Coastal Lake Paleoclimate Records: a Late Holocene Paleostorm Record for the Northeastern Gulf of Mexico Coast Jennifer Lynn Coor

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2012 Coastal Lake Paleoclimate Records: A Late Holocene Paleostorm Record for the Northeastern Gulf of Mexico Coast Jennifer Lynn Coor

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COLLEGE OF ARTS AND SCIENCES

COASTAL LAKE PALEOCLIMATE RECORDS: A LATE HOLOCENE PALEOSTORM

RECORD FOR THE NORTHEASTERN GULF OF MEXICO COAST

JENNIFER LYNN COOR

A Dissertation submitted to the Department of Earth, Ocean, and Atmospheric Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Fall Semester, 2012 Jennifer Coor defended this dissertation on July 12, 2012.

The members of the supervisory committee were:

Joseph F. Donoghue Professor Directing Dissertation

James B. Elsner University Representative

Yang Wang Committee Member

Stephen A. Kish Committee Member

Lynn M. Dudley Committee Member

Alan W. Niedoroda Committee Member

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

I dedicate this dissertation to

My Mama, Daddy, Nana, and Grandma

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ACKNOWLEDGEMENTS

I would first like to thank my advisor, Dr. Joseph Donoghue, and my committee for their unending advice, support, guidance, and encouragement throughout this endeavor.

Secondly, I would like to thank my family and friends for their support through all of the years. My parents, Nana, aunts, uncles, and cousins have all provided limitless support and encouragement. My friends have listened to the complaints and helped keep me going, even when I was discouraged.

Third, I would like to thank all of those that made my dissertation possible: The stable isotopic compositions of sediment samples were measured by Oindrila Das and Thomas Wallace in the FSU Stable Isotope Lab at the National High Magnetic Field Laboratory. Dr. James Jawitz of the University of Florida, Gainesville, FL, provided monitoring data for three of the coastal dune lakes. Dr. James Elsner provided assistance with the statistical modeling portion of the project. The Choctawhatchee Basin Alliance provided data, assistance, and field support throughout this project. The St. Vincent National Wildlife Refuge provided access and field support. The Florida Geological Survey assisted in locating core logs and samples. Eglin Air Force Base personnel provided long-term precipitation data.

Dr. Jonathan Bryan of Northwest Florida State College, Niceville, FL, assisted in foraminifera identification. The staff of the Antarctic Marine Geology Research Facility at Florida State University provided core imagery and x-radiography. Dr. William Burnett and the Environmental Radioactivity Measurement Facility at Florida State University carried out the Pb-210 analyses. Dr. Bikash Saha and Dr. Alan Niedoroda assisted in running the SLOSH model. Dr. Jill Malmstadt Trepanier assisted with obtaining historic storm data. Dr. Frank Stapor and Dr. Neil Lundberg read earlier versions of the manuscript and contributed constructive suggestions for improvement.

Finally, I would like to acknowledge the U.S. Department of Defense, through the Strategic Environmental Research and Development Program (SERDP), Project RC-1700, for financial support this project.

TABLE OF CONTENTS

LIST OF TABLES ...... viii LIST OF FIGURES ...... ix ABSTRACT ...... xi 1. INTRODUCTION...... 1 Research Justification ...... 1 Research Design and Objectives ...... 2 Principal Results and Publication Status by Chapter ...... 2 Chapter 2: Geology and Hydrology of the Coastal Dune Lakes of Northwest Florida...... 2 Chapter 3: Detecting a Storm History Signature in Coastal Lake Sediments, Northern Gulf of Mexico Coast ...... 3 Chapter 4: A Paleostorm Record for the Northwest Florida Coast...... 3 2. GEOLOGY AND HYDROLOGY OF THE COASTAL DUNE LAKES OF NORTHWEST FLORIDA ...... 4 Introduction ...... 4 Background ...... 4 Sea-Level Rise in the Northern Gulf of Mexico ...... 4 Humates ...... 5 Study Area ...... 6 Regional Geology...... 6 Coastal Dune Lakes ...... 7 Materials and Methods ...... 8 Remote Sensing...... 8 Stratigraphy ...... 8 Geochronology ...... 8 Water Level Data ...... 9 Water Quality Data...... 9 Precipitation Data ...... 9 Results ...... 9 Remote Sensing...... 9 Stratigraphy ...... 11 Geochronology ...... 11 Water Level Data ...... 12 Water Quality Data...... 12 Precipitation Data ...... 13 Discussion ...... 13 Conclusions ...... 14 3. DETECTING A STORM HISTORY SIGNATURE IN COASTAL LAKE SEDIMENTS, NORTHERN GULF OF MEXICO COAST ...... 25 Introduction ...... 25 Methods of Detecting Evidence of Major Flooding Occurrence ...... 26

Micropaleontological Methods of Paleostorm Analysis ...... 27 Sedimentological Methods of Paleostorm Analysis ...... 27 Geochemical Methods of Paleostorm Analysis ...... 28 Historic Storm Record ...... 29 Regional Setting ...... 30 Materials and Methods ...... 32 Field Sampling ...... 32 Core Preparation ...... 32 Geochemical Analysis ...... 32 Sediment Analysis ...... 33 Micropaleontology ...... 34 Geochronologic Analysis ...... 34 Remote Sensing...... 34 Results ...... 35 Lake Data ...... 35 Core Description ...... 35 Remote Sensing...... 35 Geochronology ...... 36 Isotope and Sediment Data ...... 37 Micropaleontological Data ...... 38 Discussion ...... 39 Model Development ...... 39 Storm Identification and Storm Frequency ...... 43 Conclusions ...... 46 4. A PALEOSTORM RECORD FOR THE NORTHWEST FLORIDA COAST ...... 61 Introduction ...... 61 Background ...... 62 Methods of Detecting Evidence of Major Flooding Occurrence ...... 62 Sedimentological Methods of Paleostorm Analysis ...... 62 Micropaleontological Methods of Paleostorm Analysis ...... 62 Geochemical Methods of Paleostorm Analysis ...... 63 Paleostorm Detection Model ...... 64 Historic Storm Record ...... 65 Regional Setting ...... 65 Sea-Level Rise ...... 66 Study Area ...... 66 Materials and Methods ...... 67 Field Sampling ...... 67 Remote Sensing and Surveying ...... 68 Core Preparation ...... 68 Sediment Analysis ...... 68 Micropaleontology ...... 69 Geochemical Analysis ...... 69 Geochronologic Analysis ...... 69 Determining Storm Surge Heights Using SLOSH ...... 70 Results ...... 70 vi

Lake Data ...... 70 Remote Sensing...... 71 Core Description ...... 71 Sedimentologic and Isotopic Data ...... 72 Micropaleontologic Data ...... 72 Geochronology ...... 73 Storm Surge Heights from SLOSH ...... 73 Discussion ...... 73 Model Application ...... 73 Storm Identification and Storm Frequency ...... 74 Paleostorm Return Periods and Cycles ...... 74 Reconstruction of Storm Surge ...... 75 Comparison with Other Studies ...... 76 Conclusions ...... 76 5. CONCLUSIONS ...... 89 Chapter 2: Coastal Dune Lakes ...... 89 Chapter 3: Detecting a Paleostorm History ...... 89 Chapter 4: Paleostorm Record for Northwest Florida ...... 90 Overall Conclusions ...... 91 APPENDIX A: Geochemical Data for Core 092710-03 ...... 92 APPENDIX B: Sediment Data for Core 092710-03 ...... 96 APPENDIX C: Micropaleontologic Data for Core 092710-03 ...... 99 APPENDIX D: Geochronologic Data for Core 092710-03 ...... 101 REFERENCES ...... 102 BIOGRAPHICAL SKETCH ...... 113

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LIST OF TABLES

2.1 Summary of dated humic material ...... 15

2.2 Summary of water quality data for selected coastal dune lakes ...... 16

2.3 Summary of monthly and annual precipitation totals during the study period ...... 17

3.1 Core depth and corresponding storm events associated with the five accumulation peaks as derived from the Pb-210 CRS model ...... 47

3.2 The eleven high-water events used to calibrate the storm model, from the Apalachicola, Florida tide gage records ...... 48

3.3 Summary of the statistical analysis resulting in the storm model...... 49

3.4 The thirteen peaks identified by the storm model in Figure 3.9 and the corresponding storm/surge events ...... 50

4.1 Major storms passing near Western Lake, FL, during historic time, with SLOSH storm surge model inputs and results ...... 79

4.2 Radiocarbon data ...... 80

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LIST OF FIGURES

2.1 Localities of humate-cemented beach and dune sands near the coast of the Gulf of Mexico, in the Florida panhandle ...... 18

2.2 The coastal dune lakes, located on the northwest Florida coast. Image source: Google Earth...... 19

2.3 Distribution of Florida Geological Survey borehole locations in the coastal dune lake region ...... 20

2.4 Results of LIDAR analysis of coastal dune lakes showing morphology of lake watersheds ...... 21

2.5 Sampling locations of dated humate-cemented sand and other organic sediments...... 22

2.6 Results of Fourier transform analysis of lake water level data ...... 23

2.7 Correlation of the water level data from Western Lake with the Valparaiso, FL tide gauge data during June 2010 ...... 24

3.1 Tracks of historical hurricanes (Category 1 to 5) that have passed within 140 km of Oyster Pond, FL ...... 51

3.2 Location map of St. Vincent Island, Florida, on the northeastern Gulf of Mexico coast. .... 52

3.3 LIDAR imagery of the southern portion of St. Vincent Island, showing the area surrounding Oyster Pond ...... 53

3.4 Pb-210 and Cs-137 profiles for core 092710-01 from Oyster Pond, FL ...... 54

3.5 Age-depth profile resulting from the CRS model of the Pb-210 data...... 55

3.6 X-radiograph, and profiles of percent sand, δ13C, and % N′ for core 092710-01 ...... 56

3.7 Sediment accumulation rate versus depth in core for core 092710-01 ...... 57

3.8 Schematic showing the process by which the storm model was generated, trained, and calibrated...... 58

3.9 Results of the storm model applied to the Oyster Pond core data ...... 59

3.10 Autocorrelation function of estimated storm probabilities from the storm model ...... 60

4.1 Tracks of historic storms to pass within 140 km of Western Lake, FL, 1851-2011...... 81

4.2 Location map of Western Lake, on the NW Florida coast...... 82

4.3 LIDAR imagery of the Western Lake area, NW Florida ...... 83

4.4 X-radiograph, and profiles of percent sand, δ13C, and % N’ for core 070910-03 ...... 84

4.5 The time-depth curve derived from the radiocarbon dates from Core 070910-03...... 85

4.6 Results of the storm model applied to the Western Lake core data ...... 86

4.7 Major flooding event history for the northwest Florida coast, based on the paleostorm model ...... 87

4.8 Summary of long-term storm activity on the U.S. Gulf and Atlantic coasts ...... 88

ABSTRACT Paleotempestology is the study of long-term regional storm history from geological proxy evidence. This relatively recent field of geology offers a glimpse at tropical cyclone activity extending back into geologic time. Historic documentation of tropical storms extends only about 150 years into the past and is not necessarily representative of the geologic record. The historic record can be used to assess decadal and multi-decadal cycles and provide some insight into the near future climate. In order to understand the true return period and risk of major storms to coastal regions, a longer record, revealing low-frequency changes, is necessary. Such a record is available in coastal sediments, and in particular in the bottom sediments of coastal lakes. This dissertation comprises three manuscripts on the topic of the geologic and climatic history of northwest Florida during the last 5,000 years. It focuses on long-lived coastal lakes and their sediments. The coastal dune lakes of northwest Florida are unique features found in only a few other places in the world. These lakes provided a natural laboratory for this investigation. The coastal dune lakes were formed during the mid-to late Holocene when postglacial sea-level rise slowed. They developed in close proximity to the Gulf of Mexico and are hydrologically connected to Gulf waters. An improved method for identifying storm signatures and establishing the geologic record of storm occurrence for coastal regions through analysis of coastal lake core sediments was developed as a product of this investigation. The resulting storm model is a probability analysis of the isotopic and sedimentologic characteristics of storm-associated sediment layers. Through use of a statistical model with fixed covariates, the result is a more objective indicator of storm occurrence, and therefore of hazard risk, than previous methods of paleostorm analysis. This model provides a valuable tool for understanding and quantifying long-term storm history, and better assessing storm hazard risk for coastal regions. The long-term geologic record of storm occurrence was determined for northwest Florida over the past five millennia by applying the storm model. The storm model identified storm events and storm clusters in the sediment record. The modeled storm events separate into 4 periods of increased storminess during the late Holocene. This study has quantified the long- term storm history for the northwest Florida coast and provides a tool to enhance the assessment of storm hazard risk for coastal regions.

CHAPTER 1 INTRODUCTION Research Justification Paleotempestology, the study of long-term regional storm history from geological proxy evidence, offers a glimpse at tropical cyclone activity extending back into geologic time (Liu and Fearn, 1993; Liu and Fearn, 2000; Boose et al., 2001; Donnelly et al., 2001; Donnelly et al., 2004; Nott, 2004; Scileppi and Donnelly, 2007). It is clear that the historic documentation of tropical storms is not necessarily representative of the geologic record, as it only extends about 150 years into the past. However, the historic record can be used to assess decadal and multi- decadal cycles and provides some insight into near future climate (Landsea et al., 1999; Goldenberg et al., 2001; Klotzbach and Gray, 2008). Knowledge regarding the low-frequency changes in climate, extracted from the long-term record found in coastal lake sediments, is of considerable benefit in understanding the true return period risk of major storms to coastal regions. The possibility of more intense storms in a warmer future only enhances this benefit. Coastal lakes and wetlands are the focus of paleostorm research, providing a continuous history of coastal sedimentologic events. Coastal lakes and wetlands are periodically subjected to storm surge and overwash processes during catastrophic hurricane strikes when coastal dune systems are overtopped or breached. The environmental effects of major storms include significant damage to coastal systems, such as coastal retreat, loss of low-lying coastal infrastructure, increased storm surge damage, degradation of coastal wetlands, and saltwater incursion into coastal aquifers. Many studies have quantified prehistoric storms by counting sand layers in coastal sediment cores and employing other standard methods of sediment analysis (Liu and Fearn, 1993; Liu and Fearn, 2000; Donnelly et al., 2001; Donnelly et al., 2004; Nott, 2004; Scileppi and Donnelly, 2007; Horton et al., 2008). In more recent studies, micropaleontology and stable isotope geochemistry have also been employed to identify marine inundation events in sediment core records (Hassan et al., 1997; Meyers, 1997; Lambert, 2003; Lucke et al., 2003; Lamb et al., 2005; Parker et al., 2006; Page et al., 2009).

Research Design and Objectives This project has focused on the coastal geology, stratigraphy, and geomorphology of northwest Florida. This project began as a way to determine the most robust method of identifying storms in the geologic record through a comparison of methods. However, after visiting the proposed study area, questions as to the formation and stratigraphy of coastal northwest Florida began to arise. The main objective of this project was to identify storm events throughout the mid- to late-Holocene from sediment cores collected from the coastal dune lakes. Sub-objectives included: • Determination of underlying stratigraphy of the coastal dune lakes • Determination of formation and recharge of the coastal dune lakes • Generation of a storm identification model • Identification of storm/climate cycles in the long-term geologic record Principal Results and Publication Status by Chapter This dissertation is comprised of three separate studies organized into chapters. Being a “non-traditional” dissertation, this manuscript begins with an introduction on the motivation of the project, and a conclusion that summarizes the major findings of the projects. Appendices have been added to allow the reader to view the raw data. The chapters presented in this document are made up of three manuscripts that are under review or in the process of being submitted. Because each chapter is an individual publication the reader will notice repetition in sections such as methods and background. One manuscript has been submitted to Quaternary Research, another is being submitted to Marine Geology, and the third will be submitted to the Journal of Coastal Research. The primary results and publication status for each project are given below. Chapter 2: Geology and Hydrology of the Coastal Dune Lakes of Northwest Florida Principal Results: The coastal dune lakes of northwest Florida have been proven to be a unique ecosystem which provides a natural laboratory for studying storm events. These lakes have been stable and in close proximity to the shoreline since their formation approximately, approximately 5,000 to 8,000 yr BP. There is a tidal signal evident in these lakes, and due to the lack of presence of a confining layer, is further evidence that they are the surface manifestation of a

freshwater lens sitting on a coastal salt water wedge at the marine boundary of the surficial aquifer. Authors: Jennifer L. Coor, Joseph F. Donoghue, Stephen Kish, Kristopher Barrios Journal: Journal of Coastal Research Status: In Preparation Chapter 3: Detecting a storm history signature in coastal lake sediments, northern Gulf of Mexico coast Principal Results: A new technique was developed for identifying storm events/saltwater inundation events in coastal freshwater conditions within the geologic record with the use of a statistical model. The results from this prototype study demonstrate that an accurate storm history can be obtained from coastal sediment cores, based on a combination of isotopic and sedimentologic data, and calibration with the known historic record of storms. This new methodology is more objective, and potentially more sensitive, than the standard methods of counting overwash sand layers or identifying marine microfossils in the sediments. Authors: Jennifer L. Coor, Joseph F. Donoghue, Thomas Wallace, Yang Wang, and James B. Elsner Journal: Quaternary Research Status: Submitted Chapter 4: A Paleostorm Record for the Northwest Florida Coast Principal Results: The model and method previously developed and described has been applied to a coastal freshwater lake in northwest Florida. The results of this study demonstrate that a reliable storm history can be extracted from coastal sediment cores, based on a combination of isotopic and sedimentologic data, and using a statistical model which has been calibrated with the historic storm record. This new methodology is more objective, and more robust, than the standard methods of counting overwash sand layers or identifying marine microfossils in the sediments. The isotope model employed in this study identified 53 anomalous segments in the core, representing a minimum of 229 separate storm events spread through four periods of increased storminess during the nearly 5000-year lake history represented by the core. Authors: Jennifer L. Coor, Joseph F. Donoghue, Oindrila Das, Yang Wang, and James B. Elsner Journal: Marine Geology Status: Submitted

CHAPTER 2 GEOLOGY AND HYDROLOGY OF THE COASTAL DUNE LAKES OF NORTHWEST FLORIDA Introduction A thorough understanding of the underlying geology and morphologic processes at work is essential to understanding the evolution of coastal regions. The coastal dune lakes found in northwest Florida are a distinctive morphologic feature, found only on the U.S. Pacific Northwest coast, Australia, New Zealand and Madagascar (CBA, 2012). Most dune lakes around the world are called freshwater lakes, though they have varying periods of saltwater intrusion (Hoyer and Canfield, 2008). Their mode of formation and effect on the underlying stratigraphy are the subject of this investigation. The primary hypothesis of this study was that the coastal dune lakes of the northwest Florida coast were formed during the mid- to late Holocene when sea-level rise slowed, and have remained in close proximity to the dunes at elevations at or near sea-level since formation. The secondary hypothesis is that the coastal dune lakes represent wind-formed depressions filled by runoff and rainfall, and are perched on a regional humate horizon, separating them from the surficial aquifer. Background Sea-Level Rise in the Northern Gulf of Mexico It has been well documented that changes in sea level over the near- to moderate term (i.e, time scales of decades to centuries) are both connected with and sensitive to changes in climate (IPCC, 2007; Grinsted et al., 2009). Sea level has been rising steadily, with few exceptions, since the last glacial maximum, when sea level was approximately 130 meters lower than present (Fairbanks, 1989; Bard et al., 1990; Clark et al., 2009). By 7800 yr BP the Laurentide ice sheet had nearly completely disappeared (Dyke and Prest, 1987; Smith et al., 2011). Sea-level rise in the Gulf of Mexico began to stabilize approximately 6,000 to 8,000 yr BP (Blum et al., 2001; Balsillie and Donoghue, 2004). It was during this time that a period of much slower sea-level rise began in the Gulf of Mexico (Smith et al., 2011). Rates of rise decreased from 8000 yr BP by approximately 10 mm/yr to approximately 4 mm/yr by mid- Holocene time (Blum et al., 2001). As a result of slower sea-level rise after approximately 8000

4 yr BP, the modern shoreline and barrier island systems began to form, including those of the northern Gulf of Mexico (Taylor and Stone, 1996; Blum, et al., 2001; Donoghue, 2011). Pollen data from Camel Lake, FL, located approximately 100 km to the northeast of the study area, lends additional support that this region has experienced a stable and relatively wet climate over the last approximately 8,000 years (Watts, et al., 1992). Grimm, et al. (1993) studied a longer pollen record from Lake Tulane in central Florida and concluded similarly that the climate has been stable and relatively wet for at least the past 6,000 years. Humates Under certain conditions in coastal regions, surficial aquifers which are enriched in dissolved humic compounds can precipitate an organic cement within the coastal freshwater- saltwater mixing zone. The resulting deposit, known as humate, represents a cemented sandstone, which may possess sufficiently low permeability to serve as an aquiclude (Swanson and Palacas, 1965). Such conditions have existed on the northwest Florida coast for the past several millennia, with the stabilization of postglacial sea-level rise and the development of extensive coastal wetlands as a supply of humic compounds (Figure 2.1). On the northwest Florida coast humates are commonly found in subsurface soil layers, in and beneath marsh deposits, and in shore and beach sands of bayous and bays near groundwater seeps and tea- colored streams (Swanson and Palacas, 1965). Humic substances in sediments (typically sands), have a dark brown to black coal-like composition and appearance, and have commonly been called “hardpan” or “asphalt” (Swanson and Palacas, 1965; Schnitzer and Kahn, 1972). The humic substances which make up the cementing agent for humates are widely distributed throughout nearly all natural waters on earth, and are most commonly found in soils, lakes, rivers, and the ocean (Schnitzer and Kahn, 1972). The first step in forming humates occurs when humus (formed during decomposition of organic matter) is leached and dissolved in water (Swanson and Palacas, 1965). When humic substances enter different geochemical environments (such as when transitioning from freshwater to brackish or salt water, the pH change causes flocculation and precipitation (Swanson and Palacas, 1965). Finally, after the humic substances are removed from solution, they act as cement for sand particles, creating deposits commonly 0.1 to one meter thick (Swanson and Palacas, 1965).

Study Area The study area is located on the northwest Florida coast, approximately 110 km (70 miles) east of the Alabama-Florida state border (Figure 2.2). While there are few inlets along the coastline a number of seasonal freshwater coastal dune lakes create temporary outlets through the dunes in several areas (Foster, 2000). While the shoreline is eroding in some places, mostly due to storms produced by cold fronts and hurricanes, it is relatively stable, maintaining its position with respect to sea-level rise (Stone and Penland, 1992). Sedimentation in the region is dominated by a net longshore transport toward the west, supported by sediment from the inner continental shelf and older Pleistocene shoreline deposits on the mainland (Stone and Stapor, 1996). The beach sediments in this region are nearly pure quartz with very little organic content (Randazzo and Jones, 1997). Regional Geology The northeastern Gulf of Mexico region is tectonically stable, enabling sea-level change to strongly influence the geological history of the study area. The Florida panhandle has alternately been either flooded by marine incursions or exposed as dry land and sandy coastlines throughout much of its geologic history (Randazzo and Jones, 1997). Clastic sediments overlie a carbonate platform formed during late Mesozoic and early Cenozoic marine highstands. A thick regional wedge of clastics, the Apalachicola Embayment, developed in a northeast-trending complex graben system extends from the Florida panhandle to eastern Georgia (Randazzo and Jones, 1997). The embayment dates from the mid-Mesozoic and is the result of regional extensional tectonics (Schmidt, 1984). The embayment is located between the Chattahoochee Anticline to the west and the Ocala Platform to the east. The northern end of the Apalachicola Embayment narrows and extends into the Gulf Trough in southern Georgia. The embayment then widens and deepens as it extends into the modern Gulf of Mexico. This region became a depression as the graben filled in the Early Cretaceous, and was a regional low until the Late Miocene to Pleistocene until the prograding coastal plain migrated over the area, and was then became the Apalachicola River delta in the Holocene (Schmidt, 1984). The southernmost extent of the embayment is not well established due to insufficient offshore well data (Rupert, 1997; Schmidt, 1984). The near-surface sediments in this region are primarily marine deposits from late Cenozoic interglacial periods, overlain by undifferentiated Quaternary sediments, beach and

6 dune deposits, and alluvium. The regional coastal deposits consist of a Pleistocene core of coastal and nearshore sediments, overlain by Holocene sediments comprising the modern dunes and beaches (Scott et al., 2001; Otvos, 1982; Otvos, 2004). Coastal Dune Lakes Coastal dune lakes are typically shallow, irregularly shaped basins located within two miles of the coast (CBA, 2012; QWP, 2012). The lakes are permanent, and most are moderate in size (up to 200 ha). There are seventeen named coastal dune lakes in the study region on the northwest Florida coast. The lakes which were the subject of this study are shown in Figure 2.2. Sand dunes, ranging in height from 1 to 10 meters, separate the northwest Florida coastal lakes from the Gulf of Mexico (Stone and Penland, 1992). The lake water is generally dark in color due to watershed contributions of dissolved organic matter. While these lakes are exposed to normal weather conditions, Florida coastal dune lakes are significantly affected by the infrequent tropical storms which impact the area. The coastal dune lakes are of interest in part due to their intermittent connection to the Gulf of Mexico. When lake elevation reaches a critical level, breaching water forms an outlet through the frontal dune system and empties some of the lake water into the Gulf of Mexico. The temporary inlet, or slough, which is created, may enable salt water and marine biota to enter the lake. The slough remains open until equilibrium is reached and then is closed through natural movement of coastal sediment alongshore. When there is an exchange with the Gulf of Mexico, the lake becomes a brackish water-body and a temporary estuarine ecosystem may be created. Each of the coastal dune lakes has individual outlet characteristics, with outlet openings varying in length, frequency and duration (Hoyer and Canfield, 2008). These openings occur based on each lake’s critical water level, which is driven by climatic conditions, such as precipitation. As a result, some of the dune lakes can be nearly completely freshwater, brackish, or saline (CBA, 2012). The substrate beneath the coastal dune lakes is composed primarily of muddy sands with organic material. These types of lakes commonly have slightly acidic, hard water with high mineral content (Hoyer and Canfield, 2008). Salinity levels can vary greatly, depending on local rainfall and storms. The lakes are generally oligotrophic with low nutrient levels (CBA, 2012). This study investigated six of the northwest Florida coastal dune lakes. From west to east they are: Campbell Lake, Oyster Lake, Draper Lake, Western Lake, Eastern Lake, and Camp Creek

Lake (Figure 2.2). Oyster Lake has a maximum depth of approximately 2.4 meters (8 ft) and is located in a developed area, Western Lake is located in Grayton Beach State Park, a relatively undeveloped area, and has a maximum depth of approximately 3.8 meters (12 ft), Eastern Lake is located in a developed area and has a maximum depth of approximately 1.8 meters (6 ft). Campbell Lake is located in Topsail Hill Preserve State Park, and has a maximum depth of approximately 4 meters (13 ft), Draper Lake and Camp Creek Lake are both in developed areas and have maximum depths of 2.1 meters (7 ft) and 2.4 meters (8 ft), respectively (CBA, 2012). Materials and Methods Remote Sensing Topographic data, based on LIDAR (LIight Detection And Ranging), was used to examine lake morphology and to determine dune elevations separating the six coastal dune lakes from the Gulf of Mexico. Recent LIDAR imagery was accessed from the NOAA Coastal Services Center Digital Coast (NOAA, 2012c). Stratigraphy The stratigraphy of the coastal dune lake region was investigated in several ways. Borehole logs were obtained from the Florida Geological Survey (FGS) and Northwest Florida Water Management District (NWFWMD) in order to determine the occurrence and depth of humate deposits (Figure 2.3). In addition, several long sediment cores were obtained using a Geoprobe 54LT and a vibrocoring system. Cores collected with the Geoprobe were measured, photographed, split length-wise, and described in the field (Figure 2.3). Vibracores were extruded and described in the field. Geochronology Radiocarbon dates on humate horizons were obtained from previous studies (Lawrence, 1974; Otvos, 1982, Lewis, et al., 2003; Koch, unpublished data). Radiocarbon dates were also collected from the bottom of cores collected for this investigation from Western Lake and Eastern Lake in order to determine a minimum age for the lakes. Subsamples of organic sediment from the cores were sent to the National Ocean Sciences Accelerator Mass Spectrometry Facility (NOSAMS) at Woods Hole Oceanographic Institute for radiocarbon analysis.

Water Level Data Water level monitoring for the current investigation began on Oyster Lake in July, 2009. The Northwest Florida Water Management District (NWFWMD) provided the instrumentation and collected data, including time, date, temperature, water pressure, and water depth. Data were collected on an hourly basis on Oyster Lake from July 14 to October 13, 2009. For Western Lake, data were collected from May 1, 2010 to September 28, 2010. For Eastern Lake, data were collected from October 13, 2009 to January 12, 2010. Water level monitoring on Campbell Lake and Draper Lake was carried out from April 30, 2007 to August 1, 2009 by Dr. J. Jawitz of the University of Florida. Monitoring of water level on Camp Creek Lake was carried out from May 2, 2008 to August 1, 2009. Data from that project were collected on an hourly basis and were provided for this investigation (Bahda and Jawitz, 2008; Jawitz, unpublished data). All water level data were plotted as time series. The significant frequencies within the data were identified by use of a Fourier transform. Water Quality Data Water quality data, including pH, salinity, dissolved oxygen, total nitrogen, and total phosphorus, have been collected regularly in the coastal dune lakes since 2001 (Hoyer and Canfield, 2008; CBA, 2012). These data sets were also made available for this investigation. Precipitation Data Monthly rainfall data were obtained from the Eglin Air Force Base facility at Jackson Guard, Niceville, FL, and the Choctawhatchee Basin Alliance (CBA, 2012). Statistical analyses were performed to examine any correlation between rainfall levels and water levels on the coastal dune lakes. Results Remote Sensing Lidar data was collected for each of the selected lakes and verified by field surveys. Each of the coastal dune lakes has a natural slough, which opens up to the Gulf of Mexico when either lake levels become too high due to periods of intense rainfall, or hurricanes and related surge inundate the lake. The data show that Campbell Lake is 0.27 km north of the Gulf of Mexico, and is separated from the Gulf of Mexico by foredunes and natural forest (Figure 2.4a). This lake has

9 an elevation of 1.33 meters and does not have a prominent natural slough. The minimum dune height is approximately 2.5 meters, and the mean dune height is approximately 6 meters. Oyster Lake is located 0.14 km north of the Gulf of Mexico, and is located landward of both the foredunes and Florida Highway 30A (Figure 2.4b). The lake elevation is 0.75 meters, and the lake has a natural slough to the southeast of the lake, though it has been anthropogenically altered through the installation of Florida Highway 30A. The minimum dune height at Oyster Lake is approximately 0.75 meters, located to the southeast of the lake, and the mean dune height is approximately 3.2 meters. Draper Lake is located approximately 0.1 km from the Gulf of Mexico, and is located behind a region of beach almost devoid of foredunes (Figure 2.4c). The lake has an elevation of 0.9 meters. The minimum dune height occurs where the natural slough formed by this lake is located, just south of the lake, having an elevation of approximately 1.25 meters. There are no foredunes directly in front of this lake. The mean dune height at this lake is approximately 5 meters. Western Lake is approximately 0.2 km north of the Gulf of Mexico shoreline, located just landward of the foredunes (Figure 2.4d). Western Lake has a lake elevation of 0.4 meters and is divided into two parts, arranged east to west. The eastern portion of the lake has a minimum dune height to the south of the lake of approximately 2.3 meters. The west side includes a natural slough, which is the dune minimum for the western portion of the lake, with an elevation of approximately 0.6 meters. The mean dune height for Western Lake is approximately 3.5 meters. Eastern Lake is located approximately 0.2 km north of the Gulf of Mexico, and is located behind the foredunes, with a natural slough occasionally present (Figure 2.4e). Eastern Lake has an elevation of 0.9 meters, and the dune minimum for the lake occurs at the outlet for the natural slough, having an elevation of approximately 1.0 meters. The mean dune height at Eastern Lake is approximately 4 meters. Camp Creek Lake is located 0.23 km from the Gulf of Mexico, and is located behind the foredunes (Figure 2.4f). Camp Creek Lake has an elevation of 0.9 meters, and the dune minimum is located at the natural slough, occurring to the southwest side of the lake and having an elevation of approximately 1.0 meters. The mean dune height in front of the lake is approximately 5 meters.

Stratigraphy The near-surface stratigraphy of the region was studied by collecting data from a series of Florida Geological Survey well logs, and collecting cores using a Geoprobe and vibracore system (Figure 2.3). The Geoprobe cores penetrated to a depth of approximately 10 meters. The vibracore penetrated to a depth of approximately 6 meters. Cores collected by both methods consisted nearly entirely of sand, with occasion pieces of wood or small pockets of muddy sediment. Humate-cemented sand and clay-rich layers that could act as a confining layer were not found. A study of lithologic logs of wells collected by the Florida Geological Survey in the coastal region of northwest Florida resulted in 14 lithologic well log descriptions (Figure 2.3). Three wells (labeled 8865, 8591, and 8873) showed the presence of humate-cemented horizons, at depths ranging from six to 15 meters below the ground surface. Well 8865, located near Campbell Lake, showed a layer described as hardpan from approximately 12.3 meters to 13 meters depth below surface. Well 8591, located closest to Western Lake, showed a layer described as hardpan in fine sand at a depth of 3 meters subsurface. Well 8873, located on the Intracoastal Waterway to the northeast of Camp Creek Lake, showed hardpan from approximately 1.5 meters to 1.8 meters subsurface, and also at approximately 7.6 meters subsurface. Geochronology Sediments from the bottom of a core collected from Western Lake, having a sediment depth 1.3 meters, have an age of approximately 4,500 yr BP (Coor et al., submitted). Sediments from the bottom of a core collected from Eastern Lake, having a sediment depth 1.0 meters, have an age of approximately 3,500 yr BP (Coor, unpublished data). Radiocarbon dating has been carried out in previous investigations on selected humate samples throughout the study region, primarily focused along the shoreline (Table 2.1, Figure 2.5). Humate has been found in other locations, but dating was not carried out at these locations (Figure 2.3). As previous studies have used the terms peat and humate/humus interchangeably, and erroneously, in the literature, samples with both labels have been included. Humate- cemented sand outcrops closest to the study area showed ages ranging from 23,000 yr BP to 45,000 yr BP (Koch, unpublished data). Nearby samples on Santa Rosa Island yielded ages from the present to 1,900 yr BP (Stone and Morgan, 1993; Lewis et al., 2003).

Water Level Data Hourly water level data for the six lakes was collected as described above and was subjected to time-series analysis. The time series analysis showed daily oscillations in the raw data, which was identified as a microtidal signal. Previous studies have also suggested the presence of tidal signal in the coastal dune lakes (Badha and Jawitz, 2008). The results of the Fourier transform analysis showed that five of the six lakes showed a distinct tidal signal at periods of 6.2 hours and 12.4 hours (Figure 2.6). Oyster Lake, Western Lake, Campbell Lake, Draper Lake, and Camp Creek Lake showed peaks surpassing both the 95% or 99% confidence intervals. Four lakes also showed evidence of a spring tide, occurring at 173.8 hours. Campbell Lake, Draper Lake, Western Lake, and Camp Creek Lake all showed peaks surpassing both the 95% and 99% confidence intervals (Figure 2.6). A time series analysis showed that the coastal dune lakes both have a tidal response to tidal cycles, and have a tidal lag slightly less than 12 hours (Figure 2.7). Water Quality Data The water quality data for the six coastal dune lakes utilized in this study are summarized in Tables 3 and 4. In Campbell Lake, from May 2002 to December 2010, the mean total phosphorus level was 6.6 µg/L, mean total nitrogen was 425.4 µg/L, mean bottom water dissolved oxygen was 7.0 mg/L, mean bottom water pH was 4.9, and the mean bottom water salinity was 0.1 ppt. In Oyster Lake, from February 2002 to September 2010, the mean total phosphorus level was 79.1 µg/L, the mean total nitrogen was 727.4 µg/L, the mean bottom water dissolved oxygen in bottom water was 6.3 mg/L, the mean bottom water pH was 6.8, and the mean bottom water salinity was 2.1 ppt. In Draper Lake, from February 2003 to August 2010, the mean total phosphorus level was 15.9 µg/L, the mean total nitrogen was 404.6 µg/L, the mean bottom water dissolved oxygen was 4.9 mg/L, the mean bottom water pH was 7.1, and the mean bottom water salinity was 14.8 ppt. In Western Lake, from May 2002 to November 2010, the mean total phosphorus level was 7.2 µg/L, the mean total nitrogen was 266.6 µg/L, the mean bottom water dissolved oxygen was 5.4 mg/L, the mean bottom water pH was 7.2, and the mean bottom water salinity was 10.9 ppt. In Eastern Lake, from December 2001 to November 2010, the mean total phosphorus level was 12.5 µg/L, the mean total nitrogen was 288.2 µg/L, the mean bottom water dissolved oxygen was 5.2 mg/L, the mean bottom water pH was 7.5, and the mean bottom water salinity was 12.9 ppt. In Camp Creek Lake, from February 2003 to

September 2010, the mean total phosphorus level was 8.3 µg/L, the mean total nitrogen was 385.4 µg/L, the mean bottom water dissolved oxygen was 4.5 mg/L, the mean bottom water pH was 7.0, and the mean bottom water salinity was 12.4 ppt. Precipitation Data The Eglin Air force Base Jackson Guard station has recorded monthly rainfall data for the coastal lake region from 1927 to the present. The rainfall data from 2007 to 2010 is presented in Table 2.3. These lakes received, on average, approximately 167 cm of precipitation per year during this period, though the amount of precipitation in any given month may range from as little as 1 cm to as much as 34 cm. The four-year mean was slightly lower than the long-term mean of 176 cm per year. Extreme dry conditions were experienced during September of 2008, receiving 3.02 cm of precipitation (the minimum for the year), and September and October 2010 and 6.71 cm and 2.36 cm (the minimum for the year) for those months, respectively. Discussion The coastal dune lakes of northwest Florida are unique coastal ecosystems, with distinctive characteristics not seen in other lakes of this type. These lakes are at least 4,500 years old, based on the sea-level history of the northern Gulf of Mexico and radiocarbon ages of lake bottom sediment cores collected from Eastern Lake and Western Lake (Blum et al., 2001; Balsillie and Donoghue, 2004; Coor et al., submitted; Coor, unpublished data). Coastal dune lakes have been thought to develop from coastal processes such as excavation of the region behind the dunes by wind (aeolian processes) or by a confining layer perching surficial freshwater (hydrologic processes) as is common in Australia (LIA, 2012; QWP, 2012). However, in northwest Florida the coastal dune lakes appear to begin as a tidally influenced basin or lagoon that becomes enclosed due to sand filling its inlet (Hoyer and Canfield, 2008). Once the lake becomes isolated from the direct effects of the Gulf of Mexico, the water gradually becomes less saline, though levels may vary depending on evapotranspiration and precipitation. These unique ecosystems are largely fed from lateral ground water seepage through the surrounding well-drained coastal sands and runoff from the lake watersheds (Badha and Jawitz, 2008; Hoyer and Canfield, 2008). The coastal dune lakes have been shown through this investigation to have a tidal signal, visible at both a diurnal and semi-diurnal period. This microtidal signal is visible in both the time series and the Fourier transform (Figure 2.6 and Figure 2.7). The tide gauge at Valpraiso, FL, located 39 km to the northwest of Western Lake,

had a mean high tide, relative to mean sea level, of 21 cm during June 2010. The mean high tide values ranged from 5 cm to 45 cm during this time period. The data collected from Western Lake, when combined with the Pensacola tide data, shows lag of approximately 10 hours, and a suppression of the tidal signal, as the coastal dune lakes are also affected by the local hydrology and climate (Figure 2.7). Conclusions Several significant findings have resulted from this investigation. First, the lake water level monitoring shows that there is a tidal signal present within five of the six coastal dune lakes studied, as evidenced from time series and Fourier transform analysis of long-term lake level data and correlation with tide gauge data from Valpriaso, FL. Second, the humate investigation shows that there is no evidence of a confining layer in the subsurface beneath Western Lake, which was investigated as a typical example of a coastal dune lake. The result of these two findings is the conclusion that the coastal dune lakes are not perched on a confining layer, as was originally hypothesized. Rather, they are the surface manifestation of a freshwater lens sitting on a coastal salt water wedge at the marine boundary of the surficial aquifer. Lastly, based on the data from Koch (unpublished) and Swanson and Palacas (1965) as shown in Figure 2.2, it seems that humate precipitation is not necessarily occurring strictly in the coastal mixing zone. The Stage 3 and Stage 4 ages of Koch’s samples, when sea level was approximately 100 meters below present, implies that humate precipitation can have multiple origins. This suggests that the humate formation horizon has migrated landward in response to sea-level rise throughout the Holocene. Radiocarbon dates collected from the coastal dune lakes provide a minimum age of approximately 5,000 yr BP for the coastal dune lakes. Sea-level rise and climate stability confirms the hypothesis that these lakes have been stable and in close proximity to the shoreline since their formation approximately, approximately 5,000 to 8,000 yr BP.

Table 2.1. Summary of dated humic materials.

Location Laboratory Depth Below Radiocarbon Age Reference Material Name Latitude (°N) Longitude (°W) Number Surface (m) (yr BP) (±1sd) J. Koch, unpub. data Walton County 30.31 86.11 WLR89-080205A Humate Outcrop 45,800 526 J. Koch, unpub. data Walton County 30.31 86.11 WLR89-080205B Humate Outcrop 43,641 474 J. Koch, unpub. data Walton County 30.31 86.12 WLR88-071505 Humate Outcrop 43,621 425 J. Koch, unpub. data Walton County 30.28 86.04 WLR114-080205 Humate Outcrop 37,019 348 J. Koch, unpub. data Walton County 30.29 86.05 WLR108-080205 Humate Outcrop 23,704 140 Otvos (1982) Santa Rosa Island 30.32 87.28 UGa-2688 Peat/Humate 22.5 9,230 175

Table 2.2. Summary of water quality data for selected coastal dune lakes.

Lake Name Area TP (µg/L) TN (µg/L) CHL A (µg/L) SECCHI (ft) (ha) Mean Max Min Mean Max Min Mean Max Min Mean Max Min Campbell 35.6 6.6 17.0 1.0 425.4 1710.0 70.0 2.6 14.0 0.0 6.7 15.0 2.0 Oyster 8.9 79.1 205.0 25.0 727.4 1560.0 450.0 6.7 31.0 0.0 2.6 6.0 0.5 Draper 11.2 15.9 57.0 6.0 404.6 1050.0 150.0 6.2 27.0 1.0 3.4 7.0 1.3 Western 38.0 7.2 31.0 3.0 266.6 600.0 80.0 1.6 7.0 0.0 4.9 10.0 2.0 Eastern 20.8 12.5 43.0 3.0 288.2 720.0 110.0 4.6 41.0 0.8 5.3 9.0 1.3 Camp Creek 24.0 8.3 25.0 2.0 385.4 880.0 160.0 3.9 43.0 0.0 3.7 7.3 1.3

* Data source: CBA, 2012 * Data set spans : Campbell Lake: May 2002 - December 2010 Oyster Lake: February 2002 - September 2010 Draper Lake: February 2003 - August 2010 Western Lake: May 2002 - November 2010 Eastern Lake: December 2001 - November 2010 Camp Creek Lake: February 2003 - September 2010

Table 2.2 (continued). Summary of water quality data for selected coastal dune lakes.

Dissolved Oxygen Temperature (°F) (mg/L) pH Salinity (ppt) Lake Name Bottom Bottom Bottom Bottom Mean Max Min Mean Max Min Mean Max Min Mean Max Min Campbell 70.9 88.5 8.2 7.0 10.9 0.2 4.8 8.9 3.6 0.1 1.1 0.1 Oyster 72.4 90.4 49.6 6.3 10.7 0.2 6.8 7.8 5.8 2.1 12.5 0.1 Draper 75.3 91.1 50.5 4.9 12.6 0.3 7.1 9.7 5.8 14.8 30.6 1.7 Western 73.7 89.6 50.6 5.4 17.3 0.2 7.2 8.4 0.9 10.9 24.8 1.8 Eastern 74.7 93.5 0.0 5.2 11.4 0.0 7.5 11.8 0.0 12.9 33.1 0.0 Camp Creek 73.7 92.2 49.0 4.5 10.1 0.2 7.0 8.5 4.5 12.4 77.0 0.0

Table 2.3. Summary of monthly and annual precipitation totals during the study period. Precipitation Accumulation (cm) 2007 2008 2009 2010 January 12.65 12.37 5.74 22.35 February 5.41 21.13 12.60 15.49 March 1.63 6.86 17.45 10.36 April 11.02 11.84 20.07 2.57 May 6.30 7.59 20.42 20.14 June 8.66 13.23 7.49 16.84 July 17.68 18.16 15.06 19.02 August 12.90 17.17 21.21 25.53 September 13.64 3.02 18.06 6.71 October 34.37 16.69 22.02 2.36 November 18.44 6.35 9.65 9.80 December 23.09 13.97 26.21 7.70 Annual 165.79 148.39 195.99 158.88 Monthly Mean 13.82 12.37 16.33 13.24 Monthly Minimum 1.63 3.02 5.74 2.36 Monthly Maximum 34.37 21.13 26.21 25.53

*Source: William Pizzolato, Jackson Guard Station, Eglin Air Force Base (unpublished data)

87° 86° Alabama Florida

Choctawhatchee Bay St. Andrew Bay 30° 31°

0 25 50 miles

Figure 2.1. Localities of humate-cemented beach and dune sands near the coast of the Gulf of Mexico, in the Florida panhandle (Swanson and Palacas, 1965).

Figure 2.2. The coastal dune lakes, located on the northwest Florida coast. Image source: Google Earth.

Figure 2.3. (Upper panel) Distribution of Florida Geological Survey borehole locations in the coastal dune lake region (numbered blue circles). FGS lithologic well logs (labeled, blue) and collected cores (green, red) Red rectangle indicates location of lower panel figure. (Lower panel) Distribution of Location Florida Geological Survey borehole, Geoprobe sites and vibrocore site near Western Lake.

Figure 2.4. Results of LIDAR analysis of coastal dune lakes showing morphology of lake watersheds. Profiles in blue show height of foredunes fronting each lake.

Figure 2.5. Sampling locations of dated humate-cemented sand and other organic sediments.

A) D) Campbell Lake Western Lake

173.8 hr 12.4 hr 173.8 hr

12.4 hr 6.2 hr 6.2 hr Power Power Power 1e-11 1e-05 1e+01 1e+07 1e+13 1e-11 1e+01 1e-05 1e-11 1e+07 1e+13 1e-17 1e-12 1e-07 1e-02 1e+03 Inf 10.0 5.0 3.3 2.5 2.0

Inf 10.0 5.0 3.3 2.5 2.0 1e-02 1e-07 1e-12 1e-17 1e+03 InfInf 2.50 1.251.25 0.830.83 0.62 0.62 0.500.50 PeriodPeriod (X (Hours)unit time) Period (Hours) 95% & 99% C.I. Period (X unit time) 95% and 99% C.I. 95%95% and & 99% 99% C.I. C.I.

B) Oyster Lake E) Eastern Lake

12.4 hr 6.2 hr Power Power Power e1 e1 e0 e0 1e+00 1e-04 1e-08 1e-12 1e-16 1e-17 1e-12 1e-07 1e-02 1e+03

1e-16 1e-12 1e-12 1e-16 1e-04 1e-08 1e+00 Inf 10.0 5.0 3.3 2.5 2.0 Inf 10.0 5.0 3.3 2.5 2.0 Inf 2.50 1.25 0.83 0.62 0.50 1e-17 1e-07 1e-12 1e-17 1e-02 1e+03 Inf 2.50 1.25 0.83 0.62 0.50

PeriodPeriod (X (Hours)unit time) PeriodPeriod (X (Hours) unit time) 95%95% & and 99% 99% C.I. 95%95% and & 99%99% C.I. C.I.

C) Draper Lake F) Camp Creek Lake

12.4 hr 12.4 hr 173.8 hr 173.8 hr 6.2 hr 6.2 hr Pow er P ower Power Power 1e-10 1e-05 1e+00 1e+05 1e+10 1e-10 1e-05 1e+00 1e+05 1e+10 1e-10 1e-05 1e-05 1e-10 1e+00 1e+10 1e+05 Inf 10.0 5.0 3.3 2.5 2.0 Inf 10.0 10.0 5.05.0 3.33.3 2.5 2.5 2.0 2.0 Inf 10.0 5.0 3.3 2.5 2.0 1e-10 1e-05 1e-10 1e+05 1e+00 1e+10 Period (X unit time) PeriodPeriod (Hours) (X unit time) Period95% &(Hours) 99% C.I. 95% & 99% C.I. 95% and 99% C.I. 95% and 99% C.I. Figure 2.6. Results of Fourier transform analysis of lake water level data. The dashed lines represent the 95% and 99% confidence intervals.

60 50 Lake Water Level 45 Valpraiso Tides 55 40

35 50 30

45 25

20 40 15 Tide Height, Valpraiso, FL (m) FL Valpraiso, Height, Tide 10 Western Lake Water Level (cm) Level Water LakeWestern 35 5

30 0 6/1/2010 6/5/2010 6/9/2010 6/13/2010 6/17/2010 6/21/2010 6/26/2010 6/30/2010 Date

55 40 Lake Water Level

Valpraiso Tides 35

50 20

5 (m) FL Valpraiso, Height, Tide Western Lake Water Level (cm) Level Water Lake Western 45 0 6/8/2010 6/9/2010 6/10/2010 6/11/2010 6/12/2010 Date Figure 2.7. Correlation of the water level data (black) from Western Lake with the Valpraiso, FL tide gauge data (blue) during June 2010. The upper image shows the response of the lake water level to the tidal signal throughout spring and neap tides. The lower images focuses on a five day period, from June 2010. Western Lake shows a semi-diurnal tidal signal, while Valpraiso has a diurnal tidal signal. The shows a lag of approximately 10 hours from the tidal signal.

CHAPTER 3 DETECTING A STORM HISTORY SIGNATURE IN COASTAL LAKE SEDIMENTS, NORTHERN GULF OF MEXICO COAST Introduction Coastal lakes and wetlands are periodically subjected to storm surge and overwash processes during catastrophic hurricane strikes when coastal dune systems are overtopped or breached. The environmental effects of major storms include significant damage to coastal systems. The potential effects include coastal retreat, loss of low-lying coastal infrastructure, increased storm surge damage, degradation of coastal wetlands, and saltwater incursion into coastal aquifers. Many studies have quantified prehistoric storms by counting sand layers in coastal sediment cores and employing other standard methods of sediment analysis (Liu and Fearn, 1993; Liu and Fearn, 2000; Donnelly et al., 2001; Donnelly et al., 2004; Nott, 2004; Scileppi and Donnelly, 2007; Horton et al., 2008). In more recent studies, micropaleontology and stable isotope geochemistry have also been employed to identify marine inundation events in sediment core records (Hassan et al., 1997; Meyers, 1997; Lambert, 2003; Lucke et al., 2003; Lamb et al., 2005; Parker et al., 2006; Page et al., 2009). Paleotempestology, the study of long-term regional storm history from geological proxy evidence, offers a glimpse at tropical cyclone activity extending back into geologic time (Liu and Fearn, 1993; Liu and Fearn, 2000; Boose et al., 2001; Donnelly et al., 2001; Donnelly et al., 2004; Nott, 2004; Scileppi and Donnelly, 2007). The question as to whether global warming may bring stronger and/or more frequent hurricanes has been debated among climate modelers in recent years (Emanuel 2005; Elsner et al., 2008; Knutson et al., 2010; Bender et al., 2010; Elsner et al., 2011; Grossman and Morgan, 2011). It is clear that the historic documentation of tropical storms is not necessarily a good representative of the geologic record, as it only extends about 150 years into the past. However, the historic record can be used to assess decadal and multi-decadal cycles and provides some insight into what might occur near future (Landsea et al., 1999; Goldenberg et al., 2001; Klotzbach and Gray, 2008). Knowledge regarding the low- frequency changes in climate, extracted from the long-term record found in coastal lake sediments, is of much benefit in understanding the true return period risk of major storms to

25 coastal regions. The possibility of more intense storms in a warmer future only enhances this benefit. Recent research suggests that an increase in intensity and possibly frequency of storms over the past 30 years is related to climate change and increasing sea surface temperatures (Elsner et al., 2008; McCarthy, 2009). Because hurricanes draw energy from warm ocean waters, warmer oceans have more thermal energy that can be converted to wind energy (Elsner, 2006; Elsner et al., 2008). Modeling studies indicate that an increase in sea surface temperature may result in an increase in storm intensity (Emanuel, 1995; Knutson et al., 2010). Wind shear, another important component of formation, may impact both cyclogenesis and storm intensity (Vecchi and Soden, 2007), due to the fact that vertical wind shear can inhibit the development (Goldenberg et al., 2001; Vecchi and Soden, 2007) and intensity of tropical cyclones (DeMaria, 1996; Frank and Ritchie, 2001; Vecchi and Soden, 2007). Observational and modeling studies to date may not accurately predict what may happen in the future, because they do not take into account wind shear (Vecchi and Soden, 2007). Observational studies have also shown that the strongest storms (Category 3 through 5) are becoming more frequent by approximately 30%, in addition to becoming more intense (Elsner et al., 2008). Thus the impacts of storms in hurricane-prone regions in the coming century may intensify, including increases in both surge and wind damage (Emanuel, 2005; Webster et al., 2005; Elsner, 2006; Elsner et al., 2008). Therefore, it is possible that with a longer storm record, determined through the geologic record, this important and heavily debated topic may be better understood. The primary hypothesis driving this investigation was that the long-term geologic record can provide a significantly longer baseline against which more recent changes in climate and storm frequency (on a century scale) can be measured. An additional hypothesis was that major flooding events leave an isotopic signature in coastal lake sediments, which may be discernible even in the absence of physical indicators of storm occurrence, such as coarse sediment layers deposited by overwash. The goal was to define a storm “signature” that is discernible in coastal lake sediments and which could be applied to other coastal lakes, in order to determine long-term storm frequencies. Methods of detecting evidence of major flooding event occurrence Several techniques have been developed for identifying the signatures of storms in coastal sedimentary records. The common tools of paleostorm analysis include: identification of

marine microfossils (e.g., foraminifera, diatoms), identification of storm-deposited overwash sand layers through grain-size analysis, and stable light isotope analysis of δ13C and δ15N in organic-rich sediments. Each of these tools has its advantages and disadvantages. Micropaleontological methods of paleostorm analysis Microfossil identification is important in paleostorm studies, given that microbiota, such as foraminifera, can be used as indicators of paleo-environmental conditions. Foraminifera, diatoms and other microorganism may be carried with sediments into freshwater coastal lakes or lagoons as overwash during storm events. They may be found preserved in sediment profiles even when there is no visible presence of overwash sediment. Therefore, when remains of marine organisms, such as forams, are found in coastal lake sediments, it can be inferred that they entered the lake during a period of marine inundation (Collins et al., 1999; Scott et al., 2003; Sabatier et al., 2008). This would typically occur as a result of hurricane storm surge. The presence and identity of diatoms can be determined under a microscope after making a smear slide of fine-grained sediments. Foraminifers are visible under a microscope and are commonly found in sandy sediments. Although diatoms are commonly well-preserved in sediments, foraminifera are commonly dissolved in water having a low pH. Analysis of diatoms and foraminifera can identify changes in salinity (fresh, brackish, or marine). High salinity conditions in coastal freshwater lakes can result from saltwater inundation (such as during a storm event), and then may be preserved in the microfossil record (Valero-Garces et al., 1997). The most significant limitation of this method for determining prehistoric storm events is the potentially poor preservation of microfossils due to pH levels, erosion, and/or significant changes in the environmental setting, such as periods of transgression and regression. Sedimentological methods of paleostorm analysis Overwash sand layers preserved in the sediments of coastal lakes have been used to provide a proxy record of catastrophic hurricane strikes during the late Holocene (Liu and Fearn, 1993; Liu and Fearn, 2000; Donnelly et al., 2001; Donnelly et al., 2004). Each overwash event may result in the deposition of a sand layer. The sand layer’s size and shape may vary, but is said to be generally proportional to the intensity of the hurricane and the overwash event (Liu and Fearn, 2000). The frequency, extent, thickness, and chronology of the sand layers can then be used as a proxy for reconstructing a history of prehistoric storm events, provided that the sedimentological “fingerprint” of a historic hurricane with a known intensity and geomorphic

27 impact are available to be used as a control (Liu and Fearn, 1993; Liu and Fearn, 2000; Liu et al., 2009). Thus, the larger the overwash fan and thicker the resulting sand layer, the larger the storm. The expected outcome of this method is that only the strongest storms are preserved in the geologic record (Liu and Fearn, 2000), and that the frequency of overwash sand layers in lake and wetland sediment cores provides an estimate of the return period of major storms. Geochemical methods of paleostorm analysis The use of stable isotopes as a proxy to study climate change has been pursued for decades. However the analysis of paleostorm events using stable isotopes is a new technique (Lambert, 2003; Lambert et al., 2008). This method of determining prehistoric storm impacts has been shown to closely mirror sedimentologic paleostorm analyses, but has the ability to detect storm impacts that did not deposit overwash sediment in coastal environments (Lambert, 2003). The technique of using organic geochemical proxies (OGP) for studying storms has advanced the field of paleotempestology, as it no longer requires an overwash sand layer to record the occurrence of a storm event. OGPs are important indicators in paleoenvironmental studies, as they record environmental conditions at the time of deposition (Castenada and Schouten, 2011). Although this technique has a potentially higher sensitivity and better resolution than other methods, it is time-consuming and costly. Using the OGP method, organic-rich sediments are analyzed in order to create stable light isotope profiles of the core. Siliceous (diatoms) and carbonaceous (mollusk, ostracodes, and foraminifera) sediments may also be used to create isotopic core profiles. Marine environments are typically more enriched in the heavier 13C isotope than terrestrial environments, versus the lighter and more common 12C isotope. In coastal freshwater or brackish environments, marine incursions can be detected in core profiles as shifts from more negative (terrestrial C3 vegetation) δ13C values (in the range of -22 to -35 ‰) (O’Leary, 1988; Cerling et al., 1997) to higher (marine) δ13C values (typically greater than -25 ‰) (Sackett, 1964; Shultz and Calder, 1976; Meyers, 1994; Thornton and McManus, 1994; Corbett et al., 2007; Lambert et al., 2008). An example of this behavior was observed in Chesapeake Bay sediments dating from the mid- to early Holocene, when the modern estuary was developing on the site of the former Susequehanna River mouth (Bratton et al., 2003). Similarly, δ15N values are more positive in marine settings, because they are also more enriched in the heavy 15N isotope than in freshwater settings, as observed in coastal sediments dating from the time of formation of the modern

Albemarle and Pamlico Sounds in North Carolina (Thornton and McManus, 1994; Middleburg and Nieuwenhuize, 1998; Corbett et al., 2007). Therefore, lacustrine plants and plankton have more negative δ13C values than their marine equivalents (Valero-Garces et al., 1997). Coastal lake biota develop in equilibrium with the water in which they exist. As a consequence, there is no significant lag time between a given event (e.g., a marine storm surge into a coastal freshwater lake) and the imparting of a unique isotopic signature into the sedimentary record. The OGP technique uses δ13C and δ15N values in coastal organic freshwater lake sediments to identify storm events (Lambert, 2003). Significant shifts in δ13C and δ15N values in sediment core profiles, followed by rapid returns to base values, are indicative of storm events, are caused when ocean waters which are more enriched in heavy isotopes, (13C and 15N) than fresh or brackish lacustrine waters, inundate the freshwater body during a storm event (Lambert et al., 2008). Some studies (Lambert, 2003; Lambert, 2008) have provided evidence of storm signals corresponding to a shift in δ13C and δ15N values both in the positive and negative directions, instead of only shifting in the positive direction. A negative shift from normal lake values might be expected in the case of a storm event that was dominated by precipitation (excessive coastal flooding) rather than by wind and high storm surge. In recent years, this method has been employed in several investigations (Meyers, 1997; Lambert, 2003; Parker et al., 2006; Lambert et al., 2008; Page et al., 2009). Previous paleostorm studies have shown that the frequency and magnitude of storms are highly variable. Storm occurrence may have a periodicity of several decades to centuries, and may be correlated to changes in climate, especially rapid changes in climate (Page et al., 2009). Although OGPs may be used independently, we suggest they are most effective when used in conjunction with one another and with other proxies. Historic Storm Record Since 1851, 30 named storms (Category 1-5 on the Saffir-Simpson scale) have passed within 140 km of the study area, Oyster Pond on St. Vincent Island, FL (Figure 3.1). The cutoff radius of 140 km was chosen as it is the mean radius of the extent of maximum sustained winds, as shown in models by Keim et al. (2007). Of the 30 Category 1-5 storms passing within 140 km of Oyster Pond, 8 were major storms (Cat. 3-5), resulting in a return period of approximately 5 years for Category 1-5 storms, and 20 years for Category 3-5 storms. Some of the more notable storms from the historic period are the 1985 Hurricanes Elena and Kate (Cat 3 and Cat 2 at their

nearest approach to Oyster Pond, respectively), Hurricane Alma (Cat. 2 when closest to Oyster Pond) in 1966, and Unnamed storms of 1926, 1899, 1894, 1886, 1877, 1852, and 1851 (all Category 2 and 3 storms when closest to Oyster Pond). Hurricanes Dennis (2005), Opal (1995), and Eloise (1975) did not pass within the 140 km cutoff radius, but are known to have caused significant storm surge at the nearby Apalachicola, FL, tide gauge (Figure 3.2) and were included in the storm history (NOAA, 2012a). Regional Setting The study area is on the northwest Florida shoreline (Figure 3.2), chosen as a representative segment of a complex coastal system. Sea-level change has strongly influenced the geological history of the area. The region has alternately been either flooded by marine incursions or exposed as dry land and beaches throughout much of its geologic history (Randazzo and Jones, 1997). The northeastern Gulf of Mexico region is tectonically stable, overlying a carbonate platform formed during late Mesozoic and early Cenozoic marine highstands. This region is also underlain by the Apalachicola Embayment subsurface structural feature, a northeast-trending complex graben system that extends from the Florida panhandle to eastern Georgia (Randazzo and Jones, 1997). The near-surface sediments in this region, as shown in the geologic map of Florida (Scott et al., 2001), are primarily marine deposits from late Cenozoic interglacial periods, overlain by undifferentiated Quaternary sediments, beach and dune sediments, and alluvium. The average rate of global sea-level rise during the twentieth century, determined by tide gauge measurements since the mid-1800’s, has been approximately + 1.7 mm/yr (Miller and Douglas, 2004; IPCC, 2007). Regional rates of sea level increase in the northern Gulf of Mexico, as measured by satellite altimetry, have been shown to average + 3.0 mm/yr since 1993, indicating that the rate of rise during the past two decades has been significantly higher than during the preceding century and a half (NOAA, 2012b). It has been widely suggested that the IPCC (2007) range of projections for future sea-level rise, + 0.18 to + 0.59 meters by the year 2100, may be significantly underestimated. Some models predict that the rate of sea-level rise may increase to more than + 20 mm/yr by the end of the century (Grinsted et al., 2009). Some recent projections of sea-level rise, based on temperature increase or changes in radiative forcing, range between + 0.5 and + 2 meters by 2100, depending on the rates of loss from the Greenland and Antarctic ice sheets (Rahmstorf, 2007; Rahmstorf et al., 2007; Pfeffer, 2008;

Grinsted et al., 2009; McCarthy, 2009), with + 0.8 meters most likely within the next century (McCarthy, 2009). It has been well documented that changes in sea level over the near- to moderate term (i.e, time scales of decades to centuries) are both connected with and sensitive to changes in climate (IPCC, 2007; Grinsted et al., 2009). The climate of the Holocene (11.8 ka to present) has been relatively stable compared to that of the most recent ice age (Alley et al., 1997). Sea level has been rising steadily, with few exceptions, since the last glacial maximum, approximately 20,000 years before present, when sea level was approximately 130 meters lower than present (Fairbanks, 1989; Bard et al., 1990; Clark et al., 2009). Sea-level rise in the Gulf of Mexico began to stabilize approximately 6,000 to 8,000 years ago (Blum et al., 2001). By 7800 yr BP the Laurentide ice sheet had nearly completely disappeared (Dyke and Prest, 1987; Smith et al., 2011), and a period of much slower sea-level rise began in the Gulf of Mexico (Smith et al., 2011), decreasing from rates of sea-level rise of approximately + 9 to + 11 mm/yr from 7800 to 6800 yr BP to approximately + 3 to + 4 mm/yr during the mid-Holocene (Blum et al., 2001). It was during the time of slower sea-level rise after about 8000 yr BP that the modern shoreline and barrier island systems began to form, including those of the northern Gulf of Mexico (Tanner, 1988; Stapor, et al., 1991; Liu and Fearn, 1993; Donoghue and White, 1995; Taylor and Stone, 1996; Liu and Fearn, 2000; Blum et al., 2001; Donoghue, 2011). St. Vincent Island (Figure 3.2) is the westernmost barrier island in the island chain at the mouth at the Apalachicola River. The Apalachicola, Florida’s largest river, has a watershed that includes parts of the southern Appalachians, the Piedmont region in Georgia and eastern Alabama, and the Gulf Coastal Plain. The river has played a major role in shaping the coast of Northwest Florida, by developing a series of deltas and by supplying sediment to build the modern barrier island system (Figure 3.2) (Stewart and Gorsline, 1962; Schnable and Goodell, 1968; Donoghue, 1993; Donoghue and White, 1995; Forrest, 2007). It has been well documented that the Apalachicola River was the major sand source for the northwest Florida shoreline during the Quaternary, providing quartz sands that have been reworked and distributed through processes of longshore drift and wave action (Johnson and Barbour, 1990; Donoghue, 1993). Long-term accumulation of the river’s sediment load, combined with low to moderate wave energy, has resulted in the development of the present-day barrier islands and spits that surround the mouth of the Apalachicola River (Tanner, 1964).

St. Vincent Island (Figure 3.2) is marked by an extensive series of beach ridges and several freshwater ponds, with each ridge representing a former shoreline position (Forrest, 2007). The freshwater pond closest to the Gulf of Mexico, Oyster Pond, was used in this study. St. Vincent Island has a long-term precipitation of 1.4 m of rainfall per annum. Rainfall is most abundant in the summer months, and is the primary source of the island’s surface water flow (Davis and Mokray, 2000). The St. Vincent beach ridge plain is composed of 12 ridge sets (Figure 3.2, inset image), each of which is marked by a truncation representing a change in sediment transport direction (Stapor, 1973; Stapor 1975; Forrest, 2007). Optical luminescence dating of the beach ridge set immediately landward of the pond in set I (Figure 3.3, inset image) produced an age of 800 +/- 100 years BP (Forrest, 2007), providing an upper age limit for the lake origin. The beach ridge set to the southeast of Oyster Pond in set J (Figure 3.3, inset image) produced an age of 400 +/- 100 years BP (Forrest, 2007), which provides a younger limit for the pond’s age. Materials and Methods Field Sampling Two sediment cores were collected for paleostorm study via isotopic, sedimentologic, micropaleontologic, and geochronologic analyses. Hand-held piston corers were used so that the lake sediment was not disturbed or homogenized. Lexan tubes with a 7.62 cm (3 inch) inner diameter were used for core collection. Measurements of salinity, pH, conductivity, and temperature were also collected in the overlying water column using a YSI probe. Core Preparation The cores were split lengthwise, with half of the core used for analysis, and the remaining half retained in cold storage as an archive. After splitting, cores were imaged using a digital line scan on a GEOTEK multi-sensor core logger (MSCL). X-radiographs were obtained using a Torrex 120D X-Ray Inspection System, exposed at 2 mA and 75 kV. One core, 092710- 01, was selected for geochemical and physical analyses; the other core, 092710-02 was archived. Geochemical Analysis The upper 40 cm of the core, encompassing ~1900 A.D. to present, was used to generate and train a statistical model for identifying the signature of known historic storm events. The core was sampled at the finest possible resolution, approximately 2.9 mm intervals (n=140 samples) using individual thin, hollow plastic sampling tubes similar to the method described by

Lavoie (1996). Sample preparation and geochemical analysis for δ13C, δ15N, percent C, percent N (of the organic fraction), and C:N ratio followed a method similar to that described by Lambert (2003) and Lambert et al. (2008). Samples were freeze-dried and ground with a mortar and pestle for homogeneity. Stable isotope analyses were carried out at the National High Magnetic Field Laboratory at Florida State University, on a Finnigan MAT delta PLUS XP stable isotope ratio mass spectrometer (IRMS) connected to a Carlo Erba Elemental Analyzer (EA) through a Conflo III interface. Sample analysis was initiated by weighing a given amount of sample (depending on % C and % N) into a silver cup, after which the sediment was moistened with deionized water and fumigated using concentrated HCl (12N) for 24 hours to remove any carbonate material. After fumigation, samples were dried overnight in an oven at 70°C. After drying, samples were wrapped in a tin cup for isotope analysis. Two sets of five different standards including YWOMST-1 (cane sugar), YWOMST-2 (phenylalanine), YWOMST-3 (L-phenylalanine), YWOMST-5 (urea), and urea-2 were also weighed and wrapped in tin cups for isotope analysis. Samples were then loaded into the auto sampler of the EA connected to the IRMS for isotopic measurements. Each batch of samples was loaded into an autosampler, which can hold a total of 50 samples and standards. Typically 10 samples in each batch were standards. Results are reported in the standard delta (δ) notation in per mil (‰) relative to the international VPDB standard (for δ13C) or air (for δ15N) (Sharp, 2007). The precision of C and N isotope analyses is + 0.2 ‰ or better based on repeated analyses of the laboratory standards. Sediment Analysis The working half of the core was sampled for sedimentologic analysis at 5 mm intervals. Samples were placed in pre-weighed plastic dishes, weighed, dried overnight in an oven at 65°C, and weighed again after cooling to determine the bulk dry weight of the sample. After drying, 1 g of sample was taken from each sample, placed in a pre-weighed aluminum dish, and subjected to combustion in a furnace for 2 hours at 550°C. Samples were cooled overnight and weighed again to determine percent combustible, a proxy for percent organic matter. The fraction not used for combustion analysis was weighed, wet-sieved, dried, and weighed again to determine percent sand and percent fines. The fine fraction was used for Pb-210 geochronologic analysis. Grain size analysis was carried out on the sand fraction using an automated settling tube and the GRANPLOT program (Balsillie et al., 2002). GRANPLOT calculates settling tube

size fractions in quarter-phi intervals based on settling times and velocities and application of the Gibbs equation. The program produces standard grain-size statistics (mean grain size, standard deviation, skewness, kurtosis, and dispersion) along with frequency histograms and cumulative plots. Micropaleontology Oyster Pond is known to be historically fresh. Samples were collected from each visible sand layer for marine foraminifera identification, as an indicator of marine incursion into the lake via storm surge. Geochronologic Analysis Geochronologic analyses, via Pb-210 and Cs-137, were utilized to establish core chronology and determine accumulation rates during historic time (Appleby and Oldfield, 1978; Walker, 2005). Seventeen Pb-210 and Cs-137 samples were collected. Sampling interval was every other centimeter to a depth in the core of 25 centimeters, followed by one sample every five centimeters between 25 cm and 45 cm. Pb-210 samples were obtained by wet-sieving to collect the fine fraction of the wet-sieved samples, drying the sediment, and grinding the sediment into a powder for homogeneity. Samples were placed in labeled plastic vials and sealed with epoxy, which allows the short-lived daughters of the radiogenic isotopes to accumulate and be contained in the vial for counting. The parent (Pb-210) and daughter (Po- 210) isotopes in the samples were allowed to equilibrate for approximately one month, and then placed in a gamma counter for 3 to 10 days, depending on the activity of the sediment. The samples were subjected to gamma spectrometry at the Environmental Radioactivity Measurement Facility at Florida State University, following a method similar to that of Kim and Burnett (1983). Remote Sensing Data from topographic surveys and LIDAR (LIight Detection And Ranging) imagery were obtained in order to determine the elevations of the dune ridges separating Oyster Pond from the Gulf of Mexico. The LIDAR data were collected as a part of the 2007 Florida Division of Emergency Management (FDEM) Lidar Project for Franklin County, Florida. Imagery was collected between May 4, 2007 and August 16, 2007, and had a reported vertical error of ± 9.1 cm relative to the NAVD 1988 datum (NOAA, 2012e).

Results Lake Data The YSI probe data for Oyster Pond on the core collection date were as follows: water temperature was 27.16 °C, salinity was 0.78 ppt, dissolved oxygen content was 0.89 mg/L, and pH was 7.8. Though a bathymetric map was not available for the lake, the water at the coring site was found to be approximately 1.5 m deep. Core Description Core 092710-01 was 81 cm in length. The core was composed of dark, grayish-brown, fine-grained, organic-rich sediment. The upper 5 cm of the core contained scattered plant and root fragments. Some disarticulated shells and shell fragments were visible from 5 to 15 cm, and sparse root fragments were present from 15 to 20 cm. Sparse plant matter was visible from 22- 24 cm, intermixed with some shell fragments. A large disarticulated bivalve shell was present at 28 cm, and shell fragments were scattered throughout the 25 to 30 cm segment. Several black plant stems were present found at 33 cm, with additional shell fragments at 24 cm. There was a large shell (possibly Crassostrea sp.) at 37 cm depth. There was a visible sand layer from 48 to 49.5 cm intermixed with some shell fragments. The sediment was a sandy mud between 50 and 65 cm, and dark, very fine-grained, with muddy sediment below 65 cm, and continuing to the bottom of the core at 81 cm. By using the standard method of counting sand layers through x- radiography, only six layers can be counted in the core that was analyzed for this study. Remote Sensing Oyster Pond is approximately 0.35 km from the Gulf of Mexico and has an elevation of approximately 0.25 meters above sea level. The pond is located in a swale within the beach ridge set labeled “I,” as shown in Figure 3.3. It is separated from the Gulf of Mexico by younger beach ridges within the same set to the south and west, in addition to ridges in a younger ridge set, labeled “J,” further to the south. The beach ridge set of which Oyster Pond is a part, Set I, has a minimum ridge height of approximately + 1.25 ± 0.09 meters separating the pond from the Gulf of Mexico (NOAA, 2012e). A small slough exits Oyster Pond and passes through the ridges to the south. Historically, the slough has been opened only by major storms. During recent time, the slough has been dammed. In beach ridge set J, the minimum ridge height is approximately +1.50 ± 0.09 meters (NOAA, 2012e). Therefore the minimum height of storm surge necessary to inundate the pond is + 1.25 ± 0.09 meters (NOAA, 2012e).

Geochronology Pb-210 and Cs-137 are short-lived isotopes commonly used to date sediment accumulations from the past approximately 100 years, in order to determine recent rates of sedimentation in lakes (Krishnaswamy et al., 1971; Robbins and Edgington, 1975; Stiller and Imboden, 1986; Robbins and Jasinski, 1995; Kirchner and Ehlers, 1998). Pb-210, with a 22.3 year half-life, bonds to clay minerals, fine silts, and humic materials (Olsen et al., 1982; Chanton et al., 1983; Kirchner and Ehlers, 1998) and is used for dating lake sediments, under the assumption that the atmospheric flux of 210Pb, produced by the decay of 222Rn, has been relatively constant (Appleby and Oldfield, 1978; Kirchner and Ehlers, 1998; Walker, 2005). Cs- 137 has a half life of approximately 30 years, and is an artificial fission product that occurs in the atmosphere primarily as a result of nuclear weapons testing in the 1950’s and 1960’s. First entering the atmosphere in 1954, atmospheric concentrations of Cs-137 peaked at the time of the Nuclear Test Ban Treaty in 1963 (Kirchner and Ehlers, 1998; Walker, 2005), and rapidly declined in the following decades (Kirchner and Ehlers, 1998). Cesium, like lead, adsorbs to clay minerals, silts, and humic materials (Stanners and Aston, 1981; Milan et al., 1995; Kirchner and Ehlers, 1998). The earliest occurrence of this isotope in a sediment core profile is generally taken to represent 1954, the year when it became commonly present in the atmosphere. A subsurface peak indicates 1963, a time-stratigraphic horizon for the peak year of atmospheric testing (Olsson, 1986; Kirchner and Ehlers, 1998; Walker, 2005). By utilizing these methods, sedimentation rates for the historic period, in the upper portions of lake cores, can be determined with reasonably good accuracy. Pb-210 analysis results are shown in Figure 3.4. The excess Pb-210 profile differs from the ideal exponential decay curve due to the somewhat episodic nature of sedimentation in the lake. The waters of Oyster Pond are approximately 300 meters from the open Gulf of Mexico, and have been so for at least several centuries, based on the OSL dating (Forrest, 2007). Occasional storm surge brings marine water and possibly sediment into the pond in a single event. The instantaneous input of external sediment appears as a deficit in Pb-210 profiles of the lake floor sediments. The Pb-210 activity data were analyzed using the constant rate of supply (CRS) model, which assumes that the Pb-210 input is constant from year to year, though the annual amount of sediment can vary (Appleby and Oldfield, 1978). The CRS method allows detection of episodes

36 of high sediment accumulation, which in the case of Oyster Pond is indicative of storm events. Because the CRS age model requires the input of positive values of excess Pb-210 and a complete Pb-210 inventory, the upper limit of excess activity for each Pb-210 sample was used in constructing the model. Therefore, a storm year, with an above-average input of annual sediment, would show up as a negative excursion in a Pb-210 core profile. This effect is visible in sample numbers 3, 4, 6, 9, 10, and 14. These samples correspond with values lower than the expected exponential decay profile in the Pb-210 results, shown in Figure 3.4. The uncertainty associated with each sample, as determined through the gamma counting error, is also shown in Figure 3.4. The results from the CRS model are shown in Figure 3.5, where the age uncertainty has been calculated from the analytical uncertainty (shown in Figure 3.4), and is represented by horizontal bars. The Cs-137 results are also shown in Figure 3.4. The gradual decrease in the amount of detectable Cs-137 in the upper portion of the profile does provide evidence that no mixing has occurred in the core, either post-sedimentation, or during collection and handling. Using the CRS model to match subsurface horizons with calendar years, the sediment horizon that represents 1954, the approximate year that bomb testing began releasing Cs-137 into the atmosphere, should be at 35 cm. A subsurface peak, which would be indicative of the maximum concentration of Cs-137 in the atmosphere in 1963, should be observed at 33 cm (Chanton et al., 1983; Lynch et al., 1989; Kirchner and Ehlers, 1998). Detectable Cs-137 was observed down to 45 cm, however. The explanation appears to be the fact that cesium becomes more soluble, and mobile, in sediments of nearshore (brackish or marine) environments (Tamura and Jacobs, 1960; Rogoski and Tamura, 1970; Aston and Duursma, 1973; Hetherington and Jefferies, 1974; Patel et al., 1978; Stanners and Aston, 1981; Olsen, et al., 1982; Kirchner and Ehlers, 1998; Walker, 2005). The absence of the 1963 peak is further evidence of the mobility of Cs-137 down the core. The groundwater of St. Vincent Island is both brackish and tidally influenced, and when combined with the mobility of the cesium isotope and tidal pumping, the downward movement of cesium in the sediment column might be an expected result. Other studies in this region have also shown a lack of agreement between Pb-210 and Cs-137 profiles (Hart, 2003; Mertz, 2007). Isotope and Sediment Data The isotope and sediment physical property parameters are shown in Figure 3.6. The percent sand values ranged from nearly 36% to 81%, with a mean of 62% and a standard

deviation of 9%. The δ13C values ranged from -21.4 ‰ to -25 ‰, having a mean value of -22.0 ‰ and a standard deviation of 1.0 ‰. The % N′ values, or the first derivative of % N, ranged from -0.13% to 0.07%, having a mean value of 0% and a standard deviation of 0.02%. By counting peaks in both the grain size and percent sand profiles, seven significant peaks can be discerned (above + 1 standard deviation above the mean), most of which correspond to sand layers visible in the x-radiograph. The upper 10 cm of this core, corresponding to 2000 to present, was not included in the creation of the model equation. During the past decade, following a USGS study (Davis and Mokray, 2000), the surface drainage of St. Vincent Island has been altered by closing roads and installing culverts in an attempt to restore natural overland water flow patterns to the island. As a result, the input of runoff and nutrients to the lake has changed in the past decade, and therefore the uppermost portions of the isotopic profiles show varying trends. Complete sediment and geochemical data are available in Appendix A and Appendix B. Micropaleontological Data Samples for micropaleontologic analysis were collected from visible sand layers in the core, which were assumed to represent storm surge deposits. The eight samples were taken from 1 cm, 7 cm, 9 cm, 11, cm, 24 m, 26 cm, 33 cm, and 37cm depths in the core. Each sample contained a dominance of the brackish to estuarine species Ammonia parkinsoniana. Six of the samples contained common forms of both Elphidium sp. and Haynesina sp. Similarly, ostracod, bivalve, and molluscan debris were present in nearly every sample. The samples collected at 33 cm and 37 cm showed rare examples of Quinqueloculina, which is a nearshore, normal marine indicator. Urchin spines were also present in the samples collected at 33 cm and 37 cm, as well as 11 cm and 26 cm, indicating a marine influence. Byrozoan fragments were present in the sample collected at 24 cm. The full microfossil analysis is included in Appendix C. This assemblage is similar to the Ammonia parkinsoniana – Elphidium gunteri assemblage described by Puckett (1992), which represents a primarily brackish to estuarine facies that is located near the open ocean, typical of the mid-region and mouth of an estuary. Although none of the sample assemblages appear to be strictly marine, they do all appear to be uniform and represent bay/estuarine conditions, suggesting Apalachicola Bay estuarine water was included in the storm surges that inundated Oyster Pond. The main body of the Apalachicola Bay estuary is less than 5 km from Oyster Pond (Figure 3.2).

Discussion Model Development The sediment accumulation rate profile (Figure 3.7), as derived from the CRS model, exhibits 5 peaks in accumulation rate, indicating 5 episodes during which sediment was rapidly deposited into Oyster Pond. These five peaks in accumulation coincide with core depths of 4.5 cm, 10.5 cm, 18.5 cm, 22.5 cm, and 29.5 cm. These core depths represent the approximate CRS model years 2002, 1999, 1990, 1986, and 1972, respectively (Table 3.1). Depths and years are presented using the midpoint of each range. Due to the analytical error associated with determining the Pb-210 ages through gamma counting, and shown in relation to the ages derived from the CRS model in Figure 3.5, some ages/depths could correspond with more than one historic storm or high water event. The most likely storm or event was chosen to represent each peak based on intensity, known surge, and the time of occurrence. A statistical model for identifying storm signatures using known storm events was developed using a combination of the sedimentologic and isotopic data from the upper 40 cm of the Oyster Pond core. Based on the Pb-210 data, that portion of the core represents the historic period, i.e., the past century, during which there is a detailed record of storm and surge events in the region. The model was calibrated using the Pb-210 chronology and the dates of the known storm and high water events. The data used to calibrate the model was obtained from long-term tide gauge records and other regional storm records, as described below. The storm model was created using R, a freeware data analysis program (http://www.r-project.org/). The procedure for creating the model, diagrammed in Figure 3.8, was as follows: (1) A database of known storm history for the region was assembled. All storms in the NOAA Historical Tracks database (NOAA, 2012d) that had passed within 140 km of the coring site were included. This distance represents the mean radius to hurricane force winds for category 3 storms, i.e., wind speeds of approximately 50 m/s (Keim et al., 2007). Storm parameters for all of the included storms were tabulated. (2) Tide gauge data were collected from the nearest NOAA tide gauge, at Apalachicola, Florida, in order to refine the storm record to events capable of breaching the beach ridges and inundating the pond (NOAA, 2012a). The tide gauge is located approximately 18 km from St. Vincent Island, at the mouth of the Apalachicola River, and is shown by a yellow dot on Figure 3.1. Monthly mean and monthly maximum tide data were obtained for the period January 1967

(when the tide gauge was installed) to present. The maximum water level for each month was recorded and examined to determine the maximum water level for each year, noting the month and date of the maximum. Given the elevation of the ridges fronting the pond, the tide gauge data were filtered to remove all events below the monthly maximum data for each year plus one half of a standard deviation, or + 1.1 meters. As the tide gauge at Apalachicola, FL is protected from storm wave run-up and surge in the Apalachicola River, the high tide values corresponding to storm and high water events are likely significantly less than water levels experienced on St. Vincent Island, FL, as shown by surge data collected from the surrounding coastal region. Needham (2011) showed that Hurricane Kate (1985) caused a + 3.35 meter surge in Cape San Blas, FL, where the Apalachicola tide gauge recorded a + 1.99 meter maximum tide (NOAA, 2012a). Similarly, Hurricane Agnes (1972) caused a + 2.13 meter surge in Port St. Joe, FL (Needham, 2011), where the Apalachicola tide gauge recorded a + 1.63 meter maximum tide (NOAA, 2012a). Lastly, Hurricane Earl (1998) caused a + 2.44 meter surge in Green Point, FL, (located between Carabelle, FL, and East Point, FL), and the Apalachicola tide gauge recorded a + 1.57 meter maximum tide (NOAA, 2012a). This cutoff yielded ten high-water events, as (Hurricanes Elena and Kate in 1985 were counted as a single event in the model due to their close temporal proximity (Table 3.2). Among the resulting ten events, five were correlated with the sediment accumulation peaks previously discussed from the CRS model of the Pb-210 data (Figure 3.7). The other five events included one known storm event (Hurricane Eloise, 1975) and four extreme high tides that do not correspond to dates of known storms. The five known storms matching accumulation peaks from the CRS model correspond to: Hurricane Dennis (Category 4, 2005), Hurricane Earl (Category 1, 1998), Hurricane Opal (Category 3, 1995) Hurricanes Elena and Kate (Category 3, 1985), and Hurricane Agnes (Category 3, 1972), as shown in Table 3.1 and Figure 3.7. The other five events recorded at the Apalachicola tide gage occurred in 2009, 2004, 2002, 1994, and 1975 (Hurricane Eloise), as shown in Table 3.2. (3) The known storm events with a storm surge and other events having a sea level elevation greater than +1.1 m were then used as the basis for a “storm signature.” A series of multiple regressions was carried out on the data set in order to determine which of the sedimentologic and isotopic data variables are statistically significant and most influenced the data. The parameters examined through regression were: δ13C, δ15N, % C, % N, δ13C ′, δ15N′,

% C′, % N′, % combustible, % sand, mean grain size, and standard deviation of grain size. The primed (′) variables were determined by determining the first derivative of each respective parameter (i.e., % N to % N′) in order to smooth trends in the data. The multiple linear regression analysis showed that δ13C, percent sand, and percent nitrogen derivative were the most statistically significant variables, and therefore changes in these variables during a storm would be the most identifiable (Table 3.3). These variables were then used as inputs to a generalized linear model, where the resulting coefficients were employed in the logistic regression equation (Hilbe, 2009): = −27.679−1.511 − 0.095 % − 57.151 % ′ In the above equation, is a function that describes the log of the odds ratio of storm to non-storm events, as seen in the data. The results of the equation must then be transformed, such that: = 1 + in order to generate a probability distribution from 0 to 1 of the logit expression (Hilbe, 2009), where 0 indicates no probability of a storm having occurred, and 1 indicates a 100% probability of a storm. A cutoff storm probability of 0.50 was chosen for the purposes of the model. This value maximized the number of events identified by the model, and minimized the number of false positives, based on the known history of storms. The results from applying the model to the data from the upper 40 cm of Core 092710-01 are shown in Figure 3.9. (4) The modeling process resulted in thirteen peaks above the probability cutoff (Figure 3.8), although some peaks contain more than one event. A chronology of these events was established, based on the CRS model, as shown in Table 3.4. The uppermost twelve peaks of the storm model temporally correspond to the period of record for the Apalachicola tide gauge, which began continuous operation in 1967. The model was able to be trained and calibrated through comparison with the known storm and surge events. Not all peaks in the sediment record correspond to real events, and are false positives. The five accumulation peaks from the CRS model (Hurricanes Dennis, Earl, Opal, Elena/Kate, and Agnes), and five of the other extreme high-water events from the tide gage record (events from 2009, 2004, 2002, and 1994, and 1975 Hurricane Eloise) were likewise positively identified. Note that Hurricane Dennis and the surge events from 2009, 2004, and 2002 are all included in the uppermost peak produced by

the model, as identified by events 1a-1g. Five false positives were also identified. The thirteenth peak, 13a and 13b, represents storm event(s) occurring in the early part of the 20th century, before the installation of the tide gauge. The false positives, peaks 2, 4, 5, 6, and 12, (Figure 3.9) have isotopic and sedimentologic properties similar enough to an event for the model to identify them as a storm or high water event. During the years represented by those peaks, the Apalachicola tide gauge data do not include any high water events above the + 1.1 meter minimum. Because the minimum dune/beach ridge height separating the pond from the Gulf of Mexico is + 1.25 ± 0.09 meters (NOAA, 2012e), we assume that whenever the gauge record exceeds + 1.1 meter the water height in the Gulf of Mexico is elevated enough to allow for seawater inundation of the pond due to the significant difference between the water levels of the Gulf of Mexico (Needham, 2011) and those recorded by the Apalachicola tide gauge (NOAA, 2012a) during storm events, causing an elevated water level greater than + 1.25 ± 0.09 meters (NOAA, 2012e). It is possible, nonetheless, that some high water events of lesser magnitude in the tide gauge record, combined with wind-driven waves, could have inundated the lake, causing the isotopic and sedimentologic signature observed in the model, and generating the false positives. The ten known storm or high-water events used to create the model, spread over seven model peaks, have percent sand and δ13C values above the mean, and a neutral % N′ value. Lambert (2003) and Lambert et al. (2008) showed that storm signals correspond to δ13C values significantly above the mean (i.e., indicating periods of marine isotopic values), although storm signals can also correspond to peaks significantly below the mean, as has also been observed (i.e., indicating periods of freshwater flooding). Some of the storms predicted by our model are shown to have either percent sand or δ13C values below the mean, as is most commonly seen in the five storm surge events that did not correspond to accumulation peaks from the CRS model and the broad Event 1, which was largely influenced isotopically and sedimentologically by anthropogenic alteration, as noted in Section 4.5 above. The five storms associated with accumulation peaks from the CRS model show a trend, having percent sand and δ13C values above the mean, and a neutral % N′ value, thus creating a storm signature. Nonetheless, the combined effect of the three parameters on the model output is sufficient to identify ten high water events since 1967. These events, which affected the pond, are identified by seven peaks in the model. Therefore, a “typical” storm signature, as shown by the model, has δ13C and percent

42 sand values above the mean, and a neutral (close to zero) % N′ value. A reasonable explanation for the false positives is that they were produced by an influx of sand and terrestrial (more negative) δ13C from freshwater flooding events. (5) Storm probabilities estimated at adjacent samples or groups of samples are not likely to be independent. To test this possibility, we computed the autocorrelation function of the predicted probabilities at sample lags (out to lag 18). A lag corresponds to one sample. A series of lags was created by shifting the data down one sample per lag and cross-correlating the storm probabilities as a function of the lag (Box and Jenkins, 1976). Results are shown in Figure 3.10. The autocorrelation function values drop below the 95% confidence interval at lags of three and higher (at least out through lag 12). This indicates that successive sediment samples are not independent. The relatively high autocorrelation at lags 1 and 2 argues that it is best to consider every third or fourth sample as statistically independent, and that statistical independence is achieved by considering groups of three or four samples together as a single event. This finding has an impact on Events 1 and 13 (Figure 3.9). Events 1 and 13 are the only two peaks that are made up of more than 3 or 4 samples. Event 1, spanning 0 to 7.5 cm and 26 samples, may represent as many as seven separate storm occurrences, as determined by the autocorrelation function, and as a result is divided into 7 sub-events. Of these potential seven storm occurrences, four are known storm and surge episodes from the tide gauge and known storm record (a surge episode in September 2009, Hurricane Dennis in 2005, a surge episode in September 2004, and a surge episode in September 2002) (Table 3.4). The other three occurrences may be explained by mixing in the sediment record by the extreme surge of Hurricane Dennis, and uncertainty associated with the Pb-210 analysis and the CRS model. Event 13, spanning 38.4 to 40 cm and 6 samples, may represent two potential storm occurrences between the CRS model years 1920 and 1907, , and is subdivided into two sub-events. Within the uncertainty limits of the CRS model, the possible sources of these model events are the unnamed storms from 1926, 1915, 1906, 1903, and 1899 (Table 3.4). Storm Identification and Storm Frequency The storm model identifies thirteen events (and multiple sub-events) during which storm surge impacted Oyster Pond between the year 1900 and the present (Table 3.4; Figure 3.9), carrying marine/estuarine waters into the fresh to brackish lake. The result after each inundation was a period of enhanced productivity in the lake, and a marine/estuarine geochemical signature

in the lake sediments for a brief period. The events will be described from top to bottom (youngest to oldest). The uppermost peak, Event 1, spans the upper 7.5 centimeters, and is subdivided into sub-events 1a through 1g: (1a) a + 1.11 meter high tide event in November 2009; (1d) the + 2.22 meter surge event associated with Hurricane Dennis in July 2005 (the first accumulation peak from the CRS model in Figure 3.7); (1e) a + 1.36 meter high tide event in September 2004 and (1f) a + 1.15 meter high tide event in September 2002. It is likely that so many inundations to the pond within such a relatively short period of time, including Hurricane Dennis which impacted the majority of the Florida panhandle coast (FDEP, 2005), changed the geochemical conditions of the lake. In addition, the alteration of the island’s surficial drainage system during the past decade (Davis and Mokray, 2000) has also impacted input from the drainage system and therefore the pond’s geochemistry and the uppermost portion of the isotopic profiles. These changes could readily result in the a wide peak and the false positives shown in sub-events 1b, 1c, and 1g. The number of events (7) within the peak is the result of the autocorrelation function. This broad peak contains percent sand and δ13C values both above and below the mean, though the % N′ remains neutral (at or nearly zero) at depths where storm or surge events occur. Event 2 is a false positive, the possible origin of which is described above in section 5.1. Event 3 has been identified as Hurricane Earl (1998) which caused a + 1.57 meter storm surge at Apalachicola, FL. This event corresponds to the second accumulation peak in the CRS model (Figure 3.7), and has a percent sand and δ13C value above the mean, and a neutral % N′. Events 4 through 6 are false positives, unexplained events during which conditions in the lake briefly resembled storm events. Event 7 (Figure 3.9) is identified as an extreme tide event of + 1.26 meter in October, 1994, and has a neutral percent sand value, δ13C value above the mean, and neutral % N′ value. Event 8 is Hurricane Opal, a major storm in October 1995 that resulted in a + 1.74 meter storm surge, and which has a signature resulting in δ13C values and percent sand above the mean and a neutral % N′ value. Due to the analytical error associated with determining the Pb-210 ages through gamma counting, and shown in relation to the ages derived from the CRS model in Figure 3.5, some ages/depths could correspond with more than one storm/event. The 1994 elevated tide event and the 1995 Hurricane Opal event are essentially coincident, and are located

at approximately the same point on the CRS model timescale. However, because Hurricane Opal had a larger surge than the 1994 surge event, it is more likely to be the event associated with the sediment accumulation peak observed at 18.5 cm depth in the CRS model (Figure 3.7). Event 9 is Hurricanes Elena (9/1/85), with a + 1.26 meter surge, and also Hurricane Kate (11/21/85) with a + 1.99 meter surge. The sediment record shows percent sand and δ13C values above the mean, as well as a neutral % N′ associated with this event. These storms, which were coincident in the sedimentary record, correspond to the fourth accumulation peak in the CRS model (Figure 3.7). Event 10 is Hurricane Eloise (1975), which caused a +1.27 m storm surge, and had percent sand δ13C values above the mean and a neutral % N′. Event 11 is Hurricane Agnes (1972), causing a + 1.63 meter storm surge at the Apalachicola, FL, tide gauge, and corresponding to the fifth accumulation peak in the CRS model (Figure 3.7). The sediment record shows percent sand δ13C values above the mean, as well as a neutral % N′ associated with this event. Event 12 is a false positive, an event during which conditions in the lake briefly resembled those of a storm event, having percent sand δ13C values above the mean, and a % N′ above the mean. The model (Figure 3.9) identified one event in the historic record prior to the 1967 start of data collection at the Apalachicola tide gauge. Event 13 corresponds to a CRS model age range of 1907-1920, which is subdivided into two smaller events, 13a and 13b, as a result of the autocorrelation analysis. The sediment record shows percent sand below the mean, δ13C values above the mean, and a neutral % N′ associated with this event. During this time period, multiple strong storms are known to have made landfall or passed near Apalachicola, FL, as determined from storm and surge records (NOAA, 2012d; NOAA, 2012a; Needham, 2011). Possible storms that could have contributed to this extended peak are the following unnamed storms: 1926 (Category 3); 1915 (Category 1), which caused a + 2.13 meter surge in Carrabelle, FL, 50 km east of Oyster Pond, FL (Needham, 2011) or 1906 (Category 3), which caused a + 4.27 meter surge on Santa Rosa Island, approximately 200 km to the west, (Needham, 2011) for sub-event 13a. For sub-event 13b, the likely candidates are un-named storms in 1903 (Category 1), which caused a + 3.05 meter surge in Apalachicola, FL, and 1899 (Category 2), which caused + 1.22 meter surge in Carabelle, FL (Needham, 2011). Within the limits of uncertainty in the CRS model one or all of these events could correlate with Event 13 in the model.

Conclusions The results from this prototype study demonstrate that an accurate storm history can be obtained from coastal sediment cores, based on a combination of isotopic and sedimentologic data, and calibration with the known historic record of storms. The new technique, and the model that has been developed, can be employed to identify storms in the sedimentary record that predate the historic storm database. A thorough investigation of the sedimentologic and isotopic properties of coastal lake sediments, and the creation of a model that can predict storms from that data, has shown that no single proxy is sufficient for identifying storm signatures. However, a combination of isotopic and sedimentologic data is sufficiently sensitive to discern storm surge events that have impacted lake sediment cores. The new methodology is more objective, and potentially more sensitive, than the standard methods of counting overwash sand layers or identifying marine microfossils in the sediments. By using the standard method of counting sand layers through x-radiography, only six layers can be counted in the core that was analyzed for this study. Similarly, by counting peaks in the grain size profile, seven peaks can be discerned, most of which correspond to sand layers visible in the x-radiograph. The isotope model employed in the study identified ten events verified by tide gauge. Additionally, the model identified several known historic storm events dating further back into the sediment record. Lesser storm events, which carry only marine/estuarine waters into the lake, without sediment overwash, can be detected by their isotopic and sedimentologic signature. The model that has been developed appears capable of detecting storm events regardless of the presence of an overwash deposit. The methodology developed during this investigation is capable of generating a storm history for any region in which long-lived coastal lakes are present.

Table 3.1. Core depth and corresponding storm events associated with the five accumulation peaks as derived from the Pb-210 CRS model.

Table 3.2. The eleven high-water events used to calibrate the storm model, from the Apalachicola, Florida tide gage records (NOAA, 2012a). Storm dates are taken from the historic storm record (NOAA, 2012d). Event Date Surge Height (m) Surge 9/2009 1.11 Hurricane Dennis a 7/2005 2.22 Surge 9/2004 1.36 Surge 9/2002 1.15 Hurricane Earl a 9/1998 1.57 Hurricane Opal a 10/1995 1.74 Surge 10/1994 1.26 Hurricane Elena a 9/1985 1.27 Hurricane Kate a 11/1985 1.99 Hurricane Eloise 9/1975 1.27 Hurricane Agnes a 6/1972 1.63

a Denotes event identified as an accumulation peak in the CRS model of the Pb-210 data

Table 3.3. Summary of the statistical analysis resulting in the storm model. Coefficient Std. Error Z Value Pr (>|z|) Significance Intercept -27.6797 19.4078 -1.426 0.1538 δ13C -1.5110 0.8939 -1.690 0.0910 . %Sand -0.0959 0.0494 -1.943 0.0521 . %N′ -57.1515 41.8896 -1.364 0.1725 Significance Codes 0: *** 0.001: ** 0.01: * 0.05: . 0.1:

Table 3.4. The thirteen peaks identified by the storm model in Figure 3.9 and the corresponding storm/surge events. Depth in Year (CRS Date of Surge Event core (cm) Model) Event Correlation Event Category a (m)b Surge 2009 9/2009 n.d. 1.11 Hurricane Dennisc 7/2005 H4 2.22 1 0.0-7.5 2010-2001 Surge 2004 9/2004 n.d. 1.36 Surge 2002 9/2002 n.d. 1.15 2 8.25-9.0 2000-1999 False Positive n.d. n.d. n.d. 3 10.6 1998 Hurricane Earl 9/1998 H1 1.57 4 11.7 1997 False Positive n.d. n.d. n.d. 5 12.2 1996 False Positive n.d. n.d. n.d. 6 12.75-13.0 1996 False Positive n.d. n.d. n.d. 7 14.4-14.75 1995-1994 Surge 1994 10/1994 n.d. 1.26 8 17.6 1990 Hurricane Opalc 10/1995 H3 1.74 Hurricane Elena 9/1985 H3 1.27 9 22.0-22.75 1984-1983 Hurricane Kate 11/1985 H2 1.99 10 27.1 1974 Hurricane Eloise 9/1975 H3 1.27 11 29.1 1971 Hurricane Agnes 6/1972 H1 1.63 12 30 1970 False Positive n.d. n.d. n.d. Not Named 1926 9/1926 H3 n.d. Not Named 1915 9/1915 H1 n.d. 13 38.4-40.0 1920-1907 Not Named 1906 9/1906 H3 n.d. Not Named 1903 9/1903 H1 n.d. Not Named 1899 8/1899 H2 n.d.

a Category of the storm when passing within 140 km of Oyster Pond, St. Vincent Island, FL b Surge data collected from tide gauge at Apalachicola, FL c Storms that did not pass within 140 km of Oyster Pond, but which caused a known surge at the Apalachicola, FL, tide gauge

Figure 3.1. Tracks of historical hurricanes (Category 1 to 5) that have passed within 140 km of Oyster Pond, FL. Storm tracks in gray indicate the strongest storms (Cat 3 or above).

Figure 3.2. Location map of St. Vincent Island, Florida, on the northeastern Gulf of Mexico coast. St. Vincent Island is shown in the upper inset map as the westernmost barrier island in the chain of islands at the mouth at the Apalachicola River. The middle image is an aerial photo of St. Vincent Island. The lower aerial photo shows the location of Oyster Pond and the core sample collection location.

Figure 3.3. (Upper Panel) LIDAR imagery of the southern portion of St. Vincent Island, showing the area surrounding Oyster Pond. Elevations are shown in meters. (Lower Panel) Map of St. Vincent Island showing the 12 beach ridge sets that comprise the island. Oyster Pond is included within ridge set I, and bounded by younger ridge set J to the south. Sampling location is indicated by red circle. Island location is shown in Figure 3.2.

Figure 3.4. Pb-210 and Cs-137 profiles for core 092710-01 from Oyster Pond, FL. The horizontal error bars represent the error associated with the gamma spectrometric analysis of each sample.

Figure 3.5. Age-depth profile resulting from the CRS model of the Pb-210 data. Vertical bars represent sampling interval. Horizontal bars represent the age uncertainty, based on the analytical error for each sample.

Figure 3.6. (From left) x-radiograph, and profiles of percent sand, δ13C, and % N′ for core 092710-01. Data from other parameters (percent combustible, mean grain size of the coarse fraction, δ15N, and %C) are available in the supplementary materials). Depth in core is shown in cm on the vertical axis. “Storm” events typically have percent sand and δ13C values above the mean; % N′ values are typically near zero, or neutral. The solid vertical line represents the mean of the data set. The dashed vertical lines represent ± 1 standard deviation. The dotted vertical lines represent ± 2 standard deviations.

Figure 3.7. Sediment accumulation rate versus depth in core for core 092710-01. Accumulation rate was obtained from CRS model analysis of the Pb-210 data. Accumulation peaks are assumed to result from storm surge into the lake associated with the storm events shown. Dates for the accumulation peaks were derived from the CRS model.

Figure 3.8. Schematic showing the process by which the storm model was generated, trained, and calibrated. The result is a tool for assessing the long-term the geologic storm history in coastal lakes.

Figure 3.9. Results of the storm model applied to the Oyster Pond core data. The horizontal axis shows the probability of a storm event, where 0 represents no chance of a storm having occurred and 1 represents maximum probability of storm occurrence. The 0.50 probability cutoff is shown by the dashed line. The left vertical axis depicts depth in core, in cm, and the right vertical axis represents time, as derived from the CRS model of the Pb-210 geochronology. Thirteen events were identified, as shown in Table 3.4. Eight were temporally correlated with historic storms and five were false positives.

Figure 3.10. Autocorrelation function of estimated storm probabilities from the storm model. A lag corresponds to one sample. A series of lags were created by shifting the data down one sample per lag and cross-correlating the storm probabilities as a function of the lag (Box and Jenkins, 1976). The autocorrelation function values drop below the 95% confidence interval (dashed line) at lags of three and higher (at least out through lag 12), indicating that successive sediment samples are not independent.

CHAPTER 4 A PALEOSTORM RECORD FOR THE NORTHWEST FLORIDA COAST Introduction Paleotempestology, the study of long-term regional storm history from geological proxy evidence, offers a glimpse at tropical cyclone activity extending back into geologic time (Liu and Fearn, 1993; Liu and Fearn, 2000; Boose et al., 2001; Donnelly et al., 2001; Donnelly et al., 2004; Nott, 2004; Scileppi and Donnelly, 2007). It is clear that the historic record of tropical storms, extending about 150 years into the past, can be used to assess decadal and multi-decadal cycles (Landsea et al., 1999; Goldenberg et al., 2001; Klotzbach and Gray, 2008). However, the historic record is relatively brief and inadequate for evaluating low-frequency changes in climate. Therefore, there is a need for longer-term records in assessing century-scale variability and risk from storms by utilizing paleotempestology to study the longer-term storm record. Major storms have significant impacts on both natural and human environments at the coast. The potential effects include coastal retreat, coastal flooding due to storm surge, loss of low- lying coastal infrastructure, degradation of coastal wetlands, and saltwater incursion into coastal aquifers. Coastal lakes and wetlands are periodically subjected to storm surge and overwash processes during catastrophic hurricane strikes when coastal dune systems are overtopped or breached. The long-term geologic record found in coastal sediments can be used to develop a better understanding of the long-term risk of major storms and flooding to coastal regions. Many studies have quantified prehistoric storm occurrence by counting sand layers in coastal sediment cores and employing other standard methods of sediment analysis (Liu and Fearn, 1993; Liu and Fearn, 2000; Donnelly et al., 2001; Donnelly et al., 2004; Nott, 2004; Scileppi and Donnelly, 2007; Horton et al., 2008). In more recent studies, micropaleontology and stable isotope geochemistry have also been employed to identify inundation events in sediment core records (Hassan et al., 1997; Meyers, 1997; Lambert, 2003; Lucke et al., 2003; Lamb et al., 2005; Parker et al., 2006; Lambert et al., 2008; Page et al., 2009). A better understanding of the true storm risk for a given coastal region is essential for preparing for and mitigating the effects of potentially greater storminess in a warmer future.

The primary hypothesis driving this investigation was that the long-term geologic record can provide a longer and more reliable baseline against which more recent changes in climate and storm frequency (on a century scale) can be compared. A second hypothesis was that major flooding events leave a marine isotopic signature in coastal lake sediments, which may be discernible even in the absence of physical indicators of storm occurrence, such as sediment layers from overwash. The goal was to apply a statistical model which defined a storm “signature” discernible in coastal lake sediments in order to identify prehistoric storm events and determine long-term storm frequencies. Background Methods of Detecting Evidence of Major Flooding Event Occurrence Several techniques have been developed in recent decades for identifying the signatures of storms in coastal sedimentary records. The common tools of paleostorm analysis include: identification of storm-deposited overwash sand layers through grain-size analysis, micropaleontology (e.g., foraminifera, diatoms), and stable isotope analysis of δ13C and δ15N in organic-rich sediments. Each of these tools has its advantages and disadvantages. Sedimentologic Approach to Paleostorm Study Overwash sand layers preserved in the sediments of coastal lakes can act as a proxy record of catastrophic hurricane strikes during the late Holocene (Liu and Fearn, 1993; Liu and Fearn, 2000; Donnelly et al., 2001; Donnelly et al., 2004). The frequency, extent, thickness, and chronology of the sand layers may be used as a proxy for reconstructing the history of hurricane strikes, provided that the sedimentological “fingerprints” of an historic hurricane of known intensity and geomorphic impact are available to be used as a control for comparison (Liu and Fearn, 1993; Liu and Fearn, 2000; Liu et al., 2009). Thus, the assumption is the larger the overwash fan and thicker the resulting sand layer, the larger the storm. The expected outcome is that only the strongest storms are preserved in the geologic record (Liu and Fearn, 2000), and that the frequency of overwash sand layers in lake and wetland cores provides an estimate of the return period of major storms and flooding events. Micropaleontologic Approach to Paleostorm Study In addition to overwash sediments, marine diatoms and foraminifera may also be carried into coastal lakes or lagoons during storm events, and may be preserved even if overwash

sediments are not present. While diatoms are commonly well preserved in sediments, foraminifera can undergo dissolution in water with a low pH. Analysis of diatoms and foraminifera can identify changes in salinity (fresh, brackish, or marine). High salinity conditions in lakes can result from saltwater inundation (such as during a storm event), and are then preserved in the microfossil record (Valero-Garces et al., 1997). The most significant limitation on this method for determining prehistoric storm events is the potentially poor preservation of microfossils due to pH levels, erosion, and/or significant changes in the environmental setting. Geochemical Approach to Paleostorm Study Organic geochemical proxies (OGPs) are important indicators in paleoenvironmental studies, as they record environmental conditions at the time of deposition (Castenada and Schouten, 2011). Using stable isotopes to identify prehistoric storm events has been shown to correlate with sediment-based paleostorm analyses, but has the additional ability to detect storm events that did not leave an overwash layer of sand in coastal sediments (Lambert, 2003). Using the OGP method, organic-rich sediments are analyzed in order to create δ13C and δ15N core profiles. Marine environments are typically more enriched than terrestrial environments in the heavier 13C isotope, versus the lighter and more common 12C isotope. As a result, lacustrine plants and plankton typically have more negative δ13C values than their marine counterparts (Valero-Garces et al., 1997). In coastal freshwater or brackish environments, marine incursions can be detected in core profiles as shifts from more negative δ13C values, indicating terrestrial C3 vegetation, (O’Leary, 1988; Cerling et al., 1997) to more positive δ13C values, indicating a marine environment (Sackett, 1964; Shultz and Calder, 1976; Meyers, 1994; Thornton and McManus, 1994; Corbett et al., 2007; Lambert et al., 2008). Similarly, δ15N values are generally more positive in marine settings, as they are also more enriched in the “heavy” 15N isotope than in more terrestrial settings, as observed in coastal sediments dating from the Albemarle and Pamlico Sounds in North Carolina (Thornton and McManus, 1994; Middleburg and Nieuwenhuize, 1998; Corbett et al., 2007). The technique of using organic geochemical proxies for studying storms has advanced the field of paleotempestology, as it no longer requires an overwash sand layer to record the occurrence of a storm event. The technique employs δ13C and δ15N values in organic sediments in coastal lakes (Lambert, 2003). Significant shifts in δ13C and δ15N values in sediment core

63 profiles, followed by rapid returns to base values, are indicative of storm events, as ocean waters are more enriched in heavy isotopes, both 13C and 15N, than fresh or brackish lacustrine waters (Lambert et al., 2008). However some studies (Lambert, 2003; Lambert et al., 2008) have provided evidence of storm signals corresponding to a shift in δ13C and δ15N values both in the positive and negative directions. A negative shift from normal lake values might be expected in the case of a coastal storm event dominated by precipitation (excessive coastal flooding) rather than by wind (high storm surge). In recent years, this method has been employed in several investigations (Meyers, 1997; Lambert, 2003; Parker et al., 2006; Lambert et al., 2008; Page et al., 2009). Paleostorm Detection Model A model was developed for identifying the signature of inundation events in the sedimentary record of coastal lakes (Coor et al., submitted). The model employed a sediment core from a coastal pond with a high sedimentation rate, enabling individual storm events to be discerned in the sedimentary record. Pb-210 dating provided the chronology for identifying the horizons representing major flooding events of the past century. Storm magnitude and surge data were obtained from archival records. Stable isotope and sedimentologic analyses were carried out at high resolution throughout the core. A series of multiple regressions was completed on the core data set in order to determine which of the sedimentologic and isotopic data variables were statistically significant and most influenced the data. The parameters examined through regression were: δ13C, δ15N, % C, % N, δ13C ′, δ15N′, % C′, % N′, % combustible, % sand, mean grain size, and standard deviation of grain size. The primed (′) variables represent a data smoothing process, employing the depth derivative of the variable (i.e., % N to % N′). Results of multiple linear regression analysis showed that δ13C, percent sand, and percent nitrogen′ were the most statistically significant variables (Table 3.3), i.e., changes in these variables during a storm would be the most readily identifiable indicators of inundation events affecting the coast, and of these δ13C and percent sand were the most significant. These variables were then used as inputs to a generalized linear model, resulting in the logistic regression equation (Hilbe, 2009):

= −27.67971− 1.51101 − 0.09596 % − 57.15150 % ′

In the above equation, is a function that describes the log of the odds ratio of storm to non-storm events, as observed in the data. The results of the equation were then transformed, such that: = 1 + in order to generate a probability distribution from 0 to 1 of the logit expression (Hilbe, 2009). In the above equation, P(0) would indicate minimal probability of a storm having occurred and P(1) would indicate maximal probability of a storm. A storm event cutoff probability of 0.50 was chosen for the purposes of the model. This value maximized the number of events identified by the model, and minimized the number of false positives, based on the known history of storms. Historic Storm Record Since 1851, 32 hurricanes (Category 1-5 on the Saffir-Simpson scale) have passed within 140 km of the study area, Western Lake, FL (Figure 4.1). The 140-km radius is a close approximation of the radius to hurricane-force winds for a major storm (categories 3-5), as determined by Keim et al. (2007). Of the 32 category 1-5 storms passing within 140 km of Western Lake during historic time, 14 were major storms (category 3-5) at some point during their track. Ten of these storms were major storms at their closest approach to Western Lake. Therefore, the return period over historic time for major storms approaching Western Lake is approximately 16 years. Table 4.1 provides details on these storms.

Regional Setting Sea-Level Rise The average rate of global sea-level rise during the twentieth century, averaged from tide gauge measurements since the mid-1800s, has been approximately + 1.7 mm/yr (Miller and Douglas, 2004; IPCC, 2007). Long-term tide gauge records from the northeastern Gulf of Mexico generally reflect the global trend. Pensacola, Florida, approximately 105 km west of the study area, has recorded an average 2.10 ± 0.26 mm/yr sea-level rise over the period 1923-2006. The Cedar Key, Florida, tide gauge, approximately 325 km southeast of the study area, has recorded an average 1.80 ± 0.19 mm/yr sea-level rise over the period 1914-2006 (NOAA, 2012a).

Rates of global sea level increase since 1993, as measured by satellite altimetry, have been shown to average 2.7 mm/yr, indicating that the rate of rise during the past two decades has been significantly higher than during the preceding century and a half (NOAA, 2012b). In the Gulf of Mexico, the satellite data show an average rate of rise of 2.2 mm/yr from 1993 to present (NOAA, 2012b) It has been well documented that changes in sea level over the near- to moderate term (i.e., time scales of decades to centuries) are both connected with and sensitive to changes in climate (IPCC, 2007; Grinsted et al., 2009). The climate of the Holocene (10,000 yr BP to present) has been relatively stable compared to that of the most recent ice age (Alley et al., 1997). Sea level has been rising steadily, with few exceptions, since the last glacial maximum, approximately 20,000 years before present when sea level was approximately 130 meters lower than today (Fairbanks, 1989; Bard et al., 1990; Clark et al., 2009). Sea-level rise began to stabilize approximately 6,000 to 8,000 years ago (Donoghue and White, 1995; Blum et al., 2001; Smith et al., 2011). By 7800 yr BP the Laurentide ice sheet had nearly completely disappeared (Dyke and Prest, 1987), and a period of much slower sea-level rise began in the world’s oceans (Smith et al., 2011). It was during the time of slower sea-level rise after about 8000 yr BP that the modern shoreline and barrier island systems began to form, including those of the northern Gulf of Mexico (Tanner, 1988; Stapor, et al., 1991; Donoghue and White, 1995; Taylor and Stone, 1996; Blum et al., 2001; Donoghue, 2011). Study Area The study area is on the northwest Florida shoreline (Figure 4.2). Sea-level change has strongly influenced the geological history of the area. The region has alternately been either flooded by marine incursions or exposed as dry land and sandy coastlines throughout much of its geologic history (Randazzo and Jones, 1997). The northeastern Gulf of Mexico region is tectonically stable, overlying a carbonate platform formed during late Mesozoic and early Cenozoic marine highstands. This region is also underlain by a subsurface structural feature, the Apalachicola Embayment, a northeast-trending complex graben system that extends from the Florida panhandle to eastern Georgia (Randazzo and Jones, 1997). The near-surface sediments in this region, as described by Scott et al. (2001), are primarily marine deposits from late Cenozoic interglacial periods, overlain by Quaternary undifferentiated sediments, beach and dune sediments, and alluvium.

This study focused on the western Florida panhandle coast, a representative segment of the northern Gulf of Mexico coast. The Quaternary deposits of the region are primarily undifferentiated sediments overlain by alluvium, dunes, and beach ridges. This regional coastal morphology consists of a Pleistocene core of coastal and nearshore sediments, overlain by Holocene sediments comprising the modern dunes and beach (Otvos, 1982). Surficial deposits of this region of Florida are composed primarily of late Quaternary aeolian deposits (Otvos, 2004). The regional beach sediments in this region are nearly pure quartz, with very little organic content (Randazzo and Jones, 1997; Davis, 1997). Dune lakes are common on the northwest Florida coast. Coastal dune lakes are typically shallow, non-tidal, irregularly shaped basins located directly behind beach foredunes (CBA, 2012; QWP, 2012). The lakes are permanent, and most are moderate in size. There are seventeen named coastal dune lakes in the study region, most of which are shown in Figure 4.2. Other locales where such lakes are found are the U.S. Pacific Northwest coast, Australia, New Zealand and Madagascar (CBA, 2012). Sand dunes, ranging in height from 1 to 10 meters, separate the northwest Florida coastal lakes from the Gulf of Mexico (Stone and Penland, 1992). When the dunes are breached and the lakes open to the Gulf of Mexico, a temporary brackish transition zone is created between the ocean and the uplands, in which the saltwater and freshwater mix (Florida Lakewatch, 2008). The coastal dune lake selected for the project was Western Lake (Figure 4.2). Western Lake is located in Grayton Beach State Park, a relatively undeveloped area, and has a maximum depth of approximately 3.75 meters (12 ft). Water quality data has been collected regularly in Western Lake since 2001. The salinity in Western Lake ranges from near fresh (approximately 1 ppt) to nearly 25 ppt after storm events, with a mean value (2002 to present) of approximately 7 ppt. Additionally, the lake receives, on average, approximately 125 cm of precipitation per year (CBA, 2012). Materials and Methods Field Sampling Several sediment cores were collected from Western Lake (Figure 4.2) for the purpose of assessing the paleostorm record in the lake sediments. Hand-held piston push cores were used so that the lake sediment was not disturbed or homogenized. The core tubes were made of Lexan, a polycarbonate resin thermoplastic, with a 7.62 cm (3 inch) inner diameter. A surficial sediment

67 sample was collected for radiocarbon dating to determine a reservoir age for the lake. Sampling coordinates were determined using a hand-held Garmin GPS. Remote Sensing and Surveying Topographic data, based on LIDAR (LIight Detection And Ranging), was used to determine dune elevations separating Western lake from the Gulf of Mexico. Recent LIDAR data was accessed from the NOAA Coastal Services Center Digital Coast (NOAA, 2012c). The minimum dune height separating Western Lake from the Gulf of Mexico represents the minimum storm surge height required to breach the dunes and inundate the coastal lake. Core Preparation Prior to sediment analysis, cores were split and opened. One half of the core was used for analysis, and the archive half was stored at 2°C for preservation. Cores were imaged with a digital line scan camera. X-radiographs were obtained for the half cores using a Torrex 120D digital x-radiograph set at 2 mA and 75 kV. Following the scanning, samples of organic sediment for radiocarbon analysis were collected adjacent to sand layers, based on a review of the x-radiographs. Sediment Analysis The core that was selected for intensive sampling was sampled for sedimentologic analysis at 0.5 cm intervals throughout the core. Samples were placed in pre-weighed plastic dishes, weighed immediately after sampling, then placed overnight in a drying oven at 65°C, and weighed again after cooling to determine percent moisture. After drying, approximately 0.5 g of sample was taken from each sample and placed in a pre-weighed aluminum dish, weighed, and placed in a furnace for 2 hours at 550°C. Samples were cooled overnight and weighed again to determine percent combustible, a standard method of bulk organic analysis. The fraction not used for combustion analysis was weighed, wet-sieved, dried, and weighed again to determine percent sand and percent fines. The percent fine fraction was discarded, and grain size analysis was completed on the sand fraction using an automated settling tube and the GRANPLOT program (Balsillie et al., 2002). GRANPLOT calculates settling tube size fractions in quarter- phi intervals based on settling times and velocities and application of the Gibbs equation. The program produces standard grain-size statistics (mean grain size, standard deviation, skewness, kurtosis, and dispersion) along with frequency histograms and cumulative plots.

Micropaleontology The coastal dune lakes historically are known to be fresh to brackish. When remains of marine organisms, such as shells of marine foraminifera, are found at depth in the lake sediments, it can be inferred that they entered the lakes during a period of marine inundation. This would typically occur as a result of storm surge associated with tropical storms. Sediment samples were collected from the upper and lower 5 cm of each core, in addition to each visible sand layer, for identification of foraminifera that might provide information on the occurrence of brackish or marine conditions in the lake. Geochemical Analysis The core that was selected for geochemical analysis was sampled at approximately 3 mm intervals (n=439 samples) using thin, hollow plastic tubes similar to the method described by Lavoie (1996) and Lambert (2003). Samples were freeze-dried and ground with a mortar and pestle for homogeneity. Stable isotope analyses were carried out at the National High Magnetic Field Laboratory at Florida State University, on a Finnigan MAT delta PLUS XP stable isotope ratio mass spectrometer (IRMS) connected to a Carlo Erba Elemental Analyzer (EA) through a Conflo III interface. Sample analysis was initiated by weighing a given amount of sample (depending on %C and %N) into a silver cup, after which the sediment was moistened with deionized water and exposed to concentrated HCl vapor for 24 hours to remove carbonate material. Samples were then dried overnight in an oven at 70°C. After drying, samples were wrapped in a tin cup for isotope analysis. Two sets of five different standards including YWOMST-1 (cane sugar), YWOMST-2 (phenylalanine), YWOMST-3 (L-phenylalanine), YWOMST-5 (urea), and urea-2 were also weighed and wrapped in tin cups for isotope analysis. Samples were then loaded into the auto sampler of the EA connected to the IRMS for isotopic measurements. Each batch of samples was loaded into an autosampler, which held a total of 50 samples, including standards and blanks. Typically 10 samples in each batch were standards. Results were reported in the standard delta (δ) notation in per mil (‰) relative to the international VPDB standard for δ13C, and air for δ15N (Sharp, 2007). The precision of C and N isotope analyses was ± 0.2 ‰ or better based on repeated analyses of the lab standards. Geochronologic Analysis Radiocarbon dating was utilized to establish a chronology in the core. Radiocarbon samples were collected at selected depths throughout the core, particularly near any visible sand

69 layer. During sample collection, approximately 1 cm3 of sediment was placed in a pre-weighed dish, weighed, dried in an oven overnight at 65°C, weighed after cooling, ground into a powder for homogeneity, and placed in a pre-weighed and labeled glass vial. Vials were sent to The National Ocean Sciences Accelerator Mass Spectrometry Facility (NOSAMS) at Woods Hole Oceanographic Institute for radiocarbon analysis. Determining Storm Surge Heights Using SLOSH This investigation employed the SLOSH (Sea, Lake, and Overland Surges from Hurricanes) model to estimate surge heights from known storms of the historic period. SLOSH, developed by the National Weather Service, is one of the most commonly used tools for estimating storm surge and wind resulting from historical or predicted storms (Jelesnianki et al., 1992). Comparisons of SLOSH model calculations with observed storm surge heights have shown that 79% of the predictions are within 1 standard deviation of the mean error, 97% are within two standard devations, and 99% are within three standard deviations (Jarvinen and Lawrence, 1985), though SLOSH is known to commonly underestimate observed storm surge heights (Houston et al., 1999). SLOSH’s design allows for storm surge computations to be made with incomplete knowledge of the storm’s structure and intensity (Houston et al., 1999), which makes it ideal for forecasting, as well as for back-calculating storm surges, of storms early in the historic and prehistoric record. Direct measurements of storm surge heights were not available for most of the storms which impacted the study area during historic time. As a result, surge heights were calculated using the SLOSH model. The model’s input parameters include the storm’s track, the radius to maximum winds (RMW), and the pressure deficit (P), or the deviation from one standard atmosphere noted every 6 hours during the track of the storm (Table 4.1) (Houston, et al., 1999; Lane et al., 2011, NOAA-NHC, 2012). Results Lake Data The coastal dune lakes of Northwest Florida are typically closed to marine influence, and normally experience low salinity conditions. They are opened to the sea through small, natural inlets following the impact of strong storms. As a result, the lake water can range from fresh to highly brackish, resulting in complex and diverse ecosystems during the transition periods. Long-term monitoring of the water quality shows that the lake conditions can vary between

70 mesotrophic and oligotrophic nutrient conditions (Florida Lakewatch 2008; CBA, 2012). Total phosphorus levels over the past 15 years have ranged from 3 µg/L to 31 µg/L, with a mean value of 7.2 µg/L. Total nitrogen has ranged from 80 µg/L to 600 µg/L, with a mean value of 266 µg/L. Dissolved oxygen has ranged from 0.2 mg/L to 17.3 mg/L, with a mean value of 5.4 mg/L. pH levels ranged up to to 8.4, with a mean pH of 7.2. The salinity in Western Lake has varied from fresh (1.0 ppt) to nearly marine (24.8 ppt), with a mean value of 11.1 ppt, and has been largely dependent on the amount of saltwater inundation experienced during storm events and precipitation received by the lake. Remote Sensing Western Lake is approximately 0.2 km north of the Gulf of Mexico shoreline, located just landward of the foredunes. The lake surface has an elevation of approximately 0.75 m (Figure 4.3). Western Lake is divided into two parts, arranged east to west. The east side includes a natural slough, which opens up to the Gulf of Mexico when either lake levels become too high due to periods of intense rainfall, or hurricanes and related surge inundate the lake. The eastern portion of the lake, which is the most exposed to storm surge, has two possible areas which are subject to dune breach and inundation by storm surge: one to the south at approximately 2.3 m and one to the southeast at approximately 3 meters. The storm surge associated with Hurricane Dennis, in 2005, caused a minor breach in the dunes south of the eastern portion of Western Lake, creating a small overwash fan (CBA, 2012). Core Description The sediment core selected for intensive paleostorm analyses, core 070910-03, was collected in approximately 3 meters water depth from Western Lake, FL. Core location is shown in Figure 4.2. An x-radiograph of the core is shown in Figure 4.4. The original length of the core was 131.4 cm. The upper 10 cm consisted of fluid mud. The muddy texture was dominant throughout the core. Horizontal laminations were visible throughout the core, indicating minimal bioturbation. A large shell was present from 17 to 19.5 cm. Shell hash was present from 30 to 31 cm, as well as at 58 cm and 79 cm. A sand layer was visible from 86 to 88 cm with some shell hash, and a smaller sand layer at 89.5 cm. Shell hash was again visible mixed with mud from 93 to 96 cm, 105 to 110 cm, 127 cm and 130 cm. Additional thin sand layers were visible at 110 cm and 126 cm. The sand laminae are exceptionally clear in the core x- radiograph, providing good evidence that there was minimal bioturbation in the core.

Sedimentologic and Isotopic Data The primary isotopic and sedimentologic variables of interest are those which compose the storm identification model (Figure 4.3): percent sand, %N′, and δ13C. The values for percent sand ranged from 7.3 % to 69.5 %, with a mean value of 37.1 %. There are 24 peaks above the mean value, 19 of which exceed + 1 standard deviation, and 5 of which exceed + 2 standard deviations. The δ13C values ranged from -26.2‰ to -23.1‰, with a mean value of - 24.4‰. There were significant shifts both in the positive and negative directions. The %N′, the first derivative of %N, ranged from - 0.2% to 0.2%, with a mean value of 0.0%. The mean values for Western Lake were normalized to the mean values of Oyster Pond for application of the model. In addition to the 19 significant peaks identified in the percent sand profile, the x- radiograph (Figure 4.4) showed 14 sand laminae, representing potential storm events. Two relatively thick sand layers can be discerned in the core, in addition to approximately twelve thinner laminae. The majority of the peaks in the percent sand profile correspond to sand layers visible in the x-radiograph. Micropaleontologic Data Samples were collected from 14 sand layers within core 070910-03: 7-9 cm, 35-36 cm, 48-49 cm, 55-56 cm, 69 cm, 74 cm, 79 cm, 81 cm, 84 cm, 86 cm, 89 cm, 96 cm, 117-118 cm, and 128-129 cm. Once collected, the samples were dried and subjected to microscopic analysis for the presence of foraminifera. The majority of the sand samples were shown to be barren. However, the sample collected at 79 cm had 12 Ammonia sp. individuals and various small molluscan fragments that could not be identified. Other samples also had molluscan fragments, some of which appeared to be the bivalve Rangia sp. Rangia cuneata, the most common Rangia species in the region, is native to the coastal waters of the northern Gulf of Mexico (LaSelle and de la Cruz, 1985). Rangia cuneata inhabits low salinity estuarine habitats, making it an indicator of brackish or estuarine environments, (Parker, 1966). It is most commonly found in areas with salinities from 5-15 ppt (Swingle and Bland, 1974). Similarly, Ammonia parkinsoniana, a common form of Ammonia sp. found in the northern Gulf of Mexico, represents a primarily brackish to estuarine environment, typical of the mid-region and mouth of an estuary (Puckett, 1992). Although none of the samples appear to be strictly marine, they do all appear to represent bay/estuarine conditions, suggesting that water

from the Gulf of Mexico has entered Western Lake, most likely during inundation during storm events. Geochronology A skim sample was collected from the sediment/water interface to determine the reservoir age of the lake. Additional samples were collected from 9 cm, 36 cm, 75 cm, 88 cm, and 127 cm depths in the core. The radiocarbon dates were converted using the CALIB 6.0 calibration scheme (Stuvier et al., 2012). The raw radiocarbon date for the surface sample, 230 +/- 15 yr BP, was taken as the reservoir age, which was used in calibrating the samples. The raw and calibrated dates are shown in Table 4.2. A time-depth curve is shown in Figure 4.5. Storm surge heights from SLOSH The NOAA/NWS SLOSH model was used to estimate storm surge elevation for historic storms. SLOSH was run using the regional historic hurricane tracks and other pertinent data from the HURDAT database, as shown in Table 4.1, in order to estimate the surge values in the vicinity of Western Lake for storms of known magnitude (NOAA-NHC, 2012). SLOSH input parameters for the fourteen landfalling storms in this study are listed in Table 4.1. The results are also shown in Table 4.1. Hurricane Opal (1995), a category 3 storm when passing closest to Western Lake, had the largest modeled surge of 2.7 meters. The calculated surge associated with category 3 storms ranged from 0 to 2.7 meters. Discussion Model Application A storm event, as identified by the storm model, typically exhibits percent sand and δ13C values above the mean, and a neutral %N′ value. Other studies have also shown storm signals corresponding to δ13C values significantly above the mean, indicating inundation with marine water, enriched in the heavier isotope. Storm signals are also occasionally represented by negative excursions in δ13C values, which are interpreted as inundation events caused by heavy rainfall and coastal flooding, rather than marine surge (Lambert, 2003; Lambert et al., 2008; Das, et al., submitted). In the Western Lake core, the result after each inundation event resulted in a period of enhanced productivity in the lake, and a marine/estuarine geochemical signature in the lake sediments for a brief period. The paleostorm model described above was applied to the Western

Lake data. The model resulted in 53 peaks above the cutoff probability during the last five millennia (Figure 4.6). Storm identification and storm frequency The storm model identified 53 separate segments in the core record, each representing an anomalous geochemical environment in Western Lake over the last ~5000 years. This is taken to represent episodes when storm surge had inundated the relatively freshwater lake, altering the lake’s chemistry for some period of time. It should be noted that some of these segments are broad, indicating an extended period during which the lake chemistry remained out of equilibrium. On average, each 3-mm sample collected for isotopic analysis represented approximately 11 years of lake history (Figure 4.4). The autocorrelation analysis, as described above, showed each successive sediment sample to be statistically independent. Among the 439 original samples, 229 lie under the 53 anomalous segments. It is reasonable to conclude, therefore, that the model has identified a minimum of 229 storm events over the approximately 5000-year lake history represented by the core. Paleostorm return periods and cycles The return period, and therefore the number of storm events per century, was determined for the 53 storm-influenced segments identified from the storm model (Coor, et al., submitted), as shown in Figure 4.6. Samples which exceeded the 50% cutoff interval, resulting in one of the 53 storm-influenced segments, was assigned a value of 1, given that the model predicts a high probability that at least 1 storm occurred during the approximately 11 years represented by the sampling resolution. Samples which did not exceed the 50% cutoff interval were assigned a value of 0, given that the model predicts a low probability of a storm having occurred during the period represented by the sample. The resulting minimum number of storms was summed and normalized per century. Of the 53 segments that did exceed the cutoff frequency, four distinct periods are visible, demonstrating cyclicity in relative storminess over time. The first active period observed, from approximately 4800 to 4400 cal yr BP, exhibiting a mean of 6.6 storms per century, ranging from 4 to 10 storms per century. The second period, lasting from approximately 4100 to 3200 cal yr BP, averaged 5.8 storms per century, ranging from 4 to 8 storms per century. The third period, from approximately 2500 to 1800 cal yr BP, shows a slightly shorter return period with mean of

7.6 storms per century, ranging from 4 to 9 storms per century. The fourth period, ranging from approximately 1300 cal yr BP to the present, exhibits a mean of 7.4 storms per century, ranging from 3 to 10 storms per century. The overall mean for active storm periods is 6.9 storm events per century, while the overall mean over the 5 millennia is 5.0 storm events per century. Three inactive periods of several centuries duration separate the four periods of higher storm activity. The model results can be viewed in the larger context of the northern Gulf of Mexico during the latter half of the Holocene. Hodell et al. (1991) noted that orbital changes have resulted in solar insolation for 10 degrees north latitude reaching a modern peak at about 7 ka and decreasing up to the present. Poore et al. (2003) analyzed high-resolution faunal and isotope data for two cores from the northern and western Gulf of Mexico. They concluded that, based on proxy evidence, sea surface temperature (SST) in the Gulf has decreased since the mid-Holocene. This would result in a southward movement of the Intertropical Convergence Zone (ITCZ), a decrease in the influence of easterly winds, and less incursion of warm surface waters into the Gulf of Mexico (Poore et al., 2003). Poore et al. (2003) also found evidence of century-scale variability in climate proxy date for the Gulf of Mexico, especially for the period from 7 to 2.5 yr BP. The modeled data also exhibit century-scale variability in storminess. The model results (Figure 4.7) reflect stability in the level of storminess over the past 5000 years, which is reflected in the similarity between the historic mean number of storms per century, 6.2, and the long-term mean of storms per century during the “active” periods, 6.9, both of which are significantly higher than the overall long-term mean of 5.0 storms per century. The results, therefore, appear to be consistent with Wallace and Anderson’s (2010) finding of relative stability of climate in the Gulf of Mexico during the late Holocene. Reconstruction of Storm Surge The results from the SLOSH model, shown in Table 4.1, show that Hurricane Opal (1995) had the largest surge of 2.7 m. While Opal was a category 4 storm at its most intense, it was a category 3 storm when it passed closest to Western Lake. None of the lesser storms produced enough surge to overtop the dunes that front the lake. The LIDAR topographic data show that the lowest point on the barrier dunes seaward of the lake is 2.3 meters (Figure 4.3). The historic storm data, indicate that a major (cat. 3 or greater) storm would be necessary to breach the dunes and inundate the lake. The paleostorm model results, therefore, may be taken to represent the impact of major storms on the lake.

Comparison with Other Studies Several recent paleostorm studies have shown that the frequency and magnitude of storms are highly variable over time and space. Moreover, it has been observed that major storm histories exhibit a cyclicity of several decades to centuries, which may be correlated to climate cycles (Liu and Fearn 1993; Liu and Fearn, 2000; Donnelly et al., 2001a, b; Scott et al., 2003; Donnelly et al., 2004; Donnelly and Woodruff, 2007; Scileppi and Donnelly, 2007; Woodruff et al., 2008; Mann et al., 2009; Page et al., 2009; Wallace and Anderson, 2010; Lane et al., 2011). Figure 4.8 compares several of these investigations with the current results. The northeastern Gulf of Mexico coast, specifically Western Lake, was found by Liu and Fearn (1993) to have experienced intense periods of storminess from 3500 to 940 cal yr BP. (All dates have been converted to cal yr BP, using CALIB 6.0.). Lane et al. (2011) reported seven periods of storm activity during the past 4000 years at Mullet Pond, FL, 180 km to the east of Western Lake. Lake Shelby, in coastal Alabama, 145 km to the west of Western Lake, was found to have had an increase in hurricane activity from 3500 to 700 cal yr BP (Liu and Fearn, 2000). Laguna Madre, located approximately 1200 km to the west of Western Lake on the Gulf Coast of Texas, showed a period of increased storminess from 5300 to 900 cal yr BP (Wallace and Anderson, 2010). Donnelly and Woodruff (2007) found that intervals of more intense hurricane activity occurred in the Caribbean between 4400 to 3600 cal yr BP, 2500 to 1000 cal yr BP, and 250 cal yr BP to present, corresponding to periods of fewer El Nino events and increased precipitation in Africa. A compilation study by Mann et al. (2009) showed that the U.S. Northeast coast had a nearly continuous active period of storminess from 1950 to 450 cal yr BP. The U.S. central eastern coast exhibited an active period of storminess from 950 to 650 cal yr BP, with considerably less activity before or after (Mann, et al., 2009). The current investigation’s paleostorm history includes four periods of active storminess. The results are in good agreement with the other study from the northeastern Gulf coast which was similar in scope, as reported by Lane et al. (2011) (Figure 4.8). Conclusions The results of this study demonstrate that a reliable storm history can be extracted from coastal sediment cores, based on a combination of isotopic and sedimentologic data, and using a statistical model which has been calibrated with the historic storm record. This new paleostorm identification method, and the model that has been developed, can be employed to identify

storms in the sedimentary record that predate the historic storm database. A thorough investigation of the sedimentologic and isotopic properties of coastal lake sediments has shown that no single proxy is sufficient for identifying storm signatures. However, a combination of isotopic and sedimentologic data is sufficiently sensitive to detect major flooding events that have impacted coastal lake sediments. This new methodology is more objective, and more robust, than the standard methods of counting overwash sand layers or identifying marine microfossils in the sediments. By using the standard method of counting sand layers through x-radiography, 14 sand laminae can be discerned in the core that was analyzed for this study. Similarly, by counting peaks in the percent sand profile, twenty-four peaks can be discerned, most of which correspond to sand layers visible in the x-radiograph (Figure 4.3). The isotope model employed in this study identified 53 anomalous segments in the core, representing a minimum of 229 separate storm events spread through four periods of increased storminess during the nearly 5000-year lake history represented by the core. The paleostorm record extracted from the sediments of Western Lake provides several significant findings. First, the northern Gulf of Mexico has experienced millennia-long cycles of increased storm activity, separated by visual observation by quiescent periods of shorter duration. Second, the level of major storm and flooding event activity during the historic period of the past 150 years (6.2 storms per century) is significantly greater than the long-term mean storm frequency over the past five millennia (5.0 storms per century). Third, major storm frequency during the historic period is a continuation of a trend of increased storminess that began about 1300 years ago. Fourth, the current “active” period is the longest in the observed record. Fifth, the mean frequency of major storms during the historic period is not notably different from the mean level of storm frequency during the “active” periods” of the past five millennia (6.9 storms per century). Sixth, there is little evidence of an increasing or decreasing trend in major storm frequency in the northern Gulf of Mexico during “active” periods of the past five millennia. This study has demonstrated that storm events which carry only marine storm surge into a coastal lake, without sediment overwash, can be detected through their isotopic and sedimentologic signature. The method is robust and the results are comparable to those of other studies using different storm proxies. The methodology employed during this investigation is

77 capable of generating a storm history for any similar coastal region, and represents a quantitative basis for modeling storm risk in a warming future.

Table 4.1. Major storms passing near Western Lake, FL, during historic time, with SLOSH storm surge model inputs and results. c d Storm Landfall Storm ΔP Rmw Surge Name Year Location Categorya Categoryb (mb) (km) Height (m) No Name 1851 Panama City, FL 3 3 57 40 0.3 No Name 1856 Panama City, FL 3 3 57 40 0.3 No Name 1877 Mexico Beach, FL 3 3 57 40 0.0 No Name 1882 Ft. Walton Beach, FL 3 3 57 40 1.6 No Name 1894 Panama City, FL 3 3 62 40 0.4 No Name 1917 Ft. Walton Beach, FL 3 4 72 61 1.6 No Name 1926 Dauphin Island, AL 2 4 67 35 1.0 No Name 1929 Indian Pass, FL 1 4 57 56 0.3 Florence 1953 Grayton Beach, FL 1 3 72 30 0.7 Eloise 1975 Dune Allen Beach, FL 3 3 61 29 1.3 Elena 1985 Gulfport, MS 3 3 60 27 0.4 Kate 1985 Mexico Beach, FL 2 3 59 30 0.0 Opal 1995 Pensacola, FL 3 4 94 80 2.7 Dennis 2005 Pensacola, FL 3 4 83 13 1.9

a Indicates Saffir-Simpson category of storm at closest approach to Western Lake, FL (NOAA, 2012d) b Indicates Saffir-Simpson category of storm at maximum strength (NOAA, 2012d) c Maximum change in pressure, as determined from HURDAT (NOAA-NHC, 2012) d Radius to maximum winds (NOAA-NHC, 2012)

Table 4.2. Radiocarbon data Lab Depth in Radiocarbon Date Number core (cm) (yr BP) (cal yr BP) OS-83029 0 230 ± 15 52 ± 20 OS-83030 9.5 1020 ± 20 764 ± 24 OS-83031 36 2120 ± 20 1920 ± 28 OS-83032 75 3270 ± 25 3359 ± 25 OS-83033 88 3410 ± 25 3471 ± 20 OS-83034 127 3630 ± 25 3750 ± 29

Figure 4.1. Tracks of historic storms to pass within 140 km of Western Lake, FL, 1851-present. Weaker storms are shown in black (cat 1 and 2) and major storms (cat. 3 or greater) are shown in gray.

Figure 4.2. Location map of Western Lake, on the NW Florida coast. The upper figure shows all of the coastal dune lakes of NW Florida. The lower figures show an image and a bathymetric map (contours in feet) of Western Lake. Inset map from Choctawhatchee Basin Alliance. Red circle indicates core location. Image source: Google Earth.

Explanation Western Lake LIDAR (meters) 0 - 0.50 ³ 0.50 - 1.0 1.0 - 1.5 1.5 - 2.0 2.0 - 2.5 2.5 - 3.0 3.0 - 3.5 3.5 - 4.0 4.0 - 4.5 4.5 - 5.0

0250 500 1,000 Meters

Figure 4.3. LIDAR imagery of the Western Lake area, NW Florida. Location is shown in Figure 4.2. Elevations are in meters.

Percent Sand δ13C (‰) % N Slope 0 25 50 75 -26.5-25.5 -24.5-23.5 -0.1 0.0 0.1 0 0

500

1000 30

40 1500

2000

60 Depth (cm) Depth Age BP)Age (cal yr

70 2500

3000

3500 100

110 4000

120

4500

130 4675 Figure 4.4. (From left) x-radiograph, and profiles of percent sand, δ13C, and % N’ for core 070910-03. Depth in core is shown in cm on the left vertical axis. Age, based on radiocarbon time-depth model, is shown on the right vertical axis. “Storm” events typically have percent sand and δ13C values above the mean. The solid vertical lines represent the mean of each data set. Dashed vertical lines represent ± 1 standard deviation. Dashed vertical lines represent ± 2 standard deviation. Core location is shown in Figure 4.2.

Depth (cm) 0 50 100 0 y = 35.937x R² = 0.8537

500

1000

1500

2000

2500 Age (cal yr BP) yr (cal Age

3000

3500

4000

4500 Figure 4.5. The time-depth curve derived from the radiocarbon dates from Core 070910-03. The vertical error bars represent the one-sigma range of the calibrated radiocarbon dates.

Storm Probability

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0 0

500 3

20 5 6

8 1000 9 30 10

11 12 13 14

15 40 16 1500

50 18

20 2000

22 Age (cal yr (calBP) Age yr Depth (cm) Depth 23 70 2500

24 25

3000

26 27 90 28 29 30 31 32 33 34 35 36 3500 100 37 38 39 40 41 42 43 44 110 45 46 4000 47 48 49

120 50

52 4500 53 130 Figure 4.6. Results of the storm model applied to the Western Lake core data. The horizontal axis shows the probability of a storm event, where 0 represents the minimum chance of a storm having occurred and 1 represents maximum probability of storm occurrence. The 0.50 probability cutoff is shown by the dashed line. The left vertical axis depicts depth in core, in cm, and the right vertical axis represents time, as derived from the radiocarbon time-depth model. Horizontal arrows indicate the fifty-three storm activity episodes identified by the model.

Figure 4.7. Major storm and flooding event history for the northwest Florida coast, based on the paleostorm model. The minimum value for storm occurrence per century is shown for the past five millennia. Four periods of increased activity are separated by shorter periods of low activity. An active period is defined as one averaging 4 or more major storms per century. The mean of the active periods is indicated by the solid black line. The historic mean for the past ~150 years is shown by the dotted line. The overall mean over the last five millennia is indicated by the heavy dashed line.

Western Lake, FL (Current Study)

Western Lake, FL (Liu and Fearn, 1993)

Mullet Pond, FL (Lane et al., 2011)

Lake Shelby, AL (Liu and Fearn, 2000) LagunaMadre, TX (Wallace and Anderson, 2010) Puerto Rico (Donnelly and Woodruff, 2007) U.S. Southeast (Mann et al., 2009)

U.S. Northeast (Mann et al., 2009)

0 500 1000 1500 2000 2500 3000 3500 4000 4500 Age (cal yr BP)

Figure 4.8. Summary of long-term storm activity on the U.S. Gulf and Atlantic coasts. Periods of greater-than normal storm activity are indicated for each location by horizontal bars.

CHAPTER 5 CONCLUSIONS This dissertation was designed to serve as a tool for future researchers and decision makers in a warmer and potentially stormier world. This dissertation has helped to describe the origin, hydrology, sedimentology, and water chemistry of a set of coastal dune lakes, found only in 5 other places worldwide. Analysis of sediments from several lakes in northwest Florida has produced a new method for identifying paleostorm events in the sediment record, and a long- term storm and climate record for the northeastern Gulf of Mexico. These findings will be critical in better understanding climate and storm trends within the Holocene and making predictions for the future. The following summarizes the major findings per chapter of this research. Chapter 2: Coastal Dune Lakes The coastal dune lakes are unique features created by coastal processes, found in only a few other places in the world. A stratigraphic and hydrologic analysis of the lake region has shown that the coastal dune lakes formed during the mid-to late Holocene when postglacial sea- level rise slowed, and developed in close proximity to the shoreline. There is evidence that the coastal dune lakes are hydraulically connected to the Gulf of Mexico. A tidal signal present within five of the six coastal dune lakes studied, indicating that the lake system is based on a freshwater lens sitting on a coastal salt water wedge at the marine boundary of the surficial aquifer. The humate formation horizon has migrated landward in response to sea-level rise throughout the Holocene. Radiocarbon dates collected from the coastal dune lakes provide a minimum age of approximately 5,000 yr BP for the coastal dune lakes. Sea-level rise and climate stability confirms the hypothesis that these lakes have been stable and in close proximity to the shoreline since their formation approximately, approximately 5,000 to 8,000 yr BP. Chapter 3: Detecting a Paleostorm History The third chapter details the creation of an improved method for identifying storm signatures and establishing the geologic record of storm occurrence for coastal regions through analysis of coastal lake core sediments. A sediment core representing the historic period (~1900 A.D. to present) was subjected to high resolution sedimentologic and stable isotope analysis, complemented by geochronology and micropaleontology. The data were combined with historic

storm records and tide-gauge storm surge history to develop a storm model. The storm model resulted in a probability equation based on the isotopic and sedimentologic characteristics of storm signatures. This model, built from two geochemical sediment variables (δ13C and % N′) and one physical sediment variable (% sand), accurately predicts known historic storm and surge events at Oyster Pond, a coastal lake in northwest Florida. The model represents a new method for identifying and quantifying paleostorm events. Through use of a statistical model with fixed covariates, the result is a more objective indicator of storm occurrence, and therefore of hazard risk, than previous methods of paleostorm analysis. This model provides a valuable tool for understanding and quantifying long-term storm history, and better assessing storm hazard risk for coastal regions. Chapter 4: Paleostorm Record for Northwest Florida The fourth chapter discusses the application of the new technique for identifying storm signatures in coastal lake sediments and establishing the geologic record of storm occurrence for northwest Florida. The results of this study demonstrate that a reliable storm history can be extracted from coastal sediment cores, based on a combination of isotopic and sedimentologic data, and using a statistical model which has been calibrated with the historic storm record. This new paleostorm identification method, and the model that has been developed, can be employed to identify storms in the sedimentary record that predate the historic storm database. A sediment core from a northwest Florida coastal pond, representing the past five millennia, was subjected to high resolution sedimentologic and stable isotope analysis, complemented by geochronology and micropaleontology. The data were input to a statistical storm model, based on two geochemical sediment variables (δ13C and % N′) and one physical sediment variable (% sand). The storm model identified storm events and storm clusters in the sediment record. The modeled storm events separate into 4 periods of increased storminess, ranging from as few as 4 storms per century to as a many as 10 storms per century. This study quantifies the long-term storm history for the northwest Florida coast and represents a tool to enhance the assessment of storm hazard risk for coastal regions. The paleostorm record extracted from the sediments of Western Lake provides several significant findings. First, the northern Gulf of Mexico has experienced millennia-long cycles of increased storm activity, separated by quiescent periods of shorter duration. Second, the level of major storm and flooding activity during the historic period of the past 150 years (6.2 storms

90 per century) is significantly greater than the long-term mean storm frequency over the past five millennia (5.0 storms per century). Third, major storm frequency during the historic period is a continuation of a trend of increased storminess that began about 1300 years ago. Fourth, the current “active” period is the longest in the observed record. Fifth, the mean frequency of major storms during the historic period is similar to the mean level of storm frequency during the “active” periods” of the past five millennia (6.9 storms per century). Sixth, there is little evidence of an increasing or decreasing trend in major storm frequency in the northern Gulf of Mexico during “active” periods of the past five millennia. This study has demonstrated that storm events which carry only marine storm surge into a coastal lake, without sediment overwash, can be detected through their isotopic and sedimentologic signature. The method is robust and the results are comparable to those of other studies using different storm proxies. The methodology employed during this investigation is capable of generating a storm history for any similar coastal region, and represents a quantitative basis for modeling storm risk in a warming future. Overall Conclusions This project succeeded in achieving its goals. The groundwork has been laid for future paleostorm studies in other coastal regions. Researchers will be able apply the model created by this pilot project and determine geographic effects on Holocene climate cycles. This is only the beginning for the advancement of paleotempestology research.

APPENDIX A GEOCHEMICAL DATA FOR CORE 092710-01

Depth in δ13C δ15N Percent C Percent N C:N Core (mm) (‰) (‰) (by Weight) (by Weight) 0.0 -25.75 1.99 4.17 0.56 8.73 2.8 -24.54 1.97 2.11 0.21 11.84 5.6 -24.77 1.88 1.59 0.15 12.24 8.3 -24.90 2.11 3.08 0.31 11.48 11.1 -25.15 2.17 3.14 0.34 10.71 13.9 -25.00 2.23 3.17 0.34 10.97 16.7 -25.00 2.20 3.20 0.34 10.89 19.4 -24.79 2.28 2.86 0.30 11.07 22.2 -24.62 2.42 2.75 0.29 11.20 25.0 -24.64 2.44 2.61 0.26 11.55 27.8 -24.52 2.53 2.52 0.25 11.65 30.6 -24.62 2.56 2.66 0.27 11.50 33.3 -24.58 2.75 2.76 0.28 11.64 36.1 -24.57 2.64 2.78 0.28 11.62 38.9 -24.29 2.71 2.54 0.24 12.14 41.7 -24.35 2.74 2.57 0.25 11.87 44.4 -24.18 2.88 2.23 0.21 12.15 47.2 -23.62 2.89 2.20 0.21 12.50 50.0 -23.92 2.90 1.79 0.17 12.34 52.8 -23.89 2.95 1.68 0.16 12.53 55.6 -23.32 3.15 1.56 0.14 12.70 58.3 -23.36 3.24 1.50 0.14 12.39 61.1 -23.37 3.28 1.15 0.11 12.40 63.9 -22.97 3.31 1.31 0.12 12.71 66.7 -23.02 3.39 1.28 0.12 12.15 69.4 -22.89 3.53 1.15 0.11 12.26 72.2 -22.60 3.46 1.22 0.11 12.46 75.0 -22.53 3.53 1.16 0.11 12.14 77.8 -22.37 3.52 1.19 0.11 12.09 80.6 -22.39 3.78 1.27 0.12 12.61 83.3 -22.50 3.55 1.12 0.10 12.64 86.1 -22.19 3.89 1.21 0.12 12.26 88.9 -22.02 3.60 1.03 0.09 13.93 91.7 -22.18 3.97 1.08 0.10 12.07

Depth in δ13C δ15N Percent C Percent N C:N Core (mm) (‰) (‰) (by Weight) (by Weight) 94.4 -22.11 3.84 1.02 0.09 13.54 97.2 -22.05 3.29 0.97 0.08 13.55 100.0 -22.12 3.41 0.98 0.08 13.78 102.8 -22.36 3.67 1.16 0.10 13.87 105.6 -22.18 3.66 0.88 0.07 13.72 108.3 -21.79 3.42 1.01 0.08 14.25 111.1 -22.15 3.68 1.02 0.09 14.02 113.9 -22.26 3.56 1.28 0.11 13.44 116.7 -22.02 3.38 0.97 0.08 13.75 119.4 -22.39 4.01 1.05 0.09 13.66 122.2 -22.18 3.80 1.00 0.09 13.30 125.0 -22.32 3.90 1.35 0.11 13.97 127.8 -22.03 3.82 1.24 0.11 13.25 130.6 -22.03 3.92 1.23 0.11 13.14 133.3 -21.95 4.06 1.39 0.12 13.31 136.1 -21.58 4.13 1.65 0.15 13.21 138.9 -21.62 3.75 1.44 0.12 13.60 141.7 -22.19 3.61 1.34 0.12 13.41 144.4 -22.08 3.72 1.42 0.13 13.24 147.2 -21.90 3.73 1.48 0.13 13.61 150.0 -21.98 3.72 1.43 0.12 13.72 152.9 -22.23 3.83 1.24 0.10 14.59 155.9 -22.21 3.83 1.29 0.11 14.02 158.8 -22.16 3.54 1.40 0.11 14.36 161.8 -22.14 3.90 1.36 0.12 13.45 164.7 -21.75 4.34 1.23 0.12 12.34 167.6 -21.80 4.58 1.34 0.13 11.94 170.6 -21.70 4.49 1.01 0.10 11.73 173.5 -21.49 3.90 1.12 0.30 4.32 176.5 -22.38 3.99 0.95 0.08 14.60 179.4 -21.57 4.59 1.11 0.10 12.37 182.4 -22.08 4.65 0.97 0.09 12.19 185.3 -21.81 4.63 0.86 0.08 12.48 188.2 -22.03 3.07 0.95 0.08 14.02 191.2 -21.60 3.45 0.72 0.06 13.56 194.1 -22.16 3.47 0.71 0.05 15.46 197.1 -21.75 4.10 0.78 0.07 12.94 200.0 -21.74 3.71 1.04 0.09 12.88 202.8 -21.65 3.73 1.20 0.12 11.96 93

Depth in δ13C δ15N Percent C Percent N C:N Core (mm) (‰) (‰) (by Weight) (by Weight) 205.6 -21.87 3.91 1.20 0.12 12.16 208.3 -21.56 4.02 1.11 0.11 11.98 211.1 -21.67 4.52 1.22 0.12 11.60 213.9 -21.68 4.49 1.22 0.12 11.74 216.7 -21.57 4.13 1.27 0.13 11.77 219.4 -22.06 3.85 1.32 0.13 12.24 222.2 -21.90 3.86 1.29 0.12 12.37 225.0 -21.70 4.00 1.05 0.10 12.36 227.8 -21.61 3.72 1.04 0.10 12.35 230.6 -21.91 3.70 1.12 0.10 12.61 233.3 -21.93 3.78 1.11 0.11 12.25 236.1 -22.07 3.66 0.94 0.08 12.98 238.9 -22.02 3.83 1.10 0.10 12.76 241.7 -21.86 3.96 0.96 0.09 12.68 244.4 -21.43 3.91 0.79 0.07 12.93 247.2 -22.21 3.66 0.94 0.08 14.52 250.0 -21.80 3.87 0.75 0.06 13.98 252.9 -22.00 3.88 0.79 0.07 13.56 255.9 -22.99 3.76 1.00 0.08 14.22 258.8 -22.46 3.57 1.18 0.10 13.36 261.8 -22.61 3.88 1.29 0.11 13.24 264.7 -21.81 5.42 1.17 0.12 11.41 267.6 -22.47 4.52 1.17 0.11 12.86 270.6 -22.26 3.56 1.03 0.09 13.04 273.5 -22.15 3.35 0.91 0.08 12.98 276.5 -21.78 3.33 0.94 0.09 12.62 279.4 -21.55 3.67 1.41 0.16 10.40 282.4 -22.14 4.08 1.48 0.16 10.81 285.3 -21.91 3.61 1.51 0.17 10.36 288.2 -22.56 3.25 1.61 0.17 10.72 291.2 -22.28 3.28 1.41 0.16 10.46 294.1 -22.21 3.47 1.35 0.15 10.85 297.1 -22.29 3.12 1.48 0.17 10.16 300.0 -22.16 3.49 1.43 0.14 11.53 302.8 -22.11 3.12 1.34 0.13 11.95 305.6 -22.22 3.18 1.52 0.15 11.85 308.3 -21.94 3.22 1.57 0.16 11.77 311.1 -22.00 3.10 1.36 0.13 11.85 313.9 -21.91 3.01 1.38 0.14 11.71 94

Depth in δ13C δ15N Percent C Percent N C:N Core (mm) (‰) (‰) (by Weight) (by Weight) 316.7 -22.07 3.03 1.49 0.15 11.82 319.4 -21.76 3.14 1.15 0.11 12.77 322.2 -22.22 3.17 1.10 0.10 12.78 325.0 -22.06 3.29 0.95 0.09 12.43 327.8 -22.23 3.27 1.32 0.12 12.74 330.6 -22.57 3.23 1.11 0.10 13.43 333.3 -22.22 3.13 0.96 0.09 12.57 336.1 -22.13 3.32 0.68 0.06 12.65 338.9 -22.17 3.15 0.59 0.05 12.85 341.7 -22.04 4.50 0.69 0.07 12.24 344.4 -21.94 4.34 0.67 0.06 12.75 347.2 -22.05 4.06 0.72 0.07 12.08 350.0 -21.92 4.40 0.57 0.05 12.33 353.1 -22.10 4.00 0.59 0.06 11.71 356.3 -22.08 3.70 0.48 0.05 12.16 359.4 -22.10 3.65 0.49 0.04 12.96 362.5 -21.86 4.20 0.59 0.05 12.73 365.6 -21.93 3.90 0.57 0.05 12.57 368.8 -21.94 3.72 0.88 0.08 12.89 371.9 -21.85 3.57 1.13 0.10 12.96 375.0 -21.91 3.66 1.42 0.13 12.58 378.1 -21.49 3.81 1.56 0.15 12.18 381.3 -21.74 4.18 1.93 0.20 11.48 384.4 -21.72 4.10 1.80 0.18 11.61 387.5 -21.81 3.86 1.61 0.15 12.66 390.6 -22.03 3.81 1.77 0.16 13.28 393.8 -21.89 3.92 1.78 0.16 12.83 396.9 -21.70 4.00 1.82 0.17 12.66 400.0 -21.22 3.81 1.87 0.17 12.72 Mean -22.43 3.56 1.37 0.13 12.47 St. Dev. 0.96 0.60 0.62 0.07 1.23

APPENDIX B SEDIMENT DATA FOR CORE 092710-01

Physical Sediment Analyses Grain Size Analysis Statistics Percent Percent Percent Depth in Combustible Sand Fines Mean St. Dev Kurtosis Skew Median Dispersion Core (cm) (%) (%) (%) (Phi) (Phi) 0.5 12.036 50.155 49.845 2.543 0.402 -0.372 2.783 2.454 0.297 1.0 11.798 55.246 44.754 2.469 0.422 -0.334 2.859 2.386 0.312 1.5 12.059 46.315 53.685 2.544 0.437 -0.411 2.831 2.456 0.327 2.0 11.128 45.570 54.430 2.553 0.397 -0.295 2.693 2.459 0.290 2.5 11.543 47.081 52.919 2.533 0.417 -0.358 2.797 2.438 0.309 3.0 10.117 46.239 53.761 2.576 0.392 -0.212 2.621 2.470 0.282 3.5 8.907 42.620 57.380 2.550 0.402 -0.299 2.773 2.451 0.294 4.0 9.338 44.459 55.541 2.492 0.388 -0.293 2.696 2.412 0.283 4.5 11.116 50.473 49.527 2.525 0.408 -0.506 3.218 2.439 0.310 5.0 8.395 55.995 44.005 2.554 0.386 -0.395 3.086 2.459 0.287 5.5 7.018 58.495 41.505 2.520 0.384 -0.206 2.600 2.425 0.276 6.0 6.819 59.535 40.465 2.569 0.376 -0.297 2.839 2.471 0.274 6.5 6.685 61.815 38.185 2.527 0.381 -0.280 2.723 2.436 0.277 7.0 6.876 63.154 36.846 2.491 0.396 -0.290 2.805 2.397 0.289 7.5 5.765 65.963 34.037 2.505 0.399 -0.424 3.146 2.420 0.299 8.0 6.119 61.268 38.732 2.532 0.441 -0.853 5.018 2.449 0.376 8.5 4.430 55.321 44.679 2.552 0.383 -0.398 3.068 2.458 0.284 9.0 8.910 58.691 41.309 2.546 0.431 -1.157 5.722 2.472 0.381 9.5 7.816 60.299 39.701 2.541 0.386 -0.358 3.214 2.442 0.287 10.0 5.068 59.030 40.970 2.536 0.389 -0.278 2.972 2.437 0.284 10.5 4.899 60.680 39.320 2.550 0.388 -0.573 3.666 2.463 0.299 11.0 7.628 62.792 37.208 2.581 0.391 -0.447 3.191 2.487 0.293 11.5 7.367 55.922 44.078 2.612 0.364 -0.302 3.085 2.501 0.267 12.0 6.236 57.352 42.648 2.594 0.370 -0.457 3.767 2.488 0.281 12.5 7.595 56.808 43.192 2.589 0.373 -0.348 3.064 2.489 0.274 13.0 4.589 53.017 46.983 2.631 0.358 -0.306 3.085 2.519 0.262 13.5 5.189 55.272 44.728 2.605 0.370 -0.470 3.535 2.502 0.280 14.0 8.239 51.606 48.394 2.626 0.356 -0.353 3.127 2.516 0.262 14.5 4.908 53.384 46.616 2.648 0.353 -0.277 2.952 2.537 0.257 15.0 9.659 57.902 42.098 2.652 0.331 -0.259 3.036 2.537 0.240 15.5 5.599 60.235 39.765 2.623 0.353 -0.336 3.161 2.515 0.259 16.0 6.168 59.204 40.796 2.655 0.335 -0.204 2.871 2.537 0.240

Physical Sediment Analyses Grain Size Analysis Statistics Percent Percent Percent Depth in Combustible Sand Fines Mean St. Dev Kurtosis Skew Median Dispersion Core (cm) (%) (%) (%) (Phi) (Phi) 16.5 7.538 58.685 41.315 2.645 0.357 -0.311 2.986 2.533 0.261 17.0 8.769 59.053 40.947 2.615 0.358 -0.318 3.089 2.507 0.262 17.5 7.728 53.661 46.339 2.610 0.393 -0.634 4.163 2.506 0.310 18.0 5.046 65.370 34.630 2.586 0.372 -0.286 3.198 2.475 0.273 18.5 5.906 60.372 39.628 2.632 0.347 -0.257 3.277 2.515 0.253 19.0 4.898 69.318 30.682 2.595 0.374 -0.524 3.948 2.490 0.288 19.5 4.289 67.389 32.611 2.593 0.378 -0.428 3.539 2.486 0.285 20.0 5.236 67.369 32.631 2.579 0.373 -0.253 2.914 2.473 0.271 20.5 5.958 62.867 37.133 2.600 0.370 -0.334 3.249 2.489 0.274 21.0 6.168 59.956 40.044 2.608 0.350 -0.362 3.188 2.505 0.258 21.5 4.600 54.327 45.673 2.643 0.352 -0.234 3.062 2.523 0.255 22.0 6.019 50.944 49.056 2.626 0.345 -0.417 3.879 2.512 0.259 22.5 6.537 52.281 47.719 2.633 0.354 -0.410 3.474 2.521 0.264 23.0 6.577 61.352 38.648 2.551 0.378 -0.376 3.340 2.447 0.282 23.5 4.199 65.419 34.581 2.537 0.404 -0.584 3.986 2.442 0.317 24.0 4.118 67.088 32.912 2.554 0.394 -0.541 3.807 2.452 0.305 24.5 4.497 67.585 32.415 2.585 0.363 -0.379 3.173 2.469 0.269 25.0 5.669 70.970 29.030 2.609 0.347 -0.116 3.035 2.489 0.248 25.5 5.277 68.024 31.976 2.613 0.355 -0.226 2.907 2.500 0.256 26.0 5.438 63.256 36.744 2.585 0.373 -0.366 3.347 2.478 0.278 26.5 7.568 62.693 37.307 2.624 0.389 -0.394 3.171 2.521 0.290 27.0 7.988 63.440 36.560 2.599 0.379 -0.287 3.323 2.486 0.280 27.5 3.759 60.931 39.069 2.567 0.386 -0.296 3.245 2.467 0.285 28.0 4.448 68.888 31.112 2.545 0.377 -0.324 3.163 2.442 0.278 28.5 6.348 72.407 27.593 2.486 0.409 -0.371 3.339 2.393 0.307 29.0 6.098 64.797 35.203 2.527 0.399 -0.343 3.418 2.415 0.299 29.5 5.938 64.363 35.637 2.522 0.408 -0.619 3.927 2.432 0.320 30.0 6.716 62.280 37.720 2.534 0.393 -0.569 4.186 2.441 0.308 30.5 6.299 58.494 41.506 2.579 0.364 -0.444 3.481 2.480 0.273 31.0 5.017 59.609 40.391 2.544 0.381 -0.312 3.061 2.440 0.280 31.5 3.858 61.107 38.893 2.519 0.381 -0.281 3.067 2.417 0.279 32.0 7.099 64.533 35.467 2.559 0.362 -0.456 3.172 2.466 0.270 32.5 3.760 67.096 32.904 2.483 0.425 -0.823 5.049 2.388 0.359 33.0 5.239 66.672 33.328 2.518 0.411 -0.501 3.679 2.425 0.316 33.5 3.820 66.234 33.766 2.511 0.411 -0.260 3.183 2.397 0.303 34.0 4.269 72.851 27.149 2.534 0.421 -0.841 4.472 2.456 0.346 34.5 4.209 76.530 23.470 2.537 0.361 -0.266 3.442 2.424 0.265 35.0 3.747 77.644 22.356 2.561 0.376 -0.751 4.427 2.467 0.298

Physical Sediment Analyses Grain Size Analysis Statistics Percent Percent Percent Depth in Combustible Sand Fines Mean St. Dev Kurtosis Skew Median Dispersion Core (cm) (%) (%) (%) (Phi) (Phi) 35.5 2.870 81.030 18.970 2.550 0.359 -0.540 4.245 2.446 0.276 36.0 2.709 80.771 19.229 2.571 0.330 -0.300 3.552 2.462 0.241 36.5 4.700 80.395 19.605 2.527 0.348 -0.372 3.798 2.419 0.260 37.0 3.969 76.312 23.688 2.528 0.376 -0.570 4.102 2.421 0.291 37.5 3.680 72.237 27.763 2.556 0.345 -0.435 3.745 2.454 0.259 38.0 3.999 55.262 44.738 2.545 0.377 -0.461 3.535 2.446 0.284 38.5 8.357 44.031 55.969 2.623 0.366 -0.537 3.576 2.522 0.278 39.0 8.478 37.471 62.529 2.587 0.382 -0.496 3.645 2.484 0.292 39.5 9.738 39.759 60.241 2.647 0.362 -0.483 3.279 2.548 0.271 40.0 8.268 35.989 64.011 2.671 0.338 -0.401 3.260 2.560 0.250 Mean 6.468 60.138 39.862 2.570 0.379 -0.405 3.376 2.468 0.284 St. Dev. 2.263 9.612 9.612 0.046 0.025 0.167 0.571 0.040 0.027

APPENDIX C MICROPALEOTOLOGIC DATA FOR CORE 092710-01

Depth in Core (cm) Foraminifera Assemblage 1 Ammonia parkinsoniana —dominant 2 charophyte oogonia several gastropod shell fragments (thin-shelled, freshwater taxa?) 1 ostracod valve 7 Ammonia parkinsoniana —abundant/dominant Elphidium gunteri —rare (3 specs) Haynesina sp. (Note: similar to Hanzawaia, but not planoconvex ) 9 Ammonia parkinsoniana —abundant Elphidium gunteri or galvestonens e—rare (3 specs) Haynesina sp. ostracods (3 specs.) bivalve (Gemma sp. ) gastropod (sp. undet.) other molluscan debris 11 Ammonia parkinsoniana —abundant Haynesina sp. present ostracods (rare) bivalve molluscan debris irregular urchin spine 24 Ammonia parkinsoniana —common/abundant Elphidium gunteri —present Haynesina sp. ostracods common 1 arcid bivalve (juvenile) 1 bryozoan fragment other molluscan debris 26 Ammonia parkinsoniana —abundant; larger in size than in samples above Elphidium gunteri —present Haynesina sp. ostracods present juvenile bivalves (spat) 3 irregular urchin spines

Depth in Core (cm) Foraminifera Assemblage 33 Ammonia parkinsoniana —common Elphidium gunteri —rare Haynesina sp. Quinqueloculina (1 spec.) ostracod debris molluscan debris irregular urchin spine 37 Ammonia parkinsoniana —common Elphidium gunteri —present Haynesina sp. Quinqueloculina rare ostracods present bivalve spat & debris irregular urchin spines

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APPENDIX D GEOCHRONOLOGIC DATA FOR CORE 092710-01

Depth Weight Midpoint Pb-210 Ra-226 Cs-137 Sample Interval +/- +/- +/- (g) (cm) (dpm/g) (dpm/g) (dpm/g) (cm) 1 1.71 0-1 0.5 13.69 2.60 2.02 0.87 1.81 0.21 2 1.76 2-3 2.5 9.53 1.71 1.42 0.19 1.83 0.18 3 2.08 4-5 4.5 4.72 1.08 2.05 0.29 1.55 0.09 4 2.14 6-7 6.5 3.59 0.96 1.49 0.25 0.97 0.08 5 1.70 8-9 8.5 4.66 1.97 1.75 0.64 1.36 0.17 6 0.00 10-11 10.5 0.00 1.38 2.60 0.45 0.62 0.13 7 1.62 12-13 12.5 0.00 1.34 2.29 0.17 0.47 0.10 8 1.35 14-15 14.5 6.78 1.81 2.53 0.76 0.16 0.13 9 1.55 16-17 16.5 3.49 0.87 2.13 0.31 0.20 0.08 10 1.38 18-19 18.5 3.13 1.30 2.67 0.50 0.28 0.09 11 1.39 20-21 20.5 5.98 1.20 2.33 0.57 0.03 0.05 12 1.45 22-23 22.5 4.23 1.22 2.27 0.10 0.09 0.09 16 1.24 24-25 24.5 0.00 1.26 2.02 0.28 0.22 0.09 13 1.32 29-30 29.5 2.06 1.30 2.17 0.40 0.09 0.10 14 1.28 34-35 34.5 4.91 1.22 2.11 0.06 0.28 0.09 15 1.15 39-40 39.5 3.83 1.15 2.16 0.42 0.24 0.08 17 1.18 44-45 44.5 0.00 1.46 2.13 0.49 0.19 0.12

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BIOGRAPHICAL SKETCH Jennifer Lynn Coor was born on July 12, 1983, and raised in Richmond, Virginia. She attended Douglas S. Freeman High School, graduating with honors in 2001. Jennifer attended Coastal Carolina University for undergraduate work, and received two bachelor of science degrees in May, 2006. The degrees were in marine science, specializing in marine geology, and the second in chemistry. She also received minors in coastal geology, applied mathematics, and physics. Under the direction of Dr. Paul Gayes and Dr. John Goodwin, Jennifer began participating in research projects and teaching during her second year at CCU. Her biggest projects involved Project Reefwatch, monitoring several reefs outside of Charleston Harbor, and running the sediment analysis laboratory. She enrolled in the graduate program in geology at Florida State University in August 2008. She served as a teaching assistant for Introduction to Science Laboratory (SCI 101) at Coastal Carolina University, and Dynamic Earth Laboratory (GLY 1000L) and Sedimentology and Stratigraphy Laboratory (GLY 4544C) at Florida State University. During her early graduate work, she developed an interest in research focused on identifying prehistoric hurricanes using sedimentology, stratigraphy, and stable isotope geochemistry. These interests became the basis for her dissertation work on the paleostorm history of the northwest Florida coast.

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