Karst landforms in Florida : geomorphological analysis

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Karst landforms in Florida : geomorphological analysis

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Title:
Karst landforms in Florida : geomorphological analysis
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Bahtijarevic, Aida
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Tampa, Florida
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University of South Florida
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English
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ix, 159 leaves : ill. ; 29 cm.

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Karst -- Florida ( lcsh )
Geomorphology -- Florida ( lcsh )
Dissertations, Academic -- Geology -- Masters -- USF ( FTS )

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Thesis (M.S.)--University of South Florida, 1996. Includes bibliographical references (leaves 94-96).

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University of South Florida
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Universtity of South Florida
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023507473 ( ALEPH )
37159893 ( OCLC )
F51-00121 ( USFLDC DOI )
f51.121 ( USFLDC Handle )

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KARST LANDFORMS IN FLORIDA, GEOMORPHOLOGICAL ANALYSIS by AIDA BAHTIJAREVIC A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Geology University of South Florida December 1996 Major Professor: Sam Upchurch, Ph. D

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Graduate School University of South Florida Tampa, Florida CERTIFICATE OF APPROVAL Master's Thesis This 1s to certify that the Master's Thesis of AIDA BAHTIJAREVIC with a major in Geology has been approved by the Examining Committee on August 16, 1996 as satisfactory for the thesis requirement for the Master of Science degree Examining Committee: Major Profes''O?: sf' Upchurch, '1?h'O:i2. Membef: Leonard Vacher, Ph.D. Member: \Mark Stewart, ,Ph .'D.

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To those who go where there is no path and leave a trail

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ACKNOWLEDGMENTS I am very grateful to the faculty and staff of the Geology Department, who stood by me in my pursuit of graduate work. In particular these are Dr. Leonard Vacher, Dr. Mark Stewart and Dr. Sam Upchurch. It could not have happened without their patience and understanding. A special thanks to Dr. Sam Upchurch, my long-suffering mentor, for his support, encouragement and faith. I also wish to express appreciation and thanks to Suwannee River Water Management District for access to the topographic GIS database and to Bob Riches, AGIS, Jacksonville for his guidance and help in automation of sample mapping.

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TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES ABSTRACT 1 INTRODUCTION 1.1. ASSUMPTIONS 1.2. KARST IN FLORIDA 2 PREVIOUS WORK 3. CONDITIONS FOR KARSTIFICATION AND DEVELOPMENT OF KARST LANDFORMS 3.1. GEOLOGIC STRUCTURE AND STRATIGRAPHY 3.2. HYDROSTRATIGRAPHY ...... 3.3. CLIMATIC CONDITIONS ADN DISSOLUTION RATES 3.4. GEOMORPHOLOGIC CONDITIONS 4 METHODOLOGY 4.1. CLASSIC GEOMORPHOLOGICAL ANALYSIS 4.1.1. Morphologic Method 4.1.2. Morphogenetic Method 4.1.3. Morphochronologic Method 4.2. GEOGRAPHIC INFORMATION SYSTEM 4.2.1. Data acquisition 4.2.1.1. Source of lithologic information 4 .2.1.2. Source of morphologic information 4.2.2. Data preprocessing 4 .2.2.1. Topographic database 4.2.2.2. Well-log database 4.2.3. Data management 4.2. 4 Data manipulation and analysis 4.2.5. Product generation ..... ]. iii i v vii 1 3 4 7 1 7 17 24 28 28 3 1 3 1 31 3 5 3 5 35 36 37 37 38 38 39 40 40 40

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4.3. QUANTITATIVE GEOMORPHOLOGICAL ANALYSIS 4.3.1. Sample Plan 4.3.2. Quantative geomoprhologic variables 4.3.2.1. Feature Descriptors 4.3.2.2. Sample Descriptors 4 .3.3. Quantification and statistical procedures 5 RESULTS 5.1. QUALITATIVE ANALYSIS OF CLOSED DEPRESSIONS 5.1.1. First-order Closed Depressions 5.1.2. The Second Group Closed Depressions 5 1 .3. The Third Group Closed Depressions 5.1.4. The Fourth Group Closed Depressions 5.1.5. The Fifth Group Closed Depressions 5.2. DISTRIBUTION OF CLOSED DEPRESSIONS 5.3. QUANTITATIVE GEOMORPHOLOGICAL ANALYSIS 5 .3.1. Feature statistics 5 .3.2. Sample statistics 6. DISCUSSION 7. CONCLUSIONS REFERENCES APPENDICES APPENDIX A ii 41 41 45 4 5 48 4 9 52 52 52 53 53 54 5 6 60 64 64 69 88 92 9 4 97 98

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LIST OF TABLES Table 1. INVENTORY OF CLOSED DEPRESSIONS IN THE WEST CENTRAL AND NORTH FLORIDA 2 GENERAL INFORMATION ON SAMPLE CENTER WELLS 3. FEATURE STARISTICS 4 CHANGES IN MEAN VALUES OF FEATURE DESCRIPTORS (after filtering out mixed population) 5. MEASURED SAMPLE DESCRIPTORS 6. MEAN AND EXTREME VALUES OF SAMPLE DESCRIPTORS iii 34 35 66 68 81 82

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LIST OF FIGURES Figure 1. Map showing confined and unconfined conditions in the Floridan Aquifer 2 Geographic position and county boundaries of the study area 3. Model of karst evolution according to Cvijic 4 Genetic classes of sinkholes 5. The hydrogeological system of karst poljes 6. Map and cross section of four townships near Lake City, Florida 7. Model for karst evolution on the Cody Escarpment 8. Correlation o f relict marine terraces and shorelines in Florida 9. Main geologic structures of Florida 10. Generalized geologic map of central and north Florida 11. Litostratigraphic and hydrostratigraphic units in North Florida 12. Isopach-map of Hawthorn group 13. Thickness of the Floridan Aquifer after Miller 1986 14. Elevation of the top of the Floridan aquifer system 15. Potentiometric surface of the Floridan aquifer 16. Geomorphologic macro-regions of the study area after White 1970 17. Closed depressions on the portion of the Mayo NE quadrangle iv 4 6 7 8 9 11 12 13 18 20 2 1 22 2 5 25 26 29 33

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18. Examples of samples 19. Distribution of samples in the study area 20. Examples of complex closed depressions 21. Paynes Prairie, a karst polje south of Gainesville, Allachua Co. .... 22 Coalescence of karst poljes (Arredondo Quadrangle) 23. An example of a karstified valley: Peacock Slough 24. Selected portions of topographic quadrangles illustrating differences in the distribution of closed depressions 2 5 Cartogram of the sinkhole frequency (number of sinkholes per survey section) central portion of the Mayo NE quadrangle . . 26 Frequency distribution of closed depressions 27. Relationship of area and circularity index for mixed population depressions (nongrouped data) 28 Relationships of feature descriptors for all depressions (nongrouped data) 2 9 Relationships of feature descriptors for first-order depressions (nongrouped data) 30 Relationships of feature descriptors for 41 44 55 56 57 59 62 63 64 6 7 7 0 71 depressions of higher orders (nongrouped data) 72 3 1 Relationships of feature descriptors for all depressions (confined conditions) 7 3 32. Relationships of feature descriptors for first--order depressions (confined conditions) 7 4 33. Relationships of feature descriptors for depressions of higher orders (confined conditions) 7 5 34. Relationships of feature descriptors for all depressions (unconfined conditions) 76 35. Relationships of feature descriptors for first--order depressions (unconfined conditions) 7 7 36. Relationships of feature descriptors for depressions of higher orders (unconfined conditions) 7 8 v

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37. Hypsometric distribution of samples 38. Relationship between elevation and Total area within closed depressions (all samples) 39. Relationship between elevation and Dissection index (all samples) . . 40. Relationship between elevation and Total number of closed depressions (all samples) 41. Relationship between elevation and Average depression area (all samples) 42. Relationship between elevation and Average circularity index (all samples) . . vi 79 83 84 85 86 87

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KARST LANDFORMS IN FLORIDA, GEOMORPHOLOGICAL ANALYSIS by AIDA BAHTIJAREVIC An Abstract Of a thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Geology University of South Florida December 1996 Major Professor: Sam Upchurch, Ph.D. Vll

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The karst terrains of west-central and north Florida are characterized by closed depressions superimposed on relict late Pliocene and Pleistocene marine terraces. The purpose of this work is to investigate a relationship between hypsometric position of karst landforms and their morphometric characteristics. The working hypothesis is that terrains in the morphologically older, higher hypsometric intervals are characterized by more frequent and/or larger and more complex closed depressions than terrains in the morphologically younger, lower hypsometri"c intervals. A quantitative analysis of the morphologic variability of closed depressions in Suwannee River Water Management District is performed to test this Distribution and morphologic variability of depressions is quantified on the basis of well-centered geomorphologic samples. Morphologic variables are quantified utilizing a 1:24, 000 GIS database and Arcinfo software. Total sampled terrains of 148.8 km2 include 774 closed depressions with area range 70 670000 m2 and an average depression area of 10100 m2 Percent sample area within depressions range 0.0960.72%, with an average of 8.54%. Elevations in the study area are increasing with the distance from the shore. The thickness of the siliciclastic cover overlying carbonates increases in the same direction. In the nearly half of the area, siliciclastic cover consists of sand with no or very low clay content so that Floridan aquifer is unconfined. In the rest of the area, the cover include Hawthorn Formation which confines the aquifer. viii

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Quantitative analysis of the relationship between morphologic variability and elevations of closed depressions in the study area shows the following: 1 -The number, the average area, the range of areas and the percent per unit area of closed depressions in covered, unconfined karst increase with an increase in elevation, i.e., geomorphologic age; 2 -Confined conditions of the karst aquifer in covered karst terrains and thickness and continuity of confining formation influence relationships between a) morphologic variability of closed depressions and elevations, i e geomorphologic age, and b ) d epression area and depth, i. e., lateral and vertical components o f geomorphologic processes; 3 The number of closed depressions per unit area in covered, confined karst decrease with an increase in elevation and/ or with an increase in thickness of confining formation; and 4 -Multicyclicity of the karst process in the study area related to sea level fluctuations disrupts the pattern of karst landforms and morphologic v a riability of depressions. Date Approved: ix

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1. INTRODUCTION Karst terrains are characterized by unique hydrologic and morphologic conditions that result from the interaction of water and carbonate rock masses, predominantly limestone. In karst terrains, a relatively high degree of rock solubility and the presence of secondary porosity combine with a sufficient water supply to provide conditions for the development of extensive subsurface drainage systems. As karstification progresses, precedent fluvial drainage is interrupted and internal drainage increases. Surficial runoff becomes oriented towards voids in the rock mass that conduct the water underground. Due to the solubility of the rock mass, voids and underground conduits enlarge, increase in number, and interconnect forming underground flow systems. Consequently, surficial, linear drainage patterns and related landforms disintegrate and are replaced by typical karst morphology, characterized by a preponderance of internally drained, closed depressions. Other geomorphologic processes, such as collapse and mechanical erosion by transported suspended material, can take a subordinate role in the development and modification of karst conduits and can influence karstification rates. Karst landscapes occur in both exposed limestone areas and regions where limestone is covered by relatively insoluble, silici-clastic rocks, provided that the cover allows water to percolate through and dissolve the underlying carbonate rock.

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2 In terrains where carbonates are overlain by siliciclastic cover, closed depressions can be partially or completely developed in the non-carbonate material as long as underground transport of clastic material is possible. Local variability of hydrogeologic properties of the lithologic substrate, such as porosity, intrinsic and/ or relative permeability, relative position of h ydrogeologic units, and thickness of overburden function as morphologic modifiers and may affect the number and size of closed depressions and the rates of their development. Given relatively uniform and constant physiographic, lithologic and hydrologic conditions, the morphology of closed depressions and related terrain characteristics are a function of geomorphologic age. In other words, active karstification results in an increase in size and complexity of closed depressions with time. The extent of closed depressions increases at the expense of fluvial or other precedent landforms. Much of the Florida carbonate platform is overlain by siliciclastic sediments of various thicknesses and permeabilities. Consequently, its relief has the general characteristics of covered karst with closed depressions developed entirely or partially in siliciclastic cover. Bare karst exposure is sporadic, and is limited to narrow coastal zones and areas of local cover perforation further inland. Subaerial karst landforms in Florida are superimposed on relict marine terraces. The terraces developed from the late Pliocene through Pleistocene, during regression from high sea stands of interglacial stages. Time correlations, number and

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3 elevations of terraces differ according to various authors (see Previous Work) However, higher terraces can be assumed to be older than lower ones. It follows that karst landscapes developed on the higher terraces are older than those on lower terraces. The purpose of this report is a systematic study of the relationship between morphologic variability of covered-karst landforms and related terrains and the geomorphologic age implied by hypsometric position. A combination of classic geomorphologic methods and computerized techniques is used for spatial analysis and quantification of target geomorphic features. A methodology is developed for introducing systematic sampling (based on statistical principles) into a geomorphological analysis and design of an application of Geographic Information System (GIS) for sampling and quantification of morphologic variability. 1.1. ASSUMPTIONS The two assumptions emphasized J.n this Introduction are relevant to the formulation of the working hypothesis: 1. In karstic terrains, complexity and extent of closed depressions increase with geomorphologic age1 ; and 2 Hypsometric2 levels in Florida are a 'geochronologic progression where higher hypsometric position implies older geomorphologic age. 1 Term geomorphologic age is used to distinguish between the age of a landform and the age of substrate, that is, geologic age. 2 Pertaining to the hypsometry (elevation above a datum, usually MSL) or isohypses (isopleths for elevation; Syn: contours on a topographic map, isoheights)

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4 These assumptions result in the hypothesis that: the extent and complexity of the closed depressions in Florida increases with the hypsometric interval or with the geomorphologic age. In other words, terrains in the morphologically older higher hypsometric intervals are characterized by more frequent and/or larger and more complex closed depressions than terrains in the morphologicaly younger lower hypsometric intervals. It is this hypothesis that is tested in this work. 1.2. KARST IN FLORIDA The zone of karst-dominated landscape in Florida correlates with areas of unconfined and semi-confined conditions of the Floridan Aquifer (Figure 1). Figure 1. Map showing confined and unconfined conditions in the Floridan aquifer. (From Johnston and Miller, 1988) 30 These are the areas where various GEORGIA ALABAMA I AOUiiCK. OR BREACHED D ou( _,...-UM tr OF FtQntQt.N AQUir[R SY5TEM 0

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5 karst landforms are well developed and pronounced to the extent sufficient for determination of and morphometry on the basis of available topographic data. Geographically, this area encompasses most of central Florida and the adjacent, central portion of north Florida. To test the principal hypothesis, a study area was selected that includes most major karst landforms found in Florida, but which is limited enough in area for practiacal study. The study area (Figure 2) encompasses a portion of Florida within the Suwannee River Water Management District

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6 1 I I STUDY AREA N i '-----------------------"-------------' Figure 2. Geographic posltlon and county boundaries of the study area.

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7 2. PREVIOUS WORK The first systematic study of karst geomorphology and hydrology was done by Jovan Cvijic during the late nineteenth and the early twentieth centuries. In his doctoral dissertation (1893) and his later work, Cvijic developed the fundamental karst theory (Figure 3.) and introduced morphographic and morphometric aspects in karst-landforms analysis. His theory has since been expanded by Roglic1 (19561 1961) 1 Sweeting ( 1972), Gams (197 4 1978), Jenings (1985, 1987) 1 Sustersic (1985) I White (1988) I Ford Ia I (b) I C) idl Figure 3. Model of karst evolution according to Cvijic ( 1918, from Ford & Williams, 1989).

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8 and Williams (1989), and numerous others. An outline of the theory is given in the introduction t o this paper. Cvijic (1918) developed a model of stages of maturation of karst landscape from initial sporadic appearance of closed depressions, through prevalence, to final destruction, i.e. disappearance of karst-landforms and redevelopment of fluvial drainage (Figure 3) According to Cvijic, the main morphologic types of depressions are defined as follows: 1) Vrtaca or dolina (an English term sinkhole will be used in this thesis, a cone or bowl-shaped depression developed at the intersection of joints and/or fractures is the basic karst landform. Cvijic's original classification of sinkholes as solutional, collapse, and alluvial SOLUTIO N COLLAPSE SUFFOSION SUBSIDENCE Figure 4. Genetic classes of sinkholes (From: Ford and Williams, 1989). is redefined by Ford and Williams (1989) into four genetic (basically structural) categories: solutional, collapse, suffosional and subsidencial (Figure 4.) 2) Uvala, a complex, closed depression developed by the expansion and coalescence of a number of sinkholes. 3) Karst polje, a large, flat-floored, closed depression, and the largest individual landform of a karst terrain (Figure 5) Classic karst poljes have a tectonic origin,

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9 i.e., they are developed in graben structures. Their morphology is a lso modified by nonkarstic geomorphological process (fluvial, lacustrine, glacial, etc.). sw C etW\ Atv. Sl"'h\o Polr4 Q ,... ooen.. Clc Cl llc G Meo1ot c 4 0&o'"U u N E Figures. The hydrogeological system of karst poljes (From: Ford & Williams, 1989 -compiled from A: Zotl, 1974 and B : Herak, 1972).

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10 Due to the predominance of the karst process in karst polj e evolution and the fact that the hydrology of karst poljes is essentially determined by the karst process, the term is internationally accepted. Most karst landforms, as well as the other morphogenetic types of landforms, are in fact polygenetic. A criterion for the classification of a landscape is a dominant geomorphological process (agent), that is, the process that results in specific features distinguishing a type of the landform from landforms of other genetic types. Of the other karst landforms defined by the theory, the most significant relative to this study are transitional morphologic features developed by progressive karstification of fluvial valleys. These features are termed dry or blind valleys (Milojevic, 1923) There is an apparent absence of systematic geomorphologic studies of karst in the geologic literature on Florida. The major geomorphological work (White, 1970) includes relative macro-to mezoscale geomorpholos-:c units ("physiographic provinces") of Florida. White emphasizes the existence of karst processes and their importance for hypsometric relationships and to the origin of lakes in the ridges of the Central Highlands. Some aspects of karst relief, mainly sinkhole development and sinkhole distribution, are directly or indirectly considered in a number of studies. Lawrence and Upchurch (1982) performed a factor analysis o f groundwater chemistry in the Live Oak area, Suwannee County. They

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11 distinguished a regional variation in water chemistry linked to physiographic provinces. Their conceptual model of the Floridan aquifer recharge system includes a model of karstification in J . -. A. '; T _.. ..-;. ... . Figure 6. Map and cross section of four townships near Lake City, Florida (From: Lawrence & Upchurch, 1982)

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1 2 covered karst. These conclusions support earlier work of the authors in the Lake City area (Lawrence and Upchurch, 1976). Upchurch and Lawrence (1984) identified three groundwater chemical domains, with differences in degree of saturation with respect to calcite, dolomite, and hydroxylapatite. These domains reflect three distinct geomorphological zones (Figure 6) The upland domain is underlain by the Hawthorn Group which confines the Floridan aquifer. Water in this domain has high residence time in the aquifer and i s in equilibrium with respect to calcite s..1'ldol...--c:ledlce _,.... --=--u l l 1:::::=.. T m 7--f ----::0 I I _____ ""' I l .._,_IEOC..::. -.2GROUND-WATER DOMAINS LOWLAND D OMAI N SCARP DOMAIN UPLAND DOMAIN SATURA TION STATES CALCI T E undersatur a te
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13 or dolomite, therefore, karstification is minimal. The scarp domain (the Cody Escarpment) is developed along the erosional edge of the Hawthorn, where recharge and karstification are active along fracture zones. this domain, so it is Water is aggressive and organic-rich in characterized by rapid vertical karst development. The lowlands domain, which lies at the foot of the scarp, is unconfined. Recharge in this domain is diffuse, and groundwater water is slightly undersaturated, so karstification is present, but slow. Upchurch (1989) discussed the origin and age of Florida's karst. He emphasized the multi-cyclicity of its development, proposed a model of karst evolution and set an upper limit on age of karst as Messinian (Miocene) He related morphologic variability and sinkhole distribution patterns to recharge patterns and to the thickness and structure of limestone cover. Sinclair and Stewart (1985) described the spatial relationships of morphogenetic types and distributions of sinkholes with the thickness and lithology of limestone cover in Florida. On the basis of these two criteria, they defined and mapped the distribution of four types of terrain in Florida. Littlefield et al. (1981) confirmed the relationship between sinkholes and lineament distribution. Based on the results of this research, they developed a hierarchy of sinkhole probability in karst terrains, with the conclusion that the highest sinkhole probability areas are in zones of major lineament development and lineament intersections.

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14 Kastning (1989) analyzed geologic factors that control distribution of karst landforms. The author found that karstic landforms follow linear and dendritic patterns. He suggested that these patterns are influenced by three factors: lithostratigraphy (selective dissolution), structure (lineaments), and drainage (ground water flow) Arrington and Lindquist (1987) studied sinkhole morphometry, orientation, and mechanisms of development in the "thickly mantled karst" of the Interlachen area of Florida. According to their results, sinkholes in thickly mantled karst have very large dimensions. Sinkhole area and depth are found to be inversely proportional to their frequency (number of sinkholes/unit area) The dominant mechanism of sinkhole development is piping due to the transport of clastic material by groundwater flow through conduits in underlying limestone. Bahtijarevic (1989) analyzed the sinkhole density of the Forest City Quadrangle, Florida, where the terrain is characterized by covered karst, "old" and "new" sinkholes. and compared distributions of These terms were used by the University of Central Florida, Florida Sinkhole Research Institute to distinguish between the sinkholes known have formed during the last 30 years ("new sinkholes) from ones formed prior to this period ("old" sinkholes). She found no correlation between the frequencies (number of sinkholes per 1 square km) of the two categories of sinkholes. On the contrary, new sinkholes appeared in the terrains with a low density of old sinkholes.

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15 Jensen (1987) described large closed depressions in covered karst in north-central Florida. He concluded that "some of the large solution basins formed in the covered karst terrain of Florida, such as lake Iamonia, Lake Jackson, and Paynes Prairie meet the general criteria of valley poljes." Jensen's maps and descriptions, as well as the hydrologic and morphogenetic <:>xplanations that he compiled in his work, indicate the similarity between his "valley poljes" and the classic periodically flooded and karst poljes of Cvijic. Healy (1975, Figure 8) correlated the distribution of marine terraces and shorelines in Florida as defined independently by several authors (f'vlatson, 1913; Cooke, 1939, 1935; Vernon 1942, 1951; MacNeil, 1949-50; Bermes et al., 1963; Hendry, 1966; Marsh, 1966; and Yon, 1966). The map that he developed is based mainly on the hypsometric distribution of terraces as defined by Cooke (1949-50). It includes multiple marine terrace levels related to interglacial stages. There are eight Pleistocene shorelines. The highest shoreline, 270 feet1 (82 m), is dated to the Aftoninan interglacial stage. MacNeil (1950) did not find conclt.: s i vE: evidence to indicate sea level transgressions above 150 feet (46 m) MSL. He identified no more than four major marine shorelines as peaks of marine transgression, related to the Yarmouth, Sangamon, Mid-Wisconsin and Post-Wisconsin changes in sea level. 1 English units or double notation are used in the first part of this thesis where existing data, mainly topographic contour values, are cited (references, adopted illustrations and topographic maps) This was done to enable direct correlation to original sources. All contour values and morphologic variables originated in this work are in metric units.

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Mlc.:>n Cooke Vernon vernon MlcHeu Mush Hendry Yon Bermea lnd olhen 1913 1939 1945 19-42 1951 19!0 1966 1966 1966 1963 (Eocunbla,Otrua, (Sutewide) (Holm .. and (Otrw lnd (Stat...tde) (Eseambia lnd (Leon County) (JeCCeuon (Fla&ler, Putnam, Abch1111 Counti .. ) Washlnrton LeryCoundea) Slnta Rota County) St. J ohns Countlea) Counties) Counties) 1 Hulehurst. formerly Coastwise delta --Hi&)l Pllocene Hi&)l levtl or M iooenePiioc cne Miocene dell -Brndywlne plaln terrsoe upl1nd surface delu plaJn pl1in to 21 5 270 ft 25().320 fl. t5D-280 Ct. 6().280 (t. 260 Ct. 260 (1. (Fiu
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3. CONDITIONS FOR KARSTIFICATION AND DEVELOPMENT OF KARST LANDFORMS Conditions for karstification in the study area are 17 determined by geologic (structural, stratigraphic, hydrogeologic) situation, and physiographic (climatic and geomorphologic) c'Jnditions. 3.1. GEOLOGIC STRUCTURE AND STRATIGRAPHY The northern Floridan carbonate platform overlies a continental mass of probably Precambrian core of altered and metamorphosed rocks distinguished from the deep-sea sediments by their shallow-water origin (Cooke, 1945). The core structure is overlain by Paleozoic black shale, quartzite and mica schist. Upon the Paleozoic rocks lies a thick series of Lower Cretaceous to Tertiary shallow-marine deposits, predominantly limestone, that comprise the carbonate platform. The thick sequence of shallow-water platform carbonate rocks underlies the entire Florida peninsula, southeastern Georgia and adjacent areas (Miller, 1988). Regional tectonics are subltly expressed in the subhorizontal of relatively undeformed platform structure. An exception is the broad, probably early Miocene structure of the Ocala Platform (Ocala Uplift) trending northwest-southeast through the study area (Figure 9 and 10). Along the shore-parallel crest of the uplift, Eocene and Oligocene limestones crop out and lie at or near the surface. Downward tilting of the platform toward the west and the consequent submergence of the western part of the Ocala Uplift

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< (---, } \ )_ / 0 50 Approximote edQe ___... of F loridon Ploteou (dotum: meon seo level) 100 MILES N Figure 9. Main geologic structures of Florida (From Faulkner, 197 3) 1 8 8 ,..., I

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19 (Cooke, 1945) resulted in truncation of outcropping limestones, with the oldest units subcropping at the crest of the structure. Northeast of the crest of the uplift, carbonate structures gently dip under Neogene and Quaternary marine deposits (Figure 10). The Tertiary carbonate section in the study area starts with the Paleocene Cedar Keys Formation. The lower portion of Cedar Formation consists of finely crystalline dolomite interbedded with anhydrite. The upper portion consists primarily of coarsely crystalline, porous dolomite (Copeland et al., 1991) The oldest rocks exposed in the study area, the Avon Park Formation (Middle Eocene) crop out on the crest of the Ocala Uplift in south Levy County (Figure 10.). The Avon Park Formation consists of interbedded cream to brown, soft to hard, fine to coarse-grained limestones with zones of wackestone and mudstone, and light to dark brown dolostone, porous, micro-to coarsely crystalline, with abundant fossil molds (Scott, 1989) The Ocala Limestone (Upper Eocene) consists of carbonate rocks uncomformably overlying Avon Park Formation. The group consists of white to light gray, fine to coarse-grained, highly fossiliferous grainstone, packstone, and in the upper part wackestone (Scott, 1989). The lower Ocala is generally soft and very porous with sporadic occurrences of brown dolomite. The upper Ocala i s soft to very hard with only exceptional local occurrence of dolomite. Thickness of Ocala Group ranges to greater than 400 feet (122 m), (Scott, 1989). Oligocene Suwannee Limestone uncomformably overlies Oc ala Limestone in northern and northeastern parts of the study area.

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LOCATION 0 10 lO Figure 10. a3 82. 30 63" azo a1 -\--------_, I Holo-Pieislocene undilferenlialed M io-Piiocene ( ?l unditfuenlioted OliQOcene n:::rTl Suwannee Eocene Pont ltrnestooe at Generalized geologic map of central and north Florida {from Faulkner, 1973). 20

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2 1 The Suwannee Limestone is characterized by varying proportions of white to tan, soft to very hard, fine to coarse grained, variably porous, often very fossiliferous grainestones to packstones with minor percentages of quartz sand, silt a n d clay, and tan to dark brown, moderately soft to hard, microcrystalline to mediu m grained, variably porous dolomite with minor content of siliciclastics a n d abcndant fossil molds. Suwannee Limestone ranges in thickness t o greater than 400 feet (Scott, 1989). Figure 11. NORTH FLORIDA LITHOSTRATIGRAPHIC HYOAOSTRATI SYSTEM SERIES UNIT 0AAPH1C ........ T OU ARTERNAnY HOLOCENE ---UH0tf'FEAC:NTIA1 EO SUAF I C IAL PlEI STOCEN E -HOC.OCNE AOUIFEA PlEISTOCEN E SEOIMC:NTS SYSTEW uCCOSuttEE F 0At.4ATIOH ...... TERTIAR Y CYPAESSMEAO FORMATION ...... P\.OOCENE NASHUA FOAUATIOH ....... .... IHT!II EOMTt AOUIFI:II SYSTe.. OR HAW Tl-CORN OAOUP COHFIH'IHO STA T(HVIllE FOfUolAT I OH UHI T MIOCENE COOSAWHATCHIE FW .... .... ........ ""'AIU
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22 Carbonate sediments are overlain, in most of the study area, by either the Miocene Hawthorn Group or, where the Hawthorn is absent, by undifferentiated residuum from Miocene strata and post-Miocene sediments. The Hawthorn Group uncomformably overlies carbonate strata north, east and northeast of the crest of the Ocala Uplift. The Hawthorn Group of sediments in the study area consist of 0 10 l'O kJl.CS $ 0 1 0 20 30 tClL..O
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23 interbedded phosphatic carbonates and siliciclastics with a trend of increasing siliciclastics in the younger sediments (Scott, 1988) The siliciclastic component consists of fine to coarsegrained quartz sand, quartz silt and clay minerals. The dominant carbonate sediment type is fine-grained dolostone with varying admixtures of clay, silt, sand and phosphate. In ascending order, formations are: Penney Farms, Marks Head and Coosawhatchee, and its lateral equivalent the Statenville (Scott, 1988). Thickness of the Hawthorn Group in the study area increases northeast-and eastward of the crest of the Ocala platform (Figure 12). In the zone where it is truncated against the Ocala Limestone and the Suwannee Limestone (the Cody Escarpment), Hawthorn Group sediments are thin and laterally discontinuous. In the northeasternmost part of the study area (Columbia, Union and Bradford counties), the thickness of Hawthorn exceeds 200 feet (61 m) Post-Miocene sediments in the study area consist mostly of undifferentiated Pleistocene-Holocene sediments that uncomformably overlie older formations: the Hawthorn Group, where it is present, and most of the outcropping carbonate platform. facies of these sediments are unconsolidated, The dominant polygenetic deposits: marine, eolian and fluvial. They consist mainly of fine to coarse-grained, non-indurated to poorly indurated, slightly clayey to nonclayey quartz sands with sporadic presence of peat deposits, thin clay beds, and freshwater carbonates (Copeland et al. 1991).

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24 3.2. HYDROSTRATIGRAPHY The principal aquifer system in the study area is the Floridan aquifer. It consists mostly of carbonates of Eocene age, locally including carbonate beds as old as Late Cretaceous or as recent as early Miocene (Johnston & Miller, 1988). It includes an upper portion of the Cedar Keys Formation, Oldsmar and Avon Park formations, Ocala Limestone, Suwannee Limestone, and, locally, the Penny Farms Formation (Figure 11) Due to the relative difference in hydrologic properties of formations, the permeability of the aquifer system varies through several highly permeable zones, separated by less permeable strata. However, overall permeability of the system is high, one to several orders of magnitude greater than permeability of underlying and/or overlying sediments. The base of the Floridan aquifer is defined by high permeability contrast, i.e., the underlying rocks have very low permeability relative to permeability of Floridan aquifer system. This permeability boundary does not coincide everywhere with chrono-or lithostratigraphic units. The thickness of the aquifer system increases radially from the central portion of the study area, where it is 1300 ft (396 m), to over 1800 ft (549 m) in the south and north, over 1500 ft (457m) on the east, and up to 2100 ft(640m) on the west (Fig. 13). Elevation of the top of the aquifer gradually increases inland from the coastal lowlands where it is lower than 10 ft (5 m) to higher than so ft (15 m) along the Ocala Uplift and then it abruptly decreases east-northeastward to below -300 ft (-91m) MSL. The top of the aquifer is near the land surface in the coastal

PAGE 38

,,,lf n.au:s , ro ,. tClLD'CTnS LEGEND CONTOUR INTERVAL o 100 fEET Figure 14. Elevation of the top of the Floridan aquifer system (in: Copeland et al., 1990) LEGEND CONTOUR INTERVAL 25 rEET 25 Figure 13. Thickness of the Floridan Aquifer, after Miller 1986 (in: Copeland et al., 1990 -27 5 -225 -175 125 -75 25

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26 The top of the aquifer is near the land surface in the coastal zone and along the shallow valleys of the Suwannee and Santa Fe rivers. In these areas the aquifer is unconfined. A zone of semiconfined aquifer extends further inland and away from the Suwannee River, where carbonates are covered by thin and/ or discontinuous sediments of the Hawthorn Group and/or unconsolidated Pleistocene-Holocene sediments containing thin clay beds. Most of the sediments of the Hawthorn Group characteristically have low permeabilities and form an effective aquitard, or upper confining sc.ou: 3 0 3 JO l'O fGL.L:S :S 0 :S 10 to 30 ICJLOCTDU LEGEND CONTOUR INTERVAL o 10 fEET Figure 15. Potentiometric surface of the Floridan aquifer (Copeland et al., 1991)

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27 unit of the Floridan aquifer. The continuous extent of the Hawthorn in the north, northeast and east portions of the study area therefore confines the aquifer. Locally, permeabilities of the carbonates within the Hawthorn are high enough to allow for limited development of an intermediate aquifer system (Copeland et al., 1991). The potentiometric surface of the Floridan aquifer generally increases in elevation to the northeast with disruptions by the Suwannee and Santa Fe valleys (Copeland et al. 1991) and indicates groundwater f low in a seaward direction (southwestward). As a result, the recharge potential of the Floridan aquifer decreases southwestwardly from the crest of the Ocala Uplift. The top of the low-permeability Hawthorn beds is the base o f the surficial aquifer. The surficial aquifer in the study area is the permeable hydrologic unit comprising PleistoceneHolocene unconsolidated to poorly indurated, siliciclastic deposits and well-indurated, permeable Upper Miocene to Pleistocene carbonate rocks (Copeland et al. 1991) Where an upper confining uni t of the Floridan is absent, the surficial aquifer is hydraulically connected with the Floridan aquifer system. The surficial aquifer is mainly unconfined and contains a water table that parallels the topographic surface. The presence of beds of low permeability create semi-confined and locally confined conditions in the deeper parts of the surficial aquifer.

PAGE 41

28 permeability create semi-confined and locally confined conditions in the deeper parts of the surficial aquifer. 3.3. CLIMATIC CONDITIONS AND DISSOLUTION RATES The study area has a humid, subtropical climate, with an average annual precipitation of 1330-1430 mm, annual temperature range 15.5 2 1 C (Bradley, and an average 1972). Based on these values, and according to theoretical relationships given in Ford and Williams (see Methodology, pg. 31), an estimated minimum "solutional denudation" of limestone in the study area is 100 mm ky1 ( 1 mm ky-1 = 1 m3 km-2 y1 ) Assuming this a correct value, and given that the area of the outcropping limestone is about 35,000 km2 the calculated minimum volume of anually dissolved limestone in the study area (Figure 2) is 35,000 m3 3 4 GEOMORPHOLOGIC CONDITIONS The topographic surface in the study area is relatively flat with low topographic gradients and relief. Distribution of elevations is zonal, generally shore-parallel, with a general slope toward the southwest. Consequently, the-highest elevations of about 30 meters are on the north, northeast and east of study area. Most of the study area has elevations of 10 to 30 meters, while the narrow coastal belt is below 10 m MSL. Such a zonal elevation distribution is the main basis for distinction of three macroscale geomorphic regions (Figure 16): Coastal Swamps, Gulf Coastal Lowlands and Northern Highlands (White, 1970).

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29 The main mesoscale characteristic of relief of west-central and northern peninsular Florida is the presence of broad plains. Low gradients minimize rates of surficial runoff and allow more effective vertical drainage of surficial water underground. Due to impact of the karst process, fluvial drainage is limited to major allogenic rivers (Suwannee, Santa Fe, Aucilla), and fluvial basins are poorly defined. Micro-to meso-scale relief is characterized by the dominance ) 11 .. IGl.D s esu n G.DCfOtS LEGtND Figure 16. Geomorphologic macro-regions of the study area after White, 1970 (in Copeland et al., 1991)

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30 of closed depressions with sinkholes as a prevalent landform. Prevalence of sinkholes and their tendency toward circularity result in a pockmarked pattern of the land surface (Figure 24). This pattern clearly indicates the presence of karst processes and is easily observed on. topographic maps (7.5 minute quadrangles) Most of the closed depressions are partially o r completely cteveloped in the siliciclastic cover overlying the limestone due to postburial transport of the cover material through the cavernous system below. The karst of Florida is multi-cyclic (Upchurch, 1989) Identifying and dating of karst cycles is hindered by the presence of siliciclastic cover and by the complicated dynamics of Tertiary and Quaternary sea-level changes that resulted in repeated alternations of transgressive (depositional) and regressive (erosional /dissolutional) stages across the Floridan carbonate platform. However, there are indications (Upchurch, 1989) of karstification stages from late Miocene and Pliocene to recent geologic history, such as uncomformi ties of late Miocene to recent formations, correlation of distribution of groundwater geochemical facies and geomorphological patterns, observations of active karst process and remnants of terrestrial faunas found i n sinkholes and caverns.

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31 4 METHODOLOGY Three main groups of methods are applied in this work for inventory, analysis, quantification, and interpretation of characteristics of closed depressions: -Classic geomorphological analysis, -Geographic Information System (GIS) analysis, and -Quantitative geomorphologic analysis. Descriptions of methods are give n in sections 4.1. through 4.3. 4.1. C LASSIC GEOMORPHOLOGICAL ANALYSIS 4 1.1. Morphologic Method. The preliminary inventory of types of closed depressions (Table 1) was performed by observation of morphologic characteristics of terrains on so topographic map quadrangles, 7.5 minute series, with a scale of 1:24, 000 and contour interval of 5 feet (7. 5 minute quadrangles) The quadrangles were selected so that a succession of elevations from 0 to 80 (0 250 ft) m would be included. Excluding the elevation criterion, selection of quadrangles is random in respect to geomorphologic characteristics. Closed depressions were delineated on the basis of the highest closed contour line. The classification of closed depressions designed for this study was also based on a morphologic criterion: complexity of

PAGE 45

32 closed depressions. The complexity of depression is defined as the number of foci, or sinkhole throats, within a depression. Foci, or sinkhole throats, are locations where cover sediments are entering (or have entered) a cavern system in the underlying limestone. Consequently, transport of the cover material from the sides of the depression is focussed toward these locations. The number of foci is assigned to a depression as its order, so that a depression with one focus is a depression of the first order, one with two foci is a depression of second order, one with three foci is a depression of a third order, and so on (Figure 17) Morphological information used as a basis for quantitative geomorphological analysis, excluding the number of sinkhole throats for each depression, was obtained by cartometric techniques using GIS (see Geographic Information System, pg. 35). The number of foci was obtained by count from the maps (in the preliminary inventory) or from the samples (in digital or map form) A minor portion of the morphologic information consists of approximation of dimensions of depressions of the first order observed in the field during cave trips and cave dives in the period preceding this work (1989 -1994) The approximated data are used mainly for illustration of extreme dimensions or comparison of types of closed depressions.

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Table 1. INVENTORY OF CLOSED DEPRESSIONS IN WEST-CENTRAL AND NORTH FLORIDA "34 CLOSED DEPRESS SHAPE/PLAN BOTTOM PROC E SS QUADRANGLE 1st 2nd 3rd lsome Elonlrreorder order order tric gated gular E rosi onal Alachua no Archer yes Arredondo yes Apopka yes Brooksvi l e no Brooksv. NW no Brooksv. SE yes Chiefland yes Cotton Plant yes Cross City E yes Day yes Day NW no Day SE yes Deep Creek no Dowling Park yes Dunnelon SE yes Dunnelon Fa irfi eld Falmouth Flemington Gainesville Gainsvil. W Genoa Highsprings Hillcoat Homosassa Inverness Lake Buttler Lawtey yes yes yes yes no yes no yes yes yes yes no no Live Oak E yes Live Oak W yes Madi son yes Madison SE yes Manatee Sprs. yes Mayo yes Mayo NE yes Macintosh yes Micanopy no Monteocha Newberry Nobleton Ocala E Ocala W Octahatchee Oldsmar Olustee Romeo St. Catherine Suwannee R. Trenton no yes yes no no yes yes yes yes yes yes yes n o yes yes yes yes yes yes yes yes yes yes no yes no yes yes yes yes yes yes no yes no yes yes yes yes no no yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes yes yes no no no yes yes no yes yes no no yes yes no no no no no yes no yes no no yes yes no yes no no yes no no yes no no no no yes yes yes no no no no yes yes no yes no no no no no no yes no no no no no no no no n o no n o no no no no yes no no no no no no no no no no no no yes yes no yes no yes no yes yes yes yes no no no no no n o no yes no yes no no no no no yes yes yes no yes no yes no yes no yes yes yes yes yes yes no no no yes yes no no yes yes yes yes yes yes yes no no no no no no no no no no no no no no yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes yes no no yes yes no no yes yes yes yes yes yes no yes no yes no yes yes yes yes n o no yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes yes yes yes yes no yes no yes no yes yes yes yes no no yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes no yes yes yes yes Acumu lative no yes yes yes yes no yes yes no yes yes no yes no no n o no yes no no no yes no no no yes yes no no yes yes yes yes no yes no yes yes no no no no yes yes yes yes yes yes no yes ELEVAT ION RANGE 80-190 55-125 55-100 60-250 40-150 50-200 25-55 60-150 20-60 80-95 65-90 90-150 40-100 45-100 40-100 60-150 50-90 70-200 75-180 55-190 100-135 70-170 60-150 5-100 45-100 80-150 100-200 65-175 85-150 50-100 90-185 5-35 25-80 50-125 55-150 55-150 100-170 70-150 50-200 40-100 50-100 70-150 5-95 135-195 60-140 50-150 5-50 40-65 COMMENT Fluvial process dominant Palustrine process dominant Fluv .Palustr .proc.dominant Fluvial process dominant Fluvial proces s Fluv .Palustr.proc.dominant Fluvial process dominant Fluv ia l process dominant

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4.1.2. Morphogenetic Method. 35 Karst landforms in this work were distinguished from other categories of relief in the landform inventory described above by applying morphogenetic principles. The criteria used for genetic classification of landforms were internal drainage and the underground transport of unconsolidated material. Morphogenetic principles were applied in distinguishing the depression bottoms (Table 1) as erosional or depositional. In either case, the information was obtained by observing features that indicate geomorphologic processes, such as flat bottom as an indication of predominance of depositional processes and/or concave or irregular bottom with sinkhole throats as an indication of active underground transport of a cover material. 4 .1. 3. Morphochronologic Method. Assumptions of the relationship between hypsometric position and geomorphologic age, and of the relationship of morphologic complexity and geomorphologic age utilized in formulation of the working hypothesis (Introduction, pg. 4) are based on morphochronologic principles. 4.2. GEOGRAPHIC INFORMATION SYSTEM The Geographic Information System (GIS) is a system of computer hardware and software that includes tools for identifying and analyzing of spatial relationships of objects. It utilizes objects or delimited geographic areas (points, lines,

PAGE 49

36 polygons) that are characterized by spatial and nonspatial attributes. The ability to link spatial and nonspatial data through a common reference system (geographic coordinates) makes GIS a powerful tool for spatial data management and analysis. GIS application in this study utilized aDOS-based personal computer (200 MB hard drive capacity) and the PC Arcinfo software 3. 4 (Environmental Systems Research Institute I n c ) The system has four Arcinfo modules: Starter, Data Conversion, ArcEdit and ArcPlot. The missing Overlay module was functionally replaced by ArcEdit at some expense of time. The latter three modules operate through the Starter module that automatically links processed data to the operating database (Info) The data processing was performed through both interactive (semiautomated) and Arc Macro Language (automated) software applications. For practical purposes, some Idrisi (automated image processing software) utilities were applied (see Data preprocessing, pg. 41) 4.2.1. Data acquisition. The process of identifying and collecting of data sets necessary to perform desired GIS application can be performed by field observations and survey, by acquiring existing data (documents from archives and repositories) and/or by utilizing aerial photography and satellite images (remote techniques) In this work, data acquisition was performed by querying of existing morphologic and lithologic databases.

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37 4.2.1.1. Source of lithologic information. The Geologic Information System, a county-structured database provided by the Florida Geological Survey (FGS) was to obtain lithologic data. The database contains selected well-log descriptions from the FGS archives. There were 580 well logs within the study area. The number of well logs selected for the sample population and the method of selection is described in the Sample Plan. The precision of the well-log locations is one second of longitude/latitude. The accuracy of the locations is practically unknown, due to probable differences in well-log data origin. As with other FGS sources, the minimum accuracy is based on visual locations on USGS topographic maps. 4.2.1.2. Source of morphologic information. The GIS digital topographic database provided by the Suwannee River Water Management District (SRWMD) was used to obtain topographic data. This database consists of elevation contour data from the 7.5 minute series USGS quadrangles. It contains 172 directories, each containing elevation isolines (contour lines) of one quadrangle with a 5-foot (1.524 m) contour interval. Utilizing this database results in a resolution limit of 5 ft (1. 5 m) of vertical and about 79 ft (24 m) of horizontal extent. That is, features with vertical dimensions (relative elevation) of less than 5 ft ( 1. 5 m) and with a horizontal dimension (length/width) of less than 79 ft (24 m) are not included in the database. These resolution limitations are acceptable for the scope of this work.

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. 38 4. 2 2. Data preprocessing. Several transformation procedures were used to convert and standardize data for input in the system. According to Star and Estes (1990) essential preprocessing procedures are: -format conversion(s) (data structure and medium); -data reduction and generalization; -error detection and editing; merging of points into lines, and of points and lines into polygons where appropriate; -edge matching; -rectification/registration; -interpolation; and -photointerpretation. Upon completion of preprocessing, data are entered into the G;s database either automatically, through coverage design, or by direct input. The preprocessing procedures performed on each of two databases used in this work differ. 4.2.2.1. Topographic database. This database was already in GIS format, but it was designed in the UNIX environment and is stored on floppy disks (approximately 200 disks) in Arcinfo export format. Two media conversions of the data (UNIX-to-DOS format, and Export-to-Import format) were performed to make the data suitable for input in PC GIS system. These were performed using Idrisi utilities and the Arcinfo Starter module, respectively. Because of the limited hard-disk capacity, the data was partitioned and processed sequentially.

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39 External tape drive and Trakker software were used to store the converted data. Standardization of the data was performed in later stages of work (after the query of sample variables) It consisted of conversion of english units into metric units. 4.2.2.2. Well-log database. This database was obtained from the FGS in DOS format suitable for GIS use without media conversion. However, to be used in an overlay with a topographic database, it had to be rectified. Rectification is the process that modifies one spatial arrangement of the data set into some other spatial arrangement (Star & Estes, 1990). Rectification of the well-log database consisted of transformation of original longitude-latitude coordinates of well locations (units: degrees, minutes, seconds) into state-plane coordinates (units: feet) based on UTM (Uni versa! Transverse Mercator) Map Projection. Upon the transformation performed using Arcinfo Starter, the data set was stored in an ASCII data file, ready for GIS application. 4 .2.3. Data management. The basic constraints of database management are: security, integrity, synchronization, physical data independence, and minimization of redundancy. Both databases used are constructed according to these constraints. The database management procedures used in this work facilitated the reduction in the number of well logs. Reduction was performed on the basis of the quality of well logs (see sample plan) and related quadrangles. Upon reduction, a point coverage

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40 of well locations was designed using topology of the GIS database as a reference. Another overlay (polygon coverage) was designed in order to delineate sample boundaries (see sample plan) The sample boundaries were later used to capture morphologic information, that is to query, measure, and analyze morphologic characteristics and classifications of sampled landforms. 4.2.4. Data manipulation numerous procedures for data (reclassification, aggregation, structure conversion, spatial and analysis. There are manipulation and analysis geometric operations, data operations, measurement, statistical analysis, modeling etc.) than can be automated and/or semiautomated using GIS utilities. The main data manipulation procedures used in this work were the extraction of data (contou; lines) within each sample boundary; the inventory and reclassification of geomorphic features to identify target features (closed depressions) within each sample; the measurement of polygons between the contour lines within each closed depression; and the storage of data in ASCII files. The results of measurements were completed by a count of denudation foci for each closed depression encompassed by sample boundaries. All measurements were performed interactively using the ArcEdi t module of Arcinfo, that is, by selection of target polygons and automatic storage of the measurements into a named sample file. 4.2.5. Product generation. GIS output files contain maps of sample areas, illustration maps for selected typical

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41 landforms, and tables of measured values. The files containing tables are used as input files for calculations of morphologic variables and for further data processing. Further data manipulation, calculations and graphic presentations were performed by Quattro Pro spreadsheet software. 4.3. QUANTITATIVE GEOMORPHOLOGICAL ANALYSIS 4.3.1. Sample Plan To provide comparability of the lithologic and morphologic information, both well-log and topologic databases were used for sample design. The sampling was performed b y capturing topography A B Figure 18. Examples of samples: A -Shaded areas are closed depressions; B No closed depressions captured within the sample boundary (empty sample)

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42 by generating a sample boundary. A sample boundary is defined and generated as a circle of a radius of 908.0 m (2979.0 ft) with a logged well in the center (Figure 18) Each sample, therefore, captures 2. 6 km2 ( 1 mi 2 ) of the karst terrain. Lithologic conditions and an elevation at the center well are assumed to represent conditions for the sample area. The 580 potential samples were reduced to an acceptable number according to the extent and the timeframe of the work. Reduction of the number of samples and selection of sample wells was based on the following criteria: -availability of geographic coordinates for well locations, -depth of the well, -presence of limestone in the lithologic column of the well, -quality of lithologic information, and -availability and quality of GIS data for the location. Since none of the criteria above is related to morphologic characteristics of samples, selection of sample locations was random with respect to the morphology and distribution of closed depressions. Upon elimination of the samples that did not satisfy any of the selection criteria, the number of samples was reduced to 62 (Table 2). Each sample was named after the FGS identification number for the center well. Topographic maps of all samples (scale 1:24,000) are enclosed in Appendix A. As emphasized in GIS method description, sample boundaries were delineated using Arcinfo techniques.

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Table 2 GENERAL INFORMATION ON SAMPLE CE N TER-WELLS Quadr angle Lamont SE Madison SE Dowling Park Leb a non Station Otter Creek Mayo F ourmile Lake East P ass Cedar Key White Spri ng s W Columbia Dowling Park Lake City E G ainesvill e E Newbe r ry L ake City W Live OakW Alachua Archer Lake CityW Arredondo Gainesville E Monteocha Trenton Gainesville E Gainesville E Gainesville E Gainesville E Gainesville E Gaines v ille E Lake City W lake City W Tr e nton Live Oak E Ellaville Manatee Springs Live Oak E Falmo ut h Mayo N E Well born Genoa Octahatchee Jennin gs Vista Vista Eugene Vista Eugene Genoa Wellborn Worthington Sprs Mikesville Jennings W i ll isto n Falmou th Pinetta M ad ison SE Pinetta May o Madison SE E ll isvi ll e White Springs W We ll number Geo coordinates (FGS) Latit Longit 98 187 358 379 664 1067 1092 1094 1243 1548 1915 1951 1 982 2041 2 1 90 2459 2255 2580 2746 2838 3140 3141 3 15 3 3669 4036 40 38 4039 4 043 4044 4046 4 520 4523 4 666 4914 5208 5956 8780 302214 301657 301100 291439 291747 300525 2942 1 0 291940 291040 301646 300621 301055 300734 293826 293815 30 1 112 302025 295132 293340 300758 293606 294030 295032 293652 293950 293959 293910 294325 294013 294332 30 1 040 300737 293650 301743 302333 292920 30 18 25 8 349 1 7 831835 831052 824404 82 4924 831337 824803 830210 830137 8 2 4921 824207 830921 823722 8 2 2008 823012 823739 830330 822431 823150 823739 822320 821835 822001 824917 821935 821742 821745 822210 8 22 1 25 281603 824214 823910 824844 825830 831058 8 2 5834 8 25 849 30 1 509 8 31435 30 1 449 830153 HydroDepth Dept h t o Eleva tion (m) Depth geol. to limeFloridan ( m ) cond. stone( m ) ( m ) 20 .42 27 .43 22 .86 3 .05 6 1 0 19.81 26 .82 1 .22 1.22 31.09 26 21 24 38 29.87 31. 39 44 .81 58.52 28 65 41.45 23 1 6 30 18 27.43 53 04 52 43 1 6 76 54.25 5 3.3 4 44 50 58. 22 38 .71 50.60 45.11 28 04 16 .76 32 .6 1 18.29 3 0 5 28 .96 26 82 39 32 54.86 15.24 6.10 8 53 24 69 13.11 6 .71 78. 64 18. 29 48.77 21. 34 76. 20 78 64 91.44 27.43 53. 34 22 86 31 39 30.48 64 0 1 86 87 30.48 73. 15 91. 44 59 .13 70.10 53 34 70 .10 5 1 .21 44 50 30.48 88 39 74. 98 32. 00 62.48 16.76 42 37 29 .26 45. 72 con i unc. unc u n c unc un c un c unc. unc coni. unc coni. coni. coni. coni coni. unc coni. u n c coni. coni. con i. cont. unc. coni. con i. coni. coni. con i. coni. coni. unc unc. unc unc unc u n c unc. u n c 6 10 6 .10 0 00 0 1 52 3 66 15. 85 8 53 2.74 30 05 3 05 12.19 19.81 39.62 18. 29 39 62 18. 29 44 20 1 52 15 24 15. 24 60 96 60 96 4 57 39.62 59 44 39 62 32 00 1 2 19 32 00 23.77 14 02 0 00 7 62 6 10 4 57 0 00 26 8 21. 3 24 4 41. 5 45 .72 18.3 64 35. 1 1 6.8 11753 1 2247 1 2494 1 2600 1 2625 1 2741 1 2931 12933 12935 1 2939 12941 13008 1 3116 1 3 1 28 13217 13218 1 3230 13 305 13365 13366 13775 1 5867 15881 15909 15924 30 13 57 82484 7 58 52 67. 67 coni 27 43 0 00 19.81 6.10 15. 24 12 19 3.66 3 05 0 00 0 00 0 00 3 1 1 302420 303325 303152 292735 292344 293108 292610 29320 0 302635 300807 295857 295902 303008 292305 302040 303712 302208 303335 300358 301553 300612 302115 825208 8314 44 830005 830627 830405 830002 830439 930607 825033 8250 1 4 822646 823502 830415 822612 830802 83173 7 831630 832134 831151 831702 823630 8246 4 6 38. 1 0 33 .53 30 .48 3 66 3 35 6 .71 1 83 4 88 36.88 24 38 24.38 38.40 45 .11 26.82 37 19 42 06 4 1 .76 2 4 3.84 38.40 24 38 43 59 35 05 23 77 30.48 25 .91 47.55 2 2.86 91.44 22.86 9 .7 5 30 18 22.86 25.60 50 29 27 43 24 38 21. 95 16.15 24 99 13. 72 41.15 38 1 0 40 23 45 72 unc u n c un c unc unc unc un c unc. unc coni coni. coni. unc un c unc coni. coni. unc con i co nt coni. con i 28.96 12 .19 32 00 15 24 9 .14 0 00 8 23 12.19 39 62 6 10 1 4 94 7 30 35. 05 33 53 1 5 2 1 8 3 1 5 8 36 6 43

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Figure 19. DISTRIBUTION OF SAMPLES IN THE ( STUDY AREA ... ORIENT A TION llAP STUDY AREA 5" \ I N Apr o xima l c sca l e I : 8 36 4, 000 e._; I t ._!lUI r.nu .. ,. ..... e mu 4m!u11 111t1 .. nu .ou n ... _.!J'lm _."' '.uou \w" .... ,. .. ltll A pr oxi mate sca l e I: 1 820,000 .. ... .U'" .. .. u ... .Utc v ..... ....... ..... "''"' .. IIU .. Uit .. Jill -"'!>n ..... ... ., ..... ... ... ... ... .... IU ...,. .... .. 1

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45 The review of the morphology of generated samples showed that some samples did not contai n any closed depressions (Figure 18 B). These samples were omitted from further analysis. Six samples fell into this category, further reducing the number to 5 6 samples. 4.3.2. Quantitative geomorphologic variables Quantitative geomorphologic variables fall into t w o categories: sample area descriptors and feature (closed depression) descriptors. 4.3.2.1. Feature Descriptors Feature descriptors are variables that describe closed depressions in terms of their size and shape. The variables were calculated only for depressions completely within the sample boundary. 1 Depression area m2 ) -the area within the highest closed contour encircling the depression. This is the apparent depression area, that is, an area of the projection o f a convoluted surface onto a horizontal plane and is smaller than the actual area of the topographic surface within the depressio n This variable is directly measured from the GIS coverage b y selection of a polygon (or polygons) within the depression (Arcedit application).

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46 2. Depression perimeter (Pm, m) -the length of the highest closed contour encircling the depression. This variable is directly measured from the GIS coverage by selection of an arc (Arcedit application). 3 Circularity index (Ic, dimensionless) -the measure of the circularity of a depression. This variable is calculated by I -c -(1) where Ic = circularity index, = measured depression area, and Ae = effective depression area. The effective area (Ae) is the area of a circle calculated by Ae = tJf r2 e ( 2 ) The effective radius (re) is calculated by ( 3) where Pm = depression perimeter. The index of circularity for a depression of a circular shape would equal one. For the depressions that are not circular, index is either less than or than one. The greater the difference between the index and one, the less circular is the depression, i. e., elongated features have values smaller than one and convoluted features greater than one. 4. Depression order -is a count of foci of denudation (sinkholes) within the depression. For the purpose of this analysis, sampled features are classified into two categories: first-order depressions (only one sinkhole in a depression) and

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higher-order depressions depression) (more than one sinkhole 4.3.2.2. Sample Descriptors 47 in a Sample descriptors are variables that describe the terrain within the 2.6 km2 (1 mi2 ) sample boundary in terms of the number and distribution of closed depressions, dissection of the closed depressions, and sample mean values of feature variables. 1. Total area within depressions (m2 ) -the sum of the areas of all closed depressions within the sample. This sum includes closed depressions entirely encompassed b y a sample boundary as well as the within-sample portions of closed depressions intersected by the sample boundary. 2 Total contours (m) the sum of the lengths of all contour lines within the closed depressions, including the portions of depressions intersected by a sample boundary. 3 Dissection index (Id, m -1 ) -the length of the contour lines per unit area within closed depressions. This variable is the ratio of the total length of all contours and the total area within the depressions. This measure provides information on elevation distribution and topographic gradients (the higher the index, the greater the elevation range and the greater the slope of a topographic surface. 4. Total number of depressions count of all closed depressions within a sample, including the depressions that are intersected by a sample boundary.

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48 5. Number of first-order depressions -count of first-order depressions within the sample, including the depressions that are intersected by a sample boundary. 6 Number of higher-order depressions -count of higherorder depressions within the sample, including depressions that are intersected by a sample boundary. 7 Index Two (I2 ) -number of higher-order depressions per first-order depression. This is the ratio of the number of higher-depressions to the n umber o f first-order depressions. 8 Average depression area (m2 ) -arithmetic mean of the depression areas for depressions in a sample. Only depressions completely within a sample boundary are taken into account. 9 Average first order depression area ( m2 ) -arithmetic mean of the first-order depression areas. Only depressions completely within a sample boundary are taken into account. 10. Average higher-order depression area ( m2 ) -arithmetic mean of the higher-order depression areas. Only depressions completely within a sample boundary are taken into account. 11. Index Three (I3)-relation of surfaces of higher-to first-order depressions. This i s as the ratio of average highe-:order depression area to average first-order depression area. 12. Average depression perimeter (m) -arithmetic mean o f the depression perimeters for depressions in a sample. Only depressions completely within a sample boundary are taken into account. 13. Average first-order depression perimeter (m) arithmetic mean of the first-order depression perimeters. Only

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49 depressions completely within a sample boundary are taken into account. 14. Average higher-order depression perimeter (m) arithmetic mean of the higher-order depression perimeters. Only depressions completely within a sample boundary are taken into account. 15. Average depression circularity index -ratio of the area of the depression to the area of the circle from the sample mean values for area and perimeter by the same equations as the feature circularity index. Only depressions completely within a sample boundary are taken into account. 16. Average first-order depression circularity index -ratio of the area of first-order depressions to area of a circle calculated by the same procedures as the circularity index for all depressions. 17. Average higher-order depression circularity index -ratio of the area of higher-order depressions to area of a circle. Calculated by the same procedure as the circularity index for all depressions. 4.3.3. Quantification and statistical procedures Quantification of morphologic characteristics (variables) and statistical analysis were performed by several successive procedures: 1 Qualitative analysis of captured samples and identification of closed depressions;

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so 2. Extraction of variables, i.e. automated measurements of features (polygons, arcs) from GIS coverage; 3 Development of spreadsheets for each sample and calculation of derived variables using results of measurements; 4. Development of subsets of data and spreadsheets for each sample or feature descriptor; and 5. Application of simple statistical procedures for nongrouped data: calculation of mean values, and correlation (linear regression) The same procedures (1 -5 above) were performed on grouped data. The samples were divided into two subsets according to lithologic differences. One subset consists of samples with a clay layer (s) overlying the Floridan aquifer. This subset represents confined or semi-confined aquifer conditions. The second subset includes samples in which the clay layer is absent in the lithologic column. This subset represents unconfined aquifer conditions. The morphologic variables are calculated and statistics performed for each subset of samples. Feature and sample descriptors were tested for similarity using the t-test. The t value is calculated by where X1 and X2 are the means of the two sample sets, n1 and n2 are the sample sizes of sample sets respectively; and Sp is the square root of the pooled variance (Sp2 )

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51 The pooled variance is calculated by Sp2 = [(n1 -1) S/ + (n2 -1) S/] I (n1 + n2 -1) {2) where n1 and n2 are sample sizes of the two sets of variables, and S1 and S2 are the standard deviations of the sets respectively. Degrees of freedom are calculated by d f = n1 + n2 -2 ( 3 ) The critical values at level of 0.05 are applied from Statistical Tables (Rohlf and Sokal, 1981).

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52 5. RESULTS 5.1. QUALITATIVE ANALYSIS OF CLOSED DEPRESSIONS The closed classified by order can be grouped into more general categories that correspond to classical terms of karst geomorphology (Introduction, pg. 8-10). 5. 1.1. First-order Closed Depressions. First order depressions (Figure 17-A) are elementary karst features with one focus, bowl-or cone-shaped, and are predominantly circular. They correspond to the term sinkhole. First-order closed depressions are the most abundant landforrr both in the study area and in terrains observed in preliminary map inventory. Two genetic types of first-order depressions were observed during the field examination of the terrains. Solutional firstorder depressions were observed in Levy County (Bronson Quadrangle) where the Ocala Limestone is exposed at the surface or covered with unconsolidated sediment and soil of a thickness less than 0.3 m. Observed features are shallow (2-3 m deep), circular, up to so m in diameter, and periodically or permanently flooded depending on their position relative to water table. Collapse first-order depressions are also abundant in the study area. They are generally deeper than solutional depressions. However, their dimensions vary from <1 m in diameter and < 1 m in

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. 53 depth to as large as the Devil's Millhopper, (a State Geological Site near Gainesville, Alachua Co.), the largest observed collapse first-order depression with a diameter of over 150 m and depth over 35 m Observed collapse depressions are often connected with underground rooms or passages. For example, Pothole and Orange Grov e Spring (Figure 22, pg. 64) lead into a underwater passage of the Peacock Springs Ca v e System; Sim' s Sink (a Nature Conservacy Site near Branford, Suwannee Co ) opens into a c a vern of the Floridan aquifer (Relyea & Sutton, 197 3 ) ; and entrances of the Warren's Cave (the larges t dry cav e in peninsular Florida, Gainesville, Alachua Co.) and Briar's Cave (Ocala, Marion Co.) are at the bottoms of collapse depressions of the first order (all listed connections have been confirmed by observatic'ls during cave-diving and/or caving trips in the period of 1990 1993) 5.1.2. The Second Group Closed Depressions. This group includes combinations of several coalescing, first-order depressions, more or less integrated into small multifoci depressions (Figure 17, B E). These are termed complex sinkholes. The depressions that c onstitute this group can be considered transitional features between elementary first-order depressions and more complex geomorphic features. 5.1.3. The Third Group Closed Depressions. This group consists of complex, multifoci depressions (Figure 20) c omposed

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54 of elementary or transitional features, and corresponds to uvalas in classic karst terminology. The depressions of this category form either by coalescence of first-order and/or transitional depressions or by transformation of a fluvial valley due to karstification (development of a sinking stream and broadening of the bottom of the valley). These uvalas are therefore, either monogenetic (predominantly karstic) or polygenetic (fluviokarstic) features, respectively. The shape of observed uvalas reflects their origin and the degree of fluvial impact. When monogenetic, i.e. predominantly karstic, they are characterized by irregularity of shape (Figure 20-A) and a broad, flat alluvial bottom. If the role of fluvial processes in the development of the depression is significant, it is clearly reflected in the morphology of the uvala. Fluvial uvalas are characterized by elongated shapes and pronounced secondary valleys and creeks on the sides (Figure 20-B), large numbers of first-order depressions, or by transformation of a fluvial valley due to a sinking stream. 5.1.4. The Fourth Group Closed Depressions. This group, the largest depressions observed, consists of a complex, multifoci depressions which are composed of two or more coalesced uvalas. The number of foci in these features cannot be determined by surficial and/or cartometric observations because of the alluvial fill and/or water accumulation on their bottoms.

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' Figure 20. Examples of complex closed depressions: A -Irregularly shaped karst uvala typical of covered karst of west-centra l and north Florida (Arredondo quadrangle); B -Polygenetic uvala with pronounced remnants of fluvial morphology (High Springs Quadrangle) -H ighesl closod conlour I ine s 0 Kl. :1(T(RS 2 1000 0 u 1 RS 1000 201lll B lll lll

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56 These landforms (Figure 21 and 22) are known as karst poljes. The largest depression of this category observed in Florida is Paynes Prairie (Figure 21) with a bottom area of approximately 13 km2 ( 5 mi 2 ) NEWHANS UI
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Levy Lake <....J Y IJo t?: ./:2 ),.g.(\ .... Q. \&!\)'-''' ri l!Y 6 I Figure 22. Coalesce.nce of karst polj es (Arredondo Quadrangle) Kanapaha Prairie is a partially and periodically flooded polje, while Levy Lake is an almost entirely and permanently flooded polje. -Highesl c losed conlour I ine lJ1 -..J

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58 Another karstified tributary of the Suwannee River (Figure 23) has a more complex morphology and hydrology. The downgradient portion of the karstified valley, Peacock Slough, has a depth of 5-6 m, total width of 100-500 m, and a bottom width of 30-150 m. The slough is fed by two karst springs: Bonnet Spring and Peacock Springs. In the periods when the Suwannee River is high relative to the Floridan aquifer, surface waters flood Peacock Slough and the flow of the slough and springs reverses. water flows from the Suwannee River and sinks into caverns of Peacock Springs. Karst features characterized by flow reversals are known as estavellas. The main spring is located on the northeast side of the spring pool, which is surrounded by a limestone escarpment that opens toward Peacock Slough. The bottom of the spring pool is covered by large limestone blocks indicating cavern collapse. The cavern that the spring is emerging from is the entrance of a phreatic cave system (Peacock Cave System; Exley, 1977) that is a part of the cavernous Floridan aquifer. Portions of the karstified valley upstream of Peacock and Bonnet Springs are characteristic dry valleys. Both eastern and western branches consist of linearly distributed, closed depressions of the first-order and elongated transitional depressions. Most of the depressions in both branches have lost surficial flow. They contain no water and function only as inputs for vertical underground drainage.

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,,,..,.., 23. example of a valley: The of che valley ot ?eacock 5?=- d:y valley by a cave syseem : valley has :eve:eible :low. s e 2 e:!Ciosed DepressOr"''S >'< Sor or-g tt u.f'dO\J acee 0 1000 2000 59

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60 Some of the depressions in the eastern branch (Pothole, Orange Grove) function as karst windows, that is, they are connected with phreatic caverns. Conduit flow under the eastern branch of the dry valley and connections with Pothole and Orange Grove were observed by the author during cave dives in the Peacock Cave System. 5.2. DISTRIBUTION OF CLOSED DEPRESSIONS The preliminary map inventory confirmed that a karst landscape is prevalent in west-central and north Florida. Closed depressions are the predominant micro-to meso-scale landforms of the terrains within 84% (42 out of 50) of reviewed quadrangles (Table 1) The remaining 16% of observed terrains are mainly fluvial and/or palustrine with impact of karst process that can be detected by map analysis. The features observed in the inventory have 1 to 21 foci (1st to 21st order of closed depressions) Examples of depressions of different order are given in Figure 17. The depression of highest observed order is within the boundary of sample 358 (Figure 18-A) The observed features include a number of closed depressions that have flat bottoms developed by the accumulation of unconsolidated siliciclastics. Consequently, inactive, buried foci cannot be identified by either cartometric or by surficial field observations.

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61 The distributions of closed depressions vary from dispersed, micro-scale depressions of first-and occasionally of second-order (Figure 24-C); through dense, mixed, microscale depressions of the first order and low order transitional depressions. The latter are seldom more than third-or fourth-order (Figure 24-B) ; to predominant meso depressions of a large areal extent and of a high order, with sporadic low order transitional or first-order depressions (Figure 24-A) The frequency of first-order depressions (sinkholes) is highly variable and ranges from 0 to over 27 depressions per km2 The highest sinkhole frequency observed in the study area is within the Mayo NE Quadrangle. A block of 36 survey sections in the central portion of the quadrangle was selected to illustrate the sinkhole frequency in this area (Figure 24) The total count of sinkholes in this area is 1,399. sinkhole frequency varies from 15 to 27 depressions pe c i._m2 (38-70 per mi2 Areas of maximum (27) and minimum (15) sinkholes per km2 (70 and 39 sinkholes per mi2 respectively) account for 3% of the selected area terrains with frequency of 24 2 7 sinkholesper k m2 (60 -69 sinkholes per mi2 for 14%, terrains with 20 23 sinkholes per km2 (50 -5 9 sinkholes per mi2 ) for 36%, and terraines with 15 19 sinkholes/km2 (40 -49 sinkholes per km2 ) for 44% of the area.

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A B c Figure 24. 62 Closed Depressions 5 KILOMETERS c----=-METERS 1000 Selected portions of topographic quadrangles illustrating differences in the dlstribution of closed depressions (Quadrangles: A -Dowling Park I 8 Mayo NE 1 C -Manatee Springs)

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63 Number of sinkho l es D 39 N gm 40-49 II so-59 60-69 70 R. 1 2 E R 1 3 E Figure 25 Cartogram of the sinkhole frequency (number of sinkholes per survey section) central portion of the Mayo NE quadrangle.

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64 5.3. QUANTITATIVE GEOMORPHOLOGICAL ANALYSIS 5.3.1 Feature statistics The count of depressions within the total sampled area of 16 0 2 km2 is 8 0 1 Of that, 653 or 81.4% are closed depressions completely encompassed by sample boundaries. The remaining 149 depressions are intersected by sample boundaries where a portion of a depression is outside of the sample area. Features intersected by sample boundaries are not included in the following feature statistics. g-5
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65 A cumulative frequency distribution graph (Figure 26) for nongrouped data (samples with both unconfined and confined hydrogeologic conditions included) indicates a truncated sample population. The sample population does not include features over 500,000 m2 which were observed in the study area and described in the qualitative analysis. These features account for less than 10% of the population by number. The number of small features truncated due to the map resolution limit is unknown. The shape of the distribution curve remains the same when the data are grouped and a curve plotted for the depressions within the samples with unconfined or confined hydrogeologic conditions. Table 3 contains the results of feature statistics for grouped and nongrouped data. The total of 653 closed depressions includes: a) 455 depressions or 69.7% are within samples areas classified as unconfined, and 198 depressions or 30.3% are within sample areas classified as confined hydrogeologic conditions; and b) 266 depressions of the firstorder or 40.7%, and 387 depressions of higher-orders or 59.3%. The depressions of the first order include 199 or 74.8% of features within the sample areas with unconfined conditions and 67 or 25.2% of features within the sample areas with confined conditions. Higher-order depressions include 256 or 66.1% of features within the samples with unconfined and or 33.9% feature within the sample areas with confined hydrogeologic conditions.

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Table 3. FEATURE STATISTICS 66 A All depression s Minimum Maximum Mean Median Mode All samples: Area (m2) 70 670030 10214 2384 1000-2000 Perimeter (m) 33 12569 377 195 100-200 Circularity index 0 .000 22.721 1 .593 1 271 1.1-1 2 Samples with unconfined conditions: Area (m2) 156 463321 9219 2108 0 -1000 Perimeter (m) 47 11502 355 182 100-200 Circularity index 0.916 22 721 1 .626 1 .202 1.0-1.1 Samples with confined conditions: Area (m2) 70 670030 12501 2841 13000-14000 Perimeter (m) 33 12569 424 2 1 3 100-200 Circularity index 0 .000 18.762 1 521 1.27 1.3-1.4 B First order depressions All samples: A r ea (m2) 70 13185 1422 958 700-800 Perimeter (m) 33 412 132 116 Circularity index 0 003 2 .778 1 .105 1 091 1 0 1,1 Samples with unconfined conditions: Area (m2) 156 9633 1308 896 Perimeter (m) 47 371 128 112 100-200 Circularity index 0 .916 2 .778 1 144 1.087 1 .1-1. 2 Samples with confined conditions: Area (m2) 70 13185 1761 1334 1000-2000 Perimeter (m) 33 412 146 136 100-200 Cir cularity index 0.003 2 194 0 .988 1 .097 1.1 1 2 C Depre ssions of higher orders All samples: Area (m2) 83 670030 13258 4799 1000-2000 Perimeter (m) 43 12569 544 301 200-300 Circularity index 0 .000 22 72 1 .929 1 .392 1 >' 2 Samples with unconfined conditions: Area (m2) 182 463321 15370 4530 1000-2000 Perimeter (m) 57 11502 533 295 1C'i:"''"' .... Circularity index 1 .040 22 721 1 .999 1 .423 1 1 1 2 Samples with confined conditions: Area (m2) 83 670030 17994 4936 2000-3000 Pe r imeter (m) 43 12569 566 291 200< 100 Circularity index 0 .000 18.762 1 .793 1 3 1.4 1 5

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67 0 : : !!!'.'."[ .. 0.001 ......................................................................................................................................... 10000 100000 1000000 Depre aion a r ea aquare ) Figure 2 7. Relationship of area and circularity index for mixed population depressions data) The presence of two trends in the graph of depression area versus circularity index (Figure 27) indicates populations. Separated populations were tested for similarity using the t-test. Comparison of mean population values with a t-value of 0 .21 for area and 0.15 for perimeter at a critical t-value of 0.44 confirmed the existence of two populations. The negatively trending population of 27 depressiofi6 0 4.1% of all sampled depressions lies within samples 1951 end 1982, both with confined hydrologic conditions. Average ar:a of these depressions is 11,717 m2 with average circularity index of 0 0 1 6 Available sam p l e variables for samples 1951

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Table 4 CHANGES IN MEAN VALUES OF FEATURE DESCRIPTORS (after f iltering out mixed population) A All depressions B First order depressions C. Depressions of higher orders Population Population Population mixed filtered mixed filtered mixed filtered All samples : Area (m2) 10214 10147 1422 1961 13258 15947 Perimeter (m) 377 375 132 155 544 537 C i rcularity index 1 .593 1 664 1 .105 1 .216 1 929 2.01 Samples with unconfined conditions : Area (m2) 9219 9219 1308 1308 15370 15370 Perimeter (m) 355 355 128 128 533 533 Circularity index 1 .626 1 626 1.144 1 144 1.999 1.999 Samples with confined conditions : Area (m2) 1 250 1 1 2630 1761 1850 17994 18068 Perimeter (m) 424 428 146 148 566 569 Circularity index 1 .521 1 768 0.988 1.157 1 793 2.077 0'1 (X)

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69 and 1982 relative to other samples do not indicate a cause for apparent differences in depression area and circularity. Samples 1951 and 1982 are omitted from further feature and sample statistics. Table 4 shows change of mean depression area after filtering out the population with the negative trend. The relationships between between feature descriptors for subsets of data defined in Table 3 ( A, B and C) and the results of regression analysis for each pair of variables are given in figures 28 through 36. 5.3.2. Sample statistics The total sampled area is 148.8 km2 i.e., 62 samples of 2. 6 km2 each. Samples that did not include any closed depression within their boundaries comprise 14.4 km2 The samples-containing closed depressions within their boundaries encompass a total of 134.4 km2 The area within the closed depressions themselves is 12. 4 km2 or of the total area. The percent area within the closed depressions for particular samples ranges from to and averages of the sampled terrain. Table 5 contains sample elevations, cover thickness and measured sample descriptors. This table also contains values for samples 1951 and 1982 excluded from sample statistics.

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I j ,, ( .,. !? t. (_ .... A I 00000 : ................................................................................ ........................... ............................................. .. ........................................................................... ,,,,,J.. ., !;;;; .,,. 1(1()(), 100 1()....-.,.-,.TTnfh I I 1111111 I I liiihi i illlilii I I IIIII 1 0 100 1000 10000 100000 1000000 Depression are a (m sq) Reg ress io n Output : Co nstant S t d Err o f Y Es t A Squ a r ed No. of Observatio ns Degrees o f Freedom t value 0 062 0 292836 0 074592 0 959453 625 623 )( "' >"' u B 100< :::::::::::::: :::::::::: :::::::::::::::::::::::::::::: :::::: ::::::::: :::::::::::: : :::: : ::::::::::::::: : ::::::::: ............................................................................................................... . .......................................................... ..... .. ............................................. 0 .11 ............................................ t 1 0 100 1000 10000 Depression area (m sq. ) R e gres sio n Out put: C onstant Std Err of Y Est R Squared No. of Observations De gre es of Freedom t-value 0 062 100000 0 51354 0 1 4918 3 0 38836 7 625 623 1000000 Figur e 28 R e lation s hips o f feat ure descripto r s f o r all d e pr essto ns (nongrouped data) ...J 0

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s ., 5 u. 8 ., c:. c3 A. 100004 ... . .... ......... .. ......... .... ............................... .... 1000 \00 ---......... .......... ....... ............ .... . ................................... : :: : :::::::: : : ::: :::::::::::::::::::::: :::: :::::::::::::::::::::: .. ::::::::::: -::.: :::::: : ::::;.;.-.: ::::::: : : ::::::::.:::::::::::::::::::::::::::::::::::::::::: 10-f--r-TrTrliii I I 1111111 I I iiiiiil I I 111111 1 0 100 1000 10000 \00000 D.>f>I< .. 0 .f !; (ij (} B. 10 j ----------------_______ ) Q I I I 1111 rrnl 10 tOO 1000 O.,press1M wea (01 sq) Regression Output: Constant Sid Err of Y Est A Squared No. of Observations Degrees of Freedom t value 0 138 10000 .23773 0 .091273 0 .160265 267 265 100000 F igure 29. Relations h ips of feature descriptors for fir s t -o rd e r depressions (nongrouped data) ...] 1-'

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.. .. e.. 0 A. 1 0000 1\1\ \\\\\\ :: :!11111\1: !\ 11 \1 1000 1 00 l :m m:j : ::: :: 100 1000 10000 100000 1000000 O


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'E Q, u; "' >-;;; u 10 Dep ression a rea (m sq ) Regression Output: Cons t ant Std Err of Y Est R Sq uared N o. of Observations Deg rees of Freedom t-value 0 .159 -0 38014 0 177 834 0 23591 170 1 68 F1gurt: l l Relation ships of f eature descriptors f or ar: depress ions (confined co nditions) ...J VJ

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A. 1 0 001:::::::::::::: :::::::::::: : : ::::2::2: ::2::::::::::::::2::::::::::::::::1 E' ;;-. c .Q .. ::::::::::::. : .:::::.: .. :::::::.::::::: ''' o/1 a. 0. 8 .............. '!!'" .......... .......... ........................................ .. 1 o :. I .. .. .... o .. .. .... o .. .. ooo o .... ooo l 10 1 0 0 1000 10000 1 00000 D.?presso o n area (m sq. ) Regression Outpu t Constant S t d Err o f Y Est A Squared N o. o f Obse r va t ions Degr ee s of Freedom t-value 0 273 0 597252 0 027376 0 9 826 7g 57 55 B >< 0> >-ro u I : _.rl" 1 0 ........ 0 000 '""" 0 1 0 100 1000 10000 1 00000 [X; p ression area (m sq. ) Regres sio n Output C onstant S td Err of Y Es t A Sq uared 0 095294 0 054752 0 007753 No. o f Observation s Degrees of Freedom t -va lu e 0 273 57 55 F1gur e 32 Relat io n ships of fea ture desc r ip tors t o r first o rd er depre ; sions (conf i ned condi t io ns)

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'f "' "' "' a 8 A. I 00000 ,;;; ;;;;:; :;;;;;:;;;;:;:;;;;;:;:;;:;:;; ;:;;;;; ;;:;:;:;;;;;;;:;;;;;;:;;:;;;;;;:;;;;:;;; I 1()01)0. II IIIUU II IIIIUI II IIIIUI """" I IIIIUI I 10 100 1000 10000 100000 1000000 Depression area (m sq. ) Regression Output: Constant Std Err of Y Est R Squared No. o f Observations Degrees of Freedom t value 0 195 0 385906 0 1 024 0 .9 1 6996 113 111 >< ,.. iii i} .................. .................. . ................ 100 B 1000 1 00000 I 000000 Depression area (m sq.) Regressi on Output : Constant S td E r r of Y Es t R Squared No. of Observat i ons Degrees of Freedom t value 0 195 .3274 0 .2048 0 159726 113 111 F1gure :33 R e l a t ionships o f descr i ptors for dP.pressions of highe r orders (confined conditions) -.J l1l

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E <:) c Q "' "' C> a 8 A 1 00000 4:!!!!!!!!!!!!!!:::!:!!!!!!!!!!!'!!'!''!::::::::!!!!!!!!::!!!!!!!!!!!!!!!!!:!!!!!'!!' 1 oooo :l::::::::::::::::::::::::::::=::=::::::::::='=:::::::::::::::::::=::::::::::m::::: iOt)O l/t0:: ::: .... .. .... ... ............. :: :: ::. 1 00 mmm .............. 10 1 I 100 1 000 1000 0 100000 1000000 are a (m sq ) Regression O u tpu t : C onsta nt Std Err of Y Est R Sq uared No o f Obse r vat i ons Degre e s of Freedom tva lut: 0 09 8 0 266943 0 .0 68177 0 965699 455 453 )( C> >-u B. 100<:::::::::::::::::::::::::::::::::: :::::::::::: ::::: :::::: : : : :::::::: ::::::: ::::: ::: ::: :::::: ::::::: : :::::::: : : 1 ...... ............................ ................................... . ...................................... ... ................ ............... ........... ....................................................... ..... .................................................................. 100 1000 1 00 00 area (m sq) Regress ion Output : Const an t Std Err of Y Est R Squared No o f Observat i ons Degrees of Freedom t value 0 098 1 00000 56532 0 1 36355 0.460995 455 453 1000000 Figure :, 4 Relationships of feature descriptors for all -iepressions (unconfined co nditions) ..J
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"i:' "' 21 c C ;:; 11"1 < "' :? >-ro u B I 0 100 Const an t 1000 Depression area (m SQ. ) Regression Output: S td Er r of Y Est R Squared No. of Observations Degrees o f Freedom t v alue 0 138 03016 0 058799 0 024326 1 99 1 97 10000 Figur e 35. Relation s hips of feature! descriptors for l.;s torder depressions (unconfine d conditions) -...] -...]

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A. I 00000 :::::::::;:;:;:;:;::;:;:;:;::;:;:;;:;:;;:;;:;:;:;:;;:;:;:;:;;:;:;:;:;;:;:;:;:;:; :;;:;: .............. ..................................................................... ........................................................................ E I 0000 :li c: 0 ;;, "' Ql a. 8 :::::::::: : :::: ::::::::::: :::::::::::::::::::::::::::::::::::::::: :::::::::::::::::::: 1000 1 00 1 0 1 I"""" I"""" I"'""' I """'I 1 00 1000 10000 100000 1000000 Depressio n a rea ( m sq ) Regression Output: Constant Std Er r of Y Est A Squared N o. o f Observa t io ns Degrees of Freedom t va:o:e 0. 13<1 0 218522 0 082419 0 946038 256 254 >< Ql ,.. iO u B Depression area (m sq ) Regr ession Output: Cons tant Std Err of Y Est A Squared No. of Observations Degrees of Freedom t-value 0 138 0 66217 0 164838 0.395646 256 254 F1gure 36. R elat i onships of featu re desc riptors to : i ep ressions o f higher orders (unconfined conditions) ...,J 00

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79 Sample statistics results are giv e n in Table 6. The columns on the left side of the table contain measured sample descriptors, and the columns on the right contain descriptors derived from feature descriptors for each sample. The third category of sample descriptors includes indices calculated as ratios of selected variables. The hypsometric distribution of samples (Figure 37) is given for intervals of 10m. Samples include (8) 2 0 >:l ............... ......... ........... . ...... ..... ............. . .... .. (/) 1 Q) a. E
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80 samples in the hypsometric interval 0-10 m, 9.3% (5) samples in the interval 10-20 m, 35.2 (19) samples in the interval 20-30 m, 18.5% (10) samples in the interval 30-40 m, 13.0% (7) in the interval 40-50, and, 9.3 (5) samples in the interval 50-60 m. The relationships between elevation and selected sample descriptors of nongrouped data are illustrated on Figures 38 -42 which include correlation results and critical values of coefficients.

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Table 5. MEASURED SAMPLE DESCRIPTORS Sample 98 1 87 358 1067 1092 1094 1243 1548 1915 1951 1982 2041 2190 2255 2459 2580 2746 2838 3140 3153 3669 4038 4043 4044 4520 4523 4666 4914 5208 5956 8780 11753 12247 12494 12600 12625 12741 12931 12933 12935 1 2939 1294 1 13008 13116 13128 1 3 217 13218 13 2 30 1 33 05 1 33 65 1 3 366 1 3 775 1 5 867 15881 15909 15924 Eleva tion (m) 20.42 27.43 22.86 19.81 26.82 1.22 1 .22 31.09 26.21 24.38 29.87 31.39 44.81 28.65 58.52 41.45 23.16 30 18 27.43 52.43 16.76 53.34 58.22 38.71 45.11 28. 04 16.76 32.61 18.29 3 05 28.96 16.76 29.26 58.52 38.10 33.53 30.48 3.66 3.35 6.71 1 .83 4 88 41.76 38.40 43.59 23 77 25.91 22. 86 22. 86 30 18 25. 60 27.43 21.95 24.99 41. 15 40. 23 Depth to to lime s tone (m) 6 10 6.10 0 00 3.66 15 85 8.53 2 74 30.05 3 05 1 2 19 19.81 39 62 18 .29 18 .29 39 62 44.20 1 52 15 24 15.24 60.96 4 .57 59.44 32.00 12 19 23.77 14.02 0.00 7 62 6.10 4.57 0.00 27.43 0.00 19.81 6.10 15 .24 12 19 3.66 3.05 0.00 0 00 0.00 28 96 1 2 19 32.00 15.24 9 14 0 00 8 23 12.19 39 62 6 10 14. 94 7 30 35.05 33.53 Total area (m sq uare) 23555 114938 767542 293977 2393 2850 162678 6324 187024 509806 47426 42781 307360 324652 14834 13383 631569 98533 337011 1578668 253126 16409 15980 63426 413167 134188 418796 428863 202274 14897 707633 80063 517300 98135 13603 120389 39468 2877 30796 18455 203652 19922 41963 130696 16457 555981 150520 639636 248073 227143 164598 217592 322360 10853 407336 19594 Total Total T otaJ no. T ol no. contours n o of of 1 of high. -(m) depress depress orders 1262 4476 34103 16639 180 195 6221 435 10295 16419 3067 2628 11916 12755 1090 565 22060 3905 9630 22913 14270 1 398 1 228 2927 15341 5307 15011 2 482 9 9982 2036 35042 5811 23822 1 605 614 6164 4069 194.08 1608 731 5482 1333 1705 7241 677 23821 6530 25558 12977 6698 5720 8810 18281 1447 243465 1 109 5 10 29 41 19 2 7 23 14 4 14 52 4 1 1 7 13 12 2 38 3 3 5 17 1 1 34 16 25 7 5 19 38 4 2 16 18 1 9 2 12 7 5 21 2 14 15 15 42 15 12 3 0 37 13 14 4 1 3 1 1 13 1 1 3 0 1 7 3 0 3 31 0 0 2 4 5 0 4 0 0 0 2 3 10 3 3 4 0 10 15 2 2 10 13 1 7 1 5 4 0 4 7 2 23 9 4 14 15 6 0 0 depress 4 7 18 28 0 0 16 2 6 16 11 4 11 21 4 1 15 9 7 2 '4 3 3 5 15 8 24 13 22 3 5 9 ...,.., 2 0 6 0 2 11 2 4 1 7 2 10 8 13 19 6 8 16 2 2 7 1-l 4 81

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1 able 6. MEAN AND EXTREME VALUE S OF SAMPLE DESCRIPTORS Total Total no. Average Average Average Total area Dissection no. of of 1 st-ord. depress 1st-order high.ord. (m square index depres depress Index 2 area depr.area depr. area (m sq ) (m sq) (m) A All samples Mean 219931 0 .063 14 5 2 761 11688 1885 15687 Min. 2393 0 .015 1 0 0 .000 865 352 1139 Max. 1578668 0 .598 52 31 11.000 141527 8811 141527 B Samples with confined conditions Mean 225430 0 051 9 3 2 .656 15067 2153 16851 Min 6324 0 .015 1 0 0 .667 865 352 1139 Max. 1578668 0 133 37 15 7 .500 141527 8811 141527 c Samples with unconfined conditions Mean 215852 0 .071 18 7 2 .805 9209 1774 14738 Min. 2393 0 .027 1 0 0 .000 1858 554 2730 Max. 767542 0 .598 52 31 11.000 32773 6801 61500 Average Average Average Index 3 depress. 1st-ord. h igh.ord. perim. perim perim. (m) (m) (m) 1 0 .647 408 1 49 530 0 .000 112 71 140 42 .876 2825 325 2825 10.917 482 156 537 1 790 112 71 140 40.287 2825 325 2825 11 353 147 524 0 000 180 86 249 42 876 951 307 1703 Average Avernge Circul. 1st-ord. index Circul. index 1 664 1 119 0 014 0 .020 4 .870 1 584 1 871 1 158 1 0 5 0 1 .023 4 870 1 .290 1 512 1 103 0 019 0 024 3 079 1 584 Average high.ord. C ircul. index 1 .951'. 0 .009 4 .870 1 .997 1 103 4 .870 1 .927 0 016 4 291 (X) N

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Ci' V) .s V) c 0 ;;; V) Q) U) '-a. c Q) -o c ::1: 5 -co Q) l6 0 1-1.6,----------------------, -1 4 ....... .......... . ............ 1.2 0.8 .... .. .. . 0.6 .. ...... ...................... . .... ............ ............................................................................... 0.4 ................................................................... .............. . ................................... ... '1'10 ... 0.2 : ............................................... ti':. . .... .......... ..... .......... ................................... ,. . 0 .... -er ... -0 1 0 20 30 40 50 60 Eleva t ion (m) Regress io n Output: Con stant Std Err of Y E s t R Sq u a r ed No. of Observa tion s Degrees of Freedom t value 0 .273 155508 277806 0.0151 5 4 52 83 Figure 38. Relationship between elevation and Total area within closed depressions (all samples)

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0 6r-.................. .. X 0 4 ............................................................................ .......... '0 0 3 ....................................................................................... ............................ 1/) 0 0 2 ....................................................................................................................... ,_ ................. .. 0 1 ........................................................................ ............................. ................................ .. ---... ___ ... 0 10 20 30 40 50 Elevation (m) Regression Outpu t : Constant Std Err of Y Est R Squared No of Observations Degrees of Freed om t value 0 273 0.052142 0.07820 5 0 .005344 54 5 2 60 84 Figure 39. Relationship between elevation and Dissection index (all samples)

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II) c 0 (/) (/) <1) '-Q. <1) l:J .,_ sal -I .. .............. J 40 ...................... ................. 0 30 ....... ...................................................... .................. '-<1) .D E . ._ .. E 2o .... .......................... ................................ ... ............... ..... ...................... .. ......... ... .... 0 1. ---.. . 10 .................................... ........................... .......................................................................... ---0 0 10 -. - 20 30 40 Elevation (m) Regression Output: Constant Std Err of Y Es t R Squared No. of Observations Degrees of Freedom t-val ue 0 273 50 Jl 60 19.15508 12.39263 0 .04422 54 52 85 Figure 40. Relationship between elevation and Total number of closed depressions (all samples)

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1 ........................................ .................................................................. ]::::::::::::::::::::::::::::::::::::::::::::::::::::.:::::::::::.:::::::.:::::::.:::.::::::::::: 1 00000 j.,,,,,., :.:::::::: :;:;;;;; ; ; ;; =;;;;;;;:;;;.;;;; .' .. ;:;; (/) <1) .... 0.. <1) <( 0 20 30 40 50 Elev ation (m) Regress i on Output: Constant Std Err of Y .Est R Squared No. of Observations Degrees of Freedom t-value 0 27 3 60 3 694073 0 .379585 0 044941 52 50 8 6 Figure 41. Relationship between elevatio n and Average depression are a (al l s a m p les)

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(\1 X (l) '0 1 2 1 i : 1 I 61 ..................... -............... -4 .......... ... ....... .. .......... .. -........ --: 2 ........ ........... ...................... ............... ........ ...... .......... ............ ...... ......... ...... .................. J8 ----------0 1 0 20 30 Elevation (m) 4 0 Regression Output : Constant 3 0427 Std Err of Y Est 2 .7371 R Squared No of Observation s Degrees of Freed om t value 0 304 0 003 41 39 50 60 87 Figure 42. Relationship between elevation and Average circularity index ( all samples)

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88 6 DISCUSSION The purpose of this work is to investigate a hypothesized relationship between hypsometric position of karst landforms and their morphometric characteristics. The working hypothesis is that terrains on the morphologically older, higher hypsometric intervals, i.e., marine teraces, are characterized by more frequent and/or larger and more complex closed depressions than terrains on the morphologically younger lower hypsometric intervals. Linear regression analysis of elevations of samples and morphologic variability of the sampled terrains (total area within depressions, number of depressions and average depression area) does not support the working hypothesis. Correlation coefficients which range from 0. 055 to 0. 212, below the critical value of 0. 273, and indicate a weak correlation1 However, analysis of graphs (figures 38 and 41) indicates an increase of total area within closed depressions per sample and of average depression area with an increase in elevation. The variability of depression size follows this pattern, so that range of depression sizes also increases with elevation. 1 correlation of geometric averages did not significantly differ from these results.

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8 9 Feature and sample statistics (tables 3 and 6) show that depression area ranges over 5 orders of magnitude (70-670,000 m2), sample average depression area over 3 orders (865-11,700 m2 ) and total area within depressions over 4 orders of magnitude (2,3931,578,700 m2). This large range of depression sizes is the cause of the data scatter and of the lack of correlation in linear regression analysis. The large range of values of the morphologic variables can be attributed to multicyclicity of karst process and karst landform evolution in Florida. Post-Miocene sea-level fluctuations include several major transgressions and regressions. Due to these interchanges, Karst landforms developed during low sea-level stands are later submerged by sea-level rise and buried by marine sediments. In subsequent sea-level regressions, buried karst is reactivated and buried karst landforms are "exhumed" simultaneously with the development of new depressions. Consequently, the karst landscape in the study area consists of both young, small closed depressions that are developing since the last sea regression and of the old, large depressions that have undergone two or more burial/reactivation cycles. Figure 40 indicates two trends in the distribution of number of depressions relative to elevation. In the hypsometric interval 0 -30 m the number of depressions increases with an increase of elevation. In the interval 30-60 m the data trend reverses so that the number of depressions

PAGE 103

90 decreases with an increase of elevation1 The reversal of the trend at continually increasing elevation indicates change in geomorphic processes at this boundary, that is a change of the factors influencing depression frequencies. The elevation of 30 m MSL where the change occurs correlates with the position of a boundary between two geomorphologic regions, the Coastal Lowlands and Transitional Region (Brooks, 1967), that is, with the position of the Cody escarpment. The former region is characterized by unconfined conditions in the Floridan aquifer, that is, by thin, discontinuous or absent Hawthorn Formation. In the latter, Transitional Region, the Hawthorn Formation has greater thickness and continuity. The significant clay content in the Hawthorn Formation retards and/or stops infiltration of water into deeper horizons and consequently prevents underground transport of sediment and depression development. As a result, terrains underlain by the Hawthorn Formation have a smaller number of closed depressions relative to the terrains where this formation is absent or discontinuous. The average number of depressions per sample (Table 6) for the terrains underlain by Hawthorn Formation is approximately half of the number of depressions on the terrains where the Hawthorn is absent (9 versus 18 for total number of 1 correlation coefficients of 0.11 for samples below 30m of elevation and of -0.19 for samples above 30 m confirm this observation.

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91 depressions and 3 versus 7 for depressions of the 1st order, respectively) The thickness of the Hawthorn Formation increases with the distance from the Coastal Lowlands toward the Northern Highlands, that is in the same direction as the elevation. The decrease in number of depressions with increaseing elevation in the interval above 30 m can be attributed to the presence and thickening of the Hawthorn Formation. Influence of the Hawthorn Formation on the number of depressions implies its influence on total area within depressions. Another influence of the confining formation is a relationship between lateral and vertical components of erosion within closed depressions. In the depressions in the confined terrains presence of local erosional basis causes stronger lateral component of erosion relative to vertical component. Consequently depressions have larger area relative to depressions in with unconfined conditions of the aquifer. In terrains where there is no confining influence, there is no inhibition of underground transport of sediment nor local erosional basis. Consequently depressions in these terrains tend to have smaller area and grow more in depth relative to ones in confined terrains. Although the depth of depressions in not measured in this study, the comparison of dissection indices for confined and unconfined terrains (Table 6) indicates existence of morphological effect of this relationship.

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92 7. CONCLUSION S The karst landscape of west-central and north Florida consists of closed depressions that correspond to sinkholes, uvalas, poljes and dry, or karstified valleys. Development of closed depressions is initiated by dissolution of Tertiary limestone overlain by Neogene siliciclastic deposits. Depressions are predominantly developed in Neogene deposits with sporadic exposure of limestone on the bottom. They are polygenetic features. Surficial drainage is predominantly internal (within depression basins) and is directed toward one or more conduits that conduct surficial waters and debris underground. Quantitative analysis of the relationship between morphologic variability and elevations of closed depressions in the study area results in following conclusions: 1 The number, the average area, the ra_nge of areas and the percent per unit area of closed depressions in covered, unconfined karst increase with an increase in elevation, i e geomorphologic age. 2. Confined conditions of the karst aquifer in covered karst terrains and thickness and continuity of confining

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93 formation influence relationships between: a) morphologic variability of closed depressions and elevations, i. e., geomorphologic age, and b) depression area and depth, i. e., lateral and vertical components of geomorphologic processes. 3. The number of closed depressions per unit area in covered, confined karst decrease with an increase in elevation and/or an increase in thickness of confining formation. 4. Multicyclicity of karst process in study area related to sea level fluctuations disrupts the pattern of karst landforms and morphologic variability of depressions.

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94 REFERENCES Arrington, D.B. and R.C. Lidquist 1987. Thickly Mantled Karst of the Interlachen, Florida area. In Karst Hydrogeology : Engineering and Environmental Applications: Proceedings o f the Second Multidisciplinary C o n ference on Sinkhole s and the Environmental Impacts of Karst held in Orlando, Florida 9-11 February 1987, ed. B.F. Beck, 3 1 -40. Rotterdam and Boston: A.A. Balkema Bahtijarevic, A 1989. Sinkhole Density of the Forest City Quadrangle. In Engineering and Environmental Impacts o f Sinkholes and Karst: Proceedings of the Third Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Sinkholes and Karst held in St. Petersburg, Florida 2-4 October 1989, ed. B .F. Beck, 75-82. Rotterdam: A A Balkema Bradley J. T 1972. Climate of Florida. I n Climates of the States: Climatography of the United States, 60-8 Cooke, c. w 1945. Geology of Florida. Florida Geological Survey Bulletin, 5 Copeland, R., T M. Scott and G Madox 1991. Florida's Groundwater Quality Monitoring Program Hydrogeological Framework. Florida Geological Survey, Special Publication No 32 Cvijic, J. 1893. Das Karstphaenomen. Versuch einer morphologichen Monographie. Geog. Abhandl. 5 (3), Wien: 218-329 cvij ic, J 1918. Hydrographie souterraine et evolution m orphologique du karst. Rec. Trav. Inst. Geography Alpine 6 (4) : 375-426 Faulkner, G. L 1973. Geohydrology of the Cross Florida Barge canal area with special reference to the Ocala vicinity. USGS Water Resources Investigation 1 -73 F ord, D. c and P W williams 1989. Karst Geomorphology and h ydroJ.ogy. London: Unwin Hyman Gams I 1 974. Kras. Ljubljana: Slovenska matica Gams, r. 1 97 8 The polje: the problem of its defi nition, z Geomorphology 22: 170-181

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95 Healy, H G. 1975. Terraces and shorelines of Florida. USGS Map Series No. 71. Jennings, J. N. 1985. Karst Geomorphology. Oxford and New York: Basil Blackwell Jensen, J. H. 1987. Valley Poljes in Florida Karst. In Karst Hydrogeology: Engineering and Environmental Applications, Proceedings of the Second Multidisciplinary Conference on Sinkholes held in Orlando, Florida 9 -11 February 1987, ed. B .F. Beck, 47-52. Rotterdam and Boston: A.A. Balkema Johnston, R. H and J. A. Miller 1988. Region 24, Southeastern United States. In The Geology of North America, Vol. 0-2 : Hydrogeology. Geological Society of America: 229-236. Kastning, E. H. 1989. Surficial Karst Patterns: Recognition and Interpretation. In Engineering and Environmental Impacts of Sinkholes and Karst: Proceedings of the Third Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Sinkholes and Karst held in St. Petersburg, Florida 2-4 October 1989, ed. B F Beck, 11-16. Rotterdam and Boston: A.A. Balkema Lawrence, F. w. and s. B. Upchurch 1976. Identification o f Geochemical Patterns in Ground Water by Numerical Analysis. In A A. Saleem, Advances in Ground Water Hydrology. Mineapolis: American Water Resource Association Lawrence, F w and S B Upchurch 1982. Identification of Recharge Areas Using Geochemical Factor Analysis. Ground Water, Vol. 20: 680-687. Littlefield et al. 1984. Relationship of Modern Sinkhole Development to Large Scale Photolinear Features. In Sinkholes: Their Geology, Engineering and Environmental Impact: Proceedings of the First Multidisciplinary conference on Sinkholes held in Orlando, Florida 17-17 October, ed. B.F. Beck, 189-196. Rotterdam and Boston: A.A. Balkema MacNeil, F. s. 1950. Pleistocene Shore Lines in Florida and Georgia. Geological Survey Professional Paper 221-F Milojevic, s. M. 1923 0 postanku suvih dolina. G lasnik Srpskog Geografskog drustva, Beograd Roglic, J 1956. Neki osnovni problemi krsa. Izvjestaj o radu r v kongresa geografa FNR Jugoslavije, Beograd Roglic, J. 1961. Prilog poznavanju Cvijiceve misli o krasu. Geografski glasnik 23, Zagreb.

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9 6 Sinclair, W. C. and Stuart, J. w. 1985. Sinkhole type, development and distribution in Florida. USGS Map Series No. 110. Scott, T. M. 1988. The litostratigraphy of the Hawthorn Group (Miocene) of Florida. Florida Geological Survey Bulletin 59 Scott, T. M. 1989. The lithostratigraph y and hydrostratigraphy of the Floridan aquifer system in Florida. In Fiel d Trip Guidebook T185: 28th International Geological Congress, 2-9. American Geophysical Union Sustersic, F. 1985. Zaprte kraske globeli, problematika in terpretacij e in kartografskega prikaza. Acta carsologica, XIV/XV: 91-98 Sweeting, M. M. 1972. Karst landforms. London: Macmillan Upchurch, S. B and F. W. Lawrence 1984. Impact of Groundwater Chemistry on Sinkhol e Development A long a Retreating Scarp. In Sinkho les: Their Geology, Engineering and Environmental Impact: Proceedings of the First Multidisciplinary Conference on Sinkholes held in Orlando, Florida 15-17 October, ed. B.F. Beck, 23-28. Rotterdam and Boston: A. A. Balkema Upchurch, s. B. 1989. Karst of Florida. In The Lithostrtigraphy and Hydrostratigraphy of the Floridan Aquifer System in Florida, Field Trip Guidebook T 185: 28th International Geological Congress, 46-55. Americah Geophysical Union White, w. A. 1958. Some Geomorphic Features of Peninsular Florida. Geological Bulletin 41, Geological Survey Central Florida White, w. A. 1970. The Geomorphology of the Florida Peninsula. Geological Bulleti n 51, Florida Geological Survey white, w. B. 1988. Geomorphology and Hydrology of Carbonate Terrains. Oxford: Oxford University Press

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97 APPENDICES

PAGE 111

98 APPENDIX A.

PAGE 112

SAMPLE 98 I
PAGE 113

SAMPLE 187 5 0 1 2 c::w.--= ---L 0 loTRS 2000 Cent er-Wei I Coordinates : L ot i tud e 30" 16' 57" L ongit ude 83" 18' 35 CLOSED DEPRFS$fONS l s l Order I Higher Order 4 .46% of the sample oreo wit hin c losed depressions 1'0 1'0 tr.l s H !><: n 0 !:J rt t-' g CD 0, f-' 0 0

PAGE 114

SAMPLE 358 5 0 !(l(){T[RS 2 txXI 0 llTERS '(XX) 200) -----------------------------------, Center-Wei I Coordinates: Loti tude JO" 11' 009 Longi it:de 83" 10' 52" CLOSED t s l Order Higher Order 2 9 7 9 7. of the s cmp I e ore o closed depre. .ssions I'd I'd l'1j s H !>< n 0 rt 1-' CD p, 1-' 0 1-'

PAGE 115

SAMPLE 379 .5 0 l(let.TERS 2 0 I.UERS txXl 200) Cenicr-W e l I Coordi nates : Latitud e 29" 14' 39" L o n g i tude 82 44' 04" N o closed depressions w i l h i n s omp I e o r e o ;t:l t'(j t'(j t:rj s H :>< ;t:l n 0 ::l rt 1-' g CD 0. f-' 0 t-v

PAGE 117

SAMPLE 1067 .5 0 l(l.OofTERS tro 0 t.rn:RS 00) 2(00 2 Center-Wei I Coordi nates: Lol i tude 30" 05' 25" Longi lude 83" 13' 37" CLOSED 1st Order Hig h e r Order I 1.41% o f the sample area within c l ose d depressions ;J:::i I'd I'd tr:l s H ::< ;J:::i n 0 ::l rt 1-' ::l c ro 0. 1-' 0 ""

PAGE 118

SAMPLE 1092 5 0 !a_CIE][RS 2 tXXl 0 IEJ[RS tXXl 10ll Center-Wei I Coord inates: loti tude 29' 42' 10" Longitude 82 48' 03" CLOSED lsl Order Higher Order 0 .097. of the sample area wit hin closed depressions ltj ltj tr.l s H :X: n 0 rt 1-' g CD 0. '-' 0 V1

PAGE 119

SAMPLE 1094 .5 0 ld.CMJIRS 2 OX) \ 0 llTERS OX) 200J -------------------, Center-Wei I Coordinates: Lot i tude 29 19' Longitude 83 02' 10" CLOSED 1st Order Higher Order 0 11% of the s amp I e are a w i t hi n closed depressions "0 "0 tzj H :X: n 0 l:l rt 1-'-g ro 0. 1-' 0 0'\

PAGE 120

I SAMPLE 1243 I .5 0 I(]_CJIJER$ txXl 0 I.T(RS txXl 2(XX) 2 Center-Wei I Coordinates: loti tude 29" 10' 40" longitude 83" 01' 37" CLOSF.D lsl Order Gl Higher Order 6.317. of the sample area within closed depressions I'd I'd tx:l s H X () 0 ::s (1" ..... ::s c CD 0. ...... 0 ...J

PAGE 121

SAMPLE 1548 5 0 IQ.JlTR) I 2 , ... _---lXX) 0 lXX) 200) Center-Wei I Coordinates: Loti tude Jo 16' 46" Longitude 83 49' 21" CLOSED 1st Order Higher Order 0.257. of the sample area within closed depressions :t::< lt:J lt:J tr::l H :>< :t::< n 0 rt 1-' g (!) 0.. f--1 0 (X)

PAGE 122

SAMPLE 1915 .5 0 IO..CIEITRS 2 w_w=_-\.TRS 0 tXXl 2CXXl LtXXl ---Center-Wei I Coord inates: Loti tude Jo 06' 21" Longitude 83 42' 07" CLOSED DE'PRSSIONS lsl Order !: Higher Order 7 .267. of the sample area within closed depressions ;J::I I'd I'd tij s H :>< ;J::I n 0 ("t 1-' g ([) 0, ...... 0 1.0

PAGE 123

SAMPLE 1951 5 0 IQ.(),[TER$ riii 0 t.TtRS fXX) 2<: n 0 ::l rt g CD 0. f-' f-' 0

PAGE 124

SAMPLE 1982 .5 0 l(l< ;x:,. n 0 !j rt 1-' g ro 0. 1-' 1-' 1-'

PAGE 125

SAMPLE 2041 .5 0 KI.ClCTERS 2 1XXl 0 I.ITRS 1XXl 2<: (} 0 ::l rt f-' ::l c CD 0, ...... ...... N

PAGE 126

SAMPLE 2190 .5 0 J(la.rnJlS I 2 ----to:): 0 lofJUlS too 2(XX) Center-Wei I Coordinates: loti tude 29" 38' 15" Long i tude 82" 30' 12" CLOSED 1st Order Higher Order 11.937. of the sample area within closed depressions ;x:.. tO tO tx:1 s H :><: ;x:.. n 0 ::s ("t f-' ::s c CD 0. ..... ..... w

PAGE 127

-------------------SAMPLE 2255 L:1 :.... 0 KlJl{TffiS .. 0 1oTRS txXl ----------------------------------------------, 2
PAGE 128

SAMPLE 2459 5 0 l
PAGE 129

SAMPLE 2580 1 .5 0 I -----Kl.()IJERS 2 (XX) 0 loIRS fXX) 2< () 0 rt 1-' g CD 0. ..... ..... m

PAGE 130

SAMPLE 2746 .5 0 l(l(J{'I'ERS 2 ----tro 0 ITERS tro 20CO Center-Wei I Coordinates: Latitude 29" 33' 4-0" longi tude 82 31' 50" CLOSED lsl Order I' Higher Order 24.517. of the sample area w i t h i n c I o sed depress ions ;J::I I'd I'd tr:l s H :X: ;J::I () 0 :::1 rt ..... :::1 c ro 0.. ..... ..... -J

PAGE 131

txXl -----------------------------------------------------SAMPLE 2838 .5 0 J(l
PAGE 132

SAMPLE 3140 .5 0 J Cenler-Wel I Coordinates: Lal i tude 29" 36' 06" Longitud e 82" 23' 20" CLOSED 1 st Order Higher Ord e r 1 3 .08% o f the sample oreo withi n c l ose d depressions :x::t'Q t'Q trJ s H :><: :x::-() 0 ::i rt 1-' g ro 0. 1-' 1-'

PAGE 133

SAMPLE 3141 .5 0 IQ..
PAGE 134

SAMPLE 3153 .5 0 Kl.a.Tf.RS 2 txXl 0 lUERS txXl 2lXXl Center-Wei I Coordinates: Loti tude 29" 50' 32" Longitude 82" 20' 01" CLOSED lsl Order Higher Order 6 1 2 7 7. o f t h e s amp I e a r e a within closed depressions I'd I'd tzj s H :X: n 0 ::::1 rt 1-' g ro 0. 1-' N 1-'

PAGE 135

s SAMPLE 3669 Q de 5 0 JQ.CIETIRS I 2 0)) 0 ITERS OXl 200) Center-Wei I Coordinates: Loti tude 29 36' 52" Longitude 82" 49' 17" CLOSED 1st Order Higher Order 9 .827. of the sample oreo wit hin closed depressions n 0 ::s rt 1-1 g ro 0.. 1-' tv tv

PAGE 136

SAMPLE 4036 5 0 l
PAGE 137

124 APPENDIX A. (Continued} ---------------------, .. V) "' : : 0 .r) "' 0-..... c: c: 0 -0 "0 O"l ,._ V) "' .... ......, -"' .... 0 "' 0 O .... (..) O"l c-.. 0.. .... c-.. CX) .., 0.. E .., QJ -o 0 QJ "0 QJ "' I "0 ::J V) .... ::J-0 c v--0' <.> J::: I c:-c: 0-.... 0 0 ::z: )1: u ....J-' I <-< I I I I I I I I : I ,, I I I I I I I 0) I C0 c:> "<:t' s2 0...... ::::s < (/) 0 0

PAGE 138

SAMPLE 4043 .s 0 IQ.CITRS 2 OX) 0 t.fTJ5 OX) 20Xl Center-Well Coordinates: Loti tude 29" 43' 25" Long i tude 82" 22' 10" CLOSED lsl Order Higher Order 1.01% of the somple area within closed depr essions ;J::I ttJ ttJ ti:l s H X ;J::I () 0 ::s (T 1-' ::s CD p. f-' N U1

PAGE 139

SAMPLE 4044 .5 0 I(L{I(TER) 2 ml 0 ITERS lXXl 2< p n 0 rt 1-' g CD 0. ..... IV ()\

PAGE 140

SAMPLE 4046 .5 0 I(L
PAGE 141

SAMPLE 4520 .5 0 ICL
PAGE 142

SAMPLE 4523 .5 0 IQ.()('J[R$ 2 lXXl 0 IT[R$ XXX) 200) Center-Wei I Coordinates: Latitude Jo 07' 37" Longitude 82" 39' 10" CLOSED 1st Order Higher Order 5.217. of the sample oreo within closed depress ions ;t::l 'tl 'tl ti:l s H :X: ;t::l () 0 rt 1-' CD p. 1-' IV \!)

PAGE 143

SAMPLE 4666 .5 0 l
PAGE 144

SAMPLE 4914 .5 0 )(lCJETERS 2 lXXl 0 .vERS lXXl 200) Center -Wei I Coordinates: "Latitude 30" 17' 43" longitude 82" 58' JO" CLOSED lsl Order Higher Order 16.647. of the sampl e area within closed depressions )::1 'U 'U tx:l s H :>< )::1 () 0 rt 1-' g CD p. VJ

PAGE 145

SAMPLE 5208 0 2 ml 0 II1UtS txXl 2<: () 0 it I-' g CD 0. 1-' w N

PAGE 146

This Page Blank

PAGE 147

SAMPLE 8780 .5 0 IQJITRi 2 txXl 0 lUERS '(XX) 20X) Center-Wei I Coordinates: Loti tude 3 0 18' 25" Longitude 82 58' 49" CLOSED 1st Order fl Higher Order 27.46% of the sample area with i n closed depressions ;x::.. 1'0 1'0 1.11 e H :><: ;x::.. n 0 :::1 rt 1-' :::1 c CD 0. I-' w ""

PAGE 148

SAMPLE 11753 8 .5 0 l
PAGE 149

SAMPLE 1224 7 .5 0 ICI.
PAGE 150

SAMPLE 124 94 .5 0 KL
PAGE 151

SAMPLE 12600 .5 0 KUllTERS I 2 ----lXX) 0 llTERS 00) 2(XX) Center-Wei I Coordinates: latitude 30" 24' 20" longitude 82" 52' 08" CLOSED 1st Order I Higher Order 0.537. of the sample area within closed depressions ;r::.o t'(l t'(l tr1 s H :X: ;r::.o n 0 :::1 ("t f-' g CD 0.. .... w (X)

PAGE 152

SAMPLE 12625 5 0 l
PAGE 153

SAMPLE 12741 5 0 lXXI 0 t.rnRS lXXI 2< ;J:::I (') 0 rt f-J c CD 0. ..... tl'> 0

PAGE 154

8 ,------------------------------------------------------------------------------------H SAMPLE 12931 !J 0 l 0 IET.RS ro:> 200) Centei-Wel I Coordinates: Loti tude 29" 27' 35u Longitude 83" 0 6 27" CLOSED lsl Order Higher Order 0 117. of the s amp I e are o within closed depressions n 0 !::l rt 1-' g (l) 0. ..... .....

PAGE 155

SAMPLE 12933 5 0 J< () 0 :::1 rt 1-' g CD 0. ...... IV

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SAMPLE 12935 !J 0 IO..a.ETIRS 2 ml 0 IETERS 'lXX) 2(00 Center-W ei I Coordinat es : Lot i tude 29" 31' 08" Longitude 83" 00' 02" CLOOED 1st Order ll Higher Order 0.727. of the sample area w ithin closed depressions ;1::1 tO tO t:rj s H n 0 :J rt ...... 2 CD 0. 1-' ,:::. l,.J

PAGE 157

SAMPLE 12939 s .5 0 l(l< n 0 ::s rt 1-' g CD 0.. ..... "" ""

PAGE 158

SAMPLE 1 2 941 .5 0 I(L 2(XX) Center-Wei I Coordinates : Latitude 29" 32' 00" Longitude 83 06' 07" CLOSED lst Order ea Higher Order 0 .777. of the sample area within closed depressions :t::' t'(j t'(j tr:l s H X :t::' () 0 ::s if 1-' g CD 0.. .... .1>Ul

PAGE 159

SAMPLE 13008 .s 0 lilJlITRS 2 lXX) 0 llTERS lXX) 20ll Center-Wei I Coordinates: Lati tude 30 26' 35" Longitude 82 50' 33 CLOSED 1sl Order Higher Order of the sample area within closed depressions ;J;I I'd I'd ti:l s H ;J;I n 0 rt c CD 0. 1-' ,p. 0"1

PAGE 160

SAMPLE 13116 .5 0 lI I'd I'd tz:l s H :X :J;:>I n 0 rt 1-' B CD p. ..... "" ...J

PAGE 161

SAMPLE 1 3 128 .5 0 l(l.<: n 0 ::l rt 1-' ::l c CD 0. 1-' 00

PAGE 162

SAMPLE 13217 .5 0 IQ..(I(l'fRS 2 DXl 0 m> 2(XX) Center-Wei I C0ordinotes: Latitude 29" 59' 02" Long i tude 82" 35' 02" CLOSED 1st Order Higher Order 21 58 7. o f the s amp I e are a w ithin closed depressions _ _ _j :t::' tU tU l:lj s H :t::' (') 0 ::s rr 1-' CD 0. ..... 1.0

PAGE 163

SAMPLE 13128 !J 0 l(l.OofTERS 2 00) 0 IEJl:RS 00) 200) Center-Wei I Coordinates: Latitude 29" 58' 57" longi 82" 26' 46" CLOSED 1sl Order Higher Order 0.64% o f the sample oreo within closed depressions :J::I ti:J s H X ;r::.r n 0 ::l ("t 1-' ::l c CD p. .... lJl 0

PAGE 164

SAMPLE 13230 ., .5 0 IQJlfJERS QX) 0 ITRS roJ 200) 2 Center-Wei I Coordinates: Loti tude 29 23' 05" Longitude 82 26' 12" CLOSED 1st Order I Higher Order 24.827. of the sample area w ithin closed depressions _j ):1 tU tU tlj s H :><: ):1 n 0 :J rt ...... g CD 0. ..... lJl .....

PAGE 165

5 OXl SAMPLE 13305 ...... .... 0 )t(l,ff'[RS 0 ITIJlS 0::0 200) 2 Center-Wei I Coordinates : Loti tude Jo 20' 40" Longitude 83" 08' 02" CLOSED 1st Order Higher Order 9.637. of the sample area within closed depressions ;J:::i ID ID t:J:j s H :><: ;J:::i n 0 ::s rt 1-' g CD 0. .... tn N

PAGE 166

SAMPLE 13365 4 ., u j .5 0 1XX) 0 ITERS '(XX) 200) 2 Center-Wei I Coordinates: loti tude Jo J7' 12" longitude 83" 17' J7" CLOSED DEPRFSSIONS 1st Order Higher Order 8 8 2 7. o f t he s amp I e a r eo within closed depressions ;t:< I'IJ I'IJ t:x:l s H X ;t:< n 0 :J rt 1-' :J c CD 0.. ..... Ul w

PAGE 167

SAMPLE 13366 .5 0 lXXI 0 ICIIRS OOl 2(XXl --_ _______ 2 Center-Wei I Coordinates: Latitude 30" 22' 08" Longitude 83" 16' 30" CLOSED 1st Order I Higher Order 6.39% of the s ample area within closed depressions ;J::I t-0 ro ti:I f3 H X ;J::I n 0 :::l rt ..... :::l c (l) 0. ..... V1 ""

PAGE 168

5 lXX) .. eFI d" 0 0 SAMPLE 13775 Kl.< :t:' () 0 rt 1-' CD 0. 1-' lJl lJl

PAGE 169

SAMPLE 15867 .5 0 IQ(l{lffiS 2 OX> 0 ICTEJlS OX> 200) Center-Wei I Coordinates: Loti tude 30 03' 58" Longitude 83 II' 51" CLOSED lsl Order l Higher Order 12.51% of the sample area w i thi n closed depr ession s t'd t'd t:I:l s H :>< n 0 rt 1-' g ro 0. ...... lJl 0'1

PAGE 170

SAMPLE 15881 .5 0 WJEnRS 2 1m 0 t.TRS txXl 2IXXl Center-Well Coordinates: latitude Jo 15' 53" longitude 83 17' 02" CLOSED DEPR.ES:iiONS 1st Order Higher Order 0.42% of the sample area within closed depressions ltJ ltJ tJ:j s H .:>< n 0 ("t ..... g CD p. ...... lJ1

PAGE 171

SAMPLE 15909 .5 0 I<: n 0 rt ..... c ro 0. 1-' lJ1 (X)

PAGE 172

SAMPLE 15924 .5 0 I

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