U.S. Department of the Interior

U.S. Geological Survey

Simulation of Salinity Intrusion Along the

Georgia and South Carolina Coasts

Using Climate-Change Scenarios

Prepared in cooperation with the Beaufort-Jasper Water and Sewer Authority

Scientific Investigations Report 2013–5036
Cover. Images from the freshwater-saltwater interface diagram and graph 

(see report figures 22 and 23, respectively).
Simulation of Salinity Intrusion Along the 

Georgia and South Carolina Coasts Using 

Climate-Change Scenarios

By Paul A. Conrads, Edwin A. Roehl, Jr., Ruby C. Daamen, and John B. Cook

Prepared in cooperation with the Beaufort-Jasper Water and Sewer Authority

Scientific Investigations Report 2013–5036

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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Suggested citation:

Conrads, P.A., Roehl, E.A., Jr., Daamen, R.C., and Cook, J.B., 2013, Simulation of salinity intrusion along the Georgia 

and South Carolina coasts using climate-change scenarios: U.S. Geological Survey Scientific Investigations Report 

2013–5036, 92 p. + 5 apps.

(http://pubs.usgs.gov/sir/2013/5036/)
iii

Acknowledgments

The authors thank Mr. Charles Sexton, Director of Engineering with Beaufort-Jasper 

Water and Sewer Authority, and Dr. Kenan Ozekin, Senior Account Manager with the Water 

Research Foundation (WaterRF), for their technical assistance and coordination.

v

Contents 

Acknowledgments........................................................................................................................................iii

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................4

Description of Study Areas .................................................................................................................5

Waccamaw River and Atlantic Intracoastal Waterway Study Area ..................................6

Lower Savannah River Study Area...........................................................................................8

Previous Studies .................................................................................................................................10

Approach..............................................................................................................................................12

Data-Collection Networks..........................................................................................................................14

Waccamaw Rivers and Atlantic Intracoastal Waterway ............................................................14

Lower Savannah River.......................................................................................................................15

Characterization of Historical Streamflow, Water Level, and Specific Conductance 

Dynamics .........................................................................................................................................16

Characterization of Historical Pee Dee River Streamflow...........................................................16

Characterization of Historical Lower Savannah River Streamflow............................................18

Characterization of Historical Waccamaw River and Atlantic Intracoastal Waterway 

Water Levels ..........................................................................................................................18

Characterization of Historical Lower Savannah River and Marsh Water Levels ....................22

Characterization of Historical Specific Conductance ..................................................................26

Waccamaw River and Atlantic Intracoastal Waterway .....................................................26

Lower Savannah River..............................................................................................................31

Simulation of Specific Conductance ........................................................................................................33

Artificial Neural Networks Models..................................................................................................35

Data-Mining Techniques ...................................................................................................................36

Signal Decomposition ...............................................................................................................37

Spectral Analysis.......................................................................................................................38

State-Space Reconstruction ...................................................................................................38

Signal Decorrelation .................................................................................................................39

Correlation and Sensitivity Analysis.......................................................................................40

Limitations of the Historical Data Sets............................................................................................42

Development and Evaluation of Specific-Conductance Models.........................................................42

Statistical Measures of Prediction Accuracy................................................................................47

Waccamaw River and Atlantic Intracoastal Waterway Models................................................47

North End Models......................................................................................................................48

South End Models......................................................................................................................51

Lower Savannah River and Marsh Specific-Conductance Models...........................................51

Riverine Specific-Conductance Model for Savannah River at the I-95 Bridge...............54

Marsh Specific-Conductance Model at Site B2...................................................................55

Development of the Decision Support Systems .....................................................................................56

Architecture.........................................................................................................................................56

Historical Databases..........................................................................................................................58

Decision Support System Visualization Programs........................................................................60
vi

Climate-Change Scenarios and Simulations using the PRISM-2 and M2M-2 Decision 

Support Systems.............................................................................................................................62

Sea-Level Rise: PRISM-2 and M2M-2 Decision Support Systems.............................................64

Changes in Streamflow: PRISM-2....................................................................................................71 

User-Defined Hydrographs: PRISM-2 ....................................................................................72

Streamflow as a Percentage of Historical Streamflow: PRISM-2.....................................77

Climate-Change Scenarios Using the M2M-2 DSS................................................................................80

Response of Rivers to Sea-Level Rise: M2M-2 DSS.....................................................................80

Effects of Decreased Streamflow and Sea-Level Rise on Simulated Salinity: 

M2M-2 DSS ...................................................................................................................83

Effects of Decreased Streamflow and Sea-Level Rise on Marsh Pore-Water 

Salinity: M2M-2 DSS....................................................................................................85

Summary and Conclusions.........................................................................................................................87

Selected References...................................................................................................................................89

Appendixes (provided as separate files at http://pubs.usgs.gov/sir/2013/5036/)

Appendix 1. Description of the artificial neural network models used in the Model-toMarsh Decision Support System-Version 2 (M2M-2 DSS)

Appendix 2. Descriptions of variables used in the Model-to-Marsh Decision Support 

System-Version 2 (M2M-2 DSS) artificial neural network models

Appendix 3. Summary of performance statistics for the specific conductance models used 

in the Model-to-Marsh Decision Support System-Version 2 (M2M-2 DSS)

Appendix 4. User’s Manual for the Pee Dee River and Atlantic Intracoastal Waterway 

Salinity Intrusion Model Decision Support System (Version 2)

Appendix 5. User’s Manual for Model-to-Marsh Decision Support System (Version 2)

Figures

1–9. Maps showing:

1. Vulnerability ratings for potential salinity intrusion at selected major 

municipal water-supply intakes along the coast of the Gulf of Mexico and the 

Atlantic Ocean.......................................................................................................................2

2. Location of the Waccamaw River and Atlantic Intracoastal Waterway study area 

and selected gages in the Pee Dee River Basin, South Carolina .................................3

3. Location of the lower Savannah River study area, including the Savannah 

National Wildlife Refuge and coastal South Carolina and Georgia .............................4

4. Locations of the Yadkin-Pee Dee River, Savannah River, and selected basins in 

North Carolina, South Carolina, and Georgia...................................................................5

5. Physiographic provinces and location of surface-water bodies in the drainage 

basin of the Waccamaw River and Atlantic Intracoastal Waterway study area in 

North and South Carolina ....................................................................................................6

6. Locations of selected water-quality monitoring stations and water-supply 

intakes in the Waccamaw River and Atlantic Intracoastal Waterway study area, 

Grand Strand, South Carolina .............................................................................................7

7. Location of selected surface-water bodies in the lower Savannah River basin, 

North Carolina, South Carolina, and Georgia...................................................................9

8. Location of Georgia Ports Authority facilities and the Savannah National Wildlife 

Refuge in the lower Savannah River basin, South Carolina and Georgia .................10

9. Location of the upstream extent of salinity intrusion along the Savannah and 

Back Rivers, greater than 0.5 practical salinity unit for four channel depths; 

13–15 feet in 1875; 26–30 feet in 1940; 34 feet in 1965; and 42 feet in 1997 ................11
vii

10. Schematic diagram showing the conceptual modeling approach for evaluation of 

the effects of climate change on salinity intrusion, North and South Carolina ...............13

11. Map showing the location of U.S. Geological Survey river gaging stations and 

marsh gaging stations in the vicinity of the Savannah National Wildlife Refuge, 

South Carolina and Georgia......................................................................................................16

12–21. Graphs showing:

12. Streamflow duration hydrographs for Pee Dee River at Rockingham, 

North Carolina, 1928–2010, and Little Pee Dee River at Galivants Ferry, 

South Carolina, 1942–2010.................................................................................................17

13. Hourly streamflow for the Pee Dee, Lynches, Little Pee Dee, and Black Rivers 

and 7-day average flow for the Pee Dee River and hourly streamflow for the 

Lynches, Little Pee Dee, and Black Rivers, January 1, 1996–December 31, 1997....19

14. Streamflow duration hydrographs for Savannah River near Clyo, Georgia, 

1930–2010..............................................................................................................................20

15. Daily streamflow and mean annual streamflow for Savannah River near Clyo, 

Georgia, October 1, 1994–September 30, 2010...............................................................20

16. Daily tidal range at Atlantic Intracoastal Waterway at South Carolina 

Highway 9, July 1994–June 1996 ......................................................................................21

17. Hourly water levels at four gaging stations on the Pee Dee and Waccamaw 

Rivers and the Atlantic Intracoastal Waterway, South Carolina for a low-flow 

period October 1–17, 1998, and a high-flow period January 15–March 1, 1999.......23

18. Hourly water levels at three gaging stations on the Savannah River, Georgia, 

October 1–31, 2002..............................................................................................................24

19. Daily tidal range at Savannah River at Fort Pulaski, Georgia, 

June 2001–April 2003..........................................................................................................24

20. Hourly water-level data for the four Back River marsh gaging stations and 

Little Back River gaging stations for the period December 15, 2001–

February 15, 2002.................................................................................................................25

21. Hourly water-level data for three marsh gaging stations along the Middle 

and Front Rivers, and Savannah River water level at Houlihan Bridge, Georgia, 

November 10, 2001–January 7, 2002................................................................................25

22. Conceptual model of the major factors controlling the position of the 

freshwater-saltwater interface................................................................................................26

23–28. Graphs showing:

23. Daily mean specific conductance at Waccamaw River at Hagley Landing, 

South Carolina, and daily mean streamflow at Pee Dee River at Pee Dee, 

South Carolina, October 1, 1994–September 30, 2010...................................................27

24. Daily mean specific conductance at Atlantic Intracoastal Waterway at Grand 

Strand Airport, South Carolina, and daily mean streamflow at Waccamaw River 

at Longs, South Carolina, October 1, 1994–September 30, 2010 .................................28

25. Daily mean specific conductance at Waccamaw River at Pawleys Island, daily 

mean streamflow at Pee Dee River, and daily mean gage height and daily mean 

tidal range at Little River Inlet, South Carolina, April 1, 2007–June 30, 2007.............29

26. Daily mean specific conductance at Waccamaw River at Briarcliffe Acres, 

Pee Dee River streamflow, and gage height and tidal range at Little River Inlet, 

April 1, 2008–October 31, 2008 ..........................................................................................30

27. Maximum daily specific conductance at Savannah River near Port Wentworth, 

Georgia (I-95 Bridge) and streamflow at Savannah River near Clyo, Georgia, 

January 1, 1994–September 30, 2010...............................................................................31

28. Daily streamflow at Savannah River near Clyo, Georgia, and daily specific 

conductance at the Savannah River near Port Wentworth, Georgia (I-95 Bridge), 

May–October 2009, including occurrences of the new moon ....................................32
viii

29. Conceptual model of the location of the freshwater-saltwater interface and salinity 

stratification–de-stratification cycle in estuarine rivers .....................................................32

30. Graph showing hourly surface and bottom salinities for station GPA 4, Georgia, 

and occurrences of the moon phase, July 15–September 13, 1997...................................33

31. Diagram showing tidal marsh types classified by interstitial salinity 

and average estuary surface salinities...................................................................................33

32–33. Graphs showing:

32. Hourly specific conductance at four marsh gaging stations along the Little Back 

and Back Rivers and hourly specific conductance at Houlihan Bridge on the 

Front River, Georgia, August 6–November 30, 2001.......................................................34

33. Hourly pore-water specific conductance for two marsh gaging stations on the 

Middle River, one marsh gaging station on the Front River, and one river 

gaging station at Houlihan Bridge on the Front River, Georgia, 

July 26–September 27, 2002 ..............................................................................................34

34. Diagram showing multi-layer perceptron artificial neural network architecture ...........35

35–37. Graphs showing:

35. Hourly gage heights at Atlantic Intracoastal Waterway at South Carolina, 

Highway 9, filtered gage heights for tidal influence, and 1-day change in 

filtered gage heights for the 60-day period October 4–December 4, 1998................37

36. Fourteen-day and 30-day moving-window averages of total inflow to the 

Waccamaw River and Atlantic Intracoastal Waterway study area, South 

Carolina, and the difference between the 14-day and 30-day moving-window 

averages, 1995–2009...........................................................................................................39

37. Daily mean gage height at Little River Inlet and daily mean total inflow to 

the Waccamaw River and Atlantic Intracoastal Waterway study area, 

South Carolina, 1995–2009.................................................................................................40

38. Schematic diagram showing the gage height decorrelation model..................................41

39. Graph showing measured, predicted, and residual error from the Single Input 

Single Output Artificial Neural Network model used to decorrelate gage height 

at Little River Inlet from the total flow to the Waccamaw River and Atlantic Intracoastal Waterway study area, South Carolina, August 12, 1995–August 12, 2009 ..........41

40. Schematic diagram showing the two-stage model architecture to predict specific 

conductance at a gaging station .............................................................................................47

41–44. Graphs showing:

41. Measured and simulated specific-conductance values from the daily model 

for four U.S. Geological Survey gages on the North End of the Atlantic Intracoastal Waterway, South Carolina: Myrtlewood, Briarcliffe Acres, Grand 

Strand Airport, and Little River Inlet, August 12, 1995–August 12, 2009.....................49

42. Measured and simulated specific-conductance values from the hourly model 

for four U.S. Geological Survey gages on the North End of the Atlantic Intracoastal Waterway, South Carolina: Myrtlewood, Briarcliffe Acres, Grand 

Strand Airport, and Little River Inlet, August 12, 1995–August 12, 2009.....................50

43. Measured and simulated specific-conductance values from the daily 

model for three U.S. Geological Survey gages on the Waccamaw River, 

South Carolina: Wachesaw, Pawleys Island, and Hagley Landing, 

August 12, 1995–August 12, 2009......................................................................................52

44. Measured and simulated specific-conductance values from the hourly 

model for three U.S. Geological Survey gages on the Waccamaw River, 

South Carolina: Wachesaw, Pawleys Island, and Hagley Landing, 

August 12, 1995–August 12, 2009......................................................................................53
ix

45. Map showing the location of Georgia Ports Authority river monitoring gages, 

South Carolina and Georgia......................................................................................................54

46–47. Graphs showing:

46. Measured and simulated hourly salinities at Savannah River at Interstate 95, 

Georgia, June 1–August 31, 2002 .....................................................................................55

47. Measured and simulated hourly pore-water salinities at Little Back River at 

Site B2, Georgia, June 1–August 31, 2002.......................................................................56

48. Architecture of the Pee Dee River and Intracoastal Waterway Salinity Intrusion 

Model-2 Decision Support System..........................................................................................57

49. Architecture of the Model-to-Marsh-2 Decision Support System.....................................57

50–52. Screen captures showing:

50. Model simulation control panels used to set parameters and run a simulation 

in the Pee Dee River and Intracoastal Waterway Salinity Intrusion Model-2 

Decision Support System and the Model-to-Marsh-2 Decision Support System ...60

51. The three-dimensional visualization worksheet showing the specific 

conductance of salinity intrusion at the North End of the Waccamaw River 

and Atlantic Intracoastal Waterway study area, South Carolina, for a climatechange scenario .................................................................................................................61

52. The Two-Dimensional Color-Gradient Visualization Program .....................................61

53. Map showing location of tidal gages along the coast of North Carolina, 

South Carolina, Georgia, and Florida.......................................................................................64

54–55. Graphs showing:

54. Sea-level rise curves for three sites along the Georgia and South Carolina 

coast: Springmaid Pier, Charleston, and Fort Pulaski...................................................66

55. Simulated specific conductance at Pawleys Island gage for actual conditions 

and for a 1-foot and 2-foot sea-level rise for December 1, 2001–August 20, 2009, 

and May 1, 2007–January 31, 2008...................................................................................67

56. Screen capture of three-dimensional longitudinal plots of a salinity-intrusion event 

at selected stations in the South End of the Waccamaw River and Atlantic 

Intracoastal Waterway study area, South Carolina, October 25–27, 2008........................68

57–58. Graphs showing:

57. The number of days specific conductance thresholds of 1,000, 2,000, and 

3,000 microsiemens per centimeter are exceeded for incremental sea-level 

rises at Wachesaw, Pawleys Island, and Hagley Landing gages, South Carolina, 

for the simulation period 1995–2009.................................................................................69

58. The number of days specific-conductance thresholds of 1,000, 2,000, and 

3,000 microsiemens per centimeter are exceeded for incremental sea-level 

rises at Myrtlewood, Myrtle Beach Airport, and Little River Inlet gages, 

South Carolina, for the simulation period 1995–2009 ....................................................70

59. Screen capture of three-dimensional longitudinal plots of a salinity-intrusion 

event at selected stations in the North End of the Waccamaw River and Atlantic 

Intracoastal Waterway study area, South Carolina, October 25–27, 2008........................71

60. Example of the scale of the global circulation model and the scale of the Yadkin-Pee 

Dee Basin, North Carolina, South Carolina, and Georgia....................................................72

61–75. Graphs showing:

61. The cumulative flow and the frequency distribution for measured and simulated 

total flow to the Waccamaw River and Atlantic Intracoastal Waterway study 

area, South Carolina, for 1995–2009 and projected total flow for 2055–2069............73
x

62. Daily duration hydrographs for simulated total flow to the Waccamaw River and 

the Atlantic Intracoastal Waterway study area, South Carolina, for historical 

conditions, 1980–2010, and projected future conditions, 2040–2070..........................74

63. Simulated historical, 1995–2009, and projected, 2055–2069, daily specificconductance values generated for Pawleys Island, South Carolina, using the 

simulated flows from the ECHO global circulation model and the Hydrologic 

Simulation Program-Fortran watershed model .............................................................75

64. Number of days per season in which the daily specific conductance threshold 

of 1,000 microsiemens per centimeter is exceeded at the Pawleys Island gage, 

South Carolina, for the simulated historical 1995–2009, and future, 2055–2069, 

simulation periods...............................................................................................................75

65. The number of days in each season that the specific conductance threshold of 

1,000 microsiemens per centimeter is exceeded at the Pawleys Island gage, 

South Carolina, for the historical, 1995–2009, projected, 2055–2069, climate 

changes, and projected climate changes in combination with a 1-foot sea-level 

rise, and a 2-foot sea-level rise, and the percentage of simulated specificconductance values exceeding 1,000 microsiemens per centimeter 

each season.........................................................................................................................76

66. Effects of potential climate change on simulated flow to the Waccamaw and 

Atlantic Intracoastal Waterway study area, South Carolina, expressed as the 

difference between the daily 10th percentile of flow for future, 2040–2070, and 

historical, 1980–2010, periods ...........................................................................................77

67. Number of days specific-conductance thresholds of 1,000, 2,000, and 

3,000 microsiemens per centimeter are exceeded at the Pawleys Island gage, 

South Carolina, for incremental reductions of flow for historical conditions and 

a 1-foot sea-level rise and a 2-foot sea-level rise, during 1995–2009 ........................78

68. Three-dimensional response surface showing the percentage of days the simu-

lated specific conductance exceeded the threshold of 1,000 microsiemens per 

centimeter with reductions in streamflow and incremental rises in sea level 

at the Pawleys Island gage, South Carolina, 1995–2009 ..............................................79

69. Measured daily salinity at the Houlihan Bridge gage, Lucknow Canal gage, and 

the Interstate 95 gage in the vicinity of the Savannah National Wildlife Refuge, 

South Carolina and Georgia, April 1994–April 2005.......................................................80

70. Simulated historical salinity and simulated salinities for 1-foot and 2-foot 

sea-level rises at Interstate 95 gage, Lucknow Canal gage, and Houlihan Bridge 

gage, in the vicinity of the Savannah National Wildlife Refuge, South Carolina 

and Georgia, April 1994–April 2005 ..................................................................................81

71. Frequency curves of simulated salinity conditions for historical conditions 

(baseline) and a 1-foot and 2-foot sea level rise at the Interstate 95 gage 

Lucknow Canal gage, and Houlihan Bridge gage, in the vicinity of Savannah 

National Wildlife Refuge, South Carolina and Georgia, April 1994–April 2005 .........82

72. Frequency curves of simulated salinity for historical conditions, 1-foot and 2-foot 

sea level rises, and a 1-foot sea-level rise in combination with a 10- and 

20-percent reduction in streamflow at the Interstate 95 gage and Lucknow Canal 

gage in the vicinity of Savannah National Wildlife Refuge, South Carolina and 

Georgia, April 1994–April 2005 ..........................................................................................83

73. Three-dimensional response surface showing the percentage of days the 

simulated salinity exceeded the threshold of 0.5 practical salinity units is 

exceeded for reductions in streamflow and incremental sea-level rises at the 

Interstate 95 gage in the vicinity of Savannah National Wildlife Refuge, 

South Carolina and Georgia, April 1994–April 2005.......................................................84
xi

74. Simulated salinity conditions for a historical, a 1-foot and a 2-foot sea level 

rise, and a 1-foot sea-level rise with 20-percent reduction in streamflow at the 

Lucknow Canal gage in the vicinity of Savannah National Wildlife Refuge, 

South Carolina and Georgia, June 1–December 31, 1999 ............................................85

75. Simulated pore-water salinity historical conditions and a 1-foot sea-level rise 

with 20-percent reduction in streamflow, and the difference between the two 

at the B2 marsh gage in the Savannah National Wildlife Refuge, South Carolina, 

September 19, 1999–December 31, 1999.........................................................................86

76. Two-dimensional color-gradient visualization of the tidal marshes in the vicinity 

of Savannah National Wildlife Refuge, South Carolina and Georgia, showing the 

differences in simulated average salinity of marsh pore water between historical 

conditions and a 1-foot sea-level rise with 20-percent reduction in streamflow, 

September 19 –October 19, 1999..............................................................................................87

Tables

1. Description of seven reservoirs on the Yadkin-Pee Dee River, North Carolina .................7

2. Description of U.S. Geological Survey gaging stations on the Pee Dee River and its 

major tributaries, North and South Carolina............................................................................8

3. Description of six reservoirs in South Carolina with greater than 1,000 acres on the 

Savannah River.............................................................................................................................9

4. Description of U.S. Geological Survey coastal gaging network in the Waccamaw 

River-Atlantic Intracoastal Waterway study area of South Carolina ................................14

5. Description of U.S. Geological Survey continuous river and marsh gaging networks 

used in the lower Savannah River study area .......................................................................15

6. Mean tide range, spring tide range, and mean tide levels for selected locations on 

the Atlantic Intracoastal Waterway and Waccamaw River, South Carolina ...................21

7. Summary of model input and output variables used in the Pee Dee River and 

Atlantic Intracoastal Waterway Salinity Intrusion Model Decision Support SystemVersion 2.......................................................................................................................................43

8. Description of variables used in the Pee Dee River and Atlantic Intracoastal 

Waterway Salinity Intrusion Model Decision Support System-Version 2 artificial 

neural network models ..............................................................................................................44

9. Summary of performance statistics for models used in the Pee Dee River and 

Atlantic Intracoastal Waterway Salinity Intrusion Model Decision Support SystemVersion 2.......................................................................................................................................46

10. Stations, characteristics, period of record, and time interval of data in the Pee Dee 

River and Atlantic Intracoastal Waterway Salinity Intrusion Model Decision Support 

System-Version 2 database ......................................................................................................58

11. Stations, characteristics, period of record, and time interval of data in the Model-toMarsh Decision Support System-Version 2 database..........................................................58

12. Climate-change scenarios used in the Pee Dee River and Atlantic Intracoastal 

Waterway Salinity Intrusion Model Decision Support System-Version 2 and the 

Model-to-Marsh Decision Support System-Version 2 .........................................................63

13. Local sea-level rise rates and beginning year of data collection for seven tidal 

stations along the North and South Carolina, Georgia, and Florida coast........................65
xii

14. Percentage of time in a 14-year simulation that the specific conductance threshold 

of 1,000 microsiemens per centimeter was exceeded at the Pawleys Island gage, 

South Carolina, as a result of reductions in historical streamflow and 

sea-level rises .............................................................................................................................79

15. Percentage of time that the salinity threshold of 0.5 practical salinity unit is 

exceeded at Interstate 95, Georgia, as a result of reductions in historical 

streamflow and sea-level rises ................................................................................................84

Conversion Factors and Datums

Inch/Pound to SI

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.004047 square kilometer (km2

)

square mile (mi2

) 2.590 square kilometer (km2) 

Flow rate

cubic foot per second (ft3

/s) 0.02832 cubic meter per second (m3/s)

Vertical coordinate information is referenced to the National Geodetic Vertical Datum of 1929 

(NGVD 29).

Horizontal coordinate information is referenced to either the North American Datum of 1927 

(NAD 27) or the North American Datum of 1983 (NAD 83).

Elevation, as used in this report, refers to distance above the vertical datum.

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius 

(μS/cm at 25 °C).
xiii

Abbreviations*

ADMi Advanced Data Mining International

AI Artificial Intelligence

AIW Atlantic Intracoastal Waterway

ANN Artificial Neural Network

BASINS Better Assessment Science Integrating Point and Non-Point Sources

BEP back error propagation

BJWSA Beaufort-Jasper Water and Sewer Authority

CRADA Cooperative Research and Development Agreement 

DSS decision support system

ECHO The ECHO GCM is a hybrid coupled ocean-atmosphere GCM of the Hamburg version 

of the European Center atmospheric GCM (ECHAM) and the Hamburg Primitive 

Equation ocean GCM (HOPE)

ECHO-HSPF linked ECHO GCM output with the HSPF watershed model

EFDC Environmental Fluid Dynamics Code

EIS Environmental Impact Statement

FCFWRU Florida Cooperative Fish and Wildlife Research Unit

GCM Global Circulation Model

GPA Georgia Ports Authority

GUI graphical user interface

HSPF Hydrologic Simulation Program-FORTRAN watershed model

I-95 Interstate 95

ME mean error

MLP multi-layer perceptron

MSM marsh succession model

M2M Model-to-Marsh Decision Support System

M2M-2 Updated Model-to-Marsh Decision Support System

N.C. North Carolina

NOAA National Oceanic and Atmospheric Administration

NRC National Resource Council

NWIS National Water Information System 

OLS ordinary least squares

PME percent model error

PRISM Pee Dee River and Atlantic Intracoastal Waterway Salinity Intrusion Model

PRISM-2 Updated Pee Dee River and Atlantic Intracoastal Waterway Salinity Intrusion Model 

psu practical salinity units

Q streamflow input

RMSE root mean square error

R2 coefficient of determination

SARP Sectoral Applications Research Program

S.C. South Carolina

SC specific conductance

SCDHEC South Carolina Department of Health and Environmental Control

SNWR Savannah National Wildlife Refuge

SSR state space reconstruction

TMDL total maximum daily load

USACE U.S. Army Corps of Engineers

USC University of South Carolina

USEPA U.S. Environmental Protection Agency

USGS U.S. Geological Survey

WL water level

XWL tidal range

1D one dimensional

2D two dimensional

3D three dimensional

3DVis three-dimensional visualization

*

Model variables are provided in table 7 and appendixes 1 and 3.

Simulation of Salinity Intrusion Along the Georgia and 

South Carolina Coasts Using Climate-Change Scenarios

By Paul A. Conrads,1 Edwin A. Roehl, Jr.,2 Ruby C. Daamen,2 and John B. Cook2

Abstract

Potential changes in climate could alter interactions 

between environmental and societal systems and adversely 

affect the availability of water resources in many coastal 

communities. Changes in streamflow patterns in conjunction 

with sea-level rise may change the salinity-intrusion dynamics 

of coastal rivers. Several municipal water-supply intakes are 

located along the Georgia and South Carolina coast that are 

proximal to the present day saltwater-freshwater interface 

of tidal rivers. Increases in the extent of salinity intrusion 

resulting from climate change could threaten the availability 

of freshwater supplies in the vicinity of these intakes. To 

effectively manage these supplies, water-resource managers 

need estimates of potential changes in the frequency, duration, 

and magnitude of salinity intrusion near their water-supply 

intakes that may occur as a result of climate change. This 

study examines potential effects of climate change, including 

altered streamflow and sea-level rise, on the dynamics of 

saltwater intrusion near municipal water-supply intakes in two 

coastal areas. One area consists of the Atlantic Intracoastal 

Waterway (AIW) and the Waccamaw River near Myrtle Beach 

along the Grand Strand of the South Carolina Coast, and 

the second area is on or near the lower Savannah River near 

Savannah, Georgia. The study evaluated how future sea-level 

rise and a reduction in streamflows can potentially affect 

salinity intrusion and threaten municipal water supplies and 

the biodiversity of freshwater tidal marshes in these two areas.

Salinity intrusion occurs as a result of the interaction 

between three principal forces—streamflow, mean coastal 

water levels, and tidal range. To analyze and simulate salinity 

dynamics at critical coastal gaging stations near four municipal water-supply intakes, various data-mining techniques, 

including artificial neural network (ANN) models, were used 

to evaluate hourly streamflow, salinity, and coastal water-level 

data collected over a period exceeding 10 years. The ANN 

models were trained (calibrated) to learn the specific interactions that cause salinity intrusions, and resulting models were 

able to accurately simulate historical salinity dynamics in both 

study areas.

1

U.S. Geological Survey.

2

Advanced Data Mining, LLC, Greenville, South Carolina.

Changes in sea level and streamflow quantity and timing 

can be simulated by the salinity intrusion models to evaluate 

various climate-change scenarios. The salinity intrusion 

models for the study areas are deployed in a decision support 

system to facilitate the use of the models for management 

decisions by coastal water-resource managers. The report 

describes the use of the salinity-intrusion models decision 

support system to evaluate salinity-intrusion dynamics for 

various climate-change scenarios, including incremental 

increases in sea level in combination with incremental 

decreases in streamflow. Operation of municipal watertreatment plants is problematic when the specific-conductance 

values for source water are greater than 1,000 to 2,000 microsiemens per centimeter (μS/cm). High specific-conductance 

values contribute to taste problems that require treatment. 

Data from a gage downstream from a municipal water 

intake indicate specific conductance exceeded 1,000 μS/cm 

about 5.4 percent of the time over the 14-year period from 

August 1995 to August 2008. Simulations of specific 

conductance at this gaging station that incorporates sea-level 

rises resulted in a doubling of the exceedances to 11.0 percent 

for a 1-foot increase and 17.6 percent for a 2-foot increase. 

The frequency of intrusion of water with specific conductance 

values of 1,000 μS/cm was less sensitive to incremental 

reductions in streamflow than to incremental increases in sea 

level. Simulations of conditions associated with a 10-percent 

reduction in streamflow, in combination with a 1-foot 

rise in sea level, increased the percentage of time specific 

conductance exceeded 1,000 μS/cm at this site from 11.0 

to 13.3 percent, and a 20-percent reduction in streamflow 

increased the percentage of time to 16.6 percent.

Precipitation and temperature data from a global circulation model were used, after scale adjustments, as input to a 

watershed model of the Yadkin-Pee Dee River basin, which 

flows into the Waccamaw River and Atlantic Intracoastal 

Waterway study area in South Carolina. The simulated 

streamflow for historical conditions and projected climate 

change in the future was used as input for the ANN model in 

decision support system. Results of simulations incorporating 

climate-change projections for alterations in streamflow 

indicate an increase in the frequency of salinity-intrusion 

events and a shift in the seasonal occurrence of the intrusion 

events from the summer to the fall.
2 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Introduction

Adapting to future climatic change will likely present 

numerous challenges to water-resource managers in coastal 

regions of the world. The east coast of the United States falls 

into this category, given the large number of people living 

along the Atlantic seaboard and the added strain on resources 

as populations grows, particularly in the Southeast. Increased 

temperatures, changes in regional precipitation regimes, 

and rises in sea level may have a large effect on existing 

hydrological systems in coastal regions. Many of the major 

municipal water-supply intakes along the southeastern coast of 

the United States are considered to be vulnerable to anticipated changes in climate (fig. 1; Furlow and others, 2002). 

Four of the municipal intakes in figure 1 are along the South 

Carolina and Georgia coast and were analyzed for this report. 

Two intakes are in the Waccamaw River–Atlantic Intracoastal 

Waterway (AIW) area, and two are in the lower Savannah 

River estuary along the South Carolina and Georgia border.

The balance between streamflow conditions within 

a coastal drainage basin and sea levels governs the characteristics and frequency of salinity intrusion into coastal 

rivers. Salinity intrusion into freshwater coastal rivers has 

been, and continues to be, one of the most important global 

challenges for coastal water-resource managers, industries, 

and agriculture (Bear and others, 1999). Major economic and 

environmental consequences of saltwater intrusion include 

the degradation of natural ecosystems and the contamination 

of municipal, industrial, and agricultural water supplies 

(Bear and others, 1999). Coastal communities need to find 

approaches for the sustainability of freshwater supplies while 

minimizing the impact on natural systems. Increases in the 

frequency, magnitude, and duration of salinity intrusion into 

the coastal rivers near the surface-water intakes could increase 

the vulnerability and threaten the potability of water at the 

four freshwater municipal intakes, as well as the biodiversity 

of nearby freshwater tidal marshes. 

Waccamaw River, AIW, and lower Savannah River, as 

with many major estuarine systems, supply many local and 

regional water-resource needs. The tidal parts of these systems supply water to the growing coastal communities along 

the South Carolina and Georgia coasts, provide assimilative 

capacity for municipal wastewater discharges, and offer 

recreational opportunities along the coast (figs. 2, 3). With 

increases in industrial and residential development along the 

South Carolina and Georgia coasts, there are competing, and 

often conflicting, demands on coastal water resources.

Coastal water-resource managers need planning tools 

that will provide estimates of the change in the magnitude, 

frequency, and duration of salinity intrusion events near water 

intakes projected to occur in response to anticipated changes 

in climate. Changes in climate could affect precipitation 

patterns and result in changing patterns of streamflow to the 

coast. Climate change also could affect the rate of sea-level 

rise. Concerns associated with sea-level rise are not limited 

to the flooding of low-lying coastal areas but include effects 

of altered salinity-intrusion dynamics on the availability of 

freshwater supplies along the coast. For municipalities with 

water-supply intakes in tidally affected waters proximal to the 

freshwater-saltwater interface, the convergence of changes in 

hydrologic regime and sea-level rise associated with potential 

climate change could have a dramatic effect on salinity 

intrusion and freshwater availability. Fortunately, there have 

been extensive data collection and salinity modeling efforts 

for areas near water-supply intakes in the two study areas, 

Waccamaw River and AIW, South Carolina, and lower Savannah River, Georgia. These monitoring and modeling efforts 

were initiated to address various coastal water-resources 

issues. Although these efforts did not directly address the 

effects of salinity intrusion resulting from potential climate 

change, the data and models (with modifications) can be used 

by interested parties to evaluate climate-change effects. 

Figure 1. Vulnerability 

ratings for potential 

salinity intrusion 

at selected major 

municipal water-supply 

intakes along the coast 

of the Gulf of Mexico 

and the Atlantic Ocean 

(modified from Furlow 

and others, 2002).

High vulnerability

Medium vulnerability

Low vulnerability

No data

EXPLANATION

Salinity intrusion

TEXAS

LOUISIANA

MISSISSIPPI

ALABAMA

NORTH

CAROLINA

SOUTH

CAROLINA

GEORGIA

FLORIDA

GULF OF MEXICO

ATLANTIC

OCEAN

Figure 1. Vulnerability ratings for potential salinity intrusion at selected major municipal water-supply intakes

along the coast of the Gulf of Mexico and the Atlantic Ocean(Modified from Furlow and others2002)

Georgetown

Charleston

Savannah

Myrtle Beach
Introduction 3

02136000

02135000

02132000

02131000

02110815

02110777

021108125 33 ̊30'

34 ̊

Winyah 0 10 20 MILES

Bay

Georgetown

0 10 20 KILOMETERS

Base from National Hydrography Dataset;

UTM projection, zone 17, datum NAD83

EXPLANATION

Streamflow gage and

gage number

02135000

Specific-conductance and waterlevel gage and gage number

Specific-conductance gage

and gage number

Surface-water intakes

02110777

02110815

79 ̊30' 79 ̊

Figure 2. Location of the Waccamaw River and Atlantic Intracoastal Waterway study area and selected gages

in the Pee Dee River Basin, South Carolina.

Atlantic

Intracoastal

Waterway Little

River

Inlet

ATLANTIC

OCEAN

NORTH CAROLINA

SOUTH CAROLINA

LittlePee

Dee

River

Pee

Dee

Lynches

River

River

River

Black

River

Sampit

River

Waccamaw

Myrtle Beach

02110770

02110760

02110755

02110500

02110809 Grand Strand

GA

SC

TN

WV

KY VA

NC

ATLANTIC

OCEAN

Pee Dee

River Basin

Atlanta

Raleigh

Columbia

Richmond

Hagley

Landing

Figure 2. Location of the Waccamaw River and Atlantic Intracoastal Waterway study area and selected gages in 

the Pee Dee River Basin, South Carolina.
4 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

SOUTH

CAROLINA

Area

shown

GEORGIA

ATLANTIC

OCEAN

Houlihan

Bridge

02198500

02198980

0

0 5 10 KILOMETERS

5 10 MILES

02198980

EXPLANATION

Gaging station and number

Surface-water intakes

Base from U.S. Geological Survey 1:100,000-scale digital line graphs

Projection: UTM, Zone: 17, Datum: NAD27

Bluffton

Hardeeville

Ridgeland

Rincon

Savannah

FORT

PULASKI Tybee Island

Hilton Head

Port

Wentworth

SAVANNAH

NATIONAL

WILDLIFE

REFUGE

ATLANTIC

OCEAN

Savannah

River

Broad

River

SOUTH CAROLINA

GEORGIA

N

95

17

321

278

Figure 3. Location of the lower Savannah River study area, including the

Savannah National Wildlife Refuge and coastal South Carolina and Georgia. 

Abercorn

Creek

80

16 FORT

JACKSON Savannah

Harbor

Figure 3. Location of the lower Savannah River study area, including the 

Savannah National Wildlife Refuge and coastal South Carolina and Georgia.

The U.S. Geological Survey (USGS), in cooperation with 

the Beaufort-Jasper Water and Sewer Authority (BJWSA), 

initiated a study to (1) modify existing decision support 

systems (DSS) with embedded empirical salinity intrusion 

models to evaluate sea-level rise and changes in streamflow 

patterns, (2) collaborate with researchers at the University of 

South Carolina on the simulation of streamflow hydrographs 

using a watershed model and downscaled rainfall and temperature inputs from regional and global climate models, and 

(3) document the DSSs and selected climate-change scenarios.

To meet the objectives of this study, the USGS entered into a 

Cooperative Research and Development Agreement (CRADA) 

with Advanced Data Mining International (ADMi) in 2002 

to collaborate on applying data mining and artificial neural 

network (ANN) models to water-resources investigations. 

This study was done in collaboration with the University 

of South Carolina (USC) and South Carolina Sea Grant 

Consortium (Sea Grant) (Whitehead and others, 2011). The 

USC and Sea Grant effort was funded through a 

National Oceanic and Atmospheric Administration 

(NOAA) Sectoral Applications Research Program 

(SARP; Funding# OAR-CPO-2008-2000994, 

CFDA# 11.431, Competition# 2074474).

The USGS, a non-regulatory Federal science 

agency with national scope and responsibilities, 

is uniquely positioned to serve the Nation’s needs 

in understanding and responding to global climate 

change (Burkett and other, 2011). The USGS 

Global Change Science Strategy recognizes core 

USGS strengths and applies them to address key 

societal problems. This study addresses two of the 

six programmatic goals for the USGS to improve 

understanding of (1) droughts, streamflows, and 

water availability under changing land use and 

climate, and (2) coastal response to sea-level rise, 

climate hazards, and human development.

Purpose and Scope

This report presents the results of a study in 

which the effects of changes in streamflow and tidal 

water-level conditions on salinity intrusion in the 

Waccamaw River and AIW and the lower Savannah 

River were simulated using a modified Pee Dee 

River and Atlantic Intracoastal Waterway Salinity 

Intrusion Model Decision Support System (PRISM 

DSS) (Conrads and Roehl, 2007) and the lower 

Savannah River estuary Model-to-Marsh Decision 

Support System (M2M DSS) (Conrads and others, 

2006). Modifications made to these DSSs to enable 

evaluation of potential effects of climate change are 

documented in this report. In addition, examples of 

model scenarios from these DSSs are provided for 

their respective study areas. The development and 

application of the PRISM DSS and M2M DSS were 

documented in earlier reports. Sections of those 

reports have been updated for this report.

 An important part of the USGS mission is to provide 

scientific information for the effective water-resources 

management of the Nation. To assess the quantity and quality 

of the Nation’s surface water, the USGS collects hydrologic 

and water-quality data from rivers, lakes, and estuaries using 

standardized methods and maintains the data from these 

stations in a national database. Often these databases are 

under-utilized and under-interpreted for addressing contemporary hydrologic issues. The techniques presented in this 

report demonstrate the manner in which valuable information 

can be extracted from existing USGS databases to assist local, 

state and Federal agencies. The application of data-mining 

techniques, including ANN models, to the Waccamaw River, 

AIW, and the lower Savannah River estuary, demonstrates 

how empirical models of complex hydrologic systems can be 

developed, disparate databases and models can be integrated, 

and study results can be easily disseminated to meet the needs 

of a broad range of end users.
Introduction 5

Description of the Study Areas

The Waccamaw River and AIW, and the lower Savannah 

River, study areas are the coastal parts of two major watersheds in the Carolinas and Georgia (fig. 4). The Yadkin-Pee 

Dee River and Savannah River watersheds are large drainage 

basins (18,500 and 9,850 square miles (mi2), respectively) that 

drain into the Coastal Plain of the Southeast. Both watersheds 

have large reservoirs that regulate streamflow in major parts 

of the watersheds and supply water to major coastal communities. During low-flow and drought periods, it is not unusual for 

regulated streamflows to compose more than 80 percent of the 

inflow to the coast. A general description of each watershed 

and study area follows.

Altamaha-St. Marys

84° 82° 80° 78° 76°

36°

34°30'

33°

31°30'

Base From ESRI® Data & Maps, 2009

Hydrologic Unit Boundaries From US Department of Agriculture, 2009, 1:24,000 

Universal Transverse Mercator projection, NAD 83, Zone 17

0 25 50 100 MILES

0 50 100 KILOMETERS 25

SOUTH CAROLINA

NORTH CAROLINA

VIRGINIA

TENNESSEE

FLORIDA

GEORGIA

Neuse-Pamlico

Cape Fear

Yadkin-Pee Dee

Edisto-Santee

Savannah

Altamaha-St. Marys

River basin boundary

Sub-basin boundary

RIVER BASINS

Location of study area in the United States

The Waccamaw River and

Atlantic Intracoastal Waterway

study area

The lower Savannah River

study area EXPLANATION

Figure 4. Locations of the Yadkin-Pee Dee River, Savannah River, and selected basins in North Carolina, South Carolina,

and Georgia.

ATLANTIC

OCEAN

Figure 4. Locations of the Yadkin-Pee Dee River, Savannah River, and selected basins in North Carolina, South Carolina, and Georgia.
6 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Waccamaw River and Atlantic Intracoastal 

Waterway Study Area 

The Waccamaw River and AIW study area is located in 

the Grand Strand of South Carolina, an area of rapidly growing coastal communities from Little River Inlet in the north 

to Winyah Bay in the south (fig. 2). Often the reach of the 

AIW near Little River Inlet is referred to as the “North End” 

and the reach of the Waccamaw River near Hagley Landing 

is referred to as the “South End.” The Pee Dee River basin, 

including the Waccamaw River tributary, supplies freshwater 

to the Grand Strand of South Carolina. The headwaters of 

the Pee Dee River are in the Blue Ridge Province of North 

Carolina and Virginia and drain 6,800 mi2

 in North Carolina 

above Blewett Falls Lake (fig. 5) before flowing through South 

Carolina to the Atlantic Ocean (Seaber and others, 1994). 

Atlantic

Intracoastal

Waterway

River

Waccamaw

Figure 5. Physiographic provinces and location of surface-water bodies in the drainage basin of the Waccamaw

River and Atlantic Intracoastal Waterway study area in North and South Carolina.

!

!

!

!

!

!

Sumter

Concord

Florence

Lumberton

Charlotte

Lexington

Georgetown

Wilkesboro

Statesville

Myrtle Beach

Winston-Salem

Columbia

ATLANTIC

OCEAN

Base from 1:100,000-scale National Hydrography Dataset. 

Albers Equal Area projection; central meridian-96 00 00; datum NAD83.

W. Kerr

Scott Lake

High Rock

Lake

Tuckertown

Reservoir

Falls

Lake Lake

Tillary

Blewett

Falls Lake

0 20 40 MILES

0 20 40 KILOMETERS

Badin

Lake

Pee

Dee

River

Yadkin

River

EXPLANATION

Pee Dee River basin

Waccamaw River basin

Physiographic provinces

 Valley and Ridge

 Blue Ridge

 Piedmont

 Coastal Plain

36 ̊

37 ̊

35 ̊

34 ̊

81 ̊ 80 ̊ 79 ̊ 78 ̊

VIRGINIA

NORTH CAROLINA

SOUTH CAROLINA

GEORGIA

GA

SC

TN

WV

KY VA

NC

ATLANTIC

OCEAN

Pee Dee

River Basin

Atlanta

Raleigh

Columbia

Richmond

River

Uwharrie

Figure 5. Physiographic provinces and location of surface-water bodies in the drainage basin of the Waccamaw 

River and Atlantic Intracoastal Waterway study area in North and South Carolina.
Introduction 7

Above the confluence with the Uwharrie River, the stream is 

known as the Yadkin River, and below as the Pee Dee River, 

or Great Pee Dee River (U.S. Geological Survey, 1986). The 

Pee Dee River flows through seven impoundments in North 

Carolina. The first impoundment is the W. Kerr Scott Lake, a 

reservoir west of Wilkesboro, N.C. Downstream, a chain of 

five reservoirs impounds 50 miles of the river; these are High 

Rock Lake, Tuckertown Reservoir, Badin Lake, Falls Lake, 

and Lake Tillery (North Carolina Department of Environment 

and Natural Resources, 2001). The seventh impoundment is 

Blewett Falls Lake, located approximately 15 miles upstream 

from the South Carolina State line (table 1). There are no 

impoundments or regulation of the Pee Dee streamflow in 

South Carolina.

The Pee Dee River below Blewett Falls Lake drains 

approximately 11,700 mi2 and has five major tributaries: the 

Little Pee Dee, the Lynches, the Black, the Waccamaw, and 

the Sampit Rivers (Seaber and others, 1994) (figs. 2 and 5). 

The Little Pee Dee, Lynches, Black, and Waccamaw River 

tributaries are unregulated, undeveloped, and drain rural 

areas. Downstream from U.S. Highway 701, the Pee Dee 

Table 1. Description of seven reservoirs on the Yadkin-Pee Dee 

River, North Carolina. 

[From North Carolina Department of Environment and Natural Resources, 

Division of Water Quality, 1998]

Reservoir Surface area, 

acres

Owner

W. Kerr Scott Lake 1,480 U.S. Army Corps of Engineers

High Rock Lake 12,200 Alcoa Power Generation, Inc.

Tuckertown Reservoir 2,550 Alcoa Power Generation, Inc.

Badin Lake 5,350 Alcoa Power Generation, Inc.

Falls Lake 203 Alcoa Power Generation, Inc.

Lake Tillery 5,260 Carolina Power and Light

Blewett Falls Lake 2,570 Carolina Power and Light

River branches successively into Bull, Thoroughfare, and 

Schooner Creeks (fig. 6). These three creeks eventually flow 

into the Waccamaw River and Winyah Bay. Most of the 

freshwater streamflow to the AIW from the Pee Dee River 

basin is carried by Bull Creek to the Waccamaw River. 

0

0 2 4 6 8 10 KILOMETERS

2 4 6 8 10 MILES

EXPLANATION

501

701

17

17B

Figure 6. Locations of selected water-quality monitoring stations and water-supply intakes in the

Waccamaw River and Atlantic Intracoastal Waterway study area, Grand Strand, South Carolina.

Myrtle Beach

Conway

Conway

Marina

Hagley

Landing

Nixons

Crossroads

Bucksport

Wachesaw

Landing

Socastee

North

Myrtle Beach

Longs

02110777

02110707

02110770

02110755

02110715

02110809

02110812

02110815

02135225

021108135

02110725

02135200

02110802

02110760

02110730 02110704

Enterprise

Landing

NORTH CAROLINA

SOUTH CAROLINA

021108125

02110802

544

9

N

Schooner

Creek

Thoroughfare

Creek

Bull Creek

Little

Bull

Creek

Intracoastal

Atlantic

Dee

River

Pee

17

17

Waterway

Midway Inlet

Murrells Inlet

Little River

Inlet

River

Waccamaw

USGS water-quality monitoring

station and station number

Surface-water intakes

Boat landing

Grand Strand Airport

Horry

County

Location of the study area and

Horry County, South Carolina

Winyah

Bay Atlantic

Ocean

Study

area

Atlantic Ocean

Grand

Strand

Figure 6. Locations of selected water-quality monitoring stations and water-supply intakes in the 

Waccamaw River and Atlantic Intracoastal Waterway study area, Grand Strand, South Carolina.
8 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

The Waccamaw 

River (drainage area 

of 1,440 square miles) 

originates in North Carolina 

and enters the AIW about 

10 miles north of the mouth 

of Bull Creek. Prior to the 

1930s, the Waccamaw River 

flowed southward toward 

Winyah Bay. In the 1930s, 

the U.S. Army Corps of 

Engineers constructed a 

canal to form the waterway 

from Enterprise Landing to 

the Little River Inlet, which 

enables a large portion 

of the freshwater in the 

Waccamaw River to flow 

northward through the AIW 

into the Atlantic Ocean through Little River Inlet (Drewes 

and Conrads, 1995). The Waccamaw River drains extensive 

cypress and hardwood swamps (U.S. Geological Survey, 

1986). There are seven USGS gaging stations on the Pee Dee 

River and its major tributaries (table 2).

The reach of the AIW from just south of Little River Inlet 

to just north of Hagley Landing provides freshwater for the 

coastal communities of the Grand Strand (fig. 6). In the 1980s, 

major water purveyors switched from groundwater to surfacewater sources to avoid taste and odor problems associated with 

the groundwater supplies (Carswell and others, 1988). Three 

municipal surface-water intakes are in the tidal, freshwater 

portions of the AIW, Waccamaw River, and Bull Creek (fig. 6). 

During the drought of 1998 to 2002, salinity intrusion forced a 

municipal water supplier to temporarily suspend withdrawals 

until the freshwater-saltwater interface moved downstream 

from the intake.

Lower Savannah River Study Area

The lower Savannah River estuary extends from the 

Interstate 95 bridge to confluence with the Atlantic Ocean 

(fig. 3). The headwaters of the Savannah River originates in 

the Blue Ridge physiographic province, at the confluence of 

the Seneca and Tugaloo Rivers, near Hartwell, Ga. The Savannah River forms the State boundary between South Carolina 

and Georgia to the divergence of the Little Back River 

near the coast (figs. 7, 8). The Keowee River flows through 

Lakes Jocassee and Keowee into Lake Hartwell. From Lake 

Hartwell, the Savannah River flows through the Piedmont and 

the Coastal Plain physiographic provinces (fig. 7). The city of 

Augusta, Ga., is on the Fall Line, which separates these two 

provinces. The slope of the river ranges from an average of 

about 3 feet per mile in the Piedmont to less than 1 foot per 

mile in the Coastal Plain. Upstream from the Fall Line, three 

large Federal multi-purpose dams (Lake Hartwell, Richard 

B. Russell Lake, and J. Strom Thurmond Lake) provide 

Table 2. Description of U.S. Geological Survey gaging stations on the Pee Dee River and its major 

tributaries, North and South Carolina.

[mi2, square mile; SC, South Carolina]

Station name 

(fig. 2)

Station 

number

Period of 

record

Drainage area, 

in mi2 River mile

Waccamaw River near Longs, S.C.1 02110500 1950 to 2013 1,110 185.4

Pee Dee River near Rockingham, N.C.2 02129000 1906 to 2013 6,863 1,192

Pee Dee River near Bennettsville, S.C.2 02130561 1990 to 2013 7,600 1,153

Pee Dee River at Pee Dee, S.C.1 02131000 1938 to 2013 8,830 2,100.2

Lynches River at Effingham, S.C.1 02132000 1929 to 2013 1,030 243.4

Little Pee Dee River at Galivants Ferry, S.C.1 02135000 1942 to 2013 2,790 241.7

Black River at Kingstree, S.C.1 02136000 1929 to 2013 1,252 286.7

1

Station used for streamflow input to artificial neural network models.

2

Not shown in figure 2.

hydropower, water supply, recreational facilities, and a limited 

degree of flood control. Thurmond Dam controls most of 

the streamflow regulation that affects the Savannah River at 

Augusta (Sanders and others, 1990). A list of the reservoirs 

in the Savannah River basin with surface areas greater than 

1,000 acres is presented in table 3.

The lower Savannah River is a deltaic system that 

branches into a series of interconnected distributary channels, 

including the Little Back, Middle, Back, and Front Rivers 

(fig. 8). The hydrology of the system is dependent upon 

precipitation, runoff, channel configuration, streamflow, and 

seasonal and daily tidal fluctuations (Latham, 1990; Pearlstine 

and others, 1990). As is typical of coastal rivers in Georgia 

and South Carolina, the shallow, deltaic branches of the 

Savannah River did not provide natural features for a harbor, 

such as deep embayments or natural scouring of deep channels. Historically, the Back River had the larger channel and 

the larger proportion of streamflow compared with those of the 

Front River (Barber and Gann, 1989). The Savannah Harbor 

was developed along the lower 21 miles of the Savannah River 

during the mid-1800s to the present (2012). The Savannah 

Harbor has a history of channel deepening, widening, creation 

of turning and sedimentation basins, and maintenance dredging and disposal, as the harbor was changed from a natural 

river system with a controlling depth of 10 feet (ft) at low tide 

to a modified river with a currently (2013) maintained depth of 

42 ft at low tide (Barber and Gann, 1989).

Two important entities are located in the Savannah River 

estuary—the Savannah National Wildlife Refuge (SNWR) 

and the Georgia Ports Authority (GPA) facilities (fig. 8). The 

tidal freshwater marsh is an essential part of the 28,000-acre 

SNWR. Located between river mile 18 and river mile 40, 

the SNWR is home to a diverse variety of wildlife and plant 

communities. To the east of the Little Back and Back Rivers, 

the SNWR contains a 3,000-acre impoundment system that is 

actively managed for migratory wading birds and waterfowl 

and is periodically flooded with freshwater from the Little 

Back River. Neighboring the SNWR, the GPA maintains 
Introduction 9

EXPLANATION

Savannah River basin

Physiographic provinces

 Valley and Ridge

 Blue Ridge

 Piedmont

 Coastal Plain

Lake

Jocassee Lake

Keowee

Keowee

River

Tugaloo

River

Seneca River

Lake

Hartwell Richard

B. Russell

Lake

Hartwell

Augusta

Clyo

Savannah

Savannah

River

J. Strom

Thurmond

Lake

84° 82° 80° 78° 76°

34°30'

33°

31°30'

0 25 50 100 MILES

0 50 100 KILOMETERS 25

SOUTH CAROLINA

NORTH CAROLINA

Base from U.S. Geological Survey 1:2,000,000-scale digital line graphs, Projection: Albers

1st standard parallel: 29°30'00", 2nd standard parallel: 45°30'00", Central meridian: -81°00'00"

Latitude of origin: 23°00'00", False easting: 0.0, False northing: 0.0, Datum: NAD83

FLORIDA

GEORGIA

The lower Savannah River

study area

Figure 7. Location of selected surface-water bodies in the lower Savannah River basin, North Carolina, South Carolina,

and Georgia.

ATLANTIC

OCEAN

GA

SC

TN

WV

KY VA

NC

ATLANTIC

OCEAN

Savannah

River basin

Atlanta

Raleigh

Columbia

Richmond

Figure 7. Location of selected surface-water bodies in the lower Savannah River basin, North Carolina, South Carolina, and Georgia.

Table 3. Description of six reservoirs in South Carolina with greater than 

1,000 acres on the Savannah River. 

[From South Carolina Water Resources Commision, 1983]

Reservoir Surface area, 

acres

Owner

Lake Jocassee 18,372 Duke Energy

Lake Keowee 18,372 Duke Energy

Lake Hartwell 56,000 U.S. Army Corps of Engineers

Richard B. Russell Lake 26,650 U.S. Army Corps of Engineers

J. Strom Thurmond Lake 71,100 U.S. Army Corps of Engineers

Stevens Creek1 2,400 South Carolina Electric and Gas

1

Data from Stringfield, 1995.
10 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios Savannah

River

River

Front

River

Back

River

Middle

River

Back

Little

Figure 8. Location of Georgia Ports Authority facilities and the Savannah

National Wildlife Refuge in the lower Savannah River basin,

South Carolina and Georgia.

Port Wentworth

Georgia Ports

Authority

Garden City

Terminals

Georgia Ports

Authority

Ocean Terminal

Savannah

SAVANNAH

NATIONAL

WILDLIFE

REFUGE

0

0 1 2 3 KILOMETERS

1 2 3 MILES

Houlihan

Bridge

Base from U.S. Geological Survey 1:100,000-scale digital line graphs

Projection: UTM, Zone: 17, Datum: NAD27

N

80

16

17

25

21

95 17

Figure 8. Location of Georgia Ports Authority facilities and the Savannah 

National Wildlife Refuge in the lower Savannah River basin, South Carolina 

and Georgia.

two deepwater terminal facilities—Garden City Terminal 

and Ocean Terminal (fig. 8). To support navigation and the 

terminal activities of the GPA, the river channel and turning 

basins are maintained by dredging downstream from the 

U.S. Highway 17 bridge (Houlihan Bridge) to approximately 

20 miles offshore from the harbor entrance. 

Substantial modifications made to the Savannah River 

estuary during the past 30 years include the installation and 

operation of a tide gate on the Back River in 1977, deepening 

of the shipping channel to 38 ft (from 34 ft) in 1978, decommissioning of the tide gate in 1991, and deepening of the 

shipping channel to 42 ft (from 38 ft) in 1994. The cumulative 

effect of the channel modifications can be seen in the upstream 

movement of the freshwater-saltwater interface over the past 

century. The average position of the freshwater-saltwater 

interface for four historical periods (1875, 1940, 1965, and 

1997) and their associated channel depths are shown in 

figure 9 (E. EuDaly, U.S. Fish and Wildlife Service, 

written commun., 2005). Data used in figure 9 were 

obtained from historical sources, as noted in the figure, 

and provide a qualitative comparison of the position of 

the freshwater-saltwater interface and the spatial extent 

of the freshwater marsh. 

Two municipal water-supply intakes are located 

in the freshwater part of the upper estuary. The city of 

Savannah maintains an intake on Abercorn Creek, a 

tributary to the Savannah River approximately 1 mile 

upstream from the Interstate 95 bridge (fig. 3). Abercorn Creek in Georgia, at the location of the intake, 

experiences reversing streamflow from the connection 

to the Savannah River but low salinity concentrations 

(Conrads and others, 2011). The freshwater intake 

for the Beaufort-Jasper Water and Sewer Authority 

is located at a canal off of the Savannah River near 

Hardeeville, S.C. (fig. 3). The water levels are affected 

by tidal backwater at the intake canal near Hardeeville, 

but streamflows do not reverse at this location on the 

Savannah River, and salinity intrusion has not affected 

the availability of freshwater at this intake.

Previous Studies

Many investigations have been conducted to 

address the quantity and quality of water resources in 

the study areas and the use of data-mining techniques 

to study salinity dynamics in these estuarine systems. 

Carswell and others (1988) investigated the potential 

freshwater supply of the AIW as an alternative to 

groundwater sources and, on the basis of statistical 

analysis and mechanistic models, determined that the 

AIW could provide an adequate supply of freshwater. 

Drewes and Conrads (1995) determined the assimilative capacity of the Waccamaw River and AIW using 

dynamic one-dimensional streamflow and waterquality models. These assimilative capacity models 

were subsequently used by the South Carolina Department of 

Health and Environmental Control (SCDHEC) to determine 

the total maximum daily load (TMDL) for dissolved oxygen 

(South Carolina Department of Health and Environmental 

Control, 1998).

The South Carolina Department of Natural Resources and 

a consortium of stakeholders entered into a cooperative agreement with the USGS to apply data-mining techniques to the 

long-term time-series datasets to analyze and simulate salinity 

dynamics near the freshwater intakes along the Grand Strand 

of South Carolina. Through that effort, Conrads and Roehl 

(2007) developed the Pee Dee River and Atlantic Intracoastal 

Waterway Salinity Intrusion Model (PRISM) DSS to evaluate 

the effects of regulated streamflows of the Pee Dee River on 

salinity intrusion in the Waccamaw River and AIW. 

Many ecological and hydrological studies have been 

conducted to evaluate the potential effects of the proposed 
Introduction 11

0 2 4 12 KILOMETERS

0 2 4

6

6 12 MILES

RM

Tidal freshwater marsh

Saltwater

Freshwater

Refuge impoundments

Islands

Savannah National Wildlife

Refuge boundary

River mile

EXPLANATION

Middle

River

Savannah

River

Back

River

Hutchinson

Island

Middle

River

Savannah

River

Back

River

Hutchinson

Island

Middle

River

Savannah

River

Back

River

Hutchinson

Island

RM 25

RM 20

RM 15 RM 10

RM 5

RM 0

RM 25

RM 20

Houlihan

Bridge

Houlihan

Bridge

RM 15 RM 10

RM 5

RM 0

Middle

River

Savannah

River

Back

River

Hutchinson

Island

Tide gate

RM 25

RM 20

RM 15 RM 10

RM 5

RM 0

17

17A

17

17A

17

17A

95 95

A B

C D

N

Figure 9. Location of the upstream extent of salinity intrusion along the Savannah and Back Rivers, greater than

0.5 practical salinity unit for four channel depths; (A) 13–15 feet in 1875; (B) 26–30 feet in 1940; (C) 34 feet in 1965;

and (D) 42 feet in 1997. Maps produced by the U.S. Fish and Wildlife Service, Charleston Field Office. Data

references used are (A) Granger (1968), (B) Lamar (1942), (C) U.S. Environmental Protection Agency,

STORET Database 1998 (http://www.epa.gov/STORET/), and (D) Applied Technology and Management, 1998.

Figure 9. Location of the upstream extent of salinity intrusion along the Savannah and Back Rivers, greater than 0.5 practical 

salinity unit for four channel depths; (A) 13–15 feet in 1875; (B) 26–30 feet in 1940; (C) 34 feet in 1965; and (D) 42 feet in 1997. 

Maps produced by the U.S. Fish and Wildlife Service, Charleston Field Office. Data references used are (A) Granger (1968), 

(B) Lamar (1942), (C) U.S. Environmental Protection Agency, STORET Database 1998 (http://www.epa.gov/STORET/), and 

(D) Applied Technology and Management, 1998.

deepening of Savannah Harbor (Collins and others, 2001; Will 

and Jennings, 2001; Conrads and others, 2006; Tetra Tech, 

2006; Welch and Kitchens, 2006). The three-dimensional (3D) 

hydrodynamic model Environmental Fluid Dynamics Code 

(EFDC) was used to assess the effects of proposed deepening 

of the harbor on salinity dynamics in the lower Savannah 

River (Tetra Tech, 2006). These studies were used in the 

development of the Environmental Impact Statement for the 

proposed deepening of Savannah Harbor (U.S. Army Corps of 

Engineers, 2012).
12 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Conrads and others (2006) developed a Model-to-Marsh 

DSS to integrate hydrodynamic and ecological models being 

used to evaluate a potential deepening of the Savannah Harbor. 

The 3D hydrodynamic model, EFDC, and a marsh succession 

model (MSM) were developed by different scientific teams 

(Tetra Tech, 2005, 2006; Welsh and Kitchens, 2006) to 

evaluate the environmental impacts of the harbor deepening. 

The EFDC model predicts changes in river water levels and 

salinity in the system in response to potential harbor geometry 

changes. The MSM predicts plant distribution in the tidal 

marshes in response to changes in the water-level and salinity 

conditions in the marsh. To link the riverine predictions of 

the EFDC to the MSM, a “model to marsh” (M2M) DSS 

was developed using data-mining techniques that included 

ANN models, which simulated river and marsh water levels 

and salinity in the vicinity of the Savannah National Wildlife 

Refuge for the full range of 11.5 years of data from river and 

marsh gaging networks. The EFDC, MSM, and M2M were 

integrated in a DSS for use by various regulatory and scientific 

stakeholders. 

Conrads and others (2011) developed a DSS to evaluate 

the potential effect of salinity and chloride concentration in 

Abercorn Creek, the source water for the city of Savannah’s 

water supply, from a proposed deepening of Savannah Harbor. 

The effects of proposed deepening of Savannah Harbor on 

salinity intrusion in the upper reaches of the lower Savannah 

River estuary, including Abercorn Creek, was evaluated using 

mechanistic and empirical modeling approaches to simulate 

chloride concentrations at the city’s intake. The mechanistic 

approach modified the 3D EFDC model used for evaluating 

potential harbor deepening effects for the Environmental 

Impact Statement (EIS). The empirical approach to simulate 

chloride concentrations was to develop models directly from 

the available data using ANN models. The ANN models use 

streamflow and specific conductance (field measurement for 

salinity) time series for inputs. The mechanistic and empirical 

modeling approaches were integrated into a DSS for simulation of salinity dynamics under various harbor-deepening 

scenarios. 

Approach

The emerging field of data mining addresses the issue 

of extracting information from large databases (Weiss and 

Indurkhya, 1998). Data mining is a powerful tool for converting large databases into a form usable in solving problems that 

are otherwise imponderable because of the large numbers of 

explanatory variables or poorly understood process physics. 

Data-mining methods come from different technical fields, 

such as signal processing, statistics, artificial intelligence, 

and advanced visualization. Data mining employs methods 

for maximizing the information content of data, determining which variables have the strongest correlations to the 

problems of interest, and developing models that predict future 

outcomes. This knowledge encompasses both understanding 

of cause-effect relations and predicting the consequences 

of alternative actions. Data mining is used extensively in 

financial services, banking, advertising, manufacturing, and 

e-commerce to classify the behaviors of organizations and 

individuals and to predict future outcomes. 

The ultimate goal of this study is to produce a model 

that effectively predicts salinity intrusion into the freshwater 

portions of the two study areas for a given set of streamflow, 

coastal water-level, and tidal-range conditions. The approach 

taken used all available streamflow, coastal water-level, and 

specific-conductance measurements from the individual 

gaging stations since the establishment of the coastal gaging 

networks in the two study areas. The modeling approach used 

correlation functions that were synthesized directly from data 

to predict how the change in specific conductance at each gage 

location is affected by streamflow and tidal conditions over 

time. Data-mining techniques, including ANNs, were previously applied to develop DSSs (PRISM and M2M) to predict 

salinity at gaging stations in the two study areas (Conrads 

and others, 2006; Conrads and Roehl, 2007). The two DSSs 

allowed users to simulate changes in salinity resulting from 

changes in regulated streamflow to the study areas. To evaluate salinity intrusion due to potential climate change, changes 

in streamflow and sea level need to be simulated separately 

and in combination. The PRISM and M2M were modified to 

simulate increases and decreases in sea level and changes in 

unregulated streamflow into the study areas.

Global circulation models (GCMs) are used in climatechange studies to simulate future precipitation and temperature 

resulting from different emission scenarios. The GCMs 

predict global changes in climate on large scales of 100- to 

500-square-kilometer (km2

) grids (Daly and others, 2008), 

but the output cannot be used directly as inputs to watershed 

models. Downscaling techniques are used to transform the 

GCM regional output to the scale of local interest. To generate 

streamflow input to the salinity models, CGMs need to be 

coupled with watershed-runoff models. Conceptual integration 

of these models is depicted in figure 10. Global atmospheric 

circulation models provide large-scale (>250-km2 grid) 

estimations of precipitation and temperature conditions under 

various carbon emission scenarios. To generate regional scale 

(about 12-km2

 grid) precipitation and temperature estimates, 

the global circulation model output is either dynamically 

or statistically downscaled (Wood and others, 2004). These 

regional precipitation and temperature predictions are then 

used as inputs to watershed models to predict changes in 

freshwater streamflow to the study areas.

To generate streamflow values for input to the PRISM 

DSS, researchers at the University of South Carolina (USC) 

applied the Hydrologic Simulation Program-Fortran (HSPF) 

watershed model as part of the BASINS modeling package 

distributed and support by the U.S. Environmental Protection Agency (USEPA) (http://www.epa.gov/waterscience/

basins/) to the Yadkin-Pee Dee River watershed (Whitehead 

and others, 2011). The HSPF watershed model was used for 

simulation of streamflows at the five long-term USGS gaging 
Introduction 13

Figure 10. Schematic diagram showing the conceptual modeling approach for evaluation of the effects of

climate change on salinity intrusion, North and South Carolina.

f

Dynamic or statistical down

scaling of output

Watershed model

Salinity intrusion model

Gridded rainfall

input to watershed

Precipitation

Global Circulation Model and temperatures

flow

Sea-level Change

Figure 10. Schematic diagram showing the conceptual modeling approach for evaluation of the effects of 

climate change on salinity intrusion, North and South Carolina.

stations used for inputs to PRISM (table 2). Streamflows at 

these locations can be used as streamflow inputs to the PRISM 

DSS. The USC hydrologic modeling work did not include the 

application of HSPF to the Savannah River basin. Changes 

in precipitation and temperature projected by GCMs were 

evaluated only for the Waccamaw River and AIW study area. 

The hydrologic model developed by USC for the 

Yadkin-Pee Dee River watershed included simulations of the 

potential effects of climate change on streamflows. Projected 

streamflow for future climate conditions were simulated by 

the HSPF model using forecasted regional precipitation and 

temperature scenarios. The regional precipitation and temperature scenario data were supplied by Dr. Katherine Hayhoe of 

Texas Tech University under contract with the USGS (Dalton 

and Jones, 2010). These one-eighth degree daily downscaled 

datasets include the PCM, CCSM3, GFDL 2.0, GFDL 2.1, 

HADCM3, BCM2, CGCM3, CNRM, ECHAM5, and ECHO 

GCMs. Data for each model are available for some or all of 

the A1FI, A1B, A2, and B1 emission scenarios. Each GCM 

scenario includes maximum and minimum daily temperature 

and precipitation values (http://cida.usgs.gov/climate/gdp/

accessed August 19, 2011). 

The USGS activities in the project, in collaboration 

with ADMi, included modification of the DSSs, retraining of 

selected ANN models, simulation of climate-change scenarios, 

coordination with the USC and Sea Grant researchers, and 

documentation of the study results. The PRISM and M2M 

DSSs were originally designed to evaluate the effect of 

changes in regulated streamflows from hydropower facilities 

on the Yadkin-Pee Dee and Savannah Rivers on the variability 

of salinity. Users could modify regulated streamflow using 

three input options—constant streamflow, percent of historical 

streamflows, or a user-defined hydrograph. Four of the streamflow inputs to the PRISM DSS are for unregulated tributaries 

to the Pee Dee River. The PRISM DSS was modified to allow 

users to input user-defined hydrographs for the unregulated 

tributaries generated from the HSPF watershed model. For 

the M2M DSS, there is only one regulated streamflow input 

(Savannah River at Clyo, Ga.), and the DSS did not need to be 

modified for unregulated streamflow inputs.

 The two DSSs also were modified to allow simulations 

of incremental increases in sea level. Changes in hydrology 

and sea level can be simulated separately and in combination 

by modifying streamflow and (or) coastal water-level inputs 

to the DSSs. Using the user-defined hydrograph streamflow 

input option, users can simulate changes to salinity dynamics 

on the basis of altered streamflow patterns to the coast. These 

streamflow inputs can be determined anecdotally (for example, 

10-percent wetter winters and springs and 10-percent drier 

summers and falls) or using hydrographs simulated from the 

integrated downscaled GCM and regional watershed models. 

These input hydrographs can be evaluated in combination with 

incremental sea-level rise. The updated DSSs are referred to as 

PRISM-2 and M2M-2.
14 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Data-Collection Networks

Many resource agencies (including the USGS, National 

Oceanic Atmospheric Administration, USEPA, South Carolina 

Department of Health and Environmental Control, Georgia 

Environmental Protection Division, and local colleges and 

universities) have collected data on the Waccamaw River and 

AIW, and lower Savannah River estuary. The USGS maintains 

the National Water Information System (NWIS), a distributed 

network of computers and file servers for the storage and 

retrieval of water data collected at approximately 1.5 million 

sites around the country, as part of the USGS program 

disseminating water data to the public. Descriptions of the 

data-collection networks for the two study areas follow.

Waccamaw River and Atlantic 

Intracoastal Waterway 

Data from three networks were used to build, train (calibrate), and test the seven specific-conductance ANN models in 

the Waccamaw River and AIW study area. One network is the 

long-term streamflow network in the Pee Dee and Waccamaw 

River basin, upstream from tidal effects (fig. 2). Data from 

the long-term streamflow network originated as early as 1906 

(table 2), and more than 50 years (1950–2007) of concurrent 

data are available for four stations on the principal tributaries 

to the Pee Dee River—the Waccamaw, Lynches, Little Pee 

Dee, and Black Rivers. 

The second network is the coastal network of 

specific-conductance and water-level3

 gaging stations in the 

Waccamaw River and AIW study area (fig. 2; table 4). The 

coastal gaging network does not have the temporal continuity 

of the streamflow network. Gaging stations often were 

installed to support special investigations and discontinued 

upon completion of a particular study. Specific-conductance 

data, collected at 15-minute intervals from 1983 to 2010, were 

available for the study with periods of record ranging from 3 

to 27 years for 19 stations. Water levels and streamflow were 

gaged in addition to specific conductance at some of the tidal 

gaging network sites (table 4). Data obtained from the coastal 

gaging network during the past 27 years encompass a wide 

3

The water-level data typically refers to elevation of a known vertical 

datum. Gage height is the measurement of water-level elevation above an arbitrary datum. For the Waccamaw River and AIW study area, the data are gage 

heights. Water level is used when a datum correction to the National Geodetic 

Vertical Datum of 1929 has been applied to the data. For the lower Savannah 

River study area, the water-level data were collected to NGVD 29.

Table 4. Description of U.S. Geological Survey coastal gaging network in the Waccamaw River-Atlantic Intracoastal Waterway study 

area of South Carolina. 

[S.C., South Carolina; WL, water level; Q, flow; SC, specific conductance; AIW, Atlantic Intracoastal Waterway. Artificial neural network models were developed 

for stations shown in bold text]

Station name Station 

number

Name used in this 

report

Characteristics 

measured Period of record

Waccamaw River at Conway Marina at Conway, S.C. 02110704 Conway Marina WL, Q, SC 1991–2013

Waccamaw River at Pitch Landing, S.C. 02110707 WL, SC 1986–1989

Waccamaw River at Peachtree Landing, S.C. 02110715 SC 1990–1991

AIW at S.C. Highway 544 at Socastee, S.C. 02110725 Highway 544 WL, SC 1986–1992

AIW at Vereens Marina at North Myrtle Beach, S.C. 02110730 WL, SC 1983–1991

AIW at Briarcliffe Acres, S.C. 02110755 Briarcliffe Acres SC 1983–2013

AIW at Myrtlewood Golf Course, S.C. 02110760 Myrtlewood SC 1986–1989, 1994–2013

AIW at Grand Strand Airport at North Myrtle Beach, S.C. 02110770 Grandstrand Airport SC 1987–2013

AIW at S.C. Highway 9 at Nixons Crossroads, S.C. 02110777 Little River Inlet WL, Q, SC 1986–2013

Waccamaw River at Bucksport, S.C. 02110802 WL, Q, SC 1983–1995

Waccamaw River at Wachesaw Landing, S.C. 02110809 Wachesaw SC 1986–1989, 2002–2013

Waccamaw River at Mt. Rena Landing near Murrells Inlet, S.C. 02110812 SC 1986–1989

Waccamaw River near Pawleys Island, S.C. 021108125 Pawleys Island WL, SC 2002–2013

Thoroughfare Creek at Berlin near Pawleys Island, S.C. 021108135 WL, SC 1989

Waccamaw River at Hagley Landing, S.C. 02110815 Hagley Landing WL, SC 1986–2013

Waccamaw River at U.S. Highway 17 at Georgetown, S.C.1 02110850 SC 1985–1989

Pee Dee River at U.S. Highway 701, S.C. 02135200 Highway 701 SC 1986–1994

Pee Dee River at Arundel Plantation near Jackson, S.C. 02135225 WL, SC 1989

Winyah Bay at Mouth near Georgetown, S.C.1 02136390 SC 1986–1989

1

Station not shown in figure 6.
Data-Collection Networks 15

range of meteorological conditions, including large rainfalls 

in a 24-hour period, major hurricanes, and drought conditions.

In addition to data from the USGS networks, wind speed and 

direction data from the National Weather Service meteorological network were obtained from the Charleston Harbor gage of 

the Southeast Climate Center. 

Lower Savannah River

For the lower Savannah River study area, data from two 

networks were used to develop and test five river specificconductance and seven marsh ANN models of the lower 

Savannah River estuary. The first network consists of USGS 

gaging stations (table 5; fig. 11A). Streamflow records collected at an hourly interval are available for the gage on the 

Savannah River near Clyo, Ga. (station 02198500; fig. 3), 

which was established in 1929. The USGS has maintained 

data-collection network stations on the Little Back River 

near the SNWR and in the lower Savannah River since 

the late 1980s. These stations collect water-level and (or) 

specific-conductance data at 15-minute intervals (fig. 11A). 

Locations of gaging stations that measure specific conductance, water level, and streamflow in the study are listed in 

table 5 and shown in figures 3 and 11A. 

The second network is the USGS marsh network, which 

consists of seven sites established in the tidal freshwater 

marshes to evaluate potential effects of deepening the 

Savannah Harbor on plant communities (Welch and Kitchens, 

2006). The Florida Cooperative Fish and Wildlife Research 

Unit (FCFWRU) of the USGS collected water-level and 

near-surface pore-water specific-conductance time-series 

data from the marsh network from June 1999 to March 2007. 

The USGS marsh network consists of four sites on the Little 

Back and Back Rivers, two on the Middle River, and one on 

the Front River (fig. 11B). The marsh monitoring sites consist 

of a pressure transducer and a specific-conductance probe 

just below the surface of the marsh, approximately 6 inches. 

The locations of the USGS continuous marsh gaging stations 

correspond to locations where the FCFWRU has conducted 

plant studies since the 1980s. 

Table 5. Description of U.S. Geological Survey continuous river and marsh gaging networks used in the lower Savannah River 

study area.

[NAD 83, North American Datum of 1983; Q, flow; WL, water level; SC, specific conductance; USFW, U.S. Fish and Wildlife Service; PSC, pore-water 

specific conductance. Artificial neural network models were developed for stations shown in bold text]

Station 

number

Station location and name used 

in this report

Characteristics 

measured Period of record

Longitude Latitude 

 (decimal 

degrees, 

NAD 83)

(decimal 

degrees, 

NAD 83)

U.S. Geological Survey River Gaging Network (figs. 3 and 11A)

02198500 Savannah River near Clyo Q October 1929–2013 –81.269 32.528

02198840 Savannah River at I-95 Bridge WL, SC June 1987–2013 –81.151 32.236

02198920 Front River at Houlihan Bridge WL, SC October 1987–2013 –81.151 32.166

02198977 Front River at Broad Street WL October 1987–2013 –81.096 32.184

021989784 Little Back River at Lucknow Canal SC May 1990–2013 –81.118 32.171

02198979 Little Back River near Limehouse WL June 1987–2013 –81.117 32.185

021989791 Little Back River at USFW Dock SC October 1989–2013 –81.118 32.186

02198980 Savannah River at Fort Pulaski WL October 1987–2013 –80.903 32.034

U.S. Geological Survey Marsh Network (fig. 11B)

B1 Little Back River marsh WL, PSC June 1999–December 2006 –81.128 32.192

B2 Little Back River marsh WL, PSC June 1999–March 2007 –81.127 32.173

B3 Back River marsh WL, PSC June 1999–December 2006 –81.126 32.154

B4 Back River marsh WL, PSC June 1999–December 2006 –81.109 32.131

F1 Front River marsh WL, PSC June 1999–April 2006 –81.148 32.187

M1 Middle River marsh WL, PSC June 1999–June 2006 –81.135 32.192

M2 Middle River marsh WL, PSC June 1999–December 2006 –81.133 32.184
16 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios Savannah

River

River

Front

River

Back

River

Middle

River

Back

Little

Port Wentworth

Savannah

SAVANNAH

NATIONAL

WILDLIFE

REFUGE

0

0 1 2 3 KILOMETERS

1 2 3 MILES

Houlihan

Bridge

Base from U.S. Geological Survey 1:100,000-scale digital line graphs

Projection: UTM, Zone: 17, Datum: NAD27

N

A B

80

16

17

25

21

17

Savannah

River

River

Front

River

Back

River Middle

River

Back

Little

Port Wentworth

Savannah

SAVANNAH

NATIONAL

WILDLIFE

REFUGE

0

0 1 2 3 KILOMETERS

1 2 3 MILES

Houlihan

Bridge

Base from U.S. Geological Survey 1:100,000-scale digital line graphs

Projection: UTM, Zone: 17, Datum: NAD27

N

80

16

17

Tide gate

25

21

17

Tide gate

95 95

02198840

02198979

021989791

021989784

02198920

02198977

B4

B3

B2

F1

M2

M1 B1

02198977

B1

EXPLANATION

U.S. Geological Survey river

station and number

U.S. Geological Survey marsh

station and number

Figure 11. Location of U.S. Geological Survey (A) river gaging stations and (B) marsh gaging stations in the vicinity of the

Savannah National Wildlife Refuge, South Carolina and Georgia. (Modified from Conrads and others, 2006)

Figure 11. Location of U.S. Geological Survey (A) river gaging stations and (B) marsh gaging stations in the vicinity of the 

Savannah National Wildlife Refuge, South Carolina and Georgia (modified from Conrads and others, 2006).

Characterization of Historical 

Streamflow, Water Level, and Specific 

Conductance Dynamics

Estuarine systems are complex systems that are 

constantly responding to changing hydrologic, tidal, and 

meteorological conditions. Dyer (1997) states that the challenge of studying estuaries is “... that river flow, tidal range, 

and sediment distribution are continually changing and this is 

exacerbated by the continually changing weather influences. 

Consequently, some estuaries may never really be steady-state 

systems; they may be trying to reach a balance they never 

achieve.” The estuarine portions of the AIW, Waccamaw 

River, and Savannah River are constantly integrating the 

changing streamflow of the Pee Dee River and Savannah 

River basins; the changing tidal conditions of the Atlantic 

Ocean; and the changing meteorological conditions, including 

wind direction and speed, rainfall, low- and high-pressure 

systems, and hurricanes. In the following sections, historical 

streamflow and tidal water levels and their effects on salinity 

intrusion are characterized.

Characterization of Historical Pee Dee River 

Streamflow

Streamflow in the Waccamaw River and AIW study area 

is regulated by releases from Blewett Falls Lake near Rockingham, N.C. Daily duration hydrographs, based on 82 years 

of data, are shown in figure 12. Daily duration hydrographs 

characterize the state of a stream with respect to time. For 

example, suppose daily streamflow data for a 78-year period 

are available for a station and the 75th-percentile streamflow 

is 10,000 cubic feet per second (ft3

/s) for a particular day of 
Characterization of Historical Streamflow, Water Level, and Specific Conductance Dynamics 17

80,000

70,000

60,000

50,000

40,000

30,000

20,000

10,000

0

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Streamflow, in cubic feet per second

A. Pee Dee River near Rockingham, North Carolina (02129000)

B. Little Pee Dee River at Galivants Ferry, South Carolina (02135000)

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

Figure 12. Streamflow duration hydrographs for (A) Pee Dee River near Rockingham, North Carolina

(station 02129000), 1928–2010, and (B) Little Pee Dee River at Galivants Ferry, South Carolina

(station 02135000)1942–2010.

5th percentile flows

25th percentile flows

50th percentile flows

75th percentile flows

95th percentile flows

EXPLANATION

5th percentile flows

25th percentile flows

50th percentile flows

75th percentile flows

95th percentile flows

EXPLANATION

Figure 12. Streamflow duration hydrographs for (A) Pee Dee River near Rockingham, North Carolina (station 

02129000), 1928–2010, and (B) Little Pee Dee River at Galivants Ferry, South Carolina (station 02135000) 1942–2010.
18 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

the year, say January 3, then 75 percent of all streamflows that 

occurred on January 3 of each of the 78 years of data were 

equal to or less than 10,000 ft3/s. Streamflows from the 0 to 

10th percentile typically occur during very dry conditions, 

and streamflows from the 90th to 100th percentile typically 

occur during very wet conditions. It is assumed that streamflows from the 25th to 75th percentile occur during normal 

hydrologic conditions. Streamflow at station 02129000, Pee 

Dee River near Rockingham, N.C., ranged from a minimum 

of less than 500 ft3/s during periods of low streamflow to 

greater than 60,000 ft3/s during periods of high streamflow 

(fig. 12A). Seasonally, the highest streamflows occur in late 

winter and early spring (February through March), and the 

lowest streamflows occur in late summer and early fall (July 

through October).

The large variation in the percentile streamflows, 

especially in the 95th-percentile streamflows, is a result of the 

regulated streamflow and large variation in releases from the 

Blewett Falls Lake. The Little Pee Dee River, which is the 

largest tributary to the Pee Dee River, and the other tributaries are unregulated, and the large variation in the duration 

hydrographs is not as pronounced as those of regulated 

streams, as illustrated by the duration hydrograph for the Little 

Pee Dee River at Galivants Ferry, S.C. (station 02135000; 

fig. 12B). The regulated streamflows decrease the range of the 

low and medium percentile streamflows, compared to unregulated streams. For the Little Pee Dee River, there is a larger 

distribution from the 5th to the 75th percentile streamflows, 

compared to the same percentiles for the Pee Dee River near 

Rockingham, N.C. (station 02129000; fig. 12).

Although regulation affects the natural streamflow regime 

over short-term (hours to days) periods, streamflow over 

longer term periods (weeks to months) generally is similar to 

that of unregulated streams. For example, the hourly streamflow values shown in figure 13A for the regulated Pee Dee 

River at Pee Dee (station 02131000) have a greater difference 

from the unregulated Lynches, Little Pee Dee, and Black 

Rivers (stations 02132000, 02135000, 02136000, respectively) 

than the 7-day average streamflow values for the same period 

shown in figure 13B.

Characterization of Historical Lower Savannah 

River Streamflow

Streamflow at Savannah River near Clyo, Ga. (station 

02198500), is regulated by releases from Lake Thurmond 

Dam near Augusta, Ga. From 1930 to 2010, the 5th- and 95thpercentile streamflows ranged from a minimum of 4,000 ft3

/s 

to more than 50,000 ft3/s (fig. 14). Seasonally, the highest 

streamflows occur in late winter and early spring (February 

through March), and the lowest streamflows occur in late 

summer and early fall (August through October). 

During the 11-year period from 1994 to 2005 (the period 

of record used to develop the M2M-2 DSS), the Savannah 

River experienced extreme streamflow conditions. During the 

winter and spring of 1998, floods from above-normal rainfall 

during El Niño conditions resulted in streamflows of greater 

than 50,000 ft3/s (fig. 15), which exceeded the 95th-percentile 

daily streamflow for the period of record. Following the 

El Niño, the southeastern United States experienced drought 

from 1998 to 2002, inclusive, and the Savannah River at 

Clyo, Ga., experienced minimum streamflows of 4,500 ft3/s. 

Streamflows during the drought generally ranged from the 5th 

percentile to the historical minimum for the period of record.

Characterization of Historical Waccamaw River 

and Atlantic Intracoastal Waterway 

Water Levels

The AIW and Waccamaw River experience semi-diurnal 

tides consisting of two high tides and two low tides in a 

24.8-hour period. A 14-day periodic tidal cycle also occurs, 

resulting in spring and neap tides. Spring tides are periods of 

increased tidal range that occur during the time of full and 

new moons. Neap tides are periods of decreased tidal range 

that occur around the onset of waxing and waning moons. 

There are also seasonal and annual cycles to tides. The mean 

and spring tidal ranges for Little River Inlet are 4.63 and 

5.56 ft, respectively, and for Winyah Bay are 4.60 and 5.40 ft, 

respectively (table 6; National Oceanic and Atmospheric 

Administration, 2010). As the tidal wave propagates upstream, 

the tidal range decreases with the increased freshwater 

streamflow of the Pee Dee and Waccamaw Rivers and energy 

losses due to channel geometry. In the AIW, the mean tidal 

range decreases to 2.08 ft at the S.C. Highway 544 bridge at 

Socastee (table 6). In the Waccamaw River, the mean tidal 

range decreases to 1.24 ft at Conway. An approximate 3- to 

4-hour lag of the tide occurs from Nixons Crossroad at S.C. 

Highway 9 (station 02110777) to Socastee at S.C. Highway 

544 (station 02110725) on the AIW, and an approximate 

5-hour lag occurs between Hagley Landing (station 02110815) 

and Conway Marina (02110704) on the Waccamaw River.

The tidal range, the difference in height between 

consecutive high and low water levels, at the AIW at S.C. 

Highway 9 gaging station (02110777) for the period July 1994 

to June 1996 clearly shows the 14- and 28-day spring-neap 

tidal cycles, along with seasonal and semi-annual cycles 

(fig. 16). For example, a high spring tide (tidal range greater 

than 4.5 ft) is followed 14 days later by a low spring tide (tidal 

range less than 4.5 ft). A similar 28-day pattern is apparent 

in the neap tides where a low neap tide (tidal range less than 

3.5 ft) is followed 14 days later by a high neap tide (tidal range 

greater than 3.5 ft). The biggest differences in spring and neap 

tides occur in spring (March and April) and fall (October and 

November) of the year. Minimum differences between the 

spring and neap tides occur in the summer (June and July) and 

in the winter (December and January).
Characterization of Historical Streamflow, Water Level, and Specific Conductance Dynamics 19

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

Pee Dee River - regulated

Lynches River - unregulated

Little Pee Dee River - unregulated

Black River - unregulated

A

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

B

Pee Dee River streamflow, in cubic feet per second

Lynches, Little Pee Dee, and Black Rivers streamflow, in cubic feet per second

Streamflow, in cubic feet per second

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

EXPLANATION

Pee Dee River - 7-day average (02131000)

Lynches River (02132000)

Little Pee Dee River (02135000)

Black River (02136000)

EXPLANATION

Jan.

1

Jan.

31

Mar.

1

Mar.

31

Apr.

30

May

30

June

29

1996 1997

July

29

Aug.

28

Sept.

27

Oct.

27

Nov.

26

Dec.

26

Jan.

25

Feb.

24

Mar.

26

Apr.

25

May

25

June

24

July

24

Aug.

23

Sept.

22

Oct.

22

Nov.

21

Dec.

21

Figure 13. Hourly streamflow for (A) the Pee Dee (02131000), Lynches (02132000), Little Pee Dee (02135000),

and Black Rivers (02136000) and (B) 7-day average flow for the Pee Dee River and hourly streamflow for the

Lynches, Little Pee Dee, and Black Rivers, January 1, 1996–December 31, 1997. Locations of gages are 

shown in figure 2.

Figure 13. Hourly streamflow for (A) the Pee Dee (02131000), Lynches (02132000), Little Pee Dee (02135000), and Black 

Rivers (02136000) and (B) 7-day average flow for the Pee Dee River and hourly streamflow for the Lynches, Little Pee Dee, 

and Black Rivers, January 1, 1996–December 31, 1997. Locations of gages are shown in figure 2.
20 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

60,000

50,000

40,000

30,000

20,000

10,000

0

5th percentile flows

25th percentile flows

50th percentile flows

75th percentile flows

95th percentile flows

Pee Dee River streamflow, in cubic feet per second

EXPLANATION

Jan.

1

Feb.

1

Feb.

29

Mar.

31

Apr.

30

May

31

June

30

July

31

Aug.

31

Sept.

30

Oct.

31

Nov.

30

Dec.

31

Figure 14. Streamflow duration hydrographs for Savannah River near Clyo, Ga. (station 02198500),

1930–2010.

Figure 14. Streamflow duration hydrographs for Savannah River near Clyo, Georgia (station 02198500), 1930–2010.

Streamflow - Savannah River at Clyo

Mean annual streamflow

EXPLANATION

Oct. 1

1994

Oct. 1

1995

Oct. 1

1996

Oct. 1

1997

Oct. 1

1998

Oct. 1

1999

Oct. 1

2000

Oct. 1

2001

Oct. 1

2002

Oct. 1

2003

Oct. 1

2004

Oct. 1

2005

Oct. 1

2006

Oct. 1

2007

Oct. 1

2008

Oct. 1

2009

Oct. 1

2010

Figure 15. Daily streamflow and mean annual streamflow for Savannah River near Clyo, Georgia,

October 1, 1994–September 30, 2010.

60,000

50,000

40,000

30,000

20,000

10,000

0

Streamflow, in cubic feet per second

Figure 15. Daily streamflow and mean annual streamflow for Savannah River near Clyo, Georgia, October 1, 1994–

September 30, 2010.
Characterization of Historical Streamflow, Water Level, and Specific Conductance Dynamics 21

Table 6. Mean tide range, spring tide range, and mean tide levels for selected locations on the Atlantic Intracoastal 

Waterway and Waccamaw River, South Carolina.

[Data from National Oceanic and Atmospheric Administration, 2010]

Location Mean tide range,

 in feet

Spring tide range,

 in feet

Mean tide level, 

in feet1

Atlantic Intracoastal Waterway (fig. 6)

Little River 4.63 5.56 2.47

Nixons Crossroads 4.10 4.55 2.18

Myrtle Beach Airport 2.88 3.34 1.60

North Myrtle Beach 1.78 2.10 1.25

Socastee Bridge (S.C. Highway 544) 2.08 2.45 1.18

Winyah Bay and Waccamaw River (fig. 6)

Winyah Bay Entrance2 4.60 5.40 2.50

Waccamaw River Entrance2 3.60 4.24 1.91

Hagley Landing 3.47 3.99 1.88

Thoroughfare Creek 3.34 3.94 1.84

Wachesaw Landing 2.74 3.18 1.53

Bull Creek Entrance 2.46 2.85 1.38

Bucksport 2.16 2.48 1.25

Enterprise Landing 2.00 2.40 1.10

Waccamaw River at Conway 1.24 1.44 0.76

Savannah River (fig. 3)

Fort Pulaski 6.92 8.03 3.60

Fort Jackson 8.10 8.70 3.67

Port Wentworth 8.14 9.12 4.28

U.S. Highway 17 7.63 8.55 4.06

Seaboard Coast Line Railroad Bridge (Interstate 95 bridge) 6.20 7.20 3.30

1

The arithmetic mean of high and low tides. The mean tide level is listed relative to the mean lower low water datum.

2

Not shown in figure 6.

Figure 16. Daily tidal range at Atlantic Intracoastal Waterway at South Carolina Highway 9

(station 02110777), July 1994 through June 1996.

1994 1995 1996

6.0

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

Daily tidal range, in feet

J A S O N D J F M A M J J A S O N D J F M A M J

Figure 16. Daily tidal range at Atlantic Intracoastal Waterway at South Carolina Highway 9 (station 02110777), 

July 1994–June 1996.
22 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

The water levels at four stations on the AIW and the 

Pee Dee and Waccamaw Rivers during a 17-day period in 

October 1998 are shown in figure 17A. The spring-tide period, 

characterized by the large amplitude in tidal range, occurred 

around October 7; the neap-tide period, characterized by the 

relatively small amplitude in tidal range, occurred around 

October 14. During periods of medium and high streamflow, 

the tidal signals at Waccamaw River at Conway Marina and 

Pee Dee River at S.C. Highway 701 are overwhelmed by 

streamflow. Water levels for the same four stations on the 

AIW and Pee Dee and Waccamaw Rivers are shown during 

a 45-day period of high streamflow during January and 

February 1999 in figure 17B. Streamflow at Pee Dee River at 

Pee Dee, S.C., (station 02131000) peaked at 22,400 ft3/s on 

January 29, and streamflow at Waccamaw River near Longs, 

S.C., (station 02110500, fig. 2) peaked at 7,280 ft3/s on February 7 (not shown in fig. 17B). The tidal signal was negligible 

at water levels greater than 6 ft at the gaging station Pee Dee 

River at U.S. Highway 701 and at water levels greater than 

4 ft at the Waccamaw River at Conway Marina. Water levels 

are expressed relative to the National Geodetic Vertical Datum 

of 1929 (NGVD 29). 

Characterization of Historical Lower Savannah 

River and Marsh Water Levels

Semi-diurnal tides also occur in the lower Savannah River 

study area. The tidal range increases along the South Carolina 

coast from north to south. The NOAA network of subordinate 

tide stations reports that a mean tidal range of 6.92 ft at Fort 

Pulaski (http://Co-ops.nos.noaa.gov/tides10), whereas it 

reports a mean tide range of 4.63 ft at Little River Inlet. The 

tidal ranges of the lower Savannah River can increase relative 

to that at the harbor as the tidal wave propagates upstream 

and the volume of water moves into small channel geometry. 

For example, the mean tidal ranges at Fort Jackson (near the 

confluence of the Back and Front Rivers) and Port Wentworth 

were 8.10, and 8.14, respectively (table 6). Farther upstream 

at the U.S. Highway 17 bridge and Interstate 95 bridge, the 

tidal range decreased to 7.63 and 6.2 ft, respectively, with the 

increased effects of the freshwater streamflow of the Savannah 

River and decrease in channel size. There is an approximately 

1-hour lag of the tide from Fort Pulaski to the Little Back and 

Back Rivers at the U.S. Highway 17 bridge. 

Figure 18 shows the water levels at three USGS gaging 

stations on the Savannah River during October 2002. The neap 

tidal period, characterized by the relatively small amplitude in 

tidal range, occurred around October 14 and 28, and the spring 

tidal period, characterized by the larger amplitude in tidal 

range, occurred around October 7 and 21. During the spring 

tide early in the month, the highest water levels occurred at 

the Broad Street gage (station 02198977; fig. 11) and were 

greater than the downstream water levels at Fort Pulaski 

(station 02198980; fig. 3). As the tidal range diminished 

during October, the highest water levels were experienced at 

the most upstream gaging station at the Interstate 95 bridge 

(station 02198840; fig. 11A) where the high water is affected 

by streamflow. 

The tidal range for the Fort Pulaski gage (station 

02198980) is shown in figure 19 for the period June 2001 

to May 2003; the 14-day spring-neap tidal range is clearly 

illustrated. For example, a high spring tide (tidal range greater 

than 8 ft) is followed by a low spring tide (tidal range less 

than 8 ft). A similar pattern is apparent in the neap tides where 

a low neap tide (tidal range less than 5.5 ft) is followed by a 

high neap tide (tidal range greater than 5.5 ft). Seasonal and 

semi-annual cycles of minimum and maximum tidal ranges 

can also be seen in figure 19. 

Water-level dynamics in the tidal marshes are dependent 

on the height of the water, the surface elevation of the marsh, 

and inertial effects. Tidal fluctuations in marsh water levels 

are greatest during the spring tides and least during neap tides. 

Water levels at USGS gaging stations on the Little Back, 

Middle, and Front Rivers in the SNWR marsh are shown in 

figures 20 and 21. In figure 20, hourly water levels in the Little 

Back River near Limehouse (station 02188979, left y-axis) 

are shown with marsh water levels (right y-axis). The Little 

Back River water-level time series shows the periods of spring 

tides. Multi-day periods of substantial tidal fluctuations at the 

four marsh sites along the Little Back and Back Rivers occur 

during spring tides beginning around December 15 and 29, 

2001, and January 28, 2002. In figure 21, similar water-level 

responses are seen for the marsh sites along the Middle 

River and Front River. Multi-day periods of substantial tidal 

fluctuations for the three marsh sites coincide with spring tides 

beginning around November 10, November 29, December 10, 

and December 29, 2001.
Characterization of Historical Streamflow, Water Level, and Specific Conductance Dynamics 23

Waccamaw River (02110704) at Conway Marina 

AIW (02110777) at SC Highway 9

Pee Dee River (02135200) at U.S. Highway 701

Waccamaw River (02110815) at Hagley Landing

EXPLANATION

Jan. 10 Jan. 15 Jan. 20 Jan. 25 Jan. 30 Feb. 4 Feb. 9 Feb. 14 Feb. 19 Feb. 24 Mar. 1 Mar. 6

Sept. 30 Oct. 2 Oct. 4 Oct. 6 Oct. 8 Oct. 10

1998

Oct. 12 Oct. 14 Oct. 16 Oct. 18 Oct. 20

Figure 17. Hourly water levels at four gaging stations on the Pee Dee and Waccamaw Rivers and the Atlantic

Intracoastal Waterway (AIW), South Carolina for (A) a low-flow period October 1–October 17, 1998, and

(B) a high-flow period January 15–March 1, 1999.

Waccamaw River (02110704) at Conway Marina 

AIW (02110777) at SC Highway 9

Pee Dee River (02135200) at U.S. Highway 701

Waccamaw River (02110815) at Hagley Landing

EXPLANATION

1999

8

7

6

5

4

3

2

1

0

–1

–2

5

4

3

2

1

0

–1

–2

Water level, in feet above or below NGVD 1929

A

B

Figure 17. Hourly water levels at four gaging stations on the Pee Dee and Waccamaw Rivers and the Atlantic 

Intracoastal Waterway (AIW), South Carolina, for (A) a low-flow period October 1–17, 1998, and (B) a high-flow period 

January 15–March 1, 1999.
24 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Figure 18. Hourly water levels at three gaging stations on the Savannah River, Georgia,

October 1–31, 2002.

–5

–3

–1

1

3

5

7

1 2 3 4 5 6 7 8 9 13 14 16 15 22

October

11 1210 17 1918 20 21 23 24 2625 27 28 29 3130

Water level, in feet above or below NGVD 29

I-95 Bridge

EXPLANATION

Broad Street

Fort Pulaski

Figure 18. Hourly water levels at three gaging stations on the Savannah River, Georgia, October 1–31, 2002.

June 2

June 30

July 28

Aug. 25

Sept. 22

Oct. 20

Nov. 17

Apr. 6

May 4

June 1

Dec. 15

Jan. 12

June 29

July 27

Aug. 24

Sept. 21

Oct. 19

Nov. 16

Dec. 14

Jan. 11

Feb. 9

Mar. 9

Feb. 8

Mar. 8

Apr. 5

Figure 19. Daily tidal range at Savannah River at Fort Pulaski (02198980), Georgia,

June 2001–April 2003.

2001 2002 2003

10

9

8

7

6

5

4

Daily tidal range, in feet

Figure 19. Daily tidal range at Savannah River at Fort Pulaski (02198980), Georgia, June 2001–April 2003.
Characterization of Historical Streamflow, Water Level, and Specific Conductance Dynamics 25

–2

0

2

4

6

8

10

12

River water level, in feet above or below NGVD 29

–0.2

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Marsh water level, in feet

Little Back River near Limehouse

Marsh Site B1

EXPLANATION

Marsh Site B2

Marsh Site B3

Marsh Site B4

15 22 29 5 12 19 26 2 9

December January February

2001 2002

15

Figure 20. Hourly water-level data for the four Back River marsh gaging stations and Little Back River

gaging stations, South Carolina, December 15, 2001–February 15, 2002.

Figure 20. Hourly water-level data for the four Back River marsh gaging stations and Little Back River gaging 

stations for the period December 15, 2001–February 15, 2002.

Savannah River, Houlihan Bridge

Marsh site F1

Marsh site M1

Marsh site M2

–2

–4

0

2

4

6

8

10

12

River water level, in feet above or below NGVD 29

–0.2

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Marsh water level, in feet

EXPLANATION

10 17 24 1 8 15 22 29 5

November December January

2001 2002

Figure 21. Hourly water-level data for three marsh gaging stations along the Middle and Front Rivers

and Savannah River water level at Houlihan Bridge (station 02198920), Georgia, November 10, 2001–

January 7, 2002.

Figure 21. Hourly water-level data for three marsh gaging stations along the Middle and Front Rivers, and 

Savannah River water level at Houlihan Bridge (station 02198920), Georgia, November 10, 2001–January 7, 2002.
26 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Figure 22. Conceptual model of the major factors controlling the position

of the freshwater-saltwater interface.

Riverine Flow

Tidal forcing

1) Mean water level

2) Tidal range

Saltwater

Freshwater

Figure 22. Conceptual model of the major factors controlling the position of the freshwater-saltwater interface.

Characterization of Historical 

Specific Conductance

Specific conductance, a measure of the ability of water 

to conduct electrical current, is commonly used to compute 

salinity (Miller and others, 1988). In this report, specific 

conductance and salinity are used interchangeably. Salinity in 

study areas is constantly responding to changing streamflow 

and tidal conditions. The location of the saltwater-freshwater 

interface is balanced between upstream freshwater river 

streamflows and downstream tidal forcing (fig. 22). During 

periods of high streamflow, it is difficult for salinity to intrude 

upstream, and the saltwater-freshwater interface moves 

downstream toward the ocean. During periods of low streamflow, the saltwater-freshwater interface is moved upstream by 

tidal forcing, by an increase in mean water level, a change in 

tidal range, or a combination of the two. 

Waccamaw River and Atlantic 

Intracoastal Waterway 

The location of the saltwater-freshwater interface in 

the Waccamaw River and Atlantic Intracoastal Waterway is 

affected by streamflow from the Pee Dee and Waccamaw 

Rivers. The daily mean specific conductance for the Waccamaw River at Hagley Landing (station 02110815) and the 

daily mean streamflow for the Pee Dee River at Pee Dee, S.C. 

(station 02131000) for the October 1994 to September 2010 

are shown in figure 23. The period encompasses streamflow 

conditions ranging from the high streamflows of the El Niños 

in 1998 and 2003 to the low streamflows of the extended 

droughts from 1998 to 2002 and 2007 to 2008. During periods 

of medium and high streamflows (streamflow greater than 

5,000 ft3

/s), the specific conductance is generally low. (For 

plotting purposes, streamflow was multiplied by 0.1, and 

9 ft was subtracted from the gage height.) During periods of 

low streamflow (streamflow less than 5,000 ft3/s), specificconductance values increase representing periods of salinity 

intrusion. During the low-flow periods prior to the high 

streamflows measured during the El Niño of 1998, salinity 

intrusions associated with specific-conductance values of 

3,000 to 5,000 microsiemens per centimeter at 25 degrees 

Celsius (μS/cm) were common. During droughts, such as 

after the high streamflow in 1998, extended periods of low 

streamflow occurred. The low streamflow contributed to 

increases in salinity, with daily mean specific-conductance 

values often greater than 15,000 μS/cm and occasional values 

greater than 20,000 μS/cm (fig. 23). A specific conductance of 

20,000 μS/cm is equal to a salinity of 11.9 practical salinity 

units (psu).

A similar relation between low streamflow and high 

salinity can be seen in the mean specific conductance for 

the North End of the AIW at Grand Strand Airport (station 

02110770) and the daily mean streamflow for the Waccamaw 

River at Longs, S.C. (station 02110500) for the period 

from October 1, 1994, to September 30, 2011 (fig. 24). The 

extended low-flow periods after 1999 are apparent; streamflows rarely reached 6,000 ft3

/s. During the low-flow periods 

prior to the El Niño of 1998, salinity intrusion with specificconductance values of 6,000 to 15,000 μS/cm were common; 

in contrast, salinity intrusions exceeding 15,000 μS/cm were 

common during the extended droughts after 1998.

Although salinity levels at the North End of the AIW and 

the South End of the Waccamaw River respond similarly to 

reduced streamflow, the response to changes in tidal conditions is very different. The primary tidal forces counteracting 

the river streamflows are the mean coastal water levels and 

tidal range (fig. 22). For the Pawleys Island gage (station 

021108125; fig. 2) at the South End, salinity intrusions occur 

when the streamflow of the Pee Dee River is less than about 

5,000 ft3

/s and the mean water levels are high. Figure 25 

depicts two salinity intrusion events in 2007. The streamflow 

in the Pee Dee River (station 02131000) decreased from 

20,000 ft3/s in late April 2007 to less than 5,000 ft3/s in early 

May (note that streamflow and gage height were adjusted on 

figure 25 for plotting purposes). Note that the intrusion events 

coincide with high mean water level, as indicated by the Little 

River Inlet gage heights, rather than spring tides. On May 13, 

2007, gage height exceeded 4.5 ft. Soon after, the streamflow 

exceeded 5,000 ft3/s and specific conductance decreased. The 

increase in mean water levels increased the volume of higher 

salinity ocean water in Winyah Bay and the lower Waccamaw 

River. Small decreases in streamflow allow the salinity to 

move farther upstream. Although streamflows were low, less 

than 5,000 ft3/s in mid-May, the next intrusion did not occur 

until the Little River Inlet gage height increased to 4.5 ft on 

June 4, 2007. 
Characterization of Historical Streamflow, Water Level, and Specific Conductance Dynamics 27

100,000

90,000

80,000

70,000

60,000

50,000

40,000

30,000

20,000

10,000

0

25,000

20,000

15,000

10,000

5,000

0

Streamflow, in cubic feet per second

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

EXPLANATION

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Figure 23. Daily mean specific conductance at Waccamaw River at Hagley Landing, South Carolina

(02110815), and daily mean streamflow at Pee Dee River at Pee Dee, South Carolina (02131000),

October 1, 1994–September 30, 2010.

Streamflow, Pee Dee River

Specific conductance, Waccamaw River

at Hagley Landing

Figure 23. Daily mean specific conductance at Waccamaw River at Hagley Landing, South Carolina (02110815), and 

daily mean streamflow at Pee Dee River at Pee Dee, South Carolina (02131000), October 1, 1994–September 30, 2010.
28 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

30,000

27,000

24,000

21,000

18,000

15,000

12,000

9,000

6,000

3,000

0

20,000

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Streamflow, in cubic feet per second

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

EXPLANATION

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Figure 24. Daily mean specific conductance at Atlantic Intracoastal Waterway at Grand

Strand Airport, South Carolina, and daily mean streamflow at Waccamaw River at Longs,

South Carolina, October 1, 1994–September 30, 2010.

Streamflow, Waccamaw River (02110500)

Specific conductance, Grand Strand

Airport (02110770)

Figure 24. Daily mean specific conductance at Atlantic Intracoastal Waterway at Grand Strand Airport, South Carolina, 

and daily mean streamflow at Waccamaw River at Longs, South Carolina, October 1, 1994–September 30, 2010.
Characterization of Historical Streamflow, Water Level, and Specific Conductance Dynamics 29

3,000

2,500

2,000

1,500

1,000

500

0

6

5

4

3

2

1

0

Daily mean streamflow, in cubic feet per second, and daily mean specific conductance, in microsiemens per centimeter

at 25 degrees Celsius

Gage height and tidal range, in feet

EXPLANATION

April

1

April

29

May

27

2007

June

30

Figure 25. Daily mean specific conductance at Waccamaw River at Pawleys Island

(station 021108125), daily mean streamflow at Pee Dee River (station 02131000), and daily mean

gage height and daily mean tidal range at Little River Inlet (station 02110777), South Carolina,

April 1, 2007–June 30, 2007. Note that, for plotting purposes, the Pee Dee River streamflow was

multiplied by 0.1, and 9 feet was subtracted from the Little River Inlet gage height.

Streamflow, Pee Dee River

Specific conductance, Pawleys Island 

Gage height (adjusted), Little River Inlet 

Tidal range, Little River Inlet 

Figure 25. Daily mean specific conductance at Waccamaw River at Pawleys Island (station 021108125), 

daily mean streamflow at Pee Dee River (station 02131000), and daily mean gage height and daily mean tidal 

range at Little River Inlet (station 02110777), South Carolina, April 1, 2007–June 30, 2007. Note: For plotting 

purposes, the Pee Dee River streamflow was multiplied by 0.1, and 9 feet was subtracted from the Little 

River Inlet gage height.
30 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

At the North End of the AIW, salinity increased in 

response to the tidal range rather than the mean coastal water 

levels. A series of salinity intrusion events at the Briarcliffe 

Acres gage ( station 02110755; fig. 2) on the AIW in July 

and August 2008 are shown using specific conductance in 

figure 26. (For plotting purposes, streamflow was multiplied 

by 0.1, and 9 ft was subtracted from the gage height.) Soon 

after the Pee Dee River and Waccamaw River streamflows 

dropped below approximately 3,000 and 200 ft3/s, respectively. In early June 2008, the salinity intruded until the 

streamflows increased in early September 2008. The peaks 

of the specific conductance coincide with the spring tides, 

as seen in the maximum tidal range values during June to 

September 2007. Although there is a 14-day spring-neap 

tide cycle along the South Carolina coast, three of the four 

intrusion events shown in figure 26 occurred during the high 

spring tide, which occurs on a 28-day cycle. The event on 

June 19, 2008, not associated with the spring tide, occurred 

during the lowest Waccamaw River and Pee Dee River 

streamflows.

3,000

2,500

2,000

1,500

1,000

500

0

6

5

4

3

2

1

0

Streamflow, in cubic feet per second, and specific conductance, in microsiemens per centimeter at 25 degrees Celsius

Gage height and tidal range, in feet

EXPLANATION

April May June

2008

July Aug. Sept. Oct.

Figure 26. Daily mean specific conductance at Waccamaw River at Briarcliffe Acres

(station 02110755), Pee Dee River streamflow (station 02131000), and gage height and

tidal range at Little River Inlet (station 02110777), April 1, 2008–October 31, 2008.

Note that for plotting purposes, Pee Dee River streamflow was multiplied by 0.1, and 9 feet

was subtracted from the Little River Inlet gage height.

Streamflow, Pee Dee River

Specific conductance, Briarcliffe Acres

Gage height (adjusted), Little River Inlet

Streamflow, Waccamaw River 

Tidal range, Little River Inlet

Figure 26. Daily mean specific conductance at Waccamaw River at Briarcliffe Acres (station 02110755), 

Pee Dee River streamflow (station 02131000), and gage height and tidal range at Little River Inlet (station 

02110777), April 1, 2008–October 31, 2008. Note: For plotting purposes, Pee Dee River streamflow was 

multiplied by 0.1, and 9 feet was subtracted from the Little River Inlet gage height.
Characterization of Historical Streamflow, Water Level, and Specific Conductance Dynamics 31

Lower Savannah River 

Historically, daily mean streamflows at the Savannah 

River near Clyo, Ga., (station 02198500) ranged from about 

5,000 to about 50,000 ft3/s (U.S. Geological Survey, 2012). 

Streamflow at the Clyo gage and specific conductance at 

the Interstate 95 gage (02198840) from October 1994 to 

September 2010 are shown in figure 27. As with the Grand 

Strand in the Waccamaw River and AIW study area (figs. 23 

and 24), periods of high specific conductance coincided with 

periods of low streamflow. During extended low-flow periods 

the daily maximum specific conductance commonly exceeded 

1,000 μS/cm at the Savannah River Interstate 95 gage. Like 

the North and South Ends of the Grand Strand, there is a 

convergence of conditions needed for the elevated specific 

conductance: Savannah River streamflow must be less than 

6,000 ft3/s at the occurrence of spring tide of the new moon. 

The daily maximum specific conductance at the Interstate 

95 gage, the daily mean streamflow for the Clyo gage, and 

the dates of the new moon for the summer 2009 are shown 

in figure 28. During the new moon, tidal ranges are greatest 

because the gravitational attraction between the sun and moon 

are aligned. The increased tidal energy under these conditions 

typically leads to a salinity-intrusion event. Meteorological 

conditions, such as tropical storms and high winds, may 

exacerbate salinity intrusion. 

The Savannah River estuary is considered a partially 

stratified system. A conceptual diagram of the primary factors 

that affect stratification and the position of the freshwatersaltwater interface in the Savannah River are shown in 

figure 29. During spring tides (tides with the largest tidal 

range), there is increased energy in the system and more 

mixing of the less dense freshwater of the river with the denser 

saltwater of the harbor, which results in smaller variation 

60,000

50,000

40,000

30,000

20,000

10,000

0

6,000

5,000

4,000

3,000

2,000

1,000

0

Daily mean streamflow, in cubic feet per second

Daily maximum specific conductance, in microsiemens per centimeter at 25 degrees Celsius

EXPLANATION

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Figure 27. Maximum daily specific conductance at Savannah River near Port Wentworth, Georgia

(station 02198840, I-95 Bridge) and streamflow at Savannah River near Clyo, Georgia (station 02198500),

January 1, 1994–September 30, 2010.

Daily mean streamflow, Clyo, Georgia

Daily maximum specific conductance,

Savannah River at Interstate 95

Figure 27. Maximum daily specific conductance at Savannah River near Port Wentworth, Georgia (station 02198840, 

I-95 Bridge), and streamflow at Savannah River near Clyo, Georgia (station 02198500), January 1, 1994–September 30, 2010.
32 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

May 1 June 1 July 1 Aug. 1 Sept. 1 Oct. 1 Nov. 1

Streamflow, Clyo, Georgia

Specific conductance, Savannah River

at Interstate 95

New moon

2009

EXPLANATION

Daily maximum specific conductance, in microsiemens

per centimeter at 25 degrees Celsius

Daily streamflow, in cubic feet per second

Figure 28. Daily streamflow at Savannah River near

Clyo, Georgia (station 02198500), and daily specific

conductance at the Savannah River near Port Wentworth,

Georgia (station 02198840) I-95 Bridge, May–October 2009,

including occurrences of the new moon.

Figure 28. Daily streamflow at Savannah River near Clyo, Georgia 

(station 02198500), and daily specific conductance at the Savannah 

River near Port Wentworth, Georgia (station 02198840), I-95 Bridge, 

May–October 2009, including occurrences of the new moon.

De-stratification

Stratification

Spring tides

Neap tides

Figure 29. Conceptual model of the location of the freshwatersaltwater interface and salinity stratification–de-stratification

cycle in estuarine rivers.

Saltwater

Freshwater

Saltwater

Freshwater

Figure 29. Conceptual model of the location of the freshwater-saltwater interface and 

salinity stratification–de-stratification cycle in estuarine rivers.

in vertical salinity concentrations. During neap tides (tides 

with the smallest tidal range), there is decreased energy 

in the system and less mixing between the freshwater and 

saltwater which allows freshwater to flow downstream over 

the saltwater intruding upstream. The decrease in mixing 

results in an increased stratification and increased salinity 

intrusion upstream. The stratification and de-stratification 

of salinity at station GPA 4 (a specific-conductance gaging 

station maintained by Georgia Ports Authority near the USGS 

station 02198977; fig. 11A) for 2 months during summer 1997 

is shown in figure 30. During the neap tides around Julian day 

225 (August 12), there is approximately a 15-psu difference in 

salinity between the bottom and surface of the channel. During 

the spring tides around Julian days 205 and 235, the system 

de-stratifies, and the differences between the bottom and 

surface salinities are only 3 to 5 psu. 

Tidal marshes are constantly integrating the changing 

river conditions in their water levels (frequency and duration 
Simulation of Specific Conductance 33

0

10

5

15

20

25

30

35

200 210 220 230

Julian calendar day, 1997

Bottom

Surface

Moon phases

 New

 Full

240 250

Salinity, in practical salinity units

EXPLANATION

Figure 30. Hourly surface and bottom salinities for station

GPA 4, Georgia, and occurrences of the moon phase, July 15–

September 13, 1997. (Modified from Tetra Tech, 2005)

The plot shows the de-startification of the Savannah River on

about Julian days 205 and 235 and the stratification on about

Julian day 225.

Figure 30. Hourly surface and bottom salinities for station 

GPA 4, Georgia, and occurrences of the moon phase, 

July 15–September 13, 1997 (modified from Tetra Tech, 2005). 

The plot shows the de-stratification of the Savannah River on 

about Julian days 205 and 235 and the stratification on about 

Julian day 225.

Figure 31. Tidal marsh types classified by interstitial salinity 

(Pearlstine and others, 1990) and average estuary surface 

salinities. (Cowardin and others, 1979; modified from Odum

and others, 1984) (<, less than; ≤, less than or equal to; psu, 

practical salinity units) 

Ocean

>30.0

ESTUARY TYPE

Surface salinity

MARSH TYPE

Interstitial

Limit of

tidal influence

Mesohaline

<18.0 psu

Oligohaline

<5.0 psu

Tidal

freshwater

≤0.5 psu

Tidal

freshwater

≤0.5 psu

Intermediate

0.5 to 3.0 psu

Brackish

3.0 to 7.0 psu

Subsaline

7.0 to 18.0 psu

Figure 31. Tidal marsh types classified by interstitial salinity 

(Pearlstine and others, 1990) and average estuary surface 

salinities (Cowardin and others, 1979; modified from Odum and 

others, 1984). [<, less than; ≤, less than or equal to; psu, pratical 

salinity units]

of inundations), and the salinity concentration in the interstitial 

pore water of the root zone varies accordingly. Plant community distributions in the marshes are affected by interstitial 

salinities (fig. 31). The marsh sites do not exhibit the semidiurnal variability in salinity shown by the river (fig. 32) but 

depend on the frequency and magnitude of the flooding of 

river water on the marsh. The connection between interstitial 

salinities of the marshes and water-column salinities occurs 

when riverine salinity intrusions are concurrent with the 

spring-tide water levels. The specific-conductance time series 

of the four marsh gaging stations along the Little Back and 

Back Rivers, and the specific conductance for the Front River 

at Houlihan Bridge (station 02198920), are shown in figure 32. 

The four marsh sites show a distinct gradient of increasing 

specific-conductance values from upstream (Site B1) to 

downstream (Site B4). Increased specific conductance in 

the marsh generally occurs following increased specific 

conductance in the river when water levels are high enough 

to flood the marsh. The pore-water specific-conductance time 

series of three marsh streamgaging stations along the Middle 

and Front Rivers and specific conductance for the Front River 

at the Houlihan Bridge water-level gage (station 02198920) 

are shown in figure 33. 

Simulation of Specific Conductance

Models generally fall into one of two categories: mechanistic, also referred to as deterministic models, and empirical. 

Mechanistic models are created from first-principles equations, 

whereas empirical models adapt generalized mathematical 

functions to fit a line or surface through data from one or more 

variables. Simulating the specific conductance of estuarine 

systems typically is done using dynamic mechanistic models 

that incorporate the mathematical descriptions of the coastal 

hydrodynamics. These 1D, 2D, or 3D models often require 

extensive data collection and are time consuming to apply 

to complex coastal systems with satisfactory results. Often 

mechanistic model calibration for estuaries is particularly 

difficult because of low watershed gradients, poorly defined 

drainage areas, tidal complexities, and a lack of understanding 

of watershed and marsh processes (Conrads and Roehl, 2005). 

Although mechanistic models have been the state of the 

practice for regulatory evaluations of anthropogenic effects 

on estuarine systems, developments in the field of advanced 

statistics, machine learning, and data mining offer opportunities to develop empirical ANN models that often are more 
34 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Aug. 6 Aug. 20 Sept. 3 Sept. 17 Oct. 1

2001

Oct. 15 Oct. 29 Nov. 12 Nov. 26

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

Figure 32. Hourly specific conductance at four marsh gaging stations along the Little Back and Back Rivers and hourly

specific conductance at Houlihan Bridge on the Front River (02198920), Georgia, August 6–November 30, 2001.

Front River at Houlihan Bridge

Marsh site B1

Marsh site B2

Marsh site B3

Marsh site B4

EXPLANATION

Figure 32. Hourly specific conductance at four marsh gaging stations along the Little Back and Back Rivers and hourly specific 

conductance at Houlihan Bridge on the Front River (02198920), Georgia, August 6–November 30, 2001.

0

5,000

10,000

15,000

20,000

25,000

30,000

Figure 33. Hourly pore-water specific conductance for two marsh gaging stations on the Middle River, one marsh

gaging station on the Front River, and one gaging station at Houlihan Bridge on the Front River (02198920), Georgia,

July 26–September 27, 2002.

Front River at Houlihan Bridge

Marsh site F1

Marsh site M1

Marsh site M2

EXPLANATION

July 26 Aug. 9 Aug. 23 Sept. 6 Sept. 20

2002

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

Figure 33. Hourly pore-water specific conductance for two marsh gaging stations on the Middle River, one marsh gaging station on 

the Front River, and one river gaging station at Houlihan Bridge on the Front River (02198920), Georgia, July 26–September 27, 2002.
Simulation of Specific Conductance 35

accurate. Conrads and Roehl (1999) compared the application 

of a mechanistic model and an ANN model to simulate 

dissolved oxygen concentrations for the tidally affected 

Cooper River in South Carolina. They found that the ANN 

models offer some important advantages, including faster 

development time, utilization of larger amounts of data, the 

incorporation of optimization routines, and model dissemination in spreadsheet applications.

Artificial Neural Network Models

ANN models are empirical models that are developed 

directly from data. The most common empirical modeling 

approach is ordinary least squares (OLS), which relates 

variables using straight lines, planes, or hyper-planes, whether 

the actual relations are linear or not. Calibration of either 

type of model attempts to optimally synthesize a line or 

surface through the measured data. Calibration of models 

is difficult when data have substantial measurement error 

or are incomplete, or when the variables for which data are 

available provide only a partial explanation of the sources 

of variability. The principal advantages of empirical models, 

such as ANN models, over mechanistic models are that they 

can be developed faster and are more accurate provided that 

the modeled systems are well characterized by data. Empirical 

models, however, are prone to problems when poorly applied. 

Overfitting and multicollinearity caused by correlated input 

variables can lead to invalid mappings, or relations, between 

input and output variables (Roehl and others, 2003). 

An ANN model is a flexible mathematical structure 

capable of describing complex nonlinear relations between 

input and output datasets. The structure of ANN models is 

loosely based on the biological nervous system with interconnections of neurons and synapses (Hinton, 1992). Although 

numerous types of ANN models exist, the most commonly 

used type of ANN model is the multi-layer perceptron 

(Rosenblatt, 1958), which is used in this study and described 

in detail by Jensen (1994). Multi-layer perceptron ANNs can 

synthesize functions to fit multi-dimensional, nonlinear data. 

Devine and Roehl (2003) and Roehl and Conrads (1999) 

describe the application of the multi-layer perceptron (MLP) 

ANN to model and control combined manmade and natural 

systems, including disinfection byproduct formation, industrial 

air emissions monitoring, and surface-water systems affected 

by point- and nonpoint-source pollution. 

Multi-layer perceptron ANNs are constructed from layers 

of interconnected processing elements called neurons that 

execute a simple “transfer function” (fig. 34). All input layer 

neurons are connected to all hidden layer neurons, and all 

hidden layer neurons are connected to all output layer neurons. 

Multiple hidden layers are possible, but a single layer is 

sufficient for most problems.

Output

layer

Input

layer

(Input time-series

examples)

(Output time-series

examples)

x1

x3

x2

x4

x5

x1

x3

x2

x4

x5

x1

x3

x2

x4

x5

y1

y1

y1

t

t+1

t+2

t

t+1

t+2

wi

wi+n

“Weights”

control

connections

Neurons with 

linear 

transfer

functions

Neurons with nonlinear

transfer functions

Hidden

layer

Figure 34. Multi-layer perceptron artificial neural network architecture. (From Conrads and Roehl, 2007) Figure 34. Multi-layer perceptron artificial neural network architecture (from Conrads and Roehl, 2007).
36 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Typically, linear transfer functions are used to scale input 

values from the input layer to the hidden layer and generally 

fall within the range that corresponds to the most linear part of 

the s-shaped sigmoid transfer functions used from the hidden 

layer to the output layer (fig. 34). Each connection has a 

weight, wi, associated with it, which scales the output received 

by a neuron from a neuron in an antecedent layer. The output 

of a neuron is a simple combination of the values it receives 

through its input connections and the associated weights, as 

well as the neuron’s transfer function. 

An ANN is “trained” by iteratively adjusting its weights 

to minimize the error by which it maps inputs to outputs for 

a dataset composed of input/output vector pairs. Prediction 

accuracy during and after training can be measured by a 

number of metrics, including coefficient of determination 

(R2) and root mean square error (RMSE). An algorithm that 

is commonly used to train multi-layer perceptron ANNs is the 

back error propagation (BEP) training algorithm (Rumelhart 

and others, 1986). The algorithm optimally minimizes the 

error in the objective function by adjusting the weights into 

and out of the hidden layer of the model (fig. 34).

Experimentation with various ANN model architectural 

and training parameters is a typical part of the modeling 

process. For correlation analysis or predictive modeling applications, a number of potential ANN models are trained and 

evaluated for their statistical accuracy and their representation 

of dynamics of the system. Interactions between combinations 

of variables are considered, in addition to the selection of 

the training dataset from the overall dataset. In developing 

ANN models, it is customary to set aside a subset of the data 

to provide an independent evaluation of model performance. 

Typically, datasets are bifurcated into “training” and “testing” 

datasets. There are many strategies for partitioning data into 

training and test datasets, but the most common is random 

selection of a specified percentage of the total population of 

measurements. 

The models were calibrated using the training dataset 

and evaluated with the testing dataset. For models with a large 

dataset with good representation over the range of historical 

behaviors, a small percentage of the dataset (10–25 percent) 

may be selected for the training dataset. For models with 

limited data, a larger percentage (75–100 percent) may be 

used in the training dataset. To mitigate the extrapolation 

and sparseness issues, the ANN models were conservatively 

trained using a method referred to as “stop training” to both 

fit the data and extrapolate in a minimally nonlinear and, 

therefore, predictable fashion. Stop training simply means 

stopping the training process before the ANN has fit the data 

to the maximum extent possible. Architectural and training 

parameters allow the modeler to control the geometric 

complexity of the surface that the ANN fits to the data. The 

data-mining software used for this application writes R2 and 

RMSE to the graphical user interface (GUI) during training, 

and an inflection in the rate of change in these parameters 

indicates a transition from a generally linear, multivariate 

surface fit to a progressively nonlinear fit. This inflection point 

was used to trigger stop training. 

In general, a high-quality predictive model can 

be obtained when

• the data ranges are well distributed throughout the state 

space of variables describing the physical system of 

interest,

• the input variables selected by the modeler share 

mutual information about the output variables, and

• the functional form “prescribed” or “synthesized” 

by the model to “map” (correlate) input variables 

to output variables is a good one. Machine-learning 

techniques, like ANN models, synthesize a best fit to 

the data. Techniques such as ordinary least squares and 

finite-difference models prescribe the functional form 

of the model’s fit to the calibration data. 

Subdividing a complex modeling problem into 

subproblems and then addressing each is an effective means 

of achieving the optimal results. A collection of sub-models 

whose calculations are coordinated by a computer program 

constitutes a “super-model.” For the Waccamaw River-AIW 

and lower Savannah River studies, daily ANN models 

(sub-models) were developed for specific conductance at 

a particular gaging station. These sub-models were then 

incorporated into a super-model application that integrated 

the model controls, model database, and model outputs. 

The super-models for the project are the Pee Dee River and 

Atlantic Intracoastal Waterway Salinity Intrusion Model 

(Version 2) Decision Support System (PRISM-2 DSS) and 

the Savannah Model-to-Marsh (Version 2) Decision Support 

System (M2M-2 DSS). The ANN models described in 

Conrads and others (2006), Conrads and Roehl (2007), and 

in this report were developed using the iQuestTM data-mining 

software4 (Version 2.03C DM Rev31). The ANN models were 

deployed in the DSSs using the Visual Basic run-time library 

of the iQuest R/TTM software. 

Data-Mining Techniques

Data mining extracts information from large databases 

using a variety of techniques from various disciplines. For 

estuarine systems such as the Waccamaw River and AIW, and 

the lower Savannah River study areas, most of the large databases consist of 15-minute time-series data of streamflows, 

water levels, and specific conductance values. The behavior, 

or dynamics, of a natural system results from interactions 

between multiple physical forces. For example, the specific 

conductance at a fixed location is subject to daily, seasonal, 

and annual streamflow conditions and semi-diurnal, fortnight, 

seasonal, and annual tidal conditions. The data-mining 

4

The iQuestTM software is exclusively distributed by Advanced Data 

Mining, LLC, 3620 Pelham Road, PMB 351, Greenville, SC 29615–5044, 

Phone (864) 201–8679, email info@advdatamining.com, http://www.advdmi.com.
Simulation of Specific Conductance 37

exercise characterizes the principal physical forces by extracting their response from time-series data. 

Signals, or time-series data, manifest three types of 

behavior: periodic, noise, or chaotic. Periodic behavior is 

perfectly predictable. Examples of periodic behavior are the 

diurnal sunlight and temperature patterns caused by the rising 

and setting sun or tidal water levels resulting from orbital 

mechanics. Noise refers to random components, usually 

attributed to measurement error, and is unpredictable. Chaotic 

behavior is neither totally periodic nor noise and always has 

a physical cause. Weather provides an example of chaotic 

behavior. Chaotic behavior is somewhat predictable, especially 

for small time frames and prediction horizons. 

For the application of the ANN models to coastal rivers 

in South Carolina and Georgia, data-mining techniques were 

applied to maximize the information content in time-series 

data while diminishing the influence of poor or missing 

measurements. Methods used to develop the salinity models, 

including signal decomposition, spectral analysis, state-space 

reconstruction, signal decorrelation, and correlation and 

sensitivity analysis are described below. 

Signal Decomposition

Signal decomposition involves splitting a signal into subsignals called components that are independently attributable 

to different physical forces. To analyze and model these time 

series, the periodic and chaotic components of the signals need 

to be separated. Signal decomposition can be accomplished 

through various techniques, such as digital filtering and the use 

of time derivatives, and time delays. As previously discussed, 

digital filtering techniques can be used to remove or isolate 

the chaotic component in the tidal water-level, or gage height, 

time series. Digital filtering also can diminish the effect of 

noise in a signal to improve the amount of useful information 

that it contains. Computation of the tidal range time series 

from the water-level time series isolates the periodic components of the water-level time series. An example of 61 days 

of hourly gage-height data and filtered gage-height data for 

the Little River Inlet is shown in figure 35. The filtered gage 

heights represent the chaotic component of the tidal gageheight signal.

Figure 35. Hourly gage heights at Atlantic Intracoastal Waterway at South Carolina, Highway 9 ( Little River Inlet

station 02110777), filtered gage heights for tidal influence, and 1-day change in filtered gage heights for the 60-day period

October 4–December 4, 1998.

Unfiltered gage height

Filtered gage height

1-day change in filtered gage height

EXPLANATION

Oct. 4 Oct. 11 Oct. 18 Oct. 25 Nov. 1 Nov. 8 Nov. 15 Nov. 22 Nov. 29

1998

Gage height, in feet

17

16

15

14

13

12

11

10

9

1-day change in gage height, in feet

1.0

0.8

0.6

0.4

0.2

0

–0.2

–0.4

–0.6

–0.8

–1.0

Figure 35. Hourly gage heights at Atlantic Intracoastal Waterway at South Carolina, Highway 9 (Little River Inlet station 02110777), 

filtered gage heights for tidal influence, and 1-day change in filtered gage heights for the 60-day period October 4–December 4, 1998.
38 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

The use of time derivatives is a common analytical 

method for analysis of the dynamics of a system. Time derivatives (the change in a parameter or a period of time) were 

computed from the measured, computed, and filtered variables 

for the Waccamaw River and AIW, and lower Savannah River, 

study areas to further understand the dynamics of the system. 

The 1-day change of the filtered water-level time series for a 

60-day period was plotted with the original time series and 

the filtered data (fig. 35). The 1-day derivatives show the rate 

of change of the chaotic component of the water-level time 

series. For the 60-day period, the daily change in filtered gage 

height ranged from –0.5 ft to 0.5 ft. 

Often time delays, or lags, occur between when an event 

is measured and the time that the response is observed in a 

system. Modeling a system is more complicated when two 

events of interest, a cause and an effect, do not occur simultaneously. For example, it takes approximately 2 day for the 

measured streamflow at the Clyo, Ga., gage on the Savannah 

River (station 02198500) to have an effect on downstream 

specific conductance at the Interstate 95 gage on the Savannah 

River (station 02198840). The time between cause and effect 

is called the time delay or lags. Each input variable of a model 

has its own delay. Determining the correct time delays for 

pulses and system response is critical to accurately simulate 

a dynamic system. For the Waccamaw River and the AIW, 

there are time delays between the measured streamflow at the 

Pee Dee River at Pee Dee, S.C., (station 02131000) and the 

response in specific conductance at the gaging stations in the 

Waccamaw River and the AIW. Time delays also occur among 

measured streamflow values for the four tributaries to the Pee 

Dee River. Time delays from the point when the streamflow 

enters the system to the point when the river responds to the 

streamflow were determined for each gage. 

Spectral Analysis

A signal decomposition technique that was effectively 

used for the models in the Waccamaw River and AIW study 

area is spectral component analysis. Spectral analysis provides 

a means of splitting a raw signal, in this case a set of monitoring data, into different frequency components that represent 

various lengths of time from daily to annual. These signals 

are then used as potential inputs to the ANN models. Signals 

representing different spectral frequencies were computed 

by generating various sizes of moving-window averages 

to capture temporal variability in the signal, such as daily, 

weekly, monthly, seasonal, intra-annual, and annual averages. 

The differences between a moving-window average and the 

next larger window-sized moving-window average are also 

calculated, and the resulting signal is a spectral component of 

the original signal for that particular averaging interval. This 

approach is analogous to band-pass spectral filtering in that it 

decomposes a raw signal into different spectral components. 

This approach also systematically decorrelates each spectral 

component signal. For example, the total inflow to the AIW 

and Waccamaw River was computed, and moving-window 

averages for 3-, 7-, 14, 30, and 60-day periods were computed. 

The difference between subsequent moving-window averages, 

such as 3-day and 7-day, were computed. No differences are 

computed for the largest moving-window average. The 14-day 

and 30-day moving-window averages of total inflow and the 

difference between the 14-day and 30-day moving-window 

averages for 1995 to 2009 are shown in figure 36.

State-Space Reconstruction

Chaos Theory provides a conceptual framework referred 

to as “state space reconstruction” (SSR) for representing 

dynamic relations (Abarbanel, 1996). Data collected at a point 

in time can be organized as a vector of measurements; for 

example, element one of the vector might be the water level, 

element two the streamflow, and so on. Engineers will say that 

a process evolves from one state to another in time, and that 

a vector of measurements, also referred to as a “state vector,” 

represents the process state at the moment the measurements 

were made. A sequence of state vectors represents a state 

history. Mathematicians will say that the state vector is a point 

in a “state space” having a number of dimensions equal to the 

number of elements in the vector. For example, eight vector 

elements equate to eight dimensions. Empirical modeling is 

the fitting of a multidimensional surface to the points arrayed 

in state space. 

Chaos Theory proposes that a process can be optimally 

represented (reconstructed) by a collection of state vectors, 

Y(t), using an optimal number of measurements x, equal 

to local dimension, dL, that are spaced in time, t, by integer 

multiples of an optimal time delay, τd (Abarbanel, 1996)5

. 

For a multivariate process of k independent variables, Y(t) is 

expressed as 

Y(t) = {[x1(t), x1(t − τd1),..., x1(t – (dL1 – 1)τd1)],...,

[xk(t), xk(t − τdk),..., xk(t – (dLk – 1)τdk)]} , (1)

where each x(t,τdi) represents a different dimension in state 

space and, therefore, a different element in a state vector. Values of dL1 and τd1 are estimated analytically or experimentally 

from the data. The mathematical formulations for models are 

derived from those for state vectors. A dependent variable of 

interest, y(t), can be predicted from prior measurements (also 

known as forecasting) of k independent variables (Roehl and 

Conrads, 2006) and expressed as

y(t) = F{[x1(t − τp1), x1(t − τp1 − τd1),..., x1(t − τp1 – (dM1 – 1)τd1)],...,

[xk(t − τpk), xk(t − τpk − τdk),..., xk(t − τpk – (dMk – 1)τdk)]} , (2)

where F is an empirical function, such as an ANN, each 

x(t,τpi,τdi) is a different input to F, and τpi is yet another time 

delay. For each variable, τpi is constrained to the time delay 

at which an input variable becomes uncorrelated to all other 

5

In Chaos Theory, dL and τd are called dynamical invariants, and are analogous to the amplitude, frequency, and phase angle of periodic time series.
Simulation of Specific Conductance 39

Figure 36. Fourteen-day and 30-day moving-window averages of total inflow to the Waccamaw River

and Atlantic Intracoastal Waterway study area, South Carolina, and the difference between the 14-day and

30-day moving-window averages 1995–2009. Total flow in the sum of the flow from the Pee Dee

(station 02131000), Lynches (station 02132000), Little Pee Dee (station 02135000), and Black (station 02136000),

and Waccamaw Rivers (station 02110500).

14-day average total flow

30-day average total flow

Difference between 14- and 30-day

 average total inflow

EXPLANATION

July 12

1995

July 12

1996

July 12

1997

July 12

1998

July 12

1999

July 12

2000

July 12

2001

July 12

2002

July 12

2003

July 12

2004

July 12

2005

July 12

2006

July 12

2007

July 12

2008

July 12

2009

Streamflow, in cubic feet per second

Difference between 14- and 30-day average total inflows, in cubic feet per second

100,000

90,000

80,000

70,000

60,000

50,000

40,000

30,000

20,000

10,000

0

50,000

40,000

30,000

20,000

10,000

0

–10,000

–20,000

–30,000

–40,000

–50,000

Figure 36. Fourteen-day and 30-day moving-window averages of total inflow to the Waccamaw River and Atlantic 

Intracoastal Waterway study area, South Carolina, and the difference between the 14-day and 30-day moving-window 

averages, 1995–2009. Total flow is the sum of the flow from the Pee Dee (station 02131000), Lynches (station 02132000), 

Little Pee Dee (station 02135000), Black (station 02136000), and Waccamaw Rivers (station 02110500).

inputs but can still provide useful information about y(t), or is 

constrained to the time delay of the most recent available measurement of xi

, or is constrained to the time delay at which an 

input variable is most highly correlated to y(t). In equation 2, 

the state space local dimension dLk of equation 1 is replaced 

with an empirically determined model input variable dimension dMk, less than or equal to dLk. The empirically derived 

input variable dimension parameter dMk tends to decrease with 

an increasing number of independent variables. 

Signal Decorrelation

Explanatory variables typically have a strong relation 

to the behavior of a response variable. When using multiple 

explanatory variables, it is difficult, if not impossible, to 

understand the individual effects of variables (sometimes 

referred to as “confounded or correlated variables”) on a 

response variable. Empirical models, such as the ANNs of 

this study, are not based on physical processes that govern the 

nature of interrelations between input variables. To be able to 

clearly analyze the effects of confounded variables, the unique 

informational content of each variable must be determined by 

decorrelating the confounded variables. For example, although 

the water-level signal at Little River Inlet (station 02110777; 

figs. 2, 35, and 37) is dominated by the tidal signal, it also 

responds to the total inflow signal (computed streamflow to 

the AIW and Waccamaw River). Although the coefficient of 

determination between the water level and streamflow signal 

is relatively low, 0.082, it is important to be able to determine 

the effect of total streamflow on water levels. During highflow periods, such as spring of 1998 and 2003, the higher 

streamflow contributes to an increase in the minimum water 

levels in the Little River Inlet (fig. 37). 

The water-level signal at Little River Inlet was decorrelated from the total inflow by using a Single Input Single 

Output (SISO) ANN model; the total inflow signal, as 
40 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Figure 37. Daily mean gage height at Little River Inlet (station 02110777) and daily mean total inflow to

the Waccamaw River and Atlantic Intracoastal Waterway study area, South Carolina, 1995–2009.

Total inflow is the sum of the flow from the Pee Dee (station 02131000), Lynches (station 02132000),

Little Pee Dee (station 02135000), Black (station 02136000), and Waccamaw Rivers (station 02110500).

Little River gage height

Total inflow

EXPLANATION

July 12

1995

July 12

1996

July 12

1997

July 12

1998

July 12

1999

July 12

2000

July 12

2001

July 12

2002

July 12

2003

July 12

2004

July 12

2005

July 12

2006

July 12

2007

July 12

2008

July 12

2009

Daily mean gage height, in feet

Daily mean streamflow, in cubic feet per second

140,000

120,000

100,000

80,000

60,000

40,000

20,000

0

–20,000

–40,000

18

17

16

15

14

13

12

11

10

9

8

Figure 37. Daily mean gage height at Little River Inlet (station 02110777) and daily mean total inflow to the Waccamaw 

River and Atlantic Intracoastal Waterway study area, South Carolina, 1995–2009. Total inflow is the sum of the flow from 

the Pee Dee (station 02131000), Lynches (station 02132000), Little Pee Dee (station 02135000), Black (station 02136000), 

and Waccamaw Rivers (station 02110500).

described above, was the input variable, and the water level 

at the Little River Inlet gage was the output variable (fig. 38). 

The residual error (the difference between predicted and 

measured values) is the unshared information between the two 

signals and is the decorrelated signal for water levels at Little 

River Inlet. Figure 39 shows the measured and predicted water 

levels, as well as the residual error, from the decorrelation 

model. The SISO model simulated the low-frequency portion 

of the measured time series well but did not capture the higher 

frequencies as seen in the spikes in the measured time series. 

The SISO model did simulate the effect of the high-flow 

events on 1998 and 2003 gage heights. 

Correlation and Sensitivity Analysis 

The relations between the many variables and their 

decomposed components are ascertained through correlation 

analyses to provide an additional understanding of system 

dynamics. Sensitivity analysis quantifies the relations between 

a dependent variable of interest and causal variables. For 

example, salinity intrusion is dependent on streamflow and 

tides. Computing sensitivities requires defining the relations 

among variables through modeling. The ANN computer 

program systematically correlates factors that most affect 

parameters of interest (for example, specific conductance) 

to determine combinations of controlled and uncontrolled 

variables (for example, regulated streamflow and tidal 

conditions). 
Simulation of Specific Conductance 41

Figure 38. Schematic diagram showing the 

gage height decorrelation model. F1 is a Single 

Input Single Output Artificial Neural Network 

model (SISO ANN). The residual error from the 

SISO ANN is computed by subtracting the GH_

Little_River predicted values from the measured 

values. (Total inflow is the sum of the flow from 

the Pee Dee (station 02131000), Lynches (station 

02132000), Little Pee Dee (station 02135000), Black 

(station 02136000), and Waccamaw Rivers (station 

02110500)).

Figure 38. Schematic diagram showing the gage height decorrelation model. F1 is a Single Input

Single Output Artificial Neural Network model (SISO ANN). The residual error from the

SISO ANN is computed by subtracting the GH_Little_River predicted values from the

measured values. (Total inflow is the sum of the flow from the Pee Dee (station 02131000),

Lynches (station 02132000), Little Pee Dee (station 02135000), and Black (station 02136000),

and Waccamaw Rivers (station 02110500))

GH_Little_River predicted = F1 [ total inflow ] GH_Little_River residual

GH_Little_River decorrelated

GH_Little_River measured

—

GH

GH_Little_River predicted

GH_Little_River measured

GH_Little_River residual

GH_Little_River decorrelated

Gage height

Predicted gage height at Little River inlet

Measured gage height at Little River inlet

Residual gage height error for Little River inlet

Decorrelated gage height at Little River inlet

EXPLANATION

Figure 39. Measured, predicted, and residual error from the Single Input Single Output Artificial

Neural Network model used to decorrelate gage height at Little River Inlet (station 02110777) from

the total flow to the Waccamaw River and Atlantic Intracoastal Waterway study area, South Carolina,

August 12, 1995--August 12, 2009. The decorrelated gage height is the residual error time series. The gap

in predicted Little River Inlet gage height is due to missing total inflow data.

Measured Little River Inlet gage height

Predicted Little River Inlet gage height

Residual errror-decorrelated Little River Inlet gage height

EXPLANATION

Aug. 12

1995

Aug. 12

1996

Aug. 12

1997

Aug. 12

1998

Aug. 12

1999

Aug. 12

2000

Aug. 12

2001

Aug. 12

2002

Aug. 12

2003

Aug. 12

2004

Aug. 12

2005

Aug. 12

2006

Aug. 12

2007

Aug. 12

2008

Aug. 12

2009

Gage height, in feet

20

15

10

5

0

–5

Figure 39. Measured, predicted, and residual error from the Single Input Single Output Artificial Neural 

Network model used to decorrelate gage height at Little River Inlet (station 02110777) from the total flow to the 

Waccamaw River and Atlantic Intracoastal Waterway study area, South Carolina, August 12, 1995–August 12, 

2009. The decorrelated gage height is the residual error time series. The gap in predicted Little River Inlet gage 

height is due to missing total inflow data.
42 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Limitations of the Historical Datasets

As with any modeling effort, empirical or deterministic, 

the reliability of the model is dependent on the quality of 

the data and range of measured conditions used for training 

or calibrating the model. For accurate simulations, data 

must encompass the full range of streamflow, water-level, 

tide-range, and salinity conditions that the ANN model is 

based on. As noted previously, substantial changes in the 

salinity response of the system can occur as a result of a small 

change in streamflow (figs. 23, 24, and 27). Although data are 

available for the coastal network from the early 1980s, the 

data were not always of a sufficient quality for use in developing empirical models. Environmental monitoring technology 

has changed substantially over the past 20 years (1990–2010). 

One of the most important changes for monitoring in estuarine 

systems has been improvement in the timers (or clocks) in the 

recording equipment. For monitoring estuarine environments, 

it is essential that the correct time of the passing of the tidal 

wave, and associated physical properties, is recorded. Timing 

errors are analogous to physically moving the gage upstream 

or downstream. The timer used for paper-punch recorders in 

the 1980s and early 1990s often drifted from the true time. 

The USGS South Carolina Water Science Center started using 

satellite telemetry in the mid-1980s and instrumented all gaging stations in the coastal network of the Waccamaw River and 

AIW study area in the mid-1990s. The clocks associated with 

satellite telemetry are much more accurate than the recorders, 

and the gage has a limited window (less than 15 seconds) to 

transmit the recorded data to a satellite. As a result, timing 

errors are nearly eliminated from the real-time data.

The timing errors of the early data often are not apparent 

upon inspection of a time series. Prediction errors (differences 

between measured and simulated values) of preliminary 

specific- conductance models show larger errors when using 

the data collected prior to 1995 than when using the data 

collected after 1995. Because of these timing errors, it was 

decided that only data collected after 1995 would be used 

for the development of the ANN models to minimize errors 

resulting from various instrumentation. In Savannah, the 

removal of the tide gate in 1991 (fig. 11) and the deepening 

of the harbor in 1994 from 38 ft to 42 ft had a large effect 

on the salinity dynamics of the system. As a result, only 

data collected after the deepening were used to develop the 

specific-conductance models for the lower Savannah River 

study area. The datasets for the Waccamaw River and AIW, 

and the lower Savannah River, study areas cover a large range 

in hydrologic conditions, including high-flow conditions from 

the El Niño of 1998, extended droughts, and many hurricanes 

and tropical storms. 

Development and Evaluation of 

Specific-Conductance Models

The following sections describe the development and 

evaluation of the specific-conductance (SC) models for 

selected gaging stations in the Waccamaw River and AIW, and 

lower Savannah River, study areas. For this study, the models 

for the PRISM-2 DSS were retrained with additional data 

collected since their original development; a description and 

documentation of the retrained PRISM-2 ANN models of the 

Waccamaw River and AIW study area are presented below. 

A summary of the PRISM-2 ANN models input and output 

variables, a description of the variables used in the models, 

and model performance statistics are listed in tables 7, 8, and 

9, respectively. The models for the M2M-2 DSS were not 

retrained; a detailed description and documentation of those 

models can be found in Conrads and others (2006). A brief 

discussion of the models is presented below, and a summary 

of the M2M-2 ANN models input and output variables, a 

description of the variables used in the models, and model 

performance statistics are listed in Appendixes 1, 2, and 3, 

respectively.

The specific-conductance models for both study areas 

were developed in two stages. The first stage simulates the 

daily average specific conductance to capture the long-term 

dynamics of the system. The second stage simulates the higher 

frequency hourly specific conductance, using the predicted 

daily specific conductance as input to the model as a carrier 

signal. The predicted specific conductance output from the 

first stage model cascades into the second stage model as a 

daily specific conductance input. The ANN model architecture 

for each gage with the use of model predictions from the daily 

model to the hourly model is shown in figure 40. 
Development and Evaluation of Specific-Conductance Models 43

Table 7. Summary of model input and output variables used in the Pee Dee River and Atlantic Intracoastal Waterway 

Salinity Intrusion Model Decision Support System-Version 2 (PRISM-2 DSS).

Model Input variable Output variable

North End 

sc755_cc_f25-final WL777_RANN_F25, WL777_RANN_D12H, 

WL777_RANN_F25_D1D, WL777_RANN_F25_D3D, 

XWL777_RANN_F25, XWL777_RANN_F25_D1D, 

QTOTAL_A14D, QTOTAL_D7D, QTOTAL_D14D

P_SC755_CC_F25

sc760_cc_f25-final QTOTAL_A30D, QTOTAL_D7D, QTOTAL_D14D, 

WL777_RANN_F25, WL777_RANN_D6H, 

WL777_RANN_D12H, WL777_RANN_F25_D1D, 

WL777_RANN_F25_D3D, WL777_RANN_F25_D7D

P_SC760_CC_F25

sc770_cc_f25-final QTOTAL_A7D, QTOTAL_D7D, QTOTAL_D14D, 

WL777_RANN_F25, WL777_RANN_F25_D1D, 

WL777_RANN_F25_D3D, XWL777_RANN_F25, 

XWL777_RANN_F25_D1D

P_SC770_CC_F25

sc777_cc_f25-final QTOTAL_A7D, QTOTAL_D1D, WL777_RANN_F25, 

XWL777_RANN_F25, WL777_RANN_F25_D1D, 

WL777_RANN_F25_D3D, XWL777_RANN_F25_D1D

P_SC777_CC_F25

sc755_cc-final WL777_RANN_F25_A3D, WL777_RANN_D3H, 

WL777_RANN_D6H, WL777_RANN_D12H, 

XWL777_RANN_A12H, P_SC755_CC_F25

P_SC755_CC

sc760_cc-final WL777_RANN_F25_A3D, WL777_RANN_D3H, 

WL777_RANN_D6H, WL777_RANN_D12H, 

WL777_RANN_F25_D1D, XWL777_RANN_A12H, 

P_SC760_CC_F25

P_SC760_CC

sc770_cc-final WL777_RANN_D6H, WL777_RANN_D12H, 

WL777_RANN_F25_D1D, XWL777_RANN_A12H, 

P_SC770_CC_F25

P_SC770_CC

sc777_cc-final P_SC777_CC_F25, WL777_RANN_A12H, 

WL777_RANN_D3H, WL777_RANN_D6H, 

WL777_RANN_D12H, XWL777_RANN_D12H

P_SC777_CC

South End

sc809_cc_f25-final QTOTAL_A7D, QTOTAL_D1D, WL777_RANN_F25, 

XWL777_RANN_F25, WL777_RANN_D12H, 

XWL777_RANN_D6H

P_SC809_CC_F25

sc8125_cc_f25-final QTOTAL_A7D, WL777_RANN_F25, 

XWL777_RANN_F25_D1D, WL777_RANN_D12H, 

XWL777_RANN_D6H, SINE-W0_ID12H, COS-W0_IA7D

P_SC8125_CC_F25

sc815_cc_f25-final QTOTAL_A7D, QTOTAL_D1D, WL777_RANN_F25, 

WL777_RANN_D12H, SINE-W0_ID12H, COS-W0_IA7D

P_SC815_CC_F25

sc809_cc-final P_SC809_CC_F25, WL777_RANN_F25, WL777_RANN_D3H, 

WL777_RANN_D6H, WL777_RANN_D12H, 

XWL777_RANN_A12H, XWL777_RANN_D6H

P_SC809_CC

sc8125_cc-final WL777_RANN_F25, WL777_RANN_D3H, 

WL777_RANN_D6H, WL777_RANN_D12H, 

P_SC8125_CC_F25

P_SC8125_CC

sc815_cc-final WL777_RANN_F25, WL777_RANN_D3H, 

WL777_RANN_D6H, WL777_RANN_D12H, 

P_SC815_CC_F25

P_SC815_CC
44 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios Table 8. Description of variables used in the Pee Dee River and Atlantic Intracoastal Waterway Salinity Intrusion Model Decision Support System-Version 2 (PRISM-2 DSS) artificial neural network models.—Continued [I, interpolated; Q, streamflow; D, daily; m, measured; F, filled; WL, water level; H, hourly; XWL, tidal range; SC, specific conductance; MWA, moving window average; hr, hour; A24, 24-hour MWA; F25, daily tidally filtered; ANN, artificial neural network model; RANN, residual from ANN; P, predicted] Variable Input or output variable Description COS-W0_IA7D Output Cosine of DIR-W0_IA7D DIRD Input 24-hour moving window average wind direction, degrees DIRH Input Hourly wind direction, degree DIR-W0_I Output Wind direction interpolated up to 6 hours to fill missing values, with random values used when wind speed = 0, degrees DIR-W0_IA12H Output 12-hr MWA of DIR-W0_I DIR-W0_IAXD Output XX-day MWA of DIR-W0_I dSCXXXXD(pu-pm) Input Difference in SCXXXXDp(u), predicted SC for user specified input, SCXXXXDpm, and predicted SC for measured conditions, SCXXXXDpu, microsiemens per centimeter. dSCXXXXH(pu-pm) Input SCXXXXHp(u) - SCXXXXHpm FWL777Dm Input Filled measured daily water level at gage 02110777, feet FWL777Hm Input Filled measured hourly water level at gage 02110777, feet IFXWL777Dm Input 24-hour moving window averaged IFXWL777Hm, feet IFXWL777Hm Input Interpolated filled measured hourly 12.4-hour water level maximum range at gage 02110777, feet InpOptionXXXX Input QxxxxxDu input option for gage XXXX set on Setpoints worksheet. Abreviation XXXX is the unique four-digits for inflow stations. IQ10500Dm Input Interpolated measured daily average Q5000, cubic feet per second IQ13200Dm Input Interpolated measured daily average Q132000, cubic feet per second IQ13500Dm Input Interpolated measured daily average Q135000, cubic feet per second IQ13600Dm Input Interpolated measured daily average Q136000, cubic feet per second P_SCXXXX_CC_F25 Output Predicted SCXXXX_CC_F25 by ANN model P_SCXXXX_F25 Output Predicted SCXXXX_CC by ANN model Q10500_A24 Output 24-hr MWA of flow at USGS gage 02110500, cubic feet per second Q13100_A24 Output 24-hr MWA of flow at USGS gage 02131000, cubic feet per second Q13100Dm Input Interpolated measured daily average Q131000, cubic feet per second Q13200_A24 Output 24-hr MWA of flow at USGS gage 02132000, cubic feet per second Q13500_A24 Output 24-hr MWA of flow at USGS gage 02135000, cubic feet per second Q13600_A24 Output 24-hr MWA of flow at USGS gage 02136000, cubic feet per second QOTHER_A1D Output Sum of measured inflows other than Q13100_A24, = Q10500_A24 + Q13200_A24(t=-48hrs) + Q13500_A24 + Q13600_A24(t=-24hrs). QOTHER_AXXD Output XX-day MWA of QOTHER_A1D. In PRISM XX = 1, 7, 14, 30 QOTHER_DXXD Output QOTHER_AXXD - QOTHER_AYYD, where XX and YY indicate successive MWAs, e.g., QOTHER_D14D = QOTHER_A14D - QOTHER_A30D QotherDm Input Sum of measured inflows other than Q13100D: Q5000D + Q13200 (time=-48hrs) + Q13500 + Q13600 (time=-24hrs), cubic feet per second QTOTAL_A1D Output QOTHER_A1D + Q13100_A24(t=-24hrs) QTOTAL_AXXD Output XX-day MWA of QTOTAL_A1D. In PRISM XX = 1, 7, 14, 30 QTOTAL_DXXD Output QTOTAL_AXXD - QTOTAL_AYYD, where XX and YY indicate successive MWAs, e.g., QTOTAL_D14D = QTOTAL_A14D - QTOTAL_A30D

QtotalDm Input Q13100Dm (time=24hrs) + QotherDm, cubic feet per second

SCXXXX_CC Output Hourly specific conductance at indicated gage. The “_CC” denotes that the raw siganl was corrected for errors
Development and Evaluation of Specific-Conductance Models 45

Table 8. Description of variables used in the Pee Dee River and Atlantic Intracoastal Waterway Salinity Intrusion Model Decision Support System-Version 2 (PRISM-2 DSS) artificial neural network models.—Continued[I, interpolated; Q, streamflow; D, daily; m, measured; F, filled; WL, water level; H, hourly; XWL, tidal range; SC, specific conductance; MWA, moving window average; hr, hour; A24, 24-hour MWA; F25, daily tidally filtered; ANN, artificial neural network model; RANN, residual from ANN; P, predicted]

Input or output Variable Description variable

SCXXXX_CC_F25 Output SCXXXX_CC filtered to remove diurnal and semi-diurnal variability. Filtering results from applying a 13-hr and a 25-hr MWA to WL777_RANN 

in sucession.

SCXXXXDm Input Daily measured specific conductance (SC) for gage XXXX, microsiemens per centimeter. “CC” indicated corrected time series. 

Abreviation XXXX is the unique four-digits for SC stations.

SCXXXXDm/p+dSC Input SCXXXXDm + dSCXXXXD(pu-pm) if SCXXXXDm is not missing, else = SCXXXXDpm + dSCXXXXD(pu-pm)SCXXXXDp(m) Input Daily predicted specific conductance (SC) for gage XXXX, microsiemens per centimeter. Abreviation XXXX is the unique four-digits for SC stations.SCXXXXDp(u) Input Daily predicted specific conductance (SC) for gage XXXX using input Q13100Du, microsiemens per centimeter.

Abreviation XXXX is the unique four-digits for SC stations.

SCXXXXHm Input Hourly measured SC for gage XXXX”. CC” indicated twice correctedSCXXXXHm/p+dSC Input SCXXXXHm + dSCXXXXH(pu-pm) if SCXXXXHm is not missing, else = SCXXXXHpm + dSCXXXXH(pu-pm)SCXXXXHp(m) Input Hourly predicted SC for gage XXXX using input Q13100DmSCXXXXHp(u) Input Hourly predicted SC for gage XXXX using input Q13100DuSINE-W0_ID12H Output Sine of DIR-W0_IWL777_C Output 02110777 hourly water level that has been corrected for errorsWL777_RANN Output Residual error of ANN model having QOTHER-related inputs to predict WL777_C. This decorrleates the WL boundary condition from the Q-related 

boundary condition. WL77_RANN is the the parameter used to calculate WL-related inputs to ANN models.

WL777_RANN_AXXH Output XX-hr MWA of WL777_RANN. In PRISM XX = 3, 6, 12 hoursWL777_RANN_DXXH Output WL777_RANN_AXXH - WL777_RANN_AYYH, where XX and YY indicate successive MWAs, 

e.g., WL777_RANN_D3H = WL777_RANN_A3H - WL777_RANN_A6H

WL777_RANN_F25 Output WL777_RANN filtered to remove diurnal and semi-diurnal variability. Filtering results from applying a 13-hr and a 25-hr MWA to WL777_RANN 

in sucession.

WL777_RANN_F25_AXXD Output XX-day MWA of WL777_RANN_F25. In PRISM XX = 3, 7, 14 daysWL777_RANN_F25_DXXD Output WL777_RANN_AXXD - WL777_RANN_AYYD, where XX and YY indicate successive MWAs, 

e.g., WL777_RANN_D3D = WL777_RANN_A3D - WL777_RANN_A7D

XWL777_RANN_AXXH Output XX-hr MWA of XWL777_RANN_I. In PRISM XX = 3, 6, 12 hoursXWL777_RANN_DXXH Output XWL777_RANN_AXXH - XWL777_RANN_AYYH, where XX and YY indicate successive MWAs, 

e.g., XWL777_RANN_D6H = XWL777_RANN_A6H - XWL777_RANN_A12H

XWL777_RANN_F25 Output XWL777_RANN_I filtered to remove diurnal and semi-diurnal variability. 

Filtering results from applying a 13-hr and a 25-hr MWA to XWL777_RANN_I in sucession.

XWL777_RANN_F25_AXXD Output XX-day MWA of XWL777_RANN_I. In PRISM XX = 3 daysXWL777_RANN_F25_DXXD Output XWL777_RANN_AXXD - XWL777_RANN_AYYD, where XX and YY indicate successive MWAs, 

e.g., XWL777_RANN_D1D = XWL777_RANN_A1D - XWL777_RANN_A3D

XWL777_RANN_I Output Tidal range calculated as the difference between the maximim and miniumum WL777_RANN values for each semi-diurnal tidal cycle. 

The “_I” indicates subsequent filling of missing values by linear interpolation.
46 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios Table 9. Summary of performance statistics for models used in the Pee Dee River and Atlantic Intracoastal Waterway Salinity Intrusion Model Decision Support System-Version 2 (PRISM-2 DSS). [HLN, hidden layer neurons; min, minimum; SC, specific conductance; max, maximum; N, number of input vectors; R2, coefficient of determination; RMSE, root mean square error; MSE, mean square error; PME, percent model error; Test%, percent of dataset used for testing] Training dataset Testing dataset Model name Station number OUTPUTS HLN Range of output variable Range of output variable Min Max

Ntrain R2train RMSEtrain MSEtrain PME Min Max

Ntest Test% R2test RMSEtest MSEtest PME

Daily models

sc755_cc_f25-final 02110755 P_SC755_CC_F25 2 62 6,220 2,086 0.69 223 49,779 3.6 62 4,380 2,190 51 0.69 189 35,678 4.4

sc760_cc_f25-final 02110760 P_SC760_CC_F25 2 51 282 2,100 0.74 19 366 8.3 50 270 886 30 0.72 20 382 8.9

sc770_cc_f25-final 02110770 P_SC770_CC_F25 2 79 14,700 2,958 0.87 643 413,738 4.4 82 12,600 1,291 30 0.86 625 391,070 5.0

sc777_cc_f25-final 02110777 P_SC777_CC_F25 2 80 36,100 2,853 0.88 2,672 7,137,883 7.4 79 35,500 1,250 30 0.88 2,736 7,483,335 7.7

sc809_cc_f25-final 02110809 P_SC809_CC_F25 3 70 2,430 1,396 0.86 46 2,141 2.0 69 2,810 663 32 0.87 53 2,803 1.9

sc8125_cc_f25-final 021108125 P_SC8125_CC_F25 1 70 12,100 1,485 0.85 544 296,413 4.5 68 13,000 662 31 0.88 441 194,368 3.4

sc815_cc_f25-final 02110815 P_SC815_CC_F25 1 60 21,800 2,825 0.88 1,094 1,197,662 5.0 60 21,400 1,234 30 0.85 1,171 1,370,682 5.5

Hourly models

sc755_cc-final 02110755 P_SC755_CC 2 57 13,600 12,822 0.60 694 481,334 5.1 50 13,600 103,525 89 0.58 332 110,115 2.4

sc760_cc-final 02110760 P_SC760_CC 2 50 386 14,690 0.73 20 397 5.9 40 366 58,263 80 0.73 20 388 6.0

sc770_cc-final 02110770 P_SC770_CC 2 0 30,200 20,738 0.85 1,084 1,175,120 3.6 0 32,600 82,426 80 0.84 1,076 1,157,465 3.3

sc777_cc-final 02110777 P_SC777_CC 2 54 46,900 10,367 0.89 2,147 4,607,999 4.6 54 51,500 89,915 90 0.84 4,213 17,750,919 8.2

sc809_cc-final 02110809 P_SC809_CC 3 68 7,490 12,678 0.81 143 20,397 1.9 67 7,490 49,227 80 0.76 91 8,342 1.2

sc8125_cc-final 021108125 P_SC8125_CC 2 66 18,700 6,017 0.81 1,480 2,191,534 7.9 56 18,700 53,857 90 0.77 723 522,132 3.9

sc815_cc-final 02110815 P_SC815_CC 2 60 26,900 15,366 0.84 1,345 1,809,208 5.0 60 28,100 88,512 85 0.83 1,378 1,899,750 4.9
Development and Evaluation of Specific-Conductance Models 47

Figure 40. Schematic diagram showing the two-stage model architecture to predict

specific conductance at a gaging station. This is an example of a “cascading”

model where the output from the first model (daily average specific conductance)

is used as an input to the second model.

“Daily”

MODEL

“Hourly”

MODEL

INPUTS OUTPUTS

Flow

Water level

Tidal range

Daily average

specific conductance

prediction

Hourly average

specific conductance

prediction

Water level

Tidal range

Figure 40. Schematic diagram showing the two-stage model 

architecture to predict specific conductance at a gaging station. 

This is an example of a “cascading” model where the output from 

the first model (daily average specific conductance) is used as an 

input to the second model.

Statistical Measures of Prediction Accuracy 

Statistical measures of prediction accuracy were computed for the specific conductance models. Statistics for the 

first stage (daily) and second stage (hourly) models provide 

measures of the performance of individual models in the cascading modeling approach to simulate specific conductance. 

The statistics for the individual models are not necessarily 

indicative of the quality of the final estimates, which are based 

on multiple models. Thus, the specific conductance models are 

best evaluated using the statistics for the final daily and hourly 

simulation. 

Model accuracy typically is reported in terms of coefficient of determination (R2

) and commonly is interpreted as 

the “goodness of the fit” of a model. A different interpretation 

poses the question: How much information does one variable 

or a group of variables provide about the behavior of another 

variable? For example, in the first context, an R2

 = 0.6 might 

be disappointing, whereas in the latter, it is merely an accounting of how much information is shared by the variables 

being used. The mean error (ME) and root mean square error 

(RMSE) statistics provide a measure of the prediction accuracy of the ANN models. The ME is a measure of the bias of 

model predictions—whether the model over- or under-predicts 

the measured data. The ME is presented as the adjustment to 

the simulated values to equal the measured values; therefore, a 

negative mean error indicates an over-prediction by the model, 

and a positive mean error indicates an under-prediction. 

Mean errors near zero may be misleading because negative 

and positive discrepancies in the simulations can cancel each 

other. The RMSE addresses the limitations of mean error by 

computing the magnitude, rather than the direction (sign), of 

the discrepancies. The units of the ME and RMSE statistics 

are the same as the simulated variable of the model.

The accuracy of the models, as given by RMSE, is best 

evaluated with respect to the measured range of the output 

variable. The percent model error (PME) is the ratio of the 

RMSE to the range of the output measured data. A model 

may have a low RMSE, but if the range of the output variable 

is small, the model may be accurate only for a narrow range 

of conditions and the model error may be a relatively large 

percentage of the model response. For example, if the RMSE 

for a model is 0.5 ft and the measured range is 0 to 2 ft, the 

percentage model error would be 25 percent. Likewise, a 

model may have a large RMSE, but if the range of the output 

variable is large, the model error may be a relatively small 

percentage of the total model response. For example, if the 

RMSE for a model is 2 ft and the measured range is 0 to 20 ft, 

the percentage model error would be 10 percent. 

Waccamaw River and Atlantic Intracoastal 

Waterway Models

The ANN models developed for the original Pee Dee 

River and Atlantic Intracoastal Waterway Salinity Model 

(PRISM; Conrads and Roehl, 2007) were trained using data 

from July 11, 1995, to December 31, 2002. Although there 

are extensive specific-conductance data for the Waccamaw 

River and AIW study area, critical gaging stations near the 

freshwater-saltwater interface—Wachesaw and Pawleys 

Island gaging stations (stations 02110809 and 021108125; 

table 4)—were either established or re-established in 2002 and 

had limited data when the models were first developed. For 

this study, the database was extended to August 31, 2009, and 

the previously developed ANN models (Conrads and Roehl, 

2007) were retrained using the additional data, which included 

additional drought periods and tropical storms.

The following sections describe the development of the 

specific-conductance models for seven sites in the Waccamaw 

River and AIW study area that experience salinity intrusions. 

Four of the seven gaging stations are in the north end of the 

study area near Little River Inlet, and the models are referred 

to as the “North End models” (stations 02110755, 02110760, 

02110770, and 02110777; fig. 2). Three of the seven gaging 

stations are in the south end of the system near Hagley 

Landing and Winyah Bay, and the models are referred to as 

the “South End models” (stations 02110809, 021108125, and 

02110815, fig. 2). 

The model development incorporated combinations of 

the decomposed total inflow (summation of the regulated 

and unregulated streamflows) and water-level (WL) signals 

that represented different spectral frequencies. For the daily 

models, the representation of the dynamic input signals is as 

follows: moving-window averages of 1, 3, 7, 14, and 30 days 

were calculated for total inflow (QTOTAL), and movingwindow averages of 3, 7, and 14 days were computed for the 

decorrelated water levels (WL777_RANN) and were named 
48 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

sequentially for the increasing moving-window size. For 

example, QTOTAL_A3 represented the 3-day moving-window 

average for rainfall, QTOTAL_A7 represented the 7-day average, QTOTAL_A14 represented the 14-day average, and so 

on. The moving-window sizes were selected to capture daily, 

weekly, and monthly variability of the parameters. For each 

parameter, differences between each moving-window average 

and the next larger window-sized moving window average 

were calculated, for example, QTOTAL_D7D = QTOTAL_A7 

- QTOTAL_A14, RAIN_D14D = QTOTAL_A14 - QTOTAL_A30, and so on. 

The ANN models were developed iteratively by starting 

with a candidate pool of input variables, training the ANN, 

and then using prediction accuracy statistics, such as R2 and 

input-output sensitivities, to cull the least important input 

variables. It is left to the ANN to learn which parameters and 

spectral ranges are the best predictors of behaviors that are 

manifest in the specific-conductance signal.

Datasets with numerous salinity intrusion events were 

randomly bifurcated into training and testing datasets. Percentages of the dataset used for testing the daily models ranged 

from 30 to 51 and, for testing the hourly models, 80 to 90 

(table 9). Statistical measures used to evaluate the models—

R2, ME, RMSE, and PME—were computed for the training 

and testing datasets for each model (table 9). In the following 

sections, examples of daily and hourly models are described 

for each group of models in the North and South Ends of the 

Waccamaw River and AIW study area.

North End Models

The daily specific-conductance model for Briarcliffe 

Acres (sc755_cc_f25-final, table 7) uses streamflow, 

water-level, and tidal-range inputs (table 7). The streamflow 

inputs (Q) are 14-day moving-window average total inflow 

(QTOTAL_A14D), the difference between the 7- and 14-day 

moving-window average of total inflow (QTOTAL_D7D), and 

the difference between the 14- and 30-day moving-window 

average (QTOTAL_D14D). The water-level data inputs are 

decorrelated daily tidally filtered water levels from Little 

River Inlet (WL777_RANN_F25), the difference between 

the 12- and 25-hour moving-window average of water levels 

(WL777_RANN_D12H), the difference between the 1- and 

3-day moving-window average of daily tidally filtered water 

levels (WL777_RANN_F25_D1D), and the difference 

between the 3- and 7-day moving-window average of daily 

tidally filtered water levels (WL777_RANN_F25_D3D). The 

tidal range (XWL) inputs are the decorrelated daily filtered 

tides and the difference between the 1- and 3-day moving-window average of the daily tidal range (XWL777_RANN_F25 

and XWL777_RANN_F25_D1D, respectively). For testing 

and training the daily model, 4,276 data values were available; 

and about 51 percent (2,090 data points) was used for training 

(table 9). The R2 for the training and testing are 0.69 and 0.69, 

respectively (table 9). The daily model had two hidden-layer 

neurons.

The hourly specific-conductance model (sc755_cc-final, 

table 7) uses the simulated daily specific conductance from 

the daily specific-conductance model and water-level and tidal 

inputs from the Little River Inlet gage (table 7). The simulated 

daily specific-conductance input (P_SC755_CC_F25) captures 

the long-term movement of the specific conductance that is 

characterized by the streamflow and the chaotic components 

of the water-level signal. Water-level and tidal-range data 

are used to capture the semi-diurnal tidal signal. The waterlevel inputs include the 3-day moving-window average of 

the daily tidally filtered decorrelated water level at Little 

River Inlet (WL777_RANN_F25_A3D) and the differences 

between the 3- and 6- hour, 6- and 12-hour, and 12- and 

25-hour moving-window averages of decorrelated water 

levels (WL777_RANN_D3H, WL777_RANN_D6H, and 

WL777_RANN_D12H, respectively). Tidal-range input is 

the 12-hour moving-window average of decorrelated tidal 

range (XWL777_RANN_A12H). For testing and training the 

hourly model, 116,347 data values were available; and about 

11 percent (12,822 data points) was used for training. The 

R2

 for the training and testing are 0.60 and 0.58, respectively 

(table 9). The hourly model had two hidden-layer neurons.

The measured and simulated daily specific-conductance 

values for the four North End specific-conductance models 

are shown in figure 41. The daily models effectively simulated 

the timing and range of the salinity-intrusion events. The 

measured and simulated hourly specific-conductance values 

for the four North End models are shown in figure 42. In 

general, the models were able to simulate the timing of the 

salinity-intrusion events, although the models do not always 

capture the peak values of the events.
Development and Evaluation of Specific-Conductance Models 49

Figure 42. Measured and simulated specific-conductance values from the hourly model for four

U.S. Geological Survey gages on the North End of the Atlantic Intracoastal Waterway, South Carolina:

(A) Myrtlewood (station 02110760), (B) Briarcliffe Acres (station 02110755), (C) Grand Strand Airport

(station 02110770), and (D) Little River Inlet (station 02110777), August 12, 1995–August 12, 2009.

A. Station 02110760: R2 = 0.73, n = 72,953

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

400

350

300

250

200

150

100

50

0

Measured

Simulated

EXPLANATION

B. Station 02110755: R2 = 0.58, n = 116,347

Measured

Simulated

EXPLANATION

Measured

Simulated

EXPLANATION

C. Station 02110770: R2 = 0.84, n = 103,164

Aug. 12

1995

Aug. 12

1996

Aug. 12

1997

Aug. 12

1998

Aug. 12

1999

Aug. 12

2000

Aug. 12

2001

Aug. 12

2002

Aug. 12

2003

Aug. 12

2004

Aug. 12

2005

Aug. 12

2006

Aug. 12

2007

Aug. 12

2008

Aug. 12

2009

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Measured

Simulated

EXPLANATION

D. Station 02110777: R2 = 0.84, n = 100,282

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

60,000

50,000

40,000

30,000

20,000

10,000

0

Figure 41. Measured and simulated specific-conductance values from the daily model for four U.S. Geological Survey gages on 

the North End of the Atlantic Intracoastal Waterway, South Carolina: (A) Myrtlewood (station 02110760), (B) Briarcliffe Acres (station 

02110755), (C) Grand Strand Airport (station 02110770), and (D) Little River Inlet (station 02110777), August 12, 1995–August 12, 2009.
50 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Figure 42. Measured and simulated specific-conductance values from the hourly model for four

U.S. Geological Survey gages on the North End of the Atlantic Intracoastal Waterway, South Carolina:

(A) Myrtlewood (station 02110760), (B) Briarcliffe Acres (station 02110755), (C) Grand Strand Airport

(station 02110770), and (D) Little River Inlet (station 02110777), August 12, 1995–August 12, 2009.

A. Station 02110760: R2 = 0.73, n = 72,953

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

400

350

300

250

200

150

100

50

0

Measured

Simulated

EXPLANATION

B. Station 02110755: R2 = 0.58, n = 116,347

Measured

Simulated

EXPLANATION

Measured

Simulated

EXPLANATION

C. Station 02110770: R2 = 0.84, n = 103,164

Aug. 12

1995

Aug. 12

1996

Aug. 12

1997

Aug. 12

1998

Aug. 12

1999

Aug. 12

2000

Aug. 12

2001

Aug. 12

2002

Aug. 12

2003

Aug. 12

2004

Aug. 12

2005

Aug. 12

2006

Aug. 12

2007

Aug. 12

2008

Aug. 12

2009

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Measured

Simulated

EXPLANATION

D. Station 02110777: R2 = 0.84, n = 100,282

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

60,000

50,000

40,000

30,000

20,000

10,000

0

Figure 42. Measured and simulated specific-conductance values from the hourly model for four U.S. Geological Survey gages on 

the North End of the Atlantic Intracoastal Waterway, South Carolina: (A) Myrtlewood (station 02110760), (B) Briarcliffe Acres (station 

02110755), (C) Grand Strand Airport (station 02110770), and (D) Little River Inlet (station 02110777), August 12, 1995–August 12, 2009.
Development and Evaluation of Specific-Conductance Models 51

South End Models 

The daily specific-conductance model (SC8125_cc_f25-

final, table 7; fig. 43) for Waccamaw River at Pawleys Island 

uses streamflow, water-level, and tidal-range inputs. The 

streamflow input is the 7-day moving-window average of 

total inflow (QTOTAL_A7D). The water-level inputs include 

the decorrelated daily tidally filtered water level (WL777_

RANN_F25) and the difference between the 12-hour and 

25-hour decorrelated water levels (WL777_RANN_D12H). 

The tidal range inputs are the differences between the 1- and 

3-day, and 6- and 12-hour, moving-window averages of 

decorrelated daily tidal range (XWL777_RANN_F25_D1D 

and XWL777_RANN_F25_D6H, respectively). For testing 

and training the daily model, 2,147 data values were available; 

and about 69 percent (1,485 data points) was used for training 

(table 9). The coefficients of determination, R2, for the training 

and testing are 0.85 and 0.88, respectively (table 9). The daily 

model had one hidden-layer neuron.

The hourly specific-conductance model (SC8125_ccfinal; table 7; fig. 44) uses the simulated daily specific 

conductance (P_SC8125_CC_F25) from the daily specificconductance model and water-level inputs from the Little 

River Inlet gage. The water-level inputs include decorrelated 

filtered daily water level (WL777_RANN_F25), the difference 

between the 3-hour and 6-hour moving-window average 

water level (WL777_RANN_D3H), the difference between 

the 6-hour and 12-hour moving-window average water level 

(WL777_RANN_D6H), and the difference between the 

12-hour and 25-hour moving-window average water level 

(WL777_RANN_D12H). For testing and training the hourly 

model, 59,874 data values were available; and about 10 

percent (6,017 data points) was used for training (table 9).

The R2 values for the training and testing are 0.81 and 0.77, 

respectively (table 9). The hourly model had two hidden-layer 

neurons.

Lower Savannah River and Marsh SpecificConductance Models

The ANN models for the Model-to-Marsh Decision 

Support System (M2M DSS) were not retrained for this 

study. However, the M2M DSS was updated for this study 

by limiting the models in the application to the USGS river 

and marsh specific-conductance gaging network (table 5). 

The original application included 84 ANN models for water 

level and specific conductance for river and marsh sites 

monitored by Georgia Ports Authority (Conrads and others, 

2006). The Georgia Ports Authority sites had limited temporal 

deployments, have much smaller datasets, and did not 

monitor as large a range of streamflow and tidal conditions as 

monitored at the USGS river and marsh sites. These models 

were removed from the updated application. The specific 

conductance ANN models of the USGS marsh sites include 

specific-conductance prediction for four GPA sites (GPA 10, 

GPA11, GPA11R, and GPA12) on the Savannah and Middle 

Rivers (fig. 45). The models for these four sites were left in 

the application. A description of the ANN models used in the 

M2M-2 DSS, descriptions of variables, and a summary of 

performance statistics used for models in this study can be 

found in appendixes 1, 2, and 3, respectively. Salinity model 

inputs and outputs for one of the river models and one of the 

marsh models are described in the following sections. For the 

M2M DSS, specific-conductance model output was converted 

to salinity.
52 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Figure 43. Measured and simulated specific-conductance values from the daily model for three

U.S. Geological Survey gages on the Waccamaw River, South Carolina: (A) Wachesaw (station 02110809),

(B) Pawleys Island (station 021108125), and (C) Hagley Landing (station 02110815),

August 12, 1995–August 12, 2009.

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

Measured

Simulated

EXPLANATION

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Measured

Simulated

EXPLANATION

Measured

Simulated

EXPLANATION

25,000

20,000

15,000

10,000

5,000

0

A. Station 02110809: R2 = 0.87, n = 2,059

B. Station 021108125: R2 = 0.88, n = 2,147

C. Station 02110815: R2 = 0.85, n = 4,059

Aug. 12

1995

Aug. 12

1996

Aug. 12

1997

Aug. 12

1998

Aug. 12

1999

Aug. 12

2000

Aug. 12

2001

Aug. 12

2002

Aug. 12

2003

Aug. 12

2004

Aug. 12

2005

Aug. 12

2006

Aug. 12

2007

Aug. 12

2008

Aug. 12

2009

Figure 43. Measured and simulated specific-conductance values from the daily model for three U.S. Geological Survey 

gages on the Waccamaw River, South Carolina: (A) Wachesaw (station 02110809), (B) Pawleys Island (station 021108125), 

and (C) Hagley Landing (station 02110815), August 12, 1995–August 12, 2009.
Development and Evaluation of Specific-Conductance Models 53

Figure 44. Measured and simulated specific-conductance values from the hourly model for three

U.S. Geological Survey gages on the Waccamaw River, South Carolina: (A) Wachesaw (station 02110809),

(B) Pawleys Island (station 021108125), and (C) Hagley Landing (station 02110815),

August 12, 1995–August 12, 2009.

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

Measured

Simulated

EXPLANATION

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

Measured

Simulated

EXPLANATION

Measured

Simulated

EXPLANATION

30,000

25,000

20,000

15,000

10,000

5,000

0

A. Station 02110809: R2 = 0.83, n = 61,905

B. Station 021108125: R2 = 0.77, n = 58,874

C. Station 02110815: R2 = 0.83, n = 103,878

Aug. 12

1995

Aug. 12

1996

Aug. 12

1997

Aug. 12

1998

Aug. 12

1999

Aug. 12

2000

Aug. 12

2001

Aug. 12

2002

Aug. 12

2003

Aug. 12

2004

Aug. 12

2005

Aug. 12

2006

Aug. 12

2007

Aug. 12

2008

Aug. 12

2009

18,000

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

Figure 44. Measured and simulated specific-conductance values from the hourly model for three U.S. Geological Survey 

gages on the Waccamaw River, South Carolina: (A) Wachesaw (station 02110809), (B) Pawleys Island (station 021108125), and 

(C) Hagley Landing (station 02110815), August 12, 1995–August 12, 2009.
54 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios Savannah

River

River

Front

River

Back

River

Middle

River

Back

Little

Port Wentworth

Savannah

SAVANNAH

NATIONAL

WILDLIFE

REFUGE

0

0 1 2 3 KILOMETERS

1 2 3 MILES

Houlihan

Bridge

Base from U.S. Geological Survey 1:100,000-scale digital line graphs

Projection: UTM, Zone: 17, Datum: NAD27

N

80

16

17

25

21

17

95 GPA-11

EXPLANATION

Georgia Ports Authority

river monitoring

sites and number

GPA-11

GPA-10

GPA-12

GPA-11R

Figure 45. Location of Georgia Ports Authority river monitoring gages,

South Carolina and Georgia. Data from these gages were used as inputs

to the salinity models of the U.S. Geological Survey marsh sites (figure 11B).

Figure 45. Location of Georgia Ports Authority river monitoring gages, 

South Carolina and Georgia. Data from these gages were used as inputs to 

the salinity models of the U.S. Geological Survey marsh sites (fig. 11B).

Riverine Specific-Conductance Model for 

Savannah River at the I-95 Bridge

The daily salinity model (sc8840a-2005-1, appendix 1) 

for Savannah River at I-95 (station 02198840) uses six streamflow, two water-level, and two tidal-range inputs. A brief 

description of the variables used in the M2M-2 is provided in 

appendix 2. The streamflow inputs are daily average streamflow (Q8500A), the 1-day change in streamflow (D8500A), 

daily streamflow lagged 2 days (LAQ2), the 2-day change 

in streamflow (the 2-day change in streamflow; DAQ2), the 

16-day change in streamflow (DAQ16), and the 30-day change 

in streamflow (DAQ30). The water-level inputs are the filtered 

daily water level from the Fort Pulaski gage (FWL8980A) and 

the 1-day change in tidally filtered daily water level (DWLA). 

The tidal range daily inputs are daily tidal range (XWL8980A) 

and the 1-day change in daily tidal range (DXWLA). The 

daily model had three hidden-layer neurons (appendix 1). For 

testing and training the daily model, there were 87,365 data 

values available. Approximately 12 percent of the data was 

used for training, and 88 percent was used for testing. The 

R2

 for the training and testing are 0.97 and 0.96, respectively 

(appendix 3). 

The hourly salinity model (sc8840h-2005-1, appendix 1) 

is based on simulated daily specific-conductance values 

from the daily model, one tidal-range input, and four 

water-level inputs. The simulated daily specific-conductance 

input (PSC8840A) captures the long-term movement of 
Development and Evaluation of Specific-Conductance Models 55

the specific conductance that is characterized 

by the streamflow and tidal-range data in the 

daily model. The tidal-range input is the hourly 

tidal range (NXWL). The four water-level 

inputs—LG1NWL, LG1D3NWL, LG4D3NWL, 

and LG7D3NWL—are the 1-hour lagged water 

level and the 3-hour change in the water level 

lagged 1, 4, and 7 hours, respectively. The lagged 

water-level inputs capture the periodic semidiurnal tidal cycle signal. For testing and training 

the hourly model, there were 87,969 data values. 

Approximately 12 percent of the data was used 

for training, and 88 percent was used for testing. 

The R2 for the training and testing are 0.88 and 

0.85, respectively (appendix 3). 

The measured and simulated hourly salinities 

at I-95 (station 02198840) are shown in figure 46 

for summer 2002. This period was at the end 

of the 5-year drought that began in 1998 and 

had the highest salinity intrusions of that drought 

period. The model simulations captured the salinity 

intrusions occurring on 14- and 28-day cycles. The 

model also was able to simulate the full range of 

the large salinity intrusion on about August 7, 2002.

Marsh Specific-Conductance Model at Site B2

For modeling the USGS marsh sites, simulated data for 

four of the GPA stations were used as inputs. The models for 

the four GPA stations were necessary to simulate potential 

mitigation scenarios to divert additional streamflow to the 

Middle and Little Back Rivers to mitigate an increase in 

salinity in the vicinity of the SNWR resulting from a potential 

deepening of Savannah Harbor (Conrads and others, 2006). 

One concern with using only the long-term USGS gaging 

stations to simulate the pore-water salinity in the marsh is that 

there would not have been any data on the Middle River which 

could have been important for evaluating mitigation scenarios 

involving creating or closing channels connecting the Front, 

Middle, Little Back, and Back Rivers. Data in this area are 

available from the GPA river network (fig. 45). Although the 

data from these stations are limited to conditions from either 

the summer of 1997 or 1999, data from GPA10, GPA11, 

GPA11R, and GPA12 were used as inputs to the marsh salinity 

models to address spatial distribution of the river salinity 

inputs for the marsh salinity models. 

There are two technical issues associated with using the 

short-term GPA data. The first issue is that the GPA data are 

not concurrent with the USGS marsh data (summers of 1997 

and 1999 for GPA gaging stations and 2000 to 2005 for USGS 

gaging stations). The models for the four GPA stations (fig. 45) 

were used to simulate the time series of specific conductance 

for 2000 to 2005. To generate the data for this period, the GPA 

models were used to make salinity predictions for a range 

of streamflow conditions much larger than those measured 

during the summers of 1997 and 1999. The second issue is 

Figure 46. Measured and simulated hourly salinities at Savannah River

at Interstate 95 (station 02198840), Georgia, June 1–August 31,

2002.

Measured

Simulated

EXPLANATION

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

June 1 June 29

2002

July 27 Aug. 4 Aug. 31

Salinity, practical salinity units

Figure 46. Measured and simulated hourly salinities at Savannah River at 

Interstate 95 (station 02198840), Georgia, June 1–August 31, 2002.

that the GPA data are highly correlated. To ensure that the data 

from the GPA sites provide unique information for the marsh 

models, the data for stations GPA11, GPA11R, and GPA12 

were systematically decorrelated from GPA10. 

The marsh pore-water salinities were modeled in two 

stages. For the first stage, USGS river data were used to 

simulate the marsh salinity. For the second stage, GPA river 

data were used to determine the residual error (difference 

between the simulated and measured marsh salinity) from the 

first-stage model. The final marsh salinity predictions are the 

sum of the predictions from the two models.

The first stage pore-water salinity model (pb2msc-2005-2, 

appendix 1) for Little Back River at Site B2 used 10 specificconductance inputs and differences in specific conductance 

from two USGS river gaging stations in the vicinity of SNWR. 

Two inputs are the differences in specific conductance at the 

Little Back River at U.S. Fish and Wildlife Dock (station 

021989791) and Savannah River at Interstate 95 (station 

02198840), and the difference between Little Back River at 

U.S. Fish and Wildlife Dock (station 02198979) and Front 

River at Houlihan Bridge (station 02198920), variables 

SCDIF8840A and SCDIF8920A, respectively. There were 

four moving-window average inputs of 48 hours, and 1, 2, 

and 4 weeks (variables FSC89791A48, FSC89791D1WK, 

FSC89791D2WK, FSC89791D4WK) and four timederivative inputs (variables LG672FSC89791A4WKD4WK, 

FSC89791DA48, DFSC89791DA48, and LG3DFSC89791DA48). The model had one hidden-layer neuron 

(appendix 1). For testing and training the daily model, there 

were 20,912 data values available (approximately 5 years of 

hourly data from 1999 to 2005; appendix 2). Ten percent of 

the data was used for training, and 90 percent was used for 

testing. The R2 for the training and testing is 0.83 for each. 
56 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

The second stage model (prb2msc) simulated 

the residual error from the first-stage marsh 

pore-water salinity model. The four inputs used 

to simulate the residual error are the weekly 

averages of the decorrelated specific-conductance 

variable from each GPA site and a 1-week 

moving-window average of specific conductance 

at GPA 10 (variables RSC10S_11B_A1WK, 

RSC10S_11BR_A1WK, RSC10S_12RS_A1WK, 

and PSCGPA10S_FLR_A1WK, respectively). 

The final prediction is a summation of the marsh 

and residual models; the R2 is 0.92 and the percent 

model error is 6.5 (appendix 3). The measured and 

simulated hourly pore-water salinities are shown in 

figure 47 for summer 2002. This period occurred 

at the end of the 5-year drought that began in 1998 

and had the highest salinity intrusions of the drought 

period. The model simulated long-term change 

in salinity but under-simulated the higher salinity 

changes occurring on 14- and 28-day cycles.

Development of the Decision 

Support Systems

Natural-resource managers and stakeholders face difficult 

challenges when managing interactions between natural and 

manmade systems. Even though the collective interests and 

computer skills of the community of managers, scientists, and 

other stakeholders are quite varied, equal access to the scientific knowledge is needed for them to make the best possible 

decisions. Dutta and others (1997) define decision support 

systems (DSSs) as “systems helping decision makers to solve 

various semi-structured and unstructured problems involving 

multiple attributes, objectives, and goals... Historically, the 

majority of DSSs have been either computer implementations of mathematical models or extensions of database 

systems and traditional management information systems.” 

Environmental resource managers commonly use complex 

mathematical (mechanistic) models based on first-principle 

physical equations to evaluate options for using the resource 

without damage. Although there appears to be no strict criteria 

that distinguish a DSS from other types of programs, Dutta 

and others (1997) suggest that artificial intelligence (AI) is 

a characteristic of more advanced DSSs: “With the help of 

AI techniques DSSs have incorporated the heuristic models 

of decision makers and provided increasingly richer support 

for decision making. Artificial intelligence systems also have 

benefited from DSS research as they have scaled down their 

goal from replacing to supporting decision makers.” 

The PRISM-2 and M2M-2 DSSs are spreadsheet applications that provide predictive models with real-time databases 

for ANN model simulation, graphical user interfaces, and 

Figure 47. Measured and simulated hourly pore-water salinities at

Little Back River at Site B2, Georgia, June 1–August 31, 2002.

Marsh salinity - measured

Marsh salinity - simulated

EXPLANATION

2.5

2.0

1.5

1.0

0.5

0

June 1 June 29

2002

July 27 Aug. 4 Aug. 31

Salinity, practical salinity units

Figure 47. Measured and simulated hourly pore-water salinities at Little 

Back River at Site B2, Georgia, June 1–August 31, 2002.

displays of results. As spreadsheet applications, the DSSs are 

easily distributable and immediately usable by all interested 

parties. The development of the DSSs for the Waccamaw 

River and AIW, and the lower Savannah River, required a 

number of steps (described previously), including (1) merging 

all the data into a single comprehensive database, (2) developing specific-conductance ANN sub-models, and (3) developing 

a Microsoft Office ExcelTM application that integrates the new 

database and ANN sub-models into a single package that is 

easy to use and disseminate.

Architecture

The basic architectural elements of the PRISM-2 and 

M2M-2 DSSs are shown in figures 48 and 49, respectively. 

The DSSs read and write files for the various run-time options 

that can be selected by the user through the system’s GUI. A 

historical database is read into the simulator along with the 

ANN sub-models at the start of a simulation. By using GUI 

controls, the user can evaluate various streamflow scenarios 

and sea-level-rise scenarios. The outputs generated by the 

simulator are written to files for post processing in Microsoft 

Office ExcelTM or other analysis software packages. The 

PRISM-2 DSS also provides streaming graphics for each gage 

during simulations and 3D visualization of the specificconductance response for the North End and South End 

models. The M2M-2 DSS provides a two-dimensional colorgradient visualization of pore-water salinity concentrations in 

the tidal marsh near the SNWR.
Development of the Decision Support Systems 57

Historical database

WL, SC, Q

Input-output files

ANN models

14 models

Simulation output

files

Simulator controls

Constrained optimization

routine

Simulator

(iQuest/RTTM, VBA)

Streaming graphics

3D Visualization

Any use of trade, product, or firm names is for descriptive purposes only and does not

 imply endorsement by the U.S. Government.

Graphical user interface

Users

Other analysis

packages

Figure 48. Architecture of the Pee Dee River and Intracoastal Waterway Salinity Intrusion Model-2 Decision

Support System (PRISM-2 DSS). (Modified from Conrads and Roehl, 2007) (WL, water level; SC, specific conductance;

Q, flow; ANN, artificial neural network; VBA, visual basic for applications; RT, run-time)

Pee Dee River and Intracoastal Waterway

Salinity Intrusion Model–Version 2 (PRISM-2)

Decision Support System (DSS)

Figure 48. Architecture of the Pee Dee River and Intracoastal Waterway Salinity 

Intrusion Model-2 Decision Support System (PRISM-2 DSS) (modified from Conrads 

and Roehl, 2007). [WL, water level; SC, specific conductance; Q, flow; ANN, 

artificial neural network; VBA, visual basic for applications; RT, run-time]

Historical database

37 stations, WL, SC, Q

Input-output files Microsoft® ExcelTM applications

ANN models

Percentile flow

input files

User defined

hydrographs

Simulation output

files

Simulator controls

Simulator

(iQuest/RTTM, VBA)

Streaming graphics

User-defined

hydrographs program

Any use of trade, product, or firm names is for descriptive purposes only and does not

imply endorsement by the U.S. Government.

2D color-gradient

visualization program

Graphical user interface

Users

Microsoft® ExcelTM

or other analysis

package

Figure 49. Architecture of the Model-to-Marsh-2 Decision Support System (DSS) (Modified from Conrads

and others, 2006). (WL, water level; SC, specific conductance; Q, flow; ANN, artificial neural network;

VBA, visual basic for applications; RT, real-time)

Model-to-Model-Version 2 (M2M-2)

Decision Support System (DSS)

Figure 49. Architecture of the Model-to-Marsh-2 Decision Support System (DSS) 

(modified from Conrads and others, 2006). [WL, water level; SC, specific conductance; 

Q, flow; ANN, artificial neural network; VBA, visual basic for applications; RT, real-time]
58 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Historical Databases

Review of the measured data in the historical databases 

was necessary for quality control because of a variety of 

problems previously discussed, including erroneous and 

missing values and timing errors causing phase shifts. For the 

PRISM-2 DSS, the resulting database comprises 14 years of 

hourly and daily data for 14 stations. For the M2M-2 DSS, the 

resulting database comprises 11 years of half-hourly data for 

51 stations. The streamgaging stations, characteristics, period 

of record of the data in the PRISM-2 DSS and M2M-2 DSS 

databases are listed in tables 10 and 11, respectively.

Table 10. Stations, characteristics, period of record, and time interval of data in the Pee Dee River and Atlantic Intracoastal Waterway 

Salinity Intrusion Model Decision Support System-Version 2 (PRISM-2 DSS) database.

[Q, streamflow; wl, water level; xwl, tide range; wsp, wind speed; sc, specific conductance; wdir, wind direction; SC, South Carolina]

Station location Site 

identifier

CharacteristicTime 

interval Start date End Date Agency

Waccamaw River near Longs, S.C. 2110500 Q Hourly July 1995 August 2009 U.S. Geological Survey

Pee Dee River at Pee Dee, S.C. 2131000 Q Hourly July 1995 August 2009 U.S. Geological Survey

Lynches River at Effingham, S.C. 2132000 Q Hourly July 1995 August 2009 U.S. Geological Survey

Little Pee Dee River at Galivants Ferry, S.C. 2135000 Q Hourly July 1995 August 2009 U.S. Geological Survey

Black River at Kingstree, S.C. 2136000 Q Hourly July 1995 August 2009 U.S. Geological Survey

AIW at S.C. Highway 9 at Nixons Crossroads, S.C. 2110777 wl, xwl Hourly July 1995 August 2009 U.S. Geological Survey

AIW at Briarcliffe Acres, S.C. 2110755 sc Hourly July 1995 August 2009 U.S. Geological Survey

AIW at Myrtlewood Golf Course, S.C. 2110760 sc Hourly July 1995 August 2009 U.S. Geological Survey

AIW at Grand Strand Airport at North Myrtle Beach, S.C. 2110770 sc Hourly July 1995 August 2009 U.S. Geological Survey

AIW at S.C. Highway 9 at Nixons Crossroads, S.C. 2110777 sc Hourly July 1995 August 2009 U.S. Geological Survey

Waccamaw River at Wachesaw Landing, S.C. 2110809 sc Hourly July 1995 August 2009 U.S. Geological Survey

Waccamaw River near Pawleys Island, S.C. 21108125 sc Hourly July 1995 August 2009 U.S. Geological Survey

Waccamaw River at Hagley Landing, S.C. 2110815 sc Hourly July 1995 August 2009 U.S. Geological Survey

Charleston Harbor, Charleston, S.C. wsp, wdir Hourly July 1995 August 2009 National Weather Service

Table 11. Stations, characteristics, period of record, and time interval of data in the Model-to-Marsh Decision Support System-Version 2 

(M2M-2 DSS) database.—Continued

[Q, streamflow; wl, water level; sc, specific conductance; xwl, tide range; psc, pore-water specific conductance]

Site 

identifier Station location 

River or 

marsh 

site

CharacteristicTime 

interval Start date End date Agency

2198500 Savannah River near Clyo River Q half hourly January 1994 December 2005 U.S. Geological Survey

2198840 Savannah River at I-95 Bridge River wl, sc half hourly January 1994 December 2005 U.S. Geological Survey

2198920 Front River at Houlihan Bridge River wl, sc half hourly January 1994 December 2005 U.S. Geological Survey

2198977 Front River at Broad Street River wl half hourly January 1994 December 2005 U.S. Geological Survey

21989784 Little Back River at Lucknow Canal River sc half hourly January 1994 December 2005 U.S. Geological Survey

2198979 Little Back River near Limehouse River wl half hourly January 1994 December 2005 U.S. Geological Survey

21989791 Little Back River at USFW Dock River sc half hourly January 1994 December 2005 U.S. Geological Survey

2198980 Savannah River at Fort Pulaski River wl, xwl half hourly January 1994 December 2005 U.S. Geological Survey

B1 Little Back River marsh Marsh wl, psc half hourly June 1999 December 2005 U.S. Geological Survey

B2 Little Back River marsh Marsh wl, psc half hourly June 1999 March 2007 U.S. Geological Survey

B3 Back River marsh Marsh wl, psc half hourly June 1999 December 2006 U.S. Geological Survey

B4 Back River marsh Marsh wl, psc half hourly June 1999 December 2006 U.S. Geological Survey

F1 Front River marsh Marsh wl, psc half hourly June 1999 April 2006 U.S. Geological Survey

M1 Middle River marsh Marsh wl, psc half hourly June 1999 June 2006 U.S. Geological Survey

M2 Middle River marsh Marsh wl, psc half hourly June 1999 December 2006 U.S. Geological Survey
Development of the Decision Support Systems 59

Table 11. Stations, characteristics, period of record, and time interval of data in the Model-to-Marsh Decision Support System-Version 2 

(M2M-2 DSS) database.—Continued

[Q, streamflow; wl, water level; sc, specific conductance; xwl, tide range; psc, pore-water specific conductance]

Site 

identifier Station location 

River or 

marsh 

site

CharacteristicTime 

interval Start date End date Agency

GPA04

GPA05

GPA06

GPA07

GPA08

GPA09

GPA10

GPA11

GPA11R

GPA12

GPA12R

GPA13

GPA14

GPA15

GPA21

GPA22

Site1 marsh

Site1 canal

Site2 Marsh 

Site2 Canal 

Site3 marsh 

Site3 canal 

Site4 marsh

Site4 canal

Site 5 marsh 

Site 5 canal 

Site6 marsh

Site6 canal

Site7 marsh

Site7 canal

Site8 marsh 

Site8 canal 

Site9 marsh

Site9 canal

Site10 marsh

Site10 canal

Savannah River near Fort Jackson

Back River upstream of Tide Gate

Front River upstream of Broad Street

Back River downstream of Houlihan Bridge

Front River downstream of Houlihan Bridge

Front River at Houlihan Bridge

Middle River at Houlihan Bridge

Front River upstream of Houlihan Bridge

Front River upstream of Houlihan Bridge

Middle River upstream of Houlihan Bridge

Middle River upstream of Houlihan Bridge

Little Back River downstream of Union Creek

Savannah River at I-95 Bridge

Little Back River at Houlihan Bridge

Front River downstream of U.S. Highway 17 

Bridge

Front River downstream of confluence with 

Middle River

Front River upstream of Houlihan Bridge

Front River upstream of Houlihan Bridge

Back River

Back River

Back River

Back River

Back River

Back River

Middle River upstream of Houlihan Bridge

Middle River upstream of Houlihan Bridge

Middle River upstream of Houlihan Bridge

Middle River upstream of Houlihan Bridge

Front River upstream of Houlihan Bridge

Front River upstream of Houlihan Bridge

Little Back River

Little Back River

Middle River upstream of Houlihan Bridge

Middle River upstream of Houlihan Bridge

Middle River downstream of Houlihan Bridge

Middle River downstream of Houlihan Bridge

River 

River 

River 

River 

River 

River 

River 

River 

River 

River 

River 

River 

River 

River 

River 

River 

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

Marsh

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, sc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

wl, psc

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

half hourly

July 1997

July 1999

July 1997

July 2000

July 1997

July 2001

July 1997

July 2002

July 1997

July 2003

July 1997

July 2004

July 1997

July 2005

July 1997

July 2006

July 1997

July 2007

July 1997

July 2008

July 1997

July 2009

July 1997

July 2010

July 1997

July 2011

July 1997

uly 2012

July 1997

July 2013

July 1997

July 2014

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

June 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

September 1997

October 1999

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

October 2002

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60 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Decision Support System Visualization 

Programs

The simulations provided by the DSSs integrate the 

historical database with the ANN models. The date/time 

controls on the control panels (fig. 50) enable the user to 

adjust start and end dates and time-step size for a simulation. 

The simulator allows the user to run “what-if?” simulations by 

varying streamflow and sea level from their historical values. 

There are three major inputs to the ANN models— streamflow, 

coastal water level, and tidal range. Of these three inputs, the 

user can adjust streamflow and coastal water levels. The DSSs 

assume that the tidal range will not be affected by sea-level 

rise. To evaluate climate-change scenarios, the streamflow and 

water-level inputs can be manually manipulated by the user, 

whereas tidal range is set to the historical values. The user can 

raise or lower sea level using a constant value, and the user 

has three simulation input variable options for streamflow: 

• percentage of historical streamflow to the system, 

• user-set streamflow to a constant value, and

• user-defined hydrograph. 

Explanations of how to use each of the options in the 

PRISM-2 and M2M-2 DSSs can be found in the user’s manuals in appendixes 4 and 5, respectively.

The PRISM-2 3D visualization (3DVis) worksheet 

provides graphical longitudinal specific-conductance profiles 

at the South and North Ends of the Waccamaw River and 

AIW study area. The visualization worksheet is designed to 

visualize and animate periods of special interest. Data and 

the controls for operating the 3DVis worksheet are on the left 

side of the 3DVis worksheet (fig. 51). The data are a subset of 

the data displayed on the “Run” worksheet (appendix 4) and 

are provided for reference while using the 3DVis worksheet. 

Two plots for the North End of the study area are shown in 

figure 51. The left plot shows the specific-conductance profile 

representing the actual historical data (when available), and 

the right plot shows the profile predicted by using the userspecified streamflow condition. Note that the predicted profiles 

are meaningful only if the models are set up to use exactly 

the same input streamflow condition. For example, the plots 

shown in figure 51 were created using all models with the Pee 

Dee River streamflows set at 80 percent of the actual historical 

streamflows.

To spatially visualize the marsh salinity response, the 

M2M-2 DSS is distributed with the “2D Color-Gradient 

Visualization Program” (fig. 52) that interpolates and extrapolates simulator output to fill and color a grid of the study area 

(appendix 5). The program provides a qualitative view of the 

large-scale, longitudinal gradients of the marsh parallel to the 

river, rather than a quantitative view of small-scale lateral 

gradients in the marsh perpendicular to the river. For the 

application, the seven USGS marsh gaging stations were used 

because of the large range of measured hydrologic conditions, 

especially non-drought conditions, compared with that of the 

GPA marsh sites. 

Although the marsh-data time series provides a temporally detailed description of changing salinity conditions, the 

seven sites provide information only on large-scale, longitudinal gradients in the system rather than small-scale, lateral 

variations in the marsh. Ecological interest in marsh salinity 

response typically is on seasonal and annual time scales rather 

than the smaller time scales of riverine responses of hours and 

days. For the color-gradient visualization program, users can 

select moving-window averages of 1 to 12 months from the 

M2M-2 DSS simulator results. 

Spatial visualization is based on a 100 meter (m; 

1,076 square feet) grid of the study area. The 29,000-cell grid 

covers the tidal marshes from Interstate 95 to the Highway 17 

Figure 50. Screen capture of model simulation control panels used to set parameters and run a simulation in the

(A) Pee Dee River and Intracoastal Waterway Salinity Intrusion Model-2 Decision Support System and the

(B) Model-to-Marsh-2 Decision Support System.

A B

Figure 50. Screen capture of model simulation control panels used to set parameters and run a simulation in the

(A) Pee Dee River and Intracoastal Waterway Salinity Intrusion Model-2 Decision Support System and the (B) Modelto-Marsh-2 Decision Support System.
Development of the Decision Support Systems 61

Figure 51. Screen capture of the three-dimensional visualization (3DVis) worksheet showing the specific conductance

of salinity intrusion at the North End of the Waccamaw River and Atlantic Intracoastal Waterway study area,

South Carolina, for a climate-change scenario. (From Conrads and Roehl, 2007)

Notes:

Gage numbers (first column on left) indicate last three or four digits of the eight- or nine-digit station numbers.

Pee Dee flow input (131000Du) is set at 80 percent of the historical streamflow.

Figure 51. Screen capture of the three-dimensional visualization (3DVis) worksheet showing the specific 

conductance of salinity intrusion at the North End of the Waccamaw River and Atlantic Intracoastal Waterway 

study area, South Carolina, for a climate-change scenario (from Conrads and Roehl, 2007). Note: Gage numbers 

(first column on left) indicate last three or four digits of the eight- or nine-digit station numbers. Pee Dee flow input 

(131000Du) is set at 80 percent of the historical streamflow.

Figure 52. Screen capture of the Two-Dimensional Color-Gradient Visualization Program. Left image shows

spatial distribution of marsh salinity based on data from seven U.S. Geological Survey marsh stations. Panel

in the upper right of the screen shows the user-specificed color scheme. Panel in the lower right shows the

users controls of the visualization program.

Figure 52. Screen capture of the Two-Dimensional Color-Gradient Visualization Program. Left image shows 

spatial distribution of marsh salinity based on data from seven U.S. Geological Survey marsh stations. Panel in 

the upper right of the screen shows the user-specificed color scheme. Panel in the lower right shows the users 

controls of the visualization program.
62 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

bridges on the Back and Front Rivers (fig. 3). Interpolation is 

performed using a simple ratio of linear distances between the 

nearest USGS marsh gaging station and the distance from a 

cell to the nearest gaging station. To enlarge the areal extent 

of measured marsh data (see USGS marsh network, fig. 11B), 

river gaging stations on the Savannah River at Interstate 95 

(station 02198840; fig. 11B) and Back River upstream from 

the tide gate (fig. 11B) were added to interpolate and extrapolate cells upstream from sites M1 and M2 and downstream 

from site B4. 

The program allows the user to configure the color scale 

and export all salinity values and grid parameters—cell size 

and corner coordinates—as an ASCII file for input into a 

mapping package such as ArcViewTM. In addition to the 100-m 

grid, a 10-m (107.6 square feet) grid (2,900,000 cells) was 

developed to minimize numerical computation errors when 

overlaying the grid data and irregular polygons in geographic 

information system (GIS) applications. 

Climate-Change Scenarios and 

Simulations Using the PRISM-2 and 

M2M-2 Decision Support Systems

The DSS applications for Waccamaw River and AIW, and 

the lower Savannah River, study areas provide water-resource 

managers from State and local agencies a tool for evaluating 

changes in the timing, magnitude, frequency, and duration of 

salinity at gaging locations resulting from potential climate 

change. Prior to the final determination of potential effects 

near municipal intakes, several issues concerning protection 

of coastal water intakes need to be addressed. What level of 

protection is required? What is the maximum specificconductance value that is acceptable? Is an intake required to 

stay online 100 percent of the time, or are limited intrusion 

events acceptable? Can an increase in salinity at the water-supply intake be minimized by increases in regulated streamflow? 

The DSSs allows users to simulate changes in sea level and 

streamflow, separately and in combination, and analyze the 

specific-conductance response at seven river gaging stations 

along the Grand Strand and the salinity response at five river 

gaging stations and seven marsh gaging stations in the lower 

Savannah River. The output can aid users in understanding 

the complex interaction of sea level, streamflow, and salinity 

dynamics in these estuarine systems. 

The following sections describe the application of the 

DSSs to evaluate salinity intrusions for various climate-change 

scenarios, including sea-level rise and changes in streamflow. 

Examples using PRISM-2 are followed by examples using 

M2M-2 DSS. Scenarios were selected to demonstrate the 

capabilities of the DSSs for using changes in coastal water 

level (sea level) and streamflow. Scenarios include incremental 

sea-level rise, incremental reductions in historical flows to the 

study areas, combination of incremental increases in sea-level 

rise and incremental reductions in flow, and GCM projected 

flows for 2040–2070 in combination with increment increase 

in sea level. 

The capabilities of the two DSSs are similar, and the 

scenarios could be simulated with the PRISM-2, the M2M-2 

DSS, or both. An example using the simulated output from 

the Yadkin-Pee Dee watershed model that was run with 

downscaled precipitation and temperature inputs from a GCM 

also is provided. The output obtained from simulations of 

climate-change scenarios is only intended to demonstrate the 

utility of the DSSs and is not intended to be interpreted as a 

regulatory application of the DSSs. The results of the scenario 

simulations are compared to a simulated historical baseline, 

sometimes referred to as “predicted actual” conditions. On 

some plots, the measured data are shown and can be compared 

to the model simulation of “predicted actual” condition for an 

indication of the model performance. The scenarios used in 

this study are listed in table 12.
Climate-Change Scenarios and Simulations Using the PRISM-2 and M2M-2 Decision Support Systems 63

Table 12. Climate-change scenarios used in the Pee Dee River and Atlantic Intracoastal Waterway Salinity Intrusion Model Decision 

Support System-Version 2 (PRISM-2 DSS) and the Model-to-Marsh Decision Support System-Version 2 (M2M-2 DSS).

[DSS, decision support system; SLR, sea-level rise; PRISM-2 DSS, Pee Dee River and Atlantic Intracoastal Waterway Salinity Intrusion Model Decision Support System-Version 2; M2M-2 DSS, Model-to-Marsh Decision Support System-Version 2; ft, foot; na, not applicable; Q, streamflow; GCM, global circulation 

model; HSPF, Hydrologic Simulation Program-Fortran]

Scenario Description DSS Report section Figures Tables

Changes in sea level

Incremental sea-level rise Sea-level rise in 0.5 ft increments 

from 0 to 3 ft

PRISM-2 Salinity response to sea-level rise: 

PRISM-2 DSS

56–59 na

Sea-level rise in 0.5 ft increments 

from 0 to 3 ft

M2M-2 Salinity response to sea-level rise 

and reduced streamflow: M2M-2 

DSS

70, 71 na

Changes in streamflow

GCM-HSPF generated 

flow 

GCM projected flows for 2040–2070 PRISM-2 Salinity response to user-defined 

streamflow hydrographs: 

PRISM-2

60–64, 

66

na

Incremental Reduction in 

historical Q

Percent reductions in historical flows 

by 5, 10, 15, 20, and 25 percent

PRISM-2 Percent of historical flow: 

PRISM-2

67 na

Changes in sea level and streamflow

GCM-HSPF generated 

flow 

GCM projected flows for 2040–2070 

with SLR of 1 ft and 2 ft

PRISM-2 Salinity response to user-defined 

streamflow hydrographs: 

PRISM-2

65 na

Incremental SLR and reductions in historical QSea-level rise in 0.5 ft increments 

from 0 to 3 feet and percent reductions in historical flows by 5, 10, 

15, 20, and 25 percent

PRISM-2 Salinity response to percent 

reductions of historical flow: 

PRISM-2

67, 68 14

Sea-level rise in 0.5 ft increments 

from 0 to 3 feet and percent reductions in historical flows by 5, 10, 

15, 20, and 25 percent

M2M-2 Salinity response to sea-level rise 

and reduced streamflow: 

M2M-2 DSS

72–74 15

Two dimensional color-gradient 

visualization of the pore-water 

salinity in the tidal marshes a 1 ft 

sea-level rise and a 

20 percent reduction in streamflow

M2M-2 Salinity response to sea-level rise 

and reduced streamflow on

 marsh pore-water salinity: 

M2M-2 DSS

75, 76 na
64 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Sea-Level Rise: PRISM-2 and M2M-2 Decision 

Support Systems 

In the PRISM-2 and M2M-2 DSSs, sea level can be 

decreased by as much as 1 ft or increased by as much as 3 ft 

in 0.1-ft increments. To place potential sea-level rise in the 

context of historical sea-level trends and accelerated sea-level 

rise resulting from various atmospheric-emission scenarios, a 

methodology developed by the U.S. Army Corps of Engineers 

(U.S. Army Corps of Engineers; 2011) was applied to tide data 

from three NOAA tide gages along the South Carolina and 

Georgia Coast (fig. 53).

The USACE (2011) recommends that analysts provide a 

minimum of three rates of sea-level rise. The low rate is based 

on locally measured rates of sea-level rise. These rates are 

preferably from tidal stations with a minimum of 40 years of 

data. Intermediate and high rates of sea-level rise are based on 

future accelerated rates resulting from various global-emission 

scenarios. For these rates, the USACE recommends using 

equations from the National Research Council (National 

Research Council, 1987) report “Responding to Changes 

in Sea Level: Engineering Implications” for low, moderate, 

and high accelerated sea-level-rise rates (NRC curves I, II, 

and III, respectively). Although the NRC report is more than 

20 years old, the USACE recommends the use of the equations 

in the report because the sea-level rises in the scenarios are 

greater than those presented in the Intergovernmental Panel on 

Climate Change report (Intergovernmental Panel on Climate 

Change, 2007). The IPCC 2007 report did not take into 

account rapid melting of Antarctica ice, and the sea-level-rise 

rates are considered to be too low (U.S. Army Corps of 

Engineers, 2011).

Rates of local sea-level rise for seven NOAA tidal 

stations along or near the Georgia and South Carolina 

coasts were obtained from the NOAA website (http://

tidesandcurrents.noaa.gov/ sltrends/slrmap.html, table 13). 

All of the tidal stations had more than 50 years of record. Of 

the seven sites, the Springmaid Pier site had the highest rate of 

local sea-level rise, 0.161 inch per year (in/year) or 4.09 millimeters per year (mm/year). Because of their proximity to 

EXPLANATION

NORTH CAROLINA

SOUTH CAROLINA

GEORGIA

FLORIDA

National Oceanic and Atmospheric

 Administration tidal gage

 and name

Oconee

Alabama

River

Savannah

River

River

ATLANTIC

OCEAN

Fernandina Beach

Fort Pulaski

Charleston

Springmaid Pier

Southport

Wilmington

Mayport

Mayport

83° 82° 81° 80° 79° 78° 77°

34°

33°

32°

31°

30°

0 50 100 MILES

0 50 100 KILOMETERS

Kentucky

Virginia

West

Va.

North Carolina

South

Carolina

Area

shown

Atlantic

Georgia Ocean

Florida

Alabama

Tennessee

Base from U.S. Geological Survey

1:2,000,000-scale and 1:100,000-scale digital line graphs

Lambert Conformal Conic projection Datum: NAD 83

40

95

77 26

20

74

16

95

75

10

Figure 53. Figure 53. Location of tidal gages along the coast of North Carolina, South Carolina, Georgia, and Florida. Location of tidal gages along the coast of North Carolina, South Carolina, Georgia, and Florida.
Climate-Change Scenarios and Simulations Using the PRISM-2 and M2M-2 Decision Support Systems 65

Table 13. Local sea-level rise rates and beginning year of data 

collection for seven tidal stations along the North and South 

Carolina, Georgia, and Florida coast.

[mm, millimeter]

Station (fig. 53)

Local sea-level 

rise rate

(mm/year)

Beginning 

year of 

data collection

Wilmington, N.C. 2.07 1935

Southport, N.C. 2.08 1933

Springmaid Pier, S.C. 4.09 1957

Charleston, S.C. 3.15 1921

Fort Pulaski, Ga. 2.98 1935

Fernandina Beach, Fla. 2.02 1897

Mayport, Fla. 2.4 1928

the study areas, three of the sites—Fort Pulaski, Charleston, 

and Springmaid Pier—were selected for use in generating 

projected rates of sea-level rise using the approach suggested 

in the USACE circular (2011). For each of the three sites, 

four sea-level-rise rates were determined; the local historical 

sea-level-rise trend (table 13) and three NRC curves (I, II, and 

III) were computed (fig. 54). The NRC curves are adjusted for 

the local historical sea-level rise trend. 

The sea-level-rise curves for the three sites are similar. 

The differences are the result of the differences in the local 

sea-level-rise trend. For Springmaid Pier, it is projected that 

a 1-ft (30.5-centimeter (cm)) rise will take 75 years, based on 

historical trends, or 40 years with the high accelerated sealevel-rise rate (NRC III) anticipated in response to increased 

global emission. For Fort Pulaski, a 1-ft sea-level rise will 

take more than 100 years, based on historical trends. With 

accelerated sea-level rise based on the NRC curves, a 1-ft rise 

at the three sites could take from approximately 40 to 70 years, 

depending on the emission scenario.

To simulate the effects of sea-level rise, the mean coastal 

water levels were increased in 0.5-ft increments to simulate 

sea-level rises of up to 3 ft in PRISM-2 and M2M-2. The 

specific conductance response to a 1-ft and 2-ft sea-level rise 

at the Pawleys Island gage (station 021108125, fig. 2), just 

downstream from a municipal freshwater intake, is shown for 

December 2001 to August 2009 in figure 55A. The “predicted 

actual” is the model simulation result of the measured data 

for the period. In this simulation, the magnitude and duration 

of the large intrusion events of 2002 and 2007 are increased 

with a 1- and 2-ft sea-level rise. The frequency and magnitude 

of the smaller intrusion events from 2002 to 2007 increase 

from one event with specific conductance greater than 

2,000 μS/cm to four events with specific conductance greater 

than 2,000 μS/cm for a 1-ft sea-level rise and five events 

with specific conductance greater than 6,000 μS/cm for a 2-ft 

sea-level rise.

Simulations also indicate that the duration of salinityintrusion events would increase with rising sea levels. The 

salinity intrusion during the droughts in 2002 and 2007 caused 

the municipal water supply intake upstream from the Pawleys 

Island gage to be taken offline. Figure 55B shows specific 

conductance values for the drought period of May 2007 to January 2008 and the specific conductance response simulated for 

1-ft and 2-ft rises in sea level. During this simulation period, 

the model with historical sea-level conditions (predicted 

actual conditions in the figure) predicted 73 days when values 

were greater than 2,000 μS/cm. In contrast, for a 1-ft rise in 

sea level, the number of days in which specific conductance 

exceeded 2000 μS/cm increased to 121 days and for a 2-ft rise 

to 161 days. For the same period, the number of consecutive 

days that specific conductance for actual conditions exceeded 

2,000 μS/cm was 48 (September 14 to October 31). A 1-ft rise 

increased the duration of the intrusion to 86 days (August 14 

to November 7), and a 2-ft rise increased the duration of the 

intrusion to 128 days (August 12 to December 17).

Salinity intrusion events also can be viewed with the 3D 

visualization plots in PRISM-2. Three days of the moderate 

intrusion event from October 25 to 27, 2008, are shown in 

figure 56, along with the simulated specific-conductance 

response to 1-ft and 2-ft sea-level rises. The longitudinal 

profile of the actual conditions shows the specific conductance 

increased in the range of 4,500 to 5,000 μS/cm at the Hagley 

Landing gage (02110815) and 500 to 1,000 μS/cm at the 

Pawleys Island gage (021108125). A 1-ft and a 2-ft rise in sea 

level increases the magnitude and duration of salinity intrusions at the Pawleys Island gage and farther upstream at the 

Wachesaw gage (02110809). (Note that specific conductance 

values greater than 5,000 μS/cm are truncated in the 3D plot 

of figure 56.) Under simulated actual conditions, there was 

one day (October 26, 2008) at the Pawleys Island gage when 

the daily mean specific-conductance values were greater than 

500 μS/cm. Simulations incorporating a 1-ft rise in sea level 

show that the magnitude of the event increases in the range 

of 2,500 to 3,000 μS/cm but lasts only 1 day of the 3-day 

period. For a 2-ft rise, the magnitude increased to greater than 

4,000 μS/cm, and there are 3 days with specific conductance 

greater than 4,000 μS/cm.

For the operators of municipal water-treatment plants, 

often the concern with the source water is not the magnitude 

of the salinity intrusion but whether the specific conductance 

of the source water at the intake exceeds a predetermined 

threshold level. When the specific-conductance values of 

source water are greater than 1,000 to 2,000 μS/cm, additional 

treatment or blending with lower specific-conductance water 

is required to eliminate taste problems and potential health 

concerns. Nomographs were generated from six sea-level-rise 

scenario simulations with PRISM-2. The mean coastal water 

levels were increased in increments of 0.5 ft to simulate 
66 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Figure 54. Sea-level rise curves for three 

sites along the Georgia and South Carolina 

coast: (A) Springmaid Pier, (B) Charleston, and 

(C) Fort Pulaski (National Research Council, 

1987).

3.0

2.5

2.0

1.5

1.0

0.5

0

Sea-level rise, in feet

A. Springmaid Pier

NRC I

NRC II

NRC III

Sea-level-rise trend

EXPLANATION

NRC I

NRC II

NRC III

Sea-level-rise trend

EXPLANATION

NRC I

NRC II

NRC III

Sea-level-rise trend

EXPLANATION

3.0

2.5

2.0

1.5

1.0

0.5

0

B. Charleston

Figure 54. Sea-level rise curves for three sites along the Georgia and

South Carolina coast: (A) Springmaid Pier, (B) Charleston, and (C) Fort Pulaski.

(National Research Council (NRC), 1987)

0 5 10 15 20 25 30 35 40 45 50 55

Years into the future

60 65 70 75 80 85 90 95 110 115 100 105

3.0

2.5

2.0

1.5

1.0

0.5

0

C. Fort Pulaski
Climate-Change Scenarios and Simulations Using the PRISM-2 and M2M-2 Decision Support Systems 67

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

A

Pawleys Island 2-foot sea-level rise

Pawleys Island 1-foot sea-level rise

Pawleys Island measured

Pawleys Island predicted actual

EXPLANATION

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

B

Figure 55. Simulated specific conductance at Pawleys Island gage (021108125) for actual

conditions and for a 1-foot and 2-foot sea-level rise for (A) December 1, 2001–August 20, 2009

and (B) May 1, 2007–January 31, 2008.

Dec. 1,

2001

May 1 June 1 July 1 Aug. 1

2007 2008

Sept. 1 Oct. 1 Nov. 1 Dec. 1 Jan. 1 Jan. 31

Dec. 1,

2002

Dec. 1,

2003

Dec. 1,

2004

Dec. 1,

2005

Dec. 1,

2006

Dec. 1,

2007

Dec. 1,

2008

Aug. 20,

2009

Pawleys Island 2-foot sea-level rise

Pawleys Island 1-foot sea-level rise

Pawleys Island measured

Pawleys Island predicted actual

EXPLANATION

Figure 55. Simulated specific conductance at Pawleys Island gage (021108125) for actual conditions and for a 

1-foot and 2-foot sea-level rise for (A) December 1, 2001–August 20, 2009, and (B) May 1, 2007–January 31, 2008.
68 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

2008 Baseline 1-foot SLR 2-foot SLR

October 25

October 26

October 27

4,501 – 5,000

4,001 – 4,500

3,501 – 4,000

3,001 – 3,500

2,501 – 3,000

2,001 – 2,500

1,501 – 2,000

1,001 – 1,500

501 – 1,000

0 – 500

–500 – 0

Specific conductance, in

microsiemens per centimeter

at 25 degrees Celsius

EXPLANATION

815

809

8125 815

809

8125 815

809

8125

815

809 815

809

8125 8125

815

809

8125

815

809 815

809

8125 8125 815

809

8125

Figure 56. Screen capture of three-dimensional longitudinal plots of a salinity-intrusion event

on October 25–27, 2008 at selected stations in the South End of the Waccamaw River and

Atlantic Intracoastal Waterway study area, South Carolina. The plots show the specific-conductance

response to historical conditions (baseline) and 1 foot and 2 foot sea-level rises (SLR) at gages along the

South End. Note: the gage numbers have been shortened and distances between gage locations

are approximate. Specific conductance values greater than 5,000 microsiemens per centimeter

are truncated.

Figure 56. Screen capture of three-dimensional longitudinal plots of a salinity-intrusion event on 

October 25–27, 2008, at selected stations in the South End of the Waccamaw River and Atlantic 

Intracoastal Waterway study area, South Carolina. The plots show the specific-conductance response 

to historical conditions (baseline) and 1-foot and 2-foot sea-level rises (SLR) at gages along the 

South End. Note: The gage numbers have been shortened and distances between gage locations 

are approximate. Specific conductance values greater than 5,000 microsiemens per centimeter are 

truncated.

sea-level rises of up to 3 ft, and the number of days the 

predicted specific-conductance values exceeded thresholds of 

1,000, 2,000, and 3,000 μS/cm for each 14-year simulation 

period was computed. The number of days that a specificconductance threshold is exceeded for sea-level rises of 0 

to 3 ft for the three sites on the South End and three sites on 

the North End are shown in figures 57 and 58, respectively. 

For example, under historical conditions, the simulated 

historical daily specific conductance at the Pawleys Island 

gage exceeded 2,000 μS/cm for almost 200 days during the 

14-year simulation period (fig. 57B). Simulations of the same 

conditions incorporating a 1-ft sea-level rise indicate that the 

number of days the municipal water at the intake is unsuitable 

for supply would double to 400 days, and a 2-ft rise would 

increase the number of days when the specific conductance of 

water at the intake exceeded 2,000 μS/cm to 700 days. 

The specific-conductance response for the North End 

sites differs completely from that of the South End sites. For 

the North End, unlike the South End, the number of days 

where the daily specific conductance exceeds the specified 

thresholds decreases with incremental increases in sea level. 

There is a positive net flow in the AIW from Bucksport to 

Little River Inlet (fig. 6) which maintains the freshwater in 

this reach of the AIW. The increase in sea level would also 

increase water levels from Little River Inlet to Winyah Bay 

with a larger tidal range propagating from the south (table 6). 

The increase in sea level would increase the gradient and net 

transport of freshwater along the AIW toward Little River 

Inlet. The effect is most pronounced at the Myrtlewood gage 

(fig. 58A) where the frequency of salinity intrusions exceeding 

1,000 and 2,000 μS/cm is 30 and 180 days, respectively, during the 14-year simulation period. With a 1-ft rise in sea level, 
Climate-Change Scenarios and Simulations Using the PRISM-2 and M2M-2 Decision Support Systems 69

1,400

1,200

1,000

800

600

400

200

0

1,400

1,200

1,000

800

600

400

200

0

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

Number of days the specific-conductance threshold is exceeded

A. Wachesaw (02110809)

B. Pawleys Island (021108125)

C. Hagley Landing (02110815)

0 0.5 1.0 1.5

Sea-level rise, in feet

2.0 2.5 3.0

Specific conductance, in microsiemens

per centimeter at 25 degrees Celsius

EXPLANATION

1,000

2,000

3,000

Figure 57. The number of days specific conductance thresholds of

1,000, 2,000, and 3,000 microsiemens per centimeter are exceeded for

incremental sea-level rises at (A) Wachesaw (station 02110809),

(B) PlIld (i02082)d (C) HlLdi

Figure 57. The number of days specific 

conductance thresholds of 1,000, 2,000, 

and 3,000 microsiemens per centimeter are 

exceeded for incremental sea-level rises at 

(A) Wachesaw (station 02110809), (B) Pawleys 

Island (station 021108125), and (C) Hagley 

Landing (station 02110815) gages, South 

Carolina, for the simulation period 1995–2009.
70 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

200

180

160

140

120

100

80

60

40

20

0

1,200

1,000

800

600

400

200

0

5,000

4,500

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

Number of days the specific-conductance threshold is exceeded

A. Myrtlewood (02110760)

B. Myrtle Beach Airport (02110770)

C. Little River Inlet (02110777)

0 0.5 1.0 1.5

Sea-level rise, in feet

2.0 2.5 3.0

Figure 58. The number of days specific-conductance thresholds of 1,000,

2,000, and 3,000 microsiemens per centimeter are exceeded for incremental

sea-level rises at (A) Myrtlewood (station 02110760) , (B) Myrtle Beach Airport

(station 02110770), and (C) Little River Inlet (station 02110777) gages,

Specific conductance, in microsiemens

per centimeter at 25 degrees Celsius

EXPLANATION

1,000

2,000

3,000

Figure 58. The number of days specificconductance thresholds of 1,000, 2,000, 

and 3,000 microsiemens per centimeter are 

exceeded for incremental sea-level rises at 

(A) Myrtlewood (station 02110760), (B) Myrtle 

Beach Airport (station 02110770), and (C) Little 

River Inlet (station 02110777) gages, South 

Carolina, for the simulation period 1995–2009.
Climate-Change Scenarios and Simulations Using the PRISM-2 and M2M-2 Decision Support Systems 71

2008 1-foot SLR 2-foot SLR

October 25

October 26

October 27

4,501 – 5,000

4,001 – 4,500

3,501 – 4,000

3,001 – 3,500

2,501 – 3,000

2,001 – 2,500

1,501 – 2,000

1,001 – 1,500

501 – 1,000

0 – 500

–500 – 0

Specific conductance, in

microsiemens per centimeter

at 25 degrees Celsius

EXPLANATION

Baseline

750

755

770

777

750

755

770

777

750

755

770

777

750

755

770

777

750

755

770

777

750

755

770

777

750

755

770

777

750

755

770

777

750

755

770

777

Figure 59. Screen capture of three-dimensional longitudinal plots of a salinity-intrusion event

on October 25–27, 2008 at selected stations in the North End of the Waccamaw River and

Atlantic Intracoastal Waterway study area, South Carolina. The plots show the specific-conductance

response to historical conditions (baseline) and 1 foot and 2 foot sea-level rises (SLR) at gages along the

North End. Note: the gage numbers have been shortened and distances between gage locations

are approximate. Specific conductance values greater than 5,000 microsiemens per centimeter

are truncated.

Figure 59. Screen capture of three-dimensional longitudinal plots of a salinity-intrusion event on 

October 25–27, 2008, at selected stations in the North End of the Waccamaw River and Atlantic 

Intracoastal Waterway study area, South Carolina. The plots show the specific-conductance response to 

historical conditions (baseline) and 1-foot and 2-foot sea-level rises (SLR) at gages along the North End. 

Note: The gage numbers have been shortened and distances between gage locations are approximate. 

Specific conductance values greater than 5,000 microsiemens per centimeter are truncated.

there are no occurrences of daily specific conductance greater 

than 2,000 μS/cm and only 40 days with values higher than 

1,000 μS/cm. At the Little River gage, there is only a small 

decrease in specific conductance with sea-level rise (fig. 58C).

Specific conductance during the moderate intrusion event 

on October 25 to 27, 2008, is shown in figure 59, along with 

the specific-conductance response to projected 1-ft and 2-ft 

sea-level rises for three gaging stations along the North End 

of the Grand Strand. The 3D longitudinal plots in figure 59 

show that the projected specific-conductance response at the 

North End gaging stations to a rise in sea level is smaller than 

that projected for the South End gaging stations (fig. 56). 

The longitudinal plots show limited intrusion into the AIW. 

A rise in sea level decreases streamflows to the south (as 

seen in figure 56 and the increased salinity intrusion) and 

increases streamflows to the north toward Little River Inlet. 

The effect of sea-level rise is clearly seen on the recession of 

the intrusion event on October 27, 2008. The simulated actual 

conditions (baseline) resulted in specific conductance values 

of 3,000 to 3,500 μS/cm, and a 2-ft sea-level rise decreased 

the values to 501 to 1,000 μS/cm.

Changes in Streamflow: PRISM-2

Projections of climate change for the 21st century 

generated by GCMs indicate changes in temperature and 

precipitation patterns, which will affect the timing and quantity of streamflow to the coast (Karl and others, 2009). For 

the southeastern United States, there is no consensus among 

results of the GCMs on the hydrologic response of the region. 

The GCMs predict that temperatures will increase. However, 

some models predict an overall increase in precipitation, 

whereas other models predict a decrease in precipitation (Karl 

and others, 2009). The effect of climate change projections by 

GCMs on salinity intrusion can be evaluated using PRISM-2 

(fig. 10). The precipitation and temperature predictions of 

the GCM are used as inputs to the watershed model of the 

Yadkin-Pee Dee basin developed by the University of South 

Carolina [Hydrologic Simulation Program–Fortran (HSPF)]. 

The HSPF model simulated the streamflow for the Waccamaw, 

Little Pee Dee, Pee Dee, Lynches, and Black Rivers, and these 

data were used as defined streamflow inputs to PRISM-2. The 

grid scales of the GCM and Yadkin-Pee Dee basin are shown 

in figure 60. 
72 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Figure 60. Example of the scale of the global circulation model (250-square-kilometer grid) and the scale of the

Yadkin-Pee Dee Basin (12-square-kilometer grid), North Carolina, South Carolina, and Georgia.

12 km2

250 km2

SOUTH

CAROLINA

NORTH CAROLINA

ALABAMA

GEORGIA

FLORIDA

VIRGINIA

TENNESSEE

Figure 60. Example of the scale of the global circulation model (250-square-kilometer 

grid) and the scale of the Yadkin-Pee Dee Basin (12-square-kilometer grid), North Carolina, 

South Carolina, and Georgia.

User-Defined Hydrographs: PRISM-2

The downscaled temperature and precipitation data for 

the ECHO CGM (Legutke and Voss, 1999) for the A2 carbon 

emission climate scenario, which assumes that nations will 

continue to pursue their interests individually rather than 

cooperate internationally in dealing with climate change 

(Intergovernmental Panel on Climate Change, 2007), were 

used as input to the HSPF watershed model. The ECHO GCM 

is a hybrid coupled ocean-atmosphere GCM of the Hamburg 

version of the European Center atmospheric GCM (ECHAM) 

and the Hamburg Primitive Equation ocean GCM (HOPE). 

Simulations of streamflows for the Waccamaw, Little Pee 

Dee, Pee Dee, Lynches, and Black Rivers for 1980 to 2010 

and 2040 to 2070 were obtained from the HSPF watershed 

model (D. Tufford, University of South Carolina Department 

of Biology, written commun., 2011). For the simulation period 

of PRISM-2 (1995–2009), single-mass curves of cumulative 

flow (fig. 61A) were generated for the total inflow (QTOTAL) 

for the measured and simulated data, and frequency distribution curves were generated for the ECHO-HSPF simulated 

total inflow for 1995 to 2009 (historical) and for total inflow 

projected for about 60 years in the future, 2055–2069 

(fig. 61B). Estimates of inflow from ECHO-HSPF are fairly 

good, considering the number of model simulation and data 

processing steps needed to simulate streamflow (large scale 

ECHO simulation output of precipitation and temperature, 

statistically downscaled precipitation and temperature inputs 

to HSPF, and HSPF simulated streamflow output). The single 

mass curve (fig. 61A) shows that the simulated ECHO-HSPF 

streamflows for 1995 to 2009 are approximately the same 

order of magnitude as the cumulative streamflow. The 

ECHO-HSPF streamflow projected for 2055 to 2069 shows a 

decrease in the cumulative total streamflow to the coast. The 

shapes of the simulated frequency distributions (fig. 61B) for 

the measured and simulated streamflows are similar; however, 

the ECHO-HSPF simulations underestimated the frequency 

of streamflows below 20,000 ft3

/s and overestimated the 

frequency of streamflows greater than 20,000 ft3/s. 

To evaluate seasonal shifts in the projected streamflows, 

daily duration hydrographs were plotted using the simulated 

ECHO-HSPF streamflows for 1980 to 2010 and 2040 to 

2070 for the total inflow to the coast (fig. 62). Although the 

simulated historical and simulated projected streamflows 

appear to be similar, there are important differences, especially 

with respect to potential changes in salinity intrusion. Salinity 

intrusion along the coast is a low-flow phenomenon; therefore, 

changes in the distribution of low flows are the most critical 

processes to be considered when assessing the potential for 

salinity intrusion. For the winter months (January–March), 

the simulated projected low flows (10th percentile) are less 

than the corresponding simulated historical low flows, and 

for the spring months (April–June), the projected low flows 

are only slightly less than simulated historical low flows. For 

the summer months (July–September), simulated projected 

low flows are lower than the simulated historical low flows 

in the beginning of July but higher than simulated historical 

low flows in August and September. For the fall months 
Climate-Change Scenarios and Simulations Using the PRISM-2 and M2M-2 Decision Support Systems 73

80,000,000

70,000,000

60,000,000

50,000,000

40,000,000

30,000,000

20,000,000

10,000,000

0

100

90

80

70

60

50

40

30

20

10

0

Cumulative flow, in cubic feet per second

Cumulative percent

A

Total flow measured

Total flow ECHO/HSPF historical

Total flow ECHO/HSPF projected

EXPLANATION

Measured flow

ECHO/HSPF historical flow

ECHO/HSPF projected flow

EXPLANATION

B

Figure 61. (A) The cumulative flow and (B) the frequency distribution for measured and simulated total

flow to the Waccamaw River and Atlantic Intracoastal Waterway study area, South Carolina, for

1995–2009 and projected total flow for 2055–2069. Total flow simulated using the ECHO global

circulation model and the Hydrologic Simulation Program-Fortran (HSPF) watershed model the historical

and projected periods. The ECHO GCM is a hybrid coupled ocean-atmosphere GCM of the Hamburg version

of the European Center atmospheric GCM (ECHAM) and the Hamburg Primitive Equation Ocean GCM (HOPE). 

0

2,500

5,000

7,500

10,000

12,500

15,000

17,500

20,000

22,500

25,000

27,500

30,000

32,500

35,000

37,500

40,000

42,500

45,000

47,500

50,000

Simulated flow, in cubic feet per second

Aug. 12

1995

Aug. 12

1996

Aug. 12

1957

Aug. 12

1998

Aug. 12

1999

Aug. 12

2000

Aug. 12

2001

Aug. 12

2002

Aug. 12

2003

Aug. 12

2004

Aug. 12

2005

Aug. 12

2006

Aug. 12

2007

Aug. 12

2008

Aug. 12

2009

Aug. 12

2055

Aug. 12

2056

Aug. 12

2057

Aug. 12

2058

Aug. 12

2059

Aug. 12

2060

Aug. 12

2061

Aug. 12

2062

Projected period

Historical period

Aug. 12

2063

Aug. 12

2064

Aug. 12

2065

Aug. 12

2066

Aug. 12

2067

Aug. 12

2068

Aug. 12

2069

Figure 61. (A) The cumulative flow and (B) the frequency distribution for measured and simulated total flow to 

the Waccamaw River and Atlantic Intracoastal Waterway study area, South Carolina, for 1995–2009 and projected 

total flow for 2055–2069. Total flow simulated using the ECHO global circulation model and the Hydrologic Simulation 

Program-Fortran (HSPF) watershed model for the historical and projected periods. The ECHO GCM is a hybrid coupled 

ocean-atmosphere GCM of the Hamburg version of the European Center atmospheric GCM (ECHAM) and the Hamburg 

Primitive Equation ocean GCM (HOPE).
74 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Figure 62. Daily duration hydrographs for simulated total inflow to the Waccamaw River and the Atlantic Intracoastal

Waterway study area, South Carolina, for historical conditions, 1980–2010, and projected future conditions, 2040–2070.

Historical and projected total flow were simulated using the ECHO global circulation model and the Hydrologic Simulation

Program-Fortran watershed model. The ECHO GCM is a hybrid coupled ocean-atmosphere GCM of the Hamburg version

of the European Center atmospheric GCM (ECHAM) and the Hamburg Primitive Equation ocean GCM (HOPE). 

10th percentile ECHO/HSPF historical

10th percentile ECHO/HSPF projected

50th percentile ECHO/HSPF historical

50th percentile ECHO/HSPF projected

90th percentile ECHO/HSPF historical

90th percentile ECHO/HSPF projected

EXPLANATION

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

Streamflow, in cubic feet per second

60,000

50,000

40,000

30,000

20,000

10,000

0

Figure 62. Daily duration hydrographs for simulated total inflow to the Waccamaw River and the Atlantic Intracoastal Waterway 

study area, South Carolina, for historical conditions, 1980–2010, and projected future conditions, 2040–2070. Historical and 

projected total flow were simulated using the ECHO global circulation model and the Hydrologic Simulation Program-Fortran 

watershed model. The ECHO GCM is a hybrid coupled ocean-atmosphere GCM of the Hamburg version of the European Center 

atmospheric GCM (ECHAM) and the Hamburg Primitive Equation ocean GCM (HOPE).

(October–December), the simulated projected low flows are 

less than the corresponding historical simulated low flows.

The projected ECHO-HSPF streamflows were input as 

user-defined hydrographs to simulate specific conductance for 

the 1995 to 2009 and 2055 to 2069 PRISM-2 DSS. Figure 63 

shows the simulated daily specific-conductance values for the 

Pawleys Island gage (021108125) generated for historical and 

projected streamflow conditions. Substantial differences in the 

time series are not obvious. The future period was not selected 

to be meteorologically synchronized with the historical conditions of 1995 to 2009. That is to say, there is probably little 

value or information to be gained by noting that the intrusion 

event in October 2001 did not occur 60 years in the future in 

October 2061 or that there were large salinity intrusions in 

October 2003 and October 2063. The two 14-year simulations 

(1995–2009) do provide the data to evaluate relative changes 

in the timing and frequency of salinity intrusions given a 

particular climate-change scenario.

One approach to comparing the simulations of 

historical and projected specific conductance for the Pawleys 

Island gage (021108125) is to determine whether there 

are seasonal shifts in the occurrence of salinity-intrusion 

events that correspond to the seasonal changes in projected 

low flows simulated using ECHO-HSPF (fig. 62). To do 

this, the numbers days in which the specific conductance 

exceeded a threshold of 1,000 μS/cm were counted for each 

season (fig. 64). The simulation of the historical specific 

conductance shows the highest number of days with specific 

concentrations greater than 1,000 μS/cm occurs in the summer 

(July–September) with a small decrease in the number in the 

fall (October–December). Results of the simulation of specific 

conductance for the projected streamflow condition shows 

a change in the occurrence of intrusion events with greater 

than 1,000 μS/cm. The number of intrusion events increases 

in the spring (April–June) and decreases in the summer; most 

of the events occur in the fall. Under potential future climate 
Climate-Change Scenarios and Simulations Using the PRISM-2 and M2M-2 Decision Support Systems 75

Figure 63. Simulated historical, 1995–2009, and projected, 2055–2069, daily specific-conductance values generated for

Pawleys Island, South Carolina (station 021108125) using the simulated flows from the ECHO global circulation model

and the Hydrologic Simulation Program-Fortran watershed model. The ECHO GCM is a hybrid coupled ocean-atmosphere

GCM of the Hamburg version of the European Center atmospheric GCM (ECHAM) and the Hamburg

Primitive Equation ocean GCM (HOPE).

ECHO historical

ECHO projection

EXPLANATION

Aug. 12

1995

Aug. 12

1996

Aug. 12

1997

Aug. 12

1998

Aug. 12

1999

Aug. 12

2000

Aug. 12

2001

Aug. 12

2002

Aug. 12

2003

Aug. 12

2004

Aug. 12

2005

Aug. 12

2006

Aug. 12

2007

Aug. 12

2008

Aug. 12

2009

Specific conductance, in microsiemens per centimeter at 25 degrees Celsius

12,000

10,000

8,000

6,000

4,000

2,000

0

Aug. 12

2055

Aug. 12

2056

Aug. 12

2057

Aug. 12

2058

Aug. 12

2059

Aug. 12

2060

Aug. 12

2061

Aug. 12

2062

Projected period

Historical period

Aug. 12

2063

Aug. 12

2064

Aug. 12

2065

Aug. 12

2066

Aug. 12

2067

Aug. 12

2068

Aug. 12

2069

Figure 63. Simulated historical, 1995–2009, and projected, 2055–2069, daily specific-conductance values generated for Pawleys 

Island, South Carolina (station 021108125), using the simulated flows from the ECHO global circulation model and the Hydrologic 

Simulation Program-Fortran watershed model. The ECHO GCM is a hybrid coupled ocean-atmosphere GCM of the Hamburg version 

of the European Center atmospheric GCM (ECHAM) and the Hamburg Primitive Equation ocean GCM (HOPE).

Figure 64. Number of days per season in which the daily specific conductance

threshold of 1,000 microsiemens per centimeter is exceeded at the Pawleys Island

gage (station 021108125)South Carolinafor the historical 1995–2009and future

ECHO/HSPF historical

ECHO/HSPF projected

EXPLANATION

Winter Spring Summer Fall Total

Number of days specific conductance threshold is exceeded

180

160

140

120

100

80

60

40

20

0

Figure 64. Number of days per season 

in which the daily specific conductance 

threshold of 1,000 microsiemens per centimeter 

is exceeded at the Pawleys Island gage 

(station 021108125), South Carolina, for the 

historical 1995–2009, and future, 2055–2069, 

simulation periods. Simulated historical and 

projected conditions based on total flow to the 

Waccamaw River and Atlantic Intracoastal 

Waterway study area using the ECHO 

global circulation model and the Hydrologic 

Simulation Program-Fortran (HSPF) watershed 

model. The ECHO GCM is a hybrid coupled 

ocean-atmosphere GCM of the Hamburg 

version of the European Center atmospheric 

GCM (ECHAM) and the Hamburg Primitive 

Equation ocean GCM (HOPE).
76 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

change, there will still be the convergence of conditions of 

low flow and high coastal water level to initiate an intrusion 

event. The decrease in the number of summer intrusion events 

corresponds to an increase in the 10th percentile projected 

streamflows seen in figure 62. Likewise, the increase in fall 

intrusion events corresponds to the slight decrease in the 

low flows in November and December. The decrease in the 

winter 10th percentile streamflow, although substantial, is not 

sufficient to cause an intrusion event.

The ECHO-HSPF streamflows also were simulated in 

PRISM-2 in combination with a 1-ft and 2-ft sea-level rise 

(fig. 65A). The seasonal shift in the number of days with 

specific conductance greater than the 1,000 μS/cm threshold 

from summer to fall with the projected streamflow conditions 

Figure 65. (A) The number of days in each season that the specific conductance

threshold of 1,000 microsiemens per centimeter is exceeded at the Pawleys Island

gage (021108125), South Carolina, for the historical, 1995–2009, projected, 2055–2069,

climate changes, and projected climate changes in combination with a

Winter

Spring

Summer

Fall

EXPLANATION

ECHO/HSPF historical ECHO/HSPF 

projection

ECHO/HSPF projection

and 1-foot SLR

ECHO/HSPF projection

and 2-foot SLR

70

60

50

40

30

20

10

0

ECHO/HSPF historical

ECHO/HSPF projected

ECHO/HSPF projected and 1-foot SLR

ECHO/HSPF projected and 2-foot SLR

EXPLANATION

Winter Spring Summer Fall Total

Number of days specific conductance exceeded 1,000 microsiemens

per centimeter at 25 degrees Celsius

Percentage of total specific-conductance values exceeding 1,000 microsiemens

per centimeter at 25 degrees Celsius

500

450

400

350

300

250

200

150

100

50

0

A

B Figure 65. (A) The number of days in 

each season that the specific conductance 

threshold of 1,000 microsiemens per centimeter 

is exceeded at the Pawleys Island gage 

(021108125), South Carolina, for the historical, 

1995–2009, projected, 2055–2069, climate 

changes, and projected climate changes in 

combination with a 1-foot sea-level rise, and 

a 2-foot sea-level rise, and (B) the percentage 

of simulated specific-conductance values 

exceeding 1,000 microsiemens per centimeter 

each season. Simulated historical and 

projected conditions are based on total flow to 

the Waccamaw River and Atlantic Intracoastal 

Waterway study area using the ECHO 

global circulation model and the Hydrologic 

Simulation Program-Fortran (HSPF) watershed 

model. The ECHO GCM is a hybrid coupled 

ocean-atmosphere GCM of the Hamburg 

version of the European Center atmospheric 

GCM (ECHAM) and the Hamburg Primitive 

Equation ocean GCM (HOPE).
Climate-Change Scenarios and Simulations Using the PRISM-2 and M2M-2 Decision Support Systems 77

also occurs in conjunction with 1-ft and 2-ft increases in sea 

level. The data in figure 65A are presented as the total number 

of days for each season and simulation. Although there is 

the shift in the number of days to the fall, the 1-ft and 2-ft 

sea-level rises show that the percentage of days in the fall 

decreases and the percentage of days in the winter, spring, and 

summer increases.

Streamflow as a Percentage of Historical 

Streamflow: PRISM-2

An alternative to basing future streamflow on output from 

GCMs is to reduce the historical streamflow by a specified 

percentage. As seen in the previous scenarios, decreases in 

streamflow increase the potential for salinity intrusion and 

increases in streamflow decrease the potential for salinity 

intrusion. As a result of the decrease in projected streamflow 

(fig. 61) and shifts in the timing of low flows (fig. 62), there is 

a wide range in the daily changes in the simulated historical 

and projected low flows. The daily differences between 

the ECHO-HSPF simulated 10th percentile historical and 

projected streamflows range from decreases in low flows of 

60 percent to increases in low flows of 120 percent (fig. 66). 

Overall, the net decrease in the 10th percentile streamflows is 

17.3 percent. 

To evaluate net reductions in streamflows, the historical 

streamflow inputs used in the PRISM-2 DSS were reduced 

by 5, 10, 15, 20, and 25 percent. Similar to the analysis of 

sea-level rise (fig. 57), the numbers of days the specific conductance threshold is exceeded for each 5-percent incremental 

decrease in streamflow and 0-, 1-, and 2-ft sea-level rise is 

shown in figures, 67A, B, and C, respectively. A 25-percent 

decrease in the streamflow increased the number of days the 

thresholds are exceeded by approximately 45 to 90 percent for 

each of the three sea-level scenarios. The combined effect of 

a 2-ft sea-level rise and 25-percent reduction in streamflows 

increased the number of days the thresholds are exceed by 440 

to 645 percent. For example, if the threshold is 2,000 μS/cm, 

then historically in the 14-year simulation there are 191 days 

in which the threshold is exceeded (fig. 67A). If the historical streamflow is reduced by 25 percent and there is a 2-ft 

sea-level rise, the number of days the threshold is exceeded 

increases to 1,067 days (fig. 67C), or 21 percent of the days 

Decreased

projected flows

Increased

projected flows

Figure 66. Effects of potential climate change on simulated flow to the Waccamaw and Atlantic Intracoastal Waterway

study area, South Carolina, expressed as the difference between the daily 10th percentile of flow for future, 2040–2070,

and historical, 1980–2010, periods. Positive differences represent a decrease in projected flows and negative difference 

represent a increase in projected flows.Simulated historical and projected conditions are based on total flow to the

Waccamaw River and Atlantic Intracoastal Waterway study area using the ECHO global circulation model and the

Hydrologic Simulation Program-Fortran (HSPF) watershed model. The ECHO GCM is a hybrid coupled ocean-atmosphere

GCM of the Hamburg version of the European Center atmospheric GCM (ECHAM) and the Hamburg Primitive Equation

ocean GCM (HOPE). 

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. 

Percentage of difference between historical and projected 10th percentile flows

80

60

40

20

0

–20

–40

–60

–80

–100

–120

–140

Figure 66. Effects of potential climate change on simulated flow to the Waccamaw and Atlantic Intracoastal Waterway study 

area, South Carolina, expressed as the difference between the daily 10th percentile of flow for future, 2040–2070, and historical, 

1980–2010, periods. Positive differences represent a decrease in projected flows and negative differences represent an increase 

in projected flows. Simulated historical and projected conditions are based on total flow to the Waccamaw River and Atlantic 

Intracoastal Waterway study area using the ECHO global circulation model and the Hydrologic Simulation Program-Fortran 

(HSPF) watershed model. The ECHO GCM is a hybrid coupled ocean-atmosphere GCM of the Hamburg version of the European 

Center atmospheric GCM (ECHAM) and Hamburg Primitive Equation ocean GCM (HOPE).
78 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Specific conductance, in microsiemens

per centimeter at 25 degrees Celsius

EXPLANATION

1,000

2,000

3,000

C. 2-foot sea-level rise

B. 1-foot sea-level rise

A. Historical conditions

1,400

1,200

1,000

800

600

400

200

0

1,400

1,200

1,000

800

600

400

200

0

1,400

1,200

1,000

800

600

400

200

0

Number of days the specific-conductance threshold is exceeded

Figure 67. Number of days specific-conductance thresholds

of 1,000, 2,000, and 3,000 microsiemens per centimeter are

exceeded at the Pawleys Island gage (station 021108125),

Sh Clifil dif flf

100 95 90 

Percentage of historical flows

85 80 75

Figure 67. Number of 

days specific-conductance 

thresholds of 1,000, 2,000, 

and 3,000 microsiemens per 

centimeter are exceeded at the 

Pawleys Island gage (station 

021108125), South Carolina, for 

incremental reductions of flow 

for (A) historical conditions, 

(B) a 1-foot sea-level rise, 

and (C) a 2-foot sea-level rise, 

during 1995–2009.
Climate-Change Scenarios and Simulations Using the PRISM-2 and M2M-2 Decision Support Systems 79

for the 14-year simulation, compared to 

the 4 percent of the days for the historical 

condition.

The results for 42 model scenarios 

incorporating sea-level rise and reduction in 

streamflows are provided in table 14. Rather 

than expressing the results as the number 

of days the threshold is exceeded for the 

14-year simulation period, the results are presented as the percentage of days the threshold is exceeded for the 14-year simulation 

period. For example, for simulated historical 

conditions (no reduction in streamflow 

and no rise in sea level), the threshold of 

1,000 μS/cm is exceeded 5.4 percent of 

the time for the 14-year simulation. For a 

reduction in streamflow of 10 percent and 

a rise in sea level of 1.0 ft, the percentage 

of time the threshold is exceeded is 13.3, 

or an increase of 7.9 percent. The response 

of specific conductance to combinations of 

sea-level rise and reduced streamflow also can be displayed as 

a 3D response surface. The percentage of time the threshold 

of 1,000 μS/cm is exceeded (z-axis) for 0.5-ft increments of 

sea-level rise up to 3 ft (x-axis) and for reductions in streamflow in increments of 5 percent (y-axis) for the Pawleys Island 

streamgaging station (021108125) are shown in figure 68. For 

no change in sea-level and no reduction in streamflow, the 

Table 14. Percentage of time in a 14-year simulation that the specificconductance threshold of 1,000 microsiemens per centimeter was exceeded at the 

Pawleys Island gage (station 021108125), South Carolina, as a result of reductions 

in historical streamflow and sea-level rises.

[μS/cm, microsiemens per centimeter at 25 degrees Celsius; % flow, percent reduction in historical flows; SLR, sea-level rise; ft, feet]

% Flow/SLR Percent of days with specific conductance greater than 1,000 μS/cm1

0 ft 0.5 ft 1 ft 1.5 ft 2 ft 2.5 ft 3 ft

0 5.4 8.3 11.0 14.0 17.6 20.1 22.8

–5 6.1 9.0 12.1 15.7 18.8 21.5 24.5

–10 7.0 10.0 13.3 17.2 20.0 23.1 26.1

–15 8.2 11.3 15.2 18.6 21.7 24.9 27.9

–20 8.9 15.1 16.8 20.0 23.5 26.6 29.8

–25 10.1 14.4 18.3 21.8 25.5 28.8 32.6

1

The model simulation period is June 1994 to July 2008.

1,000 μS/cm threshold is exceeded 5.4 percent of the time. For 

a 3-ft sea-level rise and a 25-percent reduction in streamflow, 

the percentage of time the threshold is exceeded increased to 

32.9 percent. The 3D response surface also shows that specific 

conductance is much more sensitive to sea-level rise than to 

reductions in streamflow, as can be seen in the difference in 

the slope along the incremental rise in sea level axis versus the 

percent reduction in streamflow axis. 

Figure 68. Three-dimensional response surface showing the percent of days the simulated

specific conductance exceeded the threshold of 1,000 microsiemens per centimeter with

reductions in streamflow and incremental rises in sea level at the Pawleys Island gage

(station 02110815), South Carolina, 1995–2009.

3.0

2.5

2.0

1.5

1.0

0.5

0

–25

–20

–15

–10

–5

0

Incremental rise in sea level, in feet

Percentage reduction in streamflow

34

32

30

28

26

24

22

20

18

16

14

12

10

8

6

4

2

0

Percentage of days specific-conductance threshold is exceeded

32–34

30–32

28–30

26–28

24–26

22–24

20–22

18–20

16–18

14–16

12–14

10–12

8–10

6–8

4–6

Number of days

EXPLANATION

Figure 68. Three-dimensional response surface showing the percentage of days the simulated specific 

conductance exceeded the threshold of 1,000 microsiemens per centimeter with reductions in streamflow 

and incremental rises in sea level at the Pawleys Island gage (station 021108125), South Carolina, 1995–2009.
80 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

Climate-Change Scenarios Using 

the M2M-2 DSS

Many of the climate-change scenarios that were simulated with PRISM-2 for the Waccamaw River and AIW study 

area can be simulated with the M2M-2 for the lower Savannah 

River study area. However, there are several differences 

between the two DSSs. The output from the M2M-2 is 

expressed in salinity units (practical salinity units) rather than 

specific conductance, as with the PRSIM-2. A salinity value of 

0.5 psu is approximately equal to 1,000 μS/cm. The M2M-2 

DSS does not have the 3D longitudinal plotting utility but does 

have a two-dimensional (2D) visualization utility to display 

a plan view of the pore-water salinity in the marsh. The 

simulation period for the M2M-2 is 11 years, April 1994 to 

May 2005. Because a watershed model to simulate streamflow 

at the Savannah River at Clyo, Ga., (the streamflow input gage 

for the M2M-2) has not been developed for use with precipitation and temperature inputs, determining streamflow under 

future climate projections, based on downscaled precipitation 

and temperature data, was not feasible. 

Two municipal water intakes are located in the lower 

Savannah River, approximately 3 miles and 15 miles upstream 

from Interstate 95. The scenarios presented in this section 

focus on the two gaging stations that are proximal to the 

salt-sensitive resources—the Little Back River (station 

021989784; fig. 11A) in the Savannah National Wildlife 

Refuge and the Interstate 95 gage (02198840; fig. 11A), the 

streamgaging station closest to the municipal water intakes.

There is great variability in the magnitude, frequency, 

and duration of salinity-intrusion events in the upper reaches 

of the lower Savannah River estuary, as illustrated by the time 

series (April 1994–April 2005) for three gaging stations in the 

upper reaches of the river (fig. 69). The salinity was highest at 

the Houlihan Bridge gage (station 02198920, fig. 11A), often 

in the 4 to 8 psu range, and high salinity intrusions occurred in 

the 10 to 14 psu range. At the Interstate 95 gage (02198840), 

6.2 miles upstream from the Houlihan Bridge gage, the 

salinity rarely was above the 0.5 psu level. At the Lucknow 

Canal gage (station 021989784; fig. 11A) on the Little Back 

River, approximately 2.2 mile to the east, salinities were often 

less than 0.5 psu; some salinities were as high as 2 and 3 psu.

Response of Rivers to Sea-Level Rise: 

M2M-2 DSS

A total of 42 scenarios encompassing reductions of 

streamflow in 5-percent increments, ranging from 0 to 

25 percent, were evaluated in combination with increases 

in sea level in 0.5 ft increments, ranging from 0 to 3 ft. 

Figure 69. Measured daily salinity at the Houlihan Bridge gage (station 02198920), Lucknow Canal gage (station 021989784),

and the Interstate 95 gage (station 02198840) in the vicinity of the Savannah National Wildlife Refuge, South Carolina and

Georgia, April 1994–April 2005.

Houlihan Bridge

Lucknow Canal

Interstate 95 gage

EXPLANATION

April

1995

April

1994

April

1996

April

1997

April

1998

April

1999

April

2000

April

2001

April

2002

April

2003

April

2004

April

2005

Salinity, in practical salinity units

16

14

12

10

8

6

4

2

0

Figure 69. Measured daily salinity at the Houlihan Bridge gage (station 02198920), Lucknow Canal gage (station 021989784),and 

the Interstate 95 gage (station 02198840) in the vicinity of the Savannah National Wildlife Refuge, South Carolina and Georgia,

April 1994–April 2005.
Climate-Change Scenarios Using the M2M-2 DSS 81

Simulations were run for the three upstream gaging stations in 

the lower Savannah River study area. The simulated salinities 

for the three gaging stations in response to a 1- and 2-ft rise 

in sea level are shown in figure 70. For the Interstate 95 gage 

and Lucknow Canal gage (fig. 70A and 70B), the number of 

salinity-intrusion events for a 1-ft sea-level rise was much 

greater than the number of events for historical conditions 

(baseline). Note that ANN models limit the extrapolation of 

the salinity response to a little over 6 psu at the Interstate 95 

gage (fig. 70A). Likewise, the extrapolation of the salinity 

Figure 70. Simulated historical salinity and simulated salinities for 1-foot and 2-foot

sea-level rises at (A) Interstate 95 gage (station 02198840), (B) Lucknow Canal gage

(station 021989784)and (C) Holihan Bridge gage (station 02198920)in the icinitof

Sept. 28, 1994

Apr. 1, 1994

Mar. 27, 1995

Sept. 23, 1995

Mar. 21, 1996

Sept. 17, 1996

Mar. 16, 1997

Sept. 12, 1997

Mar. 11, 1998

Sept. 7, 1998

Mar. 6, 1999

Sept. 2, 1999

Feb. 29, 2000

Aug. 27, 2000

Feb. 23, 2001

Aug. 22, 2001

Feb. 18, 2002

Aug. 17, 2002

Feb. 13, 2003

Aug. 12, 2003

Feb. 8, 2004

Aug. 6, 2004

Apr. 1, 2005

7

6

5

4

3

2

1

0

2-foot sea-level rise

1-foot sea-level rise

Historical condition

EXPLANATION

Salinity, in practical salinity units

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

16

14

12

10

8

6

4

2

0

A. Interstate 95 gage (02198840)

B. Lucknow gage (021989784)

C. Houlihan Bridge gage (02198920)

Figure 70. Simulated historical 

salinity and simulated salinities for 

1-foot and 2-foot sea-level rises 

at (A) Interstate 95 gage (station 

02198840), (B) Lucknow Canal 

gage (station 021989784), and 

(C) Houlihan Bridge gage (station 

02198920), in the vicinity of the 

Savannah National Wildlife Refuge, 

South Carolina and Georgia, 

April 1994–April 2005.
82 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

response at the Lucknow Canal gage is limited to a little over 

4 psu (fig. 70B). A 2-ft rise in sea level further increases the 

number and duration of the salinity-intrusion events at the 

two gaging stations and would have the effect of shifting 

the tidal marsh types from tidal freshwater to oligohaline 

(fig. 31). Salinity increased with 1- and 2-ft sea-level rises at 

the Houlihan Bridge gage (fig. 70C), but the increases in the 

frequency, magnitude, and duration of the salinity intrusions 

are not as great as at the other two gaging stations.

The time-series data also can be displayed as frequency 

curves with salinity values on the y-axis and the percentage 

ranking of the data on the x-axis (fig. 71). These plots clearly 

1-foot sea-level rise

2-foot sea-level rise

Historical conditions (baseline)

7

6

5

4

3

2

1

0

A. Interstate 95 gage (station 02198840)

Figure 71. Frequency curves of simulated salinity conditions for historical conditions

(baseline) and a 1-foot and 2-foot sea level rises at (A) the Interstate 95 gage

(station 02198840) (B) Lucknow Canal gage (station 021989784)and (C) Houlihan Bridge

0 10 20 30 40 50 60 70 80 90 100

Percentage rank of the data

Salinity, in practical salinity units

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

16

14

12

10

8

6

4

2

0

B. Lucknow Canal gage (station 021989784)

C. Houlihan Bridge gage (station 02198920)

EXPLANATION

1-foot sea-level rise

2-foot sea-level rise

Historical conditions (baseline)

EXPLANATION

1-foot sea-level rise

2-foot sea-level rise

Historical conditions (baseline)

EXPLANATION

Figure 71. Frequency curves of 

simulated salinity conditions for 

historical conditions (baseline) and 

a 1-foot and 2-foot sea level rise at 

(A) the Interstate 95 gage (station 

02198840) (B) Lucknow Canal gage 

(station 021989784), and (C) Houlihan 

Bridge gage (station 02198920), in the 

vicinity of Savannah National Wildlife 

Refuge, South Carolina and Georgia, 

April 1994–April 2005.
Climate-Change Scenarios Using the M2M-2 DSS 83

show that the salinity at all three sites increased in response 

to increases in sea level. For example, at the Interstate 95 

gage, under the historical (baseline) conditions, there are 

few occurrences of salinity greater than 2 psu (fig. 71A). A 

2-foot rise in sea level increases the occurrence of salinity to 

greater than 2 psu to approximately 5 percent of the time. At 

the Lucknow Canal gage, under historical (baseline) conditions (no rise in sea level), the salinity was 0.5 psu or less 

90 percent of the time (fig. 71B), and the salinity was greater 

than 0.5 psu 10 percent of the time. A 1-ft rise in sea level 

shifts the frequency interval for the occurrence of events of 

0.5 psu salinity by 10 percent with values greater than 0.5 psu 

occurring 20 percent of the time. A 2-ft rise increases the 

salinity at the site even more, and salinity of 0.5 psu occurred 

40 percent of the time.

Effects of Decreased Streamflow and Sea-Level 

Rise on Simulated Salinity: M2M-2 DSS

As was done with the PRISM-2 DSS for the Waccamaw 

River and AIW study area, historical streamflows for the lower 

Savannah River study area were reduced to provide estimates 

of anticipated changes in streamflow associated with climate 

change. Frequency curves for simulated salinity are shown in 

figure 72 for five scenarios of sea-level rise with and without 

streamflow reduction at the Interstate 95 and Lucknow Canal 

gaging stations. The decrease in streamflow combined with 

the 1-ft rise in sea level increased the salinity at the sites and 

moved the frequency curves closer to the salinity of a 2-ft rise 

in sea level.

Figure 72. Frequency curves of simulated salinity for historical conditions (baseline),

and a 1-foot and 2-foot sea level rises, and a 1-foot sea-level rise in combination

with a 10- and 20- percent reductions in streamflow at (A) the Interstate 95 gage

(station 02198840) and (B) Lucknow Canal gage (station 021989784) in the vicinity of

Savannah National Wildlife Refuge, South Carolina and Georgia, April 1994–April 2005.

Historical conditions (baseline)

1-foot sea-level rise

1-foot sea-level rise and

10-percent flow reduction

1-foot sea-level rise and

20-percent flow reduction

2-foot sea-level rise

EXPLANATION

0

75 80 85 90 95 100

10 20 30 40 50 60 70 80 90 100

Percentage rank of the data

7

6

5

4

3

2

1

0

Salinity, in practical salinity units

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

A. Interstate 95 gage (station 02198840)

B. Lucknow Canal gage (station 021989784)

Historical conditions (baseline)

1-foot sea-level rise

1-foot sea-level rise and

10-percent flow reduction

1-foot sea-level rise and

20-percent flow reduction

2-foot sea-level rise

EXPLANATION

Figure 72. Frequency curves of simulated salinity for historical conditions (baseline), 1-foot 

and 2-foot sea level rises, and a 1-foot sea-level rise in combination with a 10- and 20- percent 

reductions in streamflow at (A) the Interstate 95 gage(station 02198840) and (B) Lucknow Canal 

gage (station 021989784) in the vicinity of Savannah National Wildlife Refuge, South Carolina and 

Georgia, April 1994–April 2005.
84 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

The percentages of time that simulated 

salinity values exceeded a 0.5 psu threshold 

at the Interstate 95 gage for various 

scenarios of sea-level rise and streamflow 

reduction during the 11-year simulation 

period are provided in table 15 and fig. 73. 

The greater effect of sea-level rise on 

salinity, as shown in the 3D response curve 

for the Interstate 95 gage on the Savannah 

River, is similar to that for the Pawleys 

Island gage (fig. 68). The salinity response 

resulting from the percent reduction in 

streamflow became more sensitive to the 

incremental rise in sea level with higher 

sea levels. This can be seen in the increased 

slope of the reduction of streamflow with 

the increase in sea level (fig. 73). The 

number of days that salinity exceeded the 

threshold increased by 4.7 percent for a 25-percent reduction 

in streamflow from the baseline conditions (table 15). For a 

1-ft sea-level rise, the same reduction in streamflow increased 

Table 15. Percentage of time that the salinity threshold of 0.5 practical salinity 

unit is exceeded at Interstate 95 (station 02198840), Georgia, as a result of 

reductions in historical streamflow and sea-level rises.

[psu, practical salinity units; % flow, percent reduction in historical flows; SLR, sea-level rise; 

ft, foot]

% Flow/SLR Percent of days that salinity is greater than 0.5 psu

0 ft 0.5 ft 1 ft 1.5 ft 2 ft 2.5 ft 3 ft

0 0.2 0.5 1.4 3.1 5.9 9.7 13.6

–5 0.3 0.7 1.8 3.9 7.0 11.2 15.4

–10 0.4 0.9 2.3 4.6 8.6 12.5 17.7

–15 0.5 1.4 3.0 5.8 10.0 14.5 20.0

–20 0.6 1.7 3.8 7.1 11.4 16.8 22.0

–25 1.0 2.2 4.7 8.7 13.3 19.3 24.1

the number of days by 7.3 percent, and a 2-ft sea-level rise 

increased the number of days by 7.9 percent relative to the 

simulated historical conditions.

22–24

20–22

18–20

16–18

14–16

12–14

10–12

8–10

6–8

4–6

2–4

0–2

Number of days

EXPLANATION

Figure 73. Three-dimensional response surface showing the percent of days the simulated salinity

exceeded the threshold of 0.5 practical salinity units is exceeded for reductions in streamflow and

incremental sea-level rises at the Interstate 95 gage (station 02198840) in the vicinity of

Savannah National Wildlife Refuge, South Carolina and Georgia, April 1994–April 2005.

–25

–20

–15

–10

–5

0

0

0.5

1.0

1.5

2.0

2.5

3.0

Incremental rise in sea level, in feet

Percentage reduction in streamflow

24

26

22

20

18

16

14

12

10

8

6

4

2

0

Percentage of days simulated salinity exceeded 0.5 practical salinity units

Figure 73. Three-dimensional response surface showing the percentage of days the simulated salinity 

exceeded the threshold of 0.5 practical salinity units is exceeded for reductions in streamflow and incremental 

sea-level rises at the Interstate 95 gage (station 02198840) in the vicinity of Savannah National Wildlife Refuge, 

South Carolina and Georgia, April 1994–April 2005.
Climate-Change Scenarios Using the M2M-2 DSS 85

Effects of Decreased Streamflow and Sea-Level 

Rise on Marsh Pore-Water Salinity: M2M-2 DSS

The M2M-2 DSS was used to evaluate a potential change 

in pore-water salinity with an increase in sea level and a 

reduction in streamflow. The M2M-2 generates an output file 

used for 2D visualization of the pore-water salinity response 

to a user scenario. Because plant communities of tidal marshes 

respond to long-term conditions (months to seasons) and 

not episodic salinity intrusion events of days, the M2M-2 

visualization application computes and displays monthly (or 

greater) averages of salinity conditions for the marsh on the 

basis of simulated conditions at seven marsh pore-water sites 

and river conditions. The scenario of a 1-ft sea-level rise and 

a 20-percent reduction in streamflow was used to demonstrate 

the marsh visualization utility. Simulated salinity at the 

Lucknow Canal gage (021989784) for historical (baseline) 

conditions, 1-ft and 2-ft sea-level rises, and a 1-ft sea-level rise 

in conjunction with a 20-percent reduction in streamflow for 

the summer and fall 1999 are shown in figure 74. The period 

June 1 to December 31, 1999, represents average conditions 

for which the simulated historical salinity was less than 

0.5 psu most of the time and occasionally exceeded 0.5 psu. 

The salinity time series shows the higher salinity values 

occurring on a 28-day tidal cycle and correspond to spring 

tides (fig. 74).

The simulations for summer and fall 1999 at the 

Lucknow Canal gage indicate that the magnitude of salinity 

intrusion would increase with increasing sea level and decreasing streamflow. For example, salinity simulations indicate an 

increase in sea level of 1 and 2 ft would result in an increase 

in the magnitude of the salinity-intrusion event to 2.0 and 

4.0 psu, respectively, in late September 1999. Simulations 

incorporating a 1-ft sea-level rise with a 20-percent reduction 

in streamflow for the same intrusion event indicate that a 

salinity response intermediate to the responses for 1- and 2-ft 

sea-level rises would occur. The rise in sea level and reduction 

in streamflow increased the magnitude of the salinity-intrusion 

Figure 74. Simulated salinity conditions for a historical (baseline), 1-foot and 2-foot sea level rises, and a 1-foot

sea-level rise with 20-percent reduction in streamflow at the Lucknow Canal gage (station 021989784), in the vicinity

of Savannah National Wildlife Refuge, South Carolina and Georgia, June 1–December 31, 1999.

Historical conditions (baseline)

1-foot sea-level rise

1-foot sea-level rise with 20-percent 

reduction in flow

2-foot sea-level rise

EXPLANATION

June July August September

1999

October November December

Salinity, in practical salinity units

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

Figure 74. Simulated salinity conditions for a historical (baseline), a 1-foot and a 2-foot sea level rise, and a 1-foot sealevel rise with 20-percent reduction in streamflow at the Lucknow Canal gage (station 021989784) in the vicinity of Savannah 

National Wildlife Refuge, South Carolina and Georgia, June 1–December 31, 1999.
86 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

events occurring on a 14-day spring tidal cycle (fig. 74). The 

sea-level rise and streamflow reduction scenarios also affect 

the pore-water salinity in the marsh. The simulated pore-water 

salinity for historical conditions (baseline) and for a 1-ft 

sea-level rise and 20-percent reduction in streamflow at the 

B2 marsh site between the Middle and Little Back Rivers 

(fig. 11B) are shown in figure 75. The dynamics of salinity in 

the pore water is dampened in comparison to that of the Little 

Back River gage at Lucknow Canal. Unlike the simulated 

salinity response at the Little Back River gage to the scenarios, 

where the magnitude and duration of the salinity-intrusion 

event sharply increased over a period of days, the pore-water 

salinity in the marsh increased by 0.5 to 0.6 psu, effectively 

doubling the background level.

The 2D marsh Color-Gradient Visualization Program 

was used to display the 1-month average response to the 

climate-change scenario. The difference between the historical 

conditions (baseline) and the scenario 1-ft sea-level rise with 

a 20-percent reduction in streamflow was computed and 

averaged for a month. The 2D Color-Gradient Visualization 

Program interpolates and extrapolates M2M-2 output to fill 

and color a grid of the study area (fig. 76). The M2M-2 DSS 

provides a qualitative view of the large-scale, longitudinal 

USGS marsh network (fig. 11B) and river gaging stations on 

the Savannah River at Interstate 95 gage (02198840, fig. 11A) 

and Back River upstream from the tide gate (near station 

02198977; fig. 11A). For the climate-change scenario 1-ft sealevel rise with a 20-percent reduction in streamflow (fig. 76), 

the greatest effect was in the lower reaches of the marsh and in 

the marsh between the Middle and Little Back Rivers.

Figure 75. Simulated pore-water salinity historical conditions (baseline) and a 1-foot sea-level rise with 20-percent

reduction in streamflow, and the difference between the two at the B2 marsh gage in the Savannah National Wildlife

Refuge, South Carolina, September 19, 1999–December 31, 1999.

Historical condition (baseline)

1-foot sea-level rise with 20-

 percent reduction in flow

Difference

EXPLANATION

19 26 3 10 17 24 31 7 14 21 28 5 12 19 26 31

1999

September October November December

Pore-water salinity, in practical salinity units

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

Figure 75. Simulated pore-water salinity historical conditions (baseline) and a 1-foot sea-level rise with 20-percent 

reduction in streamflow, and the difference between the two at the B2 marsh gage in the Savannah National Wildlife 

Refuge, South Carolina, September 19, 1999–December 31, 1999.
Summary and Conclusions 87

Figure 76. Two-dimensional color-gradient visualization of the tidal marshes in the

vicinity of Savannah National Wildlife Refuge, South Carolina and Georgia, showing

the differences in simulated average salinity of marsh pore water between historical

conditions and a 1-foot sea-level rise with 20-percent reduction in streamflow,

September 19 –October 19, 1999. Darker green color shades indicates greater

differences between the two simulations.

N

0

0 5 KILOMETERS

5 MILES

Figure 76. Two-dimensional color-gradient visualization of the 

tidal marshes in the vicinity of Savannah National Wildlife Refuge, 

South Carolina and Georgia, showing the differences in simulated 

average salinity of marsh pore water between historical conditions 

and a 1-foot sea-level rise with 20-percent reduction in streamflow, 

September 19 –October 19, 1999. Darker green color shades 

indicate greater differences between the two simulations.

Summary and Conclusions

The availability of freshwater in coastal streams is 

frequently affected by salinity intrusion. The balance between 

freshwater and saltwater in coastal streams primarily is governed by the interaction of hydrologic streamflow conditions 

and sea level. Changes in streamflow and sea level, anticipated 

as a result of potential changes in climate, could affect the 

frequency, duration, and magnitude of salinity intrusions in 

coastal areas. Evaluation of the effects of changes in salinity 

is particularly important for coastal areas of the southeastern 

United States because of the large number of municipal 

water-supply intakes in coastal rivers. This study addressed 

salinity dynamics in the vicinity of water-supply intakes in 

the Waccamaw River and the Atlantic Intracoastal Waterway 

(AIW) study area in South Carolina and in the lower Savannah 

River study area along the South Carolina and Georgia border. 

To evaluate the effects of potential climate changes on 

salinity intrusions, anticipated alterations in hydrology and 

sea level need to be simulated separately and in combination. 

Global circulation, watershed, and salinity intrusion models 

need to be integrated to evaluate various climate-change 

scenarios. Salinity in the Waccamaw River and AIW, and 

the Savannah River, is constantly responding to changing 

streamflow and tidal conditions. The location of the saltwaterfreshwater interface is a balance between upstream river 

streamflows and downstream tidal forcing. During periods of 

high streamflow, it is difficult for salinity to intrude upstream, 

and the saltwater-freshwater interface is moved downstream 

toward the ocean. During periods of low streamflow, salinity 
88 Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios

is able to intrude upstream, and the saltwater-freshwater 

interface is moved upstream by tidal forcing, by an increase in 

mean water levels, a change in tidal range, or a combination 

of the three. The goal of this study was to develop effective 

models to predict salinity intrusion in the freshwater parts of 

the Waccamaw River and AIW, and lower Savannah River, 

study areas for a given set of streamflow, water-level, and 

tidal-range conditions. The artificial neural network (ANN) 

models for both study areas were able to simulate the salinity 

dynamics of the system and capture the long-term trends in 

the data with a relatively lower percent model error. For the 

models of the Waccamaw River and AIW, the R2 (coefficient 

of determination) for the daily models ranged from 0.69 to 

0.88, and the percent model error was 8.9 percent or less. For 

the lower Savannah River, the R2 for the models ranged from 

0.57 to 0.88, and the percent model error was 6.1 percent or 

less.

To facilitate the dissemination and use of the salinityintrusion models, the Pee Dee River and Intracoastal Waterway Salinity Intrusion Model Decision Support System 2

(PRISM-2 DSS) for Waccamaw River and AIW, and the 

Model-to-Marsh Decision Support System 2 (M2M-2 DSS) 

for the lower Savannah River, were developed as spreadsheet 

applications that provide predictive models with real-time 

databases for ANN model simulations, graphical user interfaces, and displays of results. These features make the DSSs 

easily distributable and immediately usable by all interested 

stakeholders. The PRISM-2 and M2M-2 DSSs were used to 

simulate potential climate-change scenarios of sea-level rise 

and changes in streamflow to the coast. To simulate the effects 

of sea-level rise, the mean coastal water levels were incremented by 0.5 foot to simulate sea-level rises of up to 3 feet 

in PRISM-2 and M2M-2. Nomographs were generated for six 

sea-level-rise scenario simulations that show the number of 

days the predicted specific conductance (or salinity) values 

exceeded predetermined thresholds for specific conductance or 

salinity for each incremental sea-level rise. For example, daily 

specific conductance greater than 2,000 microsiemens per 

centimeter (μS/cm) historically occurred on almost 200 days 

over the 14-year simulation period at the Pawleys Island gage. 

A 1-foot sea-level rise would double the number of days the 

municipal intake is unavailable to 400 days, and a 2-foot rise 

would increase the unavailability to nearly 2 years (700 days).

Projections of climate change for the 21st century 

by global circulation models (GCMs) indicate changes in 

temperature and precipitation patterns that will affect the 

timing and quantity of streamflow to the coast. Salinity intrusion is a low-flow phenomenon, so to evaluate net reductions 

in streamflows, the historical streamflow in PRISM-2 and 

M2M-2 was reduced by 5, 10, 15, 20, and 25 percent. To 

evaluate the combination of reduced streamflow and sea-level 

rise, the incremental reductions in streamflow were combined 

with sea-level rises of 0, 1, and 2 feet. Similar to the analysis 

of sea-level rise, the number of days the specific conductance 

or salinity threshold is exceeded for each 5-percent incremental decrease in streamflow for 0-, 1-, and 2-foot sea-level 

rises was computed. A 25-percent decrease in the streamflow 

increased the number of days for the three thresholds of 1,000, 

2,000, and 3,000 μS/cm by approximately 45 to 90 percent 

for each of the three sea-level scenarios for the 14-year 

simulation period at the Pawleys Island gage. The combined 

effect of a 2-foot sea-level rise and 25-percent reduction in 

streamflow increased the number of days the thresholds are 

exceeded by 440 to 645 percent. For example, if the threshold 

is 2,000 μS/cm, for the historical 14-year simulation, then the 

threshold is exceeded on 191 days (4 percent of the days). 

If the streamflow is reduced to 75-percent of the historical 

streamflow and there is a 2-foot sea-level rise, then the number 

of days the threshold is exceeded increases to 1,067 days, 

an increase of 559 percent, or 21 percent of the days for the 

14-year simulation. 

The responses of specific conductance to combinations 

of sea-level rise and reduced streamflow at the Pawley’s 

Island gage in the Waccamaw River and AIW study area and 

the Interstate 95 gage in the lower Savannah River study area 

were displayed as a three-dimensional (3D) response surfaces. 

The 3D response surfaces were generated from 42 model 

simulations of incremental rises in sea level (x-axis) and 

incremental reductions in streamflow (y-axis). The simulated 

percentage of days that the daily specific-conductance 

threshold is exceeded is shown on the z-axis. The 3D response 

surface showed that specific conductance is much more 

sensitive to sea-level rise than to reductions in streamflow for 

both study areas. 

The effect of climate-change projections from a global 

circulation model (GCM) on salinity intrusion was evaluated by using PRISM-2. The precipitation and temperature 

predictions from the GCM were used as inputs to the 

watershed model of the Yadkin-Pee Dee basin developed by 

the University of South Carolina [Hydrologic Simulation 

Program-Fortran (HSPF)]. The HSPF model simulated the 

streamflow for the Waccamaw, Little Pee Dee, Pee Dee, 

Lynches, and Black Rivers, and these streamflow data were 

used as user-defined flow inputs to PRISM-2. The downscaled 

temperature and precipitation data for the ECHO CGM for the 

A2 carbon emission climate scenario was used as input to the 

HSPF model, and streamflows for the Waccamaw, Little Pee 

Dee, Pee Dee, Lynches, and Black Rivers were simulated for 

1980 to 2010 and 2040 to 2070. The ECHO GCM is a hybrid 

coupled ocean-atmosphere GCM of the Hamburg version of 

the European Center atmospheric GCM (ECHAM) and the 

Hamburg Primitive Equation ocean GCM (HOPE).To evaluate 

seasonal shifts in the projected streamflows, daily duration 

hydrographs were plotted using the simulated ECHO-HSPF 

streamflows for 1980 to 2010 and 2040 to 2070 for the total 

streamflow to the coast. Although the historical and projected 

streamflows appear similar there are important differences, 

especially with respect to potential changes in salinity 

intrusion. Salinity intrusion along the coast is a low-flow 

phenomenon; therefore, changes in low flow, as represented 

by the 10th percentile streamflows, are the most critical for 

potential salinity intrusion.
Selected References 89

Results of the specific-conductance simulation with 

projected streamflow conditions shows a change in the 

occurrence of intrusion events greater than 1,000 μS/cm at 

the Pawleys Island gage. The number of intrusion events 

increase in the spring and decrease in the summer, and 

most of the events occur in the fall. Under potential climate 

change, a salinity-intrusion event will still be initiated by the 

convergence of conditions of low flow and high coastal water 

level. The decrease in the number of summer intrusion events 

corresponds to an increase in the 10th percentile projected 

streamflows. Likewise, the increase in the number of fall 

intrusion events corresponds to the slight decrease in the low 

flows in November and December. The decrease in the winter 

10th-percentile streamflow, although significant, does not 

decrease streamflow to the level that would cause a salinityintrusion event.

Selected References 

Abarbanel, H.D.I., 1996, Analysis of observed chaotic data: 

New York, Springer-Verlag, Inc., p. 4–12.

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Prepared by:

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3916 Sunset Ridge Road

Raleigh, NC 27607

Manuscript approved for publication on February 23, 2013

Editorial review by Ruth Larkins

Illustrations prepared by Jeff Corbett

Layout by Kay Naugle

Cover design by Ray Douglas

For additional information regarding this publication, contact:

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phone: 803–750–6100

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Or visit the South Carolina Water Science Center Web site at:

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Conrads and others—Simulation of Salinity Intrusion Along the Georgia and South Carolina Coasts Using Climate-Change Scenarios—Scientific Investigations Report 2013–5036