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Temporal and spatial patterns in optical properties of colored dissolved organic matter on florida's gulf coast

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Title:
Temporal and spatial patterns in optical properties of colored dissolved organic matter on florida's gulf coast shelf to stream to aquifer
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English
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Conmy, Robyn Nicole
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University of South Florida
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Tampa, Fla
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Subjects / Keywords:
Dissolved organic carbon
Fluorescence
West Florida Shelf
Groundwater
Dissertations, Academic -- Marine Science -- Doctoral -- USF   ( lcsh )
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non-fiction   ( marcgt )

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Summary:
ABSTRACT: Characterization of Colored Dissolved Organic Matter (CDOM) in surface and ground waters in South Florida was conducted using fluorescence and absorption spectroscopy. Waters of the West Florida Shelf are heavily influenced by many river systems on Florida's Gulf Coast that, to the first order control CDOM distributions on the shelf. Seasonal surveys revealed that changes in the underwater light field as a result of major hurricanes and resuspension events are linked closely with a number of factors prior to a storm's passing such as the presence of persistant blooms, rainfall and discharge. Additionally, storm track and wind direction were found to play a significant role in CDOM signatures. A study of ten riversheds located between the Mississippi / Atchafalya River system and the Shark River in the Everglades revealed a wide range in CDOM seasonality.A regional dependence of CDOM was also found, where highest aromaticity and concentration of organic material was found for the southernmost watersheds. Basin characteristics, vegetation differences, land use and climatic patterns are implicated in the cause for regional differences. In addition to surface flow, organic material in groundwater was measured in deep and shallow aquifers surrounding the Tampa Bay Estuary. As a result of strong hydrologic links between shallow aquifers and the overlying surface waters, CDOM in both reservoirs were found to be quite similar. Deep aquifers (> 150 ft) however are less concentrated and have CDOM signatures more similar to marine waters. This suggests similar biogeochemical pathways of the material, including the influence of the aquatic microbial community.Furthermore, multi-spectral CDOM fluorescence measurements were shown to be a potential indicator of groundwater presence in Tampa Bay during times of low surficial discharge to the bay, and when some rivers are almost entirely spring-fed. Investigating CDOM distribution and signatures is vital to carbon budget and cycling questions. The amount and quality of organic material has significant implications for ecosystems, thereby affecting organisms that use CDOM as a food source, light availability for photosynthesis, UV shading provided to biota, satellite estimates of chlorophyll a, metal binding, materials transport and overall water quality.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2008.
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Includes bibliographical references.
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by Robyn Nicole Conmy.
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Title from PDF of title page.
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Document formatted into pages; contains 120 pages.

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aleph - 001994149
oclc - 317403143
usfldc doi - E14-SFE0002432
usfldc handle - e14.2432
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Temporal and Spatial Patterns in Optical Properties of Colored Dissolved Organic Matter on Florida’s Gulf Coast: Shelf to Stream to Aquife r by Robyn Nicole Conmy A dissertation in partial fulfillment of the requirements for the degree of Doctor of Philosophy College of Marine Science University of South Florida Major Professor: Paula G. Coble, Ph.D. Kendall L. Carder, Ph.D. Cynthia A. Heil, Ph.D. Mark E. Luther, Ph.D. Ashanti J. Pyrtle, Ph.D. Date of Approval March 31, 2008 Keywords: Dissolved Organic Carbon, fluorescence, W est Florida Shelf, groundwater Copyright 2008, Robyn Conmy

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i TABLE OF CONTENTS List of Tables..................................... ................................................... ..............................ii List of Figures.................................... ................................................... .............................iii List of Acronyms................................... ................................................... .........................ix Abstract........................................... ................................................... .................................x Preface............................................ ................................................... ................................xii General Introduction............................... ................................................... ..........................1 Part I. Spatial distribution of CDOM on the souther n West Florida Shelf Introduction....................................... ................................................... ....................5 Methodology........................................ ................................................... .................9 Results and Discussion............................. ................................................... ..........14 Conclusions........................................ ................................................... .................48 Part II. CDOM optical properties in Florida’s Gulf Coast riversheds: A regional comparison Introduction....................................... ................................................... ..................51 Methodology........................................ ................................................... ...............53 Results and Discussion............................. ................................................... ..........56 Conclusions........................................ ................................................... .................75 Part III. Characterization of subsurface terrestria l CDOM sources to Tampa Bay, Florida Introduction....................................... ................................................... ..................77 Methodology........................................ ................................................... ...............81 Results and Discussion............................. ................................................... ..........88 Conclusions........................................ ................................................... ...............101 General Conclusions................................ ................................................... .....................102 Literature Cited................................... ................................................... ..........................104 Appendices Appendix I....................................... ................................................... .................113 Appendix II...................................... ................................................... .................117

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ii LIST OF TABLES Table 1.1. Date of WFS field experiments along wit h USGS and CHNEP discharge and flow classifications.......... ................................................... ....15 Table 1.2. Absorption data for the water types sho wn in Figure 1.10..............................34 Table 1.3. Table 1.3. Peak positions and intensiti es for EEMs shown in Figure 1.17....................................... ................................................... .........................40 Table 1.4. EEM peak positions previously found by C oble, 1996....................................41 Table 2.1. Locations (GPS positions and landmarks) of river samples.............................55 Table 3.1. Location and environmental data for gro undwater wells in the Tampa Bay region.............................. ................................................... .................84 Table 3.2. Location and environmental data for sur face samples in the Tampa Bay Estuary.......................... ................................................... ...............89

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iii LIST OF FIGURES Figure 1.1. Map of West Florida Shelf in the Gulf of Mexico.......................................... .7 Figure 1.2. Results of freezing experiment to dete rmine if a loss of chromophores were apparent in absorption spectr a................................................10 Figure 1.3. CDOM fluorescence at Ex/Em 300/430 nm for seasonal cruises on the West Florida Shelf................. ................................................... ............14 Figure 1.4. River discharge data from the Bartow S tation (Source: USGS)....................15 Figure 1.5. COM Spatial distributions on the West Florida Shelf....................................18 Figure 1.6. Salinity spatial distributions for sea sonal cruises on the West Florida Shelf............................ ................................................... ..................19 Figure 1.7. Maps of currents and wind data from buo ys operated by USF Ocean Circulation Group (http://ocg7.mari ne.usf.edu/~liu)........................20 Figure 1.8A. Enhanced RGB (R: 551,G: 488,B: 443nm) (top panels) for three days in August 2004 around the time of t he passing of Hurricane Charley................................. ................................................... ....................21 Figure 1.8B. Imagery of CHL with the removal of ge lbstoff (top panels) and adg443 (bottom panels) for three days in Augus t 2004 around the

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iv time of the passing of Hurricane Charley.......... ................................................... .......23 Figure 1.9. Maps of currents and wind data from buo ys operated by USF Ocean Circulation Group (http://ocg7.marine. usf.edu/~liu).............................24 Figure 1.10A. Enhanced RGB (R: 551, G: 488, B: 443 nm) (top panels) for two days in December 2004..................... ................................................... ...........25 Figure 1.10B. Imagery of CHL with the removal of g elbstoff (top panels) and adg443 (bottom panels) for two days in Decembe r 2004.....................................26 Figure 1.11. Karenia brevis cell concentrations from FL-FWCC plotted on top of the COM fluorescence spatial maps shown previously................................27 Figure 1.12. Maps of currents and wind data from bu oys operated by USF Ocean Circulation Group (http://ocg7.marine. usf.edu/~liu).............................29 Figure 1.13A. Enhanced RGB (R: 551,G: 488,B: 443nm ) (top panels) for two days pre and post-Hurricane Wilma......... ................................................... ....30 Figure 1.13B. Imagery of CHL with the removal of g elbstoff (top panels) and adg443 (bottom panels) for two da ys pre and post-hurricane Wilma.............................. ................................................... ..................31 Figure 1.14. Relationship of CDOM fluorescence to absorption at 312 and 440 nm for all cruises on the West Florida Shelf...........................................32 Figure 1.15. Absorption spectra for different wate r types sampled during cruises to the West Florida Shelf.......... ................................................... .........33

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v Figure 1.16. Spectral slope values for seasonal cr uises on the West Florida Shelf..................................... ................................................... .........................35 Figure 1.17A. Excitation Emission Matrices (EMS) f or three water types on the West Florida Shelf............. ................................................... .........37 Figure 1.17B. EEMS for water after the passage of Hurricanes Wilma (top) and Charley (bottom).................. ................................................... .........38 Figure 1.17C. EEMS for water before (top) and afte r (bottom) the passage of the December 2004 storm event...... ................................................... ..39 Figure 1.18. Position of fluorescence maxima for H umic Peak M for West Florida Shelf waters.............. ................................................... ........42 Figure 1.19. Spatial distributions of Humic Peak M position for seasonal cruises on the West Florida Shelf........ ................................................... .......44 Figure 1.20. Ratio of Humic Peaks A and C/M as a f unction of CDOM fluorescence intensity and salinity for all f ield experiments..........................45 Figure 1.21. The relationship between Dissolved Or ganic Carbon (DOC) and CDOM fluorescence on the West Flo rida Shelf............................................. ................................................... .............................46 Figure 1.22. Spatial distributions of DOC on the W est Florida Shelf..................................... ................................................... .........................47 Figure 2.1. Map showing locations of the rivers sa mpled in Florida (top) and in Louisiana & Mississippi (righ t)................................................. ..54

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vi Figure 2.2. CDOM fluorescence at Ex/Em 300/430 nm f or ten rivers that supply the Eastern Gulf of Mexico..... ................................................... .....56 Figure 2.3. CDOM fluorescence at Ex/Em 300/430 nm f or Tampa Bay rivers........................................ ................................................... ..........................57 Figure 2.4A. Histograms of fluorescence intensity, absorption coefficient and fluorescence efficiencies for all rivers............................................. ...59 Figure 2.4B. Histograms of DOC concentration, posi tion of Humic Peak C maximum and fluorescence ratios for a ll rivers..................................60 Figure 2.5. CDOM fluorescence at Ex/Em 300/430 nm a s a function of absorption coefficient at 312 nm...... ................................................... ......61 Figure 2.6. CDOM fluorescence at Ex/Em 300/430 nm a s a function of absorption coefficient at 312 nm for C harlotte Harbor Rivers and Manatee River in Tampa Bay............. ................................................... ....62 Figure 2.7. The relationship between CDOM fluoresce nce and DOC for river and West Florida Shelf waters for all se asons sampled.................................64 Figure 2.8. The relationship between CDOM fluoresce nce and DOC for Tampa Bay (top), Charlotte Harbor and Shark Ri vers (bottom)............................65 Figure 2.9A. EEMs contours for rivers taken at zer o salinity during dry season................................. ................................................... .....................67 Figure 2.9B. EEMs contours for rivers taken at zer o salinity during dry season.............68

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vii Figure 2.10. Position of Humic Peak C maximum as a function of salinity for river and West Florida Shelf water s for all seasons sampled........................................... ................................................... ..........................69 Figure 2.11. Normalized emission spectra at Ex = 3 00nm for all rivers..........................70 Figure 2.12. Spatial distribution of COM in an urb an locale (left) and natural locale (right) in the Hillsborough River.. ................................................... ......72 Figure 2.13. Historical time series of color at th e mouth of the Alafia River in Tampa Bay (top)................... ................................................... ...........74 Figure 3.1. Map of Tampa Bay showing calcium carbo nate content (left), total organic carbon content (mi ddle), and major sediment facies (right) in bottom sediments. ................................................... ..80 Figure 3.2. Hydrogeologic framework of Florida dep icting the three main zones of the Florida aquifer system.... ................................................... ....81 Figure 3.3. Map of Tampa Bay denoting sampling loc ations within the estuary (closed circles) and well locat ions surrounding Tampa Bay (closed triangles are aquifers deeper th an 130 ft, open triangles are aquifers shallower than130 ft) ................................................... .....82 Figure 3.4. Uranium-Thorium decay series........... ................................................... .........87 Figure 3.5. CDOM fluorescence intensity as a funct ion of salinity for estuary, river, and groundwater sampl es in Tampa Bay during March-April 2006....................... ................................................... ...........88

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viii Figure 3.6. Spatial distributions of salinity, CDO M, and Ra-226 in Tampa Bay............................... ................................................... ................93 Figure 3.7. Relationship between Dissolved Organic Carbon concentration and CDOM fluorescence intensity..... ................................................... 94 Figure 3.8. EEMS of CDOM in the Manatee River, Tam pa Bay estuary, Gulf of Mexico, surficial aquifer and deep Floridan aquifer........................................... ................................................... ............................95 Figure 3.9. Position of Humic Peak C/M for groundw ater, river water and estuary water..................... ................................................... ...............96 Figure 3.10. Normalized emission scans at Excitati on = 300 nm.....................................97 Figure 3.11. CDOM fluorescence ratio for groundwat er, river water and estuary water..................... ................................................... ...............98 Figure 3.12. Fluorescence ratio as indicator of gr oundwater. Red contours suggest CDOM derived from surface terrestrial envi ronments...................................99

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ix LIST OF ACRONYMS Adg443 Absorption due to gelbstoff and detritus at 443 nm CDOM Colored Dissolved Organic Matter CHL Chlorophyll a concentrations COM Colored Organic Matter DOC Dissolved Organic Carbon EEM Excitation Emission Matrix ERGB Enhanced Red / Green / Blue True Color Imagery Ex/Em Excitation / Emission Wavelength Pair FDOM Fluorescent Dissolved Organic Matter FLH Fluorescence Line Height QSE Quinine Sulfate Equivalents PCU Platinum Cobalt Units USGS United States Geological Survey WFS West Florida Shelf NEP National Estuary Program

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x Temporal and Spatial Patterns in Optical Properties of Colored Dissolved Organic Matter on Florida’s Gulf Coast: Shelf to Stream to Aquife r Robyn Nicole Conmy ABSTRACT Characterization of Colored Dissolved Organic Matte r (CDOM) in surface and ground waters in South Florida was conducted using fluores cence and absorption spectroscopy. Waters of the West Florida Shelf are heavily influe nced by many river systems on Florida’s Gulf Coast that, to the first order contr ol CDOM distributions on the shelf. Seasonal surveys revealed that changes in the under water light field as a result of major hurricanes and resuspension events are linked close ly with a number of factors prior to a storm’s passing such as the presence of persistant blooms, rainfall and discharge. Additionally, storm track and wind direction were f ound to play a significant role in CDOM signatures. A study of ten riversheds located between the Missi ssippi / Atchafalya River system and the Shark River in the Everglades revealed a wide r ange in CDOM seasonality. A regional dependence of CDOM was also found, where h ighest aromaticity and concentration of organic material was found for the southernmost watersheds. Basin characteristics, vegetation differences, land use a nd climatic patterns are implicated in the cause for regional differences. In addition to sur face flow, organic material in groundwater was measured in deep and shallow aquife rs surrounding the Tampa Bay Estuary. As a result of strong hydrologic links bet ween shallow aquifers and the overlying surface waters, CDOM in both reservoirs w ere found to be quite similar. Deep

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xi aquifers (> 150 ft) however are less concentrated a nd have CDOM signatures more similar to marine waters. This suggests similar b iogeochemical pathways of the material, including the influence of the aquatic mi crobial community. Furthermore, multi-spectral CDOM fluorescence measurements were shown to be a potential indicator of groundwater presence in Tampa Bay during times o f low surficial discharge to the bay, and when some rivers are almost entirely spring-fed Investigating CDOM distribution and signatures is v ital to carbon budget and cycling questions. The amount and quality of organic materi al has significant implications for ecosystems, thereby affecting organisms that use CD OM as a food source, light availability for photosynthesis, UV shading provide d to biota, satellite estimates of chlorophyll a metal binding, materials transport and overall wa ter quality.

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xii PREFACE I am forever indebted to a great many people who he lped make this dissertation possible. Completion of this work would not have occurred if it weren’t for the financial support of the USGS-USF Cooperative Agreement and NASA Earth S ystems Science Fellowships. I would like to thank the crew of R/V Suncoaster an d F.G. Walton Smith, staff of Keys Marine Lab, HAB researchers at FWCC, and the lab gr oup of Peter Ortner at NOAA / AOML for the complementary ship time, assistance wi th sample collection and the wonderful times at sea. I owe a great deal of grat itude to SWFWMD for not only access to ground water wells, but who also provided an ama zing field technician, Bob Brady, to go sampling and lift equipment for a very pregnant student. I am especially grateful to Roxanne Hastings for being there every step of the way: road trips, sample collection, sample analysis, data processing, being a sounding board and a wonderful labmate. For providing data and/or images that supported the findings of this work, I would like to thank Jennifer Cannizzaro, Barnali Dixon, Inia Soto Chuanmin Hu and Steve Meyers. I am grateful to Eric Steimle, Andy Casper, Mike Hall and Tim Elliott for the opportunity to use the GSV in the Hillsborough River. Greta Kl ungness, I can’t thank you enough for making GIS maps, substituting on cruise and being a trusted colleague and friend. Thanks to Jim Krest, Donny Smoak, Charlotte Clayton and Erik Oij for analyses, use of facilities, insightful conversations and answering so many questions regarding radionuclides. To the College of Marine Science ad ministrative staff, thank you for keeping the science running smoothly. Thank you, St Francis of Assisi for a quiet library to prepare for my comprehensive exams.

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xiii To my advisor, Paula Coble, you have made my journe y through graduate school better than any of my expectations. Your endless support, your confidence and your trust in me means a great deal. Special gratitude is extended to my graduate committee, Ken Carder, Cindy Heil, Mark Luther and Ashanti Pyrtle for thei r insight and guidance. And to my chair, David Hastings, thank you for agreeing to la st minute requests. To my friends and family at the College of Marine Science, you were a lways there to discuss science and life –you have made me and this project all that mo re complete. To my family and friends, thank you for believing i n me and helping me to achieve my goals. Especially to my father who even came out o n the boat in the Everglades and helped with groundwater sampling. As always, thank you Maddy and Ebby for resting at my feet during the writing of this dissertation. T hank you to my daughters, Sage Macy and Aris Sofia for tolerating all the field samplin g (in or out of the womb) and for keeping me company while analyzing samples. My dea rest Drew, I can’t begin to thank you for all that you do for me. Your love and supp ort means the world and I am so appreciative of the time we have had, and will have together. It is to you that this work is dedicated. Finally, thank you Arth Guinness for your beautiful creation and your inspiring philanthropic ways.

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1 GENERAL INTRODUCTION Dissolved Organic Matter (DOM) is the largest fract ion of organic carbon in oceanic and estuarine waters (Williams and Druffel, 1988), ther efore an important reservoir and an integral component of the global carbon cycle. Muc h of the DOM in the coastal environment originates from the breakdown of terres trial plants, which is transported to the ocean via rivers (Duursma, 1974; Laane, 1981; B erger et al ., 1984; Hayase et al ., 1987). Due to the chemical complexity of this mate rial, this pool even today is approximately 80% uncharacterized at the molecular level (Hilf and Tuszynski, 1990). Many studies have been dedicated to deciphering the sources and biogeochemical pathways of organic carbon in aquatic environments (Chen and Gardner, 2004; Del Castillo, 2005 and refs. therein). The quality and quantity of the material reflects information about its sources, affect on water qual ity and clarity, and ability to transport other dissolved materials through watersheds. Of p articular interest is the study of Colored Dissolved Organic Matter (CDOM). This is t he portion of the DOM pool that is chromophoric, absorbing radiation in the ultraviole t and visible portions of the spectrum. A significant fraction of DOM is photoreactive, and therefore can be easily measured with optical techniques, as compared to the remaind er of the DOM pool, which requires labor intensive practices. There have been many names used to describe CDOM. Kalle (1966) first coined the phrase ‘gelbstoff’ to describe the organic matter t hat gave waters a yellow-brown color. Other names used in the literature include yellow m atter, humics, fulvics or gilvin (Kirk, 1994). The abundance of terms to describe the mate rial is an indication of the complex mixture of compounds that comprise the pool. CDOM also has distinctive optical

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2 properties, a multitude of sources and undergoes a variety of chemical, biological and physical processes in estuaries and ocean waters (C abaniss and Shuman, 1987; Donard et al ., 1989; Cauwet et al ., 1990; Coble et al ., 1990; Blough et al ., 1993; Coble, 1996). These optical properties may be used to distinguish possible sources, as well as to determine the composition of the material. The majo r source to coastal waters is from river runoff of humic substances from soils, such a s humic acids and fulvic acids. This allochthonous gelbstoff dominates DOM composition i n nearshore waters (Duursma, 1974; Laane, 1981; Berger et al ., 1984; Hayase et al. 1987). Away from the coast, however, CDOM is of marine origin from biological p rocesses such as autotrophic productivity, zooplankton feeding and bacterial int eractions. Biological productivity is an autochthonous source of CDOM, a crucial component o f new dissolved material in the oceans (Yentsch and Reichert, 1961; Traganza, 1969; Carlson and Mayer, 1983; Chen, 1992; Coble, 1996). Changes in the spectral propert ies have also been observed during the transition of early to late phytoplankton bloom periods (Carder et al ., 1989), where protein signatures are found in the water column an d underlying sediments in regions of recent biological production. The major destructive pathway for gelbstoff has bee n shown to be degradation by sunlight (Kieber et al ., 1990; Mopper et al ., 1991), which is also known to cause alteration of Dissolved Organic Carbon (DOC) compos ition. Several studies have demonstrated that exposure to sunlight degrades lar ger molecules into smaller photoproducts that are removed from the DOM pool. The removal is via two routes; through direct volatilization of carbon gases, such as CO and CO2, and through rapid microbial consumption of labile photoproducts (Kieb er et al ., 1990; Mopper et al ., 1991; Valentine and Zepp, 1993; Miller and Zepp, 1995, Mi ller and Moran, 1997). Photodegradation has been shown to alter the optica l properties of CDOM by reducing color, resulting in new spectral signatures.

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3 In the coastal environments, CDOM measurements are used for many purposes. It is conservative with respect to salinity (Cabaniss and Shuman, 1987) and can be used to track water masses (Del Castillo et al., 1999,2001; Kowalczuk et al .,2003; Stedmon et al ., 2003; Chen et al ., 2004; Conmy et al ., 2004b; Nelson et al. 2007). Color is routinely measured by monitoring and management age ncies as it is a measure of ecosystem health and CDOM fluorescence intensity ha s been shown to be a reliable proxy for Dissolved Organic Carbon (DOC) in some re gions (Ferrari et al ., 1996; Vodacek et al ., 1997; Del Castillo et al. ,1999, Baker and Spencer, 2004, Del Castillo 2005). Furthermore, CDOM can be measured remotely, and its presence interferes with remotely sensed determinations of chlorophyll a in the surface ocean (Carder et al. 1989; Muller-Karger et al. 1989). CDOM spectra have been shown to vary widel y by region due to differences in chemical composition (Blough et al. 1993) and a better understanding of its optical properties and chemica l characteristics is needed for the improvement of bio-optical algorithms, especially i n coastal waters. Presented in this dissertation are the results of a study examining CDOM characterization and distribution on the WFS, in coastal riversheds, the Tampa Bay Estuary and the Florida Aquifer system. The optical properties of CDOM, such as absorption coefficients, fluorescence intensities and ratios ( Del Castillo et al. 2001), position of the emission maxima at varying excitation wavelengths ( Coble, 1996), spectral slopes (Blough et al ., 1993), and apparent fluorescence efficiencies we re used to distinguish sources, establish seasonality and infer compositio n of the organic material in these aquatic environments. Findings were subsequently compared to discharge patterns and specific watershed basin characteristics to explain patterns.

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4 PART I: Spatial distribution of CDOM on the southern West F lorida Shelf.

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5 INTRODUCTION Dissolved Organic Matter (DOM) in seawater is the l argest reactive reservoir of carbon on earth (Hedges, 1992). Contained within it is th e photochemically active fraction, CDOM, which mediates the sunlit-induced reactions o f non-living systems. This material plays important roles in the marine enviro nment, affecting primary productivity by determining the quality and quantity of sunlight available for photosynthesis. CDOM also provides UV shading and nutrients to marine bi ota, and scavenges pollutants and metals, all of which influence biological productio n (Aiken, 2002; Hansell, 2002 and the refs. therein). Additionally, interference by CDOM with remotely sensed ocean color measurements, make it challenging to retrieve accur ate chlorophyll a (CHL) concentrations in the world’s oceans (Carder et al ., 1989; Muller-Karger et al ., 1989; Hu et al ., 2003; Del Castillo, 2005). In addition to being an important factor controllin g light penetration in coastal waters, CDOM is also important for the study of global ocea n carbon budgets because it is the only component of DOM that can be measured with in situ and remote sensors. This has significant implications, because establishing regi onal relationships between DOC and CDOM allows for making estimates of the larger orga nic carbon pool, based on a smaller, easier to measure component. Furthermore, because CDOM appears to have longer residence times than time scales of most est uarine and coastal mixing processes, it represents a significant portion of DOM that is exp orted to the open ocean. Longer time scales also mean that CDOM is an ideal water mass tracer and can be used to examine circulation in coastal and open ocean en vironments (Del Castillo et al., 1999,2001; Kowalczuk et al .,2003; Stedmon et al ., 2003; Chen et al ., 2004; Conmy et al ., 2004b; Nelson et al. 2007). In particular, this is important in regio ns with complex mixing of marine and terrestrial organic material, where strong gradients exist in chemical and optical properties of CDOM (Del Castil lo, 2005). This is the case on the West Florida Shelf (WFS), where the dominant source is terrestrial in nature, which originates from the many rivers on the eastern marg in of the Gulf of Mexico, but ever

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6 present is also the organic material of a marine so urce. There is a critical need not only to identify the source of CDOM in the coastal ocean but also, to understand how its optical properties are changing as mixing occurs on the shelf. Linking the primary factors that determine the distribution of CDOM on river-do minated margins (seasonal currents, precipitation, river discharge, winds, storms, etc. ) with the properties themselves will allow for untangling the ambiguities regarding the cycling and fate of organic material in the ocean. This in turn could make possible predic tive capabilities of DOM concentrations in coastal environments. Investigated in this chapter are the spatial distri butions of CDOM in the southern portion of the West Florida Shelf (WFS) between Tampa Bay a nd Florida Bay over a three year period. Seasonal differences were observed using d iscrete and in situ sampling techniques (a WetLabs’ SAFIre-Spectral Absorption a nd Fluorescence Instrument for underway mapping) to generate spatial maps. Differ ences in the optical properties of CDOM were used to infer differences in the composit ion of organic material. Results from this project advance the study of CDOM in coastal environments by (1) providing valuable in field measurements of spectra l slopes and fluorescence to absorption relationships for ocean color bio-optica l algorithms. This information helps to retrieve more accurate regional estimates of season al primary productivity. (2) Assessing variability in the relationship between CDOM and DO C in shelf environments during periods of high and low river discharge. (3) Demons trating the manner in which CDOM is affected by local forcing of winds, currents, st orms, discharge. Geographic Setting The West Florida Shelf (Figure 1.1) is located in t he eastern portion of the Gulf of Mexico. It is marked by a large shelf width as a r esult of the gentle sloping of the inner shelf. The WFS is a river-dominated environment, w here freshwater enters from various river sources along the northern and eastern margin s of the Gulf. Seasonality of riverine discharge, where northern rivers peak in spring and the southern Everglades rivers peak

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7 Figure 1.1. Map of West Florida Shelf in the Gulf of Mexico. The black square highlights the study location between Tampa Bay and the Florida Keys. in summer, gives rise to temporal and spatial diffe rences in the contribution of freshwater and materials (ie. metals, nutrients, organic matte r, suspended sediments) throughout the year on the shelf. In addition, unique environment s of the head waters result in compositional differences amongst rivers, including rivers that are controlled by dams or gates (Mississippi, Hillsborough, Calooshatchee) or ones that are swamp-fed (Atchafalaya, Suwannee, Shark) or ones that travers e agricultural lands (Peace). These are just some of the factors that influence the amo unt and type of freshwater making it to the WFS. Once on the shelf, materials originating from fresh water environments are mixed with those from marine waters. Seasonal patterns in wind s and currents then impact the distribution of said material in these coastal wate rs, where dominant forcing is to the south from October-April and to the north in summer months (Weisberg et al., 2005). Additionally, intermittent weather phenomena, such as hurricanes, tropical storms and

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8 winter storm events also influence substances in sh elf waters, and can result in the resuspension of sediments and dissolved material in to the water column. Distributions of productivity-critical substances such as nutrients, metals, organics and particles are also affected by weather phenomena. It is these distrib utions that are key in determining if, what type, and where phytoplankton blooms occur on the shelf. This is particularly important on the WFS, where Karenia brevis a toxic dinoflagellate, blooms nearly annually (late fall to winter) causing red tides th at affect the coastal ecosystem. Two of the field experiments (December 2004 and November 2 005) presented in Part I of this dissertation were conducted during times of Harmful Algal Blooms (HABs) of K. brevis. The active 2004 hurricane season has been proposed as a contributing factor to the persistent blooms that initiated in Fall 2004 (Hu e t al., 2006). The bloom moved south from the Charlotte Harbor region in October to the Florida Bay and Keys region, where high cell concentrations were observed in November 2004. In January 2005, high counts were also observed 30 mi offshore of Florida’s west coast. In April-May 2005, field measurements showed diminished cell concentrations, satellite imagery using a K. brevis classification criteria (Cannizzaro et al., 2008) s howed the bloom moved north and was never sampled. The bloom reappeared between Tampa Bay and Charlotte Harbor in July-August 2005 and cell concentrations continued to increase through November 2005. The bloom finally diminished in December 2005.

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9 METHODOLOGY Sample Collection Discrete water samples were collected during season al field experiments on the West Florida Shelf as part of the Florida Bay Circulatio n and Exchange Study (NOAA/AOML) and the Florida Red Tide Program (Florida Fish and Wildlife Conservation Commission) on board the R/V F.G. Walton Smith and the R/V Sunc oaster, respectively (Figure 1.1). The months sampled are as follows: December 2003, August 2004, December 2004, April 2005, August 2005 and November 2005. Surface and subsurface samples were collected via Niskin bottles for all field experime nts. During the Florida Bay Circulation and Exchange Study, whole water was collected in am ber glass bottles and filtered through pre-combusted GF/F filters (up to 24 hours at 450oC) on board using glass filtration apparatus and a pump. During the Florid a Red Tide Program cruises, water was gravity filtered through pre-combusted GF/F filters mounted in stainless steel in-line filtration apparatus. All filtered water was then stored frozen in pre-combusted, amber glass bottles until slowly thawed for absorption, f luorescence and Dissolved Organic Carbon (DOC) analysis. To verify that the freezing process did not result in any loss of chromophores, absorption spectra were collected pri or-to and after freezing (Figure 1.2). Absorbance Spectroscopy Absorbance spectra were obtained using a Hitachi U3300 double-beam spectrophotometer with matching one and ten centime ter quartz cells. Measurements were made at 1 nm intervals between 200 and 750 nm with Milli-Q deionized water in the reference cell. Samples were scanned three tim es and then averaged to reduce noise and yield a more robust spectrum. Data were corre cted for scattering and baseline fluctuations by subtraction from each wavelength, t he measured absorption at 700 nm (Bricaud et al ., 1981). Absorbance values were converted to abso rption coefficients using the following equation, a(l ) = 2.303A(l )/r,

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10 where A is the absorbance (Log Io/I) and r is the pathlength in meters. Spectral slopes were then calculated for a variety of wavelength ra nges between 250 and 440 nm using linear least squares regression. Figure 1.2. Results of freezing experiment to dete rmine if a loss of chromophores were apparent in absorption spectra. The findings show no significant change was observed using this method. Fluorescence Spectroscopy High-resolution fluorescence spectroscopy was perfo rmed on the discrete samples according to the method of Coble (1996) using a Hor iba Jobin Yvon Inc. Fluoromax II spectrofluorometer with a 450 Watt xenon lamp and s ingle excitation and emission monochromators. Samples with absorbance values abo ve 0.02 at 300 nm using a 1 cm

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11 cell were diluted prior to fluorescence analysis to avoid self-shading of the material (Green and Blough, 1994). Samples were analyzed in ratio mode with 5 nm bandwidths for excitation and emission. Forty-eight emission scans were collected at excitation wavelengths five nanometers apart between 220 and 4 55 nm. Emission wavelengths spanned between 250 and 700 nm, with data collected every 2 nm over an interval of 0.5 seconds (Coble, 1996). Three-dimensional excitatio n-emission matrices (EEMs) were generated by conjoining the individual spectra. Th e EEMs were normalized to a fixed value for Raman scatter at Ex/Em = 275/303 nm based on a single emission scan from the Milli-Q water daily blank and then corrected for sc atter at all wavelengths by subtracting a Milli-Q EEM (determined weekly). This procedure has been found to improve removal of first and second-order Raman scattering peaks. Blank-subtracted EEMs were corrected for instrument configuration using both e mission and excitation correction factors (Coble et al., 1993). Excitation correctio n factors were determined every two weeks using a fresh solution of saturated Rhodamine in ethylene glycol (0.8g / 100 mL). Emission correction factors were provided by the ma nufacturer. Finally, corrected fluorescence intensities were converted to units of quinine sulfate equivalents (QSE) in ppb using the fluorescence of a dilution series of quinine sulfate dihydrate in 0.05M sulfuric acid at Ex/Em = 350/450 nm (Velapoldi and Mielenz, 1980), where 1 QSE = 1 ppb quinine sulfate dehydrate. All processing was c onducted using Galactic Industries’ Grams 32 software. Dissolved Organic Carbon Dissolved Organic Carbon concentrations were determ ined by thermal catalysis using a Shimadzu TOC 5000 equipped with an ASI-5000 autosam pler. Prior to analysis, approximately 20-40 ml of sample were transferred f rom amber glass bottles into preashed, foil wrapped glass vials. For every millili ter of sample, 1 m l of concentrated hydrochloric acid (12.1N) was added to the vial and subsequently capped with foil. Samples were sparged for ten minutes with low-carbo n air to remove inorganic carbon from the sample water (Del Castillo, 1998). The in jection volume was selected as 100 m l, where samples were injected up to ten times. The be st three of ten peaks, with a standard

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12 deviation of 200 or less or a coefficient of varian ce of 2.0 % or less, were then averaged. DOC concentrations were calculated using a standard dilution of phthalic acid, where the range of the dilution series depended on the origin of the samples (up to 5ppm). Concentration of DOC in a MilliQ water blank was al so determined and subtracted from the samples. To assure instrument stability, stand ards were randomly run with samples and MilliQ water was injected between samples to ve rify baseline levels. Standard curves were performed weekly, with daily one-point calibrations conducted. High-Resolution Spatial Mapping Continuous, underway mapping of organic matter fluo rescence in surface waters was performed using a SAFIre (Spectral Absorption and F luorescence Instrument manufactured by WET Labs). Fluorescence output was stored with salinity and temperature (Seabird Electronics SBE-45 thermosalin ograph) and GPS information (Garmin, Inc) using a WET Labs Data Handler (DH-4). Data streams were merged and processed using the WAP (WET Labs Archive Processin g) program which extracted time-stamped raw data from archived files and appli ed calibration coefficients for all instruments. A Matlab binning routine was used on extracted data to yield data points every 0.3 km. Spatial maps of underway data were g enerated by kriging and blanking methods in Surfer mapping software, version 8.1. Underway data were unfiltered and represent COM (Co lored Organic Matter). The SAFIre measures fluorescence at six excitation and sixteen emission wavelengths, configured for optimum organic matter detection (ex citation range: 228-436 nm and emission range: 228-687 nm). Discrete filtered sea water samples were used to intercalibrate the SAFIre to the benchtop fluoromet er (Conmy et al., 2004a). Satellite Data Level-1A MODIS (Aqua) data were retrieved from the NASA Goddard Space Flight Center (GSFC) website (http://oceancolor.gsfc.nasa. gov) and processed to Level-2 using SeaDAS (version 5.0) software. CHL concentrations a nd CDOM absorption coefficients

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13 at 443nm were estimated using the Carder et al. (19 99) semi-analytical algorithm which can differentiate between phytoplankton and CDOM ab sorption. Fluorescence line height (FLH) data were calculated according to Abbott and Letelier (1999), where FLH is based on calibrated, normal water-leaving radiances. Thi s height is the intensity of upwelled radiance at 676.7 nm above the baseline created fro m 665.1 and 746.3 nm. Overestimations associated with FLH are attributed to the presence of suspended particles and differences in chlorophylla fluorescence efficiency of plankton. Waterleaving radiance data in three MODIS bands (551, 48 8 and 443 nm) were used to derive, composite enhanced RGB (ERGB) images. All images w ere stretched to the same scale in accordance with code from Chuanmin Hu and the US FInstitute of Marine Remote Sensing. Atmospheric affects have been removed fro m imagery. All processing was conducted by the USF – Optical Oceanography Laborat ory.

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14 RESULTS & DISCUSSION The coastal waters of the West Florida Shelf are he avily influenced by the multiple rivers to the north and the east. As a result, the CDOM p ool on the shelf is mainly due to the mixing of fresh water and seawater. For the field experiments on the shelf between 2003 and 2005, this mixing line, essentially the relatio nship between CDOM fluorescence and salinity, was found to be relatively constant, with a slope of ~4 (Figure 1.3). Separating data by the amount of river discharge, denoted here as high-flow and low-flow conditions (relates to classifications and values in Table 1.1 and Figure 1.4), showed no distinct difference in the mixing line, therefore the regres sion reported in the figure is for both conditions. Figure 1.3. CDOM fluorescence at Ex/Em 300/430 nm for seasonal cruises on the West Florida Shelf. Both dry and wet seasons fall on th e same mixing line, with the exception of hypersaline waters during August 2005.

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15 Table 1.1 Date of WFS field experiments along with USGS and CHNEP discharge and flow classifications. Flows represent monthly aver ages. Data from www.chnep.org. Month and Year of Field Experiments USGS Discharge in Peace River at Bartow Station (cfs) Charlotte Harbor NEP Flow Classification December 200345Normal August 2004667.2Low, then High after Hurricane December 2004186.5Normal to High April 2005188Normal to High August 2005756.5High November 2005371.8Normal Figure 1.4. Peace River discharge data from the Bar tow Station (Source: USGS).

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16 This inverse relationship was found for all samples with salinities less than 36. Above this salinity, however, the trend reverses and CDOM was found to vary proportionately with salinity for a subset of samples taken during August 2005. This positive correlation has been previously observed on the southern portio ns of the shelf (Coble, 2004) during the rainy months of the summer season. The product ive, shallow sediments of this portion of the shelf produce large concentrations o f CDOM, which enter the water column. Coincident evaporative processes result in high salinity water masses that in turn contain this newly produced organic matter (Coble, pers. commun.). Spatial Distribution of COM Fluorescence The fact that CDOM follows a quasi-conservative mix ing line (de Souza Sierra et al ., 1997; Del Castillo et al ., 2000), isn’t necessarily worthy of note in this region, but how the spatial distributions vary by season is. Gener ally, during the South Florida rainy season (summer months) there is a higher concentrat ion of CDOM on the shelf as a result of increased contribution of rivers. Conversely, d uring times of little rainfall (winter months), lower concentrations would be expected in these coastal waters. Although this is a reasonable generalization, the field experimen ts in this project show numerous exceptions. Plotted in Figure 1.5 are the spatial distributions of COM for surface waters on the shelf for all months sampled between 2003 an d 2006. It is important to note that due to the shallow nature of the shelf and the clos e proximity to shore of the field experiments, there tended to be no vertical structu re throughout the water column. Of the approximately 200 samples collected, only 24 statio ns exhibited a two layer water mass. Given this low percentage, and that the waters in t his region of the shelf are well mixed, it is reasonable supposition that when looking at t he spatial maps, the patterns observed in the surface waters would be similar at the botto m of the water column as well. Of all the months sampled, December 2003 had the lo west flow conditions for the Bartow station, and concentrations over much of the shelf were below 12 QSE. Higher concentrations were observed in the southernmost se ctions of the cruise track, near

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17 Florida Bay, which also corresponded to a decrease in salinity (Figure 1.6). Although dry conditions dominated the region, the Everglades exp erienced some isolated rain events during this month. During April 2005, discharge wa s also very low (188 cfs) and the distribution of COM resembled that of December 2003 Freshwater contribution from from the Everglades, however, was not observed duri ng this month, therefore no elevated COM values were found. In contrast, August 2005 had the highest discharge of all the months sampled, and higher COM concentrations were observed on the shelf. This is also the experiment where the hypersaline CDOM signature was observed, just to th e west of Florida Bay. Due to the large amounts of rainfall that Florida experiences during the months of August, high organic matter concentrations would be expected ove r much of the shelf, however this was not the case for August 2004. Two field experi ments were conducted that month, and the spatial plots reveal patterns and concentra tions more similar to dry season conditions. This is due primarily to (1) a late st art to the wet season that year, where discharge was low in the early parts of August 2004 (see Figure 1.4), and (2) the passing of Hurricane Charley five days before the first fie ld experiment. This major storm (category 4 at landfall in Punta Gorda on August 13 2004) approached Florida from the southwest and forced offshore water, with lower CDO M concentrations, onto the shelf, mixing it with shelf water. Winds and currents on the shelf from the day of the storm are shown in Figure 1.7 (left panel) and illustrate the direction of the currents. During the two field experiments, the prevailing winds and cur rents were to the north (middle and right panels). The distribution of low-COM waters (and also low-CHL) on the shelf during this time was also observed in satellite ima gery from MODIS-Aqua. Dark waters in the enhanced RGB (ERGB) and the Fluorescence Lin e Height (FLH) imagery for pre and post-hurricane days are shown in Figure 1.8A. The water-leaving radiance data of ERGB gives more information on detecting spatial fe atures due to the use of three MODIS bands, as compared to using to only two bands in CHL imagery. The images reveal that prior to the hurricane, dissolved organ ic material was present between Charlotte Harbor and the Florida Keys. After the s torm, however, dark color wasn’t as

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18 Figure 1.5. COM Spatial distributions on the West Florida Shelf collected with the WetLabs Inc SAFIre Measurements obtained using continuous flow-through system of un filtered surface waters.

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19 Figure 1.6. Salinity spatial distributions for sea sonal cruises on the West Florida Shelf.

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20 Figure 1.7. Maps of currents and wind data from buo ys operated by USF Ocean Circulation Group ( http://ocg7.marine.usf.edu/~liu ). Left panel is during the passage of Hurricane Charl ey, middle panel is 4 days after the storm, and rig ht panel is 10 days after the storm in August 2004. Longitude Longitude LongitudeLatitude Longitude Longitude LongitudeLatitude

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21 Figure 1.8A. Enhanced RGB (R: 551,G: 488,B: 443nm) (top panels) for three days in August 2004 around the time of the passing of Hurricane Charley. Bottom panels are the Fluoresce nce Line Height (mW / cm2 / m /sr) for the same days. Imagery supplied by USF-Optical Oceanography Laboratory. 11Aug04 (pre-hurricane)17Aug04 (4 days post-hurrica ne)23Aug04 (10 days post-hurricane) 11Aug04 (pre-hurricane)17Aug04 (4 days post-hurrica ne)23Aug04 (10 days post-hurricane) 11Aug04 (pre-hurricane)17Aug04 (4 days post-hurrica ne)23Aug04 (10 days post-hurricane)

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22 prevalent and the observed white colors indicate ei ther suspended or bottom sediments. Given that the SAFIre measures COM, and not CDOM, t he presence of suspended particle matter (SPM) can increase the fluorescence signal in these unfiltered instruments. The observed low-COM values suggest that the white colors in the ERGB imagery are most likely from the bottom and not SPM. Hence, if SPM, CDOM and CHL were all low, the water column may have been optically clearer an d signal from the bottom sediments would be more apparent at this time. Absorption du e to gelbstoff and detritus at 443nm (adg443) and CHL estimates are shown in Figure 1.8B The imagery also shows that there was minimal biomass on the West Florida Shelf at this time and that low values of adg443 five days after the storm are in agreement w ith the in situ COM measurements. By the second field experiment, the contribution fr om terrestrial sources increased as a result of the large amounts of rainfall from the hu rricane, subsequently, higher COM concentrations were observed compared to the experi ment five days earlier, but still low compared to August 2005. Episodic storm events can also affect the underwate r light field during dry season months as was observed during December 2004. Again, there were two field experiments during this month and the distributions of COM fluorescenc e south of Charlotte Harbor are quite dissimilar. The first cruise showed low concentrat ions over the shelf that correlates with salinity patterns. The second cruise, however, show ed strong fluorescence signal over the entire southern portion of the field experiment, bu t with no corresponding change in salinity, indicating that freshwater was not the so urce of the increase. At the time of the second field experiment, a storm event occurred (De cember 15, 2004), as evidenced by the current and wind vectors (Figure 1.9, right pan el). ERGB imagery in Figure 1.10A (right panel) shows a significant increase in the p roportion of white colors, most likely the result of resuspended particles from the storm’ s passing. Such increases in non-algal particles can interfere and result in higher COM va lues, as well as any dissolved material released from the sediments at the time of resuspen sion (Boss et al ., 2004).

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23 Figure 1.8B. Imagery of CHL with the removal of ge lbstoff (top panels) and adg443 (bottom panels) for three days in August 2004 around the time of the passing of Hurricane Charley Units are mg / m3 for CHL and m-1 for adg443.11Aug04 (pre-hurricane)17Aug04 (4 days post-hurrica ne)23Aug04 (10 days post-hurricane) 11Aug04 (pre-hurricane)17Aug04 (4 days post-hurrica ne)23Aug04 (10 days post-hurricane)

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24 Figure 1.9. Maps of currents and wind data from buo ys operated by USF Ocean Circulation Group ( http://ocg7.marine.usf.edu/~liu ). Left panel is during a winter storm event and right panel a week after the storm in Dec ember 2004. LatitudeLongitude Longitude LatitudeLongitude Longitude

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25 Figure 1.10A. Enhanced RGB (R: 551, G: 488, B: 443 nm) (top panels) for two days in December 2004. Bottom panels are the Fluorescence Line Height (mW / cm2 / m /sr) for the same days. Imagery supplied by USF-Optical Oceanography Laboratory. 06Dec04 (pre-storm field experiment)15Dec04 (during storm field experiment) 06Dec04 (pre-storm field experiment)15Dec04 (during storm field experiment)

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26 The FLH images show a marked difference from before and during the storm, indicating an increase in CHL during the latter experiment. T his is also observed in the CHL images in Figure 1.10B (top right panel). This inc rease during the second field experiment was most likely the result of an observe d Karenia brevis bloom in the southern portions of the shelf. Cell concentrations from Florida Fish and Wildlife Conservation Commission – Florida Wildlife Research Institute (FWC-FWRI) are plotted over the COM spatial map in Figure 1.11. Figure 1.10B. Imagery of CHL with the removal of g elbstoff (top panels) and adg443 (bottom panels) for two days in December 2004. Uni ts are mg / m3 for CHL and m-1 for agd443. 06Dec04 (pre-storm field experiment)15Dec04 (during storm field experiment) 06Dec04 (pre-storm field experiment)15Dec04 (during storm field experiment)

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27 Figure 1.11. Karenia brevis cell counts from FWC/FWRI plotted on top of the CO M fluorescence spatial maps shown previously. Left p anel is for the second December 2004 field experiment and the right panel is for Novembe r 2005. Regions with the highest cell counts (up to a milli on cells / L) coincide with high fluorescence intensity. Additionally, the observed increase in COM fluorescence during the second December 2004 field experiment concurs w ith an increase in adg443 in the satellite imagery. A plausible explanation for the observed high-COM values is most likely a combination of factors including the (1) p resence of suspended particles as the experiment occurred during the passing of the storm (2) dissolved materials released from red tide, (3) dissolved materials resuspended from the sediments and possibly even (4) benthic diatoms that may be put into suspension. G iven the data available it is difficult to assess which factor dominated. Above, it was shown that hurricanes can drastically alter IOP concentration and distribution on the shelf, but how they are changed and to what extent depends greatly on the characteristics of the storm (from strength, to direction, to storm surge, to wind patterns) and the ambient climate before the storm passes over a region. During

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28 November 2005, South Florida was impacted by anothe r major storm, Hurricane Wilma (category 3 at landfall in Cape Romano on October 2 4, 2005), and the field experiment for this month occurred eight days after its passin g. Winds and currents on the shelf pre, during and post-hurricane are shown in Figure 1.12. Highest concentrations of organic matter were observed during this experiment and wer e widespread over much of the shelf. When reviewing the ERGB imagery (Figure 1.1 3A) it is apparent that unlike the August and December 2004 experiments, there exist e xtensive areas of dark colors prior to the passing of the hurricane. This may have res ulted from either the end of the wet season accumulation of CDOM on the shelf and / or t he presence of the persistent HAB of K. brevis that blanketed the coast (Figure 1.11, right panel). The FLH imagery shows widespread phytoplankton signal during this pre-Hur ricane Wilma time period. Imagery from eight days after the storm shows the presence of lighter colors (ERGB) at the southern portion of the WFS. This is most likely d ue to bottom sediments, as this region is shallow and the imagery is from more than a week after the storm, thereby giving ample time for particles resuspended during the sto rm to settle out. The FLH imagery shows a concentration effect of the phytoplankton b etween Tampa Bay and Charlotte Harbor, which is also seen in the CHL and adg443 im agery (Figure 1.13B). It also shows high gelbstoff in the southern and inshore po rtions near Florida Bay. In situ measurements found the highest COM fluorescence and lowest salinities near Florida Bay, but the imagery impedes comparison due to the presence of clouds in this region. If the high COM signal didn’t result from interfere nce with suspended particles, then an explanation for the high intensities during the Nov ember 2005 includes the following components: (1) The storm physically concentrating the bloom and any of the dissolved organic material produced by the prolonged K. brevis bloom. (2) Vigorous mixing of shelf waters and the HAB could have released additi onal dissolved organic material from the cells. (3) Dissolved Organic Matter from the s ediments becoming suspended in the water column and remaining in solution long after t he particles settled out.

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29 Figure 1.12. Maps of currents and wind data from bu oys operated by USF Ocean Circulation Group ( http://ocg7.marine.usf.edu/~liu ). Left panel is before Hurricane Wilma, middle pa nel is during the storm and right panel after the hurricane in November 2005. LatitudeLongitude Longitude Longitude LatitudeLongitude Longitude Longitude

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30 (4) The storm occurring at the end of the rainy sea son, when the shelf and the rivers had already accumulated high amounts of organic materia l. The rain from the storm could have resulted in a flushing of easily transported t errestrial material resulting in higher color on the shelf. Figure 1.13A. Enhanced RGB (R: 551,G: 488,B: 443nm ) (top panels) for two days pre and post-Hurricane Wilma. Bottom panels are the Fl uorescence Line Height (mW / cm2 / m /sr) for the same days. Imagery supplied by USFOptical Oceanography Laboratory 18Oct05 (pre-hurricane)02Nov05 (post-hurricane) 18Oct05 (pre-hurricane)02Nov05 (post-hurricane)

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31 Figure 1.13B. Imagery of CHL with the removal of g elbstoff (top panels) and adg443 (bottom panels) for two days pre and post-Hurricane Wilma. Units are mg / m3 for CHL and m-1 for adg443. Absorption Measurements The ability to derive CDOM absorption from fluoresc ence measurements is of great interest to coastal zone researchers, and given tha t this relationship varies by region, field observations are necessary for its determination on the West Florida Shelf. Fluorescence and absorption values of discrete samples from each of the field experiments are plotted in Figure 1.14. Strong correlations, independent o f depth, location, or season were found between fluorescence and absorption coefficients at 312 and 440 nm. This demonstrates promise for deriving CDOM absorption values from fl uorescence measurements, not only from discrete samples, but in situ measurements as well. 18Oct05 (pre-hurricane)02Nov05 (post-hurricane) 18Oct05 (pre-hurricane)02Nov05 (post-hurricane)

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32 Figure 1.14. Relationship of CDOM fluorescence to absorption at 312 and 440 nm for all cruises on the West Florida Shelf. The relationship of fluorescence and absorption fro m the shelf experiments holds true for a wide range of concentrations. In addition to thi s concentration range, there is also a wide range in spectral shape of the absorption spec tra. Figure 1.15 shows examples of seven different waters observed during the field ex periments. Steeper slopes were expectedly found for low CDOM, high salinity waters and the lowest slopes were for high CDOM, low salinity waters between 280-312 nm ( Table 1.2). The large dashed lines show the differences between the absorption c urves from waters affected by Hurricanes Charley and Wilma, where the former resu lted in waters with increased clarity over much of the shelf and the latter, resu lted in increased COM concentrations.

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33 Figure 1.15. Absorption spectra for different wate r types sampled during cruises to the West Florida Shelf. Spectra from before (gray small dashed line) and af ter (black small dashed line) the December 2004 storm event show little difference. This supports the hypothesis that the higher signal observed in the spatial maps may have resulted in part from suspended particles. Spectral slopes can be calculated over different re gions of the absorption spectrum. Two spectral slope ranges are plotted in Figure 1.16 an d show how the slope parameter changes with salinity, and the wavelength dependent differences in the parameter. The spectral slope parameter for narrow, more blue-shif ted (shorter) wavelength ranges (in

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34 Table 1.2. Absorption data for the water types sho wn in Figure 1.15. DesignationSalinity Ex/Em = 300/430 nm Spectral Slope 280-312nm (m-1) Spectral Slope 350-412nm (m-1) Spectral Slope 350-440nm (m-1) High CDOMLow Salinity24.7554.610.019380.017630.01 788 High CDOM-High Salinity38.0211.450.025070.018320.01 799 Low CDOM-High Salinity35.511.410.032250.013340.0129 4 Post Hurricane Charley36.031.310.030740.011780.0107 2 Post Hurricane Wilma34.0219.720.022190.011600.01074 December 2004 Pre Event34.5710.850.021970.018240.01 842 December 2004 Post Event34.669.040.021890.017320.01 698 Designationa(280) (m-1)a(312) (m-1)a(350) (m-1)a(440) (m-1)DOC ( M) High CDOMLow Salinity33.8818.479.541.95411.15 High CDOM-High Salinity8.153.731.740.34193.56 Low CDOM-High Salinity0.910.330.140.04148.51 Post Hurricane Charley1.290.500.240.0982.85 Post Hurricane Wilma12.946.553.481.41207.74 December 2004 Pre Event6.333.181.540.29195.29 December 2004 Post Event7.443.751.780.39155.47

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35 Figure 1.16. Spectral slope values for seasonal cr uises on the West Florida Shelf. Slopes calculated by linear least squares regression for t wo ranges: 280-312 and 350-440 nm.

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36 the top panel) show clearer differences between the various water types, as indicated by the arrows in the plot. However, parameters calcul ated out to 440 nm (bottom panel) are necessary for remote sensing algorithm applications but have greater signal to noise ratios. In general, higher slopes at lower wavelen gths indicate more refractory, less aromatic organic material, similar to what is found in the open ocean. Conversely, lower slopes point to more complex material. The top pan el in Figure 1.16 shows a progression from lower to higher slopes with salinity (a decrea se in complexity) from waters taken after Hurricane Wilma, the December 2004 storm, Hur ricane Charley, and for the hypersaline water of August 2005. Spectral Changes Related to the spectral slope parameter are differe nces in the fluorescence EEM fingerprint. These matrices allow for determinatio n of spectral shape and position of peaks, as well as how the humic peaks change relati ve to each other. These parameters serve as proxies for compositional differences of o rganic matter within sample waters. The same examples shown in the absorption curves of Figure 1.15 are plotted as EEMS in Figures 1.17 A, B and C. Refer to Table 1.3 for list of peak positions and fluorescence intensities. Similar fingerprints were found for th e high CDOM low salinity and high CDOM high salinity waters. Organic matter fluore scence after the passage of Hurricane Wilma also resembled these waters (Figure 1.17B, to p panel). Recall that the spatial maps during the November 2005 experiment showed wid espread distributions of high COM concentrations. Fluorescence matrices after th e passage of Hurricane Charley, however, are most similar to that of the low CDOM high salinity fingerprints. Figure 1.17C shows the EEMS from before (top) and after (b ottom) the December 2004 event. The matrices look similar to each other in both con centration and peak positions, indicating some similarity of the dissolved organic pool. Both EEMS also possess protein-like fluorescence peaks (Table 1.4 lists th e wavelength range for protein-like peaks). These matrices, however show differences i n the spectral shape of the humic peaks, indicating that the organic material before and after the storm event differed in some way.

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37 Figure 1.17A. Excitation Emission Matrices (EMS) f or three water types on the West Florida Shelf. Top panel is a high CDOM, low salin ity water. Middle panel is for hypersaline waters with high concentrations of CDOM Bottom panel is high salinity, low CDOM water. Humics Peaks A and M are demarked in the top right contour. Scale of contour plots are set to the maximum fluorescenc e intensity in the corresponding 3-D plots on the left. High CDOM –Low SLow CDOM –High S High CDOM –High S Peak M Peak A High CDOM –Low SLow CDOM –High S High CDOM –High S High CDOM –Low SLow CDOM –High S High CDOM –High S Peak M Peak A

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38 Figure 1.17B. EEMS for water after the passage of Hurricanse Wilma (top) and Charley (bottom). Both are high salinity waters, but posse ss differences in CDOM concentration. Scale of contour plots are set to the maximum fluor escence intensity in the corresponding 3-D plots on the left. Hur. Charley, August 2004 Hur. Wilma, November 2005 Hur. Charley, August 2004 Hur. Wilma, November 2005

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39 Figure 1.17C. EEMS for water before (top) and afte r (bottom) the passage of the December 2004 storm event. Scale of contour plots a re set to the maximum fluorescence intensity in the corresponding 3-D plots on the lef t. Pre-Storm Event Post-Storm Event

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40 Table 1.3. Peak positions and intensities for EEMs shown in Figure 1.17. EEM ID MonthYearSalinity Ex Humic Peak A (nm) Em Humic Peak A (nm) Fluor. Intensity Humic Peak A (QSE) Ex Humic Peak M (nm) Em Humic Peak M (nm) Fluor. Intensity Humic Peak M (QSE) High CDOM-Low SalintyAug-0524.75235427.85108.083054 19.8455.73 High CDOM-High SalinityAug-0538.02235419.5223.90305 407.3012.09 Low CDOM-High SalinityDec-0335.51235403.403.4830040 1.641.63 Pre-storm eventDec-0434.57235419.5222.35305411.6111 .33 Post-storm eventDec-0434.66230417.2122.84300414.349 .32 Hurricane WilmaNov-0533.90235416.0719.04305403.5810 .44 Hurricane CharleyAug-0436.03235395.302.73295382.671 .45

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41 Table 1.4. EEM peak positions previously found by Coble, 1996. ComponentPeak NameEx/Em (nm) tyrosine-like, protein-likeB275 / 305tryptophan-like, protein-likeT275 / 340unknownN280 / 370UVC humic-likeA260 / 400-460UVA marine humic-likeM290-310 / 370-410UVA humic-likeC320-360 / 420-460pigment-likeP398 / 660 One way to visualize these differences, is to plot the position of Humic Peak M emission maxima as a function of salinity (Figure 1.18). As initially reported by Coble, 1996, Peak M has been previously identified at Ex/Em = 29 0-310 / 370-410 nm (Table 1.4). The position of Peak M showed the largest range dur ing high flow conditions (379425 nm), where the most red-shifted peaks, corresponded to lower salinities and higher concentrations of organic material. Peaks at longe r wavelengths during the wet season are explained by the occurrence of higher river dis charge that carries with it CDOM of terrestrial origin to the shelf. This material has been shown to be more red-shifted, colored, labile and compositionally complex (McKnig ht et al., 2001; Aiken, 2002, Stedmon and Markager, 2005) as compared to marine o rganics (Shark River water was added to this plot for comparison). Conversely, the occurrence of peaks at shorter wavelengths during the wet season is the result of warmer temperatures and high sun exposure of these summer months. These conditions are favorable for photodegradation of organic material, which produces, smaller, less colored organically resistant material that results in a blue-shifting of the fluorescence peaks (Zepp and Schlotzhauer, 1981). The most blue-shifted peaks were found after the pa ssage of Hurricane Charley. The

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42 hypersaline waters also show peaks at shorter wavel engths. Shelf waters after the passage of Hurricane Wilma possessed peaks between 400-415 nm, suggesting a strong terrestrial signature where salinities were low. D uring November 2005 for higher salinity waters, these peak positions could have also arose from productivity in the sediments or water column. Samples taken after the winter storm event were also more red-shifted, but had higher salinities, suggesting runoff was no t the source, but from marine productivity (either within the water column or the sediments) instead. Figure 1.18. Position of fluorescence maxima for H umic Peak M for West Florida Shelf waters. The range in position is greatest during h igh flow conditions. Peak C for Shark River water is added for reference. Shark River da ta is included for reference. The position of peak maxima can also be demonstrate d with spatial maps for each field experiment (Figure 1.19). It is important to note that the contour maps shown previously in Figure 1.5 were generated with in situ unfiltered water, whereas filtered, discrete samples were used in the plots of Figures 1.19 and 1.22. Red colors in the spatial map represent more complex material and blue colors are indicative of more refractory marine

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43 CDOM. Bluest positions were found after the passag e of Hurricane Charley and conversely, reddest positions were observed during August 2005, when discharge was highest. The hypersaline waters show positions clos e to 400 nm. During November 2005, post Hurricane Wilma, peak positions of longe r wavelengths dominated the shelf (peaks > 400 nm) and are most likely related to the flux of organic material from the sediments, lysis of cells from the phytoplankton bl oom and from terrestrial runoff due to increased rain brought by the storm. For the case of December 2004, unfortunately, only a few samples were taken south of Charlotte Harbor, so the storm event cannot be documented spatially with discrete samples. At the time of lowest flow (December 2003), we also see patterns similar to August 2004, but with a smaller range in peak position. This pattern would have been expected wi th April 2005, as well, but because samples were only taken at inshore stations, no off shore blue-shifted material was sampled. Another parameter to help describe the composition of organic material on the shelf is the ratio of humic peaks A (UVC region) to that of humi c peaks C or M (UVA region) (Figure 1.20). Many of these coastal samples have ratios of approximately 2 (Shark River samples were also found to be 2). Shelf water s after Hurricane Wilma tended to be between 1 and 2, where as Hurricane Charley samples were mostly above 2. The winter storm event exhibited higher ratios as well.

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44 Figure 1.19. Spatial distributions of Humic Peak M position for seasonal cruises on the West Florida Shelf. Contours generated using discrete samples, as compared to previous spatial m aps of in situ data.

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45 Figure 1.20. Ratio of Humic Peaks A and C/M as a f unction of CDOM fluorescence intensity and salinity for all field experiments. Shark River ratios are included for comparison.

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46 CDOM and Dissolved Organic Carbon Measurement of DOC concentration is a labor intensi ve process, and there is a critical need for developing proxies for this material. In c oastal regions, recent studies have found that DOC is highly correlated with CDOM (Ferr ari et al ., 1996; Vodacek et al ., 1997; Del Castillo et al. 1999, Del Castillo 2005). Using fluorescence meas urements to estimate critical organic carbon concentrations can foster better estimates of terrestrial carbon export in coastal waters. Plotted in Figure 1.21 is the relationship of carbon to fluorescence for the West Florida Shelf field exper iments. Separating data by flow conditions results in two regression lines, where h igher color is observed per DOC unit during periods with increased discharge onto the sh elf. During December 2003 (time Figure 1.21. The relationship between Dissolved Or ganic Carbon (DOC) and CDOM fluorescence on the West Florida Shelf. Regression s calculated without samples from December 2003 or the high DOClow CDOM values.

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47 of smallest discharge), no relationship could be fo und between the two parameters on the shelf. There are also a number of data points that show elevated organic carbon relative to fluorescence, that were not included in the regr essions, as these were outliers compared to the remaining samples. The spatial plo t for December 2003 (Figure 1.22), when no correlation with CDOM could be found, shows higher values found in waters influenced by the Everglades and Florida Bay. Duri ng the experiment with the highest discharge (August 2005), highest DOC was found near the mouth of the Caloosahatchee and the Everglades rivers. Figure 1.22. Spatial distributions of DOC on the W est Florida Shelf. Left panel is December 2003, right panel is August 2005.

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48 CONCLUSIONS Generally, waters on the West Florida Shelf exhibit ed a strong correlation between CDOM and salinity. This suggests that although the re are numerous sources of this material to the WFS, to the first order, mixing is the dominant factor controlling CDOM. An inversion in the CDOM-salinity relationship was observed for waters adjacent to Florida Bay, where a hypersaline, CDOM-rich water m ass was found during the 2005 summer season. Temporal variability in distribution patterns of CDOM concentrations, spectral properties and DOC was found. This is att ributed to seasonal differences in river discharge, the presence of HABs and the occurrence of episodic storm events. The first of these events, the passage of Hurricane Charley i n August 2004 resulted in low fluorescence intensities over the entire shelf. Li ttle terrestrial organic matter was detected as evidenced by high spectral slopes at lo w wavelengths and blue-shifted fluorescence peaks. A dry season storm event in December 2004 was shown to cause an increase in fluorescence intensity using in situ instruments as compared to the field experiment ju st prior to the storm’s passing. Spectral slope and E EM data showed no significant change in the optical properties of discrete samples prior to and after the event, suggesting that the change measured by in situ sensors was due prim arily to particles in the unfiltered seawater. This was supported by satellite ERGB ima gery. Additionally, the presence of a red tide and DOM from the sediments may also have attributed to the higher signal. Highest fluorescence intensities were observed on t he shelf during November 2005 after the passing of Hurricane Wilma eight days earlier. Spectral slope values were low, fluorescence peaks were red-shifted, and both high and low-salinity waters were observed at this time. This indicates that the organic mate rial originated from rivers and marine sources. The marine sources may have been DOM from the sediments and from the persistent phytoplankton bloom that blanketed the s helf.

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49 The work presented here illustrates the dynamic nat ure of the West Florida Shelf and ways in which discharge patterns and physical forci ngs influence the distribution of CDOM. Results of this project can be used to groun d truth ocean color products for a variety of scenarios: high versus low river discha rge, hurricanes, resuspension events and bloom periods. One component, river discharge of C DOM is the focus of the next section of this dissertation, where seasonal and sp atial variability is examined.

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50 Part II: CDOM optical properties in Florida’s Gulf Coast riv ersheds: A regional comparison

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51 INTRODUCTION Colored Dissolved Organic Matter (CDOM) is photorea ctive, an important reservoir, and an integral component of the global carbon cycle. I t plays crucial roles in biogeochemical processes such as metal binding capacity (Aiken, 20 02), pollutant and material transport across reservoirs (Hansell, 2002), microbial loop d ynamics, and nutrient cycling. It also affects water clarity, dissolved organic carbon flu x, drinking water quality, and UV shading of aquatic biota. Its presence determines the coastal euphotic zone, serving as both a limitation for certain primary producers (ie seagrasses) and at the same time as essential sunscreen for organisms (ie. corals) that require protection from harmful UV radiation. Due to these reasons, CDOM serves as a measure of ecosystem health and an indicator of environmental change for restoration-c ritical species and resource managers. CDOM is chemically complex and its composition is s ource specific (Cauwet et al., 1990; Coble, 1996; Boss et al ., 2001; Stedmon et al ., 2003; Chen et al., 2004 Baker, 2001, 2002; Baker and Spencer, 2004). Its optical properties vary for waters impacted by forests, wetlands, urbanization, sewage effluent, a gricultural practices, local biology and groundwater discharge. Due to the source specific nature of CDOM composition, the optical properties of this material can be used as a water tracer. Parameters, such as absorption coefficients, fluorescence intensities a nd ratios (Del Castillo et al. 2001; McKnight et al ., 2001; Conmy et al ., 2004), positions of fluorescence maxima (Coble, 1996), spectral slopes (Blough et al ., 1993), and apparent fluorescence efficiencies al l have been used to distinguish sources and observe C DOM transformations through ecosystems. The strong conservative behavior of CDOM and salini ty in coastal waters gave rise to the notion that a true ‘endmember’ value at zero salini ty in rivers must exist and that a universal endmember could be established for a wate rshed in order to account for the conservative mixing observed in shelf waters. Find ings of recent studies dedicated to examining CDOM spectral properties in river systems have countered that perception (Del Castillo et al ., 1999; Baker, 2002; Kowalczuk, et al 2003; Chen et al ., 2004,

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52 Conmy, et al ., 2004; Coble, 2007 and ref. therein). Locally, p rojects investigating the rivers supplying the West Florida Shelf (WFS) (Boeh me, 2000; Stovall-Leonard, 2003), showed that Dissolved Organic Matter (DOM) in water sheds can possess distinguishing optical characteristics. Although these studies sh ed much light on the differences between selected watersheds on the Gulf Coast, an a ssessment of temporal variability or a comprehensive comparison of rivers by region was ou tside the scope of those projects. There is a critical need to establish the spatial a nd temporal variability of CDOM and DOC in rivers and estuaries. This is especially t rue for river-dominated margins, like the WFS, where freshwater originates from numerous rive rs. In such regions, the ability to understand regional carbon dynamics is necessitated by establishing CDOM variability in said rivers. Additionally, an understanding of how CDOM distribution is influenced by watershed-basin characteristics (soil composition, land cover, soil permeability) and climatic patterns is also required to determine how CDOM is transferred through various environmental regimes. Investigated in this study is how optical propertie s (absorption coefficients, fluorescence efficiencies, intensity, ratios and peak position) and DOC vary spatially and temporally for watersheds between the Atchafalaya/Mississippi River system in Louisiana and the Shark River in the Everglades for a two year time p eriod. Implicated in the cause for differences among rivers are differences in basin c haracteristics of the watersheds.

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53 METHODOLOGY Seasonal sampling of the Atchafalaya, Mississippi, Apalachicola, Suwannee, Hillsborough, Alafia, Manatee, Peace, Caloosahatche e, and Shark Rivers was conducted over a two year time period during 2003-2005 (Figur e 2.1). Water samples and in situ measurements were taken from the banks of rivers or from docks at various locations along the river, with the exception of the Shark Ri ver (Table 2.1). For this river, a boat from the Keys Marine Lab was used to obtain river w ater. Up to four samples were taken from each stream to encompass a salinity range from the river mouth to zero-salinity waters. In situ measurements of salinity, conductivity, temperatur e, and pH were taken via a Hydrolab Sonde. Samples for DOC, fluorescenc e and absorption analyses were collected wearing polypropylene gloves into pre-com busted glass amber bottles (450oC for 24 hours) and stored on ice (1-5 hours) until r eturning to the lab or an alternate nearby facility. Water was then filtered using the same p rotocols stated in Part I of this dissertation. For DOC measurements, sample water w as stored frozen for up to one year, until thawed for analysis. Prior to absorption and fluorescence analyses, samples were either stored refrigerated for no more than two day s or frozen and then slowly thawed depending on instrument availability. Using the pro tocols in Part I, absorption spectra were measured to determine if and to what extent di lution was needed following Green and Blough (1994). Appropriate dilutions were made and then water was reanalyzed for absorption and subsequently analyzed for excitation emission fluorescence spectroscopy. Again, stated in Part I are the procedures for samp le processing for all analyses.

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54 Figure 2.1. Map showing locations of the rivers sa mpled in Florida (top) and in Louisiana & Mississippi (right). Rivers include the Atchafalaya, Mississippi, Apalachicola, Suwannee, Hillsborough, Alafia, Manat ee, Peace, Caloosahatchee, and Shark River. Apalachicola Suwannee Hillsborough Alafia Manatee PeaceCaloosahatchee Shark Mississippi Atchafalaya Apalachicola Suwannee Hillsborough Alafia Manatee PeaceCaloosahatchee Shark Apalachicola Suwannee Hillsborough Alafia Manatee PeaceCaloosahatchee Shark Mississippi Atchafalaya Mississippi Atchafalaya

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55 Table 2.1. Locations (GPS positions and landmarks) of river samples. River Landmark Latitude Longitude AlafiaDeSoto Road27.87669-82.31293 Riverview Park27.86606-82.31868US 41 Williams Park27.86040-82.38473 ApalachicolaBreakaway boat ramp29.75860-85.02423 Water Street29.72373-84.9808198 Causeway29.95234-84.46589US 98 Bridge29.73489-84.90253St. George Island 29.66946-84.86631 AtchafalayaBerwick29.69413-91.21608CaloosahatcheePort La Belle 26.93010-82.27322 Franklin Rec. Area26.72132-81.69451Ft. Myers26.66008-81.84961Freemont St26.66013-81.84957Riverside Drive26.58992-81.89789Yacht Club Commun. Park26.54260-81.95205Dennis Drive26.52234-81.96517 HillsboroughE. Pocohontas28.00843-82.41619 Sligh Ave28.01058-82.46420W Indiana & N Ridge28.06670-82.45360Riverfront Park27.95200-82.46604Green St27.95536-82.46551Blake Co River Park28.06670-82.45360 ManateeRye Wilderness Park27.51370-82.36763 Colony Cove Marina26.98125-82.00112Wellon Ranch Rd27.52877-82.4827545th Ave E off 30127.51834-82.5187441 and US 301 Bridge27.50741-82.56266 MississippiCypress Cove Marina29.69412-91.21607PeaceNavagator Marina27.06089-82.00137 Harbor Heights26.98881-81.99413US41 Bridge26.95172-82.06118SeaTow Dock26.59078-81.89753Matlacha Park26.95720-82.06083 SharkNo landmark25.39775-80.96960 No landmark25.38892-81.01080No landmark25.37440-81.04466No landmark25.35644-81.10743 SuwanneeBradford Road29.32198-83.14429 Shellmound29.20650-83.06982Cedar Key29.16381-83.02714

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56 RESULTS & DISCUSSION Riverine Fluorescent DOM Concentrations A scatter plot of riverine CDOM fluorescence intens ity (Ex/Em = 300/430 nm) as a function of salinity is shown in Figure 2.2. For r eference, data for the West Florida Shelf (WFS) has also been included. One result of this t wo year seasonal study is that two trends were established and are based on regional d ifferences. Generally, river systems in the northern portion of the field study (Atchafa laya to Alafia River) have less fluorescence per freshwater unit compared to rivers to the south (Manatee to Shark River). One exception to this is the Suwannee (repr esented by the black outlined gray squares), which is significantly more colored than the remaining northern rivers. The Figure 2.2. CDOM fluorescence at Ex/Em 300/430 nm f or ten rivers that supply the Eastern Gulf of Mexico. Stream data are generally s eparated into two lines, based on latitude. Open black diamonds around solid symbols represent 2004 hurricane season.

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57 Suwannee is a swamp-fed river, with similar landsca pe characteristics to rivers found in South Florida. Seasonality in CDOM fluorescence wa s also observed, and is related to river discharge. Highest intensities were found fo r samples taken during the active hurricane season of 2004. This seasonality is als o responsible for the low r-squared values within each watershed. An example of this i s shown in Figure 2.3. Here, mixing lines for three of Tampa Bay’s rivers are shown. Ta mpa Bay was chosen as the example because it is geographically situated where the bre ak in mixing lines occurs. The Hillsborough and Alafia rivers plot along the North ern-river line and the Little Manatee and Manatee plot along the Southern-river line. As evident from this figure, during lowFigure 2.3. CDOM fluorescence at Ex/Em 300/430 nm f or Tampa Bay rivers. Dry seasons (solid lines) exhibited less fluorescence, compared to wet seasons (dashed lines).

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58 flow conditions (dry season), there is less color i n each of the rivers, with little variability between years. For the wet season, however, rivers were found to be more colored. Also, the response of each river to discharge patte rns varies. The Manatee River exhibits the same wet-season mixing line, regardless of year The Hillsborough and Alafia, however, show significantly higher color during 200 4 compared to 2005, where values at the freshwater endmembers approach that of the Mana tee River to the south. Again, this is most likely attributed to the large amount of ra infall and discharge due to the active 2004 hurricane season in South Florida. Regional and seasonal differences of each river are apparent in the top-panel histogram in Figure 2.4A, where dry seasons are represented by s olid bars and wet seasons are represented by striped bars. Tables of fluorescenc e, absorption and DOC data are located in Appendices I and II.

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59 Figure 2.4A. Histograms of fluorescence intensity, absorption coefficient and fluorescence efficiencies for all rivers. Latitude of rivers decreases from left to right within plots. Dry seasons are shown with solid bar s, wet seasons are shown with striped bars, with increasing year from left to right.

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60 Figure 2.4B. Histograms of DOC concentration, posi tion of humic peak C maximum and fluorescence ratios for all rivers. Latitude of ri vers decreases from left to right within plots. Dry seasons are shown with solid bars, wet seasons are shown with striped bars, with increasing year from left to right.

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61 Riverine Absorption Measurements Regional differences were less apparent in the rela tionship between fluorescence and the absorption coefficient at 312 nm (Figure 2.5). How ever, fluorescence efficiencies did exhibit seasonality Shown here is the drastic increase in fluorescence intensity relative to absorption during the 2004 wet season. To clear ly see seasonal trends in this relationship, Figure 2.6 shows a subset of the data for the Caloosahatchee and Peace Rivers in Charlotte Harbor and the Manatee River in Tampa Bay. Although the rivers Figure 2.5. CDOM fluorescence at Ex/Em 300/430 nm a s a function of absorption coefficient at 312 nm. Outlined symbols are sample s that were taken in the streams during the 2004 active hurricane season in Florida.

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62 Figure 2.6. CDOM fluorescence at Ex/Em 300/430 nm a s a function of absorption coefficient at 312 nm for Charlotte Harbor rivers a nd Manatee River in Tampa Bay. As shown here, seasonality is apparent for regional su bsets. ultimately feed two different estuaries, they are a ffected by similar weather patterns and the streams traverse similar landscapes. Source wa ters of the Manatee and Peace Rivers are similar, but the Caloosahatchee River originate s at Lake Okechobee, which experiences controlled releases. What is visible h ere is that during both dry seasons, a quasi-constant relationship exists between fluoresc ence and absorption. In contrast, the wet seasons showed two different regressions for CD OM. Implicated in the cause for the higher fluorescence efficiency in 2004 is the incre ase in discharge resulting from daily precipitation and frequency of hurricanes during th at period. Increase in discharge from landscape runoff generally yields organic material with more efficient fluorescence

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63 properties, due to the presence of large organic ma cromolecules from soils and plant litter. This is not the case, however in the wet s eason of 2005, where less fluorescence per unit absorption was observed. This is in part due to the late start of the rainy season that year and because the rivers were sampled early in the season. A possibility as to why the relationship was not only lower than the previo us wet season, but also the dry seasons may be because of higher sunlight, increased water temperature, and age of terrestrial material in the stream. At the cusp between the en d of a dry season and start of the subsequent wet season, organic material in the stre am has been in the water for longer periods of time with exposure to sunlight, higher w ater temperatures and microbiota that can degrade the material, thereby decreasing its ef ficiency (refer to Figure 2.4A middle and bottom panels for histogram representation). I nterestingly, this is also the same season where the hypersaline waters were found on t he West Florida Shelf. This illustrates the need to measure these parameters ov er more frequent temporal scales, for longer-durations and over wider ranging spatial sca les to determine the variability and how it is affected by basin and climatic patterns. This information is critical for understanding CDOM quantity and quality that supply the estuaries and ultimately the shelf. Riverine Dissolved Organic Carbon Of great importance in coastal environments is the establishment of CDOM as a proxy to derive DOC in coastal watersheds. Recent work by M cKnight et al. (2001, 2003), Baker and Spencer (2004) and others has shown good agreem ent between these parameters in certain regions. Also, an excellent summary of the relationship can be found in Del Castillo (2005). Figure 2.7 illustrates how fluor escence intensity varies with DOC for all the rivers sampled. Again, scatter in this relatio nship is due, in part, to seasonality and also regional differences in watersheds (Figure 2.4 B, top histogram). Viewing only a subset of rivers (ones supplying Tampa Bay and Char lotte Harbor) allows for calculating better regressions, especially during high-flow con ditions (Figure 2.8). These relationships hold promise for estimating freshwate r organic carbon export from

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64 fluorescence intensity during limited time periods for these estuaries. This, along with the histograms in Figure 2.4B, illustrates that alt hough the water contribution of large rivers to the Gulf of Mexico may be greater (ie. Th e Mississippi River), the organic carbon delivery by the smaller, organic-rich rivers to the south can be quite significant because of higher DOC concentrations per liter of r iver water. Figure 2.7. The relationship between CDOM fluoresce nce and DOC for river and West Florida Shelf waters for all seasons sampled.

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65 Figure 2.8. The relationship between CDOM fluoresce nce and DOC for Tampa Bay (top), Charlotte Harbor and Shark Rivers (bottom).

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66 Riverine Spectral Properties Multi-spectral fluorescence measurements produce Ex citation-Emission Matrices (EEMs), which allow for determination of spectral s hape, position of peaks and how the humic peaks change relative to each other. Example s of EEMs from each of the rivers sampled during a dry season are in Figure 2.9A & B. The contours have been scaled to the maximum fluorescence value in the Humic Peak A region (Ex ~ 240 / Em ~ 400-460 nm), so as to compare not the intensity, but spectr al differences. These optical properties relate to the chemical composition of the organic m aterial, where longer wavelengths are indicative of larger, more complex, highly aromatic compounds. Visually comparing the EEMs, it is possible to see positional and shape di fferences in Humic Peak C (Ex 320360 / Em 420/460 nm), which is also supported by th e middle histogram in Figure 2.4B. Longest wavelengths were found for waters in the Su wannee River and shortest were for the Hillsborough River. Looking at the position di fferences as a function of salinity (Figure 2.10) it is obvious that streams show much geographical and seasonal variability, not only at the endmember, but along the salinity g radient as well. One important note is the effect of the 2004 hurricane season on the posi tion of fluorescence maximum. Included in Figure 2.10 are the WFS data, where the most blue-shifted samples occurred right after the passage of Hurricane Charley, due t o offshore ocean water being pushed inshore. In the southern streams, however, the hur ricane yielded the most red-shifted material as a result of the rapid accumulation of r unoff in the stream beds. In addition to position, the spectral shape can als o offer insight regarding the chemistry of organic material. Plotting individual spectra from each EEM at excitation 300 nm, and normalizing to remove intensity differences allows for comparing the spectral shape. Figure 2.11 illustrates these differences, where co lors of rivers correspond to the colors in the histogram plots previously shown. One way to i nterpret this plot is that the steeper peaks, like the curves for the Atchafalaya, Mississ ippi, Apalachicola and Alafia Rivers, result from less complexity in the organic material The Suwannee has the lowest relative peak height and width and plots along with the Manatee and Peace Rivers, all of which are organic-rich river systems. The bold-sha ped curves found in the middle, are

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67 Figure 2.9A. EEMs contours for rivers taken at zer o salinity during dry season. River names are located in the top left corner of each pa nel. Scale set to maximum value of Humic Peak A for comparison of spectral properties.

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68 Figure 2.9B. EEMs contours for rivers taken at zer o salinity during dry season. River names are located in the top left corner of each pa nel. Scale set to maximum value of Humic Peak A for comparison of spectral properties. Manateeplot scaled to 180 QSE Caloosahatcheeplot scaled to 395 QSE Peaceplot scaled to 325 QSE Sharkplot scaled to 350 QSE Manateeplot scaled to 180 QSE Caloosahatcheeplot scaled to 395 QSE Peaceplot scaled to 325 QSE Sharkplot scaled to 350 QSE

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69 Figure 2.10. Position of Humic Peak C/M maximum as a function of salinity for river and West Florida Shelf waters for all seasons sampl ed. Shortest wavelengths at high salinities resulted from Hurricane Charley, as did the longest wavelengths in southern rivers.

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70 Figure 2.11. Normalized emission spectra at Ex = 3 00nm for all rivers. Line colors correspond to river colors in previously shown hist ograms.

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71 for the Hillsborough, Caloosahatchee and Shark, whi ch incidentally are all controlled rivers in one form or another. Calculating the rat io between the fluorescence peak maximum and the value at 500 nm for excitation 300 nm yields a quantitative description of differences between these watersheds (bottom his togram in Figure 2.4 B). Northern rivers exhibit the highest ratios, which were follo wed by the controlled-flow rivers from both the southern and northern regions. The southe rn, free flowing rivers had the lowest ratios of all rivers sampled. Once again, the Suwa nnee River is the exception, showing more similarity to the southern streams. Implicated in the cause of regional differences in riverine organic matter are basin and land use characteristics, but to date have not been thoroughly investigated for this region. Work done by Dr. Barnali Dixon and others at the US F Geo-spatial Analysis Lab comparing Tampa Bay and Charlotte Harbor streams ha s shown that land use, land elevation, soil run off, soil organic carbon conten t, and land permeability have great influences on the materials within these rivers (un published data, pers. commun.). It was established that the Manatee and Peace Rivers had a greater proportion of low-elevation lands compared to the Hillsborough and Alafia River s, significantly greater agricultural land use compared to the higher percentage of urban ization for northern watersheds, and a greater fraction of poorly drained soils. As a f irst cut, these findings help to explain some of the geographical differences observed in th e rivers sampled in this study. High-Resolution Sampling The results of this study illustrate the need for m ore effective sampling strategies. Seasonal collection of discrete samples at a few lo cations along a river is not adequate for truly resolving the spatial and temporal variabilit y within a stream. To accomplish this, both resolution scales need to be finer, which may be achieved with more frequent sampling, or ideally, with in situ sensors mounted on monitoring platforms. One technique tested during the course of this project, was the mounting of an in situ multispectral fluorometer (SAFire) on a Guided Surface V ehicle (GSV), that was deployed in the Hillsborough River to assess spatial variabilit y of water quality parameters in natural

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72 and urban locales (Casper et al., 2008). Spatial distributions of CDOM fluorescence at the two locations are shown in Figure 2.12. This f igure is merely to show that changes in fluorescence were found to occur over small distanc es and that the current sampling strategies that are used to measure materials in ma ny rivers are unable to detect these variations. Figure 2.12. Spatial distribution of COM in an urb an locale (left) and natural locale (right) in the Hillsborough River. High resolution, in situ measurements of multi-spectral COM allow for better spatial and temporal measureme nt scales.

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73 Historical Color Measurements The color of water is routinely measured by many re gional water management and monitoring agencies, as it is a visual measurement of water clarity and ecosystem health. Changes in the color of a water body can be used to infer alterations in land use practices, landscape changes and shifts in climatic patterns. In Tampa Bay, a thirty year record exists for color as well as other water quality par ameters for the estuary and rivers. Plotted in Figure 2.13 (top panel) is the color val ue in Platinum Cobalt Units for the mouth of the Alafia River for years 1973-2006 along with discharge data from the USGS (bottom panel). During the most recent years, an i ncrease in color corresponds to increases in discharge. This was not necessarily t he case in the 1970’s and 1980’s, when Tampa Bay had a lower proportion of urbanized lands A study done by Xian and Crane (2005) found that the transformation of landscape f rom natural to impervious urban land in Tampa Bay increased three-fold from 1991 to 2002 resulting in a 27% coverage of these impervious lands in Tampa Bay. Alterations l ike these to watershed landscapes are one possible explanation for changes in organic car bon content and color in coastal waters, which is demonstrated in Freeman (2001).

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74 Figure 2.13. Historical time series of color at th e mouth of the Alafia River in Tampa Bay (top). Records indicate an increase in color v alues during the past 30 years although the mean monthly discharge for the Alafia has been fairly constant (bottom). Sources of data are the EPCHC (Environmental Protection Commis sion of Hillsborough County) and USGS.

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75 CONCLUSIONS Examined here were differences in the optical prope rties of CDOM for ten rivers from the Atchafalaya/Mississippi River system to the Sha rk River. Fluorescence and absorption techniques were used to distinguish both regional and seasonal variability in these watersheds. It was found that CDOM in rivers that supply the shelf is regionally dependent, where southernmost rivers generally have higher fluorescence intensities compared to northern watersheds. This was also tru e for DOC concentrations. Spectral properties also were watershed-specific, where fluo rescence ratios were lowest for southern rivers without controlled flow. This is a ttributed to the presence of more complex, highly aromatic organic material from less urbanized settings. Comparisons between the basin characteristics (soil runoff, soi l permeability and land use) of Tampa Bay and Charlotte Harbor were also made to help exp lain differences in the optical properties of streams. Seasonal differences were also found, where high-fl ow, summer seasons exhibited the largest fluorescence intensities. The results of t he efficiency of the material, however, demonstrated that differences in climatic and disch arge patterns can have a strong influence on this parameter. Intermittent weather phenomena (ie. hurricanes) were also shown to have a significant effect on the optical p roperties of CDOM in streams. Findings were also compared to historical color val ues from 1973 to 2006 from the mouth of the Alafia River in Tampa Bay. Long-term trends show a tripling of color values that coincide with a three-fold increase in impervious urban lands and also no clear trend in discharge patterns. Recommendations for future studies of organic matte r in rivers include (1) the need to thoroughly evaluate the variability of materials of interest with differences in landscape parameters and climate patterns, and (2) the need t o implement advanced sampling strategies with in situ sensors. Combined, these t wo approaches will yield improved resolution (temporal and spatial) and allow for mak ing inferences about CDOM quantity and quality in river streams, and ultimately estuar ies and shelf environs.

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76 PART III: Characterization of subsurface terrestrial CDOM sou rces to Tampa Bay, Florida

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77 INTRODUCTION Subsurface waters constitute 95% of all global, unf rozen freshwater reserves (National Ground Water Assoc. website www.ngwa.org). Even th ough groundwater is the largest freshwater reservoir, there have been relatively fe w studies on its contribution to surface waters. This is in part due to the difficulty in c ollecting measurements of water flow and the constituents therein (Burnett et al., 2002). H ence, it can be challenging to determine the role of groundwater in budgets for materials su ch as nutrients, metals, pollutants, inorganic and organic carbon (Moore 2003). Determi ning the amount and nature of the organic material in an aquifer is significant given its reactive nature which influences other materials of interest (Aiken, 2002), its abil ity to control a number of geochemical, microbial and environmental processes (Aiken, 2002; Kroeger et al., 2007), and its role in the global carbon budget. There are various ways in which groundwater is tran sferred to surface waters. The first type of exchange is via localized springs that may supply water to streams, which in turn supply estuaries and the coastal ocean. The second is via Submarine Groundwater Discharge (SGD) where water is more diffusely excha nged through sediments beneath a water body. The latter is now acknowledged as an i mportant flux of materials to the coastal ocean (Moore, 1999; Burnett et al., 2002; K elly and Moran, 2002; Moore et al., 2002; Moore, 2003). The extent of importance of ea ch route is dependent on regional geology, hydraulic head gradients between reservoir s, and thickness of the overburden deposits. Added to these hydrogeologic characteri stics are the effects of climate and society’s increasing demand on ground water reserve s (Swarzenski et al., 2001), all of which serve to complicate our understanding of grou ndwater. In recent decades, new light has been shed on the i mportance of groundwater contribution to estuaries and the coastal ocean due to improveme nts in tracer techniques (Moore, 1999; Krest et al., 1999; Burnett et al., 2001; Swa rzenski et al., 2007). The combined use of naturally occurring isotopes and other chemical tracers (O-18, H2, Rn-222, Ra223,224,226,228, Sr87/86, 13C, 15N, and major dissolved species) along with

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78 geochemical modeling have been used to quantify int eractions between surface and subsurface water reservoirs (Katz, 2002). Although these techniques have advanced the understanding of the role of groundwater in surface environments, there still exist limitations of these techniques, such as speed of m easurement and the lack of an ideal tracer for riverine, estuarine and oceanic environs One technique not previously applied to groundwater detection, is Excitation Emission Matrix Spectroscopy (EEMS) of Colored Dissolved Org anic Matter (CDOM). This fluorescence tool has been routinely used as a trac er of surface waters due to the source dependent nature of CDOM. Additionally, fluorescen ce measurements are less time consuming than alternate techniques and have the ad ded benefit of real-time collection with in situ sensors. There have been limited stud ies investigating CDOM fluorescence intensity and spectral properties in aquifers (Voda cek, 1992; Baker and Lamont-Black, 2001; Khan et al., 2007), but none attempting to us e fluorescence as a tool to discern groundwater contributions to surface waters. Theref ore, investigating CDOM quantity and quality in groundwater is warranted (Aiken, 200 2) and may offer a relatively inexpensive and rapid way to fingerprint subsurface waters. Previous work in Tampa Bay tended to focus solely o n marine and fluvial input of CDOM but not on the contribution from groundwater, which is estimated as 50 million gallons per day, or 20% of the combined surface wat er runoff (Swarzenski et al., 2001). Given that South Florida is dominated by carbonates and sands (Figure 3.1, Brooks and Doyle, 1998), with a porosity that favors exchange of subsurface and surface waters, this groundwater input may likely be an underestimated s ource of CDOM to these coastal waters with distinct biogeochemical cycling. This work is a novel approach to (1) characterize C DOM in the groundwater endmembers in the aquifers in the Tampa Bay region and (2) determine optical proxies to detect groundwater presence in the surface waters. The results of this type of approach serve to provide better techniques to determine gro undwater sources with monitoring

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79 networks and ultimately remotely sensed measurement s from space with the advent of improved satellite technology.

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80 Figure 3.1. Map of Tampa Bay showing calcium carbo nate content (left), total organic carbon content (middle), and major sediment facies (right) in bottom sediments. Figure amended from Brooks and Doyle, 1998. LatitudeLongitude LatitudeLongitude

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81 METHODOLOGY Regional Setting / Hydrogeologic Framework of Flori da The aquifer system of Florida can be divided into t hree main zones (Miller, 1986) (Figure 3.2). The surficial aquifer system is the uppermos t aquifer and is unconfined, relatively thin, and consists of unconsolidated sand, shell an d limestone. In the Tampa Bay region this aquifer is approximately 50 ft. thick (Wolansk y et al., 1985). Below this, is the intermediate aquifer system, which is comprised of clastic sediments interbedded with carbonate rocks and is no more than 250 ft thick in Tampa Bay (Dehaven et al., 1991). Beneath the intermediate aquifer system lays the Up per Floridan aquifer, which consists of a thick vertically stratified sequence of limest one and dolomite. This aquifer is greater than 1000 ft deep (Hutchinson, 1983), where waters in this system have been estimated at >10,000 year old (Meyer, 1989). Each of the aquifer systems and permeable zones are separated by layers of interbedded clays and fine-g rained clastics (Torres et al., 2001). Figure 3.2. Hydrogeologic framework of Florida dep icting the three main zones of the Florida aquifer system. Figure amended from Tihans ky, 1999.

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82 Tampa Bay Sample Collection Surface water samples were obtained from various si tes within the Tampa Bay estuary system during March-April 2006 (Figure 3.3) aboard a 19’ Parker boat. Collection was conducted wearing polypropylene gloves, filling lar ge pre-ashed glass amber bottles with bay water from just below the surface to avoid cont amination by the presence of any microlayer. Whole water was filtered through pre-c ombusted GF/F filters (up to 24 hours at 450oC) on site using a portable glass filtration appara tus and hand pump. Filtered water was transferred to pre-combusted, amber glass bottles and then stored on ice until returning to the laboratory. Figure 3.3. Map of Tampa Bay denoting sampling loc ations within the estuary (closed circles) and well locations surrounding Tampa Bay ( closed triangles are aquifers deeper than 130 ft, open triangles are aquifers shallower than130 ft). Longitude L a t i t u d e Longitude L a t i t u d e

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83 In situ measurements of salinity, conductivity, temperatur e, and pH were also taken via a Hydrolab Sonde. Samples for DOC analysis were stor ed frozen until all collection was complete, then thawed and measured using the protoc ol stated in Part I of this dissertation. Samples for absorption and fluoresce nce analyses were stored refrigerated for no more than two days. Absorption spectra were measured to determine if and to what extent dilution was needed following Green (19 92). Appropriate dilutions were made, and then water was stored frozen until reanal yzed for absorption and analyzed for excitation emission fluorescence spectroscopy. Aquifer Sample Collection Water samples from the surficial, intermediate and deep Floridan aquifers were obtained via wells maintained by the Southwest Florida Water Management District (SWFWMD) during March 2006. Nine wells were chosen based on a range of aquifer depths, geographic proximity to the bay and to various rive rs that supply water to the estuary (Figure 3.3 and Table 3.1). In situ measurements of salinity, conductivity, temperatur e, pH and dissolved oxygen were also taken using a por table unit operated by SWFWMD. Water for CDOM and DOC analysis was collected using the methods described previously in Part I of this dissertation. Since e xisting sampling protocols for spectroscopy have not been tested for groundwater s amples, a freezing experiment was conducted for the Floridan aquifer at the Buckhorn Spring site. Absorption and EEMs analysis were conducted on refrigerated and frozen sample water to determine if there was any significant change in chromophores or fluor ophores. Results from this experiment show that the percent decrease in fluore scence peak intensity varied between 2.9 and 6.7% for various wavelength pairs in the hu mic peak regions where the percentage decreased with increasing excitation wav elength. Peak position had minimal variation with no change in excitation, and 3.6% fo r Humic Peak C and 5.2% for Humic Peak A emission.

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84 Table 3.1. Location and environmental data for gro undwater wells in the Tampa Bay region. Sample mea surements include temperature, salinity, pH, CDOM fluorescence and ab sorption, Dissolved Organic Carbon and Radium. Sup erscripts 1,2, and 3 represent the Floridan, Intermediate and Surficial aquifers, respectively. Well Name Case depth (ft)LatitudeLongitude Temper. (degrees C)SalinitypH Sample Date Local TimeDOC (M) Romp 51 Elapp 1 36527.67575-82.4218327.920.527.103/16/200614:15104. 69 Speedling Inc 1 30027.67778-82.4794424.620.527.323/20/200610:00149. 36 Buckhorn Main Spring 1 Spring27.88937-82.3027123.620.237.363/16/200612:003 0.47 CNB#3 1 12828.01521-82.3519324.550.37.443/16/20067:3071.82 Snead's Island 229 1 20027.53320-82.6250324.641.17.003/17/200610:3080.10 Palma Sola W. Davis 2 19627.51510-82.6628425.351.667.183/17/200613:25116. 87 Romp TR 10-2 3 1327.90043-82.3730920.210.826.913/16/200616:05529.1 0 Eureka Springs 682 3 428.01390-82.3452818.390.76.643/16/20068:30670.64 Romp TR 8-1 3 1727.58319-82.5460823.070.476.993/17/20068:00501.46 Well Name Ex Humic Peak A (nm) Em Humic Peak A (nm) Fluor. Intensity Humic Peak A (QSE) Ex Humic Peak C (nm) Em Humic Peak C (nm) Fluor. Intensity Humic Peak C (QSE) Ex/Em = 300/400 nm (QSE) Ex/Em = 300/430 nm (QSE) Fluor. ratio Em 430:400 nm @ Ex 300 nm Romp 51 Elapp 1 23541556.8232041228.9126.9826.010.964 Speedling Inc 1 23541869.4732041236.4833.8832.650.964 Buckhorn Main Spring 1 2354216.823204113.573.333.280.986 CNB#3 1 23042440.2831542117.8016.2516.981.044 Snead's Island 229 1 24041361.3132040832.4629.8128.180.945 Palma Sola W. Davis 2 24041962.5032041031.8229.3527.980.953 Romp TR 10-2 3 235425212.36320419105.4095.1299.571.047 Eureka Springs 682 3 235432232.22320426120.93108.71117.781.083 Romp TR 8-1 3 235434230.73315426116.98103.74113.421.093

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85 Table 3.1. (cont.) Well Name Spectral Slope 280312nm (m-1)a(312) (m-1)a(440) (m-1) Fluor. @ 300/430 nm / a312 (QSE*m) Ra-223 (dpm / 100L) Ra-224 (dpm / 100L) Ra-228 (dpm / 100L) Ra-226 (dpm / 100L) Romp 51 Elapp 1 0.010027.511.313.4722.2938.1019.22505.49 Speedling Inc 1 0.016544.170.737.8239.8733.0114.33577.40 Buckhorn Main Spring 1 0.010491.070.273.070.446.5126.09273.64 CNB#3 1 0.0044313.614.841.2523.1841.629.51670.02 Snead's Island 229 1 0.020112.410.1111.6976.2989.4729.452181.98 Palma Sola W. Davis 2 0.012306.252.314.4740.7953.1932.822173.70 Romp TR 10-2 3 0.0089832.128.793.1020.00202.3191.62968.34 Eureka Springs 682 3 0.0126740.558.932.9025.6098.8135.17167.98 Romp TR 8-1 3 0.0155117.991.766.309.6457.4622.83440.16

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86 Radium Sample Collection Water from the bay and from the aquifers was also c ollected for radium-223,224,226,228 analyses (Figure 3.4). Forty liters of water was c ollected into a plastic carboy and then subsequentially pumped through a custom made column of manganese-coated acrylic fiber to extract radium from the sample water (Moor e, 1976; Dulaiova and Burnett, 2004). Flow rate was kept below 1.4 L m-1 to ensure adequate adsorption of radium onto fiber. The fiber was then removed from the columns and placed into plastic sealable bags and brought to the laboratory. Radium Quartet Analysis In preparation for Ra-223 and Ra-224 analysis, the Mn-fiber was partially dried and placed in an air circulation system (Moore and Arno ld, 1996). Helium was circulated over the fiber and through a scintillation cell whe re alpha particles from the decay of radon and daughters were recorded with a PMT attach ed to the cell. Signals are routed to a delayed coincidence system designed by Giffin et al. (1963) and adapted by Moore and Arnold (1996). The delayed coincidence system use s the difference in decay constants of the short lived Po daughters of Rn-219 and Rn-22 0 to identify alpha particles derived from Rn-219 and Rn-220 decay. Because samples were not reanalyzed 6 weeks later, initial excess of Ra-224 could not equilibrate with Th-228 adsorbed onto the fiber. However, the thorium peaks in gamma results were us ed to correct for this. Upon completion of the short lived radium analysis, the Mn-fiber was leached with hot 6 M HCl in a Soxhlet extraction column to release Ra226 and Ra-228, which were then co-precipitated from the acid solution with BaSO4. The supernatant was decanted or aspirated, and the precipitate was concentrated by centrifuging. Activities were measured one year later using two low background, high purit y germanium well detectors manufactured by Canberra. An IAEAA Baltic sea stan dard (supplied by Dr. J.M. Smoak) was also analyzed in both detectors and served as a means to calibrate counts into activities for all samples.

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87 Figure 3.4. Uranium-Thorium decay series. Vertical arrows denote alpha decay, while diagonal arrows d enote beta decay. Ammended from Swarzenski et al 2000.

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88 RESULTS & DISCUSSION High correlation between fluorescence intensity and salinity was found for Tampa Bay estuary and river waters during March – April 2006 (Figure 3.5). Riverine waters follow the same mixing lines that were established in Part II of this dissertation. Gray squares represent the southern rivers (located down bay in the Tampa Bay Estuary) and Gray circles and triangles represent the northern rivers (located up bay in the Tampa Bay Estuary), two mixing lines were calculated based on geographic setting (northern-up bay and southern-down bay). Results of the groundwater samples are also plotted in Figure 3.5, where it was found that the surficial aquifer wells were similar in concentration to the rivers (dark circles). This is not unexpected, as this shallow unconfined aquifer exchanges water and materials with the overlying su rface waters. The deeper aquifers, however, are quite low in fluorescence (dark square s) and more similar to the higher salinity samples. Values are listed in Tables 3.1 and 3.2. Figure 3.5. CDOM fluorescence intensity as a funct ion of salinity for estuary, river, and groundwater samples in Tampa Bay during March-April 2006.

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89 Table 3.2. Location and environmental data for surf ace samples in the Tampa Bay Estuary. Sample measu rements include temperature, salinity, pH, CDOM fluorescence and ab sorption, Dissolved Organic Carbon and Radium. Station NumberLatitudeLongitude Sample Depth (m) Temperature (degrees C)SalinitypH Sample Date Local TimeDOC (M) 127.6001-82.7368020.8336.038.384/1/20069:03120.88227.5303-82.6538021.2933.228.44/1/20069:51154.70327.7245-82.4898021.9729.418.543/31/200615:4585.38427.8471-82.4087022.4326.148.533/30/200615:35355.42527.9206-82.4747021.71268.413/30/200612:50299.89627.8791-82.4577026.8826.98.473/30/200614:40325.74727.8244-82.4446021.4527.638.523/30/200616:15321.72827.9187-82.5897020.0825.678.43/30/20069:57380.94927.8697-82.5746026.5726.588.493/30/200610:56348.20 1027.7943-82.586020.5128.718.353/31/20068:50208.401127.7641-82.6029020.1328.988.363/31/20069:30214.161227.7679-82.5011021.5628.558.373/31/200613:40233.1 7 1327.7377-82.5313021.5130.318.483/31/200615:10269.0 8 1427.7038-82.5596020.9533.328.53/31/200616:24171.421527.717-82.6161021.0530.778.443/31/200610:23221.461627.6736-82.6067022.1232.568.434/1/200611:44173.311727.6359-82.6444020.9334.958.44/1/200611:06135.431827.5755-82.6651021.2633.758.364/1/200610:39155.02

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90 Table 3.2. (cont.) Station Number Ex Humic Peak A (nm) Em Humic Peak A (nm) Fluor. Intensity Humic Peak A (QSE) Ex Humic Peak C (nm) Em Humic Peak C (nm) Fluor. Intensity Humic Peak C (QSE) Ex/Em = 300/400 nm (QSE) Ex/Em = 300/430 nm (QSE) Fluor. ratio Em 430:400 nm @ Ex 300 nm 1235415.2510.48300404.355.335.254.970.9472240418.8222.95300409.9811.3711.2511.010.9793235420.1338.68300413.5119.1918.7318.710.9994235417.0151.82300409.9826.1825.8325.070.9705235417.6156.24300413.7627.5426.8926.720.9946240418.2247.96300414.3823.7123.2523.010.9907235418.8247.12300412.4923.7123.3222.710.9748235417.0152.54300409.4325.4825.0424.460.9779240418.9049.76300409.6925.0824.7524.000.970 10240417.6140.37300413.7620.3220.0319.630.98011235418.8238.52300411.2619.0718.7718.440.98212235419.9841.07300415.0020.8720.4320.160.98713235419.5233.76300410.9916.7216.3916.090.98214235422.3921.55300413.1510.8010.6010.440.98515235415.8532.01300413.1515.8715.5715.320.98416240419.9823.69300411.8711.7311.5911.300.97517240419.9815.47300409.987.617.557.410.98118240421.7819.27300412.499.619.369.340.998

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91 Table 3.2. (cont.) Station Number Spectral Slope 280312nm (m-1)a(312) (m-1)a(440) (m-1) Fluor. 300/430 nm / a312 (QSE*m) Ra-223 (dpm / 100L) Ra-224 (dpm / 100L) Ra-228 (dpm / 100L) Ra-226 (dpm / 100L) 10.032881.19-0.084.160.711.9611.2067.4120.027582.87-0.033.8320.3269.6731.73169.1730.024795.190.373.6023.5455.3049.51292.8640.023886.640.393.7733.1364.0359.89352.1150.025796.150.184.3531.1344.5555.15354.9660.023036.720.693.4229.1644.6258.90330.7670.025325.400.194.2121.3450.2964.77335.4880.028594.96-0.064.9322.7743.9593.59395.7790.024885.860.444.1012.3854.7767.67314.33 100.025854.750.244.1411.2038.9347.72248.62110.025184.740.243.892.9710.7243.62250.25120.025894.730.174.2610.6928.6350.75281.77130.026994.420.193.6415.1650.1741.33219.36140.028012.610.014.0011.8535.8626.58151.00150.026573.670.054.1711.1942.1840.03196.82160.027482.830.043.9910.9438.1130.00161.31170.028222.070.043.5811.9646.8020.88107.49180.028612.400.023.8913.4547.1327.79147.59

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92 A spatial representation of salinity and CDOM fluor escence in Tampa Bay is shown in Figure 3.6 (left and middle panels). The distribut ions of both parameters look identical, where highest fluorescence was coincident with lowe st salinities. Radium-226 concentrations plotted spatially also follow the sa me distribution pattern (Figure 3.6, right panel). Although samples were taken over a three d ay period, and therefore not synoptic, plotting the data in this way is justified based on estuarine model results from the USF Ocean Monitoring and Prediction Lab under the direc tion of Dr. Mark Luther. Their model suggests there is minimal change in the salin ity over tidal stages during the dry season in Tampa Bay (Dr. Steve Meyers, per commun.) Using only concentration of fluorescence or radium-226 does not offer much info rmation about the location of groundwater contributions for this time period. DOC in Tampa Bay is highly correlated with CDOM flu orescence. Figure 3.7 shows the regression line for the two parameters and suggest that it is possible to estimate DOC from fluorescence in the bay, in the rivers and in the aquifers. The shallow aquifers plot along the same mixing line as the bay and rivers, b ut the deeper wells are more colored with respect to DOC. Although there are difference s between reservoirs, there is still a positive correlation that could be used for carbon estimates. Investigating CDOM and DOC concentrations fails to yield information about the type of organic material that is present in these waters. To address this, spectral differences must be observed. One such difference is the position o f the Humic Peak C/M in an EEM matrix. In Figure 3.8, the movement of this peak t o longer wavelengths (red-shifting) as salinity decreases is apparent. The aquifer EEMS r eveal that the humic peak in shallow groundwater is also red shifted. The deep aquifer, however, contains humic peak positions more similar to high salinity environment s. Plotting the propagation of the peak as a function of salinity (Figure 3.9) offers an ea sy way of looking at source specific differences in the peak position. Here it is clear that the surficial aquifer is most similar to the rivers, and that the deeper aquifer is more similar to CDOM found in higher saline environments. The position of peaks is important b ecause it is related to the chemical

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93 Figure 3.6. Spatial distributions of salinity, CDO M, and Ra-226 in Tampa Bay.

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94 Figure 3.7. Relationship between Dissolved Organic Carbon concentration and CDOM fluorescence intensity. Strong correlations indica te that DOC estimates can be derived from fluorescence values in this region.

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95 Figure 3.8. EEMS of CDOM in the Manatee River, Tam pa Bay estuary, Gulf of Mexico, surficial aquifer and deep Floridan aquifer. The r ed dotted lines mark Em=400nm and assist in tracking the propagation of Humic Peak C/ M.

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96 Figure 3.9. Position of Humic Peak C/M for groundw ater, river water and estuary water. composition of the organic material (Coble, 1996; M cKnight et al., 2001; Aiken, 2002, Stedmon and Markager, 2005). Substances with short er peak positions are believed to be microbially derived, which correspond to material t hat is less complex, have less amounts of aromatic carbon, less phenolic content, and more nitrogen compared to material that is recently derived from higher plants in the terrestr ial environment (Aiken, 2002). A study in 2007 by Mahara et al. using carbon isotopes, sho wed that the age of organic matter in deep aquifers was approximately 4000 years old and was originally derived from land plants. Due to isolation for extended periods of t ime, it is possible that this material is then reworked by the microbial community (Aiken, 20 02; McKnight et al., 2001). This means that the processes controlling the organic ma terial in the deep aquifer is most similar to those in the marine environment and not to those in the surficial terrestrial

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97 environment. PARAFAC (Parallel Factor Analysis) re sults in a study by Stedmon and Markager (2005) revealed similarities in fluorophor es between marine environments and watersheds impacted by agricultural waste. It was deduced that the spectral properties of the material in that study was bacterially derived. Likewise, for the work presented here, similarities between marine and deep aquifer waters suggest similar microbe-derived sources that are unique from the organic sources of the surface terrestrial environment. In addition to peak position, spectral shape also o ffers information about the quality of organic material. Emission spectra, at Ex = 300 nm normalized at Em = 425 nm, allow for comparison of the shape of the peaks. Figure 3 .10 reveals similarities between deep Figure 3.10. Normalized emission scans at Excitati on = 300 nm. Aquifers are represented with dotted lines and surface waters ar e solid lines. 400 nm 430 nm 400 nm 430 nm

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98 aquifer and marine waters and between river waters and surficial aquifers. Although a visual comparison is informative, it doesn’t allow for a quantitative assessment of differences. For that, calculating a ratio of two wavelengths is necessary and if properly chosen, can be an indicator of peak steepness and s hape (Mcknight et al., 2001). Here, 400 and 430 nm were used, and then plotted as a fun ction of salinity (Figure 3.11). Again, the pattern looks similar to that of the pea k position scatter plot. But this parameter is possibly more useful because if only t wo wavelengths are needed, then expensive benchtop EEMS fluorometers wouldn’t be ne cessary, but the ratio could be calculated from in situ sensors configured to wavel engths of this ratio. This has huge implications for the capability to measure groundwa ter via monitoring networks. Figure 3.11. CDOM fluorescence ratio for groundwat er, river water and estuary water. Differences in ratios suggest differences in the ch emical composition of organic material.

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99 Plotting this ratio spatially for Tampa Bay, reveal s an interesting finding (Figure 3.12). Regions with red color contours (ratios closest to 1) are found where the Manatee and Little Manatee outflows occur. This is expected ba sed on the spectral shape for surface waters derived from higher terrestrial plants. The re are three main regions where blue contours were found, one down near the mouth of the bay and two up bay. This signature down bay is also expected, given the spectral shape shown in previous figures. What is interesting is the low ratios far up bay. Salinity contours shown earlier prove that this is not high salinity water that has been entrained up bay. The low ratios found in Old Figure 3.12. Fluorescence ratio as indicator of gr oundwater. Red contours suggest CDOM derived from surface terrestrial environments. Blue contours represent CDOM with marine or subsurface sources.

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100 Tampa Bay to the west are coincident with locations where Submarine Groundwater Detection (SGD) has been previously detected using isotopic methods (Swarzenski et al., 2007). The low ratios found in the eastern portion are located adjacent to the mouth of the Alafia River and appear to have a riverine sour ce. A study by Brooks et al., 1993 found that during periods of low flow in Tampa Bay, the stream flow is composed mainly of groundwater outflow from underlying aquifers. H ence, there is little or no surface water contribution to Tampa Bay at the end of the d ry season. This also means that fluorescence ratios may be the tool that is sensiti ve enough to detect groundwater from deeper aquifers.

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101 CONCLUSIONS To the author’s knowledge, there have been no other studies investigating CDOM optical properties in the Florida aquifer system. In this work, it was found that organic material in the shallow aquifers was similar to the rivers s upplying Tampa Bay. Concentrations and spectral properties suggest similar sources, wh ich was not unexpected given the strong hydrologic connections between surface and s hallow sub surface environments in Florida. Deep aquifers were found to be low in DOC and CDOM concentrations with spectral properties most analogous to higher salini ty environments. The source of dissolved organic material in the deep aquifer is b elieved to be of microbial origin, and tied to the reworking of aged plant material. Stro ng correlations between DOC and CDOM fluorescence were found for all three aquifers This indicates that fluorescence can be a reliable site and season-specific proxy of bulk organic carbon in groundwater and aide researchers and monitoring agencies in est imating organic carbon in groundwater reserves. A novel approach to identifying the presence of gro undwater was tested in this study. Fluorescence ratios were shown to hold promise for detection of deep groundwater in surface waters of Tampa Bay. Future investigations on the optical detection of groundwater are necessary and will undoubtedly prov ide essential information on the extent of discharge to the Tampa Bay Estuary. Seas onality of subsurface discharge via springs may even help to explain seasonal differenc es in the CDOM optical properties within streams that were observed in Part II of thi s dissertation.

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102 GENERAL CONCLUSIONS The work presented here examined CDOM characterizat ion and distribution on the WFS, in coastal riversheds, the Tampa Bay Estuary and th e Florida Aquifer system. Shelf environments exhibited variability in spatial distr ibution of CDOM in surface waters. This was attributed to seasonal patterns in river d ischarge, the occurrence of episodic storms, resuspension events and the presence of hyp ersaline waters. CDOM on the WFS is influenced by the rivers that su pply the shelf. To better characterize terrestrial sources to the shelf, ten rivers from the Mississippi / Atchafalaya River System to the Shark River in the Everglades w ere sampled seasonally. Southernmost rivers were found to be highly colored rich in DOC, and have spectral properties indicative of complex, highly aromatic o rganic material. These differences between river systems were linked to watershed char acteristics. Strong seasonality was also observed and intermittent weather events had a significant effect on the distribution of CDOM optical properties. River results also ill ustrated the need for improved sampling strategies to better assess the spatial an d temporal variability within riversheds. Lastly, investigated here was a novel approach to g roundwater detection using CDOM fluorescence properties. Aquifers were sampled to fingerprint the source water in the Tampa Bay region. Unique optical properties in dee p aquifers were observed and indicate similar biogeochemical processes controlli ng organic matter in deep groundwater and high saline environments. Fluoresc ence ratios were found to offer promise in detecting the presence of groundwater in the surface waters of Tampa Bay. Current detection methods use the presence of disso lved radium, which is problematic in freshwater environments and is unable to identify i f groundwater originated in deep or

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103 shallow aquifers. This is not the case with CDOM f luorescence measurements and warrants further investigation. A constant relati onship between DOC and CDOM was also discovered in the aquifers, demonstrating that fluorescence intensity could also serve as a proxy for organic carbon in groundwater reserv es. The findings of this dissertation provide insight o n the source, fate and cycling of terrestrial CDOM in coastal environments. With the advancement in sensor technology and the development of sophisticated sampling strat egies, CDOM measurements can be a powerful tool to researchers and resource managers alike. A simple, non-destructive analysis reflects much information on the source of water, the quality of the water and the watershed through which it was transferred.

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104 LITERATURE CITED Abbott, M.R. and R.M. Letelier (1999) ATBD 22-Chlor ophyll Fluorescence (MODIS product 20). NASA Algorithm Theoretica l Basis Document. http://modis.gsfc.nasa.gov/data/atbd/at bd_mod22.pdf Aiken, G.R. (2002) Organic matter in ground water. USGS Open File Report 02-89. Artificial Recharge Proceedings, April 2002, Sacram ento, CA. Baker, A. (2001) Fluorescence excitation – emission matrix characterization of some sewage impacted rivers. Environ. Sci Technol., 35: 948–53. Baker, A. and J. Lamont-Black (2001) Fluorescence of Dissolved Organic Matter as a natural tracer of groundwater. Ground Water 39, 745-750. Baker, A. (2002) Spectrophotometric discrimination of river dissolved organic matter. Hydrol. Process., 16: 3203– 13. Baker, A. and R.G.M Spencer (2004) Characterization of dissolved organic matter from source to sea using fluorescence and absorbance spe ctroscopy. Sci. Tot. Environ ., 333: 217-232. Berger, P., R.P.W.M Laane, A.G. Ilahude, M. Ewald a nd P. Courtot (1984). Comparative study of dissolved fluorescent matter in four westeuropean estuaries. Oceanol. Acta 7: 309-313. Blough, N.V., O.C. Zafiriou and J. Bonilla (1993) Optical absorption spectra of waters from the Orinoco River outflow: Terrestrial input of colored organic matter to the Caribbean. J. Geophys. Res. 98: 2271-2278. Boehme, J.R. (2000) Artificial and natural fluores cence of dissolved organic matter in the Tampa Bay estuary. Ph.D. Dissertation, Univers ity of South Florida. Boss, E., W.S Pegau, J.R.V. Zanaveld, A.H Barnard ( 2001) The spectral particulate attenuation and particle size distribution in the b ottom boundary layer of a continental shelf. Geophys. Res. Oceans 106: 9509-9516.

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113 Appendix I. Seasonal CDOM fluorescence measurement s for river samples. RiverSalinitySeason Ex Humic Peak A (nm) Em Humic Peak A (nm) Fluor. Intensity Humic Peak A (QSE) Ex Humic Peak C (nm) Em Humic Peak C (nm) Fluor. Intensity Humic Peak C (QSE) Ex/Em = 300/400 nm (QSE) Ex/Em = 300/430 nm (QSE) Fluor. ratio Em 430:400 nm @ Ex 300 nm Alafia 0 Summer 2004 240 445.79 420.36 320 442.18 221.42 210.62 168.71 1.248 Alafia 3.3 Summer 2004 240 440.94 324.81 320 439.09 175.45 170.45 142.82 1.193 Alafia 0.21 Summer 2005 235 441.17 241.00 320 439.93 137.55 128.58 108.83 1.182 Alafia 0.32 Summer 2005 235 443.06 285.00 320 438.04 161.44 150.76 127.65 1.181 Alafia 8.44 Summer 2005 235 437.79 149.66 315 432.66 85.26 80.14 67.50 1.187 Alafia 1.2 Winter 2004 235 435.45 105.77 320 431.85 55.00 55.12 48.27 1.142 Alafia 6.5 Winter 2004 235 434.01 94.74 320 433.29 47.96 48.61 43.39 1.120 Alafia 21.4 Winter 2004 235 426.81 62.88 320 426.09 31.25 32.33 31.54 1.025 Alafia 0 Winter 2005 235 430.65 152.51 305 423.68 74.69 74.96 68.74 1.091 Alafia 2 Winter 2005 235 432.54 154.14 300 427.48 76.21 76.10 69.22 1.099 Alafia 22 Winter 2005 235 426.92 65.39 300 421.10 30.96 30.44 29.58 1.029 Apalachicola 0 Summer 2004 235 438.57 122.82 320 434.96 65.68 63.02 55.91 1.127 Apalachicola 30.3 Summer 2004 240 435.40 41.62 320 433.56 19.88 21.00 19.32 1.087 Apalachicola 0.52 Summer 2005 230 429.38 88.19 315 430.01 44.93 43.79 39.20 1.117 Apalachicola 22.13 Summer 2005 235 438.56 50.87 310 430.80 26.82 26.44 23.13 1.143 Apalachicola 18.69 Summer 2005 235 436.98 65.81 305 430.65 34.59 34.26 30.14 1.137 Apalachicola 0 Winter 2004 235 437.61 104.38 320 435.03 53.76 54.23 47.14 1.150 Apalachicola 17.5 Winter 2004 235 436.17 54.84 315 431.13 28.78 28.58 25.33 1.128 Apalachicola 6.9 Winter 2004 235 432.68 17.10 315 428.97 10.74 10.77 10.07 1.070 Apalachicola 0 Winter 2005 235 430.81 105.18 300 425.00 52.07 50.10 45.24 1.107 Apalachicola 0.1 Winter 2005 235 430.81 114.22 300 428.87 50.56 52.07 47.49 1.096 Apalachicola 6.7 Winter 2005 235 435.32 65.78 300 427.58 32.43 32.48 29.56 1.099 Apalachicola 20.4 Winter 2005 235 434.03 48.42 300 420.49 24.36 23.78 22.54 1.055 Atchafalaya 0 Winter 2004 235 432.76 103.48 315 428.01 54.17 52.33 47.91 1.092 Caloosahatchee 0.1 Summer 2004 235 433.16 414.77 315 427.74 214.84 216.32 193.73 1.117 Caloosahatchee 1.2 Summer 2004 235 433.56 363.84 305 424.38 187.43 188.70 170.18 1.109 Caloosahatchee 6.3 Summer 2004 240 433.16 312.15 305 424.18 163.02 160.69 151.40 1.061 Caloosahatchee 0.21 Summer 2005 235 433.81 266.07 310 428.75 151.80 146.53 129.07 1.135 Caloosahatchee 0.88 Summer 2005 235 435.08 254.96 310 430.01 145.67 140.41 124.16 1.131 Caloosahatchee 1.22 Summer 2005 235 434.68 250.25 310 427.57 139.37 135.07 119.54 1.130

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114 Appendix I. (cont.) RiverSalinitySeason Ex Humic Peak A (nm) Em Humic Peak A (nm) Fluor. Intensity Humic Peak A (QSE) Ex Humic Peak C (nm) Em Humic Peak C (nm) Fluor. Intensity Humic Peak C (QSE) Ex/Em = 300/400 nm (QSE) Ex/Em = 300/430 nm (QSE) Fluor. ratio Em 430:400 nm @ Ex 300 nm Caloosahatchee0Winter 2004235434.01358.32315430.411 76.52177.05152.481.161 Caloosahatchee1.1Winter 2004235431.85297.35315425.3 7148.98149.11134.231.111 Caloosahatchee6.9Winter 2004235432.57243.91315428.2 5120.87121.99110.121.108 Caloosahatchee11.3Winter 2004235433.29196.10315427. 5395.2696.8886.321.122 Caloosahatchee0Winter 2005235434.03364.07300426.291 75.55174.21157.111.109 Caloosahatchee14.1Winter 2005235432.10304.57300428. 87147.02145.24130.911.109 Caloosahatchee14.5Winter 2005235430.16222.63300424. 36108.73107.5199.791.077 Hillsborough0Summer 2004235442.18460.18320443.98232 .90227.55189.031.204 Hillsborough0Summer 2004235445.79451.76320442.18224 .91224.25187.551.196 Hillsborough2.98Summer 2005235440.55195.07315432.41 106.37101.7189.321.139 Hillsborough9.15Summer 2005235438.04103.17315429.90 65.9662.1154.631.137 Hillsborough23.44Summer 2005235431.3564.92305423.71 33.4332.4130.981.046 Hillsborough0Winter 2004235428.97380.66300417.45232 .49223.28215.971.034 Hillsborough4.4Winter 2004235435.45138.07305425.377 2.2271.7064.541.111 Hillsborough10.1Winter 2004235435.45117.13305426.81 59.6958.9653.281.107 Hillsborough16Winter 2004235429.9789.36305424.2146. 6246.1842.451.088 Hillsborough0Winter 2005235437.90230.72300425.65115 .69114.79103.291.111 Hillsborough6Winter 2005235434.03106.71300425.6552. 7852.3748.251.085 Hillsborough16Winter 2005235430.2384.54300425.6241. 4840.8438.631.057 Manatee0Summer 2004240454.81420.35320451.20227.1321 5.74166.591.295 Manatee0.6Summer 2004235447.59427.26320443.98222.58 217.55175.771.238 Manatee4.8Summer 2004235443.89399.89320445.79204.73 202.41163.151.241 Manatee20.91Summer 2005235433.29101.45305426.9053.6 252.8748.721.085 Manatee24.99Summer 2005235434.5590.08305427.5347.59 47.0943.401.085 Manatee27.35Summer 2005235427.5365.70305424.9735.34 34.4032.731.051 Manatee0Winter 2004235443.37158.70320437.0384.4981. 6670.191.163 Manatee20.3Winter 2004235431.85117.44305426.0959.57 59.8354.741.093 Manatee26.2Winter 2004240430.4163.46305423.9332.023 2.3330.071.075

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115 Appendix I. (cont.) RiverSalinitySeason Ex Humic Peak A (nm) Em Humic Peak A (nm) Fluor. Intensity Humic Peak A (QSE) Ex Humic Peak C (nm) Em Humic Peak C (nm) Fluor. Intensity Humic Peak C (QSE) Ex/Em = 300/400 nm (QSE) Ex/Em = 300/430 nm (QSE) Fluor. ratio Em 430:400 nm @ Ex 300 nm Manatee0.12Winter 2005235441.77210.91300432.10103.0 6102.9990.801.134 Manatee17.73Winter 2005235430.16141.85300425.6570.5 869.0164.661.067 Manatee20.74Winter 2005235436.82123.19300426.2859.7 559.6055.031.083 Manatee25.08Winter 2005235429.5285.33300419.2040.91 40.9038.711.057 Mississippi0Winter 2004235431.8548.42315422.1324.71 24.2123.081.049 Peace0Summer 2004235448.32478.10320444.63263.84247. 59198.681.246 Peace1.4Summer 2004235440.94378.54320435.40201.6919 5.12166.581.171 Peace14Summer 2004240437.25140.16315429.8769.5570.5 763.931.104 Peace0.12Summer 2005235445.84322.34310437.61184.621 75.41141.971.236 Peace0.14Summer 2005235442.67312.56310437.61183.371 75.76145.081.211 Peace8.67Summer 2005235438.56221.58310434.04129.421 25.90106.141.186 Peace0Winter 2004235435.45273.21315431.85141.93140. 54121.581.156 Peace9.7Winter 2004235436.89224.01310428.25113.8011 3.09100.471.126 Peace16.8Winter 2004235432.57162.94305426.8182.9082 .5974.901.103 Peace0Winter 2005235442.41415.83300437.90193.28191. 67164.161.168 Peace2.9Winter 2005235438.80409.53300431.55200.3819 9.84171.761.163 Peace14.7Winter 2005235437.25249.47300429.52120.471 19.85105.561.135 Shark0.9Summer 2004240431.35269.51315427.74133.4413 6.48126.391.080 Shark8.2Summer 2004240434.08255.37310428.67128.6012 7.65116.871.092 Shark16.6Summer 2004235434.08202.27310428.67104.581 04.0393.721.110 Shark27.1Summer 2004240436.3596.59305427.1251.4350. 8646.731.089 Shark0.5Summer 2005230428.86238.33310425.62114.3610 9.9799.061.110 Shark2.67Summer 2005235435.71202.98315431.00106.931 06.1793.631.134 Shark12.53Summer 2005230436.34175.62310430.6589.408 6.9378.951.101 Shark23.59Summer 2005235429.46101.70310426.9052.975 1.7547.331.093 Shark9.01Winter 2005235433.39339.05300425.62169.521 66.64152.321.094 Shark16.58Winter 2005235437.27289.66300426.92145.83 144.83132.211.095 Shark22.79Winter 2005235443.09220.83300430.15109.61 108.0997.741.106 Shark30.79Winter 2005235432.74110.68300425.6254.715 3.5449.721.077

PAGE 130

116 Appendix I. (cont.) RiverSalinitySeason Ex Humic Peak A (nm) Em Humic Peak A (nm) Fluor. Intensity Humic Peak A (QSE) Ex Humic Peak C (nm) Em Humic Peak C (nm) Fluor. Intensity Humic Peak C (QSE) Ex/Em = 300/400 nm (QSE) Ex/Em = 300/430 nm (QSE) Fluor. ratio Em 430:400 nm @ Ex 300 nm Suwannee0Summer 2004235450.28717.81320450.28391.013 54.87280.071.267 Suwannee22.4Summer 2004235441.30240.36315435.88122. 23122.39103.251.185 Suwannee20.2Summer 2004240443.73252.95315436.35126. 98129.37108.331.194 Suwannee0.13Summer 2005230443.06233.16325447.42113. 31102.9183.671.230 Suwannee24.7Summer 2005235435.8577.21305428.7938.12 37.6233.671.117 Suwannee22.78Summer 2005235432.4191.27310422.4247.3 446.0442.741.077 Suwannee0Winter 2004235443.37314.49320442.65174.861 61.46129.301.249 Suwannee13.2Winter 2004235442.65184.90320439.0598.0 794.7679.181.197 Suwannee18.3Winter 2004235441.21129.76320438.3365.9 265.5355.951.171 Suwannee0Winter 2005230437.92273.06300439.21122.181 21.1899.821.214 Suwannee20.1Winter 2005235434.44144.09300431.2869.4 068.4059.941.141 Suwannee25.6Winter 2005235432.74109.11300430.8052.4 751.8947.741.087

PAGE 131

117 Appendix II. Seasonal absorption and DOC measureme nts for river samples. Missing data due to sample storage issues. RiverSalinitySeason Spectral Slope 350-440nm (m-1) Spectral Slope 280-312nm (m-1)a(312) (m-1)a(350) (m-1)a(440) (m-1) Fluor. 300/430 nm / a312 (QSE*m)DOC M Alafia0Summer 20040.015260.0124711.326.501.96107.67Alafia3.3Summer 20040.014270.013177.824.401.31129.9 21121.34 Alafia0.21Summer 20050.016520.0145450.0928.636.4719 .89995.20 Alafia0.32Summer 20050.015840.0145656.6532.147.5419 .991060.77 Alafia8.44Summer 20050.013620.0143341.7923.476.9811 .48845.90 Alafia1.2Winter 20040.023950.0181917.508.501.1548.0 2556.81 Alafia6.5Winter 20040.020400.0177116.548.061.7328.0 4513.26 Alafia21.4Winter 20040.019560.0191110.945.501.0431. 08409.87 Alafia0Winter 20050.021760.0179021.2511.012.1335.16 555.51 Alafia2Winter 20050.015770.0166324.9313.353.7520.30 445.50 Alafia22Winter 20050.022590.021798.834.060.7142.742 72.49 Apalachicola0Summer 20040.015210.014082.981.750.441 44.62 Apalachicola30.3Summer 20040.017160.016960.980.520. 12171.08266.23 Apalachicola0.52Summer 20050.016680.0133623.8513.89 3.7211.76377.38 Apalachicola22.13Summer 20050.018160.0163912.816.76 1.6016.50325.78 Apalachicola18.69Summer 20050.017790.0150216.098.69 2.2315.39365.16 Apalachicola0Winter 20040.014560.0134425.7715.954.7 611.40488.73 Apalachicola17.5Winter 20040.017030.0156414.888.031 .8915.10393.11 Apalachicola6.9Winter 20040.018280.0165613.467.211. 566.92478.41 Apalachicola0Winter 20050.015380.0141120.0511.392.9 516.97 Apalachicola0.1Winter 20050.015990.0136920.7812.372 .8318.37341.10 Apalachicola6.7Winter 20050.014030.0155911.596.181. 7418.64220.13 Apalachicola20.4Winter 20050.018500.018139.024.580. 9624.88252.46 Atchafalaya0Winter 20040.015530.0164529.9217.274.57 11.46552.44 Caloosahatchee0.1Summer 20040.019560.016725.912.990 .61355.18982.46 Caloosahatchee1.2Summer 20040.019530.017165.062.470 .47402.75669.84 Caloosahatchee6.3Summer 20040.018570.017834.642.350 .44368.79643.66 Caloosahatchee0.21Summer 20050.015850.0154671.1739. 6911.0113.311319.43 Caloosahatchee0.88Summer 20050.016370.0154768.5537. 5810.1713.811284.43 Caloosahatchee1.22Summer 20050.018540.0161263.4433. 978.0116.861258.68

PAGE 132

118 Appendix II. (cont.) RiverSalinitySeason Spectral Slope 350-440nm (m-1) Spectral Slope 280-312nm (m-1)a(312) (m-1)a(350) (m-1)a(440) (m-1) Fluor. 300/430 nm / a312 (QSE*m) DOC M Caloosahatchee0Winter 20040.018580.0169757.4929.056 .3327.99266.90 Caloosahatchee1.1Winter 20040.018680.0168146.2423.1 05.1528.931298.31 Caloosahatchee6.9Winter 20040.018120.0171241.4720.2 44.9224.801065.51 Caloosahatchee11.3Winter 20040.018630.0177634.2717. 453.4727.94982.49 Caloosahatchee0Winter 20050.013820.0165059.8833.649 .1419.07 Caloosahatchee14.1Winter 20050.012930.0165650.3226. 578.3917.321056.01 Caloosahatchee14.5Winter 20050.013640.0173637.6019. 445.6519.04597.84 Hillsborough0Summer 20040.015400.0150810.356.521.53 148.29 Hillsborough0Summer 20040.014010.0136211.317.102.05 109.40 Hillsborough2.98Summer 20050.013810.0140153.6531.39 9.4210.80853.46 Hillsborough9.15Summer 20050.016880.0161133.6618.53 4.0915.19682.24 Hillsborough23.44Summer 20050.019680.0167815.488.97 1.6919.21444.92 Hillsborough0Winter 20040.016750.0152472.1138.379.2 824.071256.51 Hillsborough4.4Winter 20040.021350.0169821.8810.892 .1633.26521.47 Hillsborough10.1Winter 20040.018270.0162422.5611.34 2.6921.88590.88 Hillsborough16Winter 20040.018360.0176416.498.231.8 225.43337.25 Hillsborough0Winter 20050.019750.0173734.7918.533.4 033.79738.33 Hillsborough6Winter 20050.017710.0173113.837.081.64 31.93228.25 Hillsborough16Winter 20050.019390.0190111.745.781.2 433.02277.00 Manatee0Summer 20040.015790.0122514.118.532.2197.45Manatee0.6Summer 20040.014020.0121713.247.642.4788. 02314.58 Manatee4.8Summer 20040.015590.0133310.025.821.65122 .551206.57 Manatee20.91Summer 20050.017010.0173824.7812.493.26 16.23612.63 Manatee24.99Summer 20050.017610.0177319.409.352.322 0.30521.82 Manatee27.35Summer 20050.013850.0173316.458.813.141 0.96436.22 Manatee0Winter 20040.018240.0144531.9817.363.9620.6 3615.85 Manatee20.3Winter 20040.020970.0181621.7911.072.182 7.46648.32 Manatee26.2Winter 20040.022840.0198711.505.390.8537 .92403.36

PAGE 133

119 Appendix II. (cont.) RiverSalinitySeason Spectral Slope 350440nm (m-1) Spectral Slope 280312nm (m-1)a(312) (m-1)a(350) (m-1)a(440) (m-1) Fluor. 300/430 nm / a312 (QSE*m) DOC M Manatee0.12Winter 20050.017440.0160434.9818.514.292 4.02700.68 Manatee17.73Winter 20050.018550.0186722.8411.362.34 29.46414.35 Manatee20.74Winter 20050.019770.0194617.998.131.783 3.48375.49 Manatee25.08Winter 20050.019430.0201913.416.531.303 1.40321.24 Mississippi0Winter 20040.013360.0154613.307.932.341 0.33250.89 Peace0Summer 20040.019950.014919.044.910.87285.66Peace1.4Summer 20040.017170.016106.583.440.81241.43 1479.16 Peace14Summer 20040.018350.019782.011.000.19362.974 87.41 Peace0.12Summer 20050.017770.01357114.2066.4216.051 0.931529.21 Peace0.14Summer 20050.016230.01359112.2165.3619.918 .831514.32 Peace8.67Summer 20050.017290.0146968.1738.989.4213. 371113.51 Peace0Winter 20040.019440.0151652.1027.085.2926.541 091.44 Peace9.7Winter 20040.019670.0161442.5921.554.6924.1 41063.99 Peace16.8Winter 20040.018320.0160532.0716.353.6822. 46784.49 Peace0Winter 20050.015660.0150675.2541.759.7519.67Peace2.9Winter 20050.014870.0145476.7042.5410.9618. 23813.41 Peace14.7Winter 20050.014010.0148646.4825.357.5415. 89862.86 Shark0.9Summer 20040.011880.015425.303.031.11123.30 793.10 Shark8.2Summer 20040.013400.015645.362.921.01126.71 898.15 Shark16.6Summer 20040.012780.015354.862.670.90116.0 9640.17 Shark27.1Summer 20040.013750.016942.411.220.41122.6 6428.45 Shark0.5Summer 20050.015300.0169456.9730.887.4514.7 6 Shark2.67Summer 20050.014610.0160364.1935.289.5111. 16 Shark12.53Summer 20050.012900.0155754.2629.719.708. 96 Shark23.59Summer 20050.012790.0165331.7217.645.749. 02 Shark9.01Winter 20050.018550.0189752.2428.585.6729. 39802.65 Shark16.58Winter 20050.018000.0188546.8523.715.4326 .65753.95 Shark22.79Winter 20050.021020.0200734.3918.233.2832 .95582.03 Shark30.79Winter 20050.016010.0202819.7710.052.6720 .05443.85

PAGE 134

120 Appendix II. (cont.) RiverSalinitySeason Spectral Slope 350-440nm (m-1) Spectral Slope 280-312nm (m-1)a(312) (m-1)a(350) (m-1)a(440) (m-1) Fluor. 300/430 nm / a312 (QSE*m) DOC M Suwannee0Summer 20040.017240.0118924.6915.093.42103 .76581.93 Suwannee22.4Summer 20040.016840.013316.333.660.8214 8.42789.86 Suwannee20.2Summer 20040.016850.014205.773.150.7716 8.36723.78 Suwannee0.13Summer 20050.017450.0119717.4310.382.69 38.201149.82 Suwannee24.7Summer 20050.018890.0155021.6011.882.81 13.38482.02 Suwannee22.78Summer 20050.021670.0179621.9311.362.3 719.46608.76 Suwannee0Winter 20040.016330.01377109.7864.9415.231 0.6099.53 Suwannee13.2Winter 20040.017070.0141952.1028.777.12 13.311061.50 Suwannee18.3Winter 20040.016580.0152037.7621.274.76 13.78880.66 Suwannee0Winter 20050.015220.0129067.0040.209.7012. 49 Suwannee20.1Winter 20050.014980.0145932.7517.454.71 14.53351.50 Suwannee25.6Winter 20050.017010.0170220.719.912.232 3.32


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Conmy, Robyn Nicole.
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Temporal and spatial patterns in optical properties of colored dissolved organic matter on florida's gulf coast :
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by Robyn Nicole Conmy.
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[Tampa, Fla] :
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Dissertation (Ph.D.)--University of South Florida, 2008.
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ABSTRACT: Characterization of Colored Dissolved Organic Matter (CDOM) in surface and ground waters in South Florida was conducted using fluorescence and absorption spectroscopy. Waters of the West Florida Shelf are heavily influenced by many river systems on Florida's Gulf Coast that, to the first order control CDOM distributions on the shelf. Seasonal surveys revealed that changes in the underwater light field as a result of major hurricanes and resuspension events are linked closely with a number of factors prior to a storm's passing such as the presence of persistant blooms, rainfall and discharge. Additionally, storm track and wind direction were found to play a significant role in CDOM signatures. A study of ten riversheds located between the Mississippi / Atchafalya River system and the Shark River in the Everglades revealed a wide range in CDOM seasonality.A regional dependence of CDOM was also found, where highest aromaticity and concentration of organic material was found for the southernmost watersheds. Basin characteristics, vegetation differences, land use and climatic patterns are implicated in the cause for regional differences. In addition to surface flow, organic material in groundwater was measured in deep and shallow aquifers surrounding the Tampa Bay Estuary. As a result of strong hydrologic links between shallow aquifers and the overlying surface waters, CDOM in both reservoirs were found to be quite similar. Deep aquifers (> 150 ft) however are less concentrated and have CDOM signatures more similar to marine waters. This suggests similar biogeochemical pathways of the material, including the influence of the aquatic microbial community.Furthermore, multi-spectral CDOM fluorescence measurements were shown to be a potential indicator of groundwater presence in Tampa Bay during times of low surficial discharge to the bay, and when some rivers are almost entirely spring-fed. Investigating CDOM distribution and signatures is vital to carbon budget and cycling questions. The amount and quality of organic material has significant implications for ecosystems, thereby affecting organisms that use CDOM as a food source, light availability for photosynthesis, UV shading provided to biota, satellite estimates of chlorophyll a, metal binding, materials transport and overall water quality.
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Advisor: Paula G. Coble, Ph.D.
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Dissolved organic carbon
Fluorescence
West Florida Shelf
Groundwater
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