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A GIS-Based Hydrologic Restoration Analysis for the Northern Indian River Lagoon Watershed

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A GIS-Based Hydrologic Restoration Analysis for the Northern Indian River Lagoon Watershed
Copyright Date:
2008

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Agricultural land ( jstor )
Drainage water ( jstor )
Environmental conservation ( jstor )
Land conservation ( jstor )
Land restoration ( jstor )
Land use ( jstor )
Term weighting ( jstor )
Water quality ( jstor )
Watersheds ( jstor )
Wetlands ( jstor )

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University of Florida
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8/1/2005

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A GIS-BASED HYDROLOGIC RESTORATION ANALYSIS FOR THE NORTHERN INDIAN RIVER LAGOON WATERSHED By CHRISTI M. GROVER A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENGINEERING UNIVERSITY OF FLORIDA 2003

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Copyright 2003 by Christi M. Grover

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iii ACKNOWLEDGMENTS I would like to thank The Nature Conserva ncy for funding this research. I would like to thank my supervisory committee ch airman, Dr. William Wise, for excellent guidance, encouragement, and support thr oughout my graduate studies. My sincere gratitude goes to Dr. Douglas Shaw for his committed support and direction for my research and for helping me secure fundi ng for research in the Indian River Lagoon watershed. I would also lik e to thank my other comm ittee members, Dr. Michael Annable and Dr. Andrew James, for their input and expertise in wetland hydrology and GIS. Special thanks also go to Ken Wiley of The Nature Conservancy and the rest of the IRL LCA team for their fi nancial support and guidance. I would also like to acknowledge Jim Fryer for completing Phase I of the project and supplying me with an abundant GIS database for the IRL watershed. My most important acknowledgements ar e saved for the ones who have supported me throughout the years before my graduate sc hool journey began. Fi rst, I would like to thank God for the opportunities and many blessi ng in life that He has provided me. I would like to thank my Grandmothers for supporting me and providing me the means to attend college and graduate sc hool. Lastly, my parents are due the utmost thanks for always listening, encouraging, and guiding me throughout my life.

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iv TABLE OF CONTENTS Page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES.............................................................................................................vi LIST OF FIGURES.........................................................................................................viii ABSTRACT......................................................................................................................x ii CHAPTER 1 INTRODUCTION........................................................................................................1 Description of the Study Area......................................................................................4 Predeveloped Hydrology/Landscape of the North IRL Watershed..............................5 History of Alterations to the IRL Watershed................................................................8 Current State of the Hydrol ogy of the IRL Watershed.................................................9 2 METHODS.................................................................................................................12 Analysis Based on Zones............................................................................................12 Central and Southern Zone Analysis...................................................................14 Identification of restorable freshwater wetlands..........................................19 Weighted average approach.........................................................................29 Northern Zone Analysis......................................................................................48 Protection of riparian zone buffer areas analysis.........................................49 Non-point source polluti on reduction analysis.............................................49 3 RESULTS...................................................................................................................61 Central and Southern Zone Results............................................................................61 Lands for Acquisition Scenario...........................................................................61 Consideration of all Lands Scenario....................................................................61 Northern Zone Results................................................................................................77 Protection of Riparian Z one Buffer Areas Analysis............................................77 Non-point Source Reduction Analysis................................................................77 Evaluation of the Central and Southern Zone Results................................................77 Highest Overall Ranked RFW.............................................................................77 Lands for acquisition scenario......................................................................77 Consideration of a ll lands scenario..............................................................96

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v Comparison of the two scenarios...............................................................103 Aerial/Land Use Verification............................................................................103 Proximity to Major Canals................................................................................105 Sensitivity Analysis on Weightings...................................................................114 Evaluation of the Northern Zone Results.................................................................115 Aerial/Land Use Verification of Both Analyses...............................................115 Non-point Source Pollution Reduction An alysis: Highest Ranked Agricultural Lands..............................................................................................................117 Non-point Source Pollution Reducti on Analysis: Sensitivity Analysis............123 4 DISCUSSION...........................................................................................................124 Central and Southern Zone.......................................................................................124 Northern Zone...........................................................................................................130 Recommendations for Future Research....................................................................131 5 SUMMARY AND CONCLUSIONS.......................................................................132 APPENDIX A LIST OF ACRONYMS............................................................................................136 B DATA LAYER SOURCES......................................................................................138 LIST OF REFERENCES.................................................................................................142 BIOGRAPHICAL SKETCH...........................................................................................145

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vi LIST OF TABLES Table page 2-1 Land-use acreage for SJRWMD hydric uplands......................................................26 2-2 Land-use acreage for SJRWMD par tially ditched or drained wetlands...................26 2-3 Land-use acreage for SFWMD hydric uplands........................................................27 2-4 Land-use acreage for SFWMD partia lly ditched or drained wetlands.....................27 2-5 Rankings for each basin based on Area_RFWSWB................................................34 2-6 Concentration-based pollutant loads (mg/l) obtained from Adamus & Bergman (1993).......................................................................................................................35 2-7 RFW rankings based on land use.............................................................................35 2-8 Habitat, species, and adjacency valu e rankings for the land for acquisition scenario.....................................................................................................................44 2-9 Habitat, species, and adjacency value rankings for the consideration of all lands scenario.....................................................................................................................44 2-10 Ranking based on drainage density..........................................................................46 2-11 Example of overall ranking for the lands for acquisition scenario...........................47 2-12 Example of overall ranking for the consideration of a ll lands scenario...................47 2-13 Land use acreage for agricultural la nd uses of the northern zone............................53 2-14 Agricultural land use rankings.................................................................................54 2-15 Rankings based on drainage density range..............................................................56 2-16 Rankings based on size range...................................................................................56 2-17 Habitat, species, and adjacency va lue rankings for the northern zone......................57 2-18 Example of overall rank ing for the northern zone...................................................60

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vii 3-1 Additional comments concer ning aerial comparison of RFW...............................104 3-2 Approximate distance of RFW to major canal.......................................................114 3-3 Example of sensitivity analysis for the lands for acquisition scenario...................116 3-4 Example of sensitivity analysis for th e consideration of a ll lands scenario...........116 3-5 Total area of hydric uplands, partially ditched/drained wetlands, and RFW for the lands for acquisition scenario.................................................................................117 3-6 Total area of hydric uplands, partially ditched/drained wetlands, and RFW for the consideration of all lands........................................................................................117 3-7 Example of sensitivity analysis in the northern zone watershed............................123 B-1 Contact Information for obtaining GIS data layers................................................138 B-2 Original source of data layers.................................................................................139

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viii LIST OF FIGURES Figure page 1-1 North Indian River Lagoon counties a nd contributing surface water basins.............2 1-2 Physiography of the countie s of the Indian River Lagoon.........................................6 1-3 General map of natural vegetation of Florida based on map produced by Davis, 1967........................................................................................................................... .7 1-4 SJRWMD land use 2000 and SFWMD land use 1995............................................10 2-1 Subdivisions of the north IRL Watershed................................................................13 2-2 Historic flatwood search area within th e contributing surface water basins in the central and southern zones.......................................................................................22 2-3 Hydric uplands and partially ditched or drained wetlands in the historic flatwood search area................................................................................................................23 2-4 Restorable and unrestorable land uses in the historic flatwood search area............24 2-5 Restorable freshwater wetlands in the historic flatwood search area.......................25 2-6 Restorable freshwater wetlands corr ected for wet areas and 2000 land use............28 2-7 1995 SJRWMD and SFWMD land use....................................................................30 2-8 2000 SJRWMD land use..........................................................................................31 2-9 Flow chart of the two scenar ios for identifying and ranking RFW..........................33 2-10 Representation of drainage density in the central and southern zones watershed....37 2-11 Map 1 of conservation data laye rs in the flatwood search area................................40 2-12 Map 2 of conservation data laye rs in the flatwood search area................................41 2-13 Categories and associated ranked fields for the lands for acquisition scenario.......48 2-14 Categories and associated ranked fields for the considera tion of all lands scenario.....................................................................................................................48

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ix 2-15 2000 SJRWMD land use in the northern zone watershed........................................50 2-16 Agricultural land use in the northern zone watershed..............................................52 2-17 Representation of drainage density in the northern zone.........................................55 2-18 Map 1 of conservation data layers in the northern zone watershed.........................58 2-19 Map 2 of conservation data layers in the northern zone watershed.........................59 3-1 Lands for acquisition scenar io water quantity ranking............................................62 3-2 Lands for acquisition scenario wa ter quality (land use) ranking..............................63 3-3 Lands for acquisition scenar io drainage density ranking.........................................64 3-4 Lands for acquisition scenario conservation value (HSA).......................................65 3-5 Lands for acquisition scenario conserva tion value (proximity to IRL targets)........66 3-6 Lands for acquisition s cenario equal weighting.......................................................67 3-7 Lands for acquisition scenario TNC weighting........................................................68 3-8 Consideration of all la nds water quantity ranking...................................................69 3-9 Consideration of all lands wa ter quality (land use) ranking.....................................70 3-10 Consideration of all land s drainage density ranking................................................71 3-11 Consideration of all lands c onservation value (HSA) ranking.................................72 3-12 Consideration of all la nds conservation value (proxi mity to IRL targets) ranking......................................................................................................................73 3-13 Consideration of a ll lands ease of restoration (FMLA) ranking..............................74 3-14 Consideration of all lands equal weighting..............................................................75 3-15 Consideration of a ll lands TNC weighting...............................................................76 3-16 Northern zone watershed unbuffered stream locations............................................78 3-17 Unbuffered stream 1 in the northern zone watershed...............................................79 3-18 Unbuffered stream 2 in the northern zone watershed...............................................80 3-19 Unbuffered stream 3 in the northern zone watershed...............................................81 3-20 Unbuffered stream 4 in the northern zone watershed...............................................82

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x 3-21 Unbuffered stream 5 in the northern zone watershed...............................................83 3-22 Unbuffered stream 6 in the northern zone watershed...............................................84 3-23 Water quality (land use) ranking in the northern zone watershed............................85 3-24 Water quality (drainage density) rank ing in the northern zone watershed...............86 3-25 Water quality (size) ranking in the northern zone watershed...................................87 3-26 Conservation ranking (HSA) in the northern zone watershed.................................88 3-27 Equal weighting in the northern zone watershed.....................................................89 3-28 Water quality weighting in the northern zone watershed.........................................90 3-29 Lands for acquisition scenario (equal weighting): highest ranked overall RFW.....91 3-30 Lands for acquisition scenario (TNC weighting): highest ranked overall RFW......92 3-31 Lands for acquisition scenario (equal weighting): cluster1......................................93 3-32 Lands for acquisition scenario (equal weighting): cluster 2.....................................94 3-33 Lands for acquisition scenario (equal weighting): cluster 3.....................................95 3-34 Lands for acquisition scenario (TNC weighting): cluster 4.....................................97 3-35 Consideration of all la nds scenario (equal weighti ng): highest ranked overall RFW.........................................................................................................................98 3-36 Consideration of all la nds scenario (TNC weighting): highest ranked overall RFW.........................................................................................................................99 3-37 Consideration of a ll lands scenario (equal weighting): cluster 5...........................100 3-38 Consideration of a ll lands scenario (TNC weighting): cluster 6............................101 3-39 Consideration of a ll lands scenario (TNC weighting): cluster 7............................102 3-40 Lands for acquisition scenario (equa l weighting): aerial verification of cluster 1..................................................................................................................106 3-41 Lands for acquisition scenario (equa l weighting): aerial verification of cluster 2..................................................................................................................107 3-42 Lands for acquisition scenario (equa l weighting): aerial verification of cluster 3..................................................................................................................108

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xi 3-43 Lands for acquisition scenario (TNC weighting): aerial verification of cluster 4..................................................................................................................109 3-44 Consideration of all land s scenario (equal weighting) : aerial verification of cluster 5..................................................................................................................110 3-45 Consideration of all la nds scenario (TNC weighti ng): aerial verification of cluster 6..................................................................................................................111 3-46 Consideration of all la nds scenario (TNC weighti ng): aerial verification of cluster 7..................................................................................................................112 3-47 Proximtiy of clusters 1, 4, and 6 to major canal.....................................................113 3-48 Equal weighting in the northern z one watershed: highe st ranked overall agricultural lands....................................................................................................118 3-49 Water quality weighting in the nort hern zone watershed: highest ranked overall agricultural lands........................................................................................119 3-50 Highest ranked overall agricultural lands (equal weighting): cluster 1.................120 3-51 Highest ranked overall agricultural lands: cluste r 2 (equal weight) and cluster 4 (water quality weighting).........................................................................121 3-52 Highest ranked overall ag ricultural lands (equal weighting): cluster 3 (equal weight)....................................................................................................................122

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xii Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Engineering A GIS-BASED HYDROLOGIC RESTORATION ANALYSIS FOR THE NORTHERN INDIAN RIVER LAGOON WATERSHED By Christi M. Grover August 2003 Chair: William R. Wise Major Department: Environmental Engineering Sciences Several factors have changed the quantity and quality of waters discharging to the Indian River Lagoon (IRL) including extensiv e drainage alterations, impoundment of salt marshes, and point and non-point sources of pollution. The Nature Conservancy (TNC) created a two-phase GIS-based project with the purpose of identifying target areas for acquisition or conservation easements that a ddress the water qualit y, water quantity, and conservation issues in the area. Phase two of the project, the focus of this research, includes splitting the IRL watershed into char acteristically simila r zones and performing a comprehensive geographic information systems (GIS) analysis for each. The central and southern zones analysis id entifies and ranks restorable freshwater wetlands (RFW) in the historic flatwood region west of the historic watershed boundary. This analysis applies a weighted average appr oach using GIS attribut e tables to rank and weight the RFW based on 4 categories: water quality benefits, water quantity benefits, conservation value, and ease of restoration. The four categories repr esent several fields

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xiii in the RFW attribute table and c onsist of drainage density; land use; habitat, species, and adjacency value; proximity to IRL targets; wa ter quantity value; and managed area value. This analysis is divided into two different scenarios: lands for acquisition scenario (considers only lands not currently under c onservation management) and consideration of all lands scenario. Two weighting schemes are applied to both the scenarios. This analysis successfully identifies seven clus ters of the highest ranked RFW meeting the objectives of the two scenarios and two weightin g schemes. Proximity of the clusters to major canals, aerial examination of the cluste rs using Digital Ort hographic Quarter Quads (DOQQ), and the sensitivity of the ra nked fields are also investigated. Two separate analyses, focusing on wate r quality, are performe d on the Northern Zone. The first analysis identifies six ripa rian zones adjacent to streams discharging to the IRL that are not currently managed for conservation. The second analysis identifies agricultural lands that if willingly taken out of production and/or restored could reduce the non-point source pollution loads received by th e IRL. This analysis ranks agricultural lands utilizing the weighted-average appro ach based on two categor ies: water-quality benefits and conservation value. The GIS attr ibute table contains four fields representing the two categories including drai nage density; land use; size; and habitat, species, and adjacency value. Two weighting schemes are applied. This analysis successfully identifies four clusters of the highest ranke d agricultural lands meeting the water quality and conservation value objectives for the two weighting schemes.

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1 CHAPTER 1 INTRODUCTION The Indian River Lagoon (IRL), an estuar y on the east coast of Florida, spans 155 miles and maintains the highest species diversity for an estuary in North America. The IRL stretches from the Ponce De Leon Inlet n ear New Smyrna Beach to Jupiter Inlet near West Palm Beach. The Lagoon contains thr ee interconnected estuar ies (Banana River, Indian River Proper, and Mosquito Lagoon) a nd crosses five countie s (Volusia, Brevard, Indian River, St. Lucie, and Martin) (Figure 11). The IRL sits at the intersection of the temperate climate to the North and the s ubtropical climate to the South. This climatological interaction along with the va riety of natural communities (mangrove/salt marsh, maritime hammocks, pine flatwood/we tland complex, coastal scrub, beaches and dunes, and spoil islands) in the IRL watershed allow for a high diversity of species including the West Indian Manatee ( Trichecus manatus latirostris ), juvenile green and loggerhead sea turtles ( Chelonia mydas and Caretta caretta ), bottlenose dolphin ( Tursiops truncates ), spotted sea trout ( Cynoscion nebulosus ), and Florida Scrub Jay ( Aphelocoma coerulescens ) (Indian River Lagoon Nationa l Estuary Program [IRLNEP], 1996). The 1987 Surface Water Improvement and Management (SWIM) Act identified the IRL as one of the five priority water bodies in the state requiring “restoration and special protection” (IRLNEP, 1996; St. Johns River Water Manage ment District [SJRWMD], 2003). Many issues contributed to the IRL be ing identified including drainage alterations which extended the historic watershed bounda ry for agricultural and urban purposes

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2 Figure 1-1. North Indian River Lagoon countie s and contributing surface water basins (Sources: Florida Geographic Data Library [FGDL]; SJRWMD).

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3 leading to increases in freshwater inputs to the IRL, degradation of salt marshes along the Lagoon due to impoundments, and impacted wa ter quality due to point and non-point sources of pollution (IRLNEP, 1996). Other programs incorporate the IRL includ ing the National Estuary Program (NEP) and Comprehensive Everglades Restoration Plan (CERP). NEP recognized the IRL in 1991 and developed a Comprehensive Conserva tion Management Plan (CCMP) for the region which suggests specific actions for improving the “integrity, diversity, and productivity” and specifies funding sources for these actions (IRLNEP, 1996, p. 1). Part of CERP aims at performing feasibility studi es in the IRL watershed to investigate the problems caused by hydrologic alterations and “det ermine the types of modifications that are needed to successfully restore water qu ality and ecological conditions of the Lagoon” (United States Army Corps of Engi neers [USACOE] & SJRWMD, 2003, p. 1). Due to the IRL’s ecological significance and the growing concern over the health of the system, The Nature Conservancy (TNC) also became interested in the welfare of the IRL. TNC formed a Large-Scale C onservation Area (LCA) team for the IRL watershed and quickly realized the importa nce of the use of geographic information systems (GIS) for analyzing conservation stra tegies for a watershed of this size and complexity. The LCA team proposed a two-phase project for the area. The first phase of the project involved accumulating GIS data fr om public and private sources for the IRL watershed, formatting the data, and assembling th is data into a GIS database. Phase two of the project involves performing a compre hensive investigation utilizing the GIS database to more adequately identify ta rget areas for acquisition and conservation easements that address the needs of the IRL conservation target s and provide water

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4 quality and quantity benefits to the Lagoon. Ph ase two of the GIS project is the focus of this study. A background information search was performed to determine the specific problems and conservation/restoration needs of the IRL watershed for phase two of the project and included defining 1. The study area boundaries. 2. Predeveloped hydrology/landscap e of the IRL watershed. 3. History of alterations to the IRL watershed. 4. Current state of the hydrology of the IRL watershed. Description of the Study Area Only the northern portion of the IRL waters hed from Ponce de Leon Inlet to Fort Pierce Inlet is selected for the detailed GIS investigation on water qu ality and quantity. The north IRL is chosen as the study area due to the greater potential to impact future restoration plans. The USACOE and the South Florida Water Management District (SFWMD) selected a plan for the restoration of the south IRL (Fort Pierce Inlet to Jupiter Inlet) and St. Lucie River through the CERP program and the Indian River Lagoon-South Final Feasibility Report and Supplem ental Environmental Impact Statement-August 2002 (IRLSFS) (USACOE & SFWMD, 2002) . The IRLSFS suggests a plan that includes building new reservoirs and stormwat er treatment areas and restoring hydrology in portions of the watershed to improve th e quantity, quality, and timing of freshwater flows to the St. Lucie and IRL (USACOE & SF WMD, 2002). However, for the Northern portion of the IRL, the USACOE remains in the planning stages for developing a feasibility study. The USACOE recently fi nished the Indian River Lagoon North Feasibility Study Project Management Plan (P MP) in April 2003. The PMP estimates the associated costs, timeline, and scope fo r performing the Indian River Lagoon North Feasibility Study (IRLNFS) (USACOE & SJRWMD, 2003).

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5 The study area includes only the contributing watershed to the north IRL as defined by SJRWMD “Surface Water Basins” GIS data la yer. Basins contributing to the north IRL are selected from the Surface Water Basi n GIS layer and exported to a separate GIS layer. The north IRL contributing watershed totals over 1100 sq. miles (Figure 1-1) and stretches 128 miles in length. The rest of this study refers spec ifically to the north Indian River Lagoon and references to the IRL refe r to the north portion of the Indian River Lagoon unless otherwise noted. Predeveloped Hydrology/Landscape of the North IRL Watershed Historically, several inlets opened and cl osed in the area and the IRL watershed maintained “a gentle, meandering drainage pa ttern consisting of sl oughs, creeks, rivers, and wetlands” (IRLNEP, 1996, p. 14). The IRL system soaked up floodwater in natural systems which facilitated gr oundwater recharge, absorbed nutrients, and filtered suspended matter (IRLNEP, 1996; USACOE & SJRWMD, 2003). A thin linear drainage basin existed with the western boundary followi ng the Atlantic Coastal Ridge except in a few places, i.e. Sebastian River (IRLNEP , 1996; Steward & VanArman, 1987). The Atlantic Coastal Ridge consists of Tu rnbull Hammock, Titusville Dunes, CocoaSebastian Ridge, and Sebastian-Ju piter Ridge. The Sebastian River headwaters fall in the Sebastian-St. Lucie Flats area and water from this area slowly draine d to the IRL through sloughs and wetlands. Waters fr om St. Johns Marsh drained to the IRL only after major storms. The eastern watershed boundary c onsisted of the barrier island dune line (IRLNEP, 1996). Figure 1-2 shows the physio graphic regions sited in this section. A wide range of plant communities existe d in the natural landscape in the IRL watershed (Figure 1-3). Pine flatwood forests covered the area west of the Atlantic Coastal Ridge and east of the marshland and pr airie zones of the St. Johns River. The

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6 Figure 1-2. Physiography of the counties of the Indian River Lagoon (Source: FGDL).

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7 Figure 1-3. General map of natural vegeta tion of Florida based on map produced by Davis, 1967 (Sources: FGDL; SJRWMD).

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8 pine flatwoods are “relatively flat, poorly drained, and there are numerous scattered intermittent ponds, lakes, and sloughs” (Brown et al., 1962, p. 9). From the Atlantic Coastal Ridge eastward the landscape grades from oak scrub into salt marsh/mangrove communities. The barrier islands bounding the Lagoon to the east vary from salt marsh/mangrove communities to maritime forest and then to dunes (IRLNEP, 1996). History of Alterations to the IRL Watershed Many hydrologic alterations occurred in th e IRL region for the purposes of urban and agricultural development. Henry Flagle r added railroad servic e through the IRL area starting in 1892. Railroad construction resu lted in the some of the first drainage alterations of pine flatwoods and wetlands on the Atlantic Coastal Ridge (IRLNEP, 1996). Intracoastal Waterway (ICW) construc tion was completed in 1912 which resulted in a 12-foot deep navigation channel pa ralleling the coast (IRLNEP, 1996; Clapp & Wilkening, 1984). The 1916 Drainage Act f acilitated population growth in the IRL watershed by allowing the creation of special taxing districts (298 Drai nage Districts) to encourage agricultural production, flood control, and mosquito control (IRLNEP, 1996). The Drainage Act diverted drainage from the Upper St. Johns River Basin across the Atlantic Coastal Ridge to the IRL and expanded the natural tributaries to the west (IRLNEP, 1996; Clapp & Wilkening, 1984). Th is included drainage of flatwood areas and wetlands west of the Atlantic Coasta l Ridge and resulted in lowering of the groundwater tables creating dryer land for cattle ranching and citrus groves (IRLNEP, 1996). Interbasin diversion was accomplished by diking areas on the east side of the St. Johns River and constructing canals linki ng to the IRL (Clapp & Wilkening, 1984). Another result of the Drainage Act include d impoundment of salt marshes to control mosquitoes (IRLNEP, 1996).

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9 Current State of the Hydrology of the IRL Watershed Both long-term natural physiographic changes and short-term anthropogenic alterations combine to form the current hydr ologic state of the IRL watershed. Four constructed or stabili zed inlets create permanent areas of limited tidal influence in the IRL (USACOE & SJRWMD, 2003). Hindered mixing occurs in areas of limited tidal influence and combines with the linear shape and shallow depth of the IRL to cause these areas to be more susceptible to pollutant s (IRLNEP, 1996). The IRL receives faster, more direct freshwater input s through canals which reduce th e treatment of nutrients and filtering of suspended solids as well as cau sing abrupt salinity va riations (USACOE & SJRWMD, 2003). The western boundary of th e watershed extends into portions of watershed that historically drained to the St. John’s River Basin and the Lake Okeechobee Basin. Canals extend many of the na tural tributaries west of the historic watershed boundary (IRLNEP, 1996). The e xpanded watershed causes changes in the timing, quality, and quantity of freshwater inpu ts to the IRL. Ch anges in water quality affect the health of seagrass beds in the IR L and impact the restrict ions on harvesting of shellfish. Spotted sea trout ( Cynoscion nebulosus ) populations have also declined due to diminished water quality and loss of habita t. Impoundment of over 75 percent of the IRL’s salt marshes has resulted in a loss of connection with the IRL and a decline in water quality and wildlife habitat (USACOE & SJRWMD, 2003). The current landscape contains natural habitats segmented by agricultural and urban areas. Figure 1-4 shows the 2000 land use distribution from SJRWMD over the north IRL watershed. Land use for St. Luci e County dates from 1995 since this is the most current land use available from the SFWMD. The entire IRL region contains “227,300 acres of citrus, 121,000 animal units (largely cattle) on 326,200 acres of range

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10 Figure 1-4. SJRWMD land use 2000 and SFWMD land use 1995 (Sources: SJRWMD; FGDL).

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11 and pasture, and 8,600 acres of vegetabl es” (IRLNEP, 1996, p. 10). Several urban centers exist in the watershed including Melbourne, Palm Bay, Titusville, and Vero Beach. Land use changes impact non-point source pollution. Both agricultural and highly developed urban areas incr ease the amount of pollutant loadings as compared with natural land uses (Adamus & Bergman, 1993).

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12 CHAPTER 2 METHODS Analysis Based on Zones The IRL watershed can be categorized on the basis of hydrology, tidal influence, and nature of threats to conservation targ ets into characteristically similar zones (IRLNEP, 1996; Knowles, 1995). Figure 2-1 shows a representation of the north IRL watershed zones. A separate GIS analysis is performed fo r the northern and central and southern zones within the north IRL watershed. The northern zone includes areas in the IRL watershed north of Melbourne. The northern zone exhibits broader expanses of water with significantly smaller watershed area as compared with the central and southe rn zones. The historic watershed area remains relatively similar to the current watershed area. Due to the distant proximity to the nearest inlet, the northern zone displays limited tidal ranges (less than 10 cm) and flushing. Wind and freshwater runoff primarily drive the mixi ng in the northern zone. As discussed previously, sluggish circulation creates a higher sensitivity to water quality problems. Fewer large drainage canals exist and the salinity varies less than in the central and southern zones (Steward & VanArma n, 1987; IRLNEP, 1996). The northern zone contains many citrus groves, some impr oved pastures, a few urban centers, and a prominent wetland area (Turnbull Hammock). The central and southern zones share simila r characteristics and for the purposes of this analysis were grouped t ogether. The central and southe rn zones include areas south of Melbourne to north of the Fort Pierce In let within tidal influence. Many large

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13 Figure 2-1. Subdivisions of the north IR L Watershed (Sources: FGDL; SJRWMD). canals widen the historic western watershed boundary resulting in substantially larger discharges of freshwater to the IRL (K nowles, 1995; IRLNEP, 1996). Tidal influence

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14 increases in a southward direction in thes e zones from a tidal range of 10 cm near Melbourne to a tidal range of 100 cm at the inlets. Both the central and southern zones exhibit broader salinity variab ility than the northern zone. Circulation increases in these zones due to the greater tidal influence and larger inflows (IRLNEP, 1996). More urban development exists near the Lagoon with ex tensive agricultural lands within the 298 Drainage Districts further west of the urban areas. Central and Southern Zones Analysis Analysis on the central and southern zone s was conducted first due to the larger extent of watershed area and more intense hydr ologic alterations. Analysis of the central and southern zones investigates the historic flatwood/wetland comple x (Figure 1-3) west of the Atlantic Coastal Ridge that historically did not drain or slowly drained to the IRL. This flatwood area contains many depressi onal wetlands, lakes, and sloughs (Brown, 1962). Many of these depressional wetlands we re ditched or drained for agricultural and urban purposes which eliminated many of th e benefits that wetlands provide (IRLNEP, 1996; Zedler, 2003). Wetlands on low inte nsity land uses, i.e. rangeland and pasturelands, could potentially be restored to provide flood storage, water quality benefits, and enhance biodiversity. Restor ed wetlands could redu ce the impacts of large influxes of freshwater runoff to the lagoon by providing additional storage. These wetlands could provide water quality benefits by intersecting and assimilating nutrients and filtering sediment in runoff before it reach es the IRL. Wetlands also provide habitat for various flora and fauna. Although wetla nds used for storing and treating water may not necessarily exhibit a high biodiversity, they may still provide some type of wildlife habitat (Zedler, 2003).

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15 This analysis identifies and ranks potential wetland restoration areas in the historic flatwoods area through a weighted average approach utilizing GIS. Several other studies utilize GIS to identify and rank wetlands fo r restoration or for assessment purposes. The approaches used in the first three articles (Richardson & Ga tti, 1999; Russell et al., 1997; O’Neill, 1997) focus on prioritizing sites for we tland and riparian restoration with targets of nonpoint source pollution control, flood atte nuation, species habita t, and disturbance regime. The last two articles (Cedfe ldt et al., 2000; SF WMD & USACOE, 1997), although not focused on restoration, utilize GI S to identify “functionally significant” wetlands based on “flood flow alteration, surface water quality improvement, and wildlife habitat provision” and identify suitable lands for water preserve areas (Cedfeldt et al., 2000, p. 1). Richardson and Gatti (1999) explain how th e Wisconsin Department of Natural Resources (WDNR) Bureau of Watershed Management developed an approach to prioritize sites for wetland restoration in or der to control nonpoint source pollution. Objectives of their project include the following: 1) develop a GIS database on a watershed scale to objectively locate drained wetland basins and identify their owners for potential restorati on contacts, and 2) use GIS modeling to rank the drained we tlands for restoration based on the potential amount of transported sediment reduced by their restoration. (p. 537) The authors identify drained we tlands utilizing a digi tized soil data laye r. A hydric soils data layer created from the soils data layer indicates the location of current or historic wetlands. They use several wetland inventories and LANDSAT Thematic Mapper images to create a data layer representing we tlands existing in 1990. The authors overlay the existing wetlands data layer with the hydric soils data laye r to create a map of drained wetlands. Wetland ownership is determined by digitizing tax parcel sheets into GIS and

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16 merging them with a table of names and addr esses for the parcels. The drained wetlands data layer is converted into raster format for purposes of ranking. To achieve the ranking objectives the WDNR implemented a five step method: 1. Estimate soil loss by using the Universal Soil Loss Equation (US LE) and create a GIS data layer. 2. Calculate sediment delivery using GAME S, a sediment delivery model, and translate data into sediment delivery da ta layer in Geographic Information System (GIS). 3. Delineate drained wetland mi ni-watersheds using DEM. 4. Sum sediment delivery for mini-watersheds using GIS. 5. Rank drained wetlands for restoration. Drained wetlands with the highe st elevation are viewed as th e highest priority and then drained wetland sections within strata of 10-m contour inte rvals of elevation are ranked by the sediment delivery amounts. Russell et al. (1997) determine an a ppropriate approach for choosing and prioritizing sites for riparian restoration or preservation utilizing GIS. The authors determine that flood reduction a nd species habitat are the key f actors of interest in this study. Two data layers are input into GIS to represent these fact ors, wetness potential and land use/land cover. The authors used elevation data in th e form of USGS 7.5minute digital elevation models (DEM) to derive the wetness potential. LANDSAT Thematic Mapper images are used to deve lop the land-use/land-cover layer. The two layers are combined to create a “matrix w ith unique class combinations” (p. 63). The authors utilize the matrix to identify poten tial sites for riparian restoration or preservation. The authors make the assumpti on that vegetated or bare ground would be more easily restored than developed land. Th ey then rank the restoration sites according

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17 to their wetness values, size, and distance to existing riparian vegeta tion. This method is applied for the initial site selecti on in the San Luis Rey River Basin. O’Neill et al. (1997) devise a similar appro ach to Russell et al. (1997) regarding the wetness potential and the species habitat factor s but add a disturbanceregime factor. The disturbance-regime factor is based on a “reach-based index of specific power ( )” (p. 1) representing the 10-yr flood event. This factor estimates the erosion and landform creation ability due to flooding. Specifi c powers less than or equal to 3 W/m2 (lowenergy sites with wide alluvial valley fl ows) or greater than or equal to 8 W/m2 (highenergy sites with steep narrow valley floors) are used for rating the disturbance-regime factor. High-energy sites ar e less desirable for restoration due to the “extreme and probably frequent disturbances” (p. 95) that make it harder for riparian regeneration to occur. Sites are again selected for restora tion due to the above factors and then ranked according to “size and proximity criteria” (p. 1). Cedfeldt et al. (2000) create s an approach specifically for the northeastern United States that quantifies spatial estimates of three wetland func tions: “flood flow alteration, surface water quality improvement, and wildlife habitat provision” (p. 1). This approach consumes less time and produces more objective results than traditional methods of identifying functionally important wetlands du e to the utilization of GIS. The authors develop a set of predictors for each wetland function. The predictors are statements based on the size and position of the wetland a nd physical nature of the landscape. The authors wrote programs that delineate the we tland watersheds from DEM and designate an adjacent land use to each wetland. The programs then analyze each wetland based on the predictors and output a grid of wetla nds that demonstrate the three functions

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18 according to the predictor requirements. The authors apply the programs to the Vermont portion of the Lake Champlain Basin and the re sults for water quality and wildlife habitat function compare reasonably well to manua l methods for measuring wetland function. SFWMD and USACOE (1997) desc ribe an analysis in southeastern Florida that utilizes a raster-based GIS technique to pinpoint appropriate areas for use as water preserve areas (WPA). The Everglades rest oration plan includes creating WPA to serve as a system of marshlands, reservoirs, and a quifer recharge basins that could be managed to store excess runoff, restore na tural flow patterns, recreate wetlands, provide water-quality treatment and provi de a buffer between the Everglades and developed areas. (p. 1) Two-and-one-half acre grid cells were a pplied to the study area. This analysis “processed, rated, and combined” (p. 7) la nd-use/land-cover, hydr operiod, and county soil-survey raster data layers to determine land suitability as a wetland, storage area, or aquifer recharge area. The approach used in this study is similar to some of the previous studies in that it prioritizes sites for wetland restoration ba sed on wetland function, i.e. flood storage, water quality benefits, and wildlife habitat. Previous studies differ from this approach because they do not consider ease of restor ation. The approach utilizes vector-based operations as compared to the raster-based appr oaches utilized in th e previous studies. GIS defines vector features as points, lines, and polygons and raster features as a grid. Raster techniques assign a single value to each grid cell while in vector techniques each layer of points, lines, or polygons has an asso ciated attribute table. Although processing with raster-based operations occurs faster, v ector-based operations c onserve the original shape of features (SFWMD & USACOE, 1997).

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19 All GIS work in this study was performe d using ArcGIS version 8.2. Sources for all data layers utilized in this study are documented by metadata. Metadata includes the data source, description of th e attribute tables, and projection parameters. The GIS data layers and the associated metadata for each da ta layer used in this study can be obtained from FGDL, SJRWMD and SFWMD, Florida Department of Environmental Protection (FDEP), Florida Natural Areas Inventory (FNA I), and TNC. After each figure generated from GIS data layers, the “Sources” where th e metadata can be obtained are listed. Although the data layers origin ate from reputable sources, use of the data requires the recognition that the data layers are supplied “as is” to the us er so the accuracy of the results relies on accuracy of the data laye rs. Appendix B includes a listing of the agencies from which the metadata can be obtai ned and a listing of the original sources of each data layer. Identification of restorable freshwater wetlands The first part of the approach involves id entifying restorable freshwater wetlands (RFW) on low intensity agricultural lands or na tural areas that reside in the historic flatwoods area. This study defines RFW as hydric uplands or partially ditched and drained wetlands. Hydric uplands contain so ils designated as hydric and are located on lands specified as uplands by the National Wetlands Inventory (NWI). The NWI, maintained by the United States Fish and Wildlife Service (USFWS), maps wetlands in a GIS data layer and provides information on wetland characteristics in an associated attribute table (USFWS, 2003). Hydric so ils indicate a location where wetlands are and/or were present (Richardson & Gatti, 1999). The approach for developing the hydric uplands data layer is simila r to the approach used by Richardson and Gatti (1999).

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20 Partially ditched or drained wetlands include all existing wetlands designated as ditched or drained by the NWI. For the purposes of this study, for a hydric up land or a partially ditched or drained wetland to be considered restorable it must reside on low intensity agricultural lands or natural areas. Land uses assumed restorab le include pastureland, rangeland, abandoned tree crops, upland forests, water bodies, and wetlands. The steps for identifying the RFW include the following: 1. Derive a historic flatwoods data layer (search area) (F igure 2-2) by clipping the General Natural Vegetation Map of Flor ida (Davis, 1967) (Figure 1-3) to the contributing surface water basins of the centr al and southern zones of the north IRL (Figure 2-2). Then select features from the clipped data layer attribute table considered to be pine flatwoods (incl udes all pine flatwoods and some wet/dry prairies with similar characteristics to pine flatwoods) and export selected features into a separate layer. 2. Derive an uplands data layer by selecti ng features within the NWI data layer (Source: FGDL) attribute table denoted as uplands and export the selected features into a separate layer. 3. Derive a hydric soils data layer by sel ecting soils within the SSURGO soils data layer (Source: FDEP) attribute table denot ed as hydric and export the selected features into separate layer. 4. Derive the hydric uplands da ta layer by intersecting the upl ands data layer with the hydric soils data layer. The result of the intersection creates a da ta layer of all the lands no longer considered to be wetlands but containing hydric soils, i.e. historic wetlands. 5. Derive a partially ditched or drained data layer by se lecting wetlands within the NWI data layer attribute table denoted as partially ditched or drained and export the selected features into a separate layer. 6. Search for hydric uplands a nd partially ditched or draine d wetlands in the historic flatwoods data layer (Figure 2-3) by sele cting by location features from the hydric uplands data layer and the partially ditched or drai ned data layer that are completely within the historic flatwoods da ta layer and export th e selected features into a separate layer. 7. Create a restorable land use data layer (Figure 2-4) by clipping the 1995 SJRWMD and SFWMD land use data layers (Source: FGDL) to the historic flatwoods data

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21 layer and selecting features from the cli pped data layer attrib ute table denoted as one of the restorable land uses described above and export the sele cted features into a separate layer. 8. Create an unrestorable land use data layer (Figure 2-4) by selecting features from the clipped data layer (from step 7) at tribute table not denoted as one of the restorable land uses described above and expor t the selected featur es into a separate layer. 9. Derive a RFW data layer for the hydric upl ands and partially ditched or drained wetlands in the historic flatwoods area (Figure 2-5) by selecting by location hydric uplands and partially ditched and drained wetlands that in tersect the restorable land use layer. Then remove from the selected features, the features that intersect the unrestorable land use data layer and export the selected features into a separate layer. 10. Clip 1995 SJRWMD and SFWMD land use to the RFW data layer and utilize the resultant data layer as the base layer for weighted average approach. The new attribute table displays land uses for each of the RFW and new fields are added to create categories for ranking. Table 21 through 2-4 display a summary of the land uses of the restorable freshwater wetla nds. [Mesic flatwoods, a type of pine flatwood, and improved pastures span the most acreage of all the land uses for restorable freshwater wetlands.] Wet land use hydric uplands correction. The final RFW data layer requires two corrections. The first correc tion includes deletion of hydric uplands with wet land uses (Figure 2-6). Hydric uplands classified as a wet land use (i.e., wetlands or water bodies) can not be restored to provide additional storag e or water quality benefits. Differences in classifications of wetlands from the NWI and the SJRWMD and SFWMD land use data layers contributed to some of the hydric uplands being classified as a wet land use. The wet areas taken out of the fi nal restorable freshwater we tland coverage are generally small slivers on the side of an existing ve getated wetland. Wet area hydric uplands may stem from the differences in delineation of the wetlands by different agencies or from errors due to manual digitizing. According to the FGDL metadata, SJRWMD digitize the land use data layer using aerial photography from the 1994 National Aerial Photography Program (NAPP) and combine with soils, land parcels, and FEMA floodplains for

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22 Figure 2-2. Historic flatwood s earch area within the contribu ting surface water basins in the central and southern z ones (Sources: FGDL; SJRWMD).

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23 Figure 2-3. Hydric uplands and partially ditched or draine d wetlands in the historic flatwood search area (Sources: FDEP; FGDL).

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24 Figure 2-4. Restorable and unres torable land uses in the hi storic flatwood search area (Source: FGDL).

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25 Figure 2-5. Restorable freshwat er wetlands in the historic flatwood search area (Sources: FDEP; FGDL).

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26 Table 2-1. Land-use acreage for SJRWMD hydric uplands. Land use of SJRWMD hydric uplands Sum acres Mesic flatwoods 1506.68 Improved pastures 1157.96 Shrub and brushland rangeland 676.65 Wet prairies 426.74 Mixed rangeland 409.41 Freshwater marshes 408.50 Herbaceous rangeland 405.94 Mixed scrub-shrub wetland 352.39 Wetland forested mixed 323.29 Harwood-conifer mixed 237.03 Woodland pastures 85.70 Mixed wetland hardwoods 74.38 Cypress 56.27 Wetland coniferous forest 43.16 Unimproved pastures 25.43 Upland hardwood forests 18.01 Mangrove swamps 12.95 Xeric oak 12.00 Forested depressional pine 11.72 Abandoned tree crops 10.27 Forest regeneration 9.71 Saltwater marshes 8.91 Sand pine 8.79 Cabbage palm savanna 7.04 Table 2-2. Land-use acreage for SJRWMD partially ditched or drained wetlands. Land use of SJRWMD partially ditched or drained wetlands Sum acres Improved pastures 646.18 Freshwater marshes 555.72 Wet prairies 408.68 Mixed scrub-shrub wetland 269.22 Mesic flatwoods 237.64 Herbaceous rangeland 170.44 Forest regeneration 131.04 Shrub and brushland rangeland 97.47 Woodland pastures 57.73 Wetland forested mixed 50.10 Mixed rangland 35.58 Wetland coniferous forests 31.23 Mangrove swamps 21.93 Saltwater marshes 16.88

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27 Table 2.2. Continued Land use of SJRWMD partially ditched or drained wetlands Sum acres Hardwood-conifer mixed 14.77 Cypress 13.67 Unimproved pastures 10.01 Lakes 6.81 Non-vegetated 5.20 Upland hardwood forests 3.75 Abandoned tree crops 0.67 Table 2-3. Land-use acreage for SFWMD hydric uplands. Land use of SFWMD hydric uplands Sum acres Improved pasture 145.03 Pine flatwoods 68.98 Wetland hardwood forests 33.87 Vegetated non-forested wetlands 21.79 Upland hardwood forests, other hardwoods 18.20 Wetlands 3.99 Upland forests, other pines 3.40 Water 3.20 Vegetated non-forested wetlands, saltwater marshes 0.00 Table 2-4. Land-use acreage for SFWMD pa rtially ditched or drained wetlands. Land use of SFWMD partially ditched or drained wetlands Sum acres Improved pastures 412.35 Vegetated non-forested wetlands 32.52 Upland hardwood forests, other hardwoods 5.95 Upland forests, pine flatwoods 4.82 Vegetated non-forested wetlands, saltwater marshes 0.61 Water 0.39 delineation of the land use areas. The devel opers of the SJRWMD land-use data layers compare the layers with NWI, SJRWMD wetland vegetation, and 1990 land-use/landcover data sets to correct for any major di fferences. The NWI data layer utilizes NAPP aerial photography along with soil survey s and field verification of wetland photo signatures to manually digiti ze or scan wetland boundaries.

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28 Figure 2-6. Restorable freshw ater wetlands corrected for wet areas and 2000 land use (Sources: FDEP; FGDL; SJRWMD).

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29 1995 to 2000 land-use correction. The second correcti on includes updating the RFW data layer for the 2000 SJRWMD land use. The initial analysis for the identification of RFW utilized 1995 SJRWMD and SFWMD land-use data layers (Figure 2-7). Newer (2000) land use data for the por tion of the watershed in the SJRWMD area became available after the completion of initia l identification of RFW. For the purposes of updating the land uses from the initial an alysis, the correction eliminates any RFW intersecting an “unrestorabl e” 2000 SJRWMD land use (Figure 2-8). As stated by the 2000 SJRWMD land use metadata, SJRWMD provides the 2000 land use data layer under the following condition: “data in this layer may change subject to internal review by SJRWMD project area staff.” Weighted average approach The next step in this approach uses the GIS attribute tables of the RFW data layer to rank selected features based on wetland f unctions and other factors. Rankings are based on a 0 to 10 scale. A ranking of 10 represents RFW that will provide the most benefits to water quality, wate r quantity, wildlife habitat, or ease of restoration if restored. The opposite is true for a ranking of 1. The small size of the individual RFW create the need for using a vector-based a pproach for conserving the shape and utilizing the attribute table to assign rankings for several categories for each feature. Two scenarios govern the ranking of the categories. The first scenario only examines lands available for acquisition and does not consid er lands currently under some type of conservation management. The lands for ac quisition scenario re quires that all RFW existing completely on lands currently mana ged for conservation be removed from the ranking. This step utilizes the Florida Ma naged Areas (FLMA) data layer from FNAI.

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30 Figure 2-7. 1995 SJRWMD and SFWMD land use (Source: FGDL).

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31 Figure 2-8. 2000 SJRWMD land use (Source: SJRWMD).

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32 The second scenario considers all lands in rank ing of categories. Figure 2-9 demonstrates the steps involved in ranking the categorie s of the RFW for the two scenarios. The categories for ranking the RFW include water qu antity benefits, water quality benefits, conservation value, and ease of restoration. Water Quantity. The water quantity category receives rankings based on additional storage area provided by restoring th e RFW. The RFW attribute table stores the rankings for the water quantity ca tegory in one additional field named “Water_Quant.” The steps for ranking the RFW ba sed on water quantity include 1. Finding total area of the hydric uplands in each contributing surface water basin (Area_HU). 2. Finding total area of the part ially ditched or drained we tlands in each contributing surface water basin and dividing each area by 2 (Area_PDD). Partially ditched or drained wetlands consist of existing wetlands with a re duced storage potential due to draining or ditching. Partially ditche d or drained wetlands will not provide as much contribution to storage since they al ready contribute some storage value. Therefore each total area is divided by 2 to account for the lower additional storage potential provided as compared to the hydric uplands. 3. Summing Area_HU and Area_PDD in each contributing surface water basin and dividing by each corresponding contributing surface water basin area and then multiplying this value by 100 (Area_RFWSWB). 4. Comparing and ranking each contributing surface water basin based on their Area_RFWSWB value. Table 2-5 displa ys the ranking each contributing surface water basins receives according to its value for Area_RFWSWB. 5. Ranking each RFW based on which contribu ting surface water basin it resides in and the corresponding ranking of that basin. This technique provides the same ranking to all RFW in the same contributing surface water basin and does not depend on the size of the RFW. A clustering effect governs the rankings and RFW in contributing surface water basins with the highest

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33 Figure 2-9. Flow chart of the two scen arios for identifying and ranking RFW. RFW Correct for RFW with land use chan g es from 1995 to 2000 Correct “Hydric Uplands” that were in a Wet Land Use 2 Scenarios which consider Water Quality, Water Quantity, Conservation Value, and Ease of Restoration Lands for Acquisition: Remove all Managed Areas Consideration of all Lands Ease of Restoration Water Quality Conservation Value Water Quantity Habitat/ Species/ Adjacency Drainage Density Land Use Proximity to IRL Targets Total Area of RFW in each surface water basin divided by the area of each surface water basin Ease of Restoration Managed Area Drainage Density Drainage Density

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34 Table 2-5. Rankings for each basin based on Area_RFWSWB. Ranking for each basin Range of Area_RFWSWB (%) 1 0-2.86 2 2.860001-5.72 3 5.720001-8.58 4 8.580001-11.44 5 11.440001-14.3 6 14.300001-17.15 7 17.150001-20.01 8 20.010001-22.87 9 22.870001-25.73 10 25.730001-28.60 density of RFW are ranked the highest. Ranking the RFW based on individual size is impractical due to the configuration of the RFW layer being based on land use. Water Quality Benefits. The water quality benefits category receives rankings based on two factors: land use and drainage de nsity. The RFW attrib ute table stores the rankings for the water quality category in two additional fields named “Land Use” and “Dd.” The first factor, land use, receives rankings based on the land use of each of the RFW and amount that the land use contribut es to non-point sour ce pollution using the relationships defined in Adamus & Berg man (1993) for total nitrogen (TN), total phosphorus (TP), biochemical oxygen demand (B OD), and suspended solids (SS). Table 2-6 shows the non-point source pollutant loads based on land use utilized to derive rankings. In the Adamus & Bergman (1993) st udy “pollutant loads were not calculated for the natural areas land use category because the intent of the model was to estimate nonpoint loads resulting from human influences ” (p. 16). Natural areas in this study are treated in the same manner and are cons idered to be the background condition. The land use category of abandoned tree crops did not appear in the Adamus & Bergman (1993) study. This analysis assu mes abandoned tree crops provide less nonpoint source pollution than th e other agricultural lands uses and assumes that abandoned

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35 tree crops may still release residual nutrient s and pesticides that may produce additional non-point source pollution as compared to natu ral areas. Based on the overall trends in Table 2-6, each of the RFW receive the following rankings listed in Table 2-7. Table 2-6. Concentration-based pollutant load s (mg/l) obtained from Adamus & Bergman (1993). Land use TN TP BOD SS Improved pasture 2.48 0.480 3.83 55.3 Row crops 2.68 0.420 3.83 55.3 Citrus 2.05 0.140 3.83 55.3 Rangeland, unimproved and woodland pasture 1.25 0.053 1.45 11.1 Natural areas 0 0 0 0 Table 2-7. RFW rankings based on land use. Land use of the RFW Ranking Improved pasture 10 All rangeland, unimproved past ure, woodlands pasture 5 Abandoned tree crops 2 All natural areas 0 The second factor, drainage de nsity, receives rankings ba sed on location of each of the RFW and the density of canals or ditche s at that location. Gatewood and Bedient (1975) define drainage density as “a measure of the total length of waterways per unit area” (p. 1). For this study, drainage density applies only to length of canals and ditches per unit area. Canal and ditches transpor t runoff and the corresponding water quality characteristics (Gatewood & Bedient, 1975). Bedient (1975) esta blishes a connection between drainage density and phosphorus loadings of watercourses and found for selected tributaries in the lower Kissimmee Ri ver Basin as the draina ge density increases so does the average phosphorus concentration. Higher dens ity ditching in agricultural areas allows for a faster transport of nutri ents to water bodies r eceiving the surface water runoff. The relationship between drainage de nsity and pollutant loading is also closely

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36 tied to land use and the corresponding amount of nonpoint source pollution that can be released (Gatewood & Bedient, 1975). The approach for ranking drainage dens ity based on water quality starts with deriving a drainage density data layer. Creation of the drainage density data layer utilizes raster based processing and the finest scale hyd rography data layers av ailable; the United States Geological Survey (USGS) 1: 24,000 scale line and polygon hydrography data layers (Source: FGDL). The steps for derivi ng a drainage density data layer include the following 1. Derive canals and ditches data layers (polygon and line) by selecting by attribute “canals and ditches” from USGS 1:24,000 hydrography polygon and line data layers and export the selected f eatures into a separate layer. 2. Clip the canals and ditches data layers to the contributing surface water basins. 3. Convert the canals and ditche s data layer (polygon and lin e) to raster files using Spatial Analyst extension in ArcGIS . 4. Convert the canals and ditches raster file (polygon and lin e) to a point data layer using Spatial Analyst and merge the two files. 5. Create a drainage density raster file by performing the “Density” operation (simple type) in Spatial Analyst on the ditches and canals point data layer and apply a search radius of 500 meters. Accordi ng to the “Help Menu” in ArcGIS the “Density” operation distributes the amount of points over a unit area to derive a continuous raster file. The search radius defines the distance to look for points for calculation of a density value for the individu al raster cells in the output raster. The best suited search radius is determined by several trials of th e density operation. Each raster cell represents the numbe r of points per square kilometer. 6. Convert the drainage density raster file fr om floating point raster to integer raster using raster calculator to facilitate conversi on to polygon data layer. 7. Convert the drainage density integer raster file to polygon data layer using Spatial Analyst. 8. Symbolize the drainage density polygon da ta layer by quantities using quantiles with each density range symbolized w ith a different color (Figure 2-10). 9. Select all the polygon features in each density range a nd dissolve each density range into a separa te data layers.

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37 Figure 2-10. Representation of drainage de nsity in the central and southern zones watershed (Sources: FGDL; SJRWMD)

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38 The method for determining drainage density is biased towards canals or ditches that run diagonally. Canal or ditche s running diagonally contain more points per length as compared to canals or ditches that run horizon tally or vertically due to the method Spatial Analyst converts the line and polygon data layer to raster and then to a point data layer. Therefore areas with diagona l running canals or ditches maintain higher drainage densities. The effect of di agonal canals and ditches must be considered when utilizing the drainage density data laye r but the effect is minimal b ecause the amount of diagonal canals and ditches is small compared to the am ount that run vertically or horizontally (see Figure 2-10). Drainage density affects both the water qua lity benefits and ea se of restoration categories. In reference to the water quality benefits category, RFW residing in a high drainage density provide grea ter water quality benefits if restored. Therefore high drainage density RFW would receive the highes t rankings. However, the relationship of ease of restoration to drainage density is i nversely related to the relationship of water quality to drainage density. The ease of re storation section of this study discusses a ranking that considers both water qua lity benefits and ease of restoration. Conservation Value. The conservation value categor y receives rankings based on two factors: habitat, specie s, and adjacency to current conservation lands value and proximity to IRL targets. The RFW attr ibute table stores the rankings for the conservation value category in two additional fields. The first factor, habitat, species, and adjacency value, receives rankings ba sed on whether the RFW reside in areas considered to have some type of conservati on value or adjacent to areas in conservation management. This analysis determines conser vation value by utilizi ng several data layers

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39 including FNAI Florida Managed Areas (FLMA) data layer, TNC Pe ninsular Ecoregional Plan (TNCPEP) data layer, Florida Forever Land Acquisition Projects Board of Trustees (FFBOT) data layer, and several data layers from the Florida Forever Conservation Need Assessment representing biodiversity (Figur e 2-11; Figure 2-12). According to the metadata, the FLMA data laye r (Source: FNAI) includes the boundaries and statistics for any land in Florida containing “natural resource value” and be ing managed for some type of conservation practice by federal, st ate, local, or private agencies. According to the Draft Florida Peninsul a Ecoregional Plan, TNCPEP data layer (Source: TNC) contains “368 portfolio sites (or Areas of Biodiversity Conservation Significance)” in the study area boundaries (TNC & UF Geoplan Center, 2001, p. i). The plan selects 367 species and habitat targets for the ecoregional analysis to derive the portfolio sites. The plan considers viabil ity and connectivity of habitats (TNC & UF Geoplan Center, 2001). FFBOT data layer (Source: FNAI) contains the existi ng approved project-site boundaries for the Florida Forever program. The Florida Forever program defines a series of goals and lists performance m easures upon which success can be measured. Prior to approval, the State’s Acquisition and Restoration Council eval uate potential sites based on 34 performance measures . The FFBOT data layer pr oposed project sites may or may not be currently in conservation manage ment. According to the FFBOT metadata, “these lands have been proposed for acquisiti on because of outstanding natural resources, opportunity for natural resource-based recr eation, or historical and archeological resources.”

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40 Figure 2-11. Map 1 of conservation data laye rs in the flatwood search area (Sources: FGDL; FNAI; TNC).

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41 Figure 2-12. Map 2 of conservation data laye rs in the flatwood search area (Sources: FGDL; FNAI).

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42 This analysis uses several biodiversity da ta layers utilized in the Florida Forever Conservation Needs Assessment Technical Repo rt (FNAI, 2001). The technical report describes the data layers crea ted for 15 of the 34 performan ce measures identified for the Florida Forever program. These data layers aid in evaluation of proposed Florida Forever project sites. Goal B of th e Florida Forever program invol ves increasing biodiversity and four of the fifteen data layers are designed to measure the progress of this goal. The four data layers (Source: FNAI) associated with Goal B include Flor ida Fish and Wildlife Conservation Commission (FFWCC) Strate gic Habitat Conservation Areas (SHCA), FNAI Priority Conservation Areas for Rare Species, Prioritized Ecological Greenways, and Under-represented Natural Communities (FNAI, 2001). The report, Closing the Gaps in Florida’s Wildlife Habitat Conservation System, identifies SHCA for the state of Florida (Cox et al., 1994). SHCA are “habitat areas in Florida that should be conserved if the key components of the State’s biological di versity are to be maintained” (Cox et al., 1994, p. 1). The report utilizes LANDSAT sate llite imagery and various habitat models to define SHCA (Cox et al,. 1994). FNAI de velops the Priority Conservation Areas for Rare Species data layer from rare and e ndangered species occurre nce point data (FNAI, 2001). FNAI specifies vital ha bitat areas around these point da ta. Prioritized Ecological Greenways data layer comes from the Ecol ogical Greenways Network of the Statewide Greenways System Planning Project. Green ways provide a “system of landscape hubs, linkages, and conservation corri dors…developed by the Universi ty of Florida using a GIS decision support model” (FNAI, 2001, p. 32). The Under-represented Natural Communities data layer contains community types poorly represented in on existing

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43 conservation lands. This data layer util izes DavisÂ’s 1967 General Map of Natural Vegetation of Florida (FNAI, 2001). The ranking process considers all of the above described data layers as having some type of conservation value and equal ra nking is given to RFW residing in any of the above described data layers. However, RF W receive a higher ranking if they reside within a 3-km radius adjacent to an area cu rrently managed for conservation (FLMA) and within one of the above described conserva tion areas. Adjacency is important for the dispersal of species (Cox et al., 1994). For dete rmination of the 3-km radius this analysis utilizes the element occurrence (EO) data layer (Source: FNAI) and the report Closing the Gaps in FloridaÂ’s Wildlife Habitat Cons ervation System (Cox et al., 1994). FNAI maintains a point data layer of all the occurr ences of rare plants and animals in the EO data layer. The EO data layer shows o ccurrences of the Florida Sandhill Crane ( Grus canadensis pratensis ), Red-cockaded Woodpecker ( Picoides borealis ), and Southern Bald Eagle ( Haliaeetus leucocephalus ) in the flatwood search area. Cox et al. (1994) specifies a 3-km radius as the neighborhood ar ea these species might visit over the course of a year. Up to this point, rankings for th e categories have been treated the same for the lands for acquisition scenario and the consid eration of all lands acenario. Ranking of habitat, species, and adjacency value for the two scenarios ra nkings differ. Table 2-8 and Table 2-9 demonstrate the rankings used. The second factor, proximity to IRL targ ets, receives rankings based on whether the RFW reside near or within a drainage ba sin draining to the location of an IRL target species or community. This factor emphasizes some of the IRL target species developed from the TNC Site Conservation Plans (SCP). The SCP identify focal species or

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44 Table 2-8. Habitat, species, and adjacency value rankings for th e land for acquisition scenario. Description of habitat, species, and adjacency value Ranking RFW adjacent to and within 3-km buffer around the FLMA boundary and intersect TNCPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers. 10 RFW intersect TNCPEP, FFBOT, SHCA, C onservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers and outside of 3-km buffer around FLMA boundary. 5 RFW adjacent to and within 3-km buffer around the FLMA boundary and do not intersect TNCPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers. 2 RFW do not intersect TNCPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers and outside of 3-km buffe r around the boundary of FLMA. 0 Table 2-9. Habitat, species, and adjacency value rankings for the consideration of all lands scenario. Description of habitat, species, and adjacency value Ranking RFW adjacent to and within 3-km buffer around the FLMA boundary and intersect TNCPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers. 10 RFW intersect TNCPEP, FFBOT, SHCA, C onservation Area for Rare Species, Greenways, Under-represented Natural Communities, or FLMA data layers and outside of 3-km buffer around FLMA boundary. 5 RFW adjacent to and within 3-km buffer around the FLMA boundary and do not intersect TNCPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, Under-represented Natural Communities, or FLMA data layers. 2 RFW do not intersect TNCPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, Under-represented Natural Communities data layers, or FLMA and outside of 3-km buf fer around the boundary of FLMA. 0 communities, rank them based on viability, and determine the level and sources of stress to the species or community. This analysis utilizes only IRL targ ets represented by the EO data layer including maritime hammocks, pine flatwood/wetland complex, coastal scrub, Florida scrub jay (Aphelocoma coerules cens), and manatee aggregation zones. This analysis also includes opossum pipefish (Mycrophis brachyurus lineatus) in addition to the IRL targets listed due to the importance of the IRL as a breeding habitat for this species. Gilmore (1999) states that “ambient water temperatures and predictable ocean

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45 current access limit effective breeding of opossum pipefish populations to the Loxahatchee, St. Lucie and St. Sebastian rivers of the Indian River Lagoon” (p. 2). This analysis utilizes the EO point data layer representing the IRL targets and the opossum pipefish, land-use data layers, and SHCA to determine whether the RFW reside in a location near an IRL target. The analysis utilizes the following resources to determine whether the RFW fall within a drainage ba sin draining to an a quatic target: USGS 1:24,000 hydrography (polygon and line) data laye r, SJRWMD Surface Water Basin data layer, and descriptions of surface drainage sub-basins in Steward and VanArman (1987). RFW found meeting the criteria of residing near or draining to an IRL target or opossum pipefish received a ranking of 10. Ease of Restoration. The ease of restoration ca tegory receives rankings based on drainage density and whether the RFW reside in an area managed for conservation. The ease of restoration category differs for the two scenarios. The lands for acquisition scenario bases ease of restoration only on drainage density. This study assumes that higher drainage density lands (i .e. highly ditched and drained lands) are harder to restore than lower drainage density lands (i.e. le ss ditched and drained lands). Ditching and draining lowers the water table (IRLNEP, 1996). In a heavily ditched and drained area, restoration would require filli ng in more ditches and drains to restore the water table. This study assumes more ditches and drains make restoration efforts harder to perform. The water quality section describes the creation of the drainage density data layer. As previously discussed drainage density influe nces water quality and eas e of restoration in an inverse relationship. Since only one field represents drai nage density, this study ranks drainage density considering both water quality benefits and ease of restoration. The

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46 highest ranking is given to RFW residing in medium range drainage densities and the lowest ranking is given to RFW residing on the highest and lowest drainage density ranges. Table 2-10 displays the ease of restoration rankings given to RFW based on drainage density. Table 2-10. Ranking based on drainage density. Drainage density range (No. points/sq. km) Ranking 68-91 10 50-67; 92-118 8 31-49; 119-161 6 1-30; 162-224 4 0; 225-671 2 The consideration of all lands scenario bases ease of re storation on drainage density and whether the RFW reside in an area manage d for conservation. This scenario applies the rankings for drainage de nsity using the same method as the lands for acquisition scenario. Additionally, this study assumes that restoration efforts will be easier if the RFW reside in an area curre ntly being managed for conservation purposes because additional property or conservation easements w ill not have to be purchased. FLMA data layer is used to determine whether the RFW reside in an area managed for conservation. RFW receive a ranking of 10 if they are completely within a FLMA. Weighting of Categories. The last step of the central and southern zones analysis involves weighting and combining the values of all the ranked fields to obtain an overall weighted ranking which is used to identif y RFW with the greatest potential for restoration. Calculating the overall weighted ranking includes adding a field to the RFW attribute table and using the “calculate values ” operation to input a weighting formula. Depending on the importance of the categories different weightings can be applied to emphasize a particular category. Discussion with TNC staff indicate that the water

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47 quality and water quantity benefits shoul d be of most importance and receive approximately 80% of the weighting while the conservation value category should receive 20% of the weighting. Table 2-11 and Table 2-12 show an example for calculating an overall weighted ranking for th e two scenarios using an equal-weight Table 2-11. Example of overall ranking for the lands for acquisition scenario. Dd Land use Habitat/ species/ adjacency (HSA) Proximity to IRL targets Water quantity Overall ranking Ranking 6 10 2 10 3 -Equal weight 0.375 0.125 0.125 0.125 0.25 7 TNC weight 0.3 0.2 0.19 0.01 0.3 5.18 Table 2-12. Example of overall ranking for the consideration of all lands scenario. Dd Land use Habitat/ species/ adjacency (HSA) Proximity to IRL targets Water quantity FLMA Overall ranking Ranking 10 10 5 10 3 10 -Equal weight 0.25 0.125 0.125 0.125 0.25 0.125 7.625 TNC weight 0.3 0.2 0.15 0.01 0.3 0.04 7.15 ranking scheme based on categories and vari ed weightings approach based on TNCÂ’s suggestions. The equal-weight ranking scheme applies rankings based on 25% contribution by each of the four categories. The TNC-weight ranking scheme applies weights of 35% to water quality, 30% to wate r quantity; 20% to conservation value, and 15% to ease of restor ation for the lands for acquisiti on scenario. The TNC-weight ranking schemeapplies weights of 35% to wa ter quality, 30% to water quantity, 16% to conservation value, and 19% to ease of rest oration for the consideration of all lands scenario. Figures 2-13 and 2-14 show the conne ction between the four categories and the ranked fields.

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48 Figure 2-13. Categories and associated ranke d fields for the lands for acquisition scenario. Figure 2-14. Categories and associated ranked fields for the consid eration of all lands scenario. Northern Zone Analysis The northern zone analysis focuses on wa ter quality and util izes some of the techniques developed in the central and southern zones an alysis. The first of two analyses performed on the northern zone determ ines the degree of pr otection of riparian zone wetlands surrounding freshwater inputs to the IRL and the second analysis examines ways to reduce non-point source pollution to the IRL. Ease of Restoration Water Quality Conservation Value Water Quantity Habitat/ Species/ Adjacency Land Use Proximity to IRL Targets Total Area of RFW in each surface water basin divided by the area of each surface water basin (Water Quantity field) Drainage Densit y FLMA Ease of Restoration Water Quality Conservation Value Water Quantity Habitat/ Species/ Adjacency Land Use Proximity to IRL Targets Total Area of RFW in each surface water basin divided by the area of each surface water basin (Water Quantity field) Drainage Densit y

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49 Protection of riparian zone buffer areas analysis The protection of riparian zone buffer ar eas analysis determines the degree of protection to riparian zone wetlands in the northern zone of the IRL. Mitsch and Gosselink (2000) define ri parian wetlands as ecosystems in which soils and soil moisture are influenced by the adjacent stream or river, are unique because they process large fluxes of energy and materials from upstream systems. (p. 513) Riparian zones act as filters for nutrients a nd sediment and provide habitat and “corridors for migration” for many animals (Mitsch & Gosselink, 2000, p. 513). This analysis utilizes FFBOT, FLMA, 1:24,000 USGS Hydrography, and 2000 SJRWMD land use data layers to determine if the riparian we tlands of the northern zone are in conservation management or are in an area planned fo r conservation management. The steps to accomplish this task include 1. Deriving a stream data layer by selecting by attributes all the stream features from the 1:24,000 USGS hydrography data layer. 2. Identifying streams discharging to th e IRL and overlay the 2000 SJRWMD landuse data layer to identify any ripari an zones adjacent to these streams. 3. Overlaying the FLMA and FFBOT data layers on the riparian zones to determine whether each zone is currently in conserva tion management or is part of an FFBOT project. 4. Creating map of unprotected riparian zone wetlands. Non-point source pollution reduction analysis Large portions of the northern zone’s narro w watershed contain agricultural lands directly adjacent to the IRL (Figure 2-15). Agricultural dom inated sub-basins produce 1.5 times higher non-point source pollutant lo adings than largely undeveloped areas (IRLNEP, 1996). The potential to redu ce non-point source pollution exists if unproductive agricultural lands ar e taken out of production by willing landowners and put

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50 Figure 2-15. 2000 SJRWMD land use in the northern zone watershed (Sources: SJRWMD).

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51 into conservation/restoration management. Th ese agricultural lands could be targeted for conversion to urban development due to the prime location of many of the agricultural lands being adjacent to the IRL. Urban non-point source pollutant loadings greatly exceed agricultural dominated watersheds pollu tant loadings. Limited mixing in the IRL makes the northern zone highly sensitive to non-point source pollutants (IRLNEP, 1996). Therefore identifying potential agricultural ar eas for restoration becomes critical to reducing non-point source pollution. Rest ored agricultural lands can also supply additional habitat for flora and fauna. Th e non-point source polluti on reduction analysis utilizes the weighted average approach de veloped in the central and southern zones analysis to identify and rank agricultural lands based on pot ential water quality benefits and habitat value that could be provid ed if restored to natural conditions. The first step in this technique involv es identifying all potentially restorable agricultural lands including cr oplands, pastureland, rangeland , tree crops, and other rural open lands. This step utilizes the 2000 SJRWMD land use data layer and involves creating an agricultural data layer (Figure 216) by selecting all lands classified as cropland, pastureland, rangeland, tree crops, and other rural open lands and exporting the selected features to a separate layer. Table 2-13 summarizes the land uses from the agricultural data layer. The next step follows a similar technique to the central and southern zones analysis and uses the GIS attr ibute table of the agricultural data layer to rank selected features based on two categorie s: water quality bene fits and conservation value. Rankings are based on a 0 to 10 scal e. A ranking of 10 re presents agricultural lands that will provide the most benefits to water quantity and wildlife habitat if willingly restored or taken out of production. The opposite is true for a ranking of 1.

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52 Figure 2-16. Agricultural land use in the northern zone watershed (Source: SJRWMD).

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53 Table 2-13. Land use acreage for agricultura l land uses of the northern zone. Agricultural land use of the northern zone Sum Acres Citrus groves 6876.75 Shrub and brushland rangeland 5023.60 Herbaceous rangeland 1648.27 Mixed rangeland 1249.23 Abandoned tree crops 1143.72 Improved pastures 968.91 Field crops 673.48 Woodland pastures 175.65 Row crops 94.34 Unimproved pastures 73.22 Tree crops 15.28 Fallow cropland 11.41 Three factors influence the water quality category including land use, drainage density, and size of agricultural land. The agricu ltural data layer attr ibute table stores the rankings for the water quality category in three additional fields named “Land Use”, “Dd”, and “Size.” The first factor, land use, receives rankings based on the amount that the land use contributes to nonpoint source pollution using th e relationships defined in Adamus and Bergman (1993). Table 2-6 in the central and southe rn zones analysis section shows the non-point source pollutant lo ads based on land use utilized to derive the rankings. Several land use categories incl uding abandoned tree crops, field crops, tree crops, and fallow cropland did not appear in the Adamus and Bergman (1993) study. This step ranks abandoned tree crops similarly to the central and sout hern zones analysis. Field crops are considered to produce non-poi nt source pollutant loads similar to row crops. The Florida Land Use, Cover, a nd Forms Classification System (FLUCCS) Handbook defines fallow cropland as “harvested agricultural land not currently in crop production” (Florida Department of Transportation [FDOT], 1999, p. 26). Therefore, this

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54 step ranks fallow cropland similarly to row crops. Based on the overall trends in Table 26, each agricultural land use receives the rankings listed in Table 2-14. Table 2-14. Agricultural land use rankings. Land use Ranking Improved pasture 10 Row crops 10 Fallow cropland 10 Field crops 10 Citrus groves 7 Tree crops 7 Woodlands pasture 5 Unimproved pasture 5 Abandoned tree crops 2 The second factor, drainage density, receives rankings based on the density of canals or ditches in each agricultural land use. As previously disc ussed higher drainage densities increase the transportation rate of pollutants to the IRL and provide greater benefits to water quality as compared to lower drainage densities if agricultural areas are restored. The northern zone utilizes the sa me technique developed in the central and southern zones analysis to deri ve a drainage density data la yer. Figure 2-17 displays the drainage density data layer for the northern zone. The northern zone technique uses a search radius of 400 meters and each rast er cell represents th e number of points per square kilometer. However, the ranking fo r drainage density differs between the two analyses. The northern zone does not cons ider the ease of rest oration due to less extensive ditching and absence of managed areas in the agricultural data layer. Therefore, this step bases the ranking of dr ainage density solely on water quality. Higher drainage densities receive hi gher rankings and lower draina ge densities receive lower rankings. The ranking technique involves selecting features from the agricultural data

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55 Figure 2-17. Representation of drainage dens ity in the northern zone (Sources: FGDL; SJRWMD)

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56 layer that have their centers in each density range and assign ing the associated ranking as displayed in Table 2-15. Table 2-15. Rankings based on drainage density range. Drainage density range (No. points/sq. km) Ranking 160-185 10 114-159 9 86-113 8 66-85 7 52-65 6 34-51 5 16-33 3 1-15 1 0 0 The third factor, size (i.e., area of the agri cultural land uses), a ffects the amount of non-point source pollution. This analysis assumes larger agri cultural land uses correspond to a larger amount of non-point source pollution. The largest agricultural lands receive the highest rankings based on a gr eater potential to impact to water quality conditions if the agricultural lands are restor ed or taken out of production. This step involves developing an “Acres ” field in the attribute table and symbolizing the agricultural data layer based on several si ze ranges of the “Acres” field. The GIS extension X-Tools calculates the area, acres, an d perimeter of any feature and adds a field for each to the attribute table. Table 2-16 displays the rankings corresponding to the size range of the features of th e agricultural data layer. Table 2-16. Rankings based on size range. Size range (acres) Ranking 600-750 10 450-599 8 300-449 6 150-299 4 0-149 2

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57 The conservation value category receives ra nkings based on habitat, species, and adjacency value. The agricultural data layer attribute table stores the rankings for the conservation value category in one additional field. Figures 2-18 and 2-19 show the conservation data layers in the northern zone watershed. This step utilizes a similar technique to the central and s outhern zone analysis. Howeve r, this step applies only the lands for acquisition scenario since very fe w agricultural lands intersect areas managed for conservation. The northern zone also ut ilizes the EO data la yer to determine the adjacency radius. The EO data layer shows several occurrences of the Southern Bald Eagle ( Haliaeetus leucocephalus ) in the northern zone. An ad jacency radius of 3 km is chosen due to the occurrence of the Southern Bald Eagle (Cox et al., 1994). Due to the long, slender shape and confi guration of the northern zone watershed, two levels of adjacency are established (1.5 km and 3 km). Table 2-17 demonstrates the rankings used. Table 2-17 Habitat, species, and adjacen cy value rankings for the northern zone. Description of habitat, species, and adjacency value Ranking Agricultural lands adjacent to and within 1.5-km buffer around the FLMA boundary and intersecting TNCPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers. 10 Agricultural lands adjacent to and within 3-1.5-km buffer around the FLMA boundary and intersecting TNCPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers. 7 Agricultural lands intersecting TN CPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers and outside of 3-km buffer around FLMA boundary. 5 Agricultural lands adjacent to and within 3-km buffer around the FLMA boundary and not intersecting TNCPEP , FFBOT, SHCA, Conservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers. 2 Agricultural lands not intersecting TNCPEP, FFBOT, SHCA, Conservation Area for Rare Species, Greenways, or Under-represented Natural Communities data layers and outside of 3-km buffer around the boundary of FLMA. 0

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58 Figure 2-18. Map 1 of conservation data layers in the northern zone watershed (Sources: SJRWMD; FNAI; TNC).

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59 Figure 2-19. Map 2 of conservation data layers in the northern zone watershed (Sources: FNAI; SJRWMD).

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60 The last step of the north ern zone analysis involves weighting and combining the values of all the ranked fields to obtain an overall weighted ranking as done in the central and southern zones analysis. The overall wei ghted ranking is used to identify agricultural lands with the greatest potential to impr ove water quality and conservation value if restored. Table 2-18 shows an example for calculating an overall weighted ranking for the northern zone non-point s ource reduction analysis using an equal weighting of both categories approach and varied weightings approach targeted for water quality. The equal weight approach gives a 50% weight to water quality and 50% weight to conservation value. The water quality target approach gives a 75% weight to water quality and 25% weight to conservation value. Table 2-18. Example of overall ra nking for the northern zone. Land use Dd Size Habitat/ species/ adjacency (HSA) Overall ranking Ranking 7 5 2 7 -Equal weight 0.167 0.167 0.167 0.5 5.83 WQ weight 0.25 0.25 0.25 0.25 5.25

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61 CHAPTER 3 RESULTS Central and Southern Zones Results Lands for Acquisition Scenario The lands for acquisition scenario co nsiders only lands not currently under conservation management (FLMA). Several GIS maps display the results of the RFW rankings and weightings for the lands for ac quisition scenario. Figures 3-1 through 3-5 show the RFW rankings based on water quant ity, water quality (land use and drainage density), conservation value (habitat, speci es, and adjacency and proximity to IRL targets), and ease of restoration (drainage dens ity). Figures 3-6 and 3-7 show the overallweighted ranking (equal weight and TNC weight) for the RFW. Consideration of all Lands Scenario This scenario considers all lands regard less of whether they are currently under conservation management. The next set of GIS maps display the results of the RFW rankings and weightings for the considera tion of all lands. Figures 3-8 through 3-13 show the RFW rankings based on water quant ity, water quality (land use and drainage density), conservation value (habitat, speci es, and adjacency and proximity to IRL targets), and ease of restoration (drainage density and FLMA). Figures 3-14 and 3-15 show the overall-weighted ranking (equal weight and TNC weight) for the RFW.

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62 Figure 3-1. Lands for acquisition scenario water quantity ranking (Sources: FDEP; FGDL; FNAI; SJRWMD).

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63 Figure 3-2. Lands for acquisiti on scenario water quality (l and use) ranking (Sources: FDEP; FGDL; FNAI; SJRWMD).

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64 Figure 3-3. Lands for acquisition scenario drainage density ranking (Sources: FDEP; FGDL; FNAI; SJRWMD).

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65 Figure 3-4. Lands for acquisition scenario conservation value (HSA) (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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66 Figure 3-5. Lands for acquisition scenario conservation value (proximity to IRL targets) (Sources: FDEP; FGDL; FNAI: SJRWMD; TNC).

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67 Figure 3-6. Lands for acquisition scenario equal weighting (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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68 Figure 3-7. Lands for acquisition scenario TNC weighting (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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69 Figure 3-8. Consideration of all lands wa ter quantity ranking (Sources: FDEP; FGDL; SJRWMD).

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70 Figure 3-9. Consideration of a ll lands water quality (land use) ranking (Sources: FDEP; FGDL; SJRWMD).

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71 Figure 3-10. Consideration of all lands drai nage density ranking (Sources: FDEP; FGDL; SJRWMD).

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72 Figure 3-11. Consideration of all lands conservation value (H SA) ranking (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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73 Figure 3-12. Consideration of all lands cons ervation value (proximity to IRL targets) ranking (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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74 Figure 3-13. Consideration of all lands eas e of restoration (F MLA) ranking (Sources: FDEP; FGDL; FNAI; SJRWMD).

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75 Figure 3-14. Consideration of all lands e qual weighting (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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76 Figure 3-15. Consideration of all lands TNC weighting (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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77 Northern Zone Results Protection of Riparian Zone Buffer Areas Analysis The first analysis in the northern zone includes identifying unprotected riparian buffer zones. Figure 3-16 shows the locati on of six areas identified as unprotected riparian buffer areas. Figures 3-17 through 322 show a zoomed-in view of each of the six unprotected riparian buffer areas. Non-point Source Reduction Analysis The non-point source reduction analysis identifies, ranks , and weights potential agricultural lands that coul d be restored to provide water quality benefits and conservation value. Several GIS maps disp lay the results of the agricultural lands rankings and weightings. Figures 3-23 thr ough 3-26 show the agricu ltural lands ranked for the water quality ranking (land use, drai nage density, and size) and conservation value (habitat, species, and adjacency). Figur es 3-27 and 3-28 show the overall-weighted ranking (equal weight and water quality weight) for the agricultural lands. Evaluation of the Central and Southern Zones Results Highest Overall Ranked RFW Lands for acquisition scenario This analysis considers RFW with an overall-weighted ranking greater than or equal to 7 as providing the greatest bene fits to water quantity, water quality, and conservation value and the easiest to rest ore. Figures 3-29 and 3-30 highlight in turquoise blue the highest overa ll-weighted ranked RFW (equal weight and TNC weight). The equal-weight ranking scheme produces th ree distinct clusters of RFW with an overall-weighted ranking over 7 (Figures 331 through 3-33). The TNC-weight ranking scheme produces only one distinct cluster of RFW with an overall -weighted ranking over

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78 Figure 3-16. Northern zone watershed unbu ffered stream locations (Sources: FGDL; FNAI; SJRWMD).

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79 Figure 3-17. Unbuffered stream 1 in the northern zone watershed (Sources: FGDL; FNAI; SJRWMD).

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80 Figure 3-18. Unbuffered stream 2 in the northern zone watershed (Sources: FGDL; FNAI; SJRWMD).

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81 Figure 3-19. Unbuffered stream 3 in the northern zone watershed (Sources: FGDL; FNAI; SJRWMD).

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82 Figure 3-20. Unbuffered stream 4 in the northern zone watershed (Sources: FGDL; FNAI; SJRWMD).

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83 Figure 3-21. Unbuffered stream 5 in the northern zone watershed (Sources: FGDL; FNAI; SJRWMD).

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84 Figure 3-22. Unbuffered stream 6 in the northern zone watershed (Sources: FGDL; FNAI; SJRWMD).

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85 Figure 3-23. Water quality (land use) ranking in the northern zone watershed (Sources: SJRWMD).

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86 Figure 3-24. Water quality (dra inage density) ranking in th e northern zone watershed (Sources: FGDL; SJRWMD).

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87 Figure 3-25. Water quality (size) ranking in the northern zone watershed (Sources: FGDL; SJRWMD).

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88 Figure 3-26. Conservation ranking (HSA) in the northern zone watershed (Sources: FNAI; SJRWMD; TNC).

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89 Figure 3-27. Equal weighting in the north ern zone watershed (Sources: FGDL; FNAI; SJRWMD; TNC).

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90 Figure 3-28. Water quality weighting in the northern zone watershed (Sources: FGDL; FNAI; SJRWMD; TNC).

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91 Figure 3-29. Lands for acquisition scenario (equal weighting): highest ranked overall RFW (Sources: FGDL; FNAI; SJRWMD; TNC).

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92 Figure 3-30. Lands for acquisition scenario (TNC weighting): highest ranked overall RFW (Sources: FGDL; FNAI; SJRWMD; TNC).

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93 Figure 3-31. Lands for acquisition scenario (equal weighting): clus ter1 (Sources: FGDL; FNAI; SJRWMD; TNC).

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94 Figure 3-32. Lands for acquisition scenario (equal weighting): clus ter 2 (Sources: FGDL; FNAI; SJRWMD; TNC).

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95 Figure 3-33. Lands for acquisition scenario (equal weighting): clus ter 3 (Sources: FGDL; FNAI; SJRWMD; TNC).

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96 7 and this cluster coincides with cluster 1 of the equal-weight ranking scheme (Figure 334). The TNC-weight ranking scheme gives hi gher weighting to water quality and water quantity but is highly skewed toward the dr ainage density field. The highest overall ranked RFW tend to reside on improved pasture with a drainage density of 6 or above. The equal-weight ranking scheme gives e qual weighting to the water quality, water quantity, conservation value, and ease of re storation categories and thus produces a broader range of results for the highest ranked RFW as long as the overall-weighted ranking is equal to or greater than 7. Consideration of all lands scenario Similarly to the land for acqui sition scenario, this scenar io highlights in turquoiseblue RFW with an overall-weighted ranking of greater than or equal to 7 (Figures 3-35 and 3-36). The equal-weight ranking scheme pr oduces one distinct cluster of RFW with an overall ranking over 7 (Figure 3-37) while the TNC-weight ra nking scheme produces two distinct clusters of RFW (Figures 3-38 and 3-39) with cluster 7 coinciding with the cluster 5 of the equal-weight ranking sche me. The equal-weight ranking scheme produces a variety of RFW with an overall rank ing equal to or greater than 7. Cluster 5 contains fewer higher ranked RFW than cl uster 7 due to the heavier weighting on drainage density and land use of the TNCweight ranking scheme. Many other improved pasture with higher drainage densities are in the higher overall ranking range in the TNCweight ranking scheme due to the heavier we ighting on water quality (Figure 3-38). The heavier weighting on water quality eliminates some RFW with lower rankings in the water quality category but with higher rankings in the other categories. An additional cluster of improved pastures appear in the TNC-weight ranking scheme as compared to the equal-weight ranking scheme also due to the heavier weigh ting on water quality.

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97 Figure 3-34. Lands for acquisition scenario (T NC weighting): cluster 4 (Sources: FGDL; FNAI; SJRWMD; TNC).

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98 Figure 3-35. Consideration of all lands scenar io (equal weighting): highest ranked overall RFW (Sources: FGDL; FNAI; SJRWMD; TNC).

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99 Figure 3-36. Consideration of all lands scen ario (TNC weighting): highest ranked overall RFW (Sources: FGDL; FNAI; SJRWMD; TNC).

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100 Figure 3-37. Consideration of all lands scen ario (equal weighting): cluster 5 (Sources: FGDL; FNAI; SJRWMD; TNC).

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101 Figure 3-38. Consideration of all lands scen ario (TNC weighting) : cluster 6 (Sources: FGDL; FNAI; SJRWMD; TNC).

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102 Figure 3-39. Consideration of all lands scen ario (TNC weighting) : cluster 7 (Sources: FGDL; FNAI; SJRWMD; TNC).

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103 Comparison of the two scenarios Several differences exist between the two scenarios. In the equal-weight ranking scheme, the consideration of a ll lands scenario produces fewe r and different clusters of RFW with overall rankings gr eater than or equal to 7 th an the lands for acquisition scenario. The consideration of all lands s cenario contains one additional ranked field based on whether the RFW reside in an area currently managed for conservation (FLMA). This additional FLMA field lessen s the weighting for drainage density and creates a need for the rankings in the other fiel ds to be higher to still remain equal to or over 7 in the overall-weighted ranking. This effect produces fewe r RFW equal to or greater than 7 in the consider ation of all lands scenario. In the TNC-weight ranking scheme, a similar situation occurs in cluster 4 of the lands for acqui sition scenario and in cluster 6 of the consideration of all lands scen ario due to the additional FLMA field in the consideration of all lands scenar io. A similar cluster to clus ter 5 of the consideration of all lands scenario does not occur in the lands for acquisition scenario because these RFW reside on lands currently managed for conser vation. In the TNC-weight ranking scheme, cluster 7 appears similarly to the reason clus ter 5 appears in the equal-weight ranking scheme. Aerial/Land Use Verification Comparison of the identified clusters with th e most current aerials verifies that the land uses of the RFW still remain in a “res torable” land use and checks the accuracy of the land use and hydrography data layers used in this analysis. FD EP provide a website for downloading 1999 DOQQ in Albers projec tion using HGPN datum (NAD83) (Land Boundary Information System [LABINS], 2003) . Figures 3-40 through 3-46 display the zoomed-in views of the seven clusters ove rlaying the 1-meter DOQQ. The turquoise-

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104 blue color outlines the RFW with overall-weig hted rankings over 7. All of the land uses from the aerials agreed with the SJRWMD land use data layers. Table 3-1 documents any additional differences in hydrography between the aerials and the USGS 1:24,000 hydrography data layer and any roads visible in the aerials that intersect the RFW. Table 3-1. Additional comments c oncerning aerial comparison of RFW. Additional comments Cluster 1 A Road intersects RFW and more intensive ditching occurs than USGS 1:24, 000 hydrography depicts B -Cluster 2 A More ditching occurs than USGS 1:24,000 hydrography depicts B Road intersects RFW C Appears that road intersects RFW Cluster 3 A -B -C -D -E -F -G -Cluster 4 A Road intersects RFW and more intensive ditching occurs than USGS 1:24, 000 hydrography depicts B -C More ditching occurs than USGS 1:24,000 hydrography depicts D Within 70 meters of major canal E -F -G Road intersects southern end of RFW and more intensive ditching occu rs than USGS 1:24,000 hydrography depicts Cluster 5 A Located adjacent to a major road B -C -D Road intersects RFW E Road intersects RFW

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105 Table 3-1. Continued Additional comments Cluster 6 A Road intersects RFW and more intensive ditching occurs than USGS 1:24, 000 hydrography depicts B -C Within 70 meters of major canal D -E -Cluster 7 A -B -C Road intersects RFW RFW with roads intersecting them may not be able to be restored. However if the road only intersects a small portion of large RF W, then part of the wetlands may be able to be restored. Some additional ditching appeared in the aerial when compared to USGS 1:24,000 hydrography. Therefore these areas ma y receive a different ranking if the additional ditching is considered in the drainage density calculations. Proximity to Major Canals The proximity of the RFW to major canal s affects the ability for RFW to be restored. Canals eliminate excess surface wa ter and lower the ground water table. Major canals drain greater expanses of the watershe d and federal and statew ide agencies govern their operation, so for these reasons they would be difficult if not impo ssible to restore. Therefore any RFW in any of the clusters inte rsecting, directly adjace nt, or within close drainage influence of these major canals need to be identified. This analysis identifies and creates a major canal da ta layer utilizing the USGS 1:24,000 hydrography data layer and Steward and VanArman (1987). Clusters 1, 4, and 6 reside in the same area and appear to be the only clusters close enough to a major canal to be affected. Figure 3-47 highlights the general area of clusters 1, 4, and 6 and shows the location of the major

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106 Figure 3-40. Lands for acquisition scenario (equal weighting): aer ial verification of cluster 1 (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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107 Figure 3-41. Lands for acquisition scenario (equal weighting): aer ial verification of cluster 2 (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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108 Figure 3-42. Lands for acquisition scenario (equal weighting): aer ial verification of cluster 3 (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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109 Figure 3-43. Lands for acquisition scenario (TNC weighting): aerial verification of cluster 4 (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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110 Figure 3-44. Consideration of all lands scenar io (equal weighting): ae rial verification of cluster 5 (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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111 Figure 3-45. Consideration of all lands scenar io (TNC weighting): aerial verification of cluster 6 (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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112 Figure 3-46. Consideration of all lands scenar io (TNC weighting): aerial verification of cluster 7 (Sources: FDEP; FGDL; FNAI; SJRWMD; TNC).

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113 Figure 3-47. Proximtiy of clusters 1, 4, a nd 6 to major canal (Sources: FGDL; FNAI; SJRWMD; TNC).

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114 canals. Table 3-2 documents the approximate distance of the RFW in clusters 1, 4,and 6 to the major canal. The approximate distan ce between the RFW and the major canal is determined using the measuring tool in Ar cGIS. No RFW from clusters 1, 4, or 6 directly intersect the major canal, howe ver, RFW D and C of cluster 4 and 6, respectively, are within very close proximity to the major canal and restoration of these RFW may be hindered. RFW D of cluster 4 and C of cluster 6 represent the same wetland ranked in two different scenarios. Table 3-2. Approximate dist ance of RFW to major canal. Approximate distance to major canal (meters) Cluster 1 A 1327 B 650 Cluster 4 A 1327 B 650 C 585 D 83 E 833 F 1080 G 2355 Cluster 6 A 1327 B 650 C 83 D 833 E 1080 Sensitivity Analysis on Weightings A sensitivity analysis is performed on the weighting schemes for the two scenarios in the central and southern zones. The se nsitivity analysis only investigates the SJRWMD portion of the central and southern zones since this area contains most of the RFW. The sensitivity analysis tests the e ffect of highly weighti ng one of the ranked fields and equally weighting the remaining ranked fields. Tables 3-3 and 3-4 shows

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115 weightings applied to the ranked fields for th e sensitivity analysis. The ratio of the four categories (water quality, wate r quantity, conservation value, and ease of restoration) change for each weighting scheme. For each weighting scheme, the RFW with an overall ranking greater than or equal to 7 are select ed and the area of the selected RFW (hydric uplands and partially ditched or drained wetlands) is noted. The last three columns Table 3-3 and 3-4 represent the following 1. Hydric upland area ratio = ( hydric uplands with overall ranking greater than or equal to 7 for each weighting sche me)/(total area of hydric uplands) 2. Partially ditched/drained wetland area ratio = (partially ditc hed/drained wetlands with overall ranking grea ter than or equal to 7 for each weighting scheme)/(total area of partially ditched/drained wetlands) 3. Total RFW area ratio = (RFW with overall ranking greater than or equal to 7 for each weighting scheme)/(total area of RFW) Tables 3-5 and 3-6 display the total areas of the hydric upland s, partially ditched/drained wetlands, and total RFW area for the lands for acquisition scenario and the consideration on all lands scenario respectively. The sensitivity analysis shows which ranke d fields (drainage density, land use, habitat/species/adjacency, proximity to IRL targets, water quantity, or managed area) contain the most highly ranked RFW on an areal basis as compared to the total area of RFW. For both scenarios the habitat/species /adjacency and proximity to IRL targets fields contain the most highly ranked RFW. Therefore the areal dominance of these two fields is an important factor to remember when highly weig hting either of these fields. Evaluation of the Northern Zone Results Aerial/Land Use Verificati on of Both Analyses Comparison of the unbuffered streams identif ied from protection of riparian zone buffer areas analysis and clusters identif ied from non-point source pollution reduction

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116Table 3-3. Example of sensitivity analys is for the lands for acquisition scenario. Drainage density (Dd) Land use (LU) Habitat/ species/ adjacency (HSA) Proximity to IRL targets (IRLT) Water quantity (WQ) Hydric upland area ratio Partially ditched/ drained wetland area ratio Total RFW area ratio Dd weight 0.5 0.125 0.125 0.125 0.125 0.152 0.066 0.111 LU weight 0.125 0.5 0.125 0.125 0.125 0.242 0.164 0.205 HSA weight 0.125 0.125 0.5 0.125 0.125 0.481 0.216 0.356 IRLT weight 0.125 0.125 0.125 0.5 0.125 0.390 0.169 0.286 WQ weight 0.125 0.125 0.125 0.125 0.5 0.0003 0.000 0.0002 Table 3-4. Example of sensitivity analysis for the consideration of all lands scenario. Drainage density (Dd) Land Use (LU) Habitat/ Species/ Adjacency (HSA) Proximity to IRL Targets (IRLT) Water Quantity (WQ) FLMA Hydric upland area ratio Partially ditched/ drained wetland area ratio Total RFW area ratio Dd weight 0.5 0.1 0.1 0.1 0.1 0.1 0.178 0.019 0.117 LU weight 0.1 0.5 0.1 0.1 0.1 0.1 0.140 0.098 0.124 HSA weight 0.1 0.1 0.5 0.1 0.1 0.1 0.262 0.091 0.197 IRLT weight 0.1 0.1 0.1 0.5 0.1 0.1 0.411 0.128 0.302 WQ weight 0.1 0.1 0.1 0.1 0.5 0.1 0.070 0.005 0.045 FLMA weight 0.1 0.1 0.1 0.1 0.1 0.5 0.251 0.052 0.174

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117 Table 3-5. Total area of hydric uplands, part ially ditched/drained wetlands, and RFW for the lands for acquisition scenario. Total area (acres) Hydric uplands 2831.41 Partially ditched/drained wetlands 2543.02 RFW 5374.43 Table 3-6. Total area of hydric uplands, part ially ditched/drained wetlands, and RFW for the consideration of all lands. Total area (acres) Hydric uplands 4487.16 Partially ditched/drained wetlands 2792.01 RFW 7279.17 analysis with the most current aerials verifies the accuracy of the land use and hydrography data layers used in this an alysis. The DOQQ confirm the land uses and canals and ditches are simila r to the land use and hydrography data layers for both analyses. Non-point Source Pollution Reduction An alysis: Highest Ranked Agricultural Lands This analysis considers agricultural lands with an overall rank ing greater than or equal to 7 as providing the grea test benefits to water quali ty and conservation value if restored. Figures 3-48 and 349 highlight in turq uoise-blue the highest overall ranked agricultural lands (equal wei ght and water quality weight). The equal-weight ranking scheme produces three distinct clusters of agricultural lands with an overall-weighted ranking over 7 (Figure 3-50 through 3-52). Th e water-quality weight ranking scheme produces only one distinct clus ter of agricultural lands with an overall ranking over 7 and this cluster coincides with cluster 2 of the equal-weight ranking scheme (Figure 3-51). The water-quality weight ranking scheme gi ves a higher weighting to water quality. Therefore the highest overall ranked agricu ltural lands in the water-quality weight

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118 Figure 3-48. Equal weighting in the northern zone waters hed: highest ranked overall agricultural lands (Sources: FGDL; FNAI; SJRWMD; TNC).

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119 Figure 3-49. Water quality weighting in the no rthern zone watershed: highest ranked overall agricultural lands (Sources: FGDL; FNAI; SJRWMD; TNC).

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120 Figure 3-50. Highest ranked overall agricultural lands (equal weighting): cluster 1 (Sources: FGDL; FNAI; SJRWMD; TNC).

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121 Figure 3-51. Highest ranked overall agricu ltural lands: cluster 2 (equal weight) and cluster 4 (water quality weighting) (Sources: FGDL; FNAI; SJRWMD; TNC).

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122 Figure 3-52. Highest ranked ove rall agricultural lands (equal weighting): cluster 3 (equal weight) (Sources: FGDL; FNAI; SJRWMD; TNC).

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123 ranking scheme tend to have rankings of 7 or above for land use, size, and Dd. However, for the equal-weighting ranking scheme only tw o of the three fields for water quality ranking need to be high as long as the ag ricultural land contains a high ranking for conservation value. This situation allows fo r more agricultural lands to have an overall ranking greater than or equal to 7 in the equal-weight ranking scheme. In general for the water-quality ranking scheme, the agricultura l lands with an overall-weighted ranking over 7 tend to reside on lands with greater contributions to nonpoint source pollution. Non-point Source Pollution Reduction Analysis: Sensitivity Analysis A sensitivity analysis, similarly to the central and southern zones analysis, is performed on the weighting schemes in the no rthern zone. Table 3-7 shows weightings applied to the ranked fields for the sensitivity analysis. After the weighting schemes are applied, the total area of agricultural lands with an overall ranking greater than or equal to 7 is noted and then divided by the total agricultural lands area (17953.85 acres) to produce the “agricultural lands area ratio.” The agricultural lands area ratios shows which ranked fields contain the most highly ra nked agricultural lands on an areal basis as compared to the total agricultural lands area. Similarly to the central and southern zones analysis, the habitat/species/adjacency field ha s the highest “agricultu ral lands area ratio” and thus the most areal dominance. Table 3-7. Example of sensitivity analys is in the northern zone watershed. Land use (LU) Dd Size Habitat/ species/ adjacency (HSA) Agricultural lands area ratio LU weighting 0.5 0.167 0.167 0.167 0.138 Dd weighting 0.167 0.5 0.167 0.167 0.142 Size weighting 0.167 0.167 0.5 0.167 0.100 HSA weighting 0.167 0.167 0.167 0.5 0.360

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124 CHAPTER 4 DISCUSSION Central and Southern Zones The central and southern zones analysis iden tifies 7 clusters with an overall ranking over 7 for two weighting schemes and two scen arios. The equal-weight ranking scheme identifies several clusters providing wate r quality benefits, water quantity benefits, conservation value, and ease of restorati on. The TNC-weight ranking scheme focuses mostly on the main problems in the IRL, water quality and quantity, and provides narrower results. The lands for acquisition scen ario requires that lands be purchased or conservation easements be obtained. The consid eration of all lands s cenario considers all lands for restoration of RFW but gives great er emphasis to lands already managed for conservation purposes. RFW in non-conservation areas ma y provide a longer lag time between when the land is obtained and when the land is available for restoration. However, political obstacles can slow th e process of restoring RFW on conservation lands. One RFW area appears to stand out in bot h scenarios and both weighting schemes and is represented by clusters 1, 4, and 6 (F igures 3-29, 3-30, and 336). This RFW area exists in the Mary “A” Farms sub-basin wh ich is dominated by improved pasture and a large area of RFW. This area maximizes be nefits to water quality and quantity and potentially could provide additi onal wildlife habitat. Zedler (2003) states that it seems “wise to choose restoration sites next to remn ants of original habitats” (p. 69) to enhance biodiversity. The area contai ns both hydric uplands and pa rtially ditched or drained

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125 wetlands. The partially ditched or drained wetl ands can allow dispersal of species to the RFW. The drainage density in this area varies which may va ry the ease of restoration. One draw-back for this area is the locati on near a major canal which may hinder the restoration of the southern most RFW in th e area. One other area highlighted in the consideration of all lands scenario (cluster 5 and 7) maximizes water quality and quantity, conservation value, and ease of restoration. This area exists on lands currently managed for conservation and contains various sized RFW. However, roads intersect some of these RFW which reduces the potentia l for these RFW to be restored. The focus of these analyses does not in clude methods for wetland restoration, measuring the success of wetland restoration, and cost of wetland re storation but these topics remain very important. Several ar ticles explore these t opics. Methods of restoration for the RFW would most likely include filling in or removing ditches and minor canals. However, not all ditches and canals solely drain agricultural lands; some ditches also irrigate the land. Irrigation d itches could potentially aid in rewetting the RFW. The Mary “A” Farms sub-basin contains some irrigation canals and could be used to restore this area (Steward and Van Arma n, 1987). Two articles focus on methods for wetland restoration of different types of wetlands on drained agricultural lands: a fen in Rocky Mountain National Park, Colorado and a kettle marsh in east-central Illinois (Cooper et al., 1998; Wilm et al., 1997). C ooper et al. (1998) discuss the hydrologic restoration of a fen in Rocky Mountain National Park (RMNP) in Colorado. Prior to the opening of the Park in 1915 the fen was ditc hed for agricultural pur poses. After the opening of the park the agricu ltural activities stopped but the ditch continued to lower water tables in the central portion of th e fen. Soils dried and oxidizing conditions

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126 developed due to the lower water table. Th e authors state that, “the concern was that oxidizing conditions would increase decomposition rates and result in a long-term loss of peat and substantial ecological changes in the fen” (p. 337). In the 1990’s the fen was restored by using sheet metal to block the di tch in order to rest ore the hydrology. The authors analyze three years of pre-restoration and four y ears of post-restoration waterlevel data to determine the eff ects of the ditch and the succes s of the restoration. They find that during the pre-restoration years the water table in most of the study area dropped far enough to cause aerobic conditions in the up per layer of soil for half of the summer except in high summer-precipitation years. In the post-restoration years the water table stayed close to the soil surface for the entire summer in 1991, 1992, and 1993. Due to the lack of precipitation in 1994 th e water table dropped to a leve l similar to pre-restoration levels. The redox potential data is unav ailable for these years although the authors determine from relationships between redox pot ential and water table level for the years 1987-89 that there would be anaerobic condi tions throughout the entire summer. The authors indicate blocking the ditch temporarily diverted surface water from the ditch but some surface water still entered the ditch and eroded the soil and the sheet metal. For a more permanent restoration, the authors suggest that the ditch be filled with soil and the area be revegetated with native vegetation. Wilm et al. (1997) discusses a successful kettle marsh restorat ion in east-central Illinois and resulting development of wetland pl ant community. Illinoi s drainage districts drained over 5 million acres of wetlands primarily for agricultural purposes. The restoration project in this arti cle shows a small-scale restora tion to convert an agricultural area back to a wetland with minimal cost, labor, and maintenance. Before the restoration

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127 project, the site consisted of abandoned pasture with underg round drainage tiles draining water to a central ditch. To restore the natural hydrology of the site the drainage tile system was removed. Soon after this hydrophytic vegetation returned to the site. In 1991 vegetation sampling began and continued annually through 1995. Trends in the vegetation are analyzed to determine the succ ess of the restoration. The sampling shows a “rapidly-developing, diverse flora dominate d by native, hydrophytic vegetation, similar to what might be found in a natural wetland community” (p. 216). Monitoring after the completion of the restoration project provides a way to evaluate the success of the restoration. Determining the success of a restoration project is essential for continued financial and social s upport of other restora tion projects. Kentula (2000) provides a summary of different idea s and techniques surr ounding the topic of determining success for wetland restoration. A ccording to the author, failure to define clear goals and objectives ofte n hinders the determination of success. Also the author notes that success has different meanings in different situations. Although success can generally be defined as “achieving established goals, ideally as specified in quantifiable criteria” (p. 200). Three types of success can be defined: complia nce, functional, and landscape success. Compliance success is “d etermined by evaluating whether the project complies with the terms of an agreement” ( p. 200). Functional success is “determined by evaluating whether the ecologi cal function of the system is biologically viable and sustainable” (p. 200). These two types of success rely heavily on the individual project and fail to look at the resource as a whole. Landscape success examines the restoration in a broader sense and is “a measure of how restoration (or resource management, in general) has contributed to maintaining or improving the ecological integrity of the

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128 region or landscape and to achievement of su ch goals as the maintenance of biodiversity” (p. 200). According to the aut hor, restoration projects have been evaluated in terms of progress of the project and in terms of su ccess in several ways including evaluating adherence with permit condition, comparing re ference and post-restoration conditions, documenting of project progre ss, experimental determina tion of wetland processes and characteristics, and a mixture of the above items. Various charac teristics have been measured including vegetation (most common) , soil, fauna, and hydrology. The author suggests and explains the use of success criteria. Historically success criteria have been based on occurrence and quantity of species rather than the determining whether the species is viable long term. A reference stan dard must be established before restoration goals can be defined or success can be dete rmined. Usually the reference wetland is a similar wetland in the area and hopefully enco mpasses the goals of the restoration. In some cases matching to reference condition may not be realistic due to the absence of a good reference condition so the restoration strategy relies on improving the current condition of the wetland. The author stat es that another comparison that can be accomplished is between new and old projects. The relative speed of a system’s recovery can be evaluated from this. The author then provides a case study to demonstrate some of the principles describe d previously (Kentula, 2000). Feasibility of a restoration pr oject is a key constraint to its success. The cost of restoring a natural system may prove difficult to estimate. The next article examines this issue. Thom et al. (1997) gather an d compare information from environmental restoration projects including “management measures, engineering features, monitoring techniques, and detailed costs” (p. 141). The authors f ound environmental restoration

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129 projects through a search of recent confer ence proceedings and personal contacts with restoration professionals. The authors received information from 39 restoration projects. They separate the projects into different ca tegories such as “bottomland hardwood forest restoration, enhancement of fish and wild life habitat, estuarine wetland restoration, estuarine wetland restoration and wildlife enhancement, mitigation banks, stream enhancement, stream restoration, water quality remediation, wetland creation, and wetland restoration and enhancement” (p. 141-1 42). The authors estimate the “cost of equipment, labor, materials, and supplies for component of a projec t” (p. 142). Similar components occurred in several of the restor ation projects and could be compared. The total and per unit costs as quoted by the authors were: Gravel removal activity costs range from $3.27 to $3,239 per ton. Rip rap installation costs range from $5.00 to $19.00 per ton. Culvert installation costs range from $150 (for 48” di ameter culvert) to $1,103.85 per ft. Channel cleaning costs range from $4.00 to $8.00 per cu.m. Erosion control costs range from $1.40 to $4.00 per sq. ft. Dike removal costs range from $1.92 to $2.67 per ft. Dike/dam/levees construction costs range from $5.00 to $20.00 per linear ft. (Thom et al., 1997, p. 143) The authors find that two primary variables govern restoration cost s: “(1) the specific project components required to restore th e ecosystem, from conceptual design to monitoring; and (2) how the re storation costs are allocated and reported” (p. 143). The restoration cost varied from project to project depending on site accessibility, grading, site preparation requirements, ability to establish plant community, and schedule delays.

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130 The authors stress the importa nce of accurate documentation fo r cost comparisons to be performed. Northern Zone In the northern zone of the IRL water quali ty represents the largest problem. Two analyses seek to improve or protect the water quality in this region of the IRL. The first analysis identifies six riparian areas that are not under cons ervation management or being considered by FFBOT. These areas should be considered for conservation to preserve the benefits that riparian wetl ands provide. The non-point source reduction analysis identifies four agricultural lands clusters wi th an overall weighted ranking over 7 for two weighting schemes. The equal-weight rank ing schemes identifies several clusters providing water quality benefits and conser vation value. The water quality weight ranking scheme focuses on the effects of la nd use, drainage dens ity, and size on water quality and provides narrower re sults. One agricultural area stands out in both weighting schemes and is represented by clusters 2 and 4. The area consists of a long-narrow dense grouping of many citrus groves, a few field crop s, and improved pastures and the area is directly south of lands currently managed for conservation. This area maximizes benefits to water quality and wildlife habitat. The success of improving water quality th rough acquisition of the northern zone agricultural lands depends bot h upon the willingness of th e landowners to sell their property for restoration/cons ervation purposes and the cost of obtaining the property. The cost of obtaining property is also importa nt in the central and southern zones. Although in the central and southern zones, conservation easements on the land may be obtained or the property may be purchased fo r restoration of the RFW. However, the northern zone analysis relies on taking th e agricultural lands out of production so

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131 conservation easements may or may not be possi ble. Estimates of the cost of land for both the northern and central and southern zones may be obtained through property tax data tables from the local property tax apprai ser office. According to the metadata, these tables usually provide the parc el number, land owner, special assessment value, and total value. The tables may be joined based on pa rcel numbers to parcel GIS data layers from the local property tax appraisals office to determine the specific information for the RFW and agricultural lands. Cost of property is not considered as part of this research because tax tables and parcel GIS layers for all th e counties could not be obtained in a cost effective and consistent manner. Parcel data change frequently and the available associated tax table year and parcel GIS data layers did not match which complicates the estimate of cost. Furthermore, tax appraisa l values may not accurately reflect market values. Recommendations for Future Research This report focuses on identifying and ranking RFW or agricultural lands for potential restoration ba sed on water quality benefits, wate r quantity benefits, conservation value, and ease of restoration. Other resear ch opportunities could ar ise from this analysis on the IRL. Upon the availability of consis tent property tax and parcel information, an economical analysis of obtaining the select ed RFW and agricultural lands could be performed. A detailed hydr ologic modeling analysis coul d also be performed on the cluster area in the Mary “A” Farms region or the area noted in the northern zone discussion to determine the effects of hydrologi c restoration on storag e. In the northern zone, further examination of the riparian wetl ands in the protection of the riparian zone buffer areas analysis could determine which of the six areas are most vital to the protection of water quality in the IRL.

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132 CHAPTER 5 SUMMARY AND CONCLUSIONS The IRL maintains the highest diversity for an estuary in North America. However, several factors have changed the qua ntity and quality of wa ters discharging the north IRL including drainage alterations whic h extended the historic watershed boundary for agricultural and urban purposes, degrada tion of salt marshes along the IRL due to impoundments, and impacts due to point and non -point sources of pollution. The Nature Conservancy (TNC) created a two-phase GIS anal ysis project that identifies target areas for acquisition and conservation easements to address the water quality, water quantity, and conservation issues for the area. The IRL watershed can be categorized on the basis of hydrology, tidal influence, and nature of threats to cons ervation targets into characteristically similar zones. A separate GIS analysis is perf ormed for the northern and central and southern zones. The central and southern zones contain an e xpanded watershed boundary crossed with many canals and ditches and increased circulation due to nearby in lets and large freshwater inflows. The northern zone contains a l ong narrow watershed and limited mixing occurs which creates a higher sensitivity to water quality problems. The central and southern zones analysis identifies and ranks RFW in the historic flatwood region west of the historic watershed boundary. The analysis applies a weighted average approach using GIS attribut e tables to rank and weight the RFW based on four categories: water quality benefits, water quantity benefits, conservation value, and ease of restoration. The f our categories represent several fields in the RFW attribute

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133 table. These fields consist of drainage dens ity; land use; habitat, species, and adjacency value; proximity to IRL targets; water quant ity value; and managed area value. The analysis is split into two different scenarios: lands for acquisition scenario (considers only lands not currently under conservation mana gement) and consideration of all lands scenario. Two weighting schemes are applied to both the scenarios: equal weight (equal weights the fields by category) and TNC weight. The anal ysis successfully identifies seven clusters of the highest ranked RFW meeting the objectiv es of the two scenarios and two weighting schemes. After aerial verifi cation with the DOQQ, th e cluster area in the Mary “A” Farms sub-basin stood out as havi ng the greatest pote ntial for maximizing water quality and quantity benefits, conservatio n value, and ease of restoration. The proximity of the clusters to major canals and th e sensitivity of the ra nked fields are also investigated. Examination of the proximity of the clusters to major canals shows that roads intersect some of the RFW which may make restoration efforts impossible on these RFW. The sensitivity analysis shows that for both scenarios the habitat/species/adjacency and proximity to IR L targets fields contain the most highly ranked RFW based on area. Several aspects rela ting to successful wetland restoration are discussed including methods for wetland restor ation, measuring the success of wetland restoration, and cost of wetland restoration. Two separate analyses are performed on the northern zone in cluding the protection of riparian zone buffer ar eas analysis and the non-poi nt source pollution reduction analysis. Both analyses focus on improving or maintaining water quality in the northern zone of the IRL. The first analysis iden tifies riparian zones connected to streams discharging to the IRL that ar e not currently managed for conservation or are not planned

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134 for acquisition by FFBOT projects. The second analysis identifies agricultural lands that if willingly taken out of production and/or re stored could improve the non-point source pollution received by the IRL. The analys is ranks agricultural lands utilizing the weighted average approach based on two categories: water quality benefits and conservation value. The GIS attribute tables contain four fields representing the two categories including drainage dens ity; land use; size; and habi tat, species, and adjacency value. Two weighting schemes are applied to both the scenarios: equal weight (equal weights the fields by category) and water quality weight. The analysis identifies four clusters of the highest ranked agricult ural lands meeting the water quality and conservation value objectives for the two weigh ting schemes. One clus ter of agricultural lands stands out as maximizing water quality benefits and conservation value and consists of a long-narrow dense grouping of many citrus groves, a few field crops, and improved pastures directly south of lands currently managed for conservation. The success of improving water quality through acquisition of the northern zone agricultural lands depends both upon the willingness of the la ndowner to sell their property for restoration/conservation purposes and the co st of obtaining the property. A sensitivity analysis, similarly to the central and sout hern zones analysis, is performed on the weighting schemes in the northern zone a nd shows that the hab itat, species, and adjacency field has the highest agricultural lands area ratio. Overall these analyses successfully id entify and rank the RFW and agricultural lands which if restored could provide wa ter quality and quantity benefits along with added conservation value to the IRL. Severa l other research oppor tunities benefiting the IRL could potentially arise from this resear ch including economic anal ysis of the highest

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135 ranked RFW and agricultural lands, hydrologic modeling of specific watershed areas to determine storage potential of RFW, and furthe r investigation of the riparian wetlands in the protection of the riparian zone buffer areas analysis.

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136 APPENDIX A LIST OF ACRONYMS BOD Biochemical Oxygen Demand CCMP Comprehensive Conservation Management Plan CERP Comprehensive Ever glades Restoration Plan Dd Drainage Density DEM Digital Elevation Model DOQQ Digital Orthogr aphic Quarter Quad EO Element Occurrence FDEP Florida Department of Environmental Protection FFBOT Florida Forever Land Acquisi tion Projects Board of Trustees FFWCC Florida Fish and Wild life Conservation Commission FGDL Florida Geographic Data Library FLMA Florida Managed Areas FLUCCS Florida Land Use, Cover and Forms Classification System FNAI Florida Natural Areas Inventory GIS Geographic Information Systems HSA Habitat/Species/Adjacency IRL Indian River Lagoon IRLNEP Indian River Lagoon National Estuary Program IRLNFS Indian River Lagoon North Feasibility Study IRLSFS Indian River Lagoon South Feasibility Study IWW Intracoastal Waterway LABINS Land Boundary Information System LCA Large Conservation Area NAPP National Aerial Photography Program NEP National Estuary Program NIRL North Indian River Lagoon NWI National Wetlands Inventory PMP Project Management Plan RFW Restorable Freshwater Wetlands SFWMD South Florida Wate r Management District SHCA Strategic Habitat Conservation Areas SJRWMD St. Johns River Water Management District SS Suspended Solids SWIM Surface Water Improvement and Management TN Total Nitrogen TNC The Nature Conservancy TNCPEP The Nature Conservancy Peninsular Ecoregional Plan TNC SCP The Nature Conserva ncy Site Conservation Plan TP Total Phosphorus

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137 UF University of Florida USACOE United States Army Corps of Engineers USFWS United States Fish and Wildlife Service USGS United States Geological Survey USLE Universal Soil Loss Equation WDNR Wisconsin Department of Natural Resources WPA Water Preserve Area

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138 APPENDIX B DATA LAYER SOURCES Table B-1. Contact Information for obtaining GIS data layers. Obtained from Contact Information/Website Data Layer Florida Department of Environmental Protection (FDEP) http://www.dep.state.fl.us Eric W Brockwell 2600 Blair Stone Rd Twin Towers, MS 6520 Tallahassee, FL 32399-2400 (850)245-8238 SSURGO Soils (SFWMD) SSURGO Soils (SJRWMD) FDEP Land Boundary Information System (LABINS) http://www.labins.org/2003 Stephen W. Hodge Florida Resource and Environmental Analysis Center Technical Assistance Program C2200 University Center Florida State University Tallahassee, FL 323062641 Phone: (850)644-2882 Fax: (850)644-7360 shodge@admin.fsu labins@admin.fsu.edu 1999 Albers Resolution: 1 meter; Units: MT; JPG Florida Geographic Data Library (FGDL) http://www.fgdl.org 431 Architecture Building PO Box 115706 Gainesville, FL 326115706 Florida County Boundaries Physiographic Divisi ons of Florida Polygon Florida Vegetation Map 1967 St. John’s River Water Management District Landuse 1995 South Florida Water Management District Landuse 1995 USFWS National Wetlands Inventory – Polygon USGS 1:24,000 Hydrography -Lines USGS 1:24,000 Hydrography – Polygons FDOR Property Tax Data Records for 1999

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139 Table B-1. Continued Obtained from Contact Information/Website Data Layer Florida Natural Areas Inventory (FNAI) http://www.fnai.org Chengxia You, GIS Manager Florida Resources and Environmental Analysis Center 1018 Thomasville Road, Suite 200-C Tallahassee, FL 32303 Phone: (850)224-8207 Fax: (850)681-9364 Florida Managed Areas (FLMA) – March 2003 Florida Forever/Board of Trustees Environmental Land Acquisition Projects (FFBOT) – April 2003 Element Occurrence Point Coverage Data Habitat Conservation Priorities Strategic Habitat Conservation Areas (SHCA) Ecological Greenways Under-Represented Natural Communities St. Johns River Water Management District (SJRWMD) http://www.sjrwmd.com GIS Data Manager, Information Resources Department P.O. Box 1429 Palatka, FL 32178-1429 (386)329-4500 gis_support@sjrwmd.com GISLIB.LULC_2000 (2000 SJRWMD Land Use/Land Cover) Surface Water Basins The Nature Conservancy (TNC) http://www.tnc.org Richard Hilsenbeck (TNC) Tom Hoctor (UF GeoPlan Center) 222 S. Westmonte Dr. Suite 300 Altamonte Springs, FL 32714 Phone: (407)682-3664 Fax: (407)682-3077 TNC Peninsular Ecoregional Portfolio Sites Table B-2. Original so urce of data layers. Data Layer Original Source SSURGO Soils (SFWMD) National Resources Conservation Service (NRCS) & SFWMD (561)686-8800 SSURGO Soils (SJRWMD) NRCS & SJRWMD (386)329-4500 1999 Albers Resolution: 1 meter; Units: MT; JPG LABINS

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140 Table B-2. Continued Data Layer Original Source Florida County Boundaries United St ates Census Bureau (USCB) Products and Services Staff, Geography Division Tiger Mapping Service Washington, D.C. 20233 (301)457-1128 (Geography) (301)457-4100 (Customer Service) http://www.census.gov/ftp/pub/geo/www/tiger Tiger@census.gov Physiographic Divisi ons of Florida – Polygon SJRWMD Florida Vegetation Map – 1967 FDGL St. John’s River Water Management District Landuse 1995 SJRWMD South Florida Water Management District Landuse 1995 SFWMD P.O. Box 24680 301 Gun Club Rd West Palm Beach, FL 33416-4680 1(800)662-turn (561)686-8800 http://www.sfwmd.gov USFWS National Wetlands Inventory – Polygon U.S. Fish & Wildlife Service, National Wetlands Inventory (USFWS) Chief Cartographer 9720 Executive Center Drive St. Petersberg, FL 33702 Phone: (813)570-5411 Fax: (813)570-5420 http://www.fws.gov USGS 1:24,000 Hydrography Lines Unite d State Geological Survey (USGS) USGS Mapping Applications Center National Cartographic Info Center 507 National Center Reston, VA 22092 Phone: (703)860-6045 Fax: (703)648-4165 http://www.usgs.gov MACWEBMATER@USGS.GOV USGS 1:24,000 Hydrography – Polygons USGS FDOR Property Tax Data Records for 1999 Florida Department of Revenue (FDOR) Service Center 2410 Allen Rd.

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141 Tallahassee, FL 32303 Phone: (850)488-9719 (Main) (850)488-3338 (Property Tax Appraisers Office) http://fcn.state.fl.us/dor/ Florida Managed Areas (FLMA) – March 2003 FNAI Florida Forever/Board of Trustees Environmental Land Acquisition Projects (FFBOT) – April 2003 FNAI Element Occurrence Point Coverage Data FNAI Habitat Conservation Priorities FNAI Strategic Habitat Conservation Areas (SHCA) Florida Fish and Wildlife Conservation Commission Office of Environmental Services 620 South Meridian St. Tallahassee, FL 32399-160 Fax: (850) 922-5679 http://floridaconservation.org Ecological Greenways Universi ty of Florida and FDEP Under-Represented Natural Communities FNAI GISLIB.LULC_2000 (2000 SJRWMD Land Use/Land Cover) SJRWMD Surface Water Basins SJRWMD TNC Peninsular Ecoregional Portfolio Sites TNC

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142 LIST OF REFERENCES Adamus, C. and M. Bergman. 1993. Developm ent of a nonpoint source pollution load screening model. Technical Memora ndum No. 1. St. Johns River Water Management District, Palatka, Florida. Bedient, P. B. 1975. Hydrologic-land use in teractions in a Florida river basin, PhD Dissertation. University of Fl orida, Gainesville, Florida. Brown, D. W., W. E. Kenner, J. W. Crooks, and J. B. Foster. 1962. Water resources of Brevard County, Florida. Report of Inve stigations No. 2, Florida Geological Survey, Tallahassee, FL. Cedfeldt, P. T., M. C. Watzin, and B. D. Richardson. 2000. Profile: Using GIS to identify functionally significant wetlands in the northern United States. Environmental Management. 26(1): 13-24. Clapp, D. A. and H. A. Wilkening, III. 1984. In terbasin diversion in the Upper St. Johns River Basin. Technical Publication SJ: 84-10. St. Johns River Water Management District, Palatka, Florida. Cooper, D. J., L. H. MacDonald, S. K. Wenger, and S. W. Woods. 1998. Hydrologic restoration of a fen in Rocky Mountain National Park, Colorado, USA. Wetlands. 18(3): 335-345. Cox, J., R. Kautz, M. MacLaughlin, and T. Gilbert. 1994. Closing the gaps in FloridaÂ’s wildlife habitat conservation system. Florida Game and Fresh Water Fish Commission, Tallahassee, Florida. Davis, J.H., Jr. 1967. General map of natural vegetation of Florida. Circ. S-178. Institute of Food and Agricultural Science, Agricu ltural Experiment Station, University of Florida, Gainesville, Florida. Florida Department of Transportation (F DOT). 1999. Florida land use, cover and forms classification system handbook. Third Edition. Florida Department of Transportation, Tallahassee, Florida. Florida Natural Areas Inventory (FNAI). 2001. Florida Forever conservation needs assessment technical report: Documentation for the December 2000 Summary Report. Florida Natural Areas I nventory, Tallahassee, Florida.

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143 Gatewood, S. E., and P. B. Bedient. 1975. Dr ainage density in the Lake Okeechobee drainage area: A report to the Division of State Planning for the special project to prevent the eutrophication of Lake Okeec hobee. Department of Environmental Engineering Sciences, University of Florida, Gainesville, Florida. Gilmore, R. G. 1999. Life history and critic al habitat/environment of opossum pipefish, Mycrophis brachyurus lineatus : A population viability analysis. Estuarine, Coastal and Ocean Science, Inc., Vero Beach, Florida. Indian River Lagoon National Estuary Progr am (IRLNEP). 1996. Indian River Lagoon comprehensive conservation and manageme nt plan. Indian River Lagoon National Estuary Program, Melbourne, Florida. Kentula, M. E. 2000. Perspectives on sett ing success criteria fo r wetland restoration. Ecological Engineering. 15(3): 199-209. Knowles, L. 1995. Rainfall and freshwater discha rge in the Indian Ri ver Basin within the St. Johns River Water Management Distri ct, east-central Florida, 1989-91. WaterResources Investigations Report 944193. United States Geological Survey, Tallahassee, FL. Land Boundary Information System (LAB INS). 2003. Florida Department of Environmental Protection Website (FDE P). http://www.labins.org/2003/. (June 2003) Mitsch, W. J. and J. G. Gosselink. 2000. Wetlands. Third Edition. John Wiley & Sons, Inc., New York, New York. The Nature Conservancy and the Univers ity of Florida Geop lan Center. 2001. Draft Florida peninsular ecoreg ional plan. The Nature Conservancy, Tallahassee and Gainesville, Florida. O’Neill, M.P., J.C. Schmidt, J.P. Dobrowolski, C.P. Hawkins, and C.M.U. Neale. 1997. Identifying sites for riparian wetland rest oration: Application of a model to the upper Arkansas River Basin. Restoration Ecology. 5(4S): 85-102. Richardson, M. S., and R. C. Gatti. 1999. Prio ritizing wetland restoration activity within a Wisconsin watershed using GIS mo deling. Journal of Soil and Water Conservation. 54(3): 537-542. Russell, G. D., C. P. Hawkins, and M. P. O’ Neill. 1997. The role of GIS in selecting sites for riparian restoration based on hydr ology and land use. Restoration Ecology. 5(4S): 56-68. St. Johns River Water Management Distri ct (SJRWMD). 2003. Coastal ecosystems: Indian River Lagoon. St. J ohns River Water Management District Website. http://www.sfwmd.gov/org/wrp/wrp_ce/2_w rp_ce_lagoon/irl.html. (May 2003)

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144 South Florida Water Management District and the United States Army Corps of Engineers (SFWMD and USACOE). 1997. Wa ter preserve areas: Land suitability analysis draft documentation. United States Army Corps of Engineers, Jacksonville, Florida. Steward, J. S., and J. VanArman (eds.). 1987. Indian River Lagoon joint reconnaissance report. St. Johns River Water Manageme nt District and South Florida Water Management District, Palatka a nd West Palm Beach, Florida. Thom, R. M., K. Wellman, D. K. Shreffler , and M. J. Scott. 1997. Aquatic habitat restoration in the United States: A review of design and costs. In: Macdonald, K. B., and F. Weinmann (eds.). 1997. Wetland a nd riparian restoration: Taking a broader view. Contributed papers and sele cted abstracts, Society for Ecological Restoration, 1995 Internati onal Conference, September 14-16, 1995, University of Washington, Seattle, Washington, USA. Publication EPA 910-R-97-007, USEPA, Region 10, Seattle, Washington. United States Army Corps of Engineer s (USACOE) and St. Johns River Water Management District (SJRWMD), 2003. Indian River Lagoon north feasibility study project management plan. United States Army Corps of Engineers, Jacksonville, Florida. United States Army Corps of Engineer s (USACOE) and South Florida Water Management District (SFWMD). 2002. Centra l and Southern Florida Project Indian River Lagoon--South feasibility study: Fi nal integrated feasibility report and supplemental environmental impact stat ement. United States Army Corps of Engineers, Jacksonville, Florida. United States Fish and Wildlife Servi ce (USFWS). 2003. National Wetlands Inventory. United States Fish and Wildlife Servi ce Website. http://www.nwi.fws.gov. (May 2003) Wilm, B. W., M. J. Morris, and S. D. Si mon. 1997. A successful east-central Illinois kettle marsh restoration and its de veloping wetland plant community. In: Macdonald, K. B., and F. Weinmann (eds.) . 1997. Wetland and ripa rian restoration: Taking a broader view. Contributed papers and selected abstracts, Society for Ecological Restoration, 1995 International Confer ence, September 14-16, 1995, University of Washington, Seattle, Wa shington, USA. Publication EPA 910-R-97007, USEPA, Region 10, S eattle, Washington. Zedler, J. B. 2003. Wetlands at your service: reducing impacts or agriculture at the watershed scale. Frontiers in Ecology and the Environment. 1(2): 65-72.

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145 BIOGRAPHICAL SKETCH Christi Marie Grover was born in Rockledge , Florida, in January of 1978 to David W. and Linda B. Grover. She lived in Me lbourne Beach, Florida, until she graduated from Melbourne High School in May 1996. Foll owing in the footsteps of her mother, Christi began attending the University of Fl orida in August 1996. She graduated with a bachelorÂ’s degree in environmental engin eering sciences in May 2001 and went on to attend graduate school at the University of Fl orida. Christi enjoye d the opportunity to perform her masterÂ’s research on the Indian River Lagoon watershed since she grew up benefiting from the LagoonÂ’s many resources. Upon graduation, Christi plans to return to the Brevard County area to work as an environmental engineer.