+08 98.29 
3 27085 3059608272 9.18E+08 95.62 
4 28293 3196067818 9.59E+08 99.89 
5 26998 3049780474 9.15E+08 95.32 
6 26739 3020523005 9.06E+08 94.40 
 
The percentages were calculated in relation to Scenario 1.2 since that was the base-case 
recharge used for the years 1990-2010 in all of the simulations except 1.1.  As illustrated by the 
percentage change in volume, decreasing recharge due to climate change had the largest effect 
on the groundwater resource.  Land cover had the next largest effect, while sea level rise and 
increased pumping had a significant effect on the aquifer. 
5.5 Discussions 
The sensitivity simulation results indicate that the aquifer was most sensitive to change in 
climate causing a decrease in recharge patterns.  The largest change in aquifer volume attributed 
to a single factor was from a decrease in recharge due to climate change.  The only volume 
reported smaller than the volume after the dry climate change was the scenario with LC/LU 
change.  The addition of dry climate change to the LC/LU simulation led to the greatest change 
in percentage, so it was concluded that climate change had the largest effect. 
 The other factors did not cause as much of a change in the amount of freshwater contained 
in the aquifer.  These conclusions are based on the volume of freshwater contained in the aquifer 
at the end of the simulation period.  The figures showing lateral intrusion from a plan view are 
 91 
helpful for visualization purposes, but they do not provide the full picture since only one layer 
(layer 9) is shown. 
When the simulation results are represented as percentage decrease in freshwater from the 
initial conditions, it does not appear as though the aquifer is very sensitive to changes in climate 
and pumping scenarios.  The largest decrease in percentage of freshwater was about 6%.  This 
does not indicate, however, that the aquifer should be exploited without concern. 
While it does seem that the pumping from the aquifer is very sustainable, careful attention 
should be drawn to the figures that show the lateral movement of the saltwater-freshwater 
interface.  In some of the scenarios, especially the increased pumping scenario (6), the interface 
moves in towards Well 80, located on the southeast coast of the island.  If pumping were to 
increase over what was simulated, especially in Well 80, the water being pumped could be 
significantly contaminated. 
 
 
 
  
 92 
6. Sustainable yield study for Dauphin Island 
6.1 Background 
An additional study was done in order to quantify the percentage of the annual average 
values that could be pumped without serious problems occurring.  For this study, a ?serious 
problem? was defined as saltwater being pumped into any of the wells and thus contaminating 
the water source.   
This was a valuable study because all of the scenarios presented in the previous section 
were hypothetical.  It is impossible to accurately predict how much land cover and climate 
change will occur in the coming years.  By studying what percentage of recharge the aquifer can 
safely yield, the future management practices can be tailored to fit the actual scenarios on a 
yearly basis.  Once again, the concentration identified as problematic was anything over 1.09 
lbs/ft3, or 50% saltwater concentration, detected by the wells. 
6.1.1 Research Objectives 
 
The main objective for this section was to identify what percentage of annual recharge 
could be safely extracted from the aquifer without the possibility of saltwater contaminating any 
wells.  This was found for both uniform average annual recharge as well as for recharge values 
that varied year to year. 
6.2 Input Data and Study Methodology  
The same general groundwater model developed in SEAWAT for the previous transient 
simulations was used.  The simulation period was kept the same for this study (1990-2030), as 
we already had recharge values for that time period.  Additionally, all other parameter values that 
were used in the previous section, such as hydraulic conductivity, porosity, etc. were all kept the 
same.   
 93 
In the model, we inserted a new monitoring well in the grid right next to the cell containing 
Well 80.   Refer to Figure 4-7 to see the location of Well 80, located on the southeast shore of the 
island. It was inserted in this location because Well 80 is most prone to saltwater intrusion since 
it was closest to the shore and one of the deepest wells.  By monitoring the cell next to Well 80 
we can get a conservative estimate for the amount of water that can be pumped, as we would be 
detecting the contamination right before it would reach the well.  Also, the cone of depression 
caused by pumping could become large enough to reach the surrounding cells around Well 80.  
The monitoring well is capable of monitoring head and concentration over time, without 
pumping, so it does not affect the groundwater levels or concentration. 
For the first part of the study, the model was simplified and a uniform average recharge 
value was used for every year.  Therefore, the recharge was the same year to year, and this also 
resulted in the pumping being the same year to year as that was a percentage of recharge.  Some 
conversions had to be done to calculate pumping rates.   
Since recharge is input in the model in the units of [L/T], the recharge values had to be 
multiplied by the simulated area of the island.  By doing this, recharge was obtained in the units 
[L3/T], and we were able to calculate percentages from this to be pumped.   
The first scenario simulated was 20% of the recharge, next was 30% and so on.  This was 
continued until the monitoring well detected concentrations at or above 1.09 lbs/ft3, which would 
indicate saltwater intruding and reaching the well.   
Once the concentration in the monitoring well reached or exceeded 1.09 lbs/ft3 the value 
was noted.  However, since the pumping had been increased in rather large increments (10% at a 
time) and it was possible that saltwater had also reached the well at percentages between the last 
simulation and the simulation it was detected in.  The next task was to attempt to find a smaller 
 94 
range that would provide a sustainable yield.  The pumping rates were then decreased in smaller 
increments until a level was reached where intrusion did not occur.  This provides us with a 
range of values to recommend for future pumping scenarios. 
A similar protocol was followed for the second part of the study where instead of uniform 
yearly recharge, the recharge varied from year to year.  The annual recharge estimates used in 
this section of the study were the same ones used previously for Scenario 1.2 in the previous 
chapter.  These were the SWAT recharge estimations that were run for Dauphin Island using the 
plant evapotranspiration method within SWAT, and assuming no climate change. 
6.3 Results 
This section discusses the results obtained when the uniform recharge and varying recharge 
simulations were simulated in an effort to determine what percentage of recharge could be 
pumped without introducing saltwater into any of the wells. 
6.3.1 Uniform Recharge 
Concentrations at or above 1.09 lbs/ft3 were first detected at the pumping rate of 60% of 
the average annual recharge values.  Since the last simulation that had been done was pumping at 
the rate of 50% of the recharge values, the scenario of 55% was simulated in order to try to 
narrow the range in which saltwater is introduced.  When 55% of the recharge was pumped, the 
monitoring well detected concentrations above 1.09 lbs/ft3 at about 124 months into the 
simulation, as shown in Figure 6-1.  Since the 50% pumping scenario did not yield 
concentrations above 1.09 lbs/ft3, it is gathered that saltwater flows into the well at pumping 
rates of somewhere between 50%-55% and higher.  Figure 6-2 shows the 50% isochlor reaching 
well 80.  While the percentage range could have been narrowed down farther, this is acceptable 
 95 
for our screening calculations, as there will be some degree of uncertainty caused by natural 
properties and heterogeneities not taken into account by this preliminary model. 
 
Figure 6-1. Concentration at Monitoring well, detected conc. of 1.09 lbs/ft3at 124 months 
 
 
Figure 6-2. Isochlor at 124 months, showing conc. of 1.09 lbs/ft3reaching the monitoring well. 
 
6.3.2 Varying Recharge 
Using the varying annual recharge values, concentrations at or above 1.09 lbs/ft3 were first 
detected at the pumping rate of 60% of the annual recharge values.  The concentration was 
 96 
detected at 1.09 lbs/ft3 at about 73 months into the simulation, as shown in the figure below. 
Concentration had not met or exceeded 1.09 lbs/ft3 in the 55% simulation, so saltwater 
contamination at the monitoring well is suggested to occur in the range of 55%-60% of the 
annual recharge values.   
 
Figure 6-3. Concentration at Monitoring well, detected conc. of 1.09 lbs/ft3at 73 months 
 
 
Figure 6-4. Isochlor 73 months, showing conc. of 1.09 lbs/ft3reaching the monitoring well. 
 
  
 97 
6.4 Discussions 
It is important to note that the ranges presented in the results for the two pumping scenarios 
are not claimed to be the sustainable yield pumping percentages for the entire aquifer.  They are 
simply the ranges where saltwater contaminant might not occur in any of the wells in the 
pumping system. 
While a formal optimization was not performed, it can still be conjectured that if Well 80 
were to pump smaller amounts than the other wells and the difference was distributed between 
the seven remaining wells, the sustainable yield for the aquifer would go up substantially.  The 
50% isochlor did not approach any of the other wells closely, even when the pumping was 
increased to levels of approximately 70% of the annual recharge values. 
Another important aspect observed in this study is that while the average recharge and 
pumping scenario and the varied recharge and pumping scenario gave different results, the 
results were still relatively similar.  This would suggest that in some cases where screening 
calculations are needed for a very complex model, it would most likely be sufficient to use 
average annual recharge values for the entire simulation period. 
 
  
 98 
7. Conclusions and Recommendations 
A review of literature indicated that obtaining a relationship between land cover and 
groundwater recharge amounts was achieved in several locations in the United States.  In this 
study, several methods were explored to estimate recharge at seven sites in the Southeast, and 
these recharge estimates were related to land cover by calculating the curve number for these 
sites and applying appropriate relationships.  However, a consistent relationship was not found 
between the land cover of each site and the amount of groundwater recharge.   
A calibrated, steady-state and transient model for Dauphin Island was successfully developed 
using SEAWAT.  The groundwater model was calibrated against previously published results.  
By simulating a variety of scenarios we were able to test the sensitivity of the island?s 
unconfined aquifer to parameters such as land-cover change, climate change, and increased 
pumping due to expected population growth.  The Dauphin Island models that were developed 
could also be applied in the future to simulate other groundwater situations on the island. 
The model predicted that a decrease in groundwater recharge due to climate change would 
have the greatest effect on the island?s freshwater resources.  This was determined by calculating 
the volume of freshwater contained in the aquifer at the end of each simulation.  Additionally, it 
was observed that changing land cover, sea level rise, and the expected increased pumping level 
have a very little effect on the freshwater resources of the island.  Saltwater intrusion was 
detected in several of the scenarios.  While the total volume of freshwater did not decrease 
significantly with any of the scenarios simulated, lateral intrusion did suggest that the changing 
factors could eventually lead to saltwater contamination in the outer wells. 
The existing Dauphin Island groundwater model used in the sensitivity analysis was 
subsequently utilized to determine a sustainable pumping amount for the Dauphin Island aquifer.  
 99 
A monitoring well was positioned next to the pumping well most prone to saltwater 
contamination.  Pumping amounts were expressed as a function of recharge amounts.  It was 
found that approximately 50%-55% of recharge could be withdrawn from the aquifer under the 
uniform pumping scenario.  The average of the actual percentage of recharge that was pumped in 
2009-2010 was 39%.   This indicates that the current pumping levels are relatively sustainable.  
However, further site specific field assessments need to be made to validate these predictions.  
The study also illustrated that using uniform average recharge values would likely be sufficient 
for large, complex groundwater models. 
In conclusion, additional research is needed to establish a relationship between land cover 
and groundwater recharge amounts in the Southeast.  Perhaps the study should first be done on a 
smaller scale until the method is refined, and then extended to a more regional scale.  Further 
research is also needed to develop an optimization tool for locating pumping wells in Dauphin 
Island.  By performing an optimization study, a more sustainable pumping pattern that could 
allow exploitation of the groundwater resource without the problem of saltwater intrusion would 
be developed. 
  
 100 
8. References 
Andersen, P. F., J. W. Mercer and H. O. White (1988). "Numerical Modeling of Salt Water 
Intrusion at Hallandale, Florida." Groundwater (26), 619-630. 
Arnold, J. G. and N. Fohrer (2005). "SWAT2000: current capabilities and research opportunities 
in applied watershed modelling." Hydrological Processes (19), 563-572. 
Arnold, J. G., R. S. Muttiah, R. Srinivasan and P. M. Allen (2000). "Regional estimation of base 
flow and groundwater recharge in the Upper Mississippi river basin." Journal of Hydrology 
(227), 21-40. 
Arnold, J. G., J. R. Williams, R. Srinivasan and K. W. King (1996). "The Soil and Water 
Assessment Tool (SWAT) User's Manual." Temple, TX. 
Balci, ?Verification, Validation, and Testing?, in J. Banks (Ed.), Handbook of 
Simulation:  Principles, Methodology, Advances, Applications, and Practice, John Wiley & 
Sons, New York NY, 1998, pp. 335-393. 
Barlow, P. M. and E. G. Reichard (2010). "Saltwater intrusion in coastal regions of North 
America." Hydrogeology Journal (18), 247-260. 
Bedient, P. B. and W. C. Huber (1992). Hydrology and floodplain analysis, Addison Wesley 
Publishing Company. 
Bras, R. L. (1990). Hydrology: an introduction to hydrologic science, Addison-Wesley Reading, 
Massachusetts, USA. 
Caldwell, J (1996).  ?Dauphin Island Water and Sewer Report.? 
Cecan, L., G. Nelson, C. McLane, and M. Metheny (2008). Evaluating safe yield for supply 
wells in an aquifer with fresh water/salt water interface. In Proceedings of the 20th Salt 
 101 
Water Intrusion Meeting, Naples, Florida, USA, ed. C.Langevin, L.Lebbe, M.Bakker, and 
C.Voss, 163?166. Reston, Virginia: USGS. 
Chandler, R. V., B. Gillett and S. S. DeJarnette (1983). Hydrogeologic and water-use data for 
southern Baldwin County, Alabama, Geological Survey of Alabama, Hydrogeology 
Division. 
Chang, S. W., T. Prabhakar Clement, M. J. Simpson and K. K. Lee (2011). "Does Sea-level Rise 
Have an Impact on Saltwater Intrusion?" Advances in Water Resources. (In Press) 
Cherkauer, D. S. and S. A. Ansari (2005). "Estimating ground water recharge from topography, 
hydrogeology, and land cover." Ground water (43), 102-112. 
Chesnaux, R. and D. M. Allen (2008). "Groundwater travel times for unconfined island aquifers 
bounded by freshwater or seawater." Hydrogeology Journal (16), 437-445. 
Chow, V. T., D. R. Maidment and L. W. Mays (2005). Applied hydrology, McGraw-Hill New 
York,, USA. 
Crosbie, R. S., P. Binning and J. D. Kalma (2005). "A time series approach to inferring 
groundwater recharge using the water table fluctuation method." Water Resources Research 
(41) 
Crosbie, R. S., I. D. Jolly, F. W. Leaney and C. Petheram (2005). "Can the dataset of field based 
recharge estimates in Australia be used to predict recharge in data-poor areas?" Hydrology 
and Earth System Sciences Discussions 7. 
Delin, G. N., R. W. Healy, D. L. Lorenz and J. R. Nimmo (2007). "Comparison of local- to 
regional-scale estimates of ground-water recharge in Minnesota, USA." Journal of 
Hydrology (334), 231-249.  
 102 
Diersch, H.-J. G. (2005), FEFLOW: Finite Element Subsurface Flow and Transport Simulation 
System, Inst. for Water Resources Planning and System Res., Berlin.  
Fetter, C.W., Applied Hydrogeology, 4th ed., Prentice Hall, Inc., New Jersey, 2001.  
Fritz, H. M., C. Blount, R. Sokoloski, J. Singleton, A. Fuggle, B. G. McAdoo, A. Moore, C. 
Grass and B. Tate (2007). "Hurricane Katrina storm surge distribution and field observations 
on the Mississippi Barrier Islands." Estuarine, Coastal and Shelf Science (74), 12-20. 
Goswami, R. R. and T. P. Clement (2007). "Laboratory-scale investigation of saltwater intrusion 
dynamics." Water Resources Research (43). 
Guo, W. and C. D. Langevin (2002). User's Guide to SEWAT: A Computer Program for 
Simulation of Three-Dimensional Variable-Density Ground-Water Flow, United States 
Geological Survey. 
Harbaugh, A.W., Banta, E.R., Hill, M.C., and McDonald, M.G., (2000), MODFLOW-2000, The 
U.S. Geological Survey Modular Ground-Water Model?User guide to modularization 
concepts and the ground-water flow process: U.S. Geological Survey Open-File Report 00-
92, 121 p.  
Henry, H. R. Effects of dispersion on salt encroachment in coastal aquifers. US Geological 
Survey Water-Supply Paper; 1964. p. C71?84. 
Heppner, C. S., J. R. Nimmo and M. P. C. A. Geological Survey (2005). Computer Program for 
Predicting Recharge with a Master Recession Curve, United States Geological Survey. 
Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan. (2004). Development of a 2001 National 
Landcover Database for the United States. Photogrammetric Engineering and Remote 
Sensing, (70), 829-840. 
 103 
Jocson, J. M. U., J. W. Jenson and D. N. Contractor (2002). "Recharge and aquifer response: 
northern Guam lens aquifer, Guam, Mariana Islands." Journal of Hydrology 260(1-4): 231-
254. 
Kalin, Latif.  Class Lecture.  Auburn University, Auburn, AL. 26 Jan 2010. 
Kidd, R.E., (1988). Hydrogeology and Water-Supply Potential of the Water-Table Aquifer on 
Dauphin Island, Alabama. U.S. Geological Survey Scientific Investigations Report 87-4283, 
49p. 
Larabi, A., M. Faouzi and A. H. D. Cheng (2008). Assessment of Groundwater Resources in 
Rmel Coastal Aquifer (Morocco) by SEAWAT. 
Lin, J., J. B. Snodsmith, C. Zheng and J. Wu (2009). "A modeling study of seawater intrusion in 
Alabama Gulf Coast, USA." Environmental Geology (57),119-130. 
Liu, J., Rich, K., Zheng, C., 2007, Sustainability analysis of groundwater resources in a coastal 
aquifer, Alabama, Environmental Geology DOI 10.1007/s00254-007-0791-x. 
Lo?iciga, H. A., T. J. Pingel and E. S. Garcia (2011). "Sea Water Intrusion by Sea Level Rise: 
Scenarios for the 21st Century." Ground water. 
Lyon, S. W., M.T. Walter, P. G?rard-Marchant, and T.S. Steenhuis (2004). ?Using a topographic 
index to distribute variable source area runoff predicted with the SCS curve-number 
equation.? Hydrological Processes (18), 2757?2771. 
Mahesha, A. and S. H. Nagaraja (1996). "Effect of natural recharge on sea water intrusion in 
coastal aquifers." Journal of Hydrology (174), 211-220. 
Mart?nez-Santos, P. and J. M. Andreu (2010). "Lumped and distributed approaches to model 
natural recharge in semiarid karst aquifers." Journal of Hydrology (388), 389-398. 
 104 
Masterson, J.P. (2004). Simulated interaction between freshwater and saltwater and effects of 
ground-water pumping and sea-level change, Lower Cape Cod aquifer system, 
Massachusetts. USGS Scientific Investigations Report 2004?5014. USGS. 
McElroy, Gary. ?New Dauphin Island well will provide better water, say officials.? Al.com. 8 
January 2010.  26 April 2011. 
<http://blog.al.com/live/2010/01/new_dauphin_island_well_will_p.html> 
Michel, C., A. Andreassian, and C. Perrin (2005). ?Soil Conservation Service Curve Number 
method: how to mend a wrong soil moisture accounting procedure.? Water Resources 
Research (41).  
Mishra, S. K. and V. P. Singh (1999). "Another look at SCS-CN method." Journal of Hydrologic 
Engineering (257). 
Mollema PN, Marco Antonellini, Tomaz Dentinho and Vasco R. M. Silva (2010). The Effects of 
Climate Change on the Hydrology and Groundwater of Terceira Island (Azores). In: 
Proceedings of the Salt Water Intrusion Meeting June 21-25 Sao Miguel Azores, Portugal. 
Morton, R.A. (2008). ?Historical changes in the Mississippi-Alabama barrier-island chain and 
the roles of extreme storms, sea level, and human activities.? Journal of Coastal Research, 
(24), 1587?1600. 
Murgulet, D. and G. Tick (2008). "The extent of saltwater intrusion in southern Baldwin County, 
Alabama." Environmental Geology (55), 1235-1245. 
Nachabe, M., N. Shah, M. Ross, and J. Vomacka  (2005). ?Evapotranspiration of two vegetation 
covers in a shallow water table environment.? Soil Science Society of America Journal (69), 
492?499. 
 105 
O?Donnell,  Daniel.  (2005) ?Baseline Assessment Report 2005 for Dauphin Island Water and 
Sewer Authority?s Public Water Supply Wells.?  
Praveena, S. M. and A. Z. Aris (2009). "Groundwater resources assessment using numerical 
model: A case study in low-lying coastal area." International Journal of Environmental 
Science and Technology (7), 135-146. 
Rahmstorf, S. (2007). "A semi-empirical approach to projecting future sea-level rise." Science 
(5810), 368. 
Ranjan, P., S. Kazama and M. Sawamoto (2006). "Effects of climate change on coastal fresh 
groundwater resources." Global Environmental Change (16), 388-399. 
Rao, S. V. N., V. Sreenivasulu, S. M. Bhallamudi, B. S. Thandaveswara and K. P. Sudheer 
(2004). "Planning groundwater development in coastal aquifers." Hydrological Sciences 
Journal (49), 155-170.  
Rutledge, A. T. (2007). "Update on the use of the RORA program for recharge estimation." 
Ground water (45), 374-382. 
Samper, J. and B. Pisani (2009). "Aquifer recharge evaluation by a combination of soil water 
balance and groundwater flow models." Estudios en la Zona no Saturada del Suelo 19. 
Scanlon, B. R., R. C. Reedy, D. A. Stonestrom, D. E. Prudic and K. F. Dennehy (2005). "Impact 
of land use and land cover change on groundwater recharge and quality in the southwestern 
US." Global Change Biology (11), 1577-1593. 
Sherif, M. M. and V. P. Singh (1999). "Effect of climate change on sea water intrusion in coastal 
aquifers." Hydrological Processes (13), 1277-1287. 
Singh, H. V. (2010) Modeling impact of Land Use/Cover changes on Water Quality and 
Quantity of Fish River Watershed (M.S. Thesis) Auburn University. 
 106 
Soil Survey Staff, Natural Resources Conservation Service, United States Department of 
Agriculture.  Soil Survey of Baldwin County, AL.  (Online WWW) Available URL: 
?http://soildatamart.nrcs.usda.gov/Survey.aspx?County=AL003? (Accessed 5 March 2011). 
Soil Survey Staff, Natural Resources Conservation Service, United States Department of 
Agriculture.  Soil Survey of Covington County, AL.  (Online WWW) Available URL: 
?http://soildatamart.nrcs.usda.gov/Survey.aspx?County=AL039?  (Accessed 5 March 2011). 
Soil Survey Staff, Natural Resources Conservation Service, United States Department of 
Agriculture.  Soil Survey of Montgomery County, AL.  (Online WWW) Available URL: 
?http://soildatamart.nrcs.usda.gov/Survey.aspx?County=AL101? (Accessed 5 March 2011). 
Soil Survey Staff, Natural Resources Conservation Service, United States Department of 
Agriculture.  Soil Survey of Escambia County, FL.  (Online WWW) Available URL: 
?http://soildatamart.nrcs.usda.gov/Survey.aspx?County=FL033?  (Accessed 5 March 2011). 
Sophocleous, M. A. (1991). "Combining the soilwater balance and water-level fluctuation 
methods to estimate natural groundwater recharge: practical aspects." Journal of Hydrology 
(124), 229-241. 
Sun, H. and P. S. Cornish (2005). "Estimating shallow groundwater recharge in the headwaters 
of the Liverpool Plains using SWAT." Hydrological Processes (19), 795-807. 
Wang, H. F. and M. P. Anderson (1982). Introduction to groundwater modeling, Freeman. 
Wang, R. (2011) Modeling Hydrologic and Water Quality Responses to Changing Climate and 
Land Use/Cover in the Wolf Bay Watershed, South Alabama (M.S. Thesis) Auburn 
University. 
Webb, M. D. and K. W. F. Howard (2010). "Modeling the Transient Response of Saline 
Intrusion to Rising Sea Levels." Ground water (49), 560-569. 
 107 
Zhang, Y. K. and K. E. Schilling (2006). "Effects of land cover on water table, soil moisture, 
evapotranspiration, and groundwater recharge: a field observation and analysis." Journal of 
Hydrology (319), 328-338. 
Zheng, C., 1990. MT3D ? A modular three-dimensional transport model for simulation of 
advection, dispersion and chemical reactions of contaminants in groundwater systems, 
U.S.E.P.A Report. 
  
 108 
9. Appendix 
 
9.1 Additional Data 
 
Table 9-1. Recharge values in ft/month used in Scen 1.1 obtained using Soil Moisture method in 
SWAT 
Stress 
Period Recharge 
Stress 
Period Recharge 
1 0.122 32 0.387 
2 0.122 33 0.341 
3 0.109 34 0.322 
4 0.177 35 0.145 
5 0.165 36 0.198 
6 0.044 37 0.104 
7 0.048 38 0.150 
8 0.004 39 0.060 
9 0.048 40 0.093 
10 0.139 41 0.053 
11 0.099 42 0.122 
12 0.041 43 0.122 
13 0.125 44 0.122 
14 0.135 45 0.122 
15 0.027 46 0.122 
16 0.201 47 0.122 
17 0.201 48 0.122 
18 0.136 49 0.122 
19 0.208 50 0.122 
20 0.041 51 0.122 
21 0.065 52 0.122 
22 0.128 53 0.122 
23 0.090 54 0.122 
24 0.126 55 0.122 
25 0.118 56 0.122 
26 0.239 57 0.122 
27 0.134 58 0.122 
28 0.056 59 0.122 
29 0.090 60 0.122 
30 0.095 61 0.122 
31 0.361   
 
 
 109 
 
 
Table 9-2. Recharge values in ft/month used in Scen 1.2 obtained using Plant ET method in 
SWAT 
Stress 
Period Recharge 
Stress 
Period Recharge 
1 0.103 32 0.238 
2 0.103 33 0.120 
3 0.099 34 0.100 
4 0.137 35 0.072 
5 0.081 36 0.178 
6 0.051 37 0.088 
7 0.056 38 0.139 
8 0.007 39 0.057 
9 0.053 40 0.099 
10 0.154 41 0.057 
11 0.110 42 0.103 
12 0.050 43 0.103 
13 0.140 44 0.103 
14 0.145 45 0.103 
15 0.030 46 0.103 
16 0.219 47 0.103 
17 0.148 48 0.103 
18 0.121 49 0.103 
19 0.129 50 0.103 
20 0.049 51 0.103 
21 0.074 52 0.103 
22 0.125 53 0.103 
23 0.111 54 0.103 
24 0.142 55 0.103 
25 0.139 56 0.103 
26 0.246 57 0.103 
27 0.131 58 0.103 
28 0.060 59 0.103 
29 0.099 60 0.103 
30 0.111 61 0.103 
31 0.333   
 
 
 
 110 
Table 9-3. Recharge values in ft/month used for Scenario 2 with Land Cover Change taken into 
account 
Stress 
Period Recharge 
Stress 
Period Recharge 
1 0.077 32 0.238 
2 0.071 33 0.120 
3 0.007 34 0.100 
4 0.030 35 0.072 
5 0.110 36 0.178 
6 0.072 37 0.088 
7 0.029 38 0.139 
8 0.099 39 0.057 
9 0.111 40 0.100 
10 0.024 41 0.057 
11 0.084 42 0.076 
12 0.070 43 0.076 
13 0.097 44 0.076 
14 0.100 45 0.076 
15 0.192 46 0.076 
16 0.098 47 0.069 
17 0.039 48 0.107 
18 0.062 49 0.117 
19 0.070 50 0.026 
20 0.258 51 0.025 
21 0.195 52 0.047 
22 0.105 53 0.078 
23 0.087 54 0.130 
24 0.053 55 0.089 
25 0.125 56 0.130 
26 0.062 57 0.024 
27 0.108 58 0.036 
28 0.044 59 0.074 
29 0.085 60 0.079 
30 0.049 61 0.116 
31 0.333   
 
 
 
 
 111 
Table 9-4. Recharge Values in ft/month for Scenario 3 (Dry Climate Change) 
 
Stress 
Period Recharge 
Stress 
Period Recharge 
1 0.103 32 0.238 
2 0.103 33 0.120 
3 0.099 34 0.100 
4 0.137 35 0.072 
5 0.081 36 0.178 
6 0.051 37 0.088 
7 0.056 38 0.139 
8 0.007 39 0.057 
9 0.053 40 0.100 
10 0.154 41 0.057 
11 0.110 42 0.062 
12 0.050 43 0.062 
13 0.140 44 0.062 
14 0.145 45 0.062 
15 0.030 46 0.062 
16 0.219 47 0.053 
17 0.148 48 0.090 
18 0.121 49 0.105 
19 0.129 50 0.015 
20 0.049 51 0.018 
21 0.074 52 0.033 
22 0.125 53 0.061 
23 0.111 54 0.110 
24 0.142 55 0.075 
25 0.139 56 0.114 
26 0.246 57 0.017 
27 0.131 58 0.023 
28 0.060 59 0.059 
29 0.099 60 0.062 
30 0.111 61 0.098 
31 0.333   
 
 
 
 
 112 
Table 9-5. Recharge Values in ft/month for Scenario 4 (Wet Climate Change) 
Stress 
Period Recharge 
Stress 
Period Recharge 
1 0.103 32 0.238 
2 0.103 33 0.120 
3 0.099 34 0.100 
4 0.137 35 0.072 
5 0.081 36 0.178 
6 0.051 37 0.088 
7 0.056 38 0.139 
8 0.007 39 0.057 
9 0.053 40 0.099 
10 0.154 41 0.057 
11 0.110 42 0.091 
12 0.050 43 0.091 
13 0.140 44 0.091 
14 0.145 45 0.091 
15 0.030 46 0.091 
16 0.219 47 0.084 
17 0.148 48 0.125 
18 0.121 49 0.129 
19 0.129 50 0.037 
20 0.049 51 0.031 
21 0.074 52 0.062 
22 0.125 53 0.095 
23 0.111 54 0.150 
24 0.142 55 0.103 
25 0.139 56 0.146 
26 0.246 57 0.031 
27 0.131 58 0.050 
28 0.060 59 0.088 
29 0.099 60 0.096 
30 0.111 61 0.133 
31 0.333   
 
 
 
 
 
 
 
 
 
 113 
Table 9-6.  Pumping rates (in ft3/month) for Dauphin Island wells. 
 
Month-Year Well ID # 10 20 30 40 50 60 70 80 
Sep 2000 36997 35137 39763 38550 40100 38587 33430 32130 
Oct 2000 35387 34048 41861 0 33706 33903 31659 29930 
Nov 2000 28366 27307 34825 0 28328 26666 26159 26645 
Dec 2000 30194 29490 36303 0 30877 28452 27910 26226 
Jan 2001 24641 23680 30352 0 25397 23352 23510 22314 
Feb 2001 25560 24119 32652 0 26075 24986 23381 21886 
Mar 2001 47549 33676 45521 0 38633 37290 34677 32424 
Apr 2001 39457 43353 48413 0 39657 37440 36173 33963 
May 2001 49703 52426 58810 0 48377 45539 44481 41777 
Jun 2001 55813 55168 66303 0 55237 50997 50983 47853 
Jul 2001 61829 61280 71113 0 60884 56203 56665 53006 
Aug 2001 42516 44061 50281 0 42127 41516 39535 37129 
Sep 2001 40320 43387 50013 0 37756 41300 37480 34700 
Oct 2001 37767 38820 47310 0 37514 36300 35100 32537 
Nov 2001 39313 40693 44147 0 39050 37780 36220 33700 
Dec 2001 33804 35497 33758 0 33583 32243 31229 29093 
Jan 2002 45029 46624 45009 0 44519 43177 41329 38262 
Feb 2002 30760 32072 30948 0 32676 32224 28296 26172 
Mar 2002 39611 43154 41704 0 43290 40422 38250 35765 
Apr 2002 49679 46173 44331 0 46321 42849 40666 37725 
May 2002 58281 58813 58335 0 60313 57003 51106 49203 
Jun 2002 60103 58239 60433 0 62073 59793 53820 48567 
Jul 2002 66687 65676 66338 0 69190 66655 58345 52790 
Aug 2002 58486 57897 58983 0 53584 60004 50622 49625 
?  ?  ?  ?  ?  ?  ?  ?  ?  
Jan 2009 29000 12000 23000 0 13000 19000 23000 21000 
Feb 2009 28000 15000 25000 0 29000 0 23000 20000 
Mar 2009 35000 2000 5000 0 41000 26000 34000 29000 
Apr 2009 42000 15000 5000 0 45000 16000 35000 31000 
May 2009 54000 56000 0 0 61000 0 40000 34000 
Jun 2009 74000 78000 10000 0 80000 20000 37000 31000 
Jul 2009 75000 74000 53000 0 80000 24000 34000 26000 
Aug 2009 64000 69000 50000 0 68000 25000 40000 31000 
Sep 2009 52000 61000 28000 0 56000 21000 37000 32000 
Oct 2009 36000 27000 33000 0 38000 2000 28000 23000 
Nov 2009 31000 19000 18000 0 31000 9000 18000 19000 
Dec 2009 24000 16000 0 0 21000 19000 18000 18000 
 114 
Jan 2010 32000 23000 0 0 32000 0 19000 28000 
Feb 2010 23000 17000 0 0 23000 0 0 20000 
Mar 2010 30000 22000 0 0 31000 0 0 25000 
Apr 2010 56000 36000 0 0 57000 0 5000 34000 
May 2010 64000 42000 0 0 60000 0 5000 28000 
Jun 2010 61000 39000 0 0 59000 0 0 26000 
Jul 2010 68000 45000 0 0 67000 0 0 36000 
Aug 2010 73000 48000 0 0 74000 0 0 30000 
Sep 2010 65000 43000 0 0 71000 0 0 19000 
Oct 2010 38000 35000 0 0 58000 0 0 14000 
Nov 2010 35000 28000 0 0 46000 0 10000 0 
Dec 2010 21000 19000 0 0 33000 0 14000 0 
 
Table 9-7.  Pumping Values (in ft3/month) used for Scenario 6 showing increased pumping 
SP 10 20 30 40 50 60 70 80 
1 181945 158560 117536 156748 0 0 0 0 
2 181945 158560 117536 156748 186640 99592 114446 122696 
3 181945 158560 117536 156748 186640 99592 114446 122696 
4 181945 158560 117536 156748 186640 99592 114446 122696 
5 181945 158560 117536 156748 186640 99592 114446 122696 
6 181945 158560 117536 156748 186640 99592 114446 122696 
7 181945 158560 117536 156748 186640 99592 114446 122696 
8 147259 140750 166056 78426 150151 147474 132418 126255 
9 110632 106395 136440 0 112581 105236 102697 98741 
10 193468 137021 185217 0 157191 151726 141094 131927 
11 160543 176395 196984 0 161357 152336 147181 138189 
12 226965 229039 266136 0 223104 207156 206328 193453 
13 157219 164944 174537 0 157929 155104 146192 135979 
14 202135 187869 180375 0 188472 174345 165462 153496 
15 247747 244765 248288 0 249378 247643 217573 203629 
16 181945 158560 117536 0 186640 99592 114446 122696 
17 181945 158560 117536 0 186640 99592 114446 122696 
18 181945 158560 117536 0 186640 99592 114446 122696 
19 181945 158560 117536 0 186640 99592 114446 122696 
20 181945 158560 117536 0 186640 99592 114446 122696 
21 181945 158560 117536 0 186640 99592 114446 122696 
22 181945 158560 117536 0 186640 99592 114446 122696 
23 132179 118625 146583 0 145819 101876 104387 102744 
24 117996 48826 93583 0 52895 77307 93583 85445 
25 113927 61032 101720 0 117996 0 93583 81376 
26 156649 34585 20344 0 174959 85445 140374 122064 
 115 
27 219716 227854 0 0 248198 0 162753 138340 
28 269559 286851 143426 0 288886 91548 150546 122064 
29 146477 109858 134271 0 154615 8138 113927 93583 
30 126133 77307 73239 0 126133 36619 73239 77307 
31 97652 65101 0 0 85445 77307 73239 73239 
32 130202 93583 0 0 130202 0 77307 113927 
33 93583 69170 0 0 93583 0 0 81376 
34 122064 89514 0 0 126133 0 0 101720 
35 244129 158684 0 0 238026 0 20344 126133 
36 262439 170890 0 0 256335 0 0 126133 
37 297023 195303 0 0 301092 0 0 122064 
38 264473 174959 0 0 288886 0 0 77307 
39 154615 142409 0 0 235991 0 0 56963 
40 142409 113927 0 0 187165 0 40688 0 
41 85445 77307 0 0 134271 0 56963 0 
42 181945 158560 117536 0 186640 99592 114446 122696 
43 186490 162521 120472 0 191302 102080 117305 125761 
44 191149 166581 123482 0 196081 104630 120235 128903 
45 195924 170742 126566 0 200979 107244 123239 132123 
46 200818 175007 129728 0 205999 109923 126317 135423 
47 205834 179379 132968 0 211145 112669 129472 138806 
48 210976 183860 136290 0 216419 115483 132707 142274 
49 216246 188452 139695 0 221826 118368 136022 145828 
50 221648 193160 143184 0 227367 121325 139420 149470 
51 227185 197985 146761 0 233046 124355 142902 153204 
52 232860 202931 150427 0 238868 127462 146472 157031 
53 238677 208000 154185 0 244835 130646 150131 160954 
54 244639 213196 158036 0 250951 133909 153881 164974 
55 250750 218521 161984 0 257220 137254 157725 169095 
56 257014 223980 166030 0 263645 140683 161665 173319 
57 263434 229575 170178 0 270231 144197 165703 177649 
58 270015 235310 174429 0 276981 147799 169843 182087 
59 276760 241188 178786 0 283900 151491 174085 186635 
60 283673 247213 183252 0 290992 155276 178434 191297 
61 290759 253388 187830 0 298261 159154 182891 196076 
 
 
 
 
 
 
 116 
Sample Calculations 
 
First cut calculations can be done in order to estimate the amount of water contained in the 
Dauphin Island Water-Table aquifer.  By doing these rough calculations, we can check the 
volumes that are calculated in Section 5.4 and confirm that they are the same order of magnitude. 
 
As shown in the Figure 8-1, by multiplying the area of the island by the depth of the aquifer we 
will get a volume of the study area.  To obtain the volume of water contained in the groundwater, 
we multiply the volume by the porosity, 0.3.  Next, we will assume that the saltwater takes up 
about 30% of this volume and subtract that out to obtain a rough estimate for freshwater volume. 
Area of study area = 58982294 ft2 
Depth of Aquifer= 42 ft 
Volume of study area= 58982294 ft2 * 42 ft = 2477256361 ft3 
[3067739136*0.7]*0.3= 520223836 ft3 of freshwater 
This is the same order of magnitude as the values calculated in Section 5.4.  For example, the 
volume of freshwater for the aquifer after the simulation of Scenario 1.1 was 526248576 ft3.