Estimated transmissivity – Based on 

 interpolation of aquifer tests at 1,487 

 locations, in units of feet squared per day. 

EXPLANATION

>30 to 5,000

>5,000 to 10,000

>10,000 to 25,000

>25,000 to 50,000

>50,000 to 100,000

>100,000 to 9,000,000

Aquifer pumping test site

Area of low well yields in the 

 Gulf Trough (Lester J. Williams, USGS 

 written commun. 2011)

Woodville Karst Plain,

 (Brooks, 1981)

First Magnitude Spring, 

 (Greenhalgh, 2003)

Specific capacity data site

Springshed, (modified from 

 Greenhalgh, 2003)

POLK

MIAMI-DADE

LAKE

COLLIER

LEVY

MARION

LEE

BAY

BALDWIN

AIKEN

PALM BEACH

LEE

MOBILE

WARE

CLARKE

OSCEOLA

PIKE

HENDRY

BIBB

TAYLOR

VOLUSIA

LEON

DIXIE

CLAY

HALE

WALTON

BURKE

DALLAS

GLADES

CLAY

D U VA L

ORANGE

MONROE

BROWARD

WILCOX

BERKELEY

WAYNE

PASCO

PINELLAS

CLINCH

DALE

SUMTER

GULF

JONES

PERRY

ALACHUA

SHELBY

SMITH

ATTALA PICKENS

LIBERTY

JACKSON

BUTLER

PUTNAM

TUSCALOOSA

COLLETON

PERRY

COOSA

MARENGO

LEE

CHOCTAW

HIGHLANDS

ESCAMBIA

BARBOUR

WAYNE

MONROE

KEMPER

SCOTT LEAKE

JASPER

BAKER

COVINGTON

CLARKE

JASPER

B R E VA R D

LAURENS MACON

HENRY

GREENE

COFFEE

CONECUH

MARTIN

CITRUS

GREENE

CHILTON

OKALOOSA

EARLY

WORTH

WASHINGTON

SANTA ROSA

HARDEE

ELMORE

MANATEE

LONG

COFFEE

MADISON

JACKSON

NASSAU

LAMAR

LOWNDES

G E N E VA

BULLOCH

CHARLTON

NOXUBEE

EMANUEL

RUSSELL

DE SOTO

DODGE

SUMTER

STONE

SCREVEN

BULLOCK

DECATUR

NEWTON

GRADY

TROUP

WINSTON

BRYAN

THOMAS

HILLSBOROUGH

WAKULLA

AUTAUGA

OKEECHOBEE

HARRIS

TIFT

SUWANNEE

HOUSTON

LIBERTY

IRWIN

DOOLY

JONES

BROOKS

NESHOBA

GLYNN

ST LUCIE

SUMTER

CALHOUN

HOLMES

APPLING

HAMPTON

BIBB

MACON

HARRISON

CHAMBERS

COLQUITT

GEORGE

COWETA

SARASOTA

FLAGLER

TELFAIR

ECHOLS

LAUDERDALE

LOWNDES

GADSDEN

RANDOLPH

FRANKLIN

BAKER

BARNWELL

BERRIEN

WASHINGTON

MITCHELL

JASPER

TAYLOR

TALBOT

WILCOX

STEWART

LAFAYETTE

HANCOCK

HAMILTON

UPSON

PIKE

MONROE

CRISP

PIERCE

HEARD

CHARLOTTE

HERNANDO

BRANTLEY

CLAY

PUTNAM

COOK

JENKINS

BACON

UNION

BAMBERG

MILLER

TURNER

ATKINSON

SEMINOLE

JOHNSON

CALHOUN

BUTTS

DOUGHERTY

COLUMBIA

ESCAMBIA

TALLADEGA

TALLAPOOSA

ST JOHNS

MONTGOMERY

CRENSHAW

JEFFERSON

FORREST

TATTNALL

MARION

WASHINGTON

JEFFERSON

TOOMBS

TWIGGS

CHARLESTON

CHATHAM

DORCHESTER

EFFINGHAM

MCINTOSH

RANDOLPH

WILKINSON

HOUSTON

MERIWETHER

TERRELL

ALLENDALE

INDIAN RIVER

WARREN

GILCHRIST

WHEELER

RICHMOND

BALDWIN

PULASKI

E VA N S

J E F F D AV I S

LAMAR

CRAWFORD

CANDLER

LANIER

BRADFORD

BEN HILL

SEMINOLE

PEACH

FAYETTE

WEBSTER

SCHLEY

BLECKLEY

BEAUFORT

MUSCOGEE

TREUTLEN

QUITMAN

SPAULDING

MONTGOMERY

CHATTAHOOCHEE

GLASCOCK

BRYAN

CAMDEN

Macon

Tampa

Miami

Mobile

Orlando

Savannah

Charleston

Montgomery

Tallahassee Jacksonville

Introduction

A goal of the U.S. Geological Survey Groundwater Resources Program is to assess 

the availability of water in the principal aquifers of the United States 

(http://water.usgs.gov/ogw/gwrp/activities/regional.html, August 2011). Groundwater 

withdrawals from 66 principal aquifers of the United States were estimated in 2000. 

The Floridan aquifer system (FAS) was ranked as having the fifth largest groundwater 

withdrawals, totaling 3.64 billion gallons per day (Bgal/d) for irrigation, public-supply, 

and self-supplied industrial water uses (Maupin and Barber, 2005, table 1). The FAS 

covers an area of approximately 100,000 square miles (mi2

) in Florida and parts of 

Georgia, South Carolina, Alabama, and Mississippi. Groundwater wells for water supply 

were first drilled in the late 1800s and by the year 2000, the FAS was the primary source 

of drinking water for about 10 million people. Almost 50 percent of the water withdrawn 

from the FAS is used for irrigation (Marella and Berndt, 2005, fig. 7). One of the 

methods for assessing groundwater availability is the development of regional or 

subregional groundwater flow models of the aquifer system that can be used to develop 

water budgets spatially and temporally, as well as evaluate the groundwater resource 

change over time. Understanding the distribution of transmissivity within the FAS is 

critical to the development of groundwater flow models. 

The FAS is a sequence of carbonate rocks, predominantly of Tertiary age, that 

generally thickens seaward from the northern boundary of the system. The top of the 

FAS is confined by late and middle Miocene series rocks and the bottom is confined by 

early Paleocene series rocks (Miller, 1990). From top to bottom, the major hydrogeologic units of the FAS defined by Miller (1986) are the Upper Floridan aquifer (UFA); 

middle Floridan confining units (MCU), which are discontinuous units of relatively 

lower permeability that can be semi-confining or confining; and the Lower Floridan 

aquifer (LFA). The UFA is the most productive part of the system, containing freshwater 

throughout the majority of the FAS. Ninety percent of withdrawals from the FAS in 

2000 were from the UFA (Marella and Berndt, 2005, p. 5). The LFA contains freshwater 

over some of the study area, but contains saltwater south of Lake Okeechobee, Florida; 

in addition, the coastal extent of freshwater in the LFA is generally less than that of the 

UFA (Bush and Johnston, 1988).

Miller (1986) published a generalized transmissivity map of the UFA based on 114 

aquifer tests as well as his understanding of the geologic processes that created the rocks 

that form the aquifer. Bush and Johnston (1988) modified this transmissivity map after 

their model calibration and included the aquifer test dataset. Both maps cover the most 

productive part of the FAS, which is the UFA over most of the system, but also include 

parts of the MCU and/or LFA in areas where the MCU is semi-confining or absent. 

Kuniansky and Bellino (2012) tabulated 1,487 estimates of transmissivity from aquifer 

tests (1,045 values) and specific capacity data (442 values) from wells open to the UFA 

only and the UFA with parts of the MCU and/or LFA. The map presented herein differs 

from the previous maps in that it is based on interpolation of the 1,487 values of 

transmissivity. The transmissivity values in the dataset range from 8 to 9,000,000 feet 

squared per day (ft2

/d) with the majority of the values ranging from 10,000 to 

100,000 ft2/d. The wide range in transmissivity (6 orders of magnitude) is typical of 

carbonate rock aquifers, which are characterized by a wide range in karstification. 

Commonly, the range in transmissivity is greatest in areas where groundwater flow 

creates conduits in facies that dissolve more readily or areas of high porosity units that 

have interconnected vugs, with diameters greater than 0.1 foot (ft). These are also areas 

where transmissivity is largest.

The purpose of this report is to provide an updated map of the estimated transmissivity for the most productive part of the FAS based on aquifer test data; show the 

delineated springsheds of first magnitude springs indicative of areas that may have large 

dissolution conduits; and show areas within the Gulf Trough in Georgia where it is 

unlikely that a high yielding well can be developed in the UFA. Additionally, the 

geospatial datasets are available for downloading along with the map at 

(http://pubs.usgs.gov/sim/3204). The full database documented in Kuniansky and 

Bellino (2012) is also available at (http://pubs.usgs.gov/ds/669). 

Acknowledgments

The transmissivity data used for this map are from aquifer-test data collected and 

analyzed by the U.S. Geological Survey (USGS); the South Carolina Department of 

Natural Resources (SCDNR); St. Johns River Water Management District (SJRWMD) 

and Suwannee River Water Management District (SRWMD), Florida; Northwest Florida 

Water Management District (NWFWMD); Southwest Florida Water Management 

District (SWFWMD); and South Florida Water Management District (SFWMD). The 

authors wish to thank Drennan Park (SCDNR); Rob Devlin and Camille Ransom, South 

Carolina Department of Health and Environmental Control (SCDHEC); Emily Richardson (SFWMD); Robert Peterson (SWFWMD); Chris Richards (NWFWMD); David 

Toth and Dale Jenkins (SJRWMD); Carlos Herd (SRWMD); Trey Grubbs, Christy 

Crandall , and Hal Davis (USGS-Tallahassee, Florida); Lester Williams, John Clarke, 

Jaime Painter, and Lynn Torak (USGS-Atlanta, Georgia) for providing electronic 

versions of their databases and tables of aquifer tests. Lester Williams also materially 

contributed to the map by delineating the area within the Gulf Trough (Ga.) where high 

yield wells in the UFA are unlikely to be developed. The delineation was made from 

well yields for approximately 9,000 active wells included in the agricultural well 

inventory data provided by the Georgia Environmental Protection Division Agricultural 

Permitting Unit, Tifton, Ga. 

Transmissivity Map Development

The map shows ranges of transmissivity predicted by interpolation methods and 

shows areas of first magnitude springsheds and springs. Also shown on the map are 

areas within the Gulf Trough in Georgia where well yields from approximately 

9,000 active wells and information from drillers indicate a poor likelihood of developing 

a high yielding UFA well (Lester J. Williams, U.S. Geological Survey, written commun., 

2011). The inset location map shows geologic structural features, as well as the extent of 

the UFA aquifer and where the UFA is shown as unconfined, thinly confined, and 

confined (from Miller, 1986). 

This section of the report describes the geostatistical methods used to explore the 

transmissivity data and the decision process for the final interpolation method used to 

create the map. The large number of values and the fairly good spatial distribution of 

values over most of the UFA allow the use of interpolation. Geospatial tools in ArcGIS 

(Esri ArcMap 10.0) were used to explore the dataset. The main reason for using geostatistics was to determine if there was spatial correlation in the data and over what 

distance the correlation occurs. If a dataset does not follow a Gaussian (normal) 

distribution, as in the case of this transmissivity dataset, many forms of statistical 

analysis fail. In fact, using geostatistical techniques such as kriging to predict 

transmissivity with the non-transformed dataset results in invalid (negative) predicted 

transmissivity for some areas. The transmissivity dataset fits a lognormal distribution 

(Kuniansky and Bellino, 2012), and by using a base 10 logarithmic (“log10”) transformation, geostatistical techniques can be used to evaluate the data. The use of kriging 

assumes some spatial correlation, meaning that data proximal to each other have similar 

values and data farther away have dissimilar values. First, spatial correlation of the 

transformed data was tested using Global Moran’s I tool (Esri ArcMap 10.0). The Global 

Moran’s I test indicated that the data were clustered and there was a less than 1 percent 

likelihood that this clustered pattern could be the result of random chance. Thus, 

interpolation methods that assume the data are spatially correlated, such as kriging 

or inverse distance weighting are appropriate.

There is some subjectivity to the process of kriging, as a number of different 

covariance or semivariogram models can be applied, and several of these were in fact 

examined. Additionally, different software implementations of kriging have different 

advantages and disadvantages. The ArcGIS software has an algorithm that uses 

optimization to find the best fit to the data among the different 

semivariogram/covariogram models and kriging methods applied, but does not allow 

weighting of values. Oridnary kriging assumes no spatial trend in the data. Because the 

thickness of the aquifer increases downdip, which may increase transmissivity, universal kriging was also tested. Universal kriging with constant trend removal provided the 

best fit to the dataset for prediction of log10 transmissivity. The main parameters of 

interest from the kriging are the following:

• Range—the distance at which the semivariogram model begins to asymptote, 

 which means that this is the distance where the variance in data becomes constant; 

• Nugget—the value of the semivariogram model where it intercepts the y-axis; 

• Sill—the value of the variogram at distances greater than the range; and 

• Partial sill—the sill minus the nugget. 

Additionally, the geostatistical exploration indicated that there was no spatial anisotropy 

(when values are correlated over different distances in different directions, like the two 

axes of an ellipse). The nugget was 0.10843 (log10(ft2

/d)), the sill was 0.19724 

(log10(ft2/d)), the partial sill was 0.08881 (log10(ft2/d)), and the range was calculated as 

16,571 ft (3 miles). Trend removal resulted in a fairly flat semivariogram model, with the 

sill being slightly greater than the nugget. 

The implementation of any interpolation method on a computer requires that the 

estimates be made for the surface by creating a grid in which an interpolated value is 

computed at each grid cell (grid cell size is determined by the software by dividing the 

longest extent of the data in the horizontal or vertical direction by 250−2,070 ft per side). 

Thus, the location of the estimated value may not correspond exactly to the location of 

the well and the interpolated surface will result in a predicted transmissivity that differs 

from the “measured” value. The fit of the kriging model to the log10-transformed data 

resulted in a root-mean square error of 0.53754 and mean error of 0.00290. To put these 

residuals into context, the range of the log10-transformed data was 6.08, with a mode of 

3.47, mean of 4.30, and standard deviation of 0.72. More information about geostatistics 

and kriging is provided by Isaaks and Srivastava (1989). 

The results of the geostatistical exploratory analysis were used to develop the 

parameters for the deterministic interpolation. This deterministic interpolation approach 

honors the test data, allows aquifer test estimates to have greater weighting than specific 

capacity estimates, and makes no assumptions about the spatial variogram and statistical 

distribution of the data in order to create a transmissivity surface. The final map was 

developed using inverse-distance weighting with a four-sector search rotated 45 degrees 

and a search radius of 39,170 ft. The search radius from kriging is set by the software 

and is a little more than double the range; however, if values are found closer to the grid 

centroid, then those values are used–a minimum of 10 and maximum of 15 weighted 

values. 

Inverse-distance weighting (IDW) is considered an exact interpolation method 

because, in theory, the method produces the exact values of the data at the locations of 

known values. However, the implementation on the computer uses the same griding 

method as the kriging, thus the grid size was determined in the same manner. A benefit 

of the use of IDW for the final map presented is the ability to apply additional weighting 

based on the method for estimating an individual value. Transmissivity estimates from 

single- and multi-well aquifer performance tests (APT) are better estimates than those 

calculated from specific capacity data (SPC). Therefore, weighting based on the test 

method was applied in addition to inverse-distance weighting (any details about the 

quality of individual estimates are in the reference or remark field of the database 

(Kuniansky and Bellino, 2012)). Transmissivity estimates from APT and SPC were 

given weights of 1.0 and 0.5, respectively, indicating that estimates from SPC are half 

as good as estimates from APT. If multiple values were approximately in the same 

location for the interpolation, the mean value was used. Although the search method 

and radius were set to the values determined from kriging, and the additional weighting 

of APT and SPC tests was applied in the IDW, the optimization function was used to 

determine the power of the inverse distance weighting. The optimal power is typically 

between 1 and 2, and was 1.276 in this case. The mean error (predicted minus 

measured) for the IDW interpolation of the log10-transformed transmissivity was 

0.03168 and the root mean square error was 0.54181, similar to the errors previously 

shown for universal kriging interpolation. The transmissivity ranges shown with 

differing color shading were based on rounding natural breaks in the distribution of 

the values. Only 52 (3 percent) of transmissivity values were less than 1,000 and 

21 (1 percent) were greater than 1,000,000 ft2

/d. Additionally, the final IDW transmissivity surface was converted to a raster dataset where all raster values outside a polygon 

created from the outer points of the transmissivity data and the extent of the FAS were 

deleted and the final grid cell side length for the raster shown is 1 mile per side.

Discussion of the Transmissivity Map

 The FAS contains many different facies and has been exposed to a range of 

post-depositional processes, the details of which are discussed in Miller (1986). Bush and 

Johnston (1988) did not try to determine transmissivity in the highly karstified areas of 

the FAS. In general, the greatest areas of karstification are where the limestone crops out 

or where overlying confining units are thin (Miller, 1986); however, karst features are 

present over most of the extent of the FAS (Veni and others, 2001; Tobin and Weary, 2004). 

Although the presence of sinkholes also indicates karstification, less permeable cover 

materials may fill in the sinkholes and their associated conduits (Sinclair and Stewart, 1985; 

Wilson and Shock, 1996; and Tihansky, 1999). Thus, sinkholes are not used to identify areas 

that might have large submerged cave passages, nor are they shown on the map. 

 Large dissolution conduits generally are near many of the first magnitude springs and 

springsheds. Generalized springsheds for these springs are shown in Greenhalgh (2003). 

Over the past 20 years, submerged, large-diameter interconnected caves have been mapped 

in the Woodville Karst Plain (Brooks, 1981). The Woodville Karst Plain and springshed 

areas shown are generalized from the original maps (Tom Greenhalgh, Florida Geolological 

Survey, written commun., August 2011), and the estimates of transmissivity may underestimate the actual range in transmissivity in these areas. Wells that intercept conduits greater 

than 5 ft in diameter could have a transmissivity greater than 10 million ft2

/day (Shoemaker 

and others, 2008), whereas a nearby well that did not intercept a conduit could have a 

transmissivity orders of magnitudes lower. An example of this is on the map in Lafayette 

County, Florida, where there is a low transmissivity bulls eye associated with one well in an 

area surrounded by first magnitude springs and wells with orders of magnitude greater 

transmissivity estimates.

 The interpolated transmissivity ranges shown on this map reflect the geologic structure 

and karstified areas. Transmissivity is large in the areas where the system is unconfined, 

such as west-central Florida and southwest Georgia just northwest of the Gulf Trough. 

Transmissivity is small along the Gulf Trough and Southwest Georgia Embayment (referred 

to as Apalachicola Embayment in some reports). Both features are known to have a thick 

accumulation of fine-grained sediment, and often are treated as a single low hydraulic 

conductivity feature within the FAS (Miller, 1986; Kellam and Gorday, 1990; Davis, 1996; 

Torak , Painter and Peck, 2010). Transmissivity is also small in the thin, updip part of the 

system near its northern boundary. Another area of large transmissivity coincides with the 

Southeast Georgia Embayment, where the FAS thickens and is composed of carbonate rocks 

(Miller, 1986). 

References Cited

Brooks, H.K., 1981, Physiographic divisions of Florida (map): Gainesville, Fla., University 

 of Florida Institute of Food and Agricultural Sciences.

Bush, P.W., and Johnston, R.H., 1988, Ground-water hydraulics, regional flow, and ground-

 water development of the Floridan aquifer system in Florida and in parts of Georgia and 

 South Carolina: U.S. Geological Survey Professional Paper 1403–C, 80 p.

Davis, Hal, 1996, Hydrogeologic investigation and simulation of ground-water flow in 

 the Upper Floridan aquifer of north-central Florida and southwestern Georgia and 

 delineation of contributing areas for selected city of Tallahassee, Florida, water-supply 

 wells: U.S. Geological Survey Water-Resources Investigations Report 95–4296, 61 p. 

Greenhalgh, Tom, 2003, Florida’s first magnitude springsheds: Florida Geological Survey, 

 Tallahassee, Florida, Poster 12, 1 sheet. 

Isaaks, E.H., and Srivastava, R.M., 1989, An introduction to applied geostatistics: New 

 York, Oxford University Press, 561 p. 

Kellam, M.F., and Gorday, L.L., 1990, Hydrogeology of the Gulf Trough–Apalachicola 

 Embayment area, Georgia: Georgia Geologic Survey Bulletin 94, 74 p. 

Kuniansky, E.L., and Bellino, J.C., 2012, Tabulated transmissivity and storage properties of 

 the Floridan aquifer system in Florida and parts of Georgia, South Carolina, and 

 Alabama: U.S. Geological Survey Data Series 669. 

Marella, R.L., and Berndt, M.P., 2005, Water withdrawals and trends from the Floridan 

 aquifer system in the southeastern United States, 1950–2000: U.S. Geological Survey 

 Circular 1278, 20 p. 

Maupin, M.A., and Barber, N.L., 2005, Estimated withdrawals from principal aquifers in the 

 United States, 2000: U.S. Geological Survey Circular 1279, 46 p. 

Miller, J.A., 1986, Hydrogeologic framework of the Floridan aquifer system in Florida and 

 in parts of Georgia, Alabama, and South Carolina: U.S. Geological Survey Professional 

 Paper 1403–B, 91 p. 

Miller, J.A., 1990, Groundwater atlas of the United States: Segment 6, Alabama, Florida, 

 Georgia, and South Carolina: U.S. Geological Survey Hydrologic Investigations Atlas 

 HA–730–G, 28 p. 

Shoemaker, W.B., Kuniansky, E.L., Birk, Steffen, Bauer, Sebastian, and Swain, E.D., 2008, 

 Documentation of a Conduit Flow Process for MODFLOW-2005: U.S. Geological Survey 

 Techniques and Methods, book 6, chap. A24, 50 p. 

Sinclair, W.C., and Stewart, J.W., 1985, Sinkhole type, development, and distribution in 

 Florida: U.S. Geological Survey Map Series 110, 1 plate. 

Tihansky, A.B., 1999, Sinkholes, west-central Florida, in Galloway, Devin, Jones, D.R., and 

 Ingebritsen, S.E., eds., Land subsidence in the United States: U.S. Geological Survey 

 Circular 1182, p. 121–140. 

Tobin, B.D., and Weary, D.J., 2004, Digital engineering aspects of karst map [A GIS version 

 of Davies, W.E., Simpson, J.H., Ohlmacher, G.C., Kirk, W.S., and Newton, E.G., 1984, 

 Engineering aspects of karst: U.S. Geological Survey, National Atlas of the United States 

 of America, Scale 1:7,500,000]: U.S. Geological Survey Open-File Report 2004–1352. 

Torak, L.J., Painter, J.A., and Peck, M.F., 2010, Geohydrology of the Aucilla-Suwannee-

 Ochlockonee River Basin, south-central Georgia and adjacent parts of Florida: U.S. 

 Geological Survey Scientific Investigations Report 2010–5072, 78 p. 

Veni, George, DuChene, Harvey, Crawford, N.C., Groves, C.G., Huppert, G.N., Kastning, 

 E.H., Olson, Rick, and Wheeler, B.J., 2001, Living with karst: American Geological 

 Institute, 64 p. 

Wilson, W.L., and Shock, E.J., 1996, New sinkhole data spreadsheet manual (v1.1): Winter 

 Springs, Florida, Subsurface Evaluations, Inc., 31 p., 3 app., 1 disk.

Base from U.S. Geological Survey digital data, 1:100,000, 

Albers Conical Equal Area, NAD 1983

This report is available online at

http://pubs.usgs.gov/sim/3204

Any use of trade, product, or firm names is for 

descriptive purposed only and does not imply 

endorsement by the U.S. Government. 

Transmissivity of the Upper Floridan Aquifer in Florida and 

Parts of Georgia, South Carolina, and Alabama

By 

Eve L. Kuniansky, Jason C. Bellino, and Joann F. Dixon 

2012

U.S. DEPARTMENT OF THE INTERIOR

U.S. GEOLOGICAL SURVEY

A product of the

U.S. Geological Survey Groundwater Resources Program Transmissivity of the Upper Floridan aquifer in Florida and

 parts of Georgia, South Carolina, and Alabama, 2012

SCIENTIFIC INVESTIGATIONS MAP 3204

By Eve L. Kuniansky, Jason C. Bellino, and Joann F. Dixon

A T L A N T I C O C E A N

LAKE 

OKEECHOBEE

G U L F O F M E X I C O

89° 88° 87° 86° 85° 84° 83° 82° 81° 80°

33°

32°

31°

30°

29°

28°

27°

26°

25°

APPROXIMATE UPDIP LIMIT OF THE 

FLORIDAN AQUIFER SYSTEM (MILLER, 1986)

0 50 MILES

0 50 KILOMETERS

35° 35°

30°

80°

80°

30°

85°

85°

0 100 200 KILOMETERS

0 100 200 MILES

EXPLANATION

Degree of confinement of the 

 Floridan aquifer system

Confined

Thinly Confined

Unconfined

Axis of a positive feature 

Axis of a negative feature 

Boundary of negative feature 

Direction of structural feature 

Fault – U, upthrown block; 

 D, downthrown block 

SOUTHEAST GEORGIA

EMBAYMENT

SUWANNEE

STRAIT

PENINSULAR ARCH

OCALA “UPLIFT”

SOUTH FLORIDA

BASIN

SOUTHWEST GEORGIA

EMBAYMENT

SOUTH 

CAROLINA

ALABAMA GEORGIA

FLORIDA

ATLANTIC OCEAN

GULF OF MEXICO

GILBERTOWN-PICKENSPOLLARD FAULT ZONE

MOBILE 

GRABEN

D

U

U

U D

U

U D

D

U

U

D

U

D

U

D

U

D

U

D

U

D

U

U

U

D

U

D

U

D

D

U

CHATTAHOOCHEE

“ARCH”

GULF TROUGH

U

D