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2016 The Use of Stable Isotopes Deuterium and Oxygen-18 as Natural Hydrologic Tracers in a Florida Springshed Erica Rau

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COLLEGE OF ARTS AND SCIENCES

THE USE OF STABLE ISOTOPES DEUTERIUM AND OXYGEN-18

AS NATURAL HYDROLOGIC TRACERS IN A FLORIDA SPRINGSHED

By

ERICA L. RAU

A Thesis submitted to the Department of Earth, Ocean and Atmospheric Science in partial fulfillment of the requirements for the degree of Master of Science

2016 Erica L. Rau defended this thesis on March 21, 2016. The members of the supervisory committee were:

Yang Wang Professor Co-Directing Thesis

Jeffrey P. Chanton Professor Co-Directing Thesis

William M. Landing Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the thesis has been approved in accordance with university requirements.

ii ACKNOWLEDGMENTS

I would like to acknowledge my great appreciation for the guidance and instruction from my thesis committee, Dr. Yang Wang, Dr. Jeffery Chanton, and Dr. William Landing, and would like to thank all my professors at Florida State University for their dedication and inspiration. Special thanks is in order to Glynnis Burgna at the Florida Agricultural and Mechanical University Wetlands Ecology Laboratory for accommodating and directing my use of the Los Gatos laser isotope analyzer and to Dr. Wang for instruction in and use of the mass spectrometer at the National High Magnetic Field Laboratory at FSU. Brian Katz, Hal Davis, Christy Crandall, Trey Grubbs, Rich Marella, and Dale Griffin are deeply appreciated for all the conversations about hydrology and for all their research that was carried out at the United States Geological Survey Tallahassee office. I would also like to thank Ed Chelette from the Florida Geological Survey for demonstrating their water sampling procedures at Wakulla Spring and for the wealth of information made available to the public through online access to their publications. Thanks to Gary Maddox at the Florida Department of Environmental Protection for permission to use the sampling line installed at the spring. I would like to express my gratitude to Wakulla Springs State Park biologist Scott Savery and park staff for their ongoing interest and cooperation with research done on the springs, with extra special thanks for all the break-of-day boat rides they gave me to the spring vent to collect water samples. I would also like to express my gratefulness for Robert Knight’s Springs Ecology course at the University of Florida and my great esteem for Jim Stevenson’s knowledge of the Wakulla watershed and ongoing contributions to public awareness of Florida’s environmental issues.

iii TABLE OF CONTENTS

List of Tables ...... v List of Figures ...... vi Abstract ...... viii

1. INTRODUCTION ...... 1

Purpose and Scope ...... 1 Study Area ...... 2 Previous Studies in the Wakulla Springshed ...... 27 Isotope Chemistry in Hydrology ...... 33

2. METHODS ...... 50

Field Methods ...... 50 Laboratory Methods ...... 62

3. RESULTS ...... 70

Summary ...... 70 North and Central Florida Springs during Base Flow and Non-base Flow Conditions ...... 70 Wakulla Spring Samples ...... 85

4. DISCUSSION AND CONCLUSIONS ...... 98

Sampling Method Notes ...... 98 Variability of Isotope Composition of Florida Springs ...... 99 Use of Deuterium and Oxygen-18 as Hydrologic Tracers ...... 103 Hydrograph Separation: The Integration of Streamflow and Isotope Data ...... 107 Conclusions ...... …113

APPENDICES ...... 116

A. WATER QUALITY DATA ...... 116 B. BACKGROUND INFORMATION ...... 119 C. ISOTOPE ANALYSES ...... 121

References ...... 124

Biographical Sketch ...... 145

iv LIST OF TABLES

Table 1: Classification of Springs ...... 3

Table 2: Results of Isotope Analyses of Florida Spring Samples ...... 73

Table 3: Results of Isotope Analyses of Wakulla Spring Samples ...... 96

v LIST OF FIGURES

Figure 1: Map of Spring Locations Sampled in North and Central Florida ...... 4

Figure 2: Map of the Wakulla Springshed ...... 6

Figure 3: Sinkhole Map ...... 14

Figure 4: Sellard’s Sketch of Sinking Streams ...... 15

Figure 5: Ames Sink Taking in Water ...... 16

Figure 6: Stage-discharge Relationship for Wakulla River near Crawfordville, FL(2005–2016).20

Figure 7: Stage-discharge Relationship for Lost Creek at Arran, FL(1998–2016) ...... 20

Figure 8: Land Use in the Wakulla/St. Marks Watershed ...... 24

Figure 9: Population Growth 1900–2010 ...... 24

Figure 10: Measurements of Streamflow for Wakulla Springs and River, 1907–2016 ...... 32

Figure 11: Measurements of Streamflow for Ichetucknee River, 1898–2016 ...... 33

Figure 12: Example of Water-velocity Data for Wakulla River ...... 54

Figure 13: Preparation of Water Samples for Laser-based Isotope Analysis ...... 65

Figure 14: Wakulla Spring Samples to be Analyzed by Isotope Ratio Mass Spectrometry ...... 68

Figure 15: Linear Regression of Isotope Values for Springs during Drought Conditions ...... 71

Figure 16: Isotope Composition of Springs during Base Flow Conditions ...... 74

Figure 17: Isotope Composition of Springs during Non-base Flow Conditions ...... 74

Figure 18: Palmer Drought Severity Index for Wakulla and Leon Counties ...... 83

Figure 19: Comparison of Springs’ Isotope Values from Dry to Wet Conditions ...... 84

Figure 20: Hydrograph for the 2012 Water Year, Wakulla River near Crawfordville, FL ...... 86

Figure 21: Hydrograph for the 2013 Water Year, Wakulla River near Crawfordville, FL ...... 86

Figure 22: Specific Conductance at Wakulla Spring for the 2013 Atlantic Hurricane Season .....87

vi Figure 23: Precipitation Record for the 2012 Hurricane Season (NWS) ...... 88

Figure 24: Time Series for Deuterium in Wakulla Spring Samples 2012 ...... 89

Figure 25: Time Series for Oxygen-18 in Wakulla Spring Samples 2012 ...... 90

Figure 26: Precipitation Record for the 2013 Hurricane Season ...... 92

Figure 27: Isotope Analysis of Precipitation Samples for the 2013 Hurricane Season ...... 93

Figure 28: 2013 Hurricane Season Precipitation: Time Series for Deuterium ...... 94

Figure 29: 2013 Hurricane Season Precipitation: Time Series for Oxygen-18 ...... 94

Figure 30: Time Series for Oxygen-18 in Wakulla Spring Samples 2013 ...... 95

Figure 31: Isotopic Composition of Florida Springs by Geographical Region ...... 100

Figure 32: Determination of Transit Time using Tropical Storm Rainfall as a Natural Tracer ..104

Figure 33: Plot of Wakulla Spring Samples 2012 ...... 109

Figure 34: Plot of Wakulla Spring Samples 2013 ...... 109

Figure 35: Theoretical Hydrograph Separation for Wakulla River for Flood Peak in 2012 ...... 112

Figure 36: Theoretical Hydrograph Separation for Wakulla River for Flood Peak in 2013 ...... 113

vii ABSTRACT

To determine if the distinct deuterium (D) and oxygen-18 (18O) signature of precipitation from a tropical storm or hurricane could be used as a natural tracer in a springshed, isotope analyses of water samples from Wakulla Spring in north Florida were conducted over the course of two Atlantic hurricane seasons. Water samples were collected between Feb. 10, 2012 and Aug. 27, 2012 and between March 19 and Oct. 21, 2013. Additionally, water samples and water quality data from a total of 20 springs in north and central Florida were collected between Jan. 14 and Feb. 18, 2012 during a period of prolonged drought; the springs were sampled again between Sept. 20 and Nov. 9, 2012 after rains in the summer and fall increased groundwater levels. The δD and δ18O values of the samples from the 21 springs, including Wakulla Spring, showed that the springs had much more variability in isotope composition during non-base flow conditions than they had during base flow conditions. Comparison between the two sets of samples provided a range in isotope values for springs fed by the Upper Floridan aquifer (UFA). The 2012 hurricane season had one major storm, Tropical Storm Debby, from June 23 to 27, which brought over 500 mm (20 in) of rain to the Wakulla Springs study area. A clear signal of the tropical storm was observed in the Wakulla Spring water samples, as isotopically light rain recharged the aquifer and emerged at the spring. Minimum δD (of −30‰) and δ18O (of −5.1‰) values on July 4 to 5 indicated a mean transit time of nine days from the heavy rainfall that occurred on June 25 and 26. The average isotope values during base flow prior to the storm were −17‰ for δD and −3.3‰ for δ18O. The transit time was similar to travel times found by dye- trace studies of the sinking streams in the springshed. The exact isotope composition for Tropical Storm Debby was not known, so there were not enough data to apply isotope-based hydrograph separation to the streamflow record for Wakulla River. The maximum measured streamflow was documented at 2,600 cfs by an acoustic Doppler current profiler measurement on June 26, 2012. During the 2013 hurricane season, rainfall was recorded and collected for isotope analysis. No major storms reached the study area during the 2013 Atlantic hurricane season, except for a weakened Tropical Storm Andrea, which brought a small amount of rain on June 6 (2.27 in, 58 mm). The precipitation had very negative isotope values (δD = −109‰ and δ18O = −14.7‰). A minimum δ18O value of −4.1‰ was seen in Wakulla Spring samples 29 and 33 days later, but it was not clear if these values could be attributed to the very small amount of isotopically light

viii precipitation, since precipitation samples from a few other intense summer storms during the month had δ18O values that were slightly more negative than −4.1‰. The use of tropical storm precipitation was shown to be an effective and simple method for studying the hydrology of a springshed with the potential for a very light isotope composition of rainfall distributed over a large land area and a very distinct signal in springflow. The drawbacks are that the opportunities to apply the method are limited by the unpredictable occurrence of tropical storms and hurricanes and that a two-component mixing model for hydrograph separation may not provide enough information for watersheds with complex hydrology. The use of tropical storm or hurricane precipitation as a natural tracer in a springshed would work well in ongoing studies of springs, where the information it provides could be added to geochemical tracer data and isotope data for other components of aquifer storage.

ix CHAPTER 1

INTRODUCTION

Purpose and Scope

Stable isotopes have been a prolific tool for investigating hydrologic systems such as river catchments and artesian springs. The hydrology of springs is an important area of research due to the insights provided into freshwater aquifers, which supply drinking water to large populations (Marella, 2008), unique ecological services (Odum, 1957), and even economic value through tourism (Bonn & Bell, 2003). Springs are of special interest not only because of their unique characteristics but as an expression of the water quality and aquifer properties of their entire springsheds.

At mid- to high latitudes, there is often a seasonal pattern seen in the deuterium (2H or D) and oxygen-18 (18O) values of rainfall (Dansgaard, 1964). The values during the summer months are more positive due to the warmer temperatures at which condensaton takes place and the values during the winter months are more negative than the average values for the year. These seasonal patterns have been exploited as a way to infer transit times for precipitation to make its way to rivers and springs, though the small range of isotope values for precipitation can cause limitations. Frederickson and Criss (1999), using seasonal precipitation data with a greater-than- usual range of isotope values due to an El Niño phase, found that the 10‰ amplitude of δ18O values in precipitation translated to a 3‰ amplitude in isotope values for river water but only a 1‰ amplitude in spring water samples from karst springs in southern Missouri. Especially in complex watersheds, the signal from small differences in the isotope composition of rain water can quickly be lost.

At low latitudes and in regions that experience summer monsoons, the seasonal patterns in the isotope composition of rain can show the opposite pattern to the precipitation at mid- to high latitudes. More negative isotope values of precipitation can occur in the summer due to the “amount effect,” as large storm systems become progressively lighter because water with a heavier isotope composition condenses first and is preferentially removed (Dansgaard, 1964;

1 Rozanski, Araguás-Araguás, & Gonfiantini, 1993). Extremely negative isotope values of precipitation due to the amount effect can also be found in rain from tropical storms and hurricanes (Gedzelman et al., 2003). When Dansgaard first described the amount effect, along with other observed patterns of “Stable Isotopes in Precipitation” in 1964, he rightly concluded that naturally occurring variations in the isotopic composition of precipitation would be of great use in hydrologic studies. The last sentence of his paper states, “the natural labeling of water occurs in a much larger scale than in any hydrological experiment with artificially induced tracers, e.g. a heavy thunderstorm may release enormous amounts of isotopically light water over a large area” (Dansgaard, 1964, p.467). Though many hydrologic studies have used isotope data from precipitation to gain insights into watersheds of interest, the use of precipitation from tropical storms and hurricanes has not been explored as a method of supplying natural tracers to a watershed.

The purpose of this data collection and analysis is to determine if the distinct stable oxygen and hydrogen isotope composition of precipitation from tropical storms or hurricanes can be used as a practical and effective hydrological tracer in a springshed. The main objectives are as follows: (1) to evaluate the normal variability of δD and δ18O compositions of springs in north and central Florida by sampling during prolonged drought conditions and to compare the values with the isotope composition of samples taken later during non-base flow conditions; (2) to regularly sample water from Wakulla Spring to see how the isotope composition is changed by recharge from large storms supplying rain with a distinct isotopic signature; (3) to determine if the tropical storm or hurricane signal is clear enough to be used to calculate the mean transit time of the isotopically light rainfall becoming outflow at the spring; and (4) to collect precipitation samples for isotope analysis to use in a two-component mixing model along with isotope data of spring water and springflow records to gain insight into aquifer characteristics.

Study Area

Florida has many large springs, thirty-seven of which fall into the category of “first magnitude”; these are defined as having average springflow (discharge) greater than 2.83 m3/s (cubic meters per second), 100 cfs (cubic feet per second), or 64.6 mgd (million gallons per day)

2 (Copeland, 2003; Harrington & Wang, 2008). The system of classification, which groups springs into first through eighth magnitudes, as determined by discharge, was proposed by U.S. Geological Survey (USGS) hydrologist O. E. Meinzer in 1923, alongside a metric-system-based alternative in which a first-magnitude spring would discharge greater than 10 m3/s (Table 1). In the USGS Water-Supply Paper “Large Springs in the United States,” Meinzer (1927) adopted his English-unit-based “classification suggested for practical use in the United States,” which has been in common use ever since, though the units are often converted into metric units as m3/s. The placement of a spring into a category is usually based on the median of springflow measurements, though sometimes the historic category is used if springflow has decreased over time or if the distribution of measurements is such that the median value would not be representative of the average springflow (Scott et al., 2004).

Table 1: Classification of Springs. Based on Meinzer (1923, 1927) in cubic feet per second (cfs), gallons per minute (gal/m), pints per minute (p/m), cubic meters per second (m3/s), liters per second (L/s), and liters per minute (L/m). Magnitude Average Discharge 1st > 100 cfs > 2.83 m3/s 2nd 10–100 cfs 0.283–2.83 m3/s 3rd 1–10 cfs 283 L/s–0.283 m3/s 4th 100 gal/m–1 cfs 2.83–283 L/s 5th 10–100 gal/m 0.283–2.83 L/s 6th 1–10 gal/m 3.79 L/m–0.283 L/s 7th 1 p/m–1 gal/m 0.379–3.79 L/m 8th < 1 p/m < 0.379 L/m

Springs in north and central Florida were sampled during base flow and then again during non-base flow (Fig. 1). They were first-, second-, and third-magnitude springs, and were all connected to the Upper Floridan aquifer (UFA). Six of the springs were along the Ichetucknee River, three were in the Florida panhandle, four were in the Big Bend region, five were in central Florida (three of which were in the Ocala National Forest), and the remainder were in north- central Florida, with several along the Santa Fe and Suwannee Rivers. Detailed descriptions of the springs and their water chemistry have been compiled by the Florida Geological Survey (FGS) (Ferguson, Lingham, Love, & Vernon, 1947; Rosenau, Faulkner, Hendry, & Hull, 1977; Scott et al., 2004).

3

Figure 1: Map of Spring Locations Sampled in North and Central Florida.

Wakulla Spring (Latitude N 30° 14’ 11”, Longitude W 84° 18’ 15”) is located in the Big Bend area of Florida about 22 km (14 mi) southeast of the capital city, Tallahassee. It is a first- magnitude spring and is one of the largest freshwater springs in Florida, with a median flow of 349 cfs or 9.9 m3/s (based on measurements made from 1907 to 2010) (USGS Water Data). The large spring pool is between 4 and 5 acres in surface area. Just downstream of the pool, a small amount of inflow from Sally Ward Spring gives the spring its more commonly used plural name, “Wakulla Springs.” Inflow from McBride Slough, which also has some small springs along it, adds to the principle springflow to form the start of the Wakulla River. Sally Ward Spring, located about 1 km (0.6 mi) to the northwest of Wakulla Spring, is a second-magnitude spring based on an FGS measurement in the fall of 2005 of 12.5 cfs or 0.35 m3/s (Barrios, 2006; Harrington & Wang, 2008). The Wakulla River flows roughly southeast for 13.3 km (8.25 mi) to

4 its confluence with the St. Marks River, which continues another 5.8 km (3.6 mi) to Apalachee Bay in the Gulf of Mexico.

The watershed has been delineated in slightly different ways depending on which potentiometric maps are used and which sub-basins are included. Usually it is reported as the Wakulla/St. Marks watershed at about 3,030 km2 (1,170 mi2) (Brooks, Thorpe, & Bartel, 2009). Now that connections with areas west of the spring are becoming more apparent, it has also been reported as the Wakulla/Spring Creek watershed at 3,000 km2 (1,150 mi2) (Davis & Katz, 2007). The Wakulla springshed encompasses both urban areas, such as Tallahassee and Crawfordville, and rural and forested areas, such as the Apalachicola National Forest. The Cody Scarp (escarpment) is the relic shoreline which runs roughly east–west just south of Tallahassee (Puri & Vernon, 1964). It marks a rough boundary between areas where the aquifer is confined or semi-confined, overlain with low-permeability sediments, and areas of unconfined aquifer, where overlying sand and high-permeability sediments do not act as a protective layer between the surface water and groundwater.

The Wakulla watershed can be further divided into three different physiographic regions: the southern Woodville Karst Plain Region, which is part of a larger section of the state where the aquifer is mostly unconfined, the Lakes Region in the middle and the Upper Watershed or Apalachee Highlands north of the Ochlockonee River (Fig. 2). The Woodville Karst Plain Region is distinguished by its many sinkholes, swallets and other exposed karst features (Hendry & Sproul, 1966). The area north of the Cody Scarp and east of the Ochlockonee River has several large lakes and numerous smaller lakes and fits the description of the “upland region of the peninsula,” referred to as the “Lake Region” by the USGS geologists G. Matson and S. Sanford in 1913. In 1917, State Geologist E. H. Sellards described this area as “the lake region belt.” The Upper Watershed is located west and north of the Ochlockonee River, encompasses the region known as the Tallahassee Red Hills and extends into southern Georgia. It could also be called the Apalachee Highlands, which is a part of the greater Northern Highlands as defined by White in 1970.

5

Figure 2: Map of the Wakulla Springshed. The green line is the approximate location of the Cody Scarp (Puri & Vernon, 1964); the tan lines are from the 2010 USGS potentiometric map of Florida (Kinnaman & Dixon, 2011); the black line is the approximate springshed. The sub- regions and springshed delieations are based on data from Northwest Florida Water Management District (NWFWMD) (Barrios, 2006; Florida Springs Institute, 2014).

6 Climate The climate is subtropical and humid, with an annual average temperature of 21.4 °C (70.6 °F) based on temperature records from 1895 to 2013 (Florida Climate Center). Annual precipitation for the study area is 150 cm (58.9 in) based on the average for 1886 to 2014 (excluding yearly means for 1888 and 2004, which had partial data) at the National Weather Service (NWS) station located at the Tallahassee Regional Airport (Site ID 08-8758; Latitude, N 30° 23’ 35’’, Longitude, W 84° 21’ 12’’). The minimum yearly value was 79 cm (31 in) in 1954, and the maximum was 265 cm (104.2 in) in 1964, when an active hurricane season brought a series of storms including Hurricanes Cleo and Dora (NWS). The amount of precipitation is important to know in order to calculate a water balance equation for the watershed. Another component of determining how much water reaches a spring is an estimate of the amount of the total precipitation lost back to the atmosphere through evapotranspiration (ET).

ET refers to the loss of water through evaporation and, by plants, through transpiration. It represents precipitation that does not contribute to the net total recharge to groundwater. ET values for Florida are higher than they are for more northern areas of the United States because Florida receives greater net radiation from the sun. Values of potential ET are calculated from records of net solar radiation, land surface temperature, humidity, and wind speed, and they sometimes include factors for land cover and vegetation. The amount of precipitation that is returned to the atmosphere due to ET for north Florida has been estimated to be between 81 and 90 cm/yr (32 and 35 in/yr) (based on data from 1971 to 2000) or 50 and 60% of the total (Sanford & Selnick, 2013). Estimates for the Tallahassee area can be as high as 107 cm/yr (42 in/yr) (Davis, Katz, & Griffin, 2010). Transpiration rates can be different for forested and agricultural lands and for different types of trees and plants, so land use or crop changes can alter ET values. The amount of precipitation intercepted by forest canopy or other vegetation is called throughfall and can also increase evaporation amounts. The presence of lakes, wetlands, and rivers in a watershed increases the amount of rainfall that escapes through evaporation. Mean annual lake evaporation values can also be used in calculations of potential ET. The Wakulla springshed contains many surface water features such as lakes, rivers, sloughs, and swamps, which delay the rainfall from reaching the aquifer. The lakes act as long-term storage areas for the precipitation that they receive, and the rivers and sinking streams create short-term storage.

7 Low-lying areas during heavy or prolonged rainfall can receive water faster than the water can infiltrate into the soil; in which case, the water ponds, which increases the amount of evaporation that takes place.

Water Balance Equation The equation to describe a water budget for a watershed can be written as follows:

+ ( + + ) = or = + + ∆

Where P is precipitation,� ���� Q− is�� streamflow,��� �� ET �is evapotranspiration,� � and� S�� is storage.� Sometimes, extra groundwater componenets are added to the equation, such as GWout , the amount of groundwater removed from the aquifer by pumping, and GWin, the amount returned to the same watershed after it was pumped for irrigation, public supply, or other uses. Average streamflow (Q) for the Wakulla River is 14.2 m3/s or 502 cfs, a total of 4.478 x 108 m3/yr (based on measurements nos. 1 to 400 for station no. 02327022) (USGS Water Data). That is the equivalent of 15 cm/yr of precipitation for the 3,000 km2 watershed. Yearly precipitation of 150 cm over an area of 3,000 km2 is equal to 4.5 x 109 m3/yr—an order of magnitude greater than the Wakulla springflow, assuming that long-term storage is minimal. The average streamflow for the St. Marks River (station no. 02326900), which is included in the greater watershed area, is 19.6 m3/s or 693 cfs, a total of 6.18 x 108 m3/yr (based on records from 1957 to 2014) (USGS Water Data) or the equivalent of 20.5 cm/yr over the watershed. The combined streamflows account for a recharge amount of 35.5 cm of the annual precipitation, which compares well with the rates cited in the literature. The recharge rates for the study area north of the Cody Scarp have been reported to be between 2.5 and 20 cm/yr, depending on the permeability of the overlying sediments, and 46 cm/yr below the Cody Scarp (Davis & Katz, 2007). The amount of precipitation lost through ET should be between 107 cm/yr (Davis et al., 2010) and 82.5 cm/yr (based on 55% of the yearly rainfall for Tallahassee), which compares well with the ET estimates for north Florida of between 81 and 90 cm/yr (Sanford & Selnick, 2013). Annual precipitation minus the high and low estimates for ET leaves between 7.5 and 32 cm/yr of rain to be accounted for in other ways, such as losses from groundwater pumping that are not returned to the aquifer.

8 Topography/Geology Land surface elevations in the Wakulla watershed range from about 2 m (6.5 ft) at the spring, to 10.5 m (35 ft) in the northern part of the Woodville Karst Plain, to 75 m (250 ft) in the Red Hills area of northern Leon County and southern Georgia (Rupert, 1988). The Floridan aquifer, which underlies the entire state, is formed of tertiary limestones and dolomites (Miller, 1986). The thickness of the layers of carbonate rock in north and central Florida is between 122 and 671 m (400 and 2,200 ft) (Miller, 1990). In the Wakulla/St. Marks watershed, the limestone stratigraphy shows the deepest layers to be the Avon Park limestone, formed during the Lower Eocene, overlain with Upper Eocene Ocala limestone from depths of about 120 to 180 m (400 to 600 ft) below the land surface (Dall & Harris, 1892; Miller, 1986). Above the deepest layers lie Oligocene-age Suwannee limestone, where most of the conduits are located (Scott, 1988). The youngest limestone layer is made up of the Miocene-age Chattahoochee/St. Marks limestone, which also has extensive dissolution features (Pratt et al., 1996). Most wells are in these two top layers (Pratt et al., 1996).

In the confined and semi-confined parts of the aquifer, there are layers made up of clay- rich mixtures with silt and sand in varying proportions; these make up the Miocene-age Hawthorn Formation (Dall & Harris, 1892; Matson & Sanford, 1913; Scott, 1988). The Hawthorn Formation can be overlain in some places by younger sediments, such as the late- Miocene Tamiami Formation (north-northeast of Tallahassee), Pliocene-age clays and sands, such as the Jackson Bluff Formation (along the Ochlockonee River), the Miccosukee/Citronelle Formation (characterized by red sands), and undifferentiated Pleistocene surficial deposits (Matson & Sanford, 1913; Scott, 1988; Stringfield, 1966). In the Woodville Karst Plain, which lies between the Gulf of Mexico and the Cody Scarp, the low-permeability Hawthorn Formation sediments were eroded away by higher sea levels during periods of low glaciation, leaving the upper layers of limestone covered only with a thin layer, up to about 7.5 m (25 ft), of high- permeability, undifferentiated Pleistocene sands (Rupert, 1988; Rupert & Spencer, 1988). Classifications of some of the geologic formations changed from those in early reports of Florida’s geology, which relied heavily on road cuts, including those for railroads, to give geologists a glimpse of the larger puzzle. Sellards’ early study of the Wakulla and Leon County area allowed him to intuit the direction of groundwater flow to be from the northwest to

9 southeast, as he noted in his 1917 report, which was later shown to be correct by more detailed potentiometric maps (Kinnaman & Dixon, 2011; Stringfield, 1935) and modern groundwater studies (Davis, 1996).

Hydrology The Floridan aquifer is formed by layers of permeable carbonate rock throughout the entire state and into Georgia and the coastal areas of Alabama and South Carolina (Johnston & Bush, 1988). The Floridan aquifer was recognized as a regional system and named by USGS hydrogeologist Garald Parker in an investigation of groundwater use and supply of south Florida prompted by its mid-century population boom (Parker, Ferguson, & Love, 1955). The aquifer system is divided into the Upper Floridan and the Lower Floridan in most places, with a middle semi-confining layer of dolomitic limestone between the two more permeable layers (Miller, 1986). Though the UFA is unconfined in some areas, in most areas, it is confined or semi- confined beneath layers of clay-based sediments of about 60 to 210 m (200 to 700 ft) thick; these aquitards form the base of the overlaying surficial aquifer when present (Swancar & Hutchinson, 1995). The UFA supplies drinking water for over 15 million Florida residents (Marella, 2008) and supplies an even greater volume for irrigation. It was estimated that 3,640 mgd or 5 million cubic meters per day (m3/d) were pumped from the Floridan aquifer system in 2000 (Mauphin & Barber, 2005). For Wakulla and southern Leon Counties, over 12,000 m3/d (3.17 mgd) are pumped from the aquifer from municipal and private wells (Marella, Mokray, & Hallock- Solomon, 1998) and 12,500 m3/d (3.3 mgd) irrigate land for agricultural and recreational use (Marella, 2008). In the study area, the potentiometric surface (water table elevation) is between 0 and 18 m (60 ft) above mean sea level (Kinnaman & Dixon, 2011).

Florida has a great number of springs, over 700 (Rosenau et al., 1977; Scott et al., 2004), due to the layers of limestone, which are prone to dissolution over time. Limestone karst can −2 −6 have a wide range of hydraulic conductivity values (KH), usually from 2 x 10 to 2 x 10 m/s, depending on the amount of “karstification” they have undergone (Domenico & Schwartz, 1990). A well test done for a deep well in the study area north of the Cody Scarp (near −2 Tallahassee) showed a KH value of 4.5 x 10 m/s (Davis, 1996). Mean values for KH in the −4 Woodville Karst Plain were calculated and were found to have a minimum KH of 6.7 x 10 and

10 a maximum of 3.3 x 10−2 m/s (Katz, Chelette, & Pratt, 2004). The clay-containing layers, which

form the confining units of the UFA, have much lower KH values than the carbonate rock that forms the aquifer. The confining layers protect groundwater quality by filtering surface runoff, with its potential contamination with bacteria and chemicals, preventing it from immediately entering the groundwater. The KH values determine the amount of transmissivity (T) for an aquifer and can be found by performing pumping tests on wells at different depths. In the Wakulla springshed, the transmissivity ranges between 92.9 and 1,022,000 m2/d or 1,000 and 11,000,000 ft2/d (in simplified units from volume per day per area times thickness of aquifer) (Davis, 1996; Kuniansky & Bellino, 2012).

Conduit flow is dramatically faster than the rate at which water flows through the small fractures and pores of the limestone matrix. There are many networks of conduits in the UFA, and many are wide enough for cave divers to explore. The conduits were mostly formed when the sea level was much lower than it is today. During the glacial periods, when massive ice sheets as thick as a mile covered large portions of the Northern Hemisphere’s land masses, sea levels were about 100 m below present-day levels, and the groundwater table was also much lower (Morrissey, Clark, Bennett, Richardson, & Stute, 2010). After the Last Glacial Maximum, 18,000 years ago, sea levels rose and the cave systems, formed by dissolution of the carbonate rock, became the underwater conduits which are now as deep as 100 m below the water table surface (Morrissey et al., 2010). The underwater cave system that feeds water to Wakulla Spring reaches a maximum depth of 110 m (Stone, 1989). The caves can also be very wide. Not far from the Wakulla Spring vent, the cave ceiling rises to create a room that is almost 30 m (100 ft) in diameter, though the average diameter of the surveyed conduits is between 10 and 15 m (32 and 49 ft) (Kincaid, 2006; Kincaid & Werner, 2008; Stone, 1989). There are 51.5 km (51,484 m, 32 mi) of underwater tunnels connected with Wakulla Springs, mapped through the Woodville Karst Plain Project and known as the Wakulla-Leon Sinks cave system; it is the fourth largest cave system in the world and the largest in the U.S.A., even without it having been explored in its entirety (Kincaid & Werner, 2008). The large conduits help transport surface water into the aquifer directly though sinkholes and sinking streams, making the groundwater very susceptible to contamination.

11 The residence times and transit times of water flowing from Wakulla Spring have been calculated by a variety of techniques. Residence time refers to the time the water spends travelling though the aquifer, whereas transit time also includes the amount of time from the water entering the watershed to it recharging the aquifer, along with the time spent as groundwater flow (McGuire & McDonnell, 2006). Mean residence and mean transit times vary temporally as flow conditions do not remain in a steady state. Even with this variability, knowledge of the time frame in which water moves through the aquifer is important for calculating how long contaminants such as nitrate remain in the groundwater, aquifer responses to withdrawals, and other groundwater modeling applications.

One method for these calculations is based on the radioactive isotope tritium (3H), which has a half-life of 12.5 years. Levels of 3H in the atmosphere peaked in 1963 when nuclear weapons testing regulations began. The weapons testing had inadvertently labeled Earth’s entire atmosphere with 3H and carbon-14 (14C). R. M. Brown realized early on that 3H could be used as for hydrologic tracing and started recording 3H concentrations in the precipitation, surface water, and groundwater of the Ottawa Valley in 1953 (Brown, 1961). Other researchers used 3H measurements for determining “surface ocean mixing rates” (Begemann & Libby, 1957), “estimating groundwater storage” (Eriksson, 1958), “the study of certain hydrologic aspects of river basins” (Eriksson, 1963), and “age measurements of groundwater” (Nir, 1964). Tritium is a useful tracer because its activity can provide an estimate of the average time since the water has been recharged (Vitvar, Aggarwal, & McDonnell, 2005). For Wakulla Spring, the average apparent ages of water from the time of recharge (residence time) based on the ratio of 3H to the dissolved gas helium-3 (3He) isotope, which is the decay product of tritium, have been calculated at 34 years and 39 years (±1) (Katz, 2001; Katz, 2004; Katz et al., 2004) and 50 years (Happell, Opsahl, Top, & Chanton, 2006). Tritium measurements are sometimes combined with stable isotope analyses of D and 18O and other water chemistry parameters, such as saturation indices of calcite and dolomite, to calculate residence times. An isotope and geochemical mixing model, based the lumped-parameter flow model used by Maloszewski and Zuber (1982), gave a range of 50 to 80 years for mean residence times for samples from Wakulla Spring taken in 1999 and 2000 (Katz et al., 2004). They same study found a range of 5 to 90 years for groundwater samples from various wells within the Woodville Karst Plain portion of the watershed. The large

12 range in residence times for the well samples was due to the mixture of rapid recharge by surface water and slow-moving water in the fissures and pore water of the matrix (Katz et al., 2004).The apparent age of spring water at Wakulla Spring based on sulfur hexafluoride (SF6) was calculated to be 7 to 10 years (Katz, 2004). Calculations of apparent water age for Wakulla Springs based on chlorofluorocarbons (CFCs) have not been possible due to the presence of higher CFC levels than expected from atmospheric deposition; this indicates contamination from within the watershed, probably caused by improperly discarded air conditioning units, automobiles, or refrigerators (Happell et al., 2006). The apparent ages of water based on CFC-13 from nearby Cheryl Sink and Hammock Sink were calculated to be 11 and 12 years (±2), respectively (Happell et al., 2006).

The relatively large, shallow lakes in the springshed include Lake Miccosukee, Lake Iamonia, Lake Lafayette, Lake Jackson, Lake Munson, and Lake Bradford. Lake Jackson, Lake Bradford, and Lake Iamonia each have sinkholes at the bottom, overlain with silt, which occasionally drain and reduce the lake levels dramatically (Hendry & Sproul, 1966; Stringfield, 1966). In the 1917 “FGS Ninth Annual Report,” Sellards mapped the location of two sinkholes at the bottom of Lake Jackson. The presence of sinkholes at the bottom of some Florida lakes had been documented by others. Dall and Harris (1892) and Maston and Sanford (1913) described a time when Alachua Sink in central Florida was a large lake, which abruptly drained in 1891 after the sediment and debris that had slowed or stopped it from draining washed through, “effectively ending the steamboat traffic that had developed on the lake” (Matson & Sanford, 1913, p. 27).

There are many sinkholes in the Wakulla watershed (Fig. 3). Twenty-seven of the sinkholes are known to be part of the mapped Wakulla-Leon Sinks cave system (Kincaid, 2006; Woodville Karst Plain Project, 2012). Located in the Leon Sinks Geological Area, about 10 km (6.5 mi) northwest of Wakulla Springs, Big Dismal Sink and Turner Sink have been shown to be hydraulically connected to the Wakulla Springs cave system (Kincaid, Hazlett, & Davies, 2005; Woodville Karst Plain Project, 2012). Emerald Sink and Cheryl Sink are actually karst windows that are open to large tunnels heading toward Wakulla Spring (Kincaid & Werner, 2008). Dye- trace studies have shown travel times of seven days from Emerald Sink to Wakulla Spring (Kincaid & Werner, 2008). There are an estimated 1,000 sinkholes ephemerally or perennially

13 holding water in the Woodville Karst Plain area of the watershed (Kincaid et al., 2005). River Sink Spring near Ivan, FL (station no. 02326997), a karst window northwest of Wakulla Spring, was measured by the USGS eight times between 1942 and 1977 at flow rates between 102 and 188 cfs (USGS Water Data). In 1992, Benoit documented sinkholes, including closed depressions (sinkholes now filled with sand and sediments), for all of Leon County, and mapped over 3,345 in order to delineate areas where the aquifer is more susceptible to contamination. Spencer and Lane (1995) listed 54 large sinkholes for Leon and Wakulla Counties in their index of sinkholes for the state.

Figure 3: Sinkhole Map. Sinkhole data from the Florida Department of Environmental Protection (DEP) and the FGS (Kelly, 2013).

14 The aquifer that feeds Wakulla Springs has many surface water interactions, such as sinking streams, which flow along their river channels and then into sinkholes (swallets). The sinkholes, swallets, and karst windows of Wakulla County were described by Sellards in 1917 as indicative of the subterranean streams that “make their way, as we may believe, in a general southeast direction and re-emerge, in part at least, to form the great Wakulla Spring” (Sellards, 1917, p. 137). His sketch of a map (Fig. 4), though sparsely labeled, indicates what is likely Fisher Creek ending at a “Sink,” briefly emerging as a karst window (possibly Riversink Spring alone or linked to another sink or karst window, such as Kini Spring), before continuing underground to Wakulla Spring (Sellards, 1917, p. 136)—a connection that would take another 85 years to be proven (Kincaid et al., 2005).

Most of the sinking streams are located northwest of Wakulla Springs and drain areas within the Apalachicola National Forest: Fisher Creek, Lost Creek, Blackwater Creek, and Jump Creek. Among the four, Figure 4: Sellard’s Sketch Jump Creek has the smallest amount of flow, of Sinking Streams. and Lost Creek, with a drainage area between Modified from Meinzer (1927), originally from 123 and 194 km2, has the largest amount of Sellards (1917). flow (Kulakowski, 2010; USGS Water Data). The water in the sinking streams is very tannic (high in dissolved organic matter originating from decomposing leaves, tree bark, and other materials) and can be very dark in color. This has been used as a method to estimate the amount of water the sinking streams contribute to the flow at Wakulla Springs (Kulakowski, 2010). The maximum amount of measured flow at Lost Creek above the sink (measured at Arran Road Bridge) was 4,170 cfs on Sept. 23, 2000 (USGS Water Data). In November 2014, a USGS streamgage was installed, in cooperation with the Northwest Florida Water Management District (NWFWMD), downstream of the sink to determine the amount of flow that bypasses the sink during high flows. One place where Fisher Creek can be seen entering the ground is in the Leon Sinks Geological Area. The creek drains into Fisher Sink

15 1, and overflow drains into Fisher Sink 2 and Sullivan Sink (Kulakowski, 2010). Fisher Creek, Blackwater Creek, and Lost Creek, upstream and downstream of Lost Creek Sink, currently have USGS streamflow-gaging stations, operated in cooperation with NWFWMD; these gages have been collecting real-time continuous data since November 2014 (USGS Water Data). The streamflow-gaging station of Lost Creek at Arran, FL has daily discharge records from October 1998 to September 2005, and from January 2007 to July 2010 (USGS Water Data). Munson Slough flows into the 255-acre Lake Munson, which drains into Eight-Mile Pond. During heavy rains, large amounts of water from Eight-Mile Pond drain into Ames Sink and Kelly Sink, which are about 17.2 km (10.7 mi) north of Wakulla Springs (Fig. 5). Dye-trace studies done between 2002 and 2009 have demonstrated the connection of Wakulla Springs to Fisher Creek, Black Creek, Ames and Kelly Sinks, and many other karst features in the springshed (Kincaid et al., 2005; Kincaid, Davies, Werner, & DeHan, 2012; Kincaid & Werner, 2008).

Figure 5: Ames Sink Taking in Water. Photo of debris spiralling on the surface of the sink as algae-green water enters the swallet. Photo taken on Oct. 18, 2014 after heavy rains on Oct. 14, 2014.

16 There is convincing and growing evidence for connections between Wakulla Spring and the Spring Creek group of 13 or 14 submarine springs located 18 km (11 mi) to the south- southwest of Wakulla Spring (Lane, 2001). Florescent dye injected into Lost Creek Sink, which appeared at Spring Creek after five days, appeared 47 days later at Wakulla Spring and had a clear recovery curve at Revell Sink, located between the swallet and Wakulla Spring (Kincaid, Davies, & Dyer, 2010). Observations have also been made by cave divers of southerly flow through the tunnels located to the south of Wakulla Springs (Kincaid et al., 2005). It is possible that the increase in the variability of flow and the documented increase in the long-term average springflow at Wakulla Springs are related to the connections with Spring Creek. Periods of higher discharge at the Wakulla River have been shown to correspond with the periods of low and no flow at the Spring Creek vents (Davis & Verdi, 2014).

Spring Descriptions When Wakulla Spring has been described in the past, the clarity of the water has often been remarked upon. Over the years, dark tannic water entering the aquifer has reduced the number of “clear-water days” to the rare periods of sustained drought. Early descriptions of Wakulla Spring were included in various travel writings by people who visited or were promoting it as a tourist location. In a travel book from 1866, E. H. Hall describes “Wachulla” Spring as “an immense limestone basin as yet unfathomable in the centre, with waters as transparent as crystal” (Hall, 1866, p. 89). A newspaper article in the June 15, 1877 Middleburg Register of Middlebury, Vermont described a trip to the spring by N. Y. Herald.

It is reached by an abominably sandy road lying through a wilderness of scrub oak and pine, most wearisome to the eye … you lose all glimpses of beauty until you approach, through a narrow path, densely fenced by thickets, the Wakulla spring … The water is perfectly colorless … In the southern portion of the spring, a great shelf can be distinctly seen, over which the water as it rises, flows southward—a river. It looks but a couple yards below the boat, but is in fact more than ninety feet. (Herald, 1877)

17 Wakulla Spring was mentioned in the USGS publication by M. L. Fuller, “Contributions to the Hydrology of Eastern United States 1903,” as part of a survey of groundwater resources funded by the 1894 US Congress “for the determining the water supply of the United States and the investigation of underground currents and deep wells” (Fuller, 1904, p. 15). Though he provides more details about the economically important Newport Springs, with its hotel and health resort, and a water quality analysis for nearby Panacea Mineral Springs, which was popularly believed to have medicinal properties, he gives a very brief description of Wakulla Springs, along with the comment of “very large” in reference to the volume of springflow (Fuller, 1904, p. 269). “This spring is 118 feet deep and covers an area of about 4 acres, forming a river sufficiently deep and broad to navigate large boats. It is 10 miles from the Gulf coast” (Fuller, 1904, p. 273).

Matson and Sanford (1913) listed Wakulla Spring as having swampy topographic surroundings and a boiling emergence with very large discharge. FGS Bulletin No. 31, Springs of Florida, published in 1947, stated “the low color and turbidity of the spring water (except during and after heavy rainfall) in combination with the white bottom and bright sunlight make under water visibility excellent” (Ferguson et al., 1947, p.171). Stringfield (1966, p. 208) reported that though “Silver Springs and Rainbow Springs, two of the largest [springs], are always clear…water having noticeable color enters Wakulla Spring through sinks after rains.”

FGS updated and revised their Springs of Florida Bulletin No. 31 in 1977 and included a photograph of a SCUBA diver 100 ft below the water surface with the tree line clearly visible in the background to illustrate the clarity of the water (Rosenau et al., 1977, p. 423). Springs of Florida was published again as FGS Bulletin No. 66 (Scott et al., 2004) and described a vist to Wakulla Spring in 2001 at a time of extended severe drought, when the water was clear but Hydrilla had invaded the river and spring pool, requiring physical and chemical removal. After the drought ended, the days of water clarity decreased dramatically. Kulakowski (2010) reported the amount of time the Wakulla Springs State Park’s glass-bottom boats ran between 1987 and 2009 as a proxy for water clarity. She documented a significant drop in water clarity and found that by 2003, the glass-bottom boats were limited to running less than 20% of the year.

18 Historic Streamflow Record Many rivers and streams have long-term flow data available as, in some Florida locations, the USGS has been monitoring river levels and streamflows for more than a hundred years (Verdi & Tomlinson, 2009). In the early decades of the 1900s and prior to that, the USGS data for river and spring sites were almost entirely “miscellaneous” discharge measurements. Some sites had a local resident or “observer” to supply daily river level (stage) readings. Springflow measurements were important to determine the springs’ classification based on the system suggested by Meinzer, who wrote, “Discharge of springs based on amount of flow can be determined with fair accuracy only by establishing a gaging station and obtaining a continuous record over a period of years” (Meinzer, 1927, p. 2). Due to a Federal effort to collect continuous surface water data and the availability of mechanical recording equipment, a number of USGS streamgages were established for springs and rivers in the 1930s.

Streamflow for the Wakulla River has been measured by USGS staff since 1907 but, because the river does not have a consistent relationship between streamflow (discharge) and water level (stage), continuous records of streamflow, such as hourly or daily values, could not be calculated based solely on stage data (Fig. 6). This is not uncommon for low-gradient rivers (those with little elevation change over their course) and rivers in coastal areas, which are influenced by tides. A continuous discharge record was not possible for the Wakulla River until in situ acoustic Doppler velocity meter (ADVM) technology became available, allowing the continuous measurement of tidally influenced water speeds. Even though the gage is 19 km (12 mi) from the coast, the water levels and streamflow fluctuate with the tidal cycle (water velocities slow as the river level rises and increase as the river level goes down). The resulting plot of the measured streamflow to the stage associated with the time of the streamflow measurement has a very high degree of scatter. A non-tidally affected river such as Lost Creek has a direct relationship between the stage and discharge (Fig. 7), which can be represented by a “rating curve” that is then used to calculate the daily discharge from the recorded stage data (Moyeed & Clark, 2005). The predicted values can then be adjusted based on measurement data to produce a computed streamflow record. In 2004, an index-velocity streamflow-gaging station was established by the USGS on the Wakulla River.

19 Wakulla River near Crawfordville, FL 8

7

6

gage 5 height (ft) 4

3

2

1 100 1,000 10,000 discharge (cfs) Figure 6: Stage-discharge Relationship for Wakulla River near Crawfordville, FL (2005–2016). The unstable relationship is due to tidal influence on gage height (stage). At gage-height values between 3.4 and 3.6 ft, discharge (streamflow) measurements ranged between 225 and 1,060 cfs.

Lost Creek ar Arran, FL 20 18 16 14 12 gage 10 height (ft) 8 6 4 2 0 1 10 100 1000 10000 discharge (cfs)

Figure 7: Stage-discharge Relationship for Lost Creek at Arran, FL (1998–2016). The long-term stable relationship facilitates the calculation of the continous discharge record from the 15- minute gage-height record.

20 The USGS has carried out streamflow measurements of the Wakulla River at the County Road 365 bridge (“upper bridge”) and at Wakulla Spring. Periodic measurements have been taken since 1907, and continuous streamflow records are available from Oct. 22, 2004 to Oct. 5, 2010, and from Oct. 15, 2011 to the present. The USGS streamflow-gaging station (station no. 02327022) is located just outside the Wakulla Springs State Park boundaries and about 8.8 km (5.5 mi) upstream from the confluence with the St. Marks River. The USGS measurements of flow for Wakulla Spring (station no. 02327000) were made below the spring pool or at the bridge, though some of the historic measurements did not differentiate between locations. There are more than 400 measurements for the Wakulla River and Spring, ranging from 25.2 cfs at the spring vent on June 19, 1931 to 2,600 cfs at the upper bridge on June 26, 2012, during Tropical Storm Debby (USGS Water Data). Previous maximum flows were measured between Aug. 25 and 28, 2008 by USGS and NWFWMD staff at the bridge at 2,090, 2,120, 2,270, and 2,300 cfs during flooding caused by Tropical Storm Fay. A long-standing record flow occurred during a spring flood at 1,910 cfs on April 11, 1973 (Rosenau et al., 1977; Scott et al., 2004). Another peak flow measurement was 1,710 cfs on July 13, 2005 after Hurricane Dennis (USGS Water Data).

Very early water quality measurements were taken in 1924 and showed chloride at 8 mg/L, sodium at 5.7 mg/L, calcium at 39 mg/L, magnesium at 9.6 mg/L, and sulfate at 11 mg/L (Scott et al., 2004). Fisher Creek, Black Creek, and Lost Creek have very dark-colored water that can have higher dissolved organic carbon (DOC) and lower specific conductance than the local groundwater and Wakulla Spring water samples (Katz, 2001). The color of the water at Wakulla Springs has been an issue of concern, with increasing numbers of “dark-water days” over time (Kulakowski, 2010).

History and Land Use The area has been used by humans for over 10,000 years, as evidenced by a number of Clovis points and pre-Clovis points found along with other tools of ivory and stone (Dunbar & Hemmings, 2004; Rink, Dunbar, & Burdette, 2012). Archeological research indicates that there were people living in the Wakulla Springs area about 11,000 years before the present era and most likely even earlier (Rink et al., 2012). A number of preserved megafauna bones have also

21 been found in the Wakulla cave system, including mammoth, mastodon, and giant ground sloth (Scott et al., 2004). As the climate changed at the end of the last glacial period, the Pleistocene animals disappeared, ocean levels rose, and, by about 8,000 years ago, Wakulla Spring took on an appearance similar to today. The land, previously used by nomadic hunters, became more suitable for agriculture, and more permanent settlements formed. The flint hide scrapers and bone fish hooks found at the bottom of Wakulla Spring by divers, and the man-made earthen mounds and artifacts documented near the spring, give a glimpse of what life was like for the people who used the area’s natural resources for thousands of years (Revels, 2002).

The area between the Ochlockonee and Aucilla Rivers was home to the Apalachee people, who farmed maize, squash, and many other crops for hundreds of years before the arrival of the Spanish. Evidence from a Spanish text suggests that one of the major Apalachee cities was located on the Wakulla River or the St. Marks River (possibly at the confluence of the two rivers), a day’s march from the coast, though its exact location remains unknown (Vaca, 1542/1993). The Spanish expedition led by Panfilo de Narvaez arrived in La Florida in 1528 C.E., and the Apalachee city of Aute was described in La Relación (“the Narrative”) of Álvar Núñez Cabeza de Vaca, one of the expedition’s members (Revels, 2002; Simpson, 1956; Vaca, 1542/1993). Spanish Franciscan monks arrived in the area around 1633, and approximately 14 missions were built in the surrounding area, including Mission San Luis near Tallahassee, which was built in 1656 (Revels, 2002). The Spanish clergy took the best farmlands and turned the area into a major source of grain production, supplying the Spanish in Florida and the Caribbean (Paisley, 1989). The native people experienced a long period of upheaval caused by clashes between the invading English and Spanish, the usurpation of native lands, direct attacks by the Europeans, and indirect aggression, which included the purposeful fomenting of hostilities among the native tribes. Europeans caused the enslavement, death, and displacement of over 10,000 Apalachee people (Revels, 2002). The surviving Apalachee people were displaced over time from their homelands to areas farther west, from Pensacola to Louisiana (Simpson, 1956).

One preserved letter written during a battle of colonial powers (England against Spain and France) in the early 1700s suggests that, at the time, people and goods could be transported by canoe up the Wakulla River to within about a day’s walk of the Mission San Luis (Revels,

22 2002). This could indicate the possibility that there were more surface water connections than exist presently, later drained by sinkholes; one of these might be a connection from McBride Slough to Lake Munson, and then to Lake Henrietta.

In the 1700s, the Creek and Seminole peoples moved into the area, and many set up trade with Europeans. In 1799, Andrew Ellicott was tasked with surveying the boundary between U.S. and Spanish lands. He also documented many geographic features of northern Florida, including one of the earliest descriptions of Wakulla Spring (Revels, 2002). After English and Spanish rule, Florida was ceded to the United States by Spain in an 1819 treaty and became a U.S. Territory in 1821, almost 40 years after the American Revolutionary War ended in 1783 (Verdi & Tomlinson, 2009).

As the numbers of settlers coming to the area increased, more land was converted to agricultural fields. A major crop was cotton, grown by enslaved African Americans, especially in the upland areas of Leon, Jefferson, and the surrounding counties, due to their proximity to waterways used to transport goods (Paisley, 1989; Sellards, 1917). Paisley (1989) described the antebellum counties as “Cotton Kingdom.” After the Civil War ended, logging began to take place on a large scale including the harvesting of the ancient cypress trees along Florida’s rivers. Turpentine collection also took place in the pine forests of Florida (Revels, 2002). Groundwater began to be pumped on a larger scale for the irrigation of fields and other uses. The uses of wells in Leon County were listed by Meinzer in 1913 and included the city water supply for Tallahassee, ice manufacturing, and even water to fill steam locomotives. In 1931, the FGS Annual Report by Gunter and Ponton included a prescient call for the “Need for conservation and protection of our water supply with special reference to waters from Ocala limestone.” They called attention to the susceptibility of Florida’s karst aquifer. Hendry and Sproul (1966) described that one water use in Wakulla and Leon Counties was pumping groundwater to cool buildings, including some on the Florida State University (FSU) campus, prior to modern air conditioning, and they declared their concern about heat pollution, as the water was returned to the aquifer. They also mentioned that large tracts of fields were purposely flooded to attract waterfowl, presumably for recreational hunting (Hendry & Sproul, 1966).

23 lakes and wetlands forested urban agricultural

Figure 8: Land Use in the Wakulla/St. Marks Watershed. Estimates from Katz (2004).

300 Florida Population 20 1900-2010 250 15

10 200 Millions 5

0 150

100 Thousands

50 Leon County

Wakulla County 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Figure 9: Population Growth 1900−2010. Data from U.S. Census records (Appendix B). Population growth has been slower in Wakulla County where the Wakulla Springshed is the most vulnerable to anthropogenic impacts.

Current land use estimates for the Wakulla Springs watershed have been reported to be about 45% forested, 31% urban, 14% agricultural, and 10% lakes and wetlands (Katz, 2004)

24 (Fig. 8). A portion of the watershed is located in the Apalachicola National Forest and has long- term protection from development. The population of Florida has risen exponentially (Fig. 9), and water use has followed the same trend. Between 1950 and 2005, Florida gained over 15 million residents, an increase of 550%, and groundwater withdrawals increased by 59 x 106 m3/d or 15,700 mgd, an increase of 600% (Marella, 2008). The present population within Leon and Wakulla Counties is about 275,000 and 31,000, respectively, based on 2010 U.S. Census data (Appendix B). Though there is a much smaller population in Wakulla County, more water is recharged in the unconfined Woodville Karst Plain Region of the springshed, where there are an estimated 11,400 on-site septic systems (based on 9,500 permits issued over twenty years) (Lombardo Associates, Inc., 2011).

Anthropogenic Impacts In 1934, Edward Ball purchased the land around Wakulla Springs, and in 1937, he had the large lodge built for visitors to the spring. The governor of Florida negotiated its purchase in 1986, and it became Edward Ball Wakulla Springs State Park. Presently, Wakulla Springs and its surrounding 6,000 acres, including Cherokee Sink, are managed by the state park, which receives many visitors each year—over 180,000 in 2002 (Bonn & Bell, 2003). Though thousands of people enjoy the park, especially in the summer months, their impact is minimized because most of the river and land within the park is protected from overuse. Visitors stay on trails, within the cordoned swimming area, and in the park boats on the river.

At Wakulla Springs, as with most Florida springs, the spring water shows elevated levels of anthropogenic nitrate. Along with increasing nitrate in Florida springs, there has also been an increasing presence of nuisance algal mats formed by filamentous algae, such as Lyngbya species (sp.) and the cyanobacteria Vaucheria sp. (Stevenson, Pinowska, & Wang, 2004), and invasive submerged aquatic plant growth, such as Hydrilla verticillata (Duarte, 1995). The relationship between elevated nitrate and primary production has many complexities; other biochemical and physical factors, such as levels of sunlight, dissolved oxygen, other nutrients, and trace metals, also play important roles (Heffernan, Leibowitz, Frazer, Evans, & Cohen, 2010). Wakulla River has also lost its population of Florida apple snails (Pomacea paludosa), which are the main food for the Florida limpkin (Aramus guarauna), though there does not appear to be a direct link

25 between apple snail mortality and elevated nitrate levels (Corrao, Darby, & Pomory, 2006). Background levels of nitrate-N (nitrogen from nitrate and nitrite) for the UFA, prior to significant anthropogenic inputs, are thought to have been extremely low. Levels were probably below the detection limit of .02 mg/L, as they remained in a limited area of undeveloped forest in and around the Ocala National Forest (Phelps, 2004), with more conservative estimates remaining below .05 mg/L. The addition of nitrate into an ecosystem that evolved with nitrate levels below detectible limits can change the dynamics of the entire system in various ways. The reduction of nitrate levels is an important step in Florida springs restoration efforts.

The upper Wakulla River has been classified as an “impaired waterway,” and the Florida DEP has established a total maximum daily load (TMDL) of nitrate-N of .35 mg/L (Gilbert, 2012), as required by the U.S. Environmental Protection Agency (EPA). Nitrate had increased dramatically in samples from Wakulla Springs since regular monitoring began in the 1960s. By 1972, it had already reached a level of .25 mg/L (Rosenau et al., 1977). Concentrations appear to have peaked from 1990 to 2000, with levels over 1 mg/L (Katz, Griffin, & Davis, 2009; Scott et al., 2004), and have slowly decreased to their present level of around .50 mg/L (Gilbert, 2012). Though a significant percentage of the nitrate input comes from wet and dry deposition (Chelette, Pratt, & Katz, 2002), which can only be effectively reduced by national air pollution regulations, state and local efforts have targeted the sources of chemical fertilizers and wastewater/on-site septic effluent, which are estimated to make up 25% and 51% (29% from wastewater treatment and 22% from septic tanks) of nitrate contributions, excluding atmospheric deposition (Chellete et al., 2002). The Wakulla/St. Mark’s watershed has a relatively low nitrate input from livestock (Chellete et al., 2002), and unlike most first-magnitude springsheds in Florida, chemical fertilizer contributes a smaller portion of nitrate than biological sources do (Albertin, Sickman, Pinowska, & Stevenson, 2011). Concerted efforts by citizens and local government, such as the upgrade by the City of Tallahassee Utilities to implement advanced wastewater treatment and public outreach by local organizations and residents to limit their use of fertilizers have been vital to the current downward trend in nitrate levels, which are approaching the TDML goal of .35 mg/L (Gilbert, 2012).

26 Previous Studies in the Wakulla Springshed

In the 1950s and 1960s when SCUBA technology was first being used, the Wakulla cave system was explored for the first time (Stone, 1989). One member of the SCUBA team that completed some of the first cave dives and tunnel maps was hydrologist Larry Brill (Burgess, 1976). He made an early attempt to determine the source and residence time of springflow from Wakulla Spring by using 14C as a tracer (Burgess, 1976). Discovered in the 1940s, 14C was commonly used during the 1950s and 1960s to calculate the age for waters that were too old to be dated using 3H (Stringfield, 1966; Vitvar et al., 2005). Burgess (1976) gives a brief description of Brill’s research, which included the installation of a stage sensor at the dive platform and a mechanical current meter at the spring vent. Brill calculated that the water had enough time to have travelled 60 to 70 miles to get to the spring vent and found that the tidal influence on water levels, though small, could be observed in the spring pool (Burgess, 1976). Though radiocarbon dating of water in a carbonate aquifer was not ideal due to the large corrections required to account for the inorganic carbon derived from the dissolution of limestone formed by ancient marine organisms, Brill’s conclusion that the water had come from a great distance, and his estimated northern boundary of the springshed around Cairo, Georgia are in agreement with current watershed delineations. The investigations that Brill did utilized the latest technology available at the time and, in many ways, my research replicated his work with the benefit of newer technologies, including stable isotopes as tracers and an in situ ADVM.

In 1968, 1971, and 1974, papers by researchers affiliated with FSU’s Oceanography and Geology departments and the University of Florida’s (UF)’s Water Resources Research Center were published that investigated the use of uranium isotopes 234U and 238U in tracing groundwater or “finger-printing” different sources of water in the Floridan aquifer (Osmond, Buie, Rydell, Kaufman, & Wallick, 1971; Osmond, Kaufman, & Cowart, 1974; Osmond, Rydell, & Kaufman, 1968). These earlier studies looked at the watershed of Wakulla Springs and found that there were unique uranium ratios for different water source locations, such as Lake Jackson. They used a mixing model to calculate that about two-thirds of the water at Wakulla Spring was recharged south of the Cody Scarp (Osmond et al., 1968) and less than 8% of the flow came from the upper watershed north of Havana, Florida (Osmond et al., 1971). Osmond and the other

27 researchers also used 3H and U measurements to infer that the springflow from Wakulla Spring was made up of about 65% flow from the deep part of the Floridan aquifer and 35% from the shallow part (Osmond et al., 1971). The researchers then took their investigation methods further afield and applied them to the watersheds of Silver Springs and Rainbow Springs in central Florida (Osmond et al., 1971, 1974). Overall, they found that their estimates of residence times and flow patterns compared well with those made using traditional hydrologic methods, though the “open nature” of the Wakulla watershed’s unconfined karst complicated their calculations. They concluded that their investigations, though preliminary, might have the potential “to lead to some very productive lines of further research” in calculating water age and flow patterns (Osmond et al., 1974). In 1999, a paper by Cao, Cowart, and Osmond was published using uranium isotope data to show that groundwater originating west of Wakulla Springs (toward the Woodville area) contributed flow via tunnels A and C, but sources from north of the spring (toward the Tallahassee area) contributed flow to tunnels B and D. Studies of the watershed have routinely used 3H measurements (Katz et al., 2004).

Katz (1998) used D, 18O, and 3H data from Lake Jackson, a sinkhole just southwest of the lake and wells in the surrounding area. He found that, while the well samples taken upgradient of the lake plotted on the meteoric water line, samples from wells downgradient of Lake Jackson, including samples from five municipal supply wells that drew water from deep in the aquifer, showed the evaporation signature of the lake water. The mixing of surface water with groundwater extended deeper than expected in the aquifer and may have been due to the amount of pumping at nearby supply wells, which created a zone of depression, pulling water downward (Katz, 1998). The 3H data showed that the recharge at depths of more than 100 m (328 ft) occurred within the last 40 years, and the D and 18O data showed that Lake Jackson is highly connected with the groundwater, even to deep parts of the UFA (Katz, 1998). Similar surface water/groundwater interactions were seen for nearby Lake Bradford (Katz, Coplan, Bullen, & Davis, 1997).

Periodic dye-trace studies have been done for the Wakulla springshed between 2002 and 2009 by GeoHydros LLC in cooperation with the FGS and other organizations (Kincaid et al., 2005, 2010, 2012; Kincaid & Werner, 2008). The florescent dyes eosin, uranine, and phloxine B

28 were introduced into selected sinking streams, wells, and sinkholes to determine flowpaths between mapped cave systems and transit times between injection and recovery points. Two of the largest mapped underwater cavern systems, the Wakulla cave system and the Leon Sinks cave systems, were surveyed by cave divers through the Woodville Karst Plain Project (WKPP), and a dye-trace study in 2005 showed that they were hydraulically connected (Kincaid et al., 2005). Travel times from Kelly and Ames Sinks to Wakulla Spring 17.2 km (10.7 mi) to the south were found to be 20 and 23 days, respectively (Kincaid & Werner, 2008). The main pathway appears to go through a main conduit connected to another conduit 300 m upgradient of Indian Spring, where the dye was detected 14 to 17 days after it was injected into the Ames and Kelly Sinks (Kincaid & Werner, 2008). Dye injected into Fisher Creek Sink and Black Creek Sink took about 10 days to reach Wakulla Spring and was first detected at Emerald Sink after about two or three days (Kincaid & Werner, 2008). Dye traces demonstrated connections between Lost Creek Sink, Spring Creek, and Wakulla Spring (Kincaid et al., 2010). When Spring Creek was taking in water, dye was injected at Lost Creek Sink and was detected further inland at two sinkholes east of Lost Creek Sink and west of Wakulla Springs (Kincaid et al., 2010). Importantly, the movement of groundwater from the City of Tallahassee’s wastewater sprayfield, located southeast of Tallahassee, to Wakulla Springs tunnel B was documented to take between 56 and 66 days, indicating groundwater movement of 200 to 300 m/d (700 to 1,000 ft/day) (Kincaid et al., 2012). Katz, Griffin and Davis (2009) demonstrated the connection between elevated amounts of nitrate-N from the sprayfield and the levels at Wakulla Spring by sampling for microbes, stable isotopes, nutrients, and water chemistry panels designed to detect treated wastewater by trace pharmaceuticals and other indicators. The information gained by the dye- trace and water quality studies was acted upon by the City of Tallahassee, which upgraded to tertiary treatments; these have greatly reduced the amount of nitrate in the processed wastewater. Measurements of nitrate-N for Wakulla Spring currently show a downward trend (Gilbert, 2012; Katz et al., 2009).

In 2012, a paper by Li was published that demonstrated the use of dye-trace data to calculate the recharge entering Emerald Sink and Cheryl Sink (Li, 2012). One labor-intensive method of determining recharge directly from sinkholes is by a discharge measurement done inside the conduit downstream of the sink. The discharge measurement is made based on the

29 same principles as a regular streamflow measurement but requires multiple SCUBA divers, the setup of a grid system (usually of PVC pipe), and multiple water-velocity measurements (one for each section of the grid). Each velocity value is multiplied by the area of each grid section and then combined to produce a total discharge value. The proposed alternative created a model based on the break-through curve and dilution data from the dye-trace studies and had good results for Emerald Sink, demonstrating a novel use of dye-trace data ordinarily used to determine travel times and flowpaths. The confidence in the results for nearby Cheryl Sink was lower due to low dilution values, indicating that it did not recharge much water, thus making the margin of error greater.

In 2011, a paper by Dimova, Burnett, and Speer of FSU’s Department of Earth, Ocean, and Atmospheric Science was published that described the use of radon-222 (222Rn) as a natural tracer at the submarine springs that make up Spring Creek (Dimova et al., 2011). In the local groundwater, 222Rn forms from the radium-226 (226Ra) present in the limestone. It has a short half-life of 3.8 days and, once exposed to air, 222Rn quickly enters the atmosphere, so surface water can easily be distinguished from groundwater by the 222Rn concentrations. Equipment was deployed to monitor the radon levels and salinity of the water at Spring Creek over a two-year period. The springs showed a pattern of low salinity and high 222Rn after heavy rain events produced high springflows. During drought conditions, 222Rn levels became moderately elevated, indicating that seawater was staying underground for extended periods of time. The collected data were used to create models describing the freshwater component of flow from the submarine springs (Dimova et al., 2011).

Davis and Verdi (2014) demonstrated a likely though complex connection between Wakulla Springs and Spring Creek, with periods of increased flows at Wakulla Springs correlating with periods of no flow at Spring Creek. They used USGS stream-gaging data from the Wakulla River gage (station no. 02327022) and the Spring Creek gage (station no. 02327038) located in the narrowest part of the Spring Creek channel. The measurements of velocity and flow at Spring Creek exclude flow from springs nos. 12 and 13 in nearby Stuart Cove, which is open to the gulf. They then calculated the differences in Spring Creek’s inflow and outflow every two tidal cycles, combined with the salinity of the water from specific conductance sensors, and

30 compared the flow data for the submarine springs with the discharge records for Wakulla River. The data indicated that when Spring Creek was not flowing, Wakulla River showed almost double its average flow. The data suggested that freshwater, which would have flowed out of the submarine springs, was shifted toward Wakulla Springs as denser seawater increased the pressure head in the aquifer around Spring Creek during times of little rain. When there were rainy periods, the large amount of freshwater from sinking streams such as Lost Creek (station no. 02327033) entering the aquifer increased the hydraulic gradient above the equivalent freshwater head needed to push out the saltwater at Spring Creek and allowed freshwater to flow again at the submarine springs. Wakulla River also showed increased flow after heavy rains due to the storm runoff. A third phase was observed as the streams in the watershed returned to base flow; Wakulla River then showed a decrease in flow (about half the average flow) as Spring Creek retained its freshwater flow, as demonstrated by the low-salinity values. Models using MODFLOW conduit flow process (CFP) employed by Gallegos, Hu, and Davis (2013) and Xu, Hu, Davis, and Kish (2015) further described the interactions between Wakulla Springs and the Spring Creek Springs.

More evidence for connectivity of Wakulla Springs with Spring Creek Springs comes from the long-term USGS flow measurements made on the Wakulla River from 1907 to the present day (most stored under USGS station no. 02327000 Wakulla Spring near Crawfordville, FL, which usually included flow from associated springs). In contrast with the pattern seen for other Florida springs with long-term flow data, Davis and Verdi (2014) observed that flow for Wakulla Springs increased over time, from 1930 to 2010; this pattern has continued with the USGS measurements into 2015 (Fig. 10), despite no observed increase in the groundwater levels in the springshed over time (Davis & Katz, 2007) or any increase in average rainfall to the area (NWS). Other first-magnitude Florida springs have shown trends of decreased flow over recent decades. Silver Springs’ flow has been documented to be in a slow but steady decline (Klammer, Yaquian-Luna, Jawitz, Annable, & Hatfield, 2014). Ichetucknee Springs’ combined flow, which has been measured by the USGS at the river from 1898 to 2010, has also shown a decrease in flow of about 1 cfs per year since 1970 (Grubbs, 2011) (Fig. 11). The trend of decreasing flow at Crystal Springs has been linked to increased water withdrawals (Weber & Perry, 2006).

31 Davis and Verdi (2014) provided two possible explanations for the trend of increased springflow at Wakulla Springs. One possibility is the increased development of conduits in the cave system feeding Wakulla Springs (Davis & Verdi, 2014). An estimate of conduit dissolution rates for the Santa Fe River Sink conduits in north-central Florida was made by UF researchers Moore, Martin, Screaton, and Neuhoff (2010). They found that, because the groundwater during base flow and recharge through the epikarst was saturated with respect to calcite, dissolution mainly occurred during short periods when flood waters entering the conduits were undersaturated in calcite and were more tannic (Moore et al., 2010). Their estimate of dissolution rates for that part of the UFA was 7 x 10−7 m/d, though they also pointed out that dissolution was not necessarily limited to the surface area of the conduits—it could also weaken the surrounding rock because the matrix could be very porous, allowing a greater widening of the conduits (Moore et al., 2010). Since the hydrology for Wakulla Springs and Spring Creek Springs is similar with respect to surface water, an estimate for 80 years of dissolution would be 20 cm in total, not including the weakening of the surrounding matrix. The other explanation put forth by Davis and Verdi (2014) is that sea-level rise has increased the pressure head at the submarine springs, shifting more flow toward Wakulla Springs. Between 1930 and 2010, local sea levels have shown an increase of approximately 15.5 cm (Davis & Verdi, 2014). As sea levels continue to rise, the pattern of Wakulla Springs capturing flow from Spring Creek could become more pronounced.

Wakulla Springs and River 3000 Measurements at 02327000 Measurements at 02327022 2500

2000

1500 discharge (cfs) 1000

500

0 1900 1907 1914 1921 1929 1936 1943 1950 1958 1965 1972 1980 1987 1994 2001 2009 2016 Figure 10: Measurements of Streamflow for Wakulla Springs and River, 1907-2016. Measurements were stored under USGS station no. 02327000, Wakulla Spring near Crawfordville, FL, and then under station No. 02327022, Wakulla River near Crawfordville, FL, once a streamflow-gaging station was established.

32 Ichetucknee River at Highway 27 near Hildreth, FL 600

550

500

450

400

350 discharge 300 (cfs) 250

200

150

100 1908 1916 1925 1933 1941 1950 1958 1966 1975 1983 1991 2000 2008 2016 Figure 11: Measurements of Streamflow for Ichetucknee River, 1898− 2016. The trend line is the rolling average of measurements. A decrease in flow was documented from about 1970 (Grubbs, 2011). Active hurricane seasons in 2004 with four major storms reaching the area, the most active Atlantic hurricane season on record in 2005 with two hurricanes reaching Florida, and two more hurricanes reaching the area in 2006 led to an increase in springflow during those years.

Isotope Chemistry in Hydrology

The number of protons in an atom determines which element it is, but the number of neutrons can vary slightly. Forms with different numbers of neutrons are isotopes of the element. Additional neutrons do not change the atomic number, which is determined by the number of protons, but they do increase the atomic mass. The most abundant atoms of the low mass elements have an equal number of protons and neutrons since that is the form in which they are the most stable. As atomic mass increases in the elements, additional neutrons can provide stability by slightly increasing the atomic mass. Different isotopes of an element will have different bond strengths with other atoms due to the differences in atomic mass. The bonds formed with an atom of greater atomic mass are stronger than those bonds formed by isotopes of a lower mass, and the stronger bonds require more energy to break. The molecules with higher mass isotopes react more slowly in chemical reactions or are slower to undergo physical changes than molecules bonded with isotopes of the element with a lower mass. The different rates of

33 change cause fractionation (an increased difference in the delta [δ] values between the water vapor and the source water), resulting in separate end products with differing isotope compositions.

Stable isotopes such as oxygen-18 (18O) and nitrogen-15 (15N) do not have radioactivity, whereas unstable isotopes or radioactive isotopes, such as 14C and 3H, show radioactive decay by the spontaneous release of alpha or beta particles (or sometimes gamma rays) (Kendall & Caldwell, 1998). The stable isotopes D and 18O have many applications in the field of hydrology but have also been instrumental in investigating the solar system in studies of solar wind (Collier et al., 1998), comet ice and meteors (Roberts, 2001), and water on Mars (Greenwood, Itoh, Sakamoto, Vicenzi, & Yurimoto, 2008). It is theorized that D was formed during big bang nucleosynthesis at a ratio of D/H at 3.4 x 10−5, estimated from current astronomical observations (Burles, 2000). Kendall and Caldwell (1998) describe the abundance of isotopes in the solar system as “a function of nuclear processes in stars.” On Earth, the average ratio of D/H is 1:6,410, and the average ratio of 18O/16O is 1:500 (Kendall & Caldwell, 1998).

History of Isotope Chemistry In 1922, the Nobel Prize in Chemistry was awarded to F. W. Aston of the University of Cambridge for discovering stable isotopes “by means of his mass-spectrograph” (Kuroda, 1992). In 1929, 18O was discovered by W. F. Giauque and H. L. Johnston at the University of California (Giauque & Johnston, 1929). In 1932, H. C. Urey and his team at Columbia University published an article, entitled “A Hydrogen Isotope of Mass 2 and Its Concentration,” describing their discovery of D by its isolation through the evaporation of large amounts of liquid hydrogen (Urey, Brickwedde, & Murphy, 1932). For this research, Urey received the Noble Prize in Chemistry in 1934 (Weidlein & Bass, 1935). Urey and his team identified D by “spectrum analysis” and very sensitive measurements of weight to calculate the density of the “heavy water”—modernized methods of the same techniques which were employed to identify new elements by atomic weight when they were discovered in the late 1700s and 1800s. Unfortunately, the results of water analyses that used the method of quantifying the actual amount of D by density measurements were not easily reproducible; they required the removal of any salts, minerals, and other solutes from the water samples through multi-step purification

34 processes that caused different degrees of fractionation (Friedman, Redfield, Schoen, & Harris, 1964). The methods were also not applicable to measuring 18O in water samples, so the use of mass spectrometry began to present a solution to the earlier limitations to research in the field (Botter & Nief, 1958; Nier, 1947).

During the 1930s, research was done in Europe, the U.S.A., Japan, and Russia (Rankama, 1954) to measure the abundance of isotopes on Earth and even in space from measurements of meteorites (Manian, Urey, & Bleakney, 1934). Knowledge of isotope chemistry developed with the intensive work in nuclear physics that accompanied the goal of creating atomic and nuclear weapons during World War II (Horita & Kendall, 2004; Ingraham, 1998). The chemist I. Kirshenbaum along with Urey and G. M. Murphy produced a report for the U.S. Atomic Energy Commission in 1951, entitled “Physical Properties and Analysis of Heavy Water,” on their methods and findings from working on the Manhattan Project (Dole, 1952; Kirshenbaum, Urey, & Murphy, 1951).

Standards Mass spectrometry had been used and improved on since the early days of isotope chemistry research, but since the relative amounts of isotopes were being measured instead of the empirical amounts, reference standards were required (Epstein & Mayada, 1953; Friedman, 1953). Scientists led by Nier at the University of Minnesota developed a type of mass spectrometer that reliably measured isotope ratios (Nier, 1940, 1947). This was soon improved upon by Urey and his student C. R. McKinney. The dual inlets and pair of collectors allowed the samples and reference standards to be compared under the same conditions (McKinney, McCrea, Epstein, Allen, & Urey, 1950). The Nier–McKinney double-inlet, double-collector mass spectrometer could measure “small differences in the isotope abundance ratios” of samples, and this allowed for measurements with greater precision (Gat, 1996; McKinney et al., 1950). This increased precision made possible new applications of stable isotope chemistry that had been out of reach in the past.

The late 1940s and the 1950s saw a proliferation of research into many areas of scientific investigation, such as geology, hydrology, oceanography, and paleoclimatology. In the 1950s

35 and early 1960s, refinements in analysis techniques and the increased availability of equipment opened the gates for a suite of investigations into the isotopes of water. Results were published for water samples from a vast expanse of sources, such as the deep ocean, glaciers, hail, and sea fog; they even included analyses of water from a “ditch near Woods Hole, Mass.” and “base of icicle” versus “tip of icicle” (Friedman et al., 1964).

In the still-nascent field, the standards to which samples were compared varied from lab to lab as each research team used what was conveniently available. Lake Michigan water was used by the “Chicago school”—a group of researchers assembled by Urey in the post-World War II era at the University of Chicago (Friedman et al., 1964; Gat, 1996). There was a consensus among researchers that comparing the ratios of stable isotopes to agreed-upon reference standards should become the common practice. Very early on in the evolution of stable isotope chemistry, it was observed that freshwater in rain and rivers was usually more depleted in comparison with ocean water the farther it was from the ocean, but rivers became more enriched as the water travelled back toward the ocean (Gilfillan, 1934; Oana, 1942; Teis, 1939a, 1939b). Since the ocean is the largest reservoir of water on the planet, Epstein and Mayada (1953) used it as their standard when they reported on 18O in “waters from natural sources.” These examples presented a logical candidate for a standard for isotopes of water. In 1961, standard mean ocean water (SMOW) was proposed for reporting δD and δ18O values in an article by H. Craig (Craig, 1961b). The same year, W. Dansgaard of Denmark published a paper that included standardized measurement techniques for “natural waters” and a description of the system of δ notation for the “relative deviation” values (Dansgaard, 1961). In the 1970s, scientists, mostly from the International Atomic Energy Agency (IAEA) in Vienna, formalized the international reference standards for stable isotopes and the protocols for isotope analyses (Sharp ed., 2007). The adoption of international standards for reporting results and the consensus on measurement techniques facilitated researchers across the field in sharing information and collaborating more effectively. The international reference standard used for isotopes of water, Vienna standard mean ocean water (VSMOW), has δ values of 0‰ for both 18O and D, which is the same for SMOW used in older publications (Coplen, 1995; Craig, 1961b).

36 When isotope analysis is performed in the lab, direct comparison with the international reference standard is often not used; several working standards that have known values relative to the international reference standard are analyzed with the samples, and the results are converted back to the reference standard. The reference standard itself does not need to be used as long as the δ values for the working standards are accurate and remain stable over time. Working standards for isotope analyses of water samples are stored and maintained by labs at a constant temperature in tightly sealed containers to prevent evaporation and ensure that the isotopic composition remains the same (Wassenaar, Coplen, & Aggarwal, 2014). Standards chosen by a lab can include local tap water collected at one time and stored properly, or even imported bottled water, such as glacial water, to use as a standard for analyses water with a very light isotope composition (Wassenaar, 2014). Working standards within a reasonable range of values similar to the samples being analyzed should be chosen. In VSMOW, the international standard for D and 18O, the actual ratio of D/H is 1.56 x 10−4 and 18O/16O is 2.005 x 10−3 (Baertschi, 1976; Dewit, VanderStraaten, & Mook, 1980).

The equation below is used to calculate a δ value and the unit is per mil, parts per thousand (‰). The ratio of the heavy (higher mass) to the light (lower mass) isotope of the sample is Rx and Rs is the ratio for the standard.

‰ = 1 1000 or ‰ = 1000 �� �� − �� � � − � ∗ � ∗ �� �� For example, the equation for 18O would be as follows:

‰ = 18 1 1000 � 16 18 �������� � � � 18 − � ∗ � 16 ���������� Isotopes as Hydrologic Tracers As there are different isotopes of hydrogen and oxygen, there are different combinations in water molecules. The various forms are called isotopologues. Dansgaard reported in 1964 that 16 18 16 he had found the ratio of the most abundant isotopologues, H2 O, H2 O, and HD O, to be about 99,800:2,000:320 parts per million (ppm). Both chemical reactions and phase changes

37 have reaction rates that are mass dependent and cause fractionation, resulting in a different isotopic composition in the products compared to the original composition (Gat, 1996). For example, during evaporation, the lighter isotopologues of water preferentially move into the vapor phase, leaving the heavier molecules to become concentrated in the liquid phase. During condensation, the heavier water molecules preferentially join the liquid phase while the lighter water molecules are likely to remain in the vapor phase longer. The fractionation factor (α) between two groups of molecules (A and B) can be calculated by using the following equation (Kendall & Caldwell, 1998): (1000 + ) = (1000 + �) �−� � � �� Fractionation occurs when water undergoes phase changes such as evaporation and condensation as it moves through the water cycle, so the isotopic composition of a water sample can provide information on the sources of the water. Kirschenbaum proposed that the Rayleigh distillation equation, used to calculate the amount of material removed from a system by evaporation, could also be applied to isotopes of water (Friedman et al., 1964; Kirshenbaum et al., 1951). The two main variables in the Rayleigh equation are temperature and the initial composition of the “mixed” system. The fractionation of isotopes of water fits into the following equation and can be used to describe the isotope composition of reservoirs of water as evaporation takes place (Kendall & Caldwell, 1998):

= ( ) �−1 � �0 ∗ � Where R0 is the initial composition, f is the fraction of the remaining composition, and α is the fractionation factor (Gat, 1996). As the process takes place, the product R continuously becomes the new initial composition Ro. Since temperature has a predicable effect on fractionation during condensation and evaporation, a phenomenon called the “temperature effect,” isotope data can provide information on the temperature range that a water sample was exposed to, or the climate of the area from which it came. The process of evaporation at cooler temperatures results in greater fractionation. Isotopes in Precipitation

38 Precipitation data was one of the initial lines of large-scale scientific investigation to which isotope chemistry was applied. It was a topic of interest early on and one of the first articles on the subject was published in 1939 by R.V. Teis in the Soviet Union on the “Isotopic Composition of Rain Water” (Teis, 1939a). In 1961, a short article was published in the journal Science in which the researcher H. Craig put forward the linear relationship between the δD and δ18O values that he and other researchers like I. Friedman had seen as a “rough correlation” in the preliminary isotope analyses of freshwater since the 1930s (Craig, 1961a; Friedman, 1953). Craig based his graph and equation on about 400 samples of meteoric water (rain and snow) and surface water (rivers and lakes). There were some samples from “closed basins” that showed evaporation enrichment by plotting to the right of the trend line, but the vast majority plotted on a straight line with the following equation:

= 8 + 10 18 �� ∗ � � The linear equation demonstrating the co-variance of δD and δ18O in precipitation would become commonly known as the global meteoric water line (GMWL) (Craig, 1961a). A paper in 1993 compared the original equation, based on the first few years of isotope data for precipitation from the early 1960s, with calculations based on the long-term data sets gathered over 40 years. The equation they found was very close to Craig’s original GMWL equation (Rozanski et al., 1993). A local meteoric water line (LMWL) can also be determined for specific geographic areas and can be helpful when using 18O and D as hydrologic tracers in localized systems. The GMWL and LMWLs can be used to indicate if evaporation has occurred to a water sample being analyzed. Precipitation samples that have undergone evaporation have isotope values that plot to the right of the meteoric water line, and their linear regression will have a lower slope value than those of the meteoric water lines (Coplen, 1993).

A useful analysis of precipitation samples is to compare them with the GMWL by calculating the “deuterium excess,” originally described by Dansgaard as the “d-index” (Dansgaard, 1964). A linear regression of the δD and δ18O values will produce a y-intercept value, usually between 0 and 20‰, which can be compared with the value of 10‰ for the GMWL. D-excess indicates how much the values differ from the GWML and can be used to

39 document seasonal patterns in the isotope composition of precipitation or the degree of evaporation water samples have undergone. D-excess is calculated by rearranging the equation for the GMWL:

= 8 18 � �� − ∗ � � Observed Effects Facilitated by the research demonstrating consistent patterns seen in isotopes of meteoric waters, a concerted global effort to document the variations in the 18O, D, and 3H composition of precipitation was organized by the IAEA and the World Meteorological Organization (WMO), beginning in 1961 (Dansgaard, 1964). In the 1960s, rain-gage stations were set up in all areas of the globe with the omission of Russia and China—presumably a consequence of Cold-War era politics. In the first few years of the world-wide “precipitation survey,” monthly samples were collected from between 100 and 220 stations in 65 different countries (Rozanski et al., 1993). The data from 1961 to 1964 were summarized by Dansgaard, who provided a geographic plot of δD and δ18O values explained by the physical conditions that the water molecules in precipitation were exposed to along the way (Dansgaard, 1964). The IAEA continued to collect precipitation data under its Global Network for Isotopes in Precipitation (GNIP), though the program saw a reduction in the number of stations it ran in 1977. Even as of 1993, a large void remained on the global map of station locations over central Russia and Siberia (Rozanski et al., 1993). The IAEA continues to assemble GNIP data on a voluntary basis and, though there are limited long- term data sets, there are many sets with less than 15 years of data (IAEA, n.d.). Reviews of the long-term precipitation data analyses have shown that the patterns for different geographic locations seen early on have been consistent (Rozanski et al., 1993). Recently, there has been new interest in how climate change may alter the temporal and spatial patterns in isotopic composition in precipitation and groundwater (Négrel & Petelet-Giraud, 2011).

Dansgaard had documented these geographical patterns based on a limited number of samples in 1953 but, with the results from the initial years of the global precipitation survey, he formalized the concepts in his 1964 paper “Stable Isotopes in Precipitation” (Dansgaard, 1964). The same year, these patterns seen in “natural waters in the hydrologic cycle” from a large data

40 set of water samples were also documented in another foundational paper put forward by I. Friedman of the USGS, A. Redfield of Woods Hole Oceanographic Institute, and others (Friedman et al., 1964). One of the global patterns observed early on was a gradient in isotope values for precipitation from the equator to the poles. The pattern can be explained by the temperature effect and the progressive depletion of precipitation as water cycles from the equator, where more evaporation occurs. The observation of increasingly more depleted δD and δ18O values in precipitation approaching high latitudes, and values closer to the zero value of the mean ocean water standard at low latitudes, was described by Dansgaard (1953, 1964) and became known as the “latitude effect.”

A pattern similar to the latitude effect was seen in precipitation that condensed at higher versus lower altitudes and was called the “altitude effect” or “elevation effect.” As elevation increases, there is a predicable decrease in temperature at a rate of about −.65 °C for each 100 m (Rogers & Yau, 1989). In 1956, a data analysis by Epstein explored the pattern in detail by measuring isotope values for precipitation over increasing elevations on Mt. Wilson in the San Gabriel range northeast of Los Angeles. The altitude effect can be very useful in hydrologic tracer studies. For example, water from a spring with a watershed encompassing both mountainous and lower-lying areas was tested to see what portion of springflow came from the precipitation in the highlands (Minissale & Vaselli, 2011). Minissale and Vaselli used isotope data from the karst springs in central Italy to determine the amount of recharge they received from the Apennine Mountains and suggested the method as a more efficient way of gathering precipitation data than installing numerous rain gages throughout the mountainous terrain. Fractionation due to the cooler temperatures at elevation has also been used in a study by researchers in California, where groundwater resources are under increasing demand due to population growth and an extensive agricultural industry, and are often diminished by increased droughts linked to climate change (Moore, Ekwurzel, Esser, Hudson, & Moran, 2006). Moore and her team used D and 18O isotope data to determine the percentage of the groundwater in the semi-arid Livermore Valley that had been “imported” from the San Joaquin and Sacramento Rivers via the South Bay Aqueduct and then applied that information to determine sources of nitrate pollution using 15N and 18O isotope analyses (Moore et al., 2006).

41 Another observed pattern, known as the “continental effect,” described the composition of precipitation as it becomes progressively depleted of heavier isotopes the farther it is removed from an oceanic source (Friedman et al., 1964). A review of the GNIP data in 1993 confirmed the expected pattern of lighter and lighter isotope values from the Atlantic Ocean across Europe, which has the highest density of collection sites, into eastern Russia, and stated that the pattern probably continued farther, though there were no stations past the Ural Mountains at the time (Rozanski et al., 1993). The continental effect on δD and δ18O in precipitation was employed by L. Tian and other researchers to distinguish the geographic areas of the Tibetan Plateau that were beyond the reach of the Indian monsoons (Tian, Masson-Delmotte, Stievenard, Yao, & Jouzel, 2001). They found that the region between the Himalayas and a more northerly mountain range had the most direct precipitation from the monsoons, whereas the region further north showed precipitation with the signature due to the continental effect, and the region south of the Himalayas had a signature of monsoon rain recycled over the landmass of India (Tian et al., 2001).

The “amount effect” was first described by Dansgaard in 1964, based on the observation of lower isotope values for monthly mean isotope values during times of the heaviest rainfall at tropical locations. There was an amount effect seen in the data year round at GNIP locations near the equator and a seasonal occurrence seen for locations at middle latitudes during the summer, but there was no evidence of an amount effect occurring at high latitudes (Dansgaard, 1964). The amount effect is caused by the process of evaporation forming an air mass depleted in comparison with the source water, the water vapor being transported away from the original source, the preferential removal of the isotopically heavier water molecules as precipitation, and the repetition of the cycle, resulting in lighter and lighter δ values for the precipitation. In order to observe the results of amount effect alone, rain intensity was compared with GNIP monthly mean data from stations located on islands in tropical areas, which were not subjected to altitude or continental effects, or seasonal temperature changes (Rozanski et al., 1993). The amount effect has been studied with greater resolution by looking at single storm systems (Dansgaard, 1953; Gambell & Friedman, 1965). Matsuo and Friedman (1967) even analyzed precipitation samples collected in 1 mm increments. In individual storms, the lightest isotopic composition is

42 usually seen in the samples taken during the times of the heaviest rainfall (McDonnell, Bonell, Stewart, & Pearce, 1990; Rozanski et al., 1993).

Isotopes of Seasonal Precipitation Many of the summer storms in Florida and other humid semi-tropical regions are caused by thermal convective systems. Especially during sunny days, air masses near the earth are heated and rise up while carrying water vapor into the atmosphere. The change in temperature and air pressure with altitude then causes the vapor to condense and return as rain to the same general area unless there is a strong front that moves the air mass a long distance. The convective systems of summer usually tend to recycle the local water, so the precipitation does not show a large overall difference in isotopic composition compared to the average precipitation the location receives (Ingraham, 1998). The increasingly depleted values for δD and δ18O due to the amount effect occur in tropical regions and at slightly higher latitudes.

The isotope composition of hurricanes and tropical storms has been investigated in detail by S. Gedzelman, J. Lawrence, and others. They found that rainfall from hurricanes and tropical storms can have a variable but sometimes extremely light isotope composition and they even found that isotope values were progressively lighter moving away from the center of Hurricane Opal (Gedzelman et al., 2003). They noted that Lawrence and White (1991) had previously observed that “remnants” of tropical storms had some of the lighter precipitation values, with δD values of −89‰ after Tropical Storm Dean in 1983 and −47‰ after Tropical Storm David in 1979 (Lawrence & Gedzelman, 1996). The most negative values they documented were for samples collected during Hurricane Olivia in 1994, late in the storm’s progression; the minimum values were δD = −201.5 and δ18O = −26.1‰. Research done by C. Odezulu on the “Stable Hydrogen and Oxygen Isotopic Variations in Natural Waters in North Florida” (2011) showed that the isotope composition of rainfall from tropical storms could have extremely depleted (negative) δ values compared to the mean ocean water standard. When Tropical Storm Alberto came to the Tallahassee area on June 16, 2006, rain samples had values of δD at −86.8‰ and δ18O at −12.3‰, and isotope compositions of rain from Tropical Storm Fay on Aug. 22, 2008 were −99.4‰ for δD and −13.6‰ for δ18O (Odezulu, 2011).

43 Different seasonal patterns in the δD and δ18O values of precipitation can occur from the combination of the different observed effects. Seasonal changes in air temperature in mid- latitude locations cause a pattern of more depleted δ values in winter than in summer. It is possible that the amount of ET, which peaks during late summer, also affects the isotope values for recharge during the summer; this is indicated by higher δ values than expected from increased temperatures alone (Rozanski et al., 1993). GNIP data showed that there were more pronounced seasonal differences at mid- and high-latitude locations in the Northern Hemisphere than in the Southern Hemisphere due the greater ratio of land mass to ocean in the north, causing greater seasonal air temperature fluctuations and increased continental effects (Rozanski et al. 1993).

Precipitation with distinct seasonal variations can also occur because of changes in dominant weather patterns. Different meteorological conditions responsible for the formation and transportation of storm systems can change with the seasons. For example, a U.S. east coast, mid-latitude location could experience prevailing winds from the far north in the winter that produce isotopically lighter precipitation due to the continental effect and cold temperatures. In the summer, the area could mostly receive rain from storm fronts formed over the ocean with δ values closer to zero. Data on D and 18O for monsoon precipitation were used by Négrel and other researchers in the region of Andhra Pradesh in southern India to gain insights into the water sources, storage, flow paths, and amounts of return flow from irrigation in a highly agricultural watershed (Négrel et al., 2011). They found that the summer monsoon rains rapidly recharged the aquifer with light-isotope water after the intense irrigation of rice paddies in the spring had produced a distinct evaporation signal in the aquifer, and their findings informed plans for long- term water use to prevent over-exploitation of the local water resources (Négrel et al., 2011).

The seasonal differences in isotope values do not need to be dramatic to be of use in calculating the transit times and storage characteristics of a catchment (Maloszewski, Rauert, Stichler, & Herrmann, 1983). Reddy, Schuster, Kendall and Reddy (2006) used the small seasonal changes in isotope values of lake water and precipitation, modeled as sine functions, to estimate the residence and transit times of groundwater in a small watershed. The predictable seasonal fluctuations, of about 15‰ in δ18O values in precipitation, were used to estimate transit

44 times for a watershed in Minnesota that were within the limits of uncertainty for other methods such as water aging using 3H. They found that the seasonal signal was clearer in some parts of the watershed, such as the headwaters, but that the method was limited in other parts by a high degree of surface water from lakes interacting with the groundwater (Reddy et al., 2006). An even smaller seasonal fluctuation in δ18O values of about 5‰ was used in a study of a small watershed in Japan to calculate the relatively short amount of time it took shallow groundwater to emerge from springs or to migrate into the deeper aquifer (Kabeya, Katsuyama, Kawasaki, Ohte, & Sugimoto, 2007).

Isotopes of water have been used in numerous studies of springs and have been shown to be effective tracers. In aquifers that have geothermal water and springs, the isotope values of δD and δ18O are changed by water–rock interactions for which fractionation factors are temperature dependent, making the use of isotope tracers more difficult (Truesdell & Hulston, 1980). Isotope analyses can provide information on flowpaths, transit and residence times, streamflow generation processes, and aquifer characteristics, and are often combined with analyses of geochemical tracers and springflow measurements. Al-Charideh (2011) used stable isotopes to demonstrate residence times of 50 to 60 years for karst springs in Syria; the researcher’s goal was to inform water use regulations as the springs have shown dramatic reductions in flow. Fifty years ago, Ras Al-Ain Spring had been one of the largest in the world, with a mean springflow of 40 m3/s (1,400 cfs) but, due to high demand for the local water resources, the spring now only flows during part of the year (Al-Charideh, 2011). One of the first large studies to apply stable isotope tracers to springflow was performed by researchers in Germany, who used 18O and 3H data to determine the runoff components for an Alpine catchment and calculate transit times for its karst springs (Maloszewski, Rauert, Trimborn, Herrmann, & Rau, 1992). The researchers later used hydrograph separation in order to separate conduit from matrix flow for two of the springs. They estimated that conduits connected to swallets contributed about 15% of the total springflow (Maloszewski, Stichler, Zuber, & Rank 2002).

Hydrograph Separation One of the initial hydrologic uses of water isotope measurements was to apply the isotope data to the method of determining aquifer characteristics called hydrograph separation or base

45 flow recession analysis. Previously, a graphical method was used to divide a time series of streamflow or hydrograph into “quick flow,” the rapid response of a river to a precipitation or snowmelt event, and base flow components, which was indicated by the rate of change of the recession of the rise (Tallaksen, 1994). The graphical method grew out of early hydrologic modeling equations. In 1877, Boussinesq’s work on the “theory of water currents” included a differential equation to model the movement of water from an unconfined aquifer to streams and rivers. It was combined with findings by Dupuit, the French “hydraulician,” into the Dupuit– Boussinesq equation. The model represented ideal conditions and, at the time, methods were not available to reliably test the results. Later, hydrologists such as Brutsaert and Nieber (1977) added functions based on empirical data, such as groundwater measurements, storage capacity, soil types, porosity, and transmissivity in order to adapt the theoretical model to the complexities of natural systems. The graphical method of hydrograph separation was put forth in 1905 by Maillet, the French Ingenieur des Ponts et Chaussées (“Engineer of Bridges and Metalled Roads”), who built upon equations from the German mathematician Gauss. Maillet was able to infer aquifer storage and predict the base flow in tributaries to the Seine River (Minchin, 1905). The equation was also used to predict peak flows. The equation described the recession of peak streamflow (Q), or the “falling limb” portion of a hydrograph:

/ = −� � �� �� �

In which t is the time from the peak discharge (Qp) and K is the decay constant of the curve (Buttle, 1998). More modern hydrologists made additions to the equation, such as different decay constants for laminar and turbulent flow (Schöeller, 1967). A downside to the graphical recession analysis was that the distinction between the quickflow from the storm and the return to base flow from long-term groundwater sources could be subjective. After the application of stable isotopes to hydrograph separation, the graphical approach fell out of favor, but it is still sometimes used in modern studies. One such study was a set of Bayesian analyses of springflow recessions to describe groundwater storage for springs in France, Bosnia Herzegovina, and the U.S.A., including Silver Springs in Florida (Carlier & El Khattabi, 2015).

46 Information from isotope tracers, such as D, 18O, and 3H, allowed for a more empirical and objective method of hydrograph separation. Isotope data allowed for the separation of “old” pre-event water in the aquifer from the “new” event water from precipitation or snowmelt by incorporating them into a two-component mixing model. Since the early 1960s, 3H had been known as a tracer for calculating water ages and other hydrologic applications (Brown, 1961; Eriksson 1958, 1963). Klaus and McDonnell (2013) found that 3H was the first isotope used in a published recession analysis. Hubert, Marin, Meybeck, Olive, and Siwertz (1969) documented a record flood on the Dranse River near Lake Geneva in 1968 through the collection of 3H data along with other geochemical and sediment data. Other researchers used 3H to calculate runoff and runoff coefficients (Crouzet, Hubert, Olive, Siwertz, & Marce, 1970), but stable isotopes were beginning to be applied to hydrologic investigations as well. In 1970, Dinçer, Payne, Florkowski, Martinee, and Tongiori used 3H and 18O data to calculate the percentage of runoff due to snowmelt. A publication by the IAEA in 1974 showed how 18O could be applied to hydrograph separation (Mook, Groeneveld, Brown, & Van Ganswijk, 1974). Though the isotope values of snow present some complicating factors, it became a common analysis after D was used for a study of snowmelt runoff published in 1978 (Herrmann, Martinec, & Stichler, 1978).

A geochemical two-component mixing model was put forward by Pinder and Jones in 1969 that used a mass balance equation to separate pre-event water from event water based on solute concentrations. Their study unexpectedly indicated that rain made up less than half of the streamflow during the rise and recession. Sklash, Farvolden, and Fritz published a study in 1976 in which they used 18O in a two-component mixing model (supplemented with specific conductance measurements) and found similar results to Pinder and Jones, with less than half of the storm runoff causing peak streamflows attributed to precipitation. They concluded that groundwater discharge was a major component in the total discharge during peak flows. In 1979, Sklash and Farvolden’s article “The Role of Groundwater in Storm Runoff” overturned the previous theory that slow-moving groundwater played a minimal role in the amount of streamflow after storms. Prior to the new discovery, it had been thought that precipitation delivered by Hortonian overland flow (Horton, 1933), or shallow water movement near the soil surface but above the water table (Hursh, 1936), generated the storm runoff. Overland flow had

47 been assumed to occur over the entire drainage area, but, in the 1960s, studies had already indicated that it was a more localized process.

Once the contribution of groundwater was realized to be significant to storm runoff (streamflow generation) and not just to base flow, the mechanism of groundwater storage and delivery became a pressing line of inquiry. Sklash and Farvolden (1979) installed a few piezometers close to the streambanks and looked at nearby well elevations to demonstrate a rapid increase in hydraulic head close to the stream. When investigations measured the groundwater response near the streams in more detail, it was found that the response of groundwater in and near the river banks was still less than the amount of pre-event water seen in the hydrograph separation results (Waddington, Roulet, & Hill, 1993). One mechanism of water movement to a spring or river was explained by a displacement of soil water present before a storm with water from the storm; this is sometimes called “translatory flow” (Buttle, 1998; Hewlett, & Hibbert, 1967). A study in 1986 indicated that soil water may play a larger role than previously realized (Kennedy, Kendall, Zellweger, Wyerman, & Avanzino, 1986). DeWalle, Swistock, and Sharpe (1988) used 18O in a three-component mixing model to distinguish soil water from the precipitation and groundwater components. Three-component mixing models, usually using two tracers, were increasingly applied to hydrograph separations, and they found that soil water could contribute a significant amount of water to storm runoff. Soil water accounted for 24% of the storm runoff in a study by DeWalle et al. (1988), 19% of the runoff in the study by Ogunkoya and Jenkins (1993), and 36% and 25% of the runoff for two sites in a study by Bazemore, Eshelman, and Hollenbeck (1994), with percentages even greater at peak flow in all cases. In karst aquifers, epikarst storage, beneath the soil layer but above the water-saturated karst, was discovered to also be a component in storm runoff (Aquilina, Ladouche, & Dörfliger, 2006; Hu, Chen, Nie, & Wang, 2015; Perrin, Jeannin & Zwahlen, 2003).

Hydrograph separation using stable isotopes became a widely used tool in hydrologic studies and, as its different applications, improvements, and limitations were learned, variations of the original method emerged as hydrologists tailored investigations to the particular characteristics of their watersheds of interest. Many investigations included additional geochemical tracers and were able to differentiate components more clearly than isotope data

48 alone, though the isotope and geochemical tracer results did not always agree (Hooper & Shoemaker, 1986; Rice & Hornberger, 1998). Researchers have also gained insights into the hydrology of their watersheds by using more complex models with three or more components (Lee & Kroethe, 2001; Uhlenbrook & Hoeg, 2003), and sometimes by incorporating geochemical models such as end-member mixing analysis (Christophersen, Neal, Hooper, Vogt, & Andersen, 1990; Hooper, Christophersen, & Peters, 1990).

Scientists have continued to apply the techniques in novel ways. Gremillion, Gonyeau, and Wanielista (2000) used stable isotope hydrograph separation to “detect changes in flow paths in a watershed undergoing urban development.” Talarovich and Kroethe (1998) used a three- component mixing model to inform remediation plans for a springshed with subsurface contaminants of polychlorinated biphenyls (PCBs), and found that the springflow after storm events was predominantly water from the epikarst that transported the PCBs. Researchers used

isotope hydrograph separation to link their observed pulses of dissolved CO2 entering a headwater stream in the Amazon to the soil water component of storm runoff (Johnson, Weiler, Couto, Riha, & Lehmann, 2007). Despite the variety of different hydrologic settings studied, the large body of research shows that, in most cases, pre-event water contribution to the peak discharge was at least 50% of the total (Buttle, 1998).

49 CHAPTER 2

METHODS

Field Methods

Data collection consisted of sampling water from springs and seasonal precipitation for isotope analyses of D and 18O. Water quality data was collected by YSI multiparameter sonde readings for pH, specific conductivity (SC, indicative of ions of dissolved solids), dissolved oxygen (DO), and water temperature taken at the time of water sampling. The USGS stream- gaging station designated as Wakulla River near Wakulla, FL (station no. 02327022) was maintained as part of my duties as a USGS hydrologist. Equipment at the gage recorded values for water velocity, river level (stage), water temperature, and diagnostics from the in situ ADVM at 15-minute intervals. Regular streamflow measurements were made to relate the index-velocity

(Vi) data and stage-area values to the mean measured water velocity for the entire cross section. That information was used to calculate 15-minute unit values and daily values of streamflow (discharge) for the Wakulla River. A water sampling permit issued by the Florida DEP allowed for the collection of water samples within state parks.

Spring Water Sampling Water samples were collected from 26 Florida springs under base flow conditions from Jan. 14 to Feb. 18, 2012; of the springs, 21 were sampled later during non-base flow conditions, from Sept. 20 to Nov. 9, 2012. Water samples were collected from Wakulla Spring between Feb. 10 and Aug. 27, 2012 and between March 19 and Oct. 21, 2013. The sample bottles used were Boston round 60 ml (2 oz.) clear glass bottles with Polyseal caps, which have a plastic cone underneath to prevent evaporation. Methods for the collection of water samples for isotope analysis of D and 18O were based on those recommended and used by the USGS as follows: “Do not field rinse bottle. Do not add chemical treatment. Fill bottle two-thirds full with either raw or filtered water so that if sample expands or freezes during shipping, bottle will not break. Cap bottle with Polyseal cap” (Révész & Coplen, 2008; USGS Reston Stable Isotope Laboratory, n.d.). The samples were transported by hand to the labs, so the recommendations for shipping glass bottles were not necessary. Bottles were filled close to the top to minimize evaporation in

50 the head space. Bottles were double-bagged in sealable plastic bags and stored in a refrigerator to keep temperatures stable at 5 °C (41 °F).

When sampling spring water, a minimum of two bottles were used to allow for at least one replicate. Sampling began with three replicates: two 60 mL (2 oz.) bottles with Polyseal caps and one 11 mL (0.4 oz.) glass vial with Teflon caps. The small-vial replicates were taken to provide a set of samples for easy long-term storage. However, after the first set of spring water samples were run for δD and δ18O on the Los Gatos liquid-water isotope analyzer, a slight difference was seen in the values for the replicates in the small vials, possibly due to a small amount of evaporation from the Teflon caps. Afterwards, the 60 mL Polyseal capped bottles were used exclusively with one or two replicates. As samples were analyzed, some bottles used for the duplicates or triplicates were reused after being dried completely. It is important that there is no moisture in the sample bottles when water samples are taken and that the caps are tightly secured to prevent evaporation. Replicates were taken in case a sample bottle became damaged or was not sealed properly. Bottles were labeled with printed waterproof labels with the USGS station names and numbers and the sample dates and times.

Surface water grab samples for Wakulla Spring were collected close to the visible boil over the spring vent from a state park boat operated by state park employees. Sample bottles were submerged at arm’s length and capped underwater after all the air bubbles were gone. During the period of flooding from June 26 to July 5, 2012, due to precipitation from Tropical Storm Debbie, park boats were not going out, so samples were taken from the ladder of the observation tower deck. In 2012, only surface water grab samples were collected. In June 2013, the use of a permanently installed sample line was offered by Florida DEP, and FGS staff demonstrated the vent line sampling procedures. When vent line sampling began, water samples were taken using both methods for comparison. Vent line water samples were collected by using a peristaltic pump to fill the same type of sampling bottles used for the grab samples. Before the bottles were filled, the battery-operated pump was used to purge the line of four gallons (15 L) of water (based on the pumping rate and length of tubing). Water quality readings from a YSI multi-meter were observed as the line was purged to ensure that the readings were stable prior to collecting samples. The timing of the water samples for the 2012 Atlantic hurricane season was

51 monthly during base flow conditions before the season started, bi-weekly during the hurricane season until a large storm occurred, and then daily (when possible) in the immediate days following the storm. Sampling intervals progressively increased as the high flows receded. The 2013 hurricane season had many localized summer storms but no large tropical storms or hurricanes. Samples were taken once or twice a week from May 28 to Oct. 21, 2013.

In addition to the storm sampling at Wakulla Springs, two sets of water samples for isotope analysis were collected from 20 springs in north and central Florida. The first round of sampling was done at 26 springs during a period of prolonged drought in the winter of 2012 to determine the isotopic composition of the springflow during base flow conditions. The second round of sampling was done in the late summer and fall of 2012 after the Atlantic hurricane season brought record rainfall in order to see the range in the isotopic composition of the springflow. Six of the initially sampled springs were not reached during non-base flow conditions for the second round of sampling. The base flow springs’ sampling began in January 2012; many parts of north Florida had been under drought conditions since the previous fall (Fuchs, n.d.). The springs were listed in alphabetical order and assigned a “Spring No.” to document the samples, along with the USGS name and station number used to label the sample bottles. The USGS assigns a unique name and number for each spring in the U.S.A. The same system is used for river gage or sampling locations, lakes, and wells. The site number is usually an eight-digit number based on the number of the hydrologic unit. The USGS streamflow-gaging station Wakulla Spring near Crawfordville, FL has the number 02327000, and the site three miles downstream is Wakulla River near Crawfordville, FL with the number 02327022 (numbers increase for sites downstream). If a number is needed for a new location for which upstream and downstream sites already have subsequent eight-digit numbers, a 15-digit number based on latitude and longitude is assigned. Fifteen-digit numbers contain location information, so the name of the nearest town is not included in the station name as it is for sites with eight-digit numbers. If the site location is in close proximity to the town, the station name will use “at” (i.e., Ponce de Leon Spring at Ponce de Leon, FL). Most of the station names use “near” to describe the location (i.e., Silver Glen Spring near Astor, FL).

52 Of the 26 springs sampled besides Wakulla Spring, nine were first magnitude, twelve were second magnitude, and five were classified as third magnitude. Eight of the springs had regular springflow measurements made by USGS staff near the date of the water sampling, and seven of those had calculated springflow values from USGS streamflow-gaging stations at the site. The methods for collecting grab samples for isotope analysis were the same as the methods used at Wakulla Spring. Samples were taken as close to the spring vent as possible. Sampling from a kayak was the preferred method but, at some springs, approaching the vent was done by wading, swimming, or from a dock. At remote Shepherd Spring, which is frequented by alligators, samples were taken from the bank at the point where the spring pool narrows into the spring run.

Streamflow Monitoring In addition to water sampling, the USGS streamflow-monitoring station on the Wakulla River was maintained as part of my duties as a USGS hydrologist. The gage is located just outside the Wakulla Springs State Park’s southern boundary, where the river is made up of flow from Wakulla Spring, Sally Ward Spring, and McBride Slough. Gage maintenance required the re-installation of an in situ ADVM and regular streamflow measurements (usually monthly) to correlate the measured flow with the recorded water-velocity data. The USGS monitoring equipment was powered by a solar panel and housed in a metal shelter attached to the downstream side of the County Road 365 bridge. A high-data rate Sutron® 8210 with SatLink satellite telemetry capabilities recorded data from a Design Analysis® H-310 submersible pressure transducer and a Sontek® Argonaut SL (side-looker) ADVM. The data recorder stored

15-minute values for the river level (stage or gage height), index velocity (Vi), water temperature, and diagnostics for the velocity meter (Fig. 12). Recorded data were transmitted hourly to one of the National Oceanic and Atmospheric (NOAA) geostationary operational environmental satellites (GOES), which are stationed around the Earth’s equator. The data were then relayed to a USGS internal database and onto the USGS National Water Information System (NWIS) public webpage (www.waterdata.usgs.gov/fl/nwis/rt). Current and historic data for Wakulla River near Crawfordville, FL (station no. 02327022) are published on the same website.

53

Figure 12: Example of Water-velocity Data for Wakulla River. Water velocities are computed based on 5-minute averages of ADVM index velocities recorded every 15 minutes.

The submersible pressure transducer was housed in PVC pipe and set to steel-tape readings from a reference point on the bridge, which had an elevation determined by running levels from the Florida Department of Transportation benchmark at the end of the bridge. The recorded stage values were compared to the physical tape-downs to the water’s surface at each visit to the site.

In situ velocity meters do not measure the water velocity across the entire channel but, rather, velocities within a programmable distance from the instrument, which creates a cone- shaped sample volume. The sampling volume for the ADVM at Wakulla River was set to start at a distance of 2 m (the blanking distance) and end at 5 m (the cell end) from the instrument. The sampling volume was chosen by adjusting the blanking distance and the cell-end distance to get the most consistent velocity data. Multiple cells can be set within the sampling volume to monitor the changes in velocity throughout the sampling volume and to recover velocity data if

54 cells near the cell end become unstable. The multi-cell data is also useful for monitoring the water velocities within the blanking distance and past the cell end (Levesque & Oberg, 2012).

The ADVM was mounted in the portion of the river cross section that has the highest water velocities, and its depth underwater was documented so that it could be returned to the same position if the instrument needed to be pulled up for maintenance. In situ velocity meters must be installed at a location that has water velocities consistent enough to be used as a proxy or “index” for the average water velocity across the entire channel over the range of water levels. If the location has an eddy or other inconsistency with water flow across the entire channel, the collected Vi will not have a stable relationship with the mean measured velocity (Vm) across the entire channel. At Wakulla River, the extensive width, uneven depths of the streambed, and seasonal growth of submerged aquatic vegetation slightly destabilized the relationship between

Vi and Vm, but the accuracy of the continuous streamflow data was within 10% of the regularly measured values. The ADVM’s internal memory was set to record velocity values at 15-minute intervals by averaging velocity data for five minutes. Since the ADVMs and acoustic Doppler current profilers (ADCPs) use the same physical principles to operate, they must operate at different frequencies in order to avoid interference when ADCPs are used to periodically measure streamflow (Levesque & Oberg, 2012). The ADCP models used to measure the Wakulla River operated at 1,200 kHz and 3,000 kHz, and the in situ ADVM operated at 1,500 kHz. Acoustic Doppler meters operate by producing “pings” that reflect off tiny particles assumed to travel at the same speed as the water that carries them. The ADVM measures the return time and direction of the reflected sound waves or “backscatter.” If the temperature data is not accurate, the velocity data will be biased; for every 5 °C error in temperature, there is about a 1% bias in the velocity data (Levesque & Oberg, 2012). Water temperature data is continuously collected by the ADVMs and ADCPs, and their accuracy is checked regularly with independent temperature readings, though it is rare for the temperature sensors to not read accurately.

Periodic streamflow measurements were made, usually monthly, in order to relate the Vi values measured by the in situ velocity meter with the Vm values associated with streamflow measurements. In that way, the water velocities measured by the ADVM within the set sampling

55 volume could be used as a representation of the mean water velocity across the entire cross section of the river which, at the Wakulla River, is about 100 m (330 ft). A minimum of 22 measurements, made throughout the range in water velocities at a site, are recommended for an accurate rating (Birgand, Lellouche, & Appelboom, 2013). Most of the Wakulla River flow measurements made in 2012 did not use an ADCP due to a lack of access to the type of ADCP designed to operate in shallow water. The streamflow measurements were made using a mechanical velocity meter (Price AA) suspended 0.5 ft above a 15-lb brass weight from the upstream side of the bridge rail by a steel cable on a hand-cranked reel (Type-A); this reel was mounted on a metal-framed “bridge board.” The weighted mechanical meter was used to take velocity readings (minimum 40 seconds each) at about 40 sections across the channel at water depths of .2 and .8 (20% and 80%) of the water depth at each section. This two-point method was changed to a three-point method (the addition of a weighted .6 depth velocity measurement) if a non-standard velocity profile was indicated for a section (Rantz et al., 1982). The water depths and meter depths were read from a mechanical dial that measured out the length of cable after it was zeroed out at the water’s surface. The water velocity was calculated by timing the number of rotations of the spinning cups on the meter. Each rotation was indicated by a clicking sound from a pair of headphones plugged into the reel so that, with every rotation, the axis of the meter with a small magnet or tiny wire “cat whisker” completed the electric circuit with an AA battery and moved a small metal plate in the headphone piece to create a metallic click. The ratio of the number of rotations to the time in seconds was entered into a standardized equation for the Price AA meter to produce the water-velocity values (Rantz et al., 1982). The velocity values were then multiplied by the area measurements (from water depths and widths) to calculate a total discharge value. The mechanical method for streamflow measurements has been used for over a hundred years, but new technology has replaced its use in most cases.

The advanced technologies for measuring streamflow use acoustic Doppler to measure water velocities. ADCPs were initially designed for deep, large rivers, where mechanical-meter measurements were the most challenging. At the Wakulla River, the shallowness of the water, complicated by the growth of submerged and emergent aquatic vegetation in patches across the channel, prevented the use of this method until specialized ADCP models became available. One type of ADCP, the Sontek RiverSurveyor® S5 model, which was developed for shallow water

56 (0.06 to 5 m depths), was acquired by the USGS Tallahassee Office and used for Wakulla River streamflow measurements beginning in 2013. The measurements were made with the ADCP mounted in a small, tethered boat deployed on an extendable pole from the upstream (north) side of the bridge; this location was chosen since there was less submerged aquatic vegetation along the river bed at that section. When river levels were deeper than normal during flood conditions in 2012, a measurement was made by deploying a Teledyne® RDI Rio Grande Workhorse ADCP mounted in a small trimaran-style Oceanscience® Riverboat tethered from the downstream side of the bridge.

Measurements made with ADCPs are considered more accurate than mechanical-meter measurements since they are based on numerous velocity and depth readings compared with the limited number taken manually. However, conditions such as a moving bed during high water velocities, magnetic interference with the instrument compass, and other physical site characteristics may require adjustments to be made to the ADCP measurements, and, if not accounted for, they can introduce error into the measurements (Mueller, Wagner, Rehmel, Oberg, & Rainville, 2014). A moving bed was tested for by running 5-minute stationary moving bed tests in the location of the highest water velocities prior to each measurement. A moving bed has not been documented at Wakulla River, and compass calibrations have not indicated magnetic interference. The two-point method for mechanical-meter measurements assumes a theoretical logarithmic velocity profile (the Prandtl-von Karman velocity distribution formula), which is not always the case (Mueller, 2013). The data collected from the ADCP can be averaged and, afterward, either visually assessed in the manufacturer’s software or processed with a USGS analytical program called Extrap to determine the actual velocity profile by standardizing the velocity data collected (Mueller, 2013). The settings and coefficient provided by the profile analysis can be applied to increase the accuracy of the velocity values used for the top and bottom sections of the channel, in which water velocities are not directly measured by the ADCP. The settings and coefficients that adjust the streamflow calculations usually only change the total streamflow value by less than 5%. For most of the Wakulla River measurements, the bottom extrapolation method was “no slip,” meaning that the attenuation of velocity by the streambed was represented by drawing a curve through zero at the depth of the bed—the shape of the curve is fit by an exponent that, by default, is .1667 (Mueller, 2013). At

57 Wakulla River, the water velocities near the top of the water column were usually indicated as “constant,” and the velocities measured below the ADCP were used to calculate the flow between the depth of the ADCP transducers and the water surface. Under some conditions, such as very high upstream winds, which can slow the surface velocities, a “three-point” setting can be applied to the top velocities. For the majority of sites, which have sufficient depth, normal roughness (Manning’s coefficient), and even streambeds, the “power/power” setting with the default .1667 exponent is used.

A typical ADVM has two beams, and the water-velocity components can be calculated in terms of x and y, which correspond with water velocity moving in the downstream direction and velocity in the cross-stream direction (vertical components z are recorded but should be negligible). The y velocity values are typically very small, with small fluctuations, and the x component is much larger since it represents the downstream movement of water. The Vi data were controlled for quality by running beam checks before and after each streamflow measurement. Beam checks are the live plot of the strength of the return signal to the instrument. They are used to ensure that there is sufficient signal amplitude by making sure that the signal does not diverge from the theoretical decay curve or fall below the instrument noise threshold. By plotting the x and y beam data as a time series, the instrument alignment can be checked to make sure that the instrument has not moved. Visual inspection of the signal amplitude can ensure that the beams did not have side-lobe interference from boundaries such as the water surface, the streambed, aquatic vegetation, or objects between the instrument and the cell end (Levesque & Oberg, 2012). Salinity will also alter the velocity equation for ADVMs operating in coastal and estuarine locations because the speed of sound is different and because haloclines or thermoclines can disrupt the sound waves. The Wakulla River location was far enough upstream not to be affected by salinity and shallow enough not to be affected by thermoclines. The ADVM at the USGS Spring Creek gage is located where thermoclines and haloclines are present.

In order to use the streamflow measurement data and the ADVM data to calculate a continuous record of streamflow for the river, two ratings are created to relate stage, area, Vi, and

Vm together. Streamflow (discharge) Q is equal to the area A (from depths and widths across the

channel) multiplied by the mean velocity Vm. To compute the streamflow record, the area values

58 are taken from a stage-area rating developed from a survey of the streambed and stream bank elevations—the latter are at a chosen or “standard” cross section that can be surveyed at regular

intervals to track changes to the channel geometry. The Vm across the entire width of the river is collected from streamflow measurements and is calculated by dividing the measured streamflow by the rated area (Levesque & Oberg, 2012).

For the Wakulla River, a stage-area rating was created based on the depth profile of the river bed, the bank elevations at the standardized cross section (located on the upstream side of the bridge), and river levels at the times of the measurements. Streamflow measurements made between Oct. 14, 2012 and Aug. 29, 2013 were used to create a compound-linear (segmented-

linear) regression. The two variables were the mean Vi values and the Vm values. Mean Vi was calculated from water-velocity values averaged and recorded every 60 seconds on the ADVM while the streamflow measurement was made. Mean measured velocity was calculated by dividing the measured streamflow value by the area value from the stage-area rating based on the average stage at the time of the measurement. For most sites, the relationship between the mean index velocity and measured mean velocity can be described by a simple linear regression but, in some cases, site conditions require multiple-linear regressions using stage or groundwater levels as an additional variable. In some cases, compound- or segmented-linear regressions are used (Levesque & Oberg, 2012). The linear regression statistics should have an R2 value close to 1.0, a P-value < .05, low standard deviations, and a residual plot that has a random pattern. The rating for the Wakulla River was a linear regression that showed a small break in the slope at higher water velocities, so it was drawn as a compound- or segmented-linear regression. Entire days of either velocity or stage data were not available or usable for the dates of Dec. 9 to 11, Aug. 14 to 20, and Sept. 18, 2012. The daily streamflow values during those dates were estimated based on the partial collected data and by hydrographic comparison with streamflow records from nearby USGS stations. The index-velocity rating produced from the recorded data and the periodic streamflow measurements were used along with the stage area to calculate 15-minute unit values and daily values of discharge (streamflow) for the period of Oct. 14, 2012 (when the gage was re-established) to Sept. 11, 2013 (when the index-velocity meter had to be relocated due to construction on the bridge).

59 Precipitation Sampling During the period of May 1 to Nov. 22, 2013, two tipping-bucket rain gages (one with SDI-12 communication capabilities and one with a counter system as a backup) were installed and wired to an electronic data recorder set to 15-minute intervals in order to document the amount of precipitation the study area received during the 2013 Atlantic hurricane season. The rain gages were located at the USGS Tallahassee Office warehouse lot; they were placed a few feet off the ground and more than 10 ft from any trees or structures in order to prevent droplets from splashing off nearby objects and entering the funnels. The rain gages each had a protective screen and were regularly cleaned to prevent them from getting clogged with leaves or debris. The gages were calibrated before and after the season of data collection. Calibration followed the standard procedure of measuring 450 mL of water into a bottle with a 3/18-inch nozzle and allowing it to drip into the rain-gage funnel. The results were then compared with the expected value for the 450 mL of water for each model of rain gage, which was 55 tips for the rain gages used. The SDI-12 rain gage reports precipitation in tenths of inches, and the counter reports the number of tips, which is then converted to tenths of inches. The data recorder also recorded the battery voltage to ensure that the system did not lose power. Files were downloaded from the recorder, converted to comma separated value (CSV) files, and moved into an Excel spreadsheet.

From May 1 to Nov. 30, 2013, precipitation samples were collected storm by storm for isotope analysis following the guidelines summarized by N. L. Ingraham (1998). Samples were collected at the same location as the rain gages by using a modified rain-gage funnel, which deposited the rain into a glass container with a layer of mineral oil to prevent evaporation (paraffin oil or silicon oil can also be used for this purpose). Samples were transferred from the collection container to capped glass sample bottles within a few hours to a few days, depending on the amount and intensity of rain. The sample bottles were Boston round 120 mL (4 oz.) bottles with Polyseal caps and were stored in a refrigerator at 5 °C (41 °F). The precipitation samples were all run on the Los Gatos liquid-water isotope analyzer. The samples were compared with Los Gatos supplied standards Std#1A, Std#5A, and Std#2.

During two large storms, one in August and one in October of 2013, throughfall samples were collected at the same time as the regular precipitation samples to see if there was any

60 enrichment signal. Throughfall samples were collected in a 1-gallon bucket with a thin layer of mineral oil. The bucket was placed underneath a 50-ft tall, 2 ft in diameter sweetgum tree. Since the isotope composition of throughfall can have greater δ values than the precipitation itself, especially for instances of light rain in lower humidity environments, some researchers studying forested watersheds have used the isotope values for throughfall samples instead of direct precipitation samples in order to gain insight into flow paths and streamflow generation processes through hydrograph separation (Bazemore et al., 1994; Brown, McDonell, Burns, & Kendall, 1999; Kabeya et al., 2007; Kubota & Tsuboyama, 2003). In the case of tropical storms and intense summer storms in Florida, the assumption was made that the high humidity would prevent significant enrichment of the rainwater. A limited comparison was made to see if the assumption was supported. Throughfall samples and the regular precipitation samples collected at the same time were analyzed using isotope ratio mass spectrometry.

Water Quality Data At the time that each spring water sample was taken at Wakulla Spring and the other springs, a YSI meter was used to measure water temperature, SC, DO, and pH (except for a few instances when the meter was sent off for repairs). The YSI meter with multiparameter sondes was calibrated based on the guidelines in the USGS National Field Manual for the Collection of Water-Quality Data (2005). Calibration for specific conductance was done as a two-point calibration, with a third standard as a check, using potassium chloride (KCl) standard solutions of 100, 250, or 500 μm/cm3 at ambient temperature. Calibration for pH was done to a buffer solution of 7.00 (corrected for temperature). A membrane-type DO probe was used for the 2012 water sampling dates. The optical DO probe used in2013 was calibrated to water-saturated air. Though the DO probes, especially the membrane-type probe, did not always provide reliable data (as inferred from unstable readings), DO values were recorded despite the uncertainty of their accuracy. Calibration notes, including temperatures and lot numbers of the standard, were recorded in a calibration notebook kept with the instrument. All field notes were recorded in a Rite in the Rain® all-weather field notebook.

61 Laboratory Methods

Los Gatos Liquid-water Isotope Analyzer Water samples were analyzed at the FAMU Wetlands Ecology Laboratory. Samples were run on a Los Gatos Research Inc. liquid-water laser isotope analyzer (model DLT-100) with an auto-injector array that can hold up to 200 sample vials and automatically moves the microliter syringe to each vial for sampling. The instrument operates by laser absorption spectroscopy. Since different isotopologues of water absorb light at different wavelengths, the instrument measures absorptions of the isotopologues of water to determine the δD and δ18O values of a water sample. The manufacturer’s reported precision (1σ) for δD is < 0.6‰, and the precision for δ18O is < 0.2‰ (Los Gatos Research Inc., 2008, 2010). Independent testing has shown that the instrument’s precision values are usually at or below the reported amounts. Penna et al. (2010) tested four different Los Gatos liquid-water isotope analyzers on a variety of water samples and found that precision for only one of the four instruments was found to be outside of the manufacture’s reported range; still, it had enough accuracy to match the isotope measurement results of the other instruments. Wen and others (2012) found that precision for measurements of water vapor with the Los Gatos liquid-water analyzer were at or within the reported values; the researchers used 0.4‰ for δD and 0.2‰ for δ18O for their comparison of different commercially available types of instruments. It was later discovered that some dissolved organic compounds with similar absorption properties can affect the precision of the laser-absorption-based analyzers (Singleton et al., 2009).

The method of laser absorption that the Los Gatos laser isotope analyzer uses is “off-axis integrated cavity output” spectroscopy (Leen, Berman, Liebson, & Gupta, 2012). Like other laser absorption spectrometers, it allows isotope measurements that would usually require the use of isotope ratio mass spectrometry to be done through a faster and less labor-intensive process. As this method measures δD and δ18O at the same time, it reduces the potential introduction of error through having to run samples at separate times to measure 18O and D. It operates by a near- infrared laser passing through a sample of vaporized water and a photodiode measuring the absorption of the water molecules (Los Gatos Research Inc., 2008, 2010). In order to increase the sensitivity of the instrument, the laser is reflected between two mirrors on either side of the tube-

62 shaped cavity. By reflecting the laser multiple times, it allows for “path length enhancement” by extending the effective length of the laser and precluding the need for the exact alignment of a single laser (Berman, 2014). There is a gas inlet on one side of the optical cavity and a gas outlet connected to a vacuum tube on the other side to allow the introduction and removal of the vaporized water samples by dry carrier air. The air tubing is connected to clear air-dryer canisters filled with colored silica desiccant that turns pink when it requires replacement. A wavelength detector quickly measures the frequencies of the light passing through the optical cavity (within 3 milliseconds) and produces a transmission spectrum (Berman, 2014). The isotopic composition of vaporized water samples can then be determined from the transmission spectrum (laser absorption) based on Beer-Lambert’s law, which relates light absorbance and the light’s path length to the concentration in a sample causing light attenuation (Berman, 2014; Penna et al., 2010).

Prior to running the sets of water samples on the Los Gatos liquid-water isotope analyzer, transmission spectrum graphs were checked for a set of blanks (deionized water samples). The transmission graphs show three dips in signal intensity due to light absorption by the water 16 18 16 isotopologues H2 O, H2 O, and H2H O (Leen et al., 2012). The middle peak in absorption needs to be centered near −1.0 GHz (relative frequency) to insure that the mirrors that reflect the laser are properly aligned. The transmission spectrum should be between 0.3 and 3.0 volts (V) and the absorption spectrum should be 5–60% (Los Gatos Research Inc., 2008, 2010). The transmission spectrum for pure water can be written as the following equation, where Iv is the measured laser transmission, v is the laser frequency, bn are baseline coefficients, G is the gain factor for the tube capacity, and Vn are functions for the Voigt line shape (Leen et al., 2012):

= ( 2 .) �0+�1�+�2� +⋯ �� 1+� �1+�2+�3+⋯ Three working standards were chosen with values that bracket the δ value range of the samples being tested. The standards used were Std#1A, Std#2, Std#3, Std#4, and Std#5, which are produced by Los Gatos Research Inc. Water samples and working standards were manually transferred from the glass sampling bottles by high-precision pipette in aliquots of 1000 μL into

63 small glass vials while limiting the water surface exposure to air to prevent evaporation. The vials were tightly capped with plastic threaded caps with a flexible membrane (septum) in the center to allow the auto-injector sampling needle (syringe) to easily pierce through (Fig. 13). An Excel spreadsheet with the order of the samples to be analyzed was imported into the instrument’s data storage to produce the setup configuration for the auto-run. The data run began and ended with blanks of the same de-ionized water. The first blank sample was followed by the three known standards. Every five samples run were followed by the same three standards in the same order, and the run ended with the set of three standards followed by the blank. Interspersing blanks and standards with water samples ensures that there is no instrument drift over the course of the run, which can take up to 12 hours. The working standards were chosen based on the likely range of isotope values for the water samples being analyzed. The order in which they were arranged in the setup configuration was as follows: the lower isotope values’ standard, followed by the higher isotope values, followed by mid-range isotope values or values close to the predicted values of the water samples.

The laser isotope analyzer auto-injector takes six discreet samples from each vial. Six very small amounts of water are separately injected into the optical chamber, and the analyzer takes the average of multiple measurements (about 20) from each injection. The values from the first two injections are not used in the final analysis to ensure that no cross contamination or memory effect occurs between the water sample and the sample being run before it. Once the data run was completed, a data text file was created and moved into an IAEA Water Resources Programme post-processing spreadsheet (in Excel) for data processing. Several quality checks were done on the data file that was produced. The estimated water molecule density (labeled 3 3 H2O_N_cm ) for each injection is reported in water molecules per cm . The values should be between 2 x 1016 and 5 x 1016, and they should not fluctuate by more than 2 to 4%. Changes of 5% or more for a single sample may indicate air leaking through the septum of the vial’s cap. Fluctuations greater than 5% over the course of the data run may indicate leakage from the syringe (Los Gatos Research Inc., 2008, 2010).

The standard deviation values are based on the four used injections out of the six. If the multiple measurements per injection were used to calculate the standard deviation, the value

64 would be smaller, so the uncertainty could be even less than the reported values. Other reported 16 16 18 16 18 16 values are the indicated ratios of H OD to H2 O, H2 O to H2 O, D to H, and O to O, which are used to calculate the values for δD and δ18O. The quality-control graphics produced by the IAEA template were reviewed to ensure that temperatures did not fluctuate during the run, that the δ values for the standards remained the same and matched the known values, and that the R2 values on the calibration plot linear regressions were very close to 1.0.

Figure 13: Preparation of Water Samples for Laser-based Isotope Analysis. Small glass vials are capped with a flexible membrane and are arranged for the auto-run on the Los Gatos liquid-water isotope analyzer.

A drawback to the laser absorption isotope measurements is that some water samples can contain small amounts of “interfering absorbers” of dissolved compounds, such as alcohols or organic compounds with –OH groups, which absorb light at the same frequencies as the isotopomers being measured (Leen et al., 2012). The organic contaminants can cause errors in the isotope measurements. For water samples without organic contaminants, the laser isotope water analyzers function very well with respect to reproducibility (Penna et al., 2010) and the

65 results are comparable to those from isotope ratio mass spectrometry (Lis, Wassenaar, & Hendry, 2008). However, Singleton et al. (2009) found that there could be significant discrepancies in the isotope values of water samples measured by laser absorption spectrometry and isotope ratio mass spectrometry, which is considered the “gold standard.” Substantial differences were only seen in a small number of the almost 600 water samples tested, but five samples had differences greater than 3‰ for δ18O and up to 14.5‰ for δD (Singleton et al., 2009). A more recent comparison of the two methods, done by USGS and IAEA and using water samples from the Mississippi River, found that the laser absorption spectrometer results showed differences of about 0.4‰ and 10‰ for δ18O and δD (one sample was even further off because it was contaminated with residue from an acid-washed container) (Coplen, Wassenaar, & Qi, 2015). In order to address possible errors caused by other dissolved compounds, Los Gatos Research Inc. currently produces post-analysis software with a “spectral contaminant identifier” function that evaluates the sections of the transmission spectrum to either side of the middle peak in laser absorption; it does this to identify any narrowband absorption caused by smaller compounds, such as methanol, and identifies broadband absorption caused by ethanol or larger organic compounds (Leen et al., 2012). The broadband absorbers can cause a negative shift to the baseline offset coefficient, which moves the entire transmission spectrum slightly downwards with respect to transmission (Leen et al., 2012). The post-processing software can check for the presence of alcohols and organics and then apply a drift correction to the transmission spectrum if needed (Berman, 2014).

At the time that the Wakulla water samples were run on the Los Gatos analyzer, the advanced processing software was not yet commercially available. A small number of Wakulla Spring water samples (12) were run as a test on a Picarro® analyzer (model L2130i) by a person being trained to operate the equipment. The Picarro analyzer also operates by laser absorption but uses “cavity ring-down” spectroscopy and has post-processing software called “Chem- Correct” that identifies spectral interferences by organic compounds and flags results that may have had errors introduced (Dennis, 2014). Of the 12 samples run on the Picarro instrument, Chem-Correct flagged several of the results for the presence of organic compound contaminants. Since there was evidence that the accuracy of the laser-based spectroscopy measurements might be affected by the presence of dissolved compounds, such as DOC, in the spring water samples,

66 the samples were then analyzed using isotope ratio mass spectrometry, which is not altered by the presence of dissolved organic compounds.

Precipitation samples were run on the Los Gatos liquid-water isotope analyzer because they did not contain dissolved organics or interfering compounds. They were run in two separate sets on July 25 and Aug. 18, 2014. The Los Gatos supplied standards that the samples were compared with were Std#1A, Std#5A, and Std#2. Standard deviations for the known standards compared with the results of the standards measured throughout the analysis should be below 2.0 for D and below 0.30 for 18O. The second run had slightly better standard deviations for δD but slightly higher standard deviation values than .30 (.33 and .31) for two of the standards for δ18O. The standard deviations for Std#1A (δD = −154.3‰, δ18O = −19.50‰) were 1.41 and 0.69 for D and 0.24 and 0.27 for 18O. For Std#5A (δD = −9.5‰, δ18O = −2.80‰), the standard deviations were 1.31 and 1.00 for D and 0.27 and .33 for 18O. The standard deviations for Std#2 (δD = −117.0‰, δ18O = −15.55‰) were 1.5 and 0.95 for D and 0.26 and 0.31 for 18O. The set of standards interspersed throughout the precipitation samples that had standard deviation values that put them slightly out of range for Std#5A and Std#2 were the second and third to last standards to be run. The two throughfall precipitation samples possibly contained dissolved organic compounds and were run on the mass spectrometer along with the two regular precipitation samples collected at the same time for comparison.

Isotope Ratio Mass Spectrometer The stable isotope ratio mass spectrometer at the National High Magnetic Field Laboratory on the FSU campus was used for the spring water sample analyses. The analytical precision of the instrument, based on replicate comparisons with known standards, is ±0.1‰ for δ18O and ±1‰ for δD. Tests of other isotope ratio mass spectrometers have shown comparable precision values at ±.1‰ and ±.05‰ for δ18O and ±0.4‰ and ±0.7‰ for δD (Penna et al., 2010; Wen et al., 2012). The high-resolution mass spectrometer has greater accuracy than the laser isotope analyzers and requires a smaller sample size, though, unlike the laser isotope analyzers, δ18O and δD for the samples are measured on separate runs using a GasBench II Auto-water equilibration device connected to a Finnigan MAT DELTA plus XP stable isotope ratio mass spectrometer. Water samples or vapor do not directly enter the mass spectrometer. Small

67 18 amounts of CO2 (for δ O analysis) or H2 (for δD analysis) are injected into the sample vials and allowed to equilibrate isotopically with the water. After equilibrium is reached, the CO2 or H2 is transferred by a carrier, helium (He), into the mass spectrometer for analysis. As the isotope fractionation factors for CO2-H2O and H2-H2O are known, the measured isotope ratios of CO2 or 18 H2 can be accurately converted to the δ O and δD of H2O. The hydrogen and oxygen isotopic compositions of the water samples were determined using the H2-water equilibration method and the CO2-water equilibration method (Horita & Kendall, 2004).

Figure 14: Wakulla Spring Samples to be Analyzed by Isotope Ratio Mass Spectrometry.

Clean, dry glass vials were labeled, and a small rod of platinum designed with a hydrophobic surface was placed in each vial to act as a catalyst (Rolston, den Hartog, & Butler, 1976). Without the catalyst, the reaction time for isotope exchange from water to gas occurs very slowly. The water-repellent surface of the platinum increases the efficiency even more (Horita & Kendall, 2004). Four working standards were chosen to intersperse with the water samples being analyzed. Water samples and standards were transferred from their sampling bottles by high- precision pipette in 500 μL aliquots into glass vials while minimizing exposure time to air, which would cause evaporation (Fig. 14). Each vial was tightly capped with a threaded plastic cap with a softer septum in the center for either the sampling needle or the flushing needle to access the headspace of the vial containing the water sample. The septum-sealed glass vials were arranged

68 in a grid on the gas bench (continuous-flow Finnigan GasBench II) sampling platform and covered with a lid that only had openings for the tops of the vials. Two flushing needles were

used to flush two vials at a time with a mixture of 2% H2 in He for the D analyses or with He and 18 0.3% CO2 for the O analyses. The water and CO2 or H2 were then allowed to equilibrate isotopically at 25 °C (77 °F) for 24 hours. The waiting time allowed the water samples to reach 18 equilibrium with CO2 (for δ O analyses) or H2 (for δD analyses). The headspace gas was then transported through the sample needle into the capillary tubing and into the mass spectrometer (Finnigan MAT DELTA plus XP). In the mass spectrometer, the gas molecules are ionized, and the ions are accelerated into a magnetic field, which separates them based on mass. The intensity of ion beams are then measured and calculated into the ratio of the “heavy” to “light” isotopes. For hydrogen isotope analysis, an H3+ factor was determined to account for the contribution of 3+ H ions (created by the ionization of H2) to mass 3, which allows high-precision measurements of the D/H ratios (Horita & Kendall, 2004). For each sample, the first four measurements were of the reference gas and the following ten measurements were of sample gas. The glass vials and platinum rods can be reused after being washed with distilled water and thoroughly dried.

The results from the mass spectrometer were moved to an Excel spreadsheet and raw values for each of the ten injections were averaged. The four standards used were SLCTAP-1 (δD = −121.7‰, δ18O = −16.2‰), YW-ST2-1 (δD = −11.6‰, δ18O = −2.3‰), ZN-1 (δD = 4.93‰, δ18O = 0.56‰), and QD (δD = −35.1‰, δ18O = −10.6‰). A linear regression was undertaken for known values and reported values for the three sets of standards run interspersed with the spring water samples. The linear regression for δD had an R2 value of 0.9982 and an equation of δD = 3.9032x + 2900.9. The linear regression for δ18O had an R2 value of 0.9992 and an equation of δ18O = 0.992x − 26.108. The equations were used to calculate the δ values in VSMOW from the reported values. The D analysis was run twice because the first run had higher standard deviations than was desirable. The standard deviations for the second run improved, but there were still some higher standard deviations seen, especially with the standards that were placed near the end of the run. The standard deviation values were very low for δ18O (between .02 and 0.4) and higher for δD (between 0.72 and 2.33).

69 CHAPTER 3

RESULTS

Summary

During the 2012 Atlantic hurricane season (June 1 to Nov. 30), one tropical storm reached the Wakulla Spring watershed. Tropical Storm Debby occurred between June 23 and 27, 2012 and brought large amounts of rainfall to the area (over 20 inches, 500 mm) (Kimberlain, 2013). The amount of rain from this storm brought precipitation levels back into normal range for the year; the preceding months had seen persistent drought conditions (Fuchs, n.d.). The springs that had been sampled at the beginning of 2012 under base flow/drought conditions were re-sampled in the late summer and fall and showed much more variability in isotope values than they had under base flow conditions. The isotope analysis of the Wakulla Spring samples showed a clear signal of light isotope values from the tropical storm precipitation, which was used to calculate the transit time for the precipitation to become groundwater, move through the aquifer, and emerge from the spring. Precipitation samples were collected for isotope analysis during the 2013 Atlantic hurricane season, which had below-average activity. One named storm reached the study area, though the already-weakened Tropical Storm Andrea, which occurred from June 5 to 7, 2013, only brought a couple of inches of rain. The precipitation sample collected on June 6 showed extremely light isotope values (δD = −109‰, δ18O = −15‰), but since the amount of rainfall was very small, there was not a clear correlation with the minimum isotope values of the Wakulla Spring water samples that occurred 29 and 33 days later.

North and Central Florida Springs during Base Flow and Non-base Flow Conditions

During base flow conditions when the springs were sampled in January and February of 2012, water samples from springs in north and central Florida had δD values between −19‰ and −13‰ and δ18O values between −4.1‰ and −2.9‰ (all results are reported in VSMOW) (Fig. 15). The samples were run on a Los Gatos water isotope analyzer, which is subject to errors introduced by dissolved organic chemicals in water samples; these results may not be as accurate

70 as D and 18O measurements done by isotope ratio mass spectrometry, which are not affected by other molecules in the water samples. Since the variability and trends of the isotope composition of samples from the different springs were of more interest than the exact δ values, the samples were not re-analyzed using mass spectrometry. When δ18O, δD were plotted as x,y coordinates, the base flow spring samples showed a trend line of δD = 4.1·δ18O − 2.5‰. Most results were clustered in the vicinity of the GMWL δD = 8·δ18O + 10‰ (Craig, 1961a) and the linear equation reported for the UFA by Swancar and Hutchinson (1995): δD = 5.4·δ18O + 1.5‰. The slope of a linear regression of isotope values for water samples that have undergone evaporation is usually between 3 and 6, compared with the slope of 8 seen for the GMWL (Coplen, 1993). The slope values for the base flow springs equation was 4.1, which was similar to the 5.4 slope the UFA line. Precipitation is subjected to some evaporation as it becomes throughfall, stemflow, and soil water in the unsaturated zone before it is recharged to the aquifer. The strong evaporation signal for the UFA indicates that it is partly recharged by surface water from lakes, ponds, and sinkholes affected by evaporation.

δ18O (‰) -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0

-5

-10 δD (‰) -15

y = 4.06x - 2.49 -20 R² = 0.60 -25

Figure 15: Linear Regression of Isotope Values for Springs during Drought Conditions.

A couple of the springs showed results that plotted slightly apart from the other base flow springs (Figs. 15 and 16). The water sample from Poe Spring had the highest isotope values (δD = −13‰, δ18O = −2.9‰), which were different from the isotope values of water samples from

71 nearby Ginnie Springs (δD = −18‰, δ18O = −3.6‰) and Little Devil Spring (δD = −17‰, δ18O = −3.5‰), which flow into the Santa Fe River downstream of Poe Spring and were sampled on the same day. The sample from Morrison Springs had results (δD = −17‰ and δ18O = −4.1‰) that plotted apart from the other springs sampled and to the left of the UFA line, the GMWL, the LMWL for Tallahassee, and the LMWL for Pensacola (Odezulu, 2011). Morrison Spring is located in the Florida panhandle, and the isotope values plot closest to the LWML for Pensacola (δD = 6.7·δ18O + 8.6‰) (Odezulu, 2011), which may explain the differences. Only three samples plotted to the right of the GMWL. These were the samples for Cedar Head Spring (δD = −17‰, δ18O = −3.3‰), Devil’s Eye Spring (δD = −16‰, δ18O = −3.1‰), and Homosassa Spring (δD = −16‰, δ18O = −3.1‰). The rest of the springs plotted to the left of the GMWL but to the right of the two LMWLs available. Cedar Head Spring flows into the spring pool of Blue Hole Spring, the largest spring on the Ichetucknee River. Devil’s Eye Spring also flows into the Ichetucknee River just downstream of Blue Hole. Blue Hole Spring is connected to a well- developed cave system and receives water from deeper in the aquifer than the other springs on the Ichetucknee (Upchurch & Champion, 2003). During base flow conditions, it plotted similarly to Mission Springs and Mill Pond Spring but differently than Ichetucknee Head Spring and Cedar Head Spring, located closest to it. Other springs along the Ichetucknee River were sampled on the same day and plotted very differently. Ichetucknee Head Spring water (δD = −18‰, δ18O = −3.9‰) showed the most depleted values. Even though the springs are all located within a small area, it is known that they draw water from different parts of the UFA and have different amounts of inflow from surface water. (Rose Sink and Clay Hole Swallet have been shown to have a connection with the springs by dye-trace studies.) (Champion & Upchurch, 2003; Upchurch & Champion, 2003).

When the springs were sampled again in the late summer and autumn under non-base flow conditions, the most visible changes in the isotope values of the spring water samples were that many more water samples plotted to the right of the GMWL and the UFA line, and the samples did not cluster around similar values the way most did during base flow conditions (Fig. 17). The results for Rainbow Springs and Poe Springs that plotted far to the left of the GMWL, the LMWLs, and UFA line were determined to be erroneous. The errors were probably due to a memory effect from a very light standard analyzed just prior to the two samples. For Poe

72 Springs, there was a replicate sample run later in the sequence during the same isotope analysis and it plotted near the water lines. There was no replicate run for Rainbow Springs and the non- base flow isotope composition is unknown. The sample for Shepherd Spring, which was the next sample in the auto-run sequence, had an unusaully high standard deviation and is considered unreliable (Table 2).

Table 2: Results of Isotope Analyses of Florida Spring Samples. Springs not reached during non- base flow conditions are listed as “not available.” Results determined to be unreliable or erroneous are listed in red font.

δD ‰ δ18O ‰ δD ‰ δD ‰ δ18O ‰ δ18O ‰ δD ‰ standard δ18O ‰ standard non-base standard non-base standard Spring Name base flow deviation base flow deviation flow deviation flow deviation Alexander Springs -15 0.8 -3.2 0.12 -16 0.6 -2.9 0.06 Blue Hole Spring -16 0.5 -3.4 0.04 -18 0.4 -3.0 0.05 Branford Springs -17 0.5 -3.4 0.09 -21 0.6 -3.6 0.00 Cedar Head Spring -17 0.2 -3.3 0.09 -18 0.3 -3.1 0.08 Devil's Eye Spring -16 0.4 -3.1 0.06 -18 0.6 -3.5 0.03 Fanning Springs -18 0.3 -3.8 0.18 -20 0.7 -3.3 0.04 Ginnie Spring -18 0.5 -3.6 0.06 -17 0.1 -3.2 0.03 Homosassa Springs -16 0.4 -3.1 0.11 -15 0.5 -2.9 0.03 Ichetucknee Head Spring -18 0.5 -3.8 0.06 -18 0.3 -3.4 0.04 Jackson Blue Spring -19 0.0 -3.7 0.04 n/a n/a n/a n/a Juniper Springs -19 0.5 -3.8 0.00 -18 0.2 -3.4 0.03 Lafayette Blue Spring -15 0.3 -3.3 0.10 -21 0.5 -3.1 0.14 Little Devil Springs -17 0.5 -3.5 0.13 -17 0.1 -3.1 0.24 Madison Blue Springs -17 0.4 -3.4 0.11 n/a n/a n/a n/a Manatee Spring -17 0.5 -3.4 0.07 -16 0.3 -3.3 0.04 Mill Pond Spring -16 0.9 -3.6 0.03 -17 0.2 -3.2 0.28 Mission Springs Complex -16 0.2 -3.6 0.07 -18 0.2 -3.1 0.05 Morrison Spring -17 0.3 -4.1 0.03 n/a n/a n/a n/a Newport Springs -17 0.5 -3.6 0.08 n/a n/a n/a n/a Poe Springs -13 0.4 -2.8 0.04 -18 0.1 -3.5 0.03 Ponce de Leon spring -19 0.4 -3.9 0.07 n/a n/a n/a n/a Rainbow Springs -18 0.6 -3.8 0.07 -14 0.2 -3.5 0.20 Shepherd Spring -14 0.4 -3.1 0.11 -20 2.6 -2.9 1.07 Silver Glen Springs -16 0.3 -3.4 0.13 -17 0.1 -2.3 0.04 Troy Spring -17 0.3 -3.6 0.15 -23 0.1 -3.2 0.09 (Wacissa) Big Blue Spring -17 0.9 -3.7 0.08 -17 0.5 -3.3 0.08 Wakulla Spring -16 0.3 -3.5 0.12 -23 0.7 -4.2 0.05

73 5 δ18O (‰) 0 -5 -4 -3 -2 -1 0 1 -5

-10 δD (‰) -15

-20

-25 Springs Base Flow Springs on the Ichetucknee GMWL UFA LMWL Pensecola LMWL Tallahassee

Figure 16: Isotope Composition of Springs during Base Flow Conditions.

5 δ18O (‰) 0 -5 -4 -3 -2 -1 0 1 -5

-10 δD (‰) -15

-20

-25 Springs Non-base Flow Springs on the Ichetucknee GMWL UFA LMWL Pensecola LMWL Tallahassee

Figure 17: Isotope Composition of Springs during Non-base Flow Conditions.

74 Alexander Springs near Astor, FL (station no. 02236095) was sampled on Feb. 18, 2012 and again on Nov. 9, 2012. At base flow, the isotopic composition plotted on the GMWL and the UFA line. Afterwards, it plotted to the right of both lines with slightly lighter isotope values (δD = −15‰, δ18O = −3.2‰ to δD = −16‰, δ18O = −2.9‰). Water quality values are usually very stable for Florida freshwater springs. Alexander Springs showed more variation than most of the other springs sampled, with pH and SC readings of 6.5 and 1,153 μS/cm3 during base flow and 7.6 and 1,045 μS/cm3 afterwards. Specific conductance readings for the springs in the Ocala National Forest are higher than those for most other springs flowing from the UFA because they receive some flow through the vertical movement of relic seawater from deeper in the aquifer (Adamski & Knowles, 2001).

Blue Hole Spring near Hildreth, FL (station no. 02322688) had springflow (discharge) measured on both sampling dates. Springflow was 57.2 cfs on Jan. 30, 2012 and was more than double that flow, at 138 cfs, on Oct. 17, 2012. The isotope values (δD = −16‰, δ18O = −3.4‰ to δD = −18‰, δ18O = −3.0‰) initially plotted similarly to most of the other springs during base flow (slightly to the left of the GMWL and UFA line) and later plotted to the right of the GMWL and UFA line during non-base flow. Water quality values remained mostly stable, with readings of 305 μS/cm3 for SC on both dates, and with pH values of 7.7 and 7.5.

Branford Springs near Branford, FL (station no. 02320502), which flows into the Suwannee River, was sampled on Jan. 14 and Oct. 1, 2012. The isotope values (δD = −17‰, δ18O = −3.4‰ to δD = −21‰, δ18O = −3.6‰) showed a similar pattern to the results from the other springs, with the initial composition clustered in a narrow range near the GMWL and UFA line. The base flow sample plotted on the UFA line and the non-base flow sample showed lighter isotopic composition and plotted slightly to the right of both lines. Despite the differences in isotope values, water quality readings remained almost the same for each visit, with pH readings of 7.2 and 7.3, and SC readings of 480 and 497 μS/cm3 for Jan. 14 and Oct. 1.

Cedar Head Spring near Hildreth, FL (station no. 02322687) was sampled on Jan. 30 and Oct. 17, 2012. The isotope values during base flow were one of the few samples that plotted slightly to the right of the GMWL and the UFA line (δD = −17‰, δ18O = −3.3‰). During non-

75 base flow conditions, the isotope values (δD = −18‰, δ18O = −3.1‰) plotted very close to the base flow conditions. Water quality readings were mostly stable, with a pH of 7.8 and 7.5, and SC readings of 308 and 323 μS/cm3. The non-base flow isotope values plotted close to the values for Blue Hole Spring, into which Cedar Head Spring flows, though the base flow samples were slightly different.

Devil’s Eye Spring near Hildreth, FL (station no. 02322694), which flows into the Ichetucknee River just downstream of Blue Hole Spring, was sampled on Jan. 30 and Oct. 17, 2012. The isotope values (δD = −16‰, δ18O = −3.1‰ to δD = −18‰, δ18O = −3.5‰) were some of the only ones that plotted to the right of the GMWL and UFA line during base flow. The isotope values then plotted on the GMWL and slightly to the right of the UFA line during non- base flow. This was the opposite of the trend seen for most of the other springs, which plotted to the left of the trend lines during base flow and moved to the right of the trend lines during non- base flow. Water quality readings were relatively stable, with pH readings of 7.5 and 7.3, and SC readings of 348 and 336 μS/cm3.

Fanning Springs near Wilcox, FL (station no. 02323502) flows into the Suwannee River. Water samples were taken on Jan. 24 and Sept. 20, 2012. Calculated daily discharge values for those dates were 55 cfs and 70 cfs. Springflow was measured on Jan. 10, 2012 at 61.9 cfs and on Oct. 15, 2012 at 69.1 cfs. The isotopic composition of the samples (δD = −18‰, δ18O = −3.8‰ to δD = −20‰, δ18O = −3.3‰) plotted to the left of the GMWL and UFA line during base flow and to the right of both lines during non-base flow conditions; this was the pattern seen for the majority of the springs sampled. Water quality readings remained stable, with pH values of 7.2 and 7.0, and SC readings of 494 and 498 μS/cm3 on Jan. 24 and Sept. 20. Fanning Springs has some of the highest nitrate levels seen for Florida springs (USGS Water Data) because a large percentage of the springshed is agricultural, with high chemical fertilizer use. There is also irrigation using water from the aquifer, but an evaporation signal from high-intensity irrigation was not seen in water sample isotope data for those two dates.

Ginnie Spring near High Springs, FL (station no. 02322400) flows into the Santa Fe River and was sampled on Jan. 23 and Oct. 17, 2012. The isotope values (δD = −18‰, δ18O =

76 −3.6‰ to δD = −17‰, δ18O = −3.2‰) initially plotted just to the left of the GMWL during base flow conditions and just to the right of the GMWL during non-base flow, as was the general trend for the other springs, but the isotope values did not change very much for the two sampling dates. The results showed slightly lighter isotope values during drought conditions. Water quality readings were mostly stable, with a pH of 7.1 and 7.4, and an SC of 352 and 350 μS/cm3 for Jan. 23 and Oct. 17, 2012.

Homosassa Springs at Homosassa Springs, FL (station no. 02310678) is located near the Gulf Coast and has some saltwater influence. It was sampled on Feb. 18 and Nov. 9, 2012, and the calculated daily discharge values were 74 cfs and 103 cfs on those dates. Springflow was measured on Feb. 3, 2012 at 90.9 cfs and on Oct. 12, 2012 at 115 cfs. The isotopic compositions of the water samples (δD = −16‰, δ18O = −3.1‰ to δD = −15‰, δ18O = −2.9‰) were slightly lighter during base flow conditions but did not change very much during non-base flow. During both flow regimes, the isotope values plotted slightly to the right of the GMWL and UFA line. Water quality readings for the pH were stable, with readings of 7.5 on both dates, but showed the most variability of all the springs sampled in terms of SC readings due to the slightly elevated salinity. The SC readings were 5,215 μS/cm3 during drought conditions and 2,709 μS/cm3 during non-base flow conditions, possibly indicating more freshwater influence from higher groundwater elevations after being recharged by summer and fall rains.

Ichetucknee Springs near Hildreth, FL (station no. 02322685), which is located at the start of the Ichetucknee River, was sampled on Jan. 30 and Oct. 17, 2012. The isotope values (δD = −18‰, δ18O = −3.8‰ to δD = −18‰, δ18O = −3.4‰) plotted slightly to the left of the GMWL and UFA line during drought conditions and slightly to the right of the trend lines afterwards. Water quality readings were very similar, with pH values of 7.6 and 7.5, and SC values of 319 and 322 μS/cm3. Although the springs of the Ichetucknee River are all located within a small area along the upper mile and a half of the river, the isotope values for the spring samples showed some variation, especially during base flow conditions. During drought conditions, when the water in the aquifer should have been the most homogeneous, the six springs sampled along the Ichetucknee River mostly showed more variation than they did under non-base flow conditions. The three springs that plotted closest together during base flow were Blue Hole

77 Spring, Mission Springs, and Mill Pond Spring, which are all located on the eastern side of the river. The springs closest to Blue Hole Spring (Ichetucknee Head Spring, Devil’s Eye Spring, and Cedar Head Spring) showed different isotope compositions. During non-base flow conditions, the isotope compositions of five of the six springs plotted much closer together, but Devil’s Eye Spring plotted slightly apart from the rest.

Jackson Blue Spring near Marianna, FL (station no. 02358795) was only sampled and measured on Jan. 16, 2012, at which time the isotope values plotted on the GMWL were found to be δD = −19‰ and δ18O = −3.7‰. The sample plotted to the right of the UFA line and had relatively light isotope values compared with the other springs sampled during base flow. Water quality values were found to be 7.6 for the pH and 258 μS/cm3 for the SC.

Juniper Springs near Ocala, FL (station no. 02236130) was sampled on Feb. 18, 2012 and again on Nov. 9, 2012. The isotope composition (δD = −19‰, δ18O = −3.8‰ to δD = −18‰, δ18O = −3.4‰) was near the UFA line and slightly to the left of the GMWL, with slightly lighter isotope values during base flow. During non-base flow, the isotope values plotted slightly to the right of both trend lines. Water quality readings showed some variation in pH values at 7.4 and 8.4, and the lowest SC readings of all the springs sampled, at 117 and 108 on Feb. 18 and Nov. 9, respectively. Juniper Springs is in an area where some of the springs have very high SC values and high concentrations of total dissolved solids due to the migration of relict seawater from deeper parts of the aquifer. The nearby springs (Silver Glen and Alexander Springs) had some of the highest SC values. It is known that Juniper Springs can have a wide range of SC values, as low as 25 μS/cm3 and as high as 496 μS/cm3, as well as large variations in pH from 4.4 to 8.8 (Adamski & Knowles, 2001).

Lafayette Blue Spring (station no. 282331081371101) flows into the Suwannee River and was sampled on Jan. 14 and Oct. 1, 2012. The isotope composition of the water samples (δD = −15‰, δ18O = −3.3‰ to δD = −21‰, δ18O = −3.1‰) initially plotted slightly to the left of the GMWL and UFA line and then plotted far to the right of the trend lines. The change in δD values was the greatest change seen for the spring water samples. The pH was measured at 7.2 and 7.4 and the SC values at 476 and 438 μS/cm3 on Jan. 14 and Oct. 1, respectively.

78 Little Devil Springs (station no. 294957082414700) flows into the Santa Fe River upstream of Ginnie Spring and was sampled on Jan. 23 and Oct. 17, 2012. The isotope composition of the water samples (δD = −17‰, δ18O = −3.5‰ to δD = −17‰, δ18O = −3.1‰) plotted slightly to the left of the GMWL and UFA line during base flow and slightly to the right during non-base flow conditions—a pattern seen for most of the springs sampled. The isotope values were similar to those of the Ginnie Spring samples, suggesting that the flow comes from similar water sources. Water quality readings were also similar to those at Ginnie Spring, with pH values of 7.4 and 7.3, and SC values of 347 and 338 μS/cm3 on Jan. 23 and Oct. 17, respectively.

Madison Blue Springs near Blue Springs, FL (station no. 02319302) flows into the Withlacoochee River and was only sampled on Jan. 14, 2012. The calculated daily discharge was 45 cfs on that day and was measured a few days earlier, on Jan. 9, 2012, at 48.9 cfs. The isotope values (δD = −17‰ and δ18O = −3.4‰) plotted just to the left of the GMWL and UFA line, which was similar to most of the other springs sampled during base flow conditions. Water quality readings were 7.7 for the pH and 307 μS/cm3 for the SC.

Manatee Spring near Chiefland, FL (station no. 02323566) flows into the Suwannee River and was sampled on Jan. 24 and Sept. 20, 2012. The isotope values (δD = −17‰, δ18O = −3.4‰ to δD = −16‰, δ18O = −3.3‰) were almost identical from base flow to non-base flow conditions, and both plotted just to the left of the GMWL and the UFA line. Water quality readings were also very similar, with pH readings of 7.1 and 7.0, and SC readings of 480 and 501 μS/cm3. Flow conditions were similar on both sampling dates as well, with calculated daily discharge values of 112 cfs and 117 cfs on Jan. 24 and Sept. 20, 2012. Springflow was measured at 93.7 cfs on Feb. 1 and 140 cfs on Oct. 10, 2012.

Mill Pond Spring near Hildreth, FL (station no. 02322695) flows into the Ichetucknee River and was sampled on Jan. 30 and Oct. 17, 2012. The isotope compositions of the water samples (δD = −16‰, δ18O = −3.6‰ to δD = −17‰, δ18O = −3.2‰) were initially just to the left of the GMWL and UFA line, near many of the other base flow spring water samples, and they

79 plotted just to the right of the trend lines during non-base flow conditions. Water quality readings were very stable, with the pH at 7.6 and 7.5, and with SC values of 377 and 376 μS/cm3.

Mission Springs Complex near Hildreth, FL (station no. 02322691) flows into the Ichetucknee River upstream of where Mill Pond Spring enters the river, and samples were taken on Jan. 30 and Oct. 17, 2012. Water samples were collected at Roaring Spring, which is the largest spring in the group. The isotope composition (δD = −16‰, δ18O = −3.6‰ to δD = −18‰, δ18O = −3.1‰) was similar to the samples from Mill Pond Spring and showed a similar change during non-base flow conditions, plotting to the right of the trend lines after initially plotting to the left. Water quality readings were stable, with pH values of 7.5 on both dates, and with SC readings of 329 and 316 μS/cm3.

Morrison Spring near Redbay, FL (station no. 02365580) was only sampled during base flow conditions, on Jan. 16, 2012. The isotope composition of the sample (δD = −17‰, δ18O = −4.1‰) plotted the farthest from the other springs sampled during drought conditions. The pH was measured at 7.7 and the SC value at 225 μS/cm3.

Newport Springs (station no. 301245084104300) is located near the Gulf Coast and usually has a sulfurous odor. It was sampled on Jan. 21, 2012 but was not reached under non- base flow conditions to be sampled a second time. The isotope values were −17‰ for δD and −3.6‰ for δ18O, which plotted similarly to the majority of the other spring samples during base flow conditions. The pH was found to be 7.3, and the SC was relatively high compared to the other springs sampled in the area, at 451 μS/cm3.

Poe Springs near High Springs, FL (station no. 02322140) was sampled on Jan. 23 and Oct. 17, 2012. The isotope compositions of the samples (δD = −13‰, δ18O = −2.8‰ to δD = −18‰, δ18O = −3.5‰) plotted apart from the other spring water values during base flow conditions and near the water lines for non-base flow conditions. The base flow sample had the highest isotope values out of all the samples collected. The non-base flow sample results for Poe Springs were taken from the replicate sample since the results for Poe Springs (as well as Rainbow Springs) plotted far to the left of the water lines and were likely erroneous. At the end

80 of the same isotope analysis run, several randomly chosen replicate samples were run. The replicates included a sample from Poe Springs, but not one from Rainbow Springs. Water quality readings were 7.3 and 6.7 for pH and 450 and 403 μS/cm3 for SC.

Ponce de Leon Spring at Ponce de Leon, FL (station no. 02365710) was only sampled during base flow conditions, on Jan. 16, 2012, and its isotope values of −19‰ for δD and −3.9‰ for δ18O were some of the lighter values measured during base flow. The sample results plotted to the left of the GMWL and UFA line, as most of the other spring samples did. Water samples from Jackson Blue Spring, which is also located in the Florida panhandle, also had very light isotope values. Water quality readings were 7.7 for the pH and 213 μS/cm3 for the SC.

Rainbow Springs (station no. 290608082261600) was sampled at the main pool at the start of the Rainbow River on Feb. 18 and Nov. 9, 2012. Discharge values from the USGS gage downstream at Rainbow River at Dunnellon, FL (station no. 02313100) were 462 cfs and 605 cfs at the times that the water samples were collected. The isotope values for the base flow sample (δD = −18‰, δ18O = −3.8‰) plotted just to the left of the GMWL and UFA line, as most of the spring samples had. The non-base flow sample from Rainbow Springs plotted far to the left of the other samples (δD = −14‰, δ18O = −4.7‰), except for the sample from Poe Spring that was run just prior to it, which plotted similarly—both results were found to be erroneous. The individual readings that were averaged for both erroneous results were very stable and did not show an increasing trend, as would be expected from a memory effect, but influence from the light standard in the auto-run sequence before the two samples most likely caused the errors. Water quality readings for Rainbow Springs only changed a little, with pH readings of 7.4 and 7.9, and SC readings of 147 and 139 μS/cm3 on Feb. 18 and Nov. 9, respectively.

Shepherd Spring (station no. 303612084473001) is located near the Gulf Coast and was sampled on Jan. 21 and Sept. 23, 2012. Isotopic composition of the base flow sample (δD = −14‰, δ18O = −3.1‰) plotted just to the left of the GMWL and UFA line, as many of the springs did when the area was under drought conditions. The non-base flow sample isotope values (δD = −20‰, δ18O = −2.9‰) were unreliable due to unusually high standard deviations. The standard deviation for δD was 2.6‰ compared to values of 0.1 to 0.7‰ for all the other

81 samples in the same run. A very high standard deviation of 1.07‰ for 18O was seen compared to 0.03 and 0.31‰. Water quality readings were variable, with 6.1 and 7.2 readings for the pH, and with SC readings of 804 and 332 μS/cm3.

Silver Glen Springs near Astor, FL (station no. 02236160) was sampled on Feb. 18 and Nov. 9, 2012. The calculated daily discharge was 76 cfs and 75 cfs. The measured springflow was 67.0 cfs on Feb. 14, 2012 and 70.3 cfs on Nov. 7, 2012. The discharge values were similar at both sampling dates, but the isotopic composition of the samples was variable (δD = −16‰, δ18O = −3.4‰ to δD = −17‰, δ18O = −2.3‰). The base flow sample plotted similarly to the rest of the spring water samples, just to the left of the GMWL and the UFA line. Like many of the other springs, the non-base flow sample plotted to the right of the GMWL and UFA line, though the Silver Glen results plotted further to the right than the rest. Water quality readings were 7.7 and 7.8 for the pH, and 1,837 and 1,702 μS/cm3 for the SC. Silver Glen Springs is also in the Ocala National Forest, in an area that has an upward movement of relict seawater, so SC readings were higher than those for most central Florida springs (Adamski & Knowles, 2001).

Troy Spring near Branford, FL (station no. 02320250) flows into the Suwannee River and was sampled on Jan. 14 and Oct. 1, 2012. The water sample had isotope values (δD = −17‰, δ18O = −3.6‰ to δD = −23‰, δ18O = −3.2‰) that were initially found to be just to the left of the GMWL and UFA line, as many other springs did. The non-base flow sample then plotted far to the right of the trend lines. Water quality readings were 7.5 and 7.3 for the pH and 364 and 399 μS/cm3 for the SC.

Wacissa Big Blue Spring near Wacissa, FL (station no. 02326523) was sampled on Feb. 3, 2012, and springflow was measured on Feb. 2, 2012 at 70.0 cfs. The isotope composition of the water samples (δD = −17‰, δ18O = −3.7‰ to δD = −17‰, δ18O = −3.3‰) had a similar pattern to other spring water samples, with isotope values just to the left of the GMWL and UFA line during drought conditions, which then plotted to the right of the trend lines during non-base flow conditions. Water quality readings were only taken during base flow conditions and were 7.5 for the pH and 340 μS/cm3 for the SC.

82 Wakulla Spring near Crawfordville, FL (station no. 02327000) had samples taken prior to and throughout the 2012 hurricane season. Leon and Wakulla Counties had seen a year of drought conditions, from May of 2011 up until Tropical Storm Debby arrived, with the Palmer Drought Index reporting “moderate” to “extreme” drought conditions (Fuchs, n.d.) (Fig. 18).

Palmer Drought Index Wakulla County Leon County 4

3 Drought 2 Impact Type 1

0 Oct. 2011 4, Nov.1, 2011 Feb. 7, 2012 Apr. 3, 2012 May 3, 2011 May 1, 2012 Aug. 2011 9, Mar. 2012 6, Sept. 6, 2011 Feb. 21,2012 Jan. 10, 2012 Jan. 24, 2012 July 201112, July 201126, Oct. 18, 2011 Dec. 13, 2011 Dec. 27, 2011 Apr. 17, 2012 May 17, 2011 May 31, 2011 May 15, 2012 May 29, 2012 Aug. 201123, June 26, 2012 June 14, 2011 June 28, 2011 June 12, 2012 Nov. 15, 2011 Nov. 29, 2011 Mar. 201220, Sept. 20, 2011 Figure 18: Palmer Drought Severity Index for Wakulla and Leon Counties. Impact Type D0 = Abnormally Dry, D1 = Moderate Drought, D2 = Severe Drought, D3 = Extreme Drought, D4 = Exceptional Drought. Points between whole numbers indicate roughly half the area in the county was in each catergory. Data were from the U.S. Drought Monitor records (Fuchs, n.d.).

The first water sample at Wakulla Spring was collected on Feb. 10, 2012 and had isotope values of δD = −16‰ and δ18O = −3.5‰ (from the Los Gatos isotope analyzer). The sample from Aug. 27, 2012, after drought conditions were over, had values of δD = −23‰ and δ18O = −4.2‰ (from the isotope ratio mass spectrometer). The isotope values during drought conditions plotted similarly to the majority of the other Florida springs, which plotted just to the left of the GMWL. The non-base flow sample had lighter isotope values that plotted along the same trend line just to the left of the GMWL (Fig. 19). Water quality readings during drought conditions were 6.6 for the pH and 364 μS/cm3 for the SC. Water quality readings were not available for the Aug. 27 samples because the YSI meter was sent away for repairs. Streamflow (discharge) measurements were done downstream at Wakulla River near Crawfordville, FL (02327022) on Feb. 2, 2012, at 733 cfs, and on Aug. 15, 2012, at 1,040 cfs. The calculated streamflow values at the times of the water sampling were 629 cfs on Feb. 10 and 1,480 cfs on Aug. 27, 2012.

83 δ18O δ18O δ18O -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0 0 0

-5 -5 -5

-10 -10 -10 δD δD δD -15 -15 -15

-20 -20 -20

-25 -25 -25 Alexander Springs Branford Spring Blue Hole Spring

δ18O δ18O δ18O -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0 0 0

-5 -5 -5

-10 -10 -10 δD δD δD -15 -15 -15

-20 -20 -20

-25 -25 -25 Cedar Head Spring Devil's Eye Spring Fanning Springs

δ18O δ18O δ18O -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0 0 0

-5 -5 -5

-10 -10 -10 δD δD δD -15 -15 -15

-20 -20 -20

-25 -25 -25 Ginnie Spring Homosassa Springs Ichetucknee Head Spring

δ18O δ18O δ18O -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0 0 0

-5 -5 -5

-10 -10 -10 δD δD δD -15 -15 -15

-20 -20 -20

-25 -25 -25 Juniper Springs Lafayette Blue Spring Little Devil Springs

δ18O δ18O δ18O -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0 0 0

-5 -5 -5

-10 -10 -10 δD δD δD -15 -15 -15

-20 -20 -20

-25 -25 -25 Manatee Spring Mill Pond Spring Mission Springs Complex Figure 19: Comparison of Springs’ Isotope Values from Dry to Wet Conditions. Red circles indicate samples during base flow/drought conditions and blue circles indicate wet/non-base flow conditions. Repotable values for δD are ±0.7‰ and for δ18O are ±0.2‰ (except for the non- base flow results for Shepherd Spring which are ±1.3‰ for δD and ±0.5‰ for δ18O).

84 δ18O δ18O δ18O -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0 0 0

-5 -5 -5

-10 -10 -10 δD δD δD -15 -15 -15

-20 -20 -20

-25 -25 -25 Poe Springs Shepherd Spring Silver Glen Springs

δ18O δ18O δ18O -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 0 0 0

-5 -5 -5

-10 -10 -10 δD δD δD -15 -15 -15

-20 -20 -20

-25 -25 -25 Troy Spring Wacissa Big Blue Spring Wakulla Spring

Figure 19 - continued.

Wakulla Spring Samples

The 2012 and 2013 water years (Oct. 1 through Sept. 30) had very different hydrologic conditions. For the majority of the 2012 water year, the study area experienced very little rain until Tropical Storm Debby arrived in late June and ended the drought. In contrast, the 2013 water year mostly had normal rainfall and a wet spring, but no major rainfall from tropical storms. Streamflow at Wakulla River was measured about every four to eight weeks, and daily and 15-minute discharge values were computed from the recorded data (Figs. 20 and 21).

When water sampling began at Wakulla Spring on Feb. 10, 2012, the color of the spring water was observed to be very clear; it remained clear until the arrival of Tropical Storm Debby on June 26. On July 5, 2012, there was a slight reddish tint to the springflow; though it was not pronounced, it was noticeable. The following day, the water had changed to a very dark red tannic color. The color remained but lessened throughout the remainder of the summer. In 2013, the water samples were very clear after the relatively dry winter and spring. Spring water samples began to darken around July 2, when summer storms brought regular rainfall. The strongest color occurred around July 15, after the heaviest rainfall of the season had taken place on July 3 and the surrounding days. Water samples during late August were also very tannic.

85 Water samples retained a tannic color until late September, when drier autumn weather lessened the amount of surface water recharging the aquifer. The October water samples were relatively clear.

3500 Daily Streamflow 2012 Water Year 3000

2500 Wakulla River c 2000 f s 1500

1000

500

0 9/30/2011 11/19/2011 1/8/2012 2/27/2012 4/17/2012 6/6/2012 7/26/2012 9/14/2012

Figure 20: Hydrograph for the 2012 Water Year, Wakulla River near Crawfordville, FL. Maximum instantaneous and daily discharge values for the water year occurred on June 27 due to Tropical Storm Debby and were also the maximum values for the entire period of record for the USGS streamflow-gaging station.

3500 Daily Streamflow 2013 Water Year 3000

2500 Wakulla River c 2000 f s 1500

1000

500

0 10/1/2012 11/20/2012 1/9/2013 2/28/2013 4/19/2013 6/8/2013 7/28/2013 9/16/2013

Figure 21: Hydrograph for the 2013 Water Year, Wakulla River near Crawfordville, FL. Maximum discharge values for the water year occurred on Mar. 1 due to seasonal precipitation. The second highest peak occurred on July 8 due to intense summer storms in late June and early July.

86 The water temperature, pH, SC, and DO values were limited for the 2012 season due to meter repairs. The DO values were consistently higher for the 2012 sampling dates than the 2013 dates, which was more likely due to using different meters that had different types of DO probes rather than being due to the actual water chemistry (the membrane-type DO probe used in 2012 can give less reliable results than the optical DO probe used in 2013). There was very little variation in pH values; even during the large rise due to Tropical Storm Debby, the pH values only changed from 7.3 to 7.1. The pH values during 2013 changed from 7.2 on June 18 to 7.5 on July 15. June 18 also showed the highest SC at 325 μS/cm3. The SC values decreased from the beginning of July to a minimum value of 285 μS/cm3 on July 31. Water temperatures for surface water at Wakulla Spring were between 20.8 and 21.1 °C during both hurricane seasons.

Specific Conductance 2013 330 325 320 315

SC 310 (μS/cm3) 305 300 SC 295 290 285 280 5/6 5/24 6/11 6/29 7/17 8/4 8/22 9/9 9/27 10/15 11/2

Figure 22: Specific Conductance at Wakulla Spring for the 2013 Atlantic Hurricane Season. Reportable specific conductance values are for temperatures at 25 °C.

In order to determine if the depleted values from tropical storm systems or hurricanes could be used as a hydrologic tracer in a springshed, the first set of water sampling was done at Wakulla Springs from Feb. 10 to Aug. 27, 2012. One named storm, Tropical Storm Debby, brought significant rainfall to the study area during 2012. Tropical Storm Debby was named on June 23, 2012 and made landfall on the Big Bend area of the Gulf Coast on the afternoon of June 26, near the town of Steinhatchee, FL (Kimberlain, 2013). As the storm moved across the state, it

87 weakened to a tropical depression. On the morning of June 27, the storm moved over the Atlantic Ocean and, by noon, it was a post-tropical storm (Kimberlain, 2013).

2012 Precipitation NWS Tallahassee 180

160

140

120

100 (mm) 80

60

40

20

0 5/1/2012 6/1/2012 7/1/2012 8/1/2012 9/1/2012 10/1/2012 11/1/2012 Figure 23: Precipitation Record for the 2012 Hurricane Season (NWS).

The storm brought over 500 mm (20 in) of rain to the area between the Apalachicola and St. Marks Rivers—rates of over 50 mm/hr (2 in/hr) at some points (NWS). One observation reported to the NWS from a Wakulla County resident’s rain gage was 28.8 inches (731 mm) (Kimberlain, 2013). June 25 saw 2.09 in (53 mm) of rain, but most of the rain occurred on June 26, with 6.85 in (174 mm) recorded at the NWS station at the Tallahassee Regional Airport (NWS). Other rainfalls from Tropical Storm Debby were 18.8 in (478 mm) at Wakulla Springs State Park, 12.4 in (315 mm) at Wacissa, 21.1 in (536 mm) at St. Marks, and 12.6 in (320 mm) at Apalachicola Municipal Airport (NWS).

On June 26, I made a streamflow measurement for the Wakulla River at a stage of 7.03 ft. The ADCP measurement documented the highest flow ever recorded at 73.6 m3/s, 2,600 cfs. (The previous record streamflow was 2,300 cfs, 65.1 m3/s from Tropical Storm Fay in 2008.)

88 The peak stage of 7.12 ft had occurred on June 26 from 1:45 to 2:45 am (USGS Water Data). The results of the 2012 water samples showed a distinct drop in isotope values of Wakulla Spring water after the storm. The spring water sample on June 26 had isotope values slightly lower than the mostly stable values of the previous samples from Feb. 10 to June 20. After June 27, the isotope values continued to become more negative, with minimum isotope values showing up nine days after the intense rainfall from Tropical Storm Debby. The results from isotope ratio mass spectrometry and the laser isotope analyzer were similar, but the mass spectrometry values are considered more accurate. The minimum δ18O value of −5.1‰ occurred on July 5. The minimum δD values occurred at the same time with −30‰ on July 4 and −29‰ on July 5 (±1‰). Prior to the storm, the June 20 spring water composition had been δD at −18‰ and δ18O at −3.9‰. Around July 20, the isotope values began to level off at slightly lighter values than those seen prior to the storm, indicating the continued influence of the tropical storm precipitation. The isotope composition of the tropical storm precipitation was very light, but the exact isotope values were not known. Precipitation was collected for isotope analysis during the 2013 season to see if the isotope values of the tropical storm system could be documented.

-10 δD Time Series

June 26, 2012 -15 June 27, 2012

June 28, 2012 July 28, 2012 May 14, 2012

June 20, 2012 July 20, 2012

-20

July 17, 2012 Aug. 27, 2012 June 29, 2012 August 4, 2012 δD (‰) August 15, 2012 June 30, 2012 July 11, 2012 -25 July 10, 2012

July 1, 2012 July 9, 2012

July 8, 2012

July 2, 2012 July 7, 2012 -30 July 6, 2012 los gatos July 5, 2012

July 4, 2012 mass spec

-35

Figure 24: Time Series for Deuterium in Wakulla Spring Samples 2012.

89 δ18O Time Series

-2.0

-2.5

June 20, 2012 May 14, 2012

-3.0 June 26, 2012 June 27, 2012

-3.5

18 August 4, 2012 δ O (‰) July 28, 2012 June 28, 2012 -4.0 August 15, 2012 August 27, 2012 June 29, 2012 July 20, 2012 July 11, 2012 July 17, 2012 June 30, 2012 -4.5 July 1, 2012 July 10, 2012 July 9, 2012 July 8, 2012 July 2, 2012 July 7, 2012 -5.0 July 6, 2012 July 4, 2012 los gatos July 5, 2012 mass spec -5.5

-6.0 Figure 25: Time Series for Oxygen-18 in Wakulla Spring Samples 2012.

The isotope values for the spring water samples had initially been measured using the Los Gatos liquid-water isotope analyzer, which is susceptible to errors introduced by dissolved organic molecules. In March of 2013, ten Wakulla Spring samples were analyzed using isotope ratio mass spectrometry. The comparison of the two methods showed that the isotope values did show inconsistencies and that the samples should be re-analyzed using mass spectrometry. After the water samples were re-analyzed, the differences in δ18O values were found to be as close as .02‰ but had a range between −0.9‰ and 0.3‰. There were greater differences between the δD results because the analytical precision for δD (based on the analysis of standards processed along with the samples) was ±2‰; the precision for δ18O was ±0.1‰. The closest that δD values came to agreement for a sample was 1‰, but there was a large range of between −8‰ and 3‰. For both isotopes, the results from the Los Gatos laser analyzer were more often slightly less negative than the results from mass spectrometry, but there was no consistent pattern to the discrepancies between the results. Some of the larger differences in δ18O values were seen in the samples from May 14 and June 20, 2012, prior to Tropical Storm Debby, when the spring water was very clear, but differences of similar magnitude were also seen in the tannic water samples on July 15 and July 19, 2012.

90 The standard deviation values for the isotope ratio mass spectrometry were very low for δ18O (between .02 and 0.22) but higher than the usual precision for δD (between 1.99 and 3.03). One sample (the July 1, 2012 surface water sample) had an unusually high standard deviation (0.59‰) for the δ18O value for unknown reasons. The δ18O analysis results from isotope ratio mass spectrometry were more reliable than the δD results and were used to describe trends and calculate transit times.

For the 2013 Atlantic hurricane season, precipitation amounts were recorded and precipitation samples were collected for isotope analysis. One named storm reached the study area, Tropical Storm Andrea, which only brought a small amount of rain. The data collected from the rain gages in Tallahassee showed that there was very little rain in May (about a half inch or 15 mm), but June had almost 9 in (231 mm) and July had the most rainfall of the season, at 15 in (382 mm), from almost daily storms. August had about 11 in of rain (272 mm). September had two and a half inches (63 mm) and October had an inch and a half (35 mm). November showed about three-quarters of an inch of precipitation (19 mm). In total, precipitation for the season measured at Tallahassee was 39.6 in (1005 mm). The rainfall compared well with the NWS station at Tallahassee Regional Airport record, which reported total rainfall of 38.1 in (967 mm) from May 1 to Nov. 30, except for Aug. 26, 28–30, for which records were missing (NWS). For the missing days, the rain gages at the USGS warehouse recorded 0.7 in (19 mm). Daily rainfall for the hurricane season, between May 1 and Nov. 30, 2013, showed a maximum of 3.7 in (94 mm) on July 3. Other days that received an inch or more were July 20 (2.5 in, 64 mm), June 6 (2.3 in, 58 mm) from Tropical Storm Andrea, Aug. 17 (1.9 in, 49 mm), July 2 (1.8 in, 45 mm), Aug. 19 (1.6 in, 41 mm), June 26 (1.3 in, 34 mm), July 4 (1.3 in, 34 mm), June 19 and June 29 (1.3 in, 32 mm), June 30 (1.1 in, 28 mm), July 25 (1.0 in, 27 mm), and Aug. 14 (1.0 in, 26 mm). A small amount of rain (0.8 in, 20 mm) fell on Oct. 7 from Tropical Storm Karen, which was offshore in the Gulf of Mexico.

Precipitation samples for isotope analysis were collected over varying time intervals, from three days to several hours or less, so there were some samples from a single storm, multiple samples over the course of a large storm, and samples from two or three days that included a few small rain events together. The date and time associated with the precipitation

91 samples was the date/time between the start of a rain event and the end of a rain event, as determined from the 15-minute recorded rain-gage data. Some samples were not collected because the collection container overflowed during large storms.

2013 Precipitation Tallahassee 100 90 80 70 60 50 (mm) 40 30 20 10 0 5/3/2013 6/3/2013 7/3/2013 8/3/2013 9/3/2013 10/3/2013 11/3/2013

Figure 26: Precipitation Record for the 2013 Hurricane Season. Rain gages were placed at the USGS Tallahassee Office.

Of the 53 precipitation samples collected from May through Nov. 2013, 13 were not used because they plotted slightly to the right of the GMWL and might have had some evaporation. The remaining samples showed a trend line of δD = 8.4·δ18O + 15.4‰. The lightest sample of precipitation had isotope values of δD = −109‰, δ18O = −14.7‰ and was collected from 9 am to 6 pm on June 6, 2013, during Tropical Storm Andrea. Rainfall began with a small shower between 5:45 am and 6:45 am, which dropped less than a tenth of an inch of rain; this rain was included in the isotope sample for the previous day. Rainfall during the isotope sample collection time was 2.1 in (54 mm) and the total rainfall for the day was 2.3 in (58 mm). Some light rainfall occurred later into the night that was not included in the precipitation sample, but it was less than a tenth of an inch. There were also depleted isotope values for three samples of rainfall prior to the arrival of Tropical Storm Andrea. These were collected on June 4 from 7:00 am to 2:00 pm, June 5 from 8:00 am to 2:00 pm, and from 2:00 pm on June 5 to 9:00 am on June 6. Rainfall on

92 those days was light, with only 0.2 in (4 mm) on both June 4 and June 5. Two of these samples plotted slightly to the right of the GMWL (δD = −29‰, δ18O = −4.2‰ and δD = −40‰, δ18O = −5.6‰) and were suspected to have had some evaporation. The isotope analysis of the precipitation samples showed a large drop in isotope values that began on June 4, with isotope values of δD = −41‰, δ18O = −6.5‰, a minimum on June 6 at δD = −109‰, δ18O = −15‰, and the beginning of a return to normal values on June 8 at δD = −58‰, δ18O = −8.4‰. There were precipitation samples with slightly lighter isotope values than average for the rest of the season but none with values as low as those during Tropical Storm Andrea. Samples from July 4, 11, and 12, Aug. 16, and Sept. 23, 2013 had values between −45‰ and −31‰ for δD and −6.8‰ and −5.1‰ for δ18O. Some rainfall occurred on Oct. 6 from Tropical Storm Karen, which was located in the middle of the gulf, but the rainfall did not show low isotope values. Some intense summer storm activity occurred from June 29 to July 5, and that week received over ten and a half inches (255 mm) of rain, with the most rainfall occurring on July 3 at 3.7 in (94 mm). The isotope values were lower the next day, though there was less rainfall on July 4 of 1.3 in (34 mm). Of the two samples collected on July 4, the sample of precipitation (δD = −38‰, δ18O = −6.3‰) that occurred between 3:30 pm and 6:45 pm had lower isotope values than the samples from earlier in the day and from the previous day, possibly indicating the amount effect for the storm system.

30

18 δ O (‰) 10

-15 -13 -11 -9 -7 -5 -3-10 -1 1 3

-30

-50 δD (‰)

-70

δD = 8.4·δ18O + 15.4 -90

-110

Figure 27: Isotope Analysis of Precipitation Samples for the 2013 Hurricane Season.

93 20

0

-20

-40 ‰ D precipitation -60

-80

-100

-120 Figure 28: 2013 Hurricane Season Precipitation: Time Series for Deuterium.

0.0

-2.0

-4.0

-6.0 ‰ -8.0

-10.0 O-18 precipitation

-12.0

-14.0

-16.0 Figure 29: 2013 Hurricane Season Precipitation: Time Series for Oxygen-18.

The Wakulla Spring water samples for the 2013 hurricane season showed δ18O values with a trend similar to the 2012 hurricane season. The 2013 samples showed a lot of variation in δD values for unknown reasons, so the more reliable δ18O data were used for calculations. The δ18O values remained relatively stable from March 18 to June 19 and then showed a decrease in values, with a minimum of δ18O = −4.1‰ occurring on July 15 for the surface water sample and a minimum of −4.1‰ on July 19 for the vent line sample. The minimum isotope values in the spring water samples occurred 29 and 33 days after the small amount of rain from the tropical storm, but several other large summer storms brought rain during that period.

94 δ18O Time Series -3.0

-3.3

July 23, 2013 September 9, 2013

June 10, 2013 May 1, 2013 September 4, 2013 March 19, 2013 May 28, 2013 July 1, 2013 September 22, 2013 -3.5 June 18, 2013 August 20, 2013 April 24, 2013 July 26, 2013 September 30, 2013 August 16, 2013 18O (‰) July 2, 2013 August 29, 2013 δ May 13, 2013 June 4, 2013 August 12, 2013 August 23, 2013 October 21, 2013 August 3, 2013 October 16, 2013 -3.8 July 31, 2013 October 9, 2013

July 5, 2013 October 4, 2013

July 11, 2013

-4.0

July 19, 2013 surface water vent line water

July 15, 2013

-4.3

Figure 30: Time Series for Oxygen-18 in Wakulla Spring Samples 2013.

The surface water samples and vent line samples were usually similar, but there were larger differences on July 1 and 2 as the isotope values began to decrease. The surface water sample taken on July 23 had a less negative value than surface water collected on the nearest sampling dates, but, due to equipment issues, no vent line sample was taken on the same date with which to compare it. There was no clear correlation of the spring water sample δ18O minimum in late July with the very light composition precipitation from the very small amount of rainfall from Tropical Storm Andrea on June 6, but the drop in isotope values followed the large amounts of rainfall in early July. There was a consistent trend in isotope values of precipitation from July 1 to July 4, with values less than the spring water sample values. There was also a small drop observed in the isotope values of the spring water samples from Oct. 4 and 9, 2013. There were two earlier rainfall samples, from Aug. 16 (δD = −35‰, δ18O = −5.9‰) and Sept. 22 (δD = − 45‰, δ18O = −6.8‰), that had lighter values than the spring water samples and may have influenced the lighter spring water compositions toward the end of the sampling period.

Two samples of throughfall were collected on Aug. 17 and Oct. 7, 2013, at the same time as two regular precipitation samples were collected for isotope analysis. All four samples were analyzed using isotope ratio mass spectrometry instead of the laser isotope analyzer because the

95 throughfall samples may have contained dissolved organics. Both throughfall samples had a tannic color, but the October sample was darker in color, probably because the leaves of the sweetgum tree under which the precipitation was collected had changed color and were beginning to fall. There was no clear evaporation signal in the August precipitation and throughfall samples. The precipitation sample had isotope values of δD = −17‰ and δ18O = −3.8‰, and the throughfall samples had an almost identical δ18O value of −3.8‰ and a slightly less negative δD value of −14‰.

Table 3: Results of Isotope Analyses of Wakulla Spring Samples. Laser Isotope Analyzer Isotope Ratio Mass Spectrometry

18O (‰) 18O (‰) δD (‰) 18O δ δD (‰) 18O δ Sample Date δD standard δ standard Sample Date δD standard δ standard (‰) (‰) (‰) (‰) deviation deviation deviation deviation February 10, 2012 -16 0.3 -3.5 0.12 June 20, 2012 -18 0.7 -3.7 0.04 March 20, 2012 -17 1.8 -2.7 0.18 June 26, 2012 -16 1.3 -3.5 0.05 May 14, 2012 -16 0.5 -2.7 0.04 June 27, 2012 -19 0.5 -3.7 0.05 June 20, 2012 -17 0.3 -2.8 0.08 June 28, 2012 -18 1.8 -3.5 0.06 June 26, 2012 -16 0.5 -2.9 0.08 June 29, 2012 -22 0.7 -4.2 0.05 June 27, 2012 -16 0.2 -3.1 0.05 June 30, 2012 -23 1.6 -4.1 0.08 June 28, 2012 -17 0.6 -3.6 0.04 July 1, 2012 -25 1.5 -4.3 0.59 June 29, 2012 -20 0.1 -4.2 0.03 July 2, 2012 -28 0.9 -4.8 0.03 June 30, 2012 -20 0.3 -4.3 0.05 July 4, 2012 -30 1.3 -4.8 0.07 July 1, 2012 -22 1.1 -4.6 0.08 July 5, 2012 -29 0.6 -5.1 0.03 July 2, 2012 -25 0.1 -4.9 0.06 July 6, 2012 -27 1.0 -4.8 0.06 July 4, 2012 -26 0.2 -4.7 0.06 July 7, 2012 -26 1.3 -4.4 0.06 July 5, 2012 -26 0.3 -4.7 0.06 July 8, 2012 -27 0.6 -4.9 0.03 July 6, 2012 -27 1.3 -4.6 0.10 July 9, 2012 -25 1.2 -4.4 0.05 July 7, 2012 -26 0.4 -4.6 0.06 July 11, 2012 -25 0.7 -4.5 0.04 July 8, 2012 -25 0.4 -4.6 0.03 July 17, 2012 -23 0.9 -4.3 0.04 July 9, 2012 -25 0.5 -4.2 0.03 July 20, 2012 -22 0.7 -4.0 0.05 July 10, 2012 -24 0.3 -4.3 0.06 July 28, 2012 -26 0.9 -3.9 0.06 July 11, 2012 -22 0.5 -4.2 0.03 August 4, 2012 -26 1.3 -3.8 0.05 July 17, 2012 -20 0.4 -4.0 0.11 August 15, 2012 -27 0.9 -3.9 0.06 July 20, 2012 -20 0.2 -4.0 0.05 August 27, 2012 -23 0.7 -4.2 0.05 July 28, 2012 -20 0.5 -3.9 0.04 surface water samples August 4, 2012 -19 0.3 -3.9 0.08 March 19, 2013 -21 1.0 -3.5 0.06 August 15, 2012 -19 0.6 -3.9 0.04 April 24, 2013 -21 0.7 -3.6 0.08 August 27, 2012 -20 0.3 -3.9 0.03 May 1, 2013 -20 1.1 -3.5 0.06 surface water samples May 13, 2013 -22 1.0 -3.6 0.05 June 18, 2013 -18 0.8 -3.1 0.10 May 28, 2013 -24 1.2 -3.5 0.05 July 2, 2013 -17 0.6 -3.3 0.16 June 4, 2013 -21 1.2 -3.6 0.06 July 11, 2013 -16 0.8 -3.6 0.05 June 10, 2013 -21 1.3 -3.5 0.05 July 15, 2013 -17 0.3 -3.3 0.06 June 18, 2013 -22 1.5 -3.6 0.05 July 19, 2013 -17 0.6 -3.3 0.03 July 1, 2013 -15 0.8 -3.5 0.05 July 23, 2013 -17 0.4 -3.3 0.07 July 2, 2013 -20 0.8 -3.6 0.05 July 31, 2013 -18 0.5 -3.5 0.07 July 11, 2013 -20 1.1 -3.9 0.05 August 12, 2013 -17 0.6 -3.4 0.15 July 15, 2013 -20 1.1 -4.2 0.03 August 29, 2013 -18 0.5 -3.7 0.07 July 19, 2013 -20 1.1 -4.0 0.03 September 4, 2013 -17 0.6 -3.8 0.09 July 23, 2013 -19 0.6 -3.3 0.04 July 31, 2013 -22 0.8 -3.7 0.04 August 12, 2013 -21 1.4 -3.6 0.05 August 29, 2013 -19 1.0 -3.5 0.03 September 4, 2013 -16 0.8 -3.5 0.05

96

Table 3 – continued.

Laser Isotope Analyzer Isotope Ratio Mass Spectrometry

18O (‰) 18O (‰) δD (‰) 18O δ δD (‰) 18O δ Sample Date δD standard δ standard Sample Date δD standard δ standard (‰) (‰) (‰) (‰) deviation deviation deviation deviation vent line samples vent line samples June 18, 2013 -17 1.0 -3.2 0.14 June 18, 2013 -20 1.1 -3.6 0.06 July 2, 2013 -16 0.7 -3.6 0.12 July 2, 2013 -18 0.9 -3.8 0.06 July 11, 2013 -17 0.5 -3.3 0.10 July 5, 2013 -22 1.6 -3.8 0.06 July 15, 2013 -17 0.1 -3.2 0.07 July 11, 2013 -21 1.1 -3.9 0.04 July 23, 2013 -19 1.2 -3.6 0.11 July 15, 2013 -19 0.8 -4.0 0.05 July 31, 2013 -18 0.4 -3.6 0.06 July 19, 2013 -17 0.7 -4.1 0.05 August 12, 2013 -16 0.2 -3.5 0.02 July 23, 2013 -18 0.9 -4.0 0.05 August 29, 2013 -18 0.8 -3.7 0.04 July 26, 2013 -20 0.8 -3.6 0.05 September 4, 2013 -17 0.8 -3.7 0.09 July 31, 2013 -20 0.8 -3.7 0.05 August 3, 2013 -19 0.6 -3.7 0.05 August 12, 2013 -14 0.7 -3.6 0.07 August 16, 2013 -19 0.9 -3.6 0.04 August 20, 2013 -20 1.0 -3.6 0.04 August 23, 2013 -20 1.0 -3.6 0.05 August 29, 2013 -15 0.7 -3.6 0.05 September 4, 2013 -14 1.1 -3.6 0.07 September 9, 2013 -17 0.4 -3.4 0.04 September 22, 2013 -18 0.7 -3.5 0.03 September 30, 2013 -18 0.7 -3.6 0.05 October 4, 2013 -13 0.5 -3.8 0.06 October 9, 2013 -13 0.6 -3.8 0.08 October 16, 2013 -13 0.5 -3.6 0.08 October 21, 2013 -15 0.5 -3.6 0.06

97 CHAPTER 4

DISCUSSION AND CONCLUSIONS

Sampling Method Notes

Now that I have learned which techniques seem to work better, I would make two small changes to the study methods. The first set of spring samples included triplicate samples in smaller glass vials with Teflon-lined caps. When the first set of water samples was run on the Los Gatos liquid-water isotope analyzer, a few samples from the smaller glass vials were run to compare the results. They showed slightly different δD and δ18O values from the samples that were taken at the same time but stored in the larger glass bottles with the Polyseal caps. Sampling instructions were taken from chapter 2 of C. Kendall and E. A. Caldwell’s 1998 book Isotope Tracers in Catchment Hydrology. The authors state, “Our experience suggests that that caps with conical plastic inserts (e.g. ‘Polyseal’ caps) are the most reliable, followed by teflon- lined caps.” The difference seen may have been due to the different caps used, or it may have been caused by the smaller sampling volume, but the Polyseal caps nonetheless appear to be the better option. The final data were all based on the water samples stored in the 60 mL (2 oz.) Boston round clear glass bottles with Polyseal caps.

The guidelines for precipitation collection for isotope analysis recommend the addition of mineral oil to 1/4 to 1/2 in depth in the collection container to prevent evaporation (Ingraham, 1998). The mineral oil that I used was purchased at a pharmacy and was very viscous. It might have inadvertently caused a small amount of evaporation in the samples collected; a few times, a small layer of water was observed floating on top of the mineral oil layer. A number of the samples run during the first precipitation analysis plotted slightly to the right of the GMWL, indicating that evaporation had taken place. The thickness of the mineral oil was reduced to about 1/8 in and the issue was not seen again. Paraffin or silicon oil, which are less viscous, might be better options.

98 Variability of Isotope Composition of Florida Springs

The pattern for the majority of the isotope values of springs from base flow to non-base flow conditions was a shift to the right and lightly downward on a plot of δ18O to δD values (Fig. 31). During dry conditions, a linear regression of the spring water isotope values showed an R2 value of .61 with a slope of 4.1, but the non-base flow spring samples had no linear relationship (the R2 value was close to zero). This suggests that water sources were more variable than the sources under base flow conditions. Base flow comes primarily from the UFA during times when it has not received as much recent recharge from the vadose zone or from surface water entering swallets or sinkholes, and represents more of the long-term average of the isotope values from its source waters. Fritz (1981, p.179) wrote that the “most groundwater bodies are isotopically constant and closely reflect the average annual isotopic composition of local precipitation.” This appears to be the case for the springs sampled during drought/base flow conditions in the winter of 2012, since they plotted around the LMWL and GMWL. The δ18O values for the springs during base flow conditions plotted between −3 and −4‰. The weighted mean of δ18O values for the 2013 precipitation in Tallahassee was −4.8‰ and the weighted mean for precipitation from the longer-term data set of 2006 to 2011 reported by Odezulu (2011) was −4.5‰. The isotopic compositions of the springs under base flow conditions are close approximations of the mean isotope composition of annual precipitation shifted slightly to less negative values by enriched (relative to local rainfall) recharge from more permanent sources of surface water such as lakes. The base flow samples still showed some variability, indicating that the springsheds have unique mixtures of water, even without recent recharge.

The isotope values for the north and central Florida springs during non-base flow conditions showed much more variability, except for the springs along the Ichetucknee River that were more similar during wet conditions than during drought conditions. Many of the spring samples showed an evaporation signal by plotting to the right of the LMWL and GMWL. The springs were grouped by geographical location. Central Florida springs were made up of Homosassa Spring, Rainbow Springs, and the three Ocala National Forest springs: Alexander, Silver Glen, and Juniper Springs. Northwest Florida springs included Jackson Blue, Ponce de Leon, and Morrison Springs, which are located in the panhandle area (which were not reached

99 during non-base flow conditions), and the springs near the Tallahassee area: Newport, Shepherd, (Wacissa) Big Blue, and Wakulla Springs. The Ichetucknee Springs (Blue Hole, Cedar Head, Devil’s Eye, Mill Pond, Mission Springs Complex, and Ichetucknee Head Spring) and springs on the Santa Fe River (Ginnie, Little Devil, and Poe Springs) were grouped together since the isotope values plotted similarly. Springs on the Suwannee River that were sampled were Branford, Fanning, Lafayette Blue, Manatee, and Troy Springs. The comparison of isotopic compositions of the spring samples during dry and wet conditions showed that the springs are very responsive to local rainfall events, which is especially true for the Suwannee River and central Florida springs. The non-base flow samples for Juniper Spring, which is in the Ocala National Forest, and Manatee Spring, which is on the lower Suwannee River, were the exceptions, since the samples plotted close to the GMWL. The evaporation signal seen in the samples collected during wet conditions indicates the larger fraction of the springflow came from surface water sources during the wet conditions compared to the sources during the dry conditions. The D and 18O data for the wet season reflect the numerous sink holes that recharge directly to the UFA and the other surface water bodies such as lakes, swamps, and ephemeral ponds and streams that also recharge to the aquifer feeding the springs, and emphasize the UFA’s vulnerability to contamination, especially in the central Florida and Suwannee River regions.

-5.0 -5.0 (a) (b) Santa Fe and -10.0 -10.0 Ichetucknee Suwannee River

Central Florida -15.0 -15.0 Northwest δD (‰) δD (‰) Florida -20.0 -20.0 Linear (GMWL)

Linear (LMWL Tallahassee) -25.0 -25.0 -5.0 -4.0 -3.0 -2.0 -1.0 -5.0 -4.0 -3.0 -2.0 -1.0 18 18 δ O (‰) δ O (‰) Figure 31: Isotopic Composition of Florida Springs by Geographic Region. The isotope values of spring water samples collected in (a) base flow/drought conditions (dry season) and (b) non-base flow conditions (wet season) were grouped by spring location. The non-base flow δD and δ18O values show an evaporation signal for most of the spring water samples.

100 A couple of the springs sampled during non-base flow showed clues that they contained more recent recharge. At Alexander Springs near Astor, the initial SC readings were 1,153 μS/cm3 but later they were 1,045 μS/cm3. The high SC values for the springs in the Ocala National Forest have been attributed to the upward movement of small amounts of relict seawater from deep in the aquifer, so the recharge from summer rains would have diluted the older water (Adamski & Knowles, 2001). The isotope values moved to the right of the GWML during non-base flow conditions, which could indicate that there was more surface water influence than during base flow conditions. Nearby, Silver Glen Springs near Astor showed a very similar change in SC readings from 1,837 to 1,702 μS/cm3. Shepherd Spring, located near the St. Marks Wildlife Refuge, had SC readings of 804 μS/cm3 during base flow and 332 μS/cm3 during non-base flow conditions. The readings might indicate a slight influence of coastal salinity during the drought and a return to SC values similar to those of the UFA after groundwater levels returned to normal.

The isotope values for the springs, especially during non-base flow, were very similar to the results found by other isotope measurements of the springs. A study of isotope concentration in springs in the St. Johns Water Management District of central and northeastern Florida, done by Toth in 1999, found that the δD and δ18O values plotted mostly on and to the right on the GMWL. The only spring that plotted to the left was Juniper Spring. Toth measured isotope values for Alexander Spring (δD = −15.6‰, δ18O = −3.00‰), Juniper Spring (δD = −18.4‰, δ18O = −3.72‰), and Silver Glen (δD = −17.0‰, δ18O = −3.25‰). The values were very similar to those during the base flow conditions in early 2012 for Alexander Spring (δD = −15‰, δ18O = −3.2‰) and Juniper Spring (δD = −19‰, δ18O = −3.8‰), and very similar to the non-base flow sample taken at Silver Glen Spring (δD = −17‰, δ18O = −2.3‰). In 1997 and 1998, Katz and other researchers measured the isotope values of water samples for springs along the Santa Fe and Suwannee Rivers (Katz, Hornsby, Bohlke, & Mokray, 1999). They reported isotope values for Ginnie Springs (δD = −17.3‰, δ18O = −3.57‰), Poe Spring (δD = −16.3‰, δ18O = −3.35‰), Blue Hole Spring (δD = −18.6‰, δ18O = −3.48‰), Troy Spring (δD = −20.1‰, δ18O = −3.78‰ in 1997, δD = −18.8‰, δ18O = −3.80‰ in 1998), Lafayette Blue Spring (δD = −18.0‰, δ18O = −3.42‰), Fanning Springs (δD = −18.1‰, δ18O = −3.65‰), and Manatee Springs (δD = −16.3‰, δ18O = −3.55‰). The similarity in isotope values suggests that the

101 isotope composition might be relatively stable over time. The differences from nearby springs that was seen in the isotope chemistry in Poe Spring water samples was also documented by Katz and others (1999).

A comparison of the base flow spring samples to only the GMWL and the UFA line did not explain why the majority of them plotted to the left of the trend lines. The local meteoric water lines for Tallahassee and Pensacola from the isotope analyses of five and six years of precipitation (the research done by Odezulu [2011]) put the base flow isotope composition into perspective and provided the explanation that the springs were plotting closer to the LMWLs of north Florida. No LMWL is available for central Florida. Over a hundred precipitation samples were analyzed for a study of Biscayne Bay by Swart et al. (1989), but they found that the samples were similar to the GMWL and so used it in their research instead of an LMWL. There are GNIP records for Ocala from 1969 to 2003 (IAEA), but there is some scatter in the plot of the δ18O and δD values, making a local line unclear. The differences between the LMWLs (Tallahassee and Pensacola) and the GMWL illustrate the importance of local precipitation isotope data but, because there is limited information about the isotopes in precipitation for many locations, an LMWL is usually not available. Many researchers use the GMWL for a variety of study locations, but it might be more informative to determine an LMWL that would then also be available to other researchers. Interestingly, the LMWL for areas along the north coast of the Mediterranean Sea is very similar to the LMWL for Tallahassee, with d-excess values of +15 and +14, respectively (Gat & Carmi, 1970; Odezulu, 2011). The d-excess value was explained by Gat and Carmi (1970) as the enrichment of the Mediterranean Sea compared to mean ocean water by evaporation. There may be a similar relationship with north Florida precipitation and the Gulf of Mexico.

The springs along the Ichetucknee River each had unique isotope signatures on both sampling dates. It is known that the water chemistry is different for each of the springs (Martin & Gordon, 2000), so it is not surprising that the isotope chemistry would also vary. During base flow conditions, the springflow comes mainly from the UFA without much surface water influence. The variation in isotope composition suggests that each spring has a unique mixture of water. The most similar isotope values during drought conditions came from the three springs all

102 located on the east side of the Ichetucknee River, hinting that they had a similar water source, possibly one flowing from the east. Counterintuitively, the six springs had more similar isotope compositions during non-base flow conditions, possibly due to a larger surface water component or recent recharge, with a similar isotope composition contributing to all the springflows.

Use of Deuterium and Oxygen-18 as Hydrologic Tracers

The water samples from Wakulla Spring showed a very clear signal from the large amounts of rainfall brought by Tropical Storm Debby; this is despite the amount of evaporation that likely occurred before it was recharged to the aquifer and received inflow from swallets. Unfortunately, there was no streamflow data available for the sinking streams, which have been shown to have hydraulic connections with Wakulla Springs. The USGS stream-gaging station at Fisher Creek near Hilliardville, FL (station no. 02326993) was discontinued between July 2010 and Dec. 2014. The connection between Spring Creek and Lost Creek has been demonstrated by dye-trace studies, and there is a probable connection with Wakulla Springs (Davis & Verdi, 2014; Kincaid et al., 2010). The USGS stream-gaging station at Spring Creek near Spring Creek, FL (station no. 02327031) was discontinued between Oct. 2010 and Oct. 2013. Lost Creek near Arran, Fl (station no. 02327033) was discontinued between Oct. 2010 and Nov. 2015, so the amount of streamflow it experienced during the flooding caused by the storm is unknown (USGS Water Data). The USGS stream-gaging station at Sopchoppy River near Sopchoppy, FL (station no. 02327100) had the highest river level recorded, and the river rose even higher, destroying the stream-gaging equipment. High water marks indicated that the peak stage was 36.78 ft, which was used to calculate an instantaneous peak flow of 11,900 cfs on June 26, 2012. I made a flood measurement several hours after the peak, at which point the water was more than 5 ft above the bridge, so it is likely that Lost Creek, which is a few miles away, also had record streamflow. Fisher Creek, Black Creek, Lost Creek (above and below the main sinkhole), and Spring Creek are currently being monitored by the USGS with cooperation from NWFWMD (USGS Water Data). The streamflow data they could have provided would have been interesting to compare with the streamflow data from the Wakulla River near Crawfordville, FL gage.

103 The mean transit time of water from the Tropical Storm Debby rainfall to emerge from Wakulla Spring, calculated from the isotope data, was 9 days (Fig. 32), which was similar to some of the travel times between the sinking streams and Wakulla Spring seen in dye-trace studies done by Kincaid and others (Kincaid et al., 2005, 2012; Kincaid & Werner, 2008). The dye-trace studies showed travel times to Wakulla Spring of 9.5 days from Fisher Creek Sink and 10.3 days from Black Creek Sink via the karst window Emerald Sink, and travel times of 23.1 days from Ames Sink and 19.1 days from Kelly Sink via the connection to Indian Spring (Kincaid & Werner, 2008). Though there was not a clear connection between the extremely light precipitation from Tropical Storm Andrea and the minimum δ18O values at Wakulla Spring 29 and 33 days later, it would not be an unreasonable mean transit time. The δD values did not show the same pattern as the δ18O values for the 2013 spring water samples. Since the D and 18O analyses were run at separate times, it could have been due to higher than desirable standard deviation values for the D analysis. The 18O results had better precision, and the values were more reliable than the δD values.

Figure 32: Determination of Transit Time using Tropical Storm Rainfall as a Natural Tracer. Hydrograph for Wakulla River near Crawfordville, FL shows flood peak. Wakulla Spring isotope data shows the minimum values observed nine days after the rainfall from Tropical Storm Debby.

104 The short mean transit times during periods of high flow can belie the longer transit times of the other half of the water, with longer flowpaths forming a “trailing end” that takes much more time to move through the aquifer. The transport of contaminants can move quickly through the aquifer via the conduits during high flows, but the increased flow could also move water into the lower permeability matrix, where very slow water movement means it can remain there for many years. As seen in isotope hydrograph separations, the mobilization of pre-event water can be a significant portion of the peak flow. Interestingly, results found by Martin, Kurz and Khadka (2016) of apparent water ages for the six main springs on the Ichetucknee River showed an increase in age as flood waters caused by Tropical Storm Debby receded, possibly indicating the mobilization of much older water stored in the matrix.

Precipitation and Throughfall Despite the limited number of samples, the 40 precipitation samples from May through Nov. 2013 that had reliable results for the isotope analysis showed a trend line of δD = 8.4·δ18O + 15.4‰, very similar to the calculated LMWL for Tallahassee by Odezulu (2011), which was δD = 8.3·δ18O + 14.0‰ and was based on isotope analyses of precipitation samples collected from May 2006 to Jan. 2011. The d-excess values for the 2013 precipitation samples were between 9 and 20‰, which compare well with the average d-excess value for Tallahassee of 14‰ found by Odezulu (2011). The weighted mean δ18O value for the small sample set of 2013 Atlantic hurricane season rainfall was −4.8‰, which compared well with the weighted mean of −4.5‰ based on the long-term data set (Odezulu, 2011).

Though the rainfall from Tropical Storm Andrea in Florida was very low, the storm system that formed into the tropical storm had brought about 12 in (300 mm) to the Yucatan Peninsula, which would explain the very light isotope values caused by the amount effect (NWS). The precipitation samples taken during Tropical Storm Andrea showed a pattern of decreasing isotope values, with the lowest values occurring in the samples taken on June 6 between 9 am and 6 pm. The findings are in agreement with the observation on large variations over a short period of time for isotope values in precipitation, which was shown by McDonnell, Bonell, Stewart, and Pearce (1990). They suggest using a “rainfall weighting technique” instead

105 of bulk sample collection if applying the isotope data to hydrograph separation (McDonnell et al., 1990).

For some isotope hydrograph studies, throughfall is collected in place of precipitation samples for isotope analysis (Bazemore et al., 1994; Brown et al., 1999). DeWalle and Swistock (1994) noted that throughfall could undergo enough evaporation to significantly enrich the isotope values compared to the rainfall from the same event, and investigated how isotope values for throughfall changed for three types of forests: deciduous, pine, and spruce. They found that differences averaged at around 0.24‰ for δ18O, however, they found little difference during intense summer storms (DeWalle & Swistock, 1994).

The results of the two throughfall samples, taken to test the differences between throughfall and rainfall samples, showed that the Aug. 17 samples both had δ18O values of −3.8‰. The Oct. 7 samples had isotope values showing possible evaporation in the throughfall sample. The precipitation sample had an isotope value of δ18O = −5.0‰, and the throughfall sample had a value of δ18O = −4.5‰. It is possible that, because the August samples were collected at warmer temperatures, the higher humidity reduced evaporation. The October samples were collected in much cooler weather and may have been more susceptible to evaporation. If isotope values for precipitation were applied to isotope hydrograph separation in the Wakulla Spring watershed, it might be worth collecting throughfall samples as well as open precipitation samples for comparison. Though there might not be a large difference between throughfall and rainfall isotope composition, the variations could be documented and used to fine tune a mixing model. If there was a significant difference seen, it could better define the isotope composition on the water being recharged to the aquifer since there is a large amount of forested lands in the springshed, especially in the Woodville Karst Plain, where a greater portion of recharge occurs. Kubota and Tsuboyama (2003) compared results of hydrograph separation using isotope data from weighted and bulk rainfall, and they also compared the results using isotope values for open precipitation to results using throughfall. They found that the small differences in isotope values could translate to differences of 5 to 10% in storm runoff components (Kubota & Tsuboyama, 2003).

106 Hydrograph Separation: The Integration of Streamflow and Isotope Data

Although the Wakulla Springs watershed characteristics do not meet all the criteria for isotope hydrograph separation delineated by Sklash and Farvolden in 1979 or by Buttle in 1994, they could still be a useful tool for learning about the watershed, especially if they were part of a more complex modeling project. One requirement for the accuracy of the two-component mixing model of isotope hydrograph separation is that “contributions from surface storage are negligible” (Buttle, 1994). In the Wakulla Springs watershed, there is a lot of groundwater/surface water interaction. Katz and other researchers found that large amounts of surface water contribute to the UFA through sinking streams and lake water from the large lakes such as Lake Jackson and Lake Bradford (Katz et al., 1997, 2004). With the large portion of the springshed in the unconfined aquifer of the Woodville Karst Plain and the surface water contributions from the Lakes Region of the springshed, Wakulla Springs receives a portion of surface water, especially during large storms, and does not meet the requirement. Surface storage can complicate a two-component mixing model because, even if the surface water only has a short time to undergo evaporation such as with sinking streams, the evaporative enrichment can weaken the new water (event-precipitation) signal. A study of a karst spring in Indiana by Lakey and Krothe (1996) found that a graph of the δD and δ18O values for spring water samples moved away from a trend line drawn between the isotope values for groundwater and the isotope values for the event precipitation. They concluded that a third component, with isotope values that had undergone evaporation, made up a significant portion of the springflow, and the two-component mixing model was not complex enough to apply to the springshed (Lakey & Krothe, 1996). Further investigation of the spring showed that a four-component mixing model, with epikarstic water and soil water added, better described the hydrology of the springshed (Lee & Krothe, 2001). Despite all the groundwater/surface water interactions in the Wakulla springshed, there was not a strong evaporative signal in the springwater samples, but a mostly consistent moderate signal, likely due to the large lakes in the watershed. The Wakulla Springs water samples do not plot directly between the groundwater and precipitation isotope values; rather, they plot mostly a bit to the right, indicating a degree of evaporation (Figs. 33 and 34). After Tropical Storm Debby, the isotope values of Wakulla Spring samples plotted along the LMWL though slightly offset to the right. If there had been a source of water with a strong evaporative signal after the

107 storm, the isotope values would have moved away from the LMWL instead of moving along it. The isotope values of surface water with clear evaporative enrichment compared with local rainfall have been used to trace that water’s connection with groundwater in other local studies (Katz, 1998), so it might still be useful in a more complex mixing model. Lake Munson supplies water to Ames and Kelly Sinks during floods and would likely have a distinct isotope signature. Since the enrichment signal from surface waters does not appear to be very strong at Wakulla Springs, the isotope data, in combination with other geochemical tracer data from the other sinking streams, with flow paths to Wakulla Spring already documented by dye-trace studies, could be combined with the newly available streamflow data for the swallets.

Another assumption required by two-component mixing models is that there is not a large difference in isotope values for other components of pre-event water, such as soil water in the vadose zone or water stored in the epikarst above the water table (Aquilina et al., 2006; DeWalle et al., 1998; Sklash & Farvolden, 1979). A more in-depth investigation would require that these other sources of recharge to the aquifer have similar isotope values to the water in the saturated zone of the aquifer or incorporate the different isotope values into the mixing model as an additional component or components. A plot of the 2012 Wakulla Spring water samples shows that the majority of the samples fall in a line just to the right of the LMWL for Tallahassee with no clear sign of another isotopically distinct water source (Fig. 33). The exceptions were the samples taken in sequence on July 28, August 4, and 15, which plotted together, further to the right of the other measurements. The sample on August 27 returned to the plot with the other 2012 samples. The shift in the isotope values might be due to a large influx of surface water for those days. The isotope values for the 2013 Wakulla Spring samples clustered on and near the LMWL with δ18O values around 4‰ (Fig. 34). The mean value was -3.7‰ which closely matched the weighted mean of the precipitation samples collected in 2013, which was -3.8‰. The Wakulla Spring samples that had the most evaporative signal in 2013 were the samples collected between March and May. In constrast with the drought conditions in spring of 2012, there was a very wet spring in 2013. The isotope values of the early 2012 samples plotted close to the LMWL, but the 2013 samples showed the recent recharge to the area by plotting towards the lower right.

108 Wakulla Spring 2012

-6.0 -5.0 -4.0 -3.0 -2.0 -1.0 -5

-10

δD (‰) -15 2012 base flow -20 LMWL

-25

-30 δ18O (‰) -35

Figure 33: Plot of Wakulla Spring Samples 2012. Samples started out with the negative isotope values during drought conditions. Base flow samples were taken prior to Tropical Storm Debby.

Wakulla Spring 2013

-6.0 -5.0 -4.0 -3.0 -2.0 -1.0 -5

-10

δD (‰) -15 2013 March to May -20 LMWL

-25

-30 δ18O (‰) -35

Figure 34: Plot of Wakulla Spring Samples 2013. Samples from March to May plotted differently than the 2012 samples taken during those months under drought conditions. There had been persistent storms in February, 2013, which caused the maximum peak in Wakulla River streamflow on March 1.

The application of isotope hydrograph separation to larger basins has been limited due to the difficulty in ensuring that the entire catchment receives precipitation with a consistent isotope composition. For rivers, this can complicate things because tributaries can bring inflow that did

109 not receive the same precipitation as the streamgage location. In a review of the use of isotopic hydrographic separation, Buttle (1994) found that the majority of published research applied the technique to watersheds of less than 100 km2. Though there could be some variability in the isotope composition of tropical storm rains, the size of the storm systems would probably overcome the limitation of needing consistent isotope values in precipitation over the entire watershed.

One of the most important criteria for isotope hydrograph separation is to have isotopically distinct event water from the pre-event water already in the aquifer (Buttle, 1994; Sklash & Farvolden, 1979). Due to the very light isotope composition of tropical storms marking the event water, there can be substantial differences between the isotope values of the rain and the groundwater. The isotope values for tropical storm and hurricane precipitation have been studied by several researchers. Lawrence and Gedzelman (1996) compiled isotope data of precipitation for a number of tropical storms and hurricanes between 1988 and 1993. They found that δ18O values ranged from −14.3‰ to −6.0‰, with a mean of −9.4‰, and noted that Lawrence and White (1991) had previously seen that “remnants” of tropical storms had some of the lighter precipitation values, with δD values of −89‰ after Tropical Storm Dean in 1983 and −47‰ after Tropical Storm David in 1979. Gedzelman et al. (2003) measured isotopes of precipitation throughout the course of hurricanes. They found that isotope values during Hurricane Olivia in 1994 ranged from δD = −95.6 and δ18O = −13.9 in the first sample collected, to δD = −201.5 and δ18O = −26.1 later in the storm’s progress. Hurricane Opal in 1996 had a similar minimum value for δ18O, at −25.8‰ (δD was not reported) (Gedzelman et al., 2003). Precipitation samples collected in Tallahassee and documented by Odezulu (2011) from Tropical Storm Alberto (June 16, 2006; δD = −86.8‰, δ18O = −12.3‰) and Tropical Storm Fay (Aug. 22, 2008; δD = −99.4‰, δ18O = −13.6‰) had similar values to the tropical storms reported by Lawrence and Gedzelman (1996). The lightest precipitation sample collected during the 2013 hurricane season, from Tropical Storm Andrea, had an isotope composition similar to the other tropical storms reported, with the lightest sample of precipitation measuring δD = −109‰, δ18O = −14.7‰.

110 Though there was not enough information to apply isotope hydrograph separation to the Tropical Storm Debby flood peak, if the rain it brought is assumed to have had a very light isotope signature similar to other tropical storms with a documented isotope composition, calculations can be made using the isotope values of spring water samples to describe the isotope composition of the groundwater in the springshed (Figs. 35 and 36). A paper by Buttle, published in 1994, detailed the calculations for hydrograph separation using stable isotopes, which use a mass balance and two-component (binary) mixing model.

= +

��= �� +� � ���� ���� ���� Where Qt is the total streamflow, Qp is the fraction of streamflow from pre-event water

(groundwater that was in the aquifer prior to the rainfall), Qe is the fraction of streamflow from event water (precipitation or snow melt), and the C values are the concentrations of a solute in a regular mass balance equation, and, in the case of isotope hydrograph separation, are the isotope values for either δD or δ18O. This can also be written to solve for the percentage (fraction) of total streamflow that came from precipitation (f) as follows:

= + (1 )

= �� +��� �� =− � + ( ) �� =�� � ( �� − �)/(�� �� )� �� − �� � �� − �� �� − �� For Qt values, the daily mean streamflow data for the USGS stream-gaging station on the 18 Wakulla River was used. The δ O and δD values used for Ct were taken from the analysis of

Wakulla Spring water samples. The Cp value was taken from the δ values of the spring water samples during base flow (prior to the rain event) and the Ce value was taken from the δ values for storm precipitation samples. The average of isotope values of tropical storm precipitation from Tropical Storm Alberto, Tropical Storm Fay, and Tropical Storm Andrea were δD = −98‰ and δ18O = −13.5‰. The average values of Wakulla Spring water samples prior to the storm (Feb. 20, May 14, and June 20) were δD = −17‰ and δ18O = −3.5‰. The fraction of storm runoff attributed to rainfall can then be calculated as follows:

111

δD + 17‰ δ O + 3.5‰ = or = 98‰ + 17‰ 1318.5‰ + 3.5‰ � � − − The results of the hydrograph separation were very similar for the δD and δ18O values, as expected. Even with the large amount of assumed isotopically light rainfall, the maximum amount of springflow that could be attributed to event water was 17%, occurring on July 5 and based on δ18O, or 16%, occurring on July 4, and based on δD. For a binary mixing model, this would indicate that 84 or 83% of the springflow at that point was groundwater present before the tropical storm. When the peak streamflow occurred, on June 27, the mixing model attributes 97% of the flow to groundwater. If the same hydrograph separation is applied to the peak in 2013, which had a maximum daily streamflow on July 8, the highest percentage that can be attributed to the very light Tropical Storm Andrea rain is 5% between July 17 and 21. A more complex model might be able to further divide the runoff due to groundwater into better-defined components.

3500 total streamflow 3000

2500

c 2000 f s 1500

1000

500

0 6/15 6/20 6/25 6/30 7/5 7/10 7/15 7/20 2012

Figure 35: Theoretical Hydrograph Separation for Wakulla River for Flood Peak in 2012.

112 1700

total streamflow pre-event 1500

1300

c 1100 f s 900

700

500 6/30 7/10 7/20 7/30 8/9

2013 Figure 36: Theoretical Hydrograph Separation for Wakulla River for Flood Peak in 2013.

Conclusions

Several conclusions can be drawn from the isotope analyses of water samples fromWakulla Spring and other north and central Florida springs:

(1) The freshwater springs in north and central Florida have unique isotope values and a degree of variability, especially seen at the central and Suwannee River locations during wet conditions when the springs were highly responsive to rainfall events and surface water influence. The base flow values appear to be relatively stable and reflective of the mean isotopic composition of precipitation for their geographical locations. Wakulla Spring samples showed a smaller degree of evaporative signal from surface water sources indicated by the isotope values plotting near the LMWL during wet and dry conditions and the 2013 mean δ18O value (-3.7‰) matching the weighted mean of the precipitation samples collected (-3.8‰).

113 (2) Tropical storm or hurricane precipitation can cover large areas with very negative isotope values (i.e. δD = -109‰ and δ18O = -15‰ during Tropical Storm Andrea) that can be effectively used as natural tracers in watersheds. The extremely depleted isotope values compared to normal rain can translate into a clear signal in springflow and can be used to calculate transit times for water in the springshed. A mean transit time of nine days was found for Tropical Storm Debby rainfall to become outflow at Wakulla Spring.

(3) The δ18O and δD values of precipitation and spring water can be used for isotope-based hydrograph separation, and though a two-component mixing model is not complex enough to describe the Wakulla River watershed, it could be supplemental to a more detailed springshed model.

(4) Since the isotopic composition of rainfall changes throughout the duration of the storm, as seen in the precipitation samples from Tropical Storm Andrea, weighted mean isotope values of precipitation would be useful in isotope-based hydrograph separation calculations.

Possible future research on Wakulla Springs or other Florida springs could make improvements on applying isotope tracer data to springshed modeling by including geochemical tracers. Aquilina, Ladouche, and Dörfliger (2006) used chloride (Cl−) and bromide (Br−) ions along with D and 18O isotope data to determine how much springflow from four springs in the Pyrenees was pre-event water from epikarst storage. Solutes such as calcium (Ca+), magnesium (Mg+), and silicate have been used as additional tracers (Wels, Cornett, & LaZerte, 1991). One benefit of using water isotopes alongside geochemical tracers is that the conservative tracers D and 18O can confirm that solutes have been conservative and have not reacted within the aquifer. Multiple tracers could also include other isotopes, such as sulfur-34 (34S) and strontium-87 (87Sr), which can be chosen to inform a specific part of the watershed processes (Vitvar et al., 2005). A multi-tracer separation method called End-Member-Mixing-Analysis (EMMA), described by Christophersen et al. (1990), allows for the separation of more components contributing water to the river or spring. Many hydrologic investigations have incorporated 18O and D data into more complex watershed modeling (Beven & Freer, 2001; Doctor et al., 2006;

114 Döll, Kaspar, & Lehner, 2003; Gurtz et al., 2003; Hoeg, Ulenbrook, & Leibundgut, 2000; Leavesley Markstrom, Restrepo, & Viger, 2002; Uhlenbrook & Hoeg, 2003).

Intra-storm variability in the isotopic composition of precipitation has been documented by Kubota and Tsuboyama (2003), though Ulenbrook and Hoeg (2003) found that the “impacts … were low when compared with the impacts of other effects” such as uneven tracer concentrations within the study area. The impacts may be amplified with the greater range in isotope values of precipitation during tropical storms and hurricanes. The precipitation sample collected during Tropical Storm Andrea prior to the storm sample that had minimum values, reported at δD= −109‰ and δ18O = −15‰, had isotope values of δD= −41‰ and δ18O = −6.5‰. The precipitation sample collected after the extremely light sample had isotope values of δD= −58‰ and δ18O = −8.4‰. Weighted mean precipitation isotope values, using precipitation samples collected a few times a day during a tropical storm or hurricane instead of daily or from composite rainfall samples, could assist with decreasing some of the uncertainty inherent in the hydrograph separation calculations. The differences in isotope values of open rainfall and throughfall samples documented under certain conditions by Kubota and Tsuboya (2003) did not appear to be significant during the intense rainfall and humid conditions during which the two throughfall samples were collected in Tallahassee. A greater number of samples could be collected for comparison but might not increase the accuracy of a mixing model for the Wakulla springshed.

For watersheds that lie in the paths of hurricanes and tropical storms, it could be advantageous to use the natural tracers the storms provide, along with other data collection as part of larger studies to gain insights into their hydrology and aquifer characteristics.

115 APPENDIX A

WATER QUALITY DATA

North and Central Springs Water Quality Data

base flow conditions non-base flow conditions Spec. Spec. Sample Date Sample Date Temp. Cond. DO DO Temp. Cond. DO DO USGS Station No. Spring Name Base Flow Non-base Flow °C pH μS/cm3 mg/L % °C pH μS/cm3 mg/L % 02236095 Alexander Springs 2/18/12 11/9/12 23.6 6.5 1153 5.8 67.1 22.9 7.6 1045 2.3 26.5 02322688 Blue Hole Spring 1/30/12 10/17/12 21.5 7.7 305 3.0 33.6 21.7 7.5 305 2.0 22.9 02320502 Branford Springs 1/14/12 10/1/12 21.7 7.2 480 1.5 16.5 22.2 7.3 497 1.2 14.2 02322687 Cedar Head Spring 1/30/12 10/17/12 21.6 7.8 308 3.2 36.2 21.7 7.5 323 3.1 35.2 02322694 Devil's Eye Spring 1/30/12 10/17/12 21.8 7.5 348 1.3 15.0 21.9 7.3 336 0.5 6.0 02323502 Fanning Springs 1/24/12 9/20/12 22.5 7.2 494 2.4 28.2 22.8 7.0 498 1.9 22.9 02322400 Ginnie Spring 1/23/12 10/17/12 22.5 7.1 352 4.7 54.6 22.5 7.4 350 3.4 39.7 02310687 Homosassa Springs 2/18/12 11/9/12 23.3 7.5 5215 5.4 65.0 23.4 7.5 2709 4.4 52.1 02322685 Ichetucknee Head Spring 1/30/12 10/17/12 21.9 7.6 319 4.4 50.5 21.8 7.5 322 3.8 42.9 02236130 Juniper Springs 2/18/12 11/9/12 21.9 7.4 117 9.2 104.7 21.8 8.4 108 7.3 83.1 282331081371101 Lafayette Blue Spring 1/14/12 10/1/12 21.5 7.2 476 0.8 9.0 22.1 7.4 438 0.9 10.4 294957082414700 Little Devil Springs 1/23/12 10/17/12 22.5 7.4 347 5.0 57.7 22.5 7.3 338 4.1 47.4 02324566 Manatee Spring 1/24/12 9/20/12 22.4 7.1 480 2.3 27.0 22.4 7.0 501 3.0 35.1 02322695 Mill Pond Spring 1/30/12 10/17/12 21.9 7.6 377 0.3 3.5 21.9 7.5 376 0.2 2.4 02322691 Mission Springs Complex 1/30/12 10/17/12 21.8 7.5 329 0.3 3.8 21.8 7.5 316 0.3 3.2 02322140 Poe Springs 1/23/12 10/17/12 22.7 7.3 450 2.7 31.0 22.5 6.7 403 0.3 4.0 290608082262600 Rainbow Springs 2/18/12 11/9/12 23.1 7.4 147 8.1 95.1 23.2 7.9 139 7.3 85.8 303612084473001 Shepherd Spring 1/21/12 9/23/12 20.4 6.1 804 3.0 35.1 21.6 7.2 332 1.3 15.1 02246160 Silver Glen Springs 2/18/12 11/9/12 23.3 7.7 1837 6.5 77.7 23.1 7.8 1702 3.3 39.2 02320250 Troy Spring 1/14/12 10/1/12 21.5 7.5 364 0.7 8.2 21.9 7.3 399 1.0 11.2 02327022 Wakulla Spring 2/10/12 8/27/12 20.8 6.6 480 2.5 28.0 n/a n/a n/a n/a n/a

116

North and Central Springs Water Quality Data –continued.

observed change mean values Mean ΔSpec. Spec. Mean ΔTemp. Cond. Mean Cond. Mean DO DO USGS Station No. Spring Name °C ΔpH μS/cm3 ΔDO mg/L ΔDO % Temp. °C Mean pH μS/cm3 mg/L % 02236095 Alexander Springs 0.7 -1.1 108 3.5 40.6 23.2 7.0 1099 4.0 46.8 02322688 Blue Hole Spring -0.2 0.2 0 1.0 10.7 21.6 7.6 305 2.5 28.3 02320502 Branford Springs -0.5 -0.1 -17 0.2 2.3 21.9 7.3 489 1.3 15.4 02322687 Cedar Head Spring -0.1 0.3 -15 0.1 1.0 21.6 7.6 316 3.1 35.7 02322694 Devil's Eye Spring 0 0.2 12 0.8 9.0 21.8 7.4 342 0.9 10.5 02323502 Fanning Springs -0.3 0.1 -4 0.5 5.3 22.6 7.1 496 2.2 25.6 02322400 Ginnie Spring 0 -0.3 2 1.3 14.9 22.4 7.3 351 4.2 47.2 02310687 Homosassa Springs -0.1 0 2506 1.0 12.9 23.3 7.5 3962 4.9 58.6 02322685 Ichetucknee Head Spring 0.1 0.1 -3 0.7 7.6 21.8 7.6 321 4.2 46.7 02236130 Juniper Springs 0.1 -1.0 9 1.9 21.6 21.9 7.9 113 8.2 93.9 282331081371101 Lafayette Blue Spring -0.6 -0.2 38 -0.1 -1.4 21.8 7.3 457 0.8 9.7 294957082414700 Little Devil Springs 0 0.1 9 0.9 10.3 22.5 7.4 343 4.6 52.6 02324566 Manatee Spring 0 0.1 -21 -0.7 -8.1 22.4 7.1 491 2.7 31.1 02322695 Mill Pond Spring 0 0.1 1 0.1 1.1 21.9 7.5 377 0.2 3.0 02322691 Mission Springs Complex 0 0 13 0.1 0.6 21.8 7.5 323 0.3 3.5 02322140 Poe Springs 0.2 1.0 47 2.3 27.0 22.6 7.1 427 1.5 17.5 290608082262600 Rainbow Springs -0.1 -0.5 8 0.8 9.3 23.1 7.6 143 7.7 90.5 303612084473001 Shepherd Spring -1.2 -1.1 472 1.7 20.0 20.9 6.6 568 2.8 25.1 02246160 Silver Glen Springs 0.2 -0.1 135 3.2 38.5 23.1 7.7 1770 4.9 58.5 02320250 Troy Spring -0.4 0.2 -35 -0.3 -3.0 21.7 7.4 382 0.9 9.7

117

North and Central Springs Water Quality Data –continued.

base flow conditions

Spec. Sample Date Base Cond. USGS Station No. Spring Name Flow Temp. °C pH μS/cm3 DO mg/L DO % 02358795 Jackson Blue Spring 1/16/12 20.7 7.6 258 8.0 89.1 02319302 Madison Blue Spring 1/14/12 20.9 7.7 307 1.4 16.1 02365580 Morrison Spring 1/16/12 20.2 7.7 225 4.0 44.5 301245084104300 Newport Spring 1/21/12 20.4 7.3 451 2.4 27.0 02354710 Ponce de Leon Spring 1/16/12 19.9 7.7 213 4.9 53.5 02326523 (Wacissa) Big Blue Spring 2/3/12 20.4 7.5 340 1.0 11.2

118 APPENDIX B

BACKGROUND INFORMATION Florida Springs List Spring No. USGS Station No. Name Lat Long 01 02236095 Alexander Springs near Astor, FL 29.0813 -81.5759 02 02322688 Blue Hole Spring near Hildreth, FL 29.9805 -82.7584 03 02320502 Branford Spring near Branford, FL 29.9549 -82.9284 04 02322687 Cedar Spring near Hildreth, FL 29.9833 -82.7587 05 02322694 Devil's Eye Spring near Hildreth, FL 29.9737 -82.7600 06 02323502 Fanning Springs near Wilcox, FL 29.5876 -82.9353 07 02322400 Ginnie Spring near High Springs, FL 29.8363 -82.7001 08 02310687 Homosassa Springs at Homosassa, FL 28.7991 -82.5885 09 02322685 Ichetucknee Head Spring near Hildreth, FL 29.9842 -82.7619 10 02358795 Jackson Blue Spring near Marianna, FL 30.7905 -85.1401 11 02236130 Juniper Springs near Ocala, FL 29.1837 -81.7124 12 282331081371101 Lafayette Blue Spring 30.1258 -83.2261 13 294957082414700 Little Devil Springs 29.8346 -82.6970 14 02319302 Madison Blue Springs near Blue Springs, FL 30.4804 -83.2444 15 02324566 Manatee Spring near Chiefland, FL 29.4895 -82.9769 16 02322695 Mill Pond Spring near Hildreth, FL 29.9667 -82.7600 17 02322691 Mission Springs Complex near Hildreth, FL 29.9762 -82.7579 18 01265580 Morrison Spring near Redbay, FL 30.6579 -85.9040 19 301245084104300 Newport Springs 30.2127 -84.1785 20 02322140 Poe Springs near High Springs, FL 29.8257 -82.6490 21 02365710 Ponce de Leon Spring at Ponce de Leon, FL 30.7212 -85.9307 22 290608082262600 Rainbow Springs 29.1025 -82.4375 23 303612084473001 Shepherd Spring 30.1253 -84.2855 24 02246160 Silver Glen Springs near Astor, FL 29.2458 -81.6435 25 02320250 Troy Spring near Branford, FL 30.0060 -82.9975 26 02326523 (Wacissa) Big Blue Spring near Wacissa, FL 30.3277 -83.9848 27 02327000 Wakulla Spring near Crawfordville, FL 30.2352 -84.3026

119 U.S. Census Records

Year Florida Leon Wakulla 1900 528,542 19,887 5,149 1910 752,619 19,427 4,802 1920 968,470 18,059 5,129 1930 1,468,211 23,476 5,468 1940 1,897,414 31,646 5,463 1950 2,771,305 51,590 5,258 1960 4,951,560 74,225 5,257 1970 6,789,443 103,047 6,408 1980 9,746,324 148,655 10,887 1990 12,937,926 192,493 14,202 2000 15,982,387 239,452 22,863 2010 18,801,310 275,487 30,776

120 APPENDIX C

ISOTOPE ANALYSES

Standard Deviation for Isotope Ratio Mass Spectrometer

δD δD δ18O δ18O Reportable Known Reportable Known Sample_name Value Value ∆δD Value Value Δδ18O SLCTAP-1 -124.79 -121.7 -3.09 -15.99 -16.2 0.21 SLCTAP-1 -123.07 -121.7 -1.37 -16.23 -16.2 0.03 SLCTAP-1 -119.40 -121.7 2.30 -16.24 -16.2 0.04 Average -122.42 Average -16.15 Std. Std. Deviation 2.75 Deviation 0.14 YW-ST2-1 -12.08 -11.6 -0.48 -2.16 -2.3 0.14 YW-ST2-1 -12.95 -11.6 -1.35 -2.06 -2.3 0.24 YW-ST2-1 -14.44 -11.6 -2.84 -1.98 -2.3 0.32 Average -13.16 Average -2.07 Std. Std. Deviation 1.19 Deviation 0.09 ZN-1 7.10 4.93 2.17 0.41 0.56 0.15 ZN-1 0.93 4.93 -4.00 0.18 0.56 0.38 ZN-1 1.99 4.93 -2.94 0.61 0.56 0.05 Average 3.34 Average 0.40 Std. Std. Deviation 3.30 Deviation 0.22 QD -32.26 -35.1 2.84 -10.70 -10.6 0.10 QD -32.41 -35.1 2.69 -10.74 -10.6 0.14 QD -28.72 -35.1 6.38 -10.71 -10.6 0.11 Average -31.13 Average -10.71 Std. Std. Deviation 2.09 Deviation 0.02 δ18O δ 2H Average Average Std. Std. Deviation 2.3 Deviation 0.1

121

Hydrograph Separation Calculations for 2012 Date f rain Daily Q Q - rain June 20, 2012 0% 525 525 June 21, 2012 0% 507 507 June 22, 2012 0% 516 516 June 23, 2012 0% 495 495 June 24, 2012 0% 478 478 June 25, 2012 0% 660 660 June 26, 2012 2% 2370 2323 June 27, 2012 4% 3390 3254 June 28, 2012 2% 3330 3263 June 29, 2012 8% 3050 2806 June 30, 2012 8% 2690 2475 July 1, 2012 10% 2130 1917 July 2, 2012 15% 1750 1488 July 3, 2012 15% 1250 1063 July 4, 2012 15% 1190 1012 July 5, 2012 17% 1070 888 July 6, 2012 15% 969 824 July 7, 2012 10% 957 861 July 8, 2012 15% 934 794 July 9, 2012 11% 899 800 July 10, 2012 11% 838 746 July 11, 2012 12% 730 642 July 12, 2012 11% 687 611 July 13, 2012 11% 643 572 July 14, 2012 10% 612 551 July 15, 2012 10% 590 531 July 16, 2012 9% 573 521 July 17, 2012 9% 581 529 July 18, 2012 8% 537 494 July 19, 2012 7% 514 478 July 20, 2012 7% 494 459

122 Hydrograph Separation Calculations for 2013 Date f rain Daily Q Q - rain July 1, 2013 0% 623 623 July 2, 2013 2% 683 667 July 3, 2013 2% 810 791 July 4, 2013 2% 962 939 July 5, 2013 2% 1330 1298 July 6, 2013 3% 1420 1383 July 7, 2013 3% 1530 1487 July 8, 2013 3% 1580 1533 July 9, 2013 3% 1480 1433 July 10, 2013 3% 1390 1344 July 11, 2013 4% 1280 1235 July 12, 2013 4% 1200 1156 July 13, 2013 4% 1130 1086 July 14, 2013 4% 1050 1008 July 15, 2013 4% 1060 1016 July 16, 2013 4% 1010 965 July 17, 2013 5% 971 926 July 18, 2013 5% 873 830 July 19, 2013 5% 819 777 July 20, 2013 5% 835 794 July 21, 2013 5% 887 845 July 22, 2013 4% 960 917 July 23, 2013 4% 997 955 July 24, 2013 3% 1050 1017 July 25, 2013 2% 1110 1086 July 26, 2013 1% 1150 1138 July 31, 2013 1% 1140 1124 August 3, 2013 1% 966 953 August 12, 2013 1% 825 819 August 16, 2013 0% 737 737 August 20, 2013 1% 993 987 August 23, 2013 0% 1030 1030 August 29, 2013 0% 862 862 September 4, 2013 0% 860 860 September 9, 2013 0% 770 770 September 22, 2013 0% 535 538 September 30, 2013 0% 506 506

123 REFERENCES

Adamski, J. C., & Knowles Jr., L. (2001). Ground-water quality of the surficial aquifer and Upper Floridan aquifer, Ocala National Forest and Lake County, Florida 1990–99 (USGS Water-Resources Investigations Report 01-4008).

Albertin, A. R., Sickman, J. O., Pinowska, A., & Stevenson, R. J. (2011). Identification of nitrogen sources and transformations within karst springs using isotope tracers of nitrogen. Biogeochemistry, 108(1), 219–232.

Al-Charideh, A. (2011). Environmental isotope study of groundwater discharge from the large karst springs in West Syria: A case study of Figeh and Al-sin springs. Environmental Earth Sciences, 63, 1–10.

America Psychological Association. (2009). Publication manual of the American Psychological Association (6th ed.). Washington DC: Author.

Aquilina, L., Ladouche, B., & Dorfliger, N. (2006). Water storage and transfer in the epikarst of karstic systems during high flow periods. Journal of Hydrology, 327, 472–485.

Baertschi, P. (1976). Absolute 18O content of standard mean ocean water. Earth Planet Science Letters, 31, 341–344.

Bazemore, D. E., Eshelman, K. N., & Hollenbeck, K. J. (1994). The role of soil water in stormflow generation in a forested headwater catchment: Synthesis of natural tracer and hydrometric evidence. Journal of Hydrology, 152(1–2), 47–75.

Barrios, K. (2006) St. Marks River and Wakulla River Springs inventory Leon and Wakulla Counties, FL (NWFWMD Water Resources Special Report 06-03). Havana, FL: Northwest Florida Water Management District.

Begemann, F., & Libby, W. F. (1957). Continental water balance groundwater inventory and storage time, surface ocean mixing rates and worldwide water circulation patterns from cosmic-ray and bomb tritium. Geochimica et Cosmochimica Acta, 21, 227–296.

Benoit, A. T. (1992). Characterization of karst development in Leon County, Florida, for the delineation of wellhead protection areas (Northwest Florida Water Management District Water Resources Special Report 92-8). Havana, FL: Northwest Florida Water Management District.

Berman, E. S. F. (2014). Advances in laser-based instrumentation for isotopic measurements [Abstract]. Consortium of Universities for the Advancement of Hydrologic Science, Inc. Virtual workshop, Jan. 27, 2014. Retrieved from http://www.cuashi.org/LaserSpecs

124 Beven, K., & Freer, J. (2001). A dynamic topmodel. Hydrological Processes, 15(10), 1993– 2011.

Birgand, F., Lellouche, G., & Appelboom, T. W. (2013). Measuring flow in non-ideal conditions for short-term projects: Uncertainties associated with the use of stage-discharge rating curves. Journal of Hydrology, 503, 186–195.

Bonn, M. A., & Bell, F. W. (2003). Economic impact of selected Florida springs on surrounding local areas (Report prepared for the Florida Department of Environmental Protection, Florida Springs Task Force).

Botter, R., & Nief, G. (1958). Joint conference on mass spectrometry, Sept. 1958. London, England: Pergamon Press.

Boussinesq, J. (1877). Essai sur la théorie des eaux courantes [Essay on the theory of water currents]. In Mémoires présénts par divers savants à l’Academie des Sciences de l’Institute National de France (Tome XXIII, No.1). Paris: Imprimerie Nationale Press.

Brooks, L., Thorpe, P., & Bartel, R. (2009). St. Marks River watershed surface water improvement and management plan update (Northwest Florida Water Management District Program Development Series 2009-2). Retrieved from http://wakulla springs.org/wp-content/uploads/2014/09/St.-Marks-Watershed-2009-Update-from- NWFWMD.pdf

Brown, R. M. (1961). Hydrology of tritium in the Ottawa Valley. Geochimica et Cosmochimica Acta, 21, 199–216.

Brown, V., McDonell, J. J., Burns, D. A., & Kendall, C. (1999). The role of event water, a rapid shallow flow component, and catchment size in summer stormflow. Journal of Hydrology, 217, 171–190.

Brutsaert, W., & Nieber. J. L. (1977). Regional drought flow hydrographs from mature glaciated plateau. Water Resources Research, 13(3), 637-643.

Burgess, R. F. (1976). The cave divers. New York, NY: Dodd and Mead.

Burles, S. (2000). Deuterium and big bang nucleosysnthesis. Nuclear Physics, A663&664, 861c– 864c.

Buttle, J. M. (1994). Isotope hydrograph separations and rapid delivery of pre-event water from drainage basins. Progress in Physical Geography, 18, 16–41.

Buttle, J. M. (1998). Fundamentals of small catchment hydrology. In C. Kendall & J. J. McDonnell (Eds.), Isotope tracers in catchment hydrology (pp. 1–49). New York, NY: Elsevier.

125 Cao, H., Cowart, J. B., & Osmond, J. K. (1999). Water sources of Wakulla Springs, Wakulla County, Florida: Physical and uranium isotopic evidences. Southeastern Geology, 39(1), 51–55.

Carlier, E., & El Khattabi, J. (2015). A probabilistic approach for spring recession flows analysis. Open Journal of Modern Hydrology, 5, 11–18.

Chelette A., Pratt, T. R., & Katz, B. G. (2002). Nitrate loading as an indicator of nonpoint source pollution in the lower St. Marks-Wakulla Rivers watershed (Northwest Florida Water Management District Water Resources Special Report 02-1).

Champion, K. M., & Upchurch, S. B. (2003). Delineation of spring-water source areas in the Ichetucknee springshed (Report prepared for the Florida Department of Environmental Protection). Tallahassee, FL: SDII Global Corporation.

Christophersen, N., Neal, C., Hooper, R. P., Vogt, R. D., & Andersen, S. (1990). Modeling streamwater chemistry as a mixture of soil water end-members: A step towards second generation acidification models. Journal of Hydrology, 116, 307–320.

Clark, I. D., & Fritz, P. (Eds.). (1997). Environmental isotopes in hydrogeology. Boca Raton, FL: CRC Press/Lewis Publishers. Retrieved from http://mysite.science.uottawa.ca/eih

Collier, M. R., Hamilton, D. C., Gloeckler, G., Ho, G., Bochsler, P., Bodmer, R., & Sheldon, R. (1998). Oxygen 16 to oxygen 18 abundance ratio in the solar wind observed by Wind/MASS. Journal of Geophysical Research: Space Physics, 103(A1), 7–13.

Corrao, N. M., Darby, P. C., & Pomory, C. M. (2006). Nitrate impacts on the Florida apple snail, Pomacea paludosa. Hydrobiologia, 568, 135–143.

Copeland, R. (2003). Florida spring classification system and spring glossary. (Florida Geological Survey Special Publication No. 52). Tallahassee, FL: Florida Geological Survey.

Coplen, T. B. (1993). Uses of environmental isotopes. In W. Alley (Ed.), Regional groundwater quality. New York, NY: V. N. Reinhold.

Coplen, T. B. (1995). Discontinuance of SMOW and PDB. Nature, 375(6529), 285.

Coplen, T. B., Wassenaar, L. I., & Qi, H. (2015). Laser absorption spectrometry for δ18O and δ2H measurements in environmental studies: Part 1, experiences and comparisons with dual-inlet isotope-ratio mass spectrometry [Abstract]. Consortium of Universities for the Advancement of Hydrologic Science, Inc. Virtual workshop, Jan. 27, 2014. Retrieved from http://www.cuashi.org/LaserSpecs

Craig, H. (1961a). Isotope variations in meteoric waters. Science, 133, 1702–1703.

126 Craig, H. (1961b). Standard for reporting concentrations in deuterium and oxygen-18 in natural waters. Science, 133, 1833–1834.

Crouzet, E., Hubert, P., Olive, P., Siwertz, E., & Marce, A. (1970). Le tritium dans les measures d’hydrologique de surface: Determination experimentale du coefficient de ruissellement [Tritium in surface water hydrology measurements: Experimental determination of the runoff coefficient]. Journal of Hydrology, 11(3), 217–229.

Dall, W. H., & Harris, G. D. (1892). Correlation papers: Neocene (USGS Bulletin 84). Washington DC: US Government Printing Office.

Dansgaard, W. (1953). The abundance of 18O in atmospheric water and water vapour. Tellus, 5, 461–469.

Dansgaard, W. (1961). The isotopic composition of natural waters. Meddelelser om Grenland, 185, 232–233.

Dansgaard, W. (1964). Stable isotopes in precipitation. Tellus, 16, 436–468.

Davis, H. (1996). Hydrogeologic investigation and simulation of ground-water flow in the Upper Floridan aquifer of North-Central Florida and Southwestern Georgia and delineation of contributing areas for selected city of Tallahassee, Florida, water-supply wells (USGS Water-Resources Investigations Report 95-4296).

Davis, J. H., & Verdi, R. (2014). Groundwater flow cycling between a submarine spring and an inland fresh water spring. Groundwater, 52(5), 705–716.

Davis, J. H., & Katz, B. G. (2007). Hydrogeologic investigation, water-chemistry analysis, and model delineation of contributing areas for City of Tallahassee public-supply wells, Tallahassee Florida (USGS Scientific Investigations Report 2007-5070).

Davis, J. H., Katz, B. G., & Griffin, D. W. (2010). Nitrate-N movement in ground water from land application of treated municipal wastewater and other sources in the Wakulla Springs springshed, Leon and Wakulla Counties, Florida, 1966–2018 (USGS Scientific Investigations Report 2010-5099).

Dennis, K. (2014). Using CRDS to measure stable isotopes of water: An overview of the technology and applications [Abstract]. Consortium of Universities for the Advancement of Hydrologic Science, Inc. Virtual workshop, Jan. 27, 2014. Retrieved from http://www.cuashi.org/LaserSpecs

DeWalle, D. R., & Swistock, B. R. (1994). Differences in oxygen-18 content of throughfall and rainfall in hardwood and coniferous forests. Hydrological Processes, 8, 75–82.

127 DeWalle, D. R., Swistock, B. R., & Sharp, W. E. (1988). Three-component tracer model for stormflow on a small Appalachian forested catchment. Journal of Hydrology, 104(1–4), 301–310.

Dewit, J. C., VanderStraaten, C. M., & Mook, W. G. (1980). Determination of the absolute hydrogen isotopic ratio of V-SMOW and SLAP. Geostandards Newsletter, 4, 33–36.

Dimova, N. T., Burnett, W. C., & Speer, K. (2011). A natural tracer investigation of the hydrological regime of Spring Creek Springs, the largest submarine spring system in Florida. Continental Shelf Research, 31, 731–738.

Dinçer, T., Payne, R., Florkowski, T., Martinee, J., & Tongiori, E. (1970). Snowmelt run-off from measurements of tritium and oxygen-18. Water Resources Research, 5, 110–124.

Dinçer, T., & Davis, G. H. (1984). Application of environmental isotope tracers to modeling in hydrology. Journal of Hydrology, 68, 95–113.

Doctor, D. H., Alexander, E. C., Petric, M., Kogovsek, J., Urbanc, J., Lojen, S., & Stichler, W. (2006). Quantification of karst aquifer discharge components during storm events through end-member mixing analysis using natural chemistry and stable isotopes as tracers. Hydrogeology Journal, 14(7), 1171–1191.

Dole, M. (1952). Chemistry and biochemistry book reviews: Physical properties and analysis of heavy water. In I. Kirschenbaum, H. Urey, & G. M. Murphy (Eds.), Science, 115(2990), 428.

Döll, P., Kaspar, F., & Lehner, B. (2003). A global hydrological model for deriving water availability indicators: Model tuning and validation. Journal of Hydrology, 279(1), 105– 134.

Domenico, P. A., & Schwartz, F. W. (1990). Physical and chemical hydrogeology. New York, NY: John Wiley and Sons.

Duarte, C. M. (1995). Submerged aquatic vegetation in relation to different nutrient regimes. Ophelia, 41, 87–112.

Dunbar, J. S., & Hemmings, C. A. (2004). Florida Paleoindian points and knives. In B. T. Lepper & R. Bonnichen (Eds.), New perspectives on the first American (pp.65–72). Columbus, OH: The Center for the Study of the First Americans.

Epstein, S. (1956). Variation of the O18/O16 ratios of fresh waters and natural ice (National Academy of Sciences Nuclear Science Series Report. No. 19, NAS-NRC publ. 400).

Epstein S., & Mayeda, T. (1953). Variation of O18 content of waters from natural sources. Geochimica et Cosmochimica Acta, 4, 213–224.

128 Eriksson, E. (1958). The possible use of tritium for estimating groundwater storage. Tellus, 10, 472–478.

Eriksson, E. (1963). Atmospheric tritium as a tool for the study of certain hydrologic aspects of river basins. Tellus, 15, 303–308.

Faure, G. (1986). Principles of isotope geology (2nd ed.). New York, NY: John Wiley and Sons.

Ferguson, G. E., Lingham, C. W., Love, S. K., & Vernon, R. O. (1947). Springs of Florida (Florida Geological Survey Bulletin No. 31). Tallahassee, FL: Florida Geological Survey.

Florida Climate Center. Retrieved from Florida State University Florida Climate Center Products and Services website: www.climatecenter.fsu.edu/products-services/data

Florida Springs Institute (2014) Wakulla Spring restoration plan. Retrieved from www.floridaspringsinstitute.ord/Resources/Documents/2014.08%20V3%20Wakulla%20 Restoration%20Plan.pdf

Frederickson, G. C., & Criss, R. E. (1999). Isotope hydrology and residence rimes of the unimpounded Meramec River Basin, Missouri. Chemical Geology, 157, 303–317.

Friedman, I. (1953). Deuterium content of natural waters and other substances. Geochemica et Cosmochemica Acta, 4, 89–103.

Friedman, I., Redfield, A. C., Schoen, B., & Harris, J. (1964). The variation of the deuterium content of natural waters in the hydrologic cycle. Review of Geophysics, 2(1), 177–224.

Fritz, P. (1981). River waters. In J. R. Gat & R. Gonfiantini (Eds.), Stable isotopic hydrology: Deuterium and oxygen-18 in the water cycle (International Atomic Energy Agency Technical Report Series 210, pp. 177–210). Vienna: IAEA.

Fuchs, B. (n.d.). Drought monitor. National Drought Mitigation Center. Retrieved from http://droughtmonitor.unl.edu

Fuller, M. L. (1904). Contributions to the hydrology of the eastern United States 1903 (USGS Water-Supply Paper No. 102, Series 0, Underground Waters, 24). Washington, DC: US Government Printing Office.

Gallegos, J. J., Hu, B. X., and Davis, H. (2013). Simulating flow in karst aquifers at laboratory and sub-regional scales using MODFLOW-CFP. Hydrogeology Journal, 21(8), 1749– 1760.

Gambell, A. W., & Friedman, I. (1965). Note on the areal variation of deuterium/hydrogen ratios in rainfall for a single storm event. Journal of Applied Meteorology, 4(4), 533–535.

129 Gat, J. R. (1996). Oxygen and hydrogen isotopes in the hydrologic cycle. Annual Review of Earth Planet Science, 24, 225–262.

Gat, J. R., & Carmi, I. (1970). Evolution of the isotopic composition of atmospheric waters in the Mediterranean Sea area. J Geophys Res, 75, 3039–3048.

Gedzelman, S., Lawrence, J., Gamache, J., Black, M., Hindman, E., Black, R., … Zhang, X. (2003). Probing hurricanes with stable isotopes of rain and water vapor. Monthly Weather Review, 131, 1112–1127.

Genereux, D. (1998). Quantifying uncertainty in tracer-based hydrograph separations. Water Resources Research, 34, 915–919.

Genereux, D. P., & Hooper, R. P. (1998). Oxygen and hydrogen isotopes in rainfall-runoff studies. In J. J. McDonnell & C. Kendall (Eds.), Isotope tracers in catchment hydrology (pp. 319–346). Amsterdam: Elsevier.

Giauque, W. F., & Johnston, H. L. (1929). An isotope of oxygen, mass 18. Journal of the American Chemical Society, 51, 1436.

Gilbert, D. (2012). Nutrient (Biology) TMDL for the upper Wakulla River (WBID 1006). Tallahassee, FL: Florida Department of Environmental Protection.

Gilfillan Jr., E. S. (1934). The isotopic composition of sea water. Journal of the American Chemical Society, 56, 406–408.

Gonfiantini, R. (1981). The δ-notation and the mass-spectrometric measurement techniques. In J. R. Gat & R. Gonfiantini (Eds.), Stable isotope hydrology: Deuterium and oxygen-18 in the water cycle (IAEA Technical Report Series No. 210). Vienna: IAEA.

Greenwood, J. P., Itoh, S., Sakamoto, N., Vicenzi, E. P., & Yurimoto, H. (2008). Hydrogen isotope evidence for loss water from Mars through time. Geophysical Research Letters, 35(5), 1–5.

Gremillion, P., Gonyeau, A., & Wanielista, M. (2000). Application of alternative hydrograph separation models to detect changes in flow paths in a watershed undergoing urban development. Hydrologic Processes, 14(8), 1485–1501.

Grubbs, J. W. (2011). Analysis of long-term trends in flow from a large springs complex in northern Florida. In E. L. Kuniansky (Ed.), Proceedings from US Geological Survey Karst Interest Group (pp. 160–167). Fayetteville, AR: USGS.

Gunter, H., & Ponton, G. M. (1931). Need for conservation and protection of our water supply with special reference to waters from the Ocala limestone. In H. Gunter (Ed.), Florida Geological Survey21st–22nd Annual Reports (pp. 43–58). Tallahassee, FL: Florida Geological Survey.

130 Gurtz, J., Zappa, M., Jasper, K., Lane, H., Verbunt, M., Badoux, A., & Vitvar, T. (2003). A comparative study in modelling runoff and its components in two mountainous catchments. Hydrological Processes, 17(2), 297–311.

Hall, E. H. (1866). Appletons’ hand-book of American travel: A southern tour; being a guide through Maryland, District of Colombia, Virginia, North Carolina, South Carolina, Georgia, Florida, Alabama, Mississippi, Louisiana, Texas, Arkansas, Tennessee, and Kentucky. New York, NY: D. Appleton & Co. Retrieved from http://name.umdl.umich.edu/AFJ8846.0001.001

Harrington, D., & Wang, J. (2008). Florida’s springs 2008 master list. Tallahassee, FL: Florida Department of Environmental Protection.

Happell, J. D., Opsahl, S., Top, Z., & Chanton, J. P. (2006). Apparent CFC and 3H/3He age differences in water from Floridan Aquifer springs. Journal of Hydrology, 319, 410–426.

Heffernan, J. B., Liebowitz, D. M., Frazer, T. K., Evans, J. M., & Cohen, M. J. (2010). Algal blooms and the nitrogen enrichment hypothesis in Florida springs: Evidence, alternatives, and adaptive management. Ecological Applications, 20(3), 816–829.

Hendry Jr., C. W., & Sproul, C. R. (1966). Geology and ground-water resources of Leon County, Florida (Florida Geological Survey Bulletin No. 47). Tallahassee, FL: Florida Geological Survey.

Herald, N. Y. (1877, June 15). A beautiful and wonderful river source near Tallahassee, Fl. Middleburg Registar. Retrieved from http://newspaperarchive.com/us

Herrmann, A., Martinec, J., & Stichler, W. (1978). Study of snowmelt runoff using isotope measurements. In S. C. Colbeck & M. Ray (Eds.), Modeling of snow cover runoff. US Army Cold Regions Research and Engineering Laboratory. Hanover, NH: US Army.

Hewlett, J. D., & Hibbert, A. R. (1967). Factors affecting the response of small watersheds to precipitation in humid areas. In W. E. Sopper & H. W. Lull (Eds.), Forest hydrology (pp. 275–290). New York, NY: Pergamon Press.

Hoeg, S., Uhlenbrook, S., & Leibundgut, C. (2000). Hydrograph separation in a mountainous catchment: Combining hydrochemical and isotope tracers. Hydrological Processes, 14(7), 1199–1216.

Hooper, R. P., Christophersen, N., & Peters, N. E. (1990). Modelling streamwater chemistry as a mixture of soilwater end-members: An application to the Panola Mountain catchment, Georgia, USA. Journal of Hydrology, 116(1), 321–343.

Hooper, R. P., & Shoemaker, C. A. (1986). A comparison of chemical and isotope hydrograph separation. Water Resources Research, 22(10), 1444–1454.

131 Horita, J., & Kendall, C. (2004). Stable isotope analysis of water and aqueous solutions by conventional dual-inlet mass spectrometry. In P. A. de Groot (Ed.), Handbook of stable isotope analytical techniques (pp. 1–37). Amsterdam: Elsevier.

Horton, R. E. (1933). The role of infiltration in the hydrologic cycle. Transactions of the American Geophysical Union, 14(1), 446–460.

Hu, K., Chen, H., Nie, Y., & Wang, K. (2015). Seasonal recharge and mean residence times of soil and epikarst water in a small karst catchment of southwest China. Scientific Reports, 5, 1–12.

Hursh, C. R. (1936). Storm-water and absorption. Transactions of the American Geophysical Union, 17(2), 301–302.

Hubert, P., Marin, E., Meybeck, M., Olive, P., & Siwertz, E. (1969). Aspects hydrologique, gèochimica et sèdimentologique de la crue exceptionelle de la Dranse du Chablais du 22 Septembre 1968 [Hydrological, geochemical and sedimentological aspects of the record flood on the Dranse River in Chablais on September 22, 1968]. Archives des Sciences, 22(3), 581–604.

Ingraham, N. L. (1998). Isotopic variations in precipitation. In C. Kendall & J. J. McDonnell (Eds.), Isotope tracers in catchment hydrology (pp. 87–118). New York, NY: Elsevier.

International Atomic Energy Agency. (n.d.). Water resources programme: Global network of isotopes in precipitation (GNIP). Retrieved from www.naweb.iaea.org/napc/ih/HIS_resources_gnip.html

Johnson, M. S., Weiler, M., Couto, E. G., Riha, S. J., & Lehmann, J. (2007). Storm pulses of dissolved CO2 in a forested headwater Amazonian stream explored using hydrograph separation. Water Resources Research, 43(11), 1–8.

Johnston, R. H., & Bush, P. W. (1988). Summary of the hydrology of the Floridan Aquifer system in Florida and parts of Georgia, South Carolina, and Alabama (USGS Professional Paper 1403-A).

Kabeya, N., Katsuyama, M., Kawasaki, M., Ohte, N., & Sugimoto, A. (2007). Estimation of mean residence times of subsurface waters using seasonal variation in deuterium excess in a small headwater catchment in Japan. Hydrological Processes, 21(3), 308–322.

Katz, B. G. (1998). Using δ18O and δD to quantify ground-water surface-water interactions in karst systems of Florida. In Monitoring: Critical foundations to protect our waters, Proceedings of National Water Quality Monitoring Council National Conference. Washington DC: US Environmental Protection Agency.

Katz, B. G. (2001). A multitracer approach for assessing the susceptibility of ground-water contamination in the Woodville Karst Plain, Northern Florida. In E. L. Kuniansky (Ed.),

132 Proceedings of US Geological Survey Karst Interest Group, Water Resources Investigations Report 01-4011 (pp. 167–176). St. Petersburg, FL: USGS.

Katz, B. G. (2004). Sources of nitrate contamination and age of water in large karstic springs of Florida. Environmental Geology, 46, 689–706.

Katz, B. G., Chelette, A. R., & Pratt, T. R. (2004). Use of chemical and isotopic tracers to assess nitrate contamination and ground-water age, Woodville Karst Plain, USA. Journal of Hydrology, 289, 36–61.

Katz, B. G., Coplen, T. B., Bullen, T. D., & Davis, J. H. (1997). Use of chemical and isotopic tracers to characterize the interactions between ground water and surface water in mantled karst. Ground Water, 35(6), 1014–1028.

Katz, B. G., Griffin, D. W., & Davis, J. H. (2009). Groundwater quality impacts from the land application of treated municipal wastewater in a large karstic spring basin: Chemical and microbiological indicators. Science of the Total Environment, 407, 2872–2886.

Katz, B. G., Hornsby, H. D., Bohlke, J. F., & Mokray, M. F. (1999). Sources and chronology of nitrate contamination in spring waters, Suwannee River basin, Florida (USGS Water- Resources Investigations Report 99-4252). Tallahassee, FL: USGS.

Kelly, M. (2013). Florida sinkholes. GIS data from Florida Department of Environmental Protection and the Florida Geological Survey. Retrieved from http://arcgis.com/home/item.html?id=ec6f4b915036468ca208a1ac411aa48d

Kendall, C., & Caldwell, E. A. (1998). Fundamentals of isotope geochemistry. In C. Kendall & J. J. McDonnell (Eds.), Isotope tracers in catchment hydrology. New York, NY: Elsevier.

Kendall, C., & McDonnell, J. J. (Eds.). (1998). Isotope tracers in catchment hydrology. New York, NY: Elsevier.

Kimberlain, T. (2013). Tropical cyclone report Tropical Storm Debby (AL042012) 23–27 June 2012. National Weather Service National Hurricane Center. Retrieved from http://consejo.bz/weather/2012/pdf/debby.pdf

Kennedy, V. C., Kendall, C., Zellweger, G. W., Wyerman, T. A., & Avanzino, R. J. (1986). Determination of the components of stormflow using water chemistry and environmental isotopes, Mattole River basin, California. Journal of Hydrology, 84(1), 107-140.

Kincaid, T. (2006). Hydrogeology of the Woodville Karst Plain: Wakulla and St. Marks River Basins. Field trip overview Dec. 2, 2006. Southeastern Geological society (SEGS). Retrieved from http://segs.org/wp/wp-content/uploads/2012/04/SEGS-Guidebook-46.pdf

133 Kincaid, T. R., Davies, G. J., & Dyer, S. B. (2010). Tracing reversing groundwater flows in the coastal Floridan aquifer. Poster presented at Geological Society of America Annual Meeting, Denver, CO.

Kincaid, T., Davies, G., Werner, C., & DeHan, R. (2012). Demonstrating interconnection between a wastewater application facility and a first magnitude spring in a karstic watershed: Tracer study of the Southeast Farm Wastewater Reuse Facility, Tallahassee, Florida (Florida Geological Survey Report of Investigation No. 111). Tallahassee, FL: Florida Geological Survey.

Kincaid, T. R., Hazlett, T. J., & Davies, G. J. (2005). Quantitative groundwater tracing and effective numerical modeling in karst: An example from the Woodville Karst Plain of North Florida. In B. F. Beck (Ed.), Sinkholes and the engineering and environmental impacts of karst. Reston, VA: American Society of Civil Engineers.

Kincaid, T. R., & Werner, C. L. (2008). Conduit flow paths and conduit/matrix interactions defined by quantitative groundwater tracing in the Floridan aquifer. Retrieved from http://wakullaspringsalliance.org/wp- content/uploads/2013/04/Kincaid_Werner_karst08_tracing_final.pdf

Kinnaman, S. L., & Dixon, J. F. (2011). Potentiometric surface of the Upper Floridan aquifer in Florida and parts of Georgia, South Carolina, and Alabama, May–June 2010 (USGS Groundwater Resources Program Scientific Investigations Map 3182).

Kirschenbaum, I., Urey, H., & Murphy, G. M. (1951). Physical properties and analysis of heavy water. New York, NY: McGraw-Hill.

Klammer, H., Yaquian-Luna, J. A., Jawitz, J. W., Annable, M. D., & Hatfield, K. (2014). Parsimonious hydrologic and nitrate response models for Solver Springs, Florida [Abstract]. Geophysical Research Abstracts 16 (EGU2014-10876).

Klaus, J., & McDonnell, J. J. (2013). Hydrograph separation using stable isotopes: Review and evaluation. Journal of Hydrology, 505, 47–64.

Kubota, T., & Tsuboyama, Y. (2003). Intra- and inter-storm oxygen-18 and deuterium variations of rain, throughfall, and stemflow, and two-component hydrograph separation in a small forested catchment in Japan. Journal of Forest Research, 8(3), 179–190.

Kulakowski, Z. P. (2010). Chromophoric dissolved organic carbon loading of five intermittent streams recharging Wakulla Springs, Florida. Master’s thesis. Florida State University Department of Earth, Ocean and Atmospheric Sciences, Tallahassee, FL.

Kuniansky, E. L., & Bellino, J. C. (2012). Tabulated transmissivity and storage properties of the Floridan aquifer system in Florida and parts of Georgia, South Carolina, and Alabama (USGS Data Series 669).

134 Kuroda, P. K. (1992). My early days at the Imperial University of Tokyo. Manuscript mailed to R. Ganapathy. Retrieved from http://karlsruhernuklidkarte.com/wiki/images/3/32/Kuroda.pdf

Lakey, B., & Krothe, N. C. (1996). Stable isotope variation of storm discharge from a perennial karst spring, Indiana. Water Resources Research, 32(3), 721–731.

Lane, E. (2001). The Spring Creek Submarine Springs Group, Wakulla County, Florida (Florida Geological Survey Special Publication 47). Tallahassee, FL: Florida Geological Survey.

Lawrence, J. R., & Gedzelman, S. D. (1996). Low stable isotope ratios of tropical cyclone rains. Geophysical Research Letters, 23(5), 527–530.

Lawrence, J. R., & White, J. W. C. (1991). The elusive climate signal in the isotopic composition of precipitation. In H. P. Taylor, J. R. O’Neil, & I. R. Kaplan (Eds.), Stable isotope geochemistry: A tribute to Samuel Epstein (pp. 169–185). The Geochemical Society Special Publication 3.

Leavesley, G. H., Markstrom, S. L., Restrepo, P. J., & Viger, R. J. (2002). A modular approach to addressing model design, scale, and parameter estimation issues in distributed hydrological modelling. Hydrological Processes, 16(2), 173–187.

Lee, E. S., & Krothe, N. C. (2001). A four-component mixing model for water in a karst terrain in south-central Indiana, USA using solute concentration and isotopes as tracers. Chemical Geology, 179(1–4), 129–143.

Leen, J. B., Berman, E. S. F., Liebson, L., & Gupta, M. (2012). Spectral contaminant identifier for off-axis integrated cavity output spectroscopy measurements of liquid water isotopes. Review of Scientific Instruments, 83(044305), 1–5.

Levesque, V. A., & Oberg, K. (2012). Computing discharge using the index-velocity method (USGS Techniques and Methods 3-A23).

Li, G. (2012). Calculation of karst conduit flow using dye tracing experiments. Transport in Porous Medium, 95, 551–562.

Lis, G., Wassenaar, L. I., & Hendry, M. J. (2008). High-precision laser spectroscopy D/H and 18O/16O measurements of microliter natural water samples. Analytical Chemistry, 80, 287–293.

Lombardo Associates, Inc. (2011) Onsite sewage treatment and disposal and management options – final report for Wakulla Springs, Leon County, Wakulla County & City of Tallahassee. Newton, MA: Lombardo Associates, Inc.

Loper, D. E., Werner, C. L., Chicken, E., Davies, G., & Kincaid, T. (2005). Coastal carbonate aquifer sensitivity to tides. Transactions American Geophysical Union, 86(39), 353–364.

135 Los Gatos Research, Inc. (2008). Liquid-water isotope analyzer, automated injection [Instruction manual]. San Jose, CA: Los Gatos Research, Inc.

Los Gatos Research, Inc. (2010). Liquid-water isotope analyzer, version 2, automated injection [Instruction manual]. San Jose, CA: Los Gatos Research, Inc.

Lyon, S. W., Desilets, S. L. E., & Troch, P. A. (2008). Characterizing the response of a catchment to an extreme rainfall event using hydrometric and isotopic data. Water Resources Research, 44(6), 1–12.

Lyon, S. W., Desilets, S. L. E., & Troch, P. A. (2009). A tale of two isotopes: Differences in hydrograph separation for a runoff event when using δD versus δ18O. Hydrological Processes, 23, 2095–2101.

Maillet, E. (1905). Essais d’hydraulique souterraine et fluvial [Essays on groundwater and river hydrology]. Paris: A. Hermann.

Maloszewski, P., & Zuber, A. (1982). Determining the turnover time of groundwater systems with the aid of environmental tracers, 1: Models and their applicability. Journal of Hydrology, 57, 207–231.

Maloszewski, P., Stichler, W., Zuber, A., & Rank, D. (2002). Identifying the flow systems in a karstic-fissured-porous aquifer, Schneealpe, Austria, by modelling of environmental 18O and 3H isotopes. Journal of Hydrology, 256, 48–59.

Maloszewski, P., Rauert, W., Stichler, W., & Herrmann, A. (1983). Application of flow models in an alpine catchment area using tritium and deuterium data. Journal of Hydrology, 66(1), 319–330.

Maloszewski, P., Rauert, W., Trimborn, P., Herrmann, A., & Rau, R. (1992). Isotope hydrologic study of mean transit times in an alpine basin (Wimbachtal, Germany). Journal of Hydrology, 140, 343–360.

Manian, S. H, Urey, H. C., & Bleakney, W. (1934). An investigation of the relative abundance of the oxygen isotopes O-16:O-18 in stone meteorites. Journal of the America Chemical Society, 56(12), 2601–2609.

Marella, R. L. (2008). Water use in Florida, 2005 and trends 1950–2005 (USGS Fact Sheet 2008-3080).

Marella, R. L., Mokray, M. F., & Hallock-Solomon, M. J. (1998). Water use trends and projections in the Northwest Florida Water Management District (USGS Open File Report 98-269).

Martin, J. B., & Gordon, S. L. (2000). Surface and ground water mixing, flow paths, and temporal variations in chemistry compositions of karst springs. In I. E. Sasowsky & C.

136 M. Wicks (Eds.), Groundwater flow and contaminant transport in carbonate aquifers (pp. 65–92). Rotterdam, Netherlands: A. A. Balkema.

Martin, J. B., Kurz, M., & Khadka, M. B. (2016). Controls and effects of time-varying apparent ages in karst spring water [Abstract]. 5th University of Florida Water Institute Symposium. Retrieved from www.http//waterinstitute.ufl.edu/symposium2016/agenda.html

Matson, G. C., & Sanford, S. (1913). Geology and ground waters of Florida (USGS Water- Supply Paper 319). Washington DC: US Government Printing Office.

Matsuo, S., & Friedman, I. (1967). Deuterium content in fractionally collected rainwater. Journal of Geophysical Research, 72(24), 6374–6376.

Mauphin, M. A., & Barber, N. L. (2005). Estimated withdrawal from the principle aquifers in the United States, 2000 (USGS Circular 1279).

McDonnell, J. J., Bonell, M., Stewart, M. K., & Pearce, A. J.(1990). Deuterium variations in storm rainfall: Implications for stream hydrograph separation. Water Resources Research, 26(4), 455–458.

McDonnell, J. J., & Kendall, C. (1992). Isotope tracers in hydrology—Report to the Hydrology Section: EOS. Transactions American Geophysical Union, 73, 260–261.

McGuire, K. J., & McDonnell, J. J. (2006). A review and evaluation of catchment transit time modeling. Journal of Hydrology, 330, 543–563.

McKinney, C. R., McCrea, J. M., Epstein, S., Allen, H. A., & Urey, H. A. (1950). Improvements in mass-spectrometers for the measurement of small differences in isotope abundance ratios. Review of Scientific Instruments, 21, 724–730.

Meinzer, O. E. (1923). Outline of ground-water hydrology with definitions (USGS Water-Supply Paper 494). Washington, DC: US Government Printing Office.

Meinzer, O. E. (1927). Large springs of the United States (USGS Water-Supply Paper 557). Washington, DC: US Government Printing Office.

Miller, J. A. (1986). Hydrogeologic framework of the Floridan Aquifer system in Florida and in parts of Georgia, Alabama, and South Carolina: Regional aquifer-system analysis (USGS Professional Paper 1402-B). Washington, DC: US Government Printing Office.

Miller, J. A. (1990). Groundwater atlas of the United States: Alabama, Florida, Georgia, and South Carolina (HA 730-G). USGS Archive. Retrieved from www.pubs.usgs.gov/ha/ha730/

137 Minchin, G. M. (1905). Flow of underground and river waters [Book review]. Nature, 1854(72), 25–26.

Minissale, A., & Vaselli, O. (2011). Karst springs as “natural” pluviometers: Constraints on the isotopic composition of rainfall in the Apennines of central Italy. Applied Geochemistry, 26, 838–852.

Mook, W., Groenenveld, D., Brown, A., & Van Ganswijk, A. (1974). Analysis of runoff hydrograph by means of natural 18O. In Isotope Techniques in Groundwater Hydrology Vol. 1. Proceedings of a symposium. Vienna: IAEA.

Moore, K. B., Ekwurzel, B., Esser, B. K., Hudson, G. B., & Moran, J. E. (2006). Sources of nitrate revealed using residence time and isotope methods. Applied Geochemistry, 21, 1026–1029.

Moore, P. J., Martin, J. B., Screaton, E. J., & Neuhoff, P. S. (2010). Conduit enlargement in an eogenetic karst aquifer. Journal of Hydrology, 393(3–4), 143–155.

Morrissey, S. K., Clark, J. F., Bennett, M., Richardson, E., & Stute, M. (2010). Groundwater reorganization in the Floridan aquifer following Holocene sea-level rise. Nature Geoscience, 3, 683–687.

Moyeed, R. A., & Clark, R. T. (2005). The use of Bayesian methods for fitting rating curves, with case studies. Advances in Water Resources, 28, 807–818.

Mueller, D. S. (2013). Extrap: Software to assist the selection of extrapolation methods for moving-boat ADCP streamflow measurements. Computers & Geosciences, 54, 211–218.

Mueller, D. S., Wagner, C. R., Rehmel, M. S., Oberg, K. A., & Rainville, F. (2014). Measuring discharge with acoustic Doppler current profilers from a moving boat (Ver. 2.0, first release 2009). In USGS techniques and methods (book 3, chap. A22). Retrieved from http://dx/doi.org/10.3133/tm3A22

National Weather Service (NWS) Forecast Office, Tallahassee, FL. NWS Local Data/Records website: Retrieved from http://w2.weather.gov/climate/local_data.php

Négrel, P., Pauwels, H., Dewandel, B., Gandolfi, J. M., Mascré, C., & Ahmed, S. (2011). Understanding groundwater systems and their functioning through the study of stable water isotopes in a hard-rock aquifer (Maheshwaram watershed, India). Journal of Hydrology, 397(1–2), 55–70.

Négrel, P., & Petelet-Giraud, E. (2011). Isotopes in groundwater as indicators of climate change. Trends in Analytical Chemistry, 30(8), 1279–1290.

Nier, A. O. (1940). A mass spectrometer for routine isotope abundance measurements. Review of Scientific Instruments, 11, 212.

138 Nier, A. O. (1947). A mass spectrometer for isotope and gas analysis. Review of Scientific Instruments, 18, 398–404.

Nir, A. (1964). On the interpretation of tritium age measurements of groundwater. Journal of Geophysical Research, 69, 2589–2595.

Oana, S. (1939). Geochemical studies of volcanoes in Japan. XXVI. Density measurements of the river water. Bulletin of the Chemical Society Japan, 17(7), 314–320.

Odezulu, C. I. (2011). Stable hydrogen and oxygen isotopic variations in natural waters in north Florida: Implications for hydrological and paleoclimatic studies. Master’s thesis. Florida State University Department of Earth, Ocean and Atmospheric Sciences, Tallahassee, FL.

Odum, H. T. (1957). Primary production in eleven Florida springs and a marine turtle-grass community. Limnology and Oceanography, 1(2), 85–97.

Ogunkoya, O. O., & Jenkins, A. (1991). Analysis of runoff pathways and flow distributions using deuterium and stream chemistry. Hydrological Processes, 5, 271–282.

Osmond, J. K., Buie, B. F., Rydell, H. S., Kaufman, M. I., & Wallick, E. I. (1971). Uranium and tritium as natural tracers in the Floridan Aquifer (Florida Water Resources Research Center Publication No. 14).

Osmond, J. K., Kaufman, M. I., & Cowart, J. B. (1974). Mixing volume calculations, sources and aging trend of Floridan aquifer water by uranium isotopic methods. Geochemica et Cosmochimica Acta, 38, 1088–1100.

Osmond, J. K., Ryell, H. S., & Kaufman, M. I. (1968). Uranium disequilibrium in groundwater: An isotope dilution approach in hydrologic investigations. Science, 162, 997–999.

Paisley, C. (1989). The red hills of Florida 1528–1865. Tuscaloosa, AL: University of Alabama Press.

Parker, G. G., Ferguson, G. E., & Love, S. K. (1955). Water resources of southeastern Florida (USGS Water-Supply Paper 1255). Washington DC: US Government Printing Office.

Penna, D., Stenni, B., Sanda, M., Wrede, S., Bogaard, T. A., Gobbi, A., … Chárová, Z. (2010). On the reproducibility and repeatability of laser absorption spectroscopy measurements for H and O isotopic analysis. Hydrology and Earth System Sciences, 14, 1551–1566.

Perrin, J., Jeannin, P.-Y., & Cornaton, F. (2007). The role of tributary mixing in chemical variations at a karst spring, Milandre, Switzerland. Journal of Hydrology, 332, 158–173.

Perrin, J., Jeannin, P.-Y., & Zwahlen, F. (2003). Epikarst storage in a karst aquifer: A conceptual model based on isotopc data, Milandre test site, Switzerland. Journal of Hydrology, 279(1), 106–124.

139 Pinder, G. F., & Jones, J. F. (1969). Determination of the groundwater component of peak discharge from the chemistry of total runoff water. Water Resources Research, 5, 438– 455.

Pratt, T. R., Richards, C., Milla, K., Wagner, J., Johnson, J., & Curry, R. (1996). Hydrogeology of the Northwest Florida Management District (Northwest Florida Water Management District Water Resources Special Report 96-4).

Puri, H. S., & Vernon, R. O. (1964). Summary of the geology of Florida and a guidebook to the classic exposures (Florida Geological Survey Special Publication 5. Revised). Tallahassee, FL: Florida Geological Survey.

Rankama, K. (1954). Isotope geology. New York, NY: McGraw-Hill Book Co., Inc.

Rantz, S. E., et al. (1982). Measurements and computations of streamflow—Volume 1: Measurement of stage and discharge (USGS Water-Supply Paper 2175).

Reddy, M. M., Schuster, P., Kendall, C., & Reddy, M. B. (2006). Characterization of surface and ground water δ18O seasonal variation and its use for estimating groundwater residence times. Hydrologic Processes, 20, 1753–1772.

Revels, T. J. (2002). Watery Eden: A history of Wakulla Springs. Friends of Wakulla Springs State Park: Sentry Press.

Révész, K., & Coplen, T. B. (Eds.). (2008). Methods of the Reston Stable Isotope Laboratory. In USGS Techniques and Methods 10-C1. Retrieved from www.isotope.usgs.gov/lab/methods/RSIL_SOP_1574.pdf and www.isotope.usgs.gov/lab/methods/RSIL_SOP_489.pdf

Rice, K. C., & Hornberger, G. M. (1998). Comparison of hydrochemical tracers to estimate source contributions to peak flow in a small, forested, headwater catchment. Water Resources Research, 34(7), 1755–1766.

Rink, W. J., Dunbar, J. S., & Burdette, K. E. (2012). The Wakulla Springs Lodge Site (8Wa329) 2008 excavations and new OSL evidence. The Florida Anthropologist, 65, 5–23.

Roberts, F. (2001). The origin of water on Earth. Science, 293(5532), 1056–1058.

Rogers, R. R., & Yau, M. K. (1989). A short course in cloud physics. Burlington, MA: Elsevier.

Rolston, J. H., den Hartog, J., & Butler, J. P. (1976). The deuterium isotope separation factor between hydrogen and liquid water. Journal of Physical Chemistry, 80(10), 1064–1067.

Rosenau, J. C., Faulkner, G. L., Hendry Jr., C. W., & Hull, R. W. (1977). Springs of Florida (Florida Geological Survey Bulletin No. 31. Revised). Tallahassee, FL: Florida Geological Survey.

140 Rozanski, K., Araguás-Araguás L., & Gonfiantini, R. (1993). Isotopic patterns in modern global precipitation: Climate change in continental isotopic records. Geophysical Monograph, 78, 1–36.

Rupert, F. R. (1988). The geology of Wakulla Springs (Florida Geological Survey Open File Report No. 22). Tallahassee, FL: Florida Geological Survey.

Rupert, F., & Spencer, S. (1988). Geology of Wakulla County (Florida Geological Survey Bulletin No. 60). Tallahassee, FL: Florida Geological Survey.

Sanford, W. E., & Selnick, D. L. (2013). Estimation of evapotranspiration across the conterminous United States using a regression with climate and land-cover data. Journal of the American Water Resources Association, 49(1), 217–230.

Schöeller, H. (1967). Hydrodynamique dans le karst (écoulement et emmagasinement) [Hydrodynamics in karst (flow and storage)]. In Hydrologie des roches fissurées [Hydrology of fractured rocks] (Vol. 1, pp. 3–20), Proceedings of the Dubrovnik Symposium. Paris, France: UNESCO.

Scott, T. M. (1988). The lithography of the Hawthorn Group (Miocene) of Florida (Florida Geological Survey Bulletin No. 59). Tallahassee, FL: Florida Geological Survey.

Scott, T. M., Means, G. H., Meegan, R. P., Means, R. C., Upchurch, S. B., Copeland, … Willet, A. (2004). Springs of Florida (Florida Geological Survey Bulletin No. 66). Tallahassee, FL: Florida Geological Survey.

Sellards, E. H. (1917). Florida State Geological Survey ninth annual report 1917. Tallahassee, FL: Florida Geological Survey.

Sharp, Z. (Ed.). (2007). Principles of stable isotope geochemistry. Upper Saddle River, NJ: Pearson Education Prentice Hall.

Simpson, J. C. (1956). A professional gazetteer of Florida place-names of Indian derivation either obsolescent or retained together with others of recent application (Florida Geological Survey Special Publication No. 1). Tallahassee, FL: Florida Geological Survey.

Singleton, G. L., Coplen, T. B., Qi, H., & Lorenz, J. M. (2009). Laser-based stable hydrogen and oxygen analyses: How reliable can measurement results be? (Geophysical Research Abstracts 11, EGU2009-3290). EGU General Assembly.

Sklash, M. G., Farvolden, R. N., & Fritz, P. (1976). A conceptual model of watershed response to rainfall, developed through the use of oxygen-18 as a natural tracer. Canadian Journal of Earth Science, 13, 271–283.

141 Sklash, M. G., & Farvolden, R. N. (1979). The role of groundwater in storm runoff. Journal of Hydrology, 43, 45–65.

Spencer, S. M., & Lane, E. (1995). Florida sinkhole index (Florida Geological Survey Open File Report No. 58). Tallahassee, FL: Florida Geological Survey.

Stone, W. C. (1989). The Wakulla Springs Project. Derwood, MD: US Deep Caving Team Inc.

Stevenson, J. R., Pinowska, A., & Wang, Y. (2004). Ecological condition of algae and nutrients in Florida springs (Florida Department of Environmental Protection, DEP Contract No. WM858).

Stringfield, V. T. (1935). Artesian water in tertiary limestone in the southeastern states (USGS Professional Paper 517). Washington, DC: US Government Printing Office.

Stringfield, V. T. (1966). Artesian water in tertiary limestone in the southeastern states (USGS Professional Paper 517). Washington DC: US Government Printing Office.

Swancar, A., & Hutchinson, C. B. (1995). Chemical and isotopic composition and potential for contamination of water in the Upper Floridan aquifer, West-Central Florida, 1986–89 (USGS Water-Supply Paper 2409). Washington DC: US Government Printing Office.

Swart, P. K., Berier, D., McNiell, D., Guzikoski, M., Harrison, S., & Dedick, E. (1989). Interstitial water geochemistry and carbonate diagenesis in the sub-surface of a Holocene mud island in Florida Bay. Bulletin of Marine Science, 44(1), 490–514.

Talarovich, S. G., & Krothe, N. C. (1998). Three-component storm hydrograph separation of a karst spring contaminated by polychlorinated biphenyls in central Indiana. AAPG Division of Environmental Geosciences Journal, 5(4), 162–176.

Tallaksen, L. M. (1994). A review of baseflow recession analysis. Journal of Hydrology, 165, 349–370.

Teis, R. V. (1939a). Isotopic composition of rain water. Doklady Akademia Nauk SSSR: Comptes Rendus de l’Académie des Sciences de l’URSS [Proceedings of the USSR Academy of Sciences], 23, 674.

Teis, R. V. (1939b). Isotopic composition of water from some rivers and lakes of the USSR. Doklady Akademia Nauk SSSR: Comptes Rendus de l’Académie des Sciences de l’URSS [Proceedings of the USSR Academy of Sciences], 24, 779.

Tian, L., Masson-Delmotte, V., Stievenard, M., Yao, T., & Jouzel, J. (2001). Tibetan Plateau summer monsoon northward extent revealed by measurements of water stable isotopes. Journal of Geophysical Research, 106(D22), 28,081–28,088.

142 Toth, D. J. (1999). Water quality and isotope concentrations from selected springs in the St. Johns River Water Management District (St. Johns Water Management District .Technical Publication SJ99-2). Palatka, FL: St. Johns Water Management District.

Truesdell, A. H., & Hulston, J. R. (1980). Isotopic evidence on environments of geothermal systems. In P. Fritz & J. Fontes (Eds.), Handbook of environmental isotope geochemistry (Vol. 1, pp. 179–226). Amsterdam: Elsevier.

Uhlenbrook, S., & Hoeg, S. (2003). Quantifying uncertainties in tracer-based hydrograph separations: A case study for two-, three- and five-component hydrograph separations in a mountainous catchment. Hydrological Processes, 17, 431–453.

Upchurch, S. B., & Champion, K. M. (2003). Geostatistical analysis of water-level and water- quality data for the Ichetucknee springshed (Suwannee River Water Management District Report #004-00398). Live Oak, FL: Suwannee River Water Management District.

Urey, H. C., Brickwedde, F. G., & Murphy, G. M. (1932). A hydrogen isotope of mass 2 and its concentration. Physical Review, 40(1), 1.

US Geological Survey. (2015). National field manual for the collection of water-quality data. In USGS techniques of water-resources investigations (Book 9). Retrieved from http://water.usgs.gov/owq/FieldManual/compiled/NFM_complete.pdf

US Geological Survey. (n.d.). Reston Stable Isotope Laboratory (RSIL) instructions for collecting samples. Retrieved from http://isotopes.usgs.gov/lab/instructions.html

Vaca, A. N., Favata, M. A., & Fernández, J. B. (1993). The Account: Alvar Núñez Cabeza de Vaca’s Relación. Houston, TX: Arte Público Press. (Original work published 1542)

Verdi, R. J., & Tomlinson, S. A. (2009). A centennial tribute, 1906–2006: History of US Geological Survey streamgaging activities for the Suwannee River at White Springs, Florida (USGS Circular 1330). Reston, VA: USGS.

Vitvar, T., Aggarwal, P. K., & McDonnell, J. J. (2005). A review of isotope applications in catchment hydrology. In P. K. Aggarwal, J. R. Gat, & K. F. P. Froehlich (Eds.), Isotopes in the water cycle: Past, present and future of a developing science (pp. 151–169). Netherlands: International Energy Agency.

Waddington, J. M., Roulet, N. T., & Hill, A. R. (1993). Runoff mechanisms in a forested groundwater discharge wetland. Journal of Hydrology, 147(1–4), 37–60.

Wassenaar, L. I. (2014). Approaches for achieving long-term accuracy and precision of δ18O and δ2H for water analyzed using laser absorption spectrometers [Power Point]. Consortium of Universities for the Advancement of Hydrologic Science, Inc. Virtual workshop, Jan. 27, 2014. Retrieved from http://www.cuashi.org/LaserSpecs

143 Wassenaar, L. I., Coplen, T. B., & Aggarwal, P. (2014). Approaches for achieving long-term accuracy and precision of δ18O and δ2H for water analyzed using laser absorption spectrometers. Environmental Science and Technology, 48(2), 1123–1131.

Weber, K. A., & Perry, R. G. (2006). Groundwater abstraction impacts on spring flow and base flow in the Hillsborough River Basin, Florida, USA. Hydrogeology Journal, 14, 1252– 1264.

Weidlein, E. R., & Bass, L. W. (1935). American chemical events 1931–1935. Industrial Engineering and Chemistry, 27(4), 360–361.

Wels, C., Cornett, R. J., & LaZerte, B. D. (1991). Hydrograph separation: A comparison of geochemical and isotopic analysis. Journal of Hydrology, 122, 252–274.

Wen, X., Lee, X., Sun, X., Wang, J., Tang, Y., Li, S., & Yu, G. (2012). Intercomparison of four commercial analyzers for water vapor isotope measurements. Journal of Atmospheric & Oceanic Technology, Feb, 235–247.

White, W. A. (1970). The geomorphology of the Florida Peninsula (Florida Geological Survey Bulletin No. 51). Tallahassee, FL: Florida Geological Survey.

Woodville Karst Plain Project. (2012). Global underwater explorers. Retrieved from http://www.globalunderwaterexplorers.org/Projects/WKPP and http://wkpp.org

Xu, Z., Hu, B. X., Davis, H., & Kish, S. (2015). Numerical study of groundwater flow cycling controlled by seawater/freshwater interaction in a coastal karst aquifer through conduit network using CFPv2. Journal of Contaminant Hydrology, 182, 131–145.

144 BIOGRAPHICAL SKETCH

Erica L. Rau received a Bachelor of Science in Ecology from Florida Tech in Melbourne. After graduation, she worked as an intern at a marine virus lab for South Carolina’s Department of Natural Resources. A move from the east to west coast was made for another internship to study desert plant ecology in Joshua Tree National Park in cooperation with USGS soil scientists. She returned to Tallahassee to work at the USGS office as a hydrologist and enrolled in the Graduate Program at FSU as a part-time student. With graduate school near an end, she accepted a new position with the USGS in Colorado and is looking forward to exploring a new environment with as much natural beauty and outdoor activities as Tallahassee has offered.

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