Submitted by: Mote Marine Laboratory 1600 Ken Thompson Parkway Sarasota, Florida 34236 (941) 388-4441 Scott E. Stevens, P.E., Project Manager Southwest Florida Water Management District Surface Water Improvement and Management Section of the Resource Projects Department 7601 U.S. 301 North Tampa, FL 33637 Submitted to: DATA INVENTORY, TREND ANALYSIS, RECOMMENDED MONITORING: FLORIDA SPRINGS COAST Volume I. Report FINAL L. Kellie Dixon Principal Investigator This document is printed on recycled paper. Mote Marine Laboratory Technical Report Number 547 December 5, 1997 Suggested reference Dixon LK. 1997. Data inventory, trend analysis, and recommended monitoring: Florida Springs coast. Southwest Florida Water Management District. Mote Marine Laboratory Technical Report no 547. 66 p. and appendices. Available from: Mote Marine Laboratory Library.
Scott E. Stevens, P.E., Project ManagerSouthwest Florida Water Management DistrictSurface Water Improvement and ManagementSection of the Resource Projects Department7601 U.S. 301 NorthTampa, FL 33637
Submitted to:
DATA INVENTORY, TREND ANALYSIS,
RECOMMENDED MONITORING:FLORIDA SPRINGS COAST
Volume I. ReportFINAL
L. Kellie DixonPrincipal Investigator
This document is printed on recycled paper.
Mote Marine Laboratory Technical Report Number 547
December 5, 1997
Suggested reference Dixon LK. 1997. Data inventory, trend analysis,
and recommended monitoring: Florida Springs coast. Southwest Florida
Water Management District. Mote Marine Laboratory Technical Report
no 547. 66 p. and appendices. Available from: Mote Marine Laboratory
Appendix IA. Citations of Data SummariesAppendix IB. Selected Illustrations of LOWESS Based Trend Analyses
VOLUME II. DATA SUMMARIES
Appendix IIA. Data SummariesAppendix IIB. SWFWMD Monitoring StationsAppendix IIC. Summary of USGS Stations and Periods of RecordAppendix IID. STORET Inventory of Study AreaAppendix IIE. Literature CitedAppendix IIF. Summary of Aerial Photographs InventoriedAppendix IIG. Abbreviations of Agencies Water Quality Parameters
Figure 2. Conceptual model of nutrient transfer and pools within freshwater,estuarine, and coastal environments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-7
Figure 3. An example of conductivity against time (3A), LOWESS smooth of theconductivity against fraction of the year (3B), and analysis of residuals fromthe LOWESS smooth against time (3C) to determine trend. Linearrepresentation only, as significance of trends was detected with non-parametric rank-correlation analyses. . . . . . . . . . . . . . . . . . . . . . . . . . . I-21
Figure 4. An example of total kjeldahl nitrogen against time (4A), LOWESS smoothof the parameter against sixty (60) day cumulative rainfall (4B), andanalysis of residuals from the LOWESS smooth against time (4C) todetermine trend. Linear representation only, as significance of trends wasdetected with non-parametric rank-correlation analyses. . . . . . . . . . . . . . . . I-23
Figure 5. Station locations of data analyzed for trend: Waccasassa region (5A),Rainbow River (5B), and Withlacoochee River (5C). . . . . . . . . . . . . . . . . I-51
Figure 6. Station locations of data analyzed for trend: Crystal River (6A),Homosassa and Chassahowitzka Rivers (6B), and Weeki Wachee River6C) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-53
Figure 7. Station locations of data analyzed for trend: Pithlachascotee and AncloteRivers (7A), NADP rainfall sites (7B). . . . . . . . . . . . . . . . . . . . . . . . . . I-55
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LIST OF TABLES
Table 1.
Table 2.
Table 3.
Table 4.
Table 5.
Table 6.
Table 7.
Table 8.
Table 9.
Table 10.
Table 11.
Table 12.
Well designations of groundwater elevation data used for indications ofdischarge at stations with substantial groundwater influences. . . . . . . . . . . . I-27
Independent variables to which water quality parameters at each stationwere smoothed prior to performing temporal trend analysis on residuals. . . . . I-28
Stations maintained in STORET. Results of trend analyses on LOWESSresiduals against time (Kendall’s tau). Direction of slope indicatedfollowing significance: “***” = p < 0.001, "***" = p < 0.01, "*" =p < 0.05, “ns” = not significant, “/” = not analyzed since n< 10. . . . . . . . . I-29
The Crystal and Withlacoochee river stations collected by theLAKEWATCH Program. Results of trend analyses on LOWESS residualsagainst time (Kendall’s tau). Direction of slope indicated followingsignificance: “***” = p<0.001, "**” = p<0.01, "*" = p<0.05,” ns ” = not significant, “/” = not analyzed since n< 10. . . . . . . . . . . . . . . I-45
Rainfall quantity and quality collected by NADP/NTN near Starke andSarasota, FL. Results of trend analyses on LOWESS residuals against time(Kendall’s tau). Direction of slope indicated following significance:" *** " = p <0.001, “**” = p <0.01, "*" = p <0.05, “ns” = notsignificant, “/” = not analyzed since n< 10. . . . . . . . . . . . . . . . . . . . . . . I-47
Existing data, by region, suitable for baseline or as initiating data for statusand trend analyses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-57
Page
Major rivers of the study area and associated springs with drainage area(km2) and discharge (cfs). (Compiled from Coffin and Fletcher, 1997;Jones et al., 1995; Roseneau et al., 1977; Wetterhall, 1965; Wolfe, 1990;Yobbi, 1989; Yobbi, 1992). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-5
Station designations, descriptions, locations, and responsible entity, asretrieved from the EPA STORET data base. . . . . . . . . . . . . . . . . . . . . . . I-18
STORET remark codes considered acceptable for inclusion in the data base.” K” and ” U” values transformed to one half the numerical value. . . . . . . . . . I-19
Mote Marine Laboratory sincerely appreciates the generosity and helpfulness of all investigatorswho provided information, further contacts, and data sets. Many provided data that we wereconstrained from analyzing by time, funding, and the focus of the study. We would in particularlike to thank all reviewers of the document, Ms. Sandy Fisher and Ms. Laura Mataraza of theFlorida LAKEWATCH Program for the use of their data, Mr. Gregg W. Jones andMs. Theresa A. Williamson of the Southwest Florida Water Management District for theirassistance in obtaining District-held data sets, and the National Atmospheric Deposition Programfor the use of the rainfall quality data from selected sites in Florida.
I. EXECUTIVESUMMARY
Increasing nitrogen concentrations in a number of west-central Florida spring discharges hasaroused concern for potential nutrient-related impacts to the comparatively pristine, oligotrophicspring runs downstream. This report documents an inventory, retrieval, and analysis of existingdata to determine if nutrient-related trends could be observed in other data sets.
The project was performed within the framework of a conceptual model of nutrient flow withinidealized freshwater, estuarine, and coastal environments. Data were summarized and selected foranalysis based on the degree of dependence of the monitored parameter on nutrient concentrations,and emphasized water quality and primary producers. Data inventories were prepared by regionwith descriptions of pertinent sampling efforts. Data selected for further analysis included thosefrom stations maintained in the EPA STORET database and LAKEWATCH data from the Crystaland Withlacoochee rivers. In addition, National Atmospheric Deposition/National Trend Networkdata on rainfall quality and quantity were analyzed both as concentrations and as weekly loads toevaluate trends of an increasingly recognized nitrogen source.
Trend analyses employed LOWESS (LOcally WEighted Scatterplot Smooth; Cleveland, 1979) tosmooth water quality parameters against a variety of independent variables, such as season,temperature, cumulative rainfall amounts, well levels, conductivity, and pH. The residuals fromthe smoothed line were then tested non-parametrically against sampling date to determine if, withrespect to the independent variable, parameter concentrations were increasing. The procedure isparticularly suited to non-normal environmental data and the processing of large data sets. Morethan 5,900 parameter-variable combinations were assessed with approximately 1,000 of thesesignificant at the 0.05 level. Of the significant trends, more than 500 were for nitrogen orphosphorus species.
Where trends in nutrients were significant, they were generally increasing. Increases wereobserved both for spring runs and in watershed rivers such as the Anclote and Withlacoocheerivers. Increasing nitrogen concentrations in spring discharges, as observed by other investiga-tions, were similarly apparent in the data sets examined for this project. Exceptions to increasingnutrient trends were observed at the Crystal River/Kings Bay LAKEWATCH stations. The rainfallconcentrations and loading of nitrogen for a given weekly rainfall amount is increasing in somelocations. Data gaps were identified, as were those studies which, on repetition, could provideinformation on trend.
Monitoring recommendations were hierarchical and focused on water quality and primaryproducers in order to document baselines, evaluate trends, determine functional links, and forecastimpacts from increased nitrogen loads. Monitoring should recognize existing data and emphasizethose regions and parameters that are comparatively data-poor. Recommended methods includedsynoptic and comparative surveys, empirical analyses, and multiple hypothesis formulation. Themonitoring should be initiated as soon as possible in light of the pace of development of the region,and should employ the Chassahowitzka River as a demonstration area.
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II. INTRODUCTION
Groundwater discharges to the multiple spring systems along the west coast of Florida haveexperienced substantial increases in nitrogen content (Jones and Upchurch, 1994; Jones et al.,1995; SWFWMD, 1994; Jones et al., 1997). Within the region, Rainbow Springs and WeekiWachee Springs (SWFWMD, 1994) and the Homosassa, Chassahowitzka, and Weeki WacheeSprings (Jones et al., 1997) have each been identified as having increased nitrate concentrations.While questions may arise as to consistency of sampling locations, analytical methods, and otherbiases over the period of record, the magnitude of the recent increases, both in combined andsingle agency records, is a clear indication of degrading water quality trends.
Detailed investigations of nutrient loadings from several systems indicate a variety of development-related causes which are not limited to the coastal springs study area (Jones and Upchurch, 1993;Jones et al., 1997). On-site disposal systems (septic tanks), wastewater treatment plant effluentdisposal, fertilization, and animal wastes are some of the indicated causes. Stormwater percolatinginto karst formations of the region is responsible for much of the transport, and increasing levelsof inorganic nitrogen in atmospheric deposition, while discounted as the major source (Jones andUpchurch, 1994), have also nearly doubled in the last ten years in some regions (Dixon et al.,1996). As plumes of groundwater with additional development-related contaminants reach theheads of spring runs, nitrogen loads to spring runs are expected to increase.
III. PROJECT SUMMARY AND GOALS
The concern is that increased nutrient loadings from spring runs (more than 360 tons annually;Jones et al., 1997) have led or will lead to increases in ‘nuisance’ algae, periphyton, overallbiomass and community metabolism, and diurnal ranges of dissolved oxygen; diurnal depressionsin oxygen; shifts to “undesirable” faunal species; increases in trophic status, and eventualeutrophication (Bass and Cox, 1985; Williams et al., 1985). Comparatively short spring runs(Estevez et al., 1991) also result in an intimate connection to pristine and oligotrophic systems inthe estuarine environment. Impacts can be subtle, but substantial nutrient loads typically result inreductions in water clarity through phytoplankton blooms, or species shifts to dinoflagellates(Kimor, 1992; Vollenweider et al., 1992), drift, filamentous, or blue green algal species.Increased epiphyte growth can also occur on seagrasses, and the combined increase in lightattenuation in both the water column and at the surface of the grass blade can result in inadequatelight levels for grasses to persist (Cambridge and McComb, 1984; Silberstein et al., 1986;Neverauskas, 1987; Tomasko and LaPointe, 1991). As a result, the Southwest Florida WaterManagement District (SWFWMD) funded a retrieval and analysis of existing data sets whichcould be used to document and evaluate the effects of increased nitrate loading from springson spring runs, associated estuaries, and coastal waters. Generalized monitoringrecommendations were to be produced.
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IV. GEOGRAPHIC SCOPE AND REGIONAL DESCRIPTION
The geographic scope of the investigation was centered on the “Springs Coast” region of westernpeninsular Florida and included the estuarine areas off of Levy, Citrus, Hernando, and Pascocounties, the spring runs of the major spring groups (Crystal, Homosassa, Chassahowitzka, andWeeki Wachee), the Withlacoochee River up to the Rainbow River, and Rainbow Springs andRiver (Figure 1). Several authors have provided summaries of the region’s hydrology and geology(Hine and Belknap, 1986; Wolfe, 1990; Yobbi, 1992; Jones et al., 1997), with some hydrologicalaspects of the spring-fed systems studied in detail. Groundwaters of the region are containedprimarily in the limestone Floridan aquifer. Recharge of groundwater can be rapid by downwardpercolation of rainfall and surface waters through porous surficial sediments and solution features.Groundwater discharge, as springs, occurs in numerous locations along the coast, generally at thehead of the coastal swamps. The larger named springs of the region are compiled from severalreferences in Table 1, but many other small discharges exist, both onshore and in the submarineenvironment. A number of smaller springs and seeps detailed in earlier works have reportedlyceased flowing. Larger rivers with a preponderance of surface drainage border the region; theWaccasassa and Withlacoochee rivers to the north and the Pithlachascotee and Anclote rivers tothe south. Centrally, the spring-fed rivers formed by Crystal, Homosassa, Chassahowitzka, WeekiWachee Springs and the Aripeka spring complex are the prominent features of the coast.
The coastline consists of a low-energy, marsh dominated, sediment-poor region controlled bylimestone bedrock topography. Karstification dominates the coastal geological processes. Themorphology of tidal creeks and marsh archipelagos is controlled by fracture and solution features.Shelf embayments have formed at the mouths of the spring-fed rivers as the result of increasedsolution by estuarine waters. Seagrasses, macroalgal beds, and oyster reefs are prominent coastalfeatures of the region.
Surface water quality of the region varies between acidic and highly colored drainage from thecoastal swamps and watershed rivers to the more alkaline, mineralized groundwater discharges.The source of many of the spring discharges is at or near the salt-fresh boundary of the underlyinggroundwater, resulting not only in a seawater component to discharges, but also in tidal fluctuationsin discharge quality. Many spring runs also are subject to tidal fluctuations in surface water levelsand therefore tidal variations in discharge quantity as well. Seasonal variations in rainfall, surfacedrainage, groundwater elevations, and spring discharges, together with varying watershed areasamong the rivers, result in complex seasonal and spatial patterns of surface water quality.
V. CONCEPTUAL MODEL
Existing data were inventoried, selected for analysis, and monitoring recommendations formulatedusing a conceptual model of processes linking nutrient and energy flow through an aquaticenvironment (Figure 2). Marsh, sediment, atmospheric, faunal, and non-spring, surface waterdrainage influences were also considered. Terrestrial interactions and contributions were notdescribed individually but were included in the surface drainage component. Nitrogen was ofprimary interest due to the documented increases in groundwater nitrogen concentrations
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Figure 1. West-central Florida Springs coast study area.
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Table 1. Major rivers of the study area and associated springs with drainage area (km2)and discharge (cfs). (Compiled from Coffin and Fletcher, 1997; Jones et al.,1995; Roseneau et al., 1977; Wetterhall, 1965; Wolfe, 1990; Yobbi, 1989;Yobbi, 1992).
Waccasassa River (2,425 km2) 315 cfs near Gulf HammockBlue Spring 8.87 cfsWekiva Springs 55.7 cfs
Withlacoochee River (5,230 km2)Rainbow Springs
(18 vents reported)
422 cfs to CFBC, 1,131 cfs to Bypass Channel707 cfs at SR 484
Wilson Head Spring 2.4 cfsBlue Spring 11-20 cfs
Crystal RiverCrystal River Springs
(30 vents reported)
975 cfs at Salt River
Homosassa RiverMain stem
Homosassa Main 1, 2, and 3Homosassa River #1
Halls River Head Spring(plus many others)
Southeast Fork(6 vents reported)
Hidden River Head Spring(plus others)
354
104
162
69.1
9.29
Total
cfs
cfs
cfs
cfs
Chassahowitzka RiverChassahowitzka #l
(Bubba Spring)Chassahowitzka MainCrab Creek SpringsPotter Creek Springs
268 Total
Ruth SpringBaird CreekSalt Creek Head SpringBetteeJay SpringRita Maria SpringBlue RunRyle Creek Head SpringRyle Creek Lower Spring
30.092.648.718.6-13.313.35.60.61
10.44.786.62.48
11.8-2.48
cfscfscfs
cfscfscfscfscfscfscfscfs
cfs
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Table 1. Continued.
Weeki Wachee RiverWeeki Wachee Main SpringTwin Dees (Little) SpringSalt SpringMud River SpringHospital Hole and SpringJenkins SpringWilderness Spring
265 Total176 cfs
11.0 cfs33.0 cfs45.0 cfs
Hammock Creek (Aripeka) < 10Aripeka #1 < 5Aripeka #2 < 5Boat Spring 1.25Bobhill Springs 0.69Magnolia Springs 2.56Gator Spring 0.26
Other Coastal SpringsIsabella SpringLittle SpringHorseshoe SpringCedar Island SpringHudson SpringSalt Springs
6-10
-10 to 10
Pithlachascotee Riverat New Port Richey (466 km2) 31.1
Anclote River at Elfers (188 km2)Seven SpringsTarpon Springs
64.2< l
*up to 1,000
Total (estimated)cfscfscfscfscfscfs
cfs
cfs
cfs
cfscfscfs
* - Periodic siphon drain from Lake Tarpon, plugged in the 1980s.
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Figure 2. Conceptual model of nutrient transfer and pools within freshwater, estuarine, and coastal environments.
(SWFWMD, 1994) and since recent work indicates that receiving coastal waters are stronglynitrogen limited (Dixon and Estevez, 1997).
Potential impacts attributable to increased nutrient loading from spring runs (i.e., changes in poolsof nitrogen, or rates of transfer) were qualitatively identified using the conceptual model andeutrophication experiences in other, similar estuaries. The practical utility of physical andbiological systems to detect changes due to nitrogen loading is based on:
0 the directness of the relationship with nutrients in the conceptual model,0 the magnitude of change expected,0 the sensitivity, reliability, and feasibility of available analytical techniques,0 the inherent variability and periodicity of the sampled system, the
geographic scale of the expected impact, and0 the knowledge of and the ability to quantify forcing factors other than
nutrient loads from the spring discharges (i.e. long-term climatic cycles,endogenous seasonality, light, temperature, rainfall, flow, salinity, andloadings from other sources).
Ultimately, the successful detection of trends attributable to increases in nutrient discharges willdepend on how direct a relationship exists between the system measured and the increased nutrientload. Effects will be observed most readily at the source of the increasing load, i.e., in the waterquality of spring discharges. Biological systems which exhibit direct responses to water columnnutrients (i.e., macroalgae or phytoplankton), also reflect increasing loads, but may also be subjectto endogenous seasonal rhythms and other environmental variables (salinity, light, etc.). Somesystems do not respond directly to increases in water column nutrients, but may exhibit responsesfrom shading due to algal or phytoplankton overgrowth, or other inter-specific competition.
The level of response to increased nutrients will also change depending on the proportion ofnutrients represented by the spring discharge, the residence time of nutrients in the particularsystem, and whether stations examined are near the spring discharge, or are in the estuarineenvironment. At some distance from the discharge, a host of processes has occurred, includingmacrophytic uptake, phytoplankton uptake, conversion of inorganic nutrients to particulate ororganic forms, remobilization as inorganic or detrital particles, and dilution with coastal waters tonear analytical detection limits. As a result, an increase in loading (either as a percentage or anabsolute amount) may have very different results in estuaries with differing residence times.
For water quality data, while monitoring of inorganic nutrient species may be sufficient in springdischarges, the farther away from the spring vent, the more necessary it will be to havedeterminations of total nutrients. Additionally, discharge information is needed if spring dischargesvary either seasonally or tidally, or if a spring discharge represents only a portion of the total basindischarge. Salinity or conductivity is required if spring discharges vary tidally or seasonally inquality, or if water quality is being measured in the estuarine portions of a river, in order toaccount for the varying dilution of spring discharges with coastal waters.
The further away a sample is from the increased load, either in time, spatial distance, or numberof trophic transfers, the more variability and uncertainty are expected in the nutrient:parameter
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relationship. As a result, to detect trends and to resolve external influences (such as nitrogen loadsfrom larger regional surface water sources) from the impacts of nutrient increases in springdischarges, additional variables will need to be considered.
In order to select data sets for trend analysis and to recommend monitoring approaches, datasummaries were categorized for potential utility based on directness of theoretical response tonutrient additions as follows:
1. Water quality at springs, in river2. Macroalgae near springs, in rivers3. Rooted macrophytes near springs, in rivers4. Water quality of estuarine, coastal areas5. Phytoplankton, macroalgae, epiphytes of estuarine, coastal areas6. Rooted macrophytes of estuarine, coastal areas7. Mapping efforts
Regional mapping efforts were included as a separate category to represent a combination ofcategories 2, 3, 5, and 6.
VI. DATA INVENTORY
Water quality and biological data (seagrasses, algae, or other biological communities) which couldreflect the impacts of nutrient enrichment, were inventoried (Dixon and Nissanka, 1997;Appendix IIA). Ongoing measurement efforts were described to the extent possible. In order toobtain data from the coastal waters between Levy and Pasco counties, search terms during the dataretrieval were expanded from the above geographical description to include Cedar Key(s), SeahorseKey, Cross Florida Barge Canal, Lake Rousseau, and the Waccasassa, Wekiva, Pithlachascotee,and Anclote Rivers.
Water quality parameters sought included nitrogen and phosphorus species, and othereutrophication-related or photosynthesis-related parameters such as dissolved oxygen, Secchi depths,light attenuation, chlorophyll, total suspended solids, turbidity, color, and biochemical oxygendemand. Ancillary data such as salinity or conductivity, temperature, major ions, and dissolvedsolids were identified provided nutrient or salinity data were also available.
The biological data sets of interest were identified with reference to the conceptual model and werelimited to those communities which either utilize water column nutrients directly or to the sessileorganisms with documented responses to water column eutrophication. Communities for whichdata were sought included phytoplankton, algae, seagrasses, freshwater macrophytes, and benthicfauna (infauna, and oysters). While communities of higher organisms (such as planktivorous orherbivorous fish) also respond tonutrient additions, their increased trophic status and mobilitymake it more difficult to link population variations with nutrient loads.
Searches for data were conducted at the federal, state, and local levels. Federal agencies includedthe U.S. Army Corps of Engineers, U.S. Environmental Protection Agency, the U.S. Geological
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Survey (USGS), the USGS Biological Division (formerly the National Biological Service), and theU.S. Fish and Wildlife Service. State agencies included the Florida Game and Fresh Water FishCommission, the Florida Department of Health and Rehabilitative Services, and the FloridaDepartment of Environmental Protection (FDEP); Office of Water Policy, National PollutantDischarge Elimination System, Point Source Evaluation, Wastewater Program ManagementSection, Water Quality Assessment Section, Biology Section, Bureau of Water Facilities, Bureauof Aquatic Plant Management, and Florida Marine Research Institute. Staff at the NationalWildlife Refuges and Aquatic Preserves within the study area were interviewed as were staff withinthe environmental and monitoring sections of SWFWMD (the Ambient Ground Water MonitoringProgram; the Ambient Monitoring Program).
County contacts included planning, engineering, and utilities departments, water and waste waterprograms, and environmental health departments from Levy, Marion, Citrus, Hernando, and Pascocounties. Pinellas County was contacted for its monitoring on the Anclote River. Others withactive interests in the region were also pursued, including the Crystal River Environmental Center,and university professors from University of Florida, University of South Florida, and Texas A&Mknown to have worked in the region. Several literature reviews prepared for the region (CampDresser & McKee, Inc., 1988; CH2M Hill, 1983; Miller et al., 1981; Maturo, 1983), as well asthe bibliographies of all documents examined were relied upon, particularly to find earlier greyliterature. Another source of additional document citations and data programs was the series ofbiannual water quality inventories prepared under Section 305(b) of the Clean Water Act by FDEP,describing the State’s Florida Trend Network (FTN), ecoregion bioassessment efforts, and SurfaceWater Ambient Monitoring Program (SWAMP).
Library searches were performed via the Florida University system on-line catalog (LUIS) andDissertation Abstracts, to identify holdings from Florida State University, University of WestFlorida, University of Florida, University of Central Florida, University of South Florida, andUniversity of Miami. The on-line catalog of SWFWMD’s library was also examined. CurrentContents and DIALOG databases (Aquatic Sciences Fisheries Abstracts, Conference Papers Index,Oceanic Abstracts) were searched. Other on-line data repositories were searched; National Oceanicand Atmospheric Administration (NOAA) Office of Ocean Resources Conservation and Assessmentcatalog of data products on environmental monitoring; NOAA’s Coastwatch; National Oceanic DataCenter’s (NODC) catalog and inventory; the National Environmental Satellite Data and InformationServices (a compilation of available satellite products from National Climatic Data Center, NODC,and NOAA); National Marine Fisheries Service; and the National Environmental Data ReferralService. The Gulf of Mexico Regional Marine Research Programs inventories of research effortswere also examined. The Earth Resource Observation System (EROS) Data Center was queriedfor aerial photography coverage within the study region.
A major source of available water quality data consisted of SWFWMD itself, which coordinatesa network of rainfall, well and lake levels, and monitoring stations under ambient ground andsurface water monitoring programs (Appendix IIB). Rainfall stations number approximately 40within the study area and the water quality of approximately 38 spring discharges are currentlyunder investigation. Routine or older monitoring data are available through the centralized datasystem, while special purpose monitoring of individual sections are maintained in specialized data
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bases. The main data system contents are not listed specifically, but individual or special purposemonitoring data are described.
The USGS data base (WATSTORE/ADEPS) contains surface water quality and dischargeinformation from that agency’s monitoring network. Like many agencies, however, monitoringcoverage has declined substantially. In 1970, USGS had 99 stations statewide with seven waterquality stations within the study area (Kaufman and Dysart, 1978). Methods and detection limitsfor the monitoring program were described by Joyner (1973). By 1995, only five water qualitystations remained active within the study area (Coffin and Fletcher, 1996; Franklin and Meadows,1995; USGS, 1990a, 1990b) with one of the stations with the longest record (National StreamWater Quality Accounting Network - Withlacoochee River at Holder, 02313000) described asdiscontinued for water quality (USGS, 1995). Routine USGS monitoring data are also containedwithin STORET and are not described in the data summaries. A summary of USGS discharge andwater quality monitoring stations appears in Appendix IIC. Individual projects performed byUSGS, however, are not, as a general rule, incorporated into WATSTORE/ADEPS (or STORET),and those data sets pertinent for the region are described in Appendix IIA. The U.S.Environmental Protection Agency’s STORET data system incorporates USGS, FDEP, and manycounty environmental monitoring records. STORET inventories for the region appear inAppendix IID.
Citations for work which did not meet parameter or data density characterization criteria describedbelow, but were performed within the study area, are listed in Appendix IIE. Additionally,bibliographic entries from the various documents reviewed which were not relevant for thisinvestigation, but which did appear to pertain to the designated study area, were also included.References to aerial photography are summarized in Appendix IIF and abbreviations are tabulatedin Appendix IIG.
VII. DATA SUMMARIES
Data sets were summarized (Appendix IIA) provided they met certain criteria. A least a portionof the stations sampled had to be located within the designated study area. Additionally, for olderdata sets, either a number of stations had to be sampled during at least a few events, or at least afew stations had to be repetitively sampled over several seasons. Newer data, where improvedquality assurance measures improve comparability, were included even if they represented fewerdata points. Regardless of whether data sets were summarized or not, however, all referencesfound to work conducted within the study area were included in the Pertinent Literature(Appendix IIE). Sediment surveys were not specifically sought, nor were groundwater availabilityor geological investigations, although again, reference to these types of studies within the studyarea were included in the Literature Cited section. Where monitor well sampling accompanied asurface water investigation, that information was included in the data summary as well.
Data sets or pertinent reports were summarized as follows; citation, general geographic region,funding entity, and project goals. Study design was broadly categorized, and the sampling period,frequency, and number of samplings and stations detailed. Comments as to the geographic rangeof stations were generally included, together with the level of detail of location information.
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Individual water quality parameters sampled were listed and whether information on analyticalmethodology or quality assurance was readily available. The analytical laboratory, if it wasexplicitly stated or could be reasonably assumed, was listed. Associated biological data aredescribed and a summary of results provided. For some multidisciplinary projects, a single reportwas divided into several data characterizations, each addressing a major work effort. Datapresentations as they appear in the report were described, as well as whether the raw data areavailable, or are in magnetic form. Contacts, generally project managers, are given for projectswhich appeared to have a high degree of usefulness for this project.
Citations of data summaries appear chronologically in Appendix IA, Tables A1-A9, summarizinginformation available by region. Data utility categories identified from the conceptual model(Section IV) of 1-7 were included, with some data sets encompassing more than one category.Regional subdivisions included:
0 Cedar Keys, Seahorse Key0 Waccasassa River, Wekiva River0 Rainbow Springs and River, Lake Rousseau, Cross Florida Barge
Canal, and the Withlacoochee River0 Crystal Springs and River0 Homosassa Springs and River0 Chassahowitzka Springs and River0 Weeki Wachee Springs and River0 Pithlachascotee River0 Anclote River
Studies which pertain to more than one region are reproduced within each region’s tabularsummary. The large body of work performed at the Crystal River Power Plant for Florida PowerCorporation is not tabulated separately, but in general appears either in the Withlacoochee Riveror Crystal River summaries or both, depending on location of control stations.
A trend analysis category was assigned to the potential data sets as either acceptable (Y), or notacceptable (N) for the present purpose. To be sufficient for trend analysis in itself, a three yearperiod of record with at least ten observations were required as a minimum. Within this threeyear period, provided station locations and methods remain comparable, any trend evident wouldbe expected to be representative of true conditions; any consistent method bias (accuracy) wouldnot obscure or produce false trends, and issues of method precision and sensitivity would simplydictate the level of trend or degree of difference which could be detected.
TO combine data sets for trend analyses, however, methods must be comparable and so a largemeasure of quality assurance is required. Lack of sensitivity or poor precision in one data setcompared to another will not in general obscure or produce false trends, but will make truedifferences much harder to detect. Poor precision may, however, produce incorrect results if manydata are below detection limits, depending on the treatment of the censored data, i.e., if one-halfof the MDL is selected for statistical analyses. Differing accuracy or method bias betweencombined data sets will produce false trends, generally evident as a step trend that coincides withthe different study.
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If data sets had information in one of the seven data utility categories described above, qualityassurance categorizations were assigned as either acceptable (Yes), unknown (Unk) but potentiallyacceptable, or unacceptable (No) for the present purpose of combining data sets into a single entityfor analysis. As such, a rating of unacceptable from this project does not imply that the work socategorized was invalid or made conclusions in error, but is used to indicate that methods wereincompatible with those in widespread use today (with which monitoring will be continuing); thatcombining the data with other projects was likely to produce a step trend; that insufficient evidencecould be found to document methods, or that a personal contact could not be found from whomto obtain further information. In assigning QA categorizations, benefit of the doubt was given tolarger organizations which may have had broader experience with quality assurance issues andwhich were better able to support the additional work load that an effective QA Program imposes.Some data sets may have acceptable QA, but a successful trend analysis will depend on the futurerepetition of a study with the same methods and stations.
VIII. SELECTION OF DATA SETS AND MINIMUM CRITERIA
Data sets were selected for analysis with the knowledge that SWFWMD was pursuing a similarinvestigation of groundwater water quality which focused on the major spring complexes of theregion, and included investigations of hydrology, sources of nitrogen, and nitrogen loading (Joneset al., 1997). In addition, previous work by SWFWMD had investigated trends in spring dischargewater quality of five selected springs (SWFWMD, 1994) which was of lesser importance to repeat.Accordingly, water quality stations selected for analysis under this project focused on surfacewater stations in order to investigate whether water quality impacts were discernable at locationsother than spring vents. The techniques employed, however, were applied to several stations atspring vents for confirmation of techniques and updating of trend analysis where additional datawere available.
Data Selection and Retrieval Process
Data selected for statistical analyses included the water quality data of surface stations availablethrough STORET and produced by a number of agencies, data available from the FloridaLAKEWATCH Program, and the National Atmospheric Deposition Program/National TrendNetwork (NADP/NTN) data on rainfall quality from stations near Sarasota and near Starke,Florida. In addition, rainfall data were evaluated both as concentrations and as weekly loads.STORET data were not supplemented by SWFWMD’s own sampling data of the region since thisinformation was under analysis internally by SWFWMD.
No biological data sets suitable for temporal trend analysis were identified. The U.S. Army Corpsof Engineers and the U.S. Fish and Wildlife Service cooperatively monitor the vegetation of KingsBay in the Crystal River. Investigations have been conducted since 1979 and are quantitative innature but the data are not available magnetically and some has been previously analyzed (Bishopand Canfield, 1995). The Florida Department of Environmental Protection Aquatic PlantManagement Division surveys aquatic vegetation (as areas covered) annually with surveys limitedto the three major exotic species on alternate years. Without detailed records on control efforts,it was considered more promising to concentrate on water quality data. Few other biological
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investigations extended over more than a year or two. Many studies were well designed and, onrepetition, would provide valuable information. In particular, vegetation mapping efforts on theRainbow and Weeki Wachee Rivers lend themselves to graphical analysis with additional studies.
Quality Assurance on Selected Data Sets
For the STORET data, the very qualities which are the most valuable (i.e., the length of recordand broad distribution of stations) also produces one of the major drawbacks, a lack of readilyaccessible documentation. Data can be expected to reflect improvements in sampling and analyticaltechniques over the years, and the question for the analysis of trends is whether the changes haveimposed a consistent bias and therefore a spurious trend. Agency-sponsored quality assuranceprocedures, with formalized assessments of precision and accuracy of all measurements, only cameinto general use in the early 1980s in Florida. While these issues were likely addressed, especiallyby larger agencies, the data base does not include information on methodologies, data validation,quality assurance of field and analytical measurements, and associated metadata. Varyingapproaches to reporting censored data are apparent and obvious errors in both station descriptionsand analytical data persist. In addition, the practice of entering data produced by a contractor ora county program under the funding agency code, make verification of outlier data points verydifficult. Despite these drawbacks, the length of record is too valuable to discard, and providedany observed trends are examined for evidence of step trends, the record can provide indicationsof long-term patterns in water quality. There is little biological data (including chlorophyll) orwater clarity information contained in the database.
The LAKEWATCH data represent an SWFWMD-supported volunteer monitoring program begunin 1990 for the Withlacoochee River and in 1992 for Kings Bay of the Crystal River. Spatialcoverage is good and the program has an enviable sampling history and frequency. Whenvolunteers miss a sample, LAKEWATCH personnel endeavor to sample within one week topreserve the sampling frequency. Parameter coverage is limited but include total nutrient quantities(nitrogen and phosphorus) as well some of the few chlorophyll data available. Volunteer samplershave been trained by the University of Florida’s Department of Fisheries and Aquatic Sciences.Following collection and delivery of samples to the LAKEWATCH program, samples are analyzedat the University of Florida, Institute of Food and Agricultural Sciences, Department of Fisheriesand Aquatic Sciences, LAKEWATCH Water Quality Laboratory. The laboratory has a FDEP-approved Comprehensive Quality Assurance Plan (#910157) which gained final approval in 1994although most of the procedures had been in use for many years (Ms. Laura Mataraza, personalcommunication). Analytical methods of the LAKEWATCH program have changed over the years.Chlorophyll analyses were modified in the spring of 1994 from acetone to ethanol solventextraction, and total nitrogen methods changed in July 1991. The methods in use are not thosetypically employed by regulatory agencies, but method equivalency studies (Bachmann andCanfield, 1996) have been performed. The effects of procedural changes in sample preservation(instituting freezing of samples for the Kings Bay program, for example) were also investigatedand found insignificant. With this level of knowledge, accuracy of analyses is consideredexcellent, and data can be examined for the presence or absence of step trends coincident withmethods changes.
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Quality assurance measures on the weekly NADP/NTN rainfall data are excellent. Site installation,operation, analytical, and sample assessment protocols are documented in instruction manuals andprogram quality assurance manuals (Bigelow, 1984; Bigelow and Dossett, 1988; Aubertin et al.,1991). Field validity codes are assigned to each sample based on one of 13 screening criteria,including assessment of sampling protocol, contamination, equipment malfunction, duration ofsampling, laboratory error, incomplete chemical analyses, and the availability of rainfall amounts.Field measurements of pH and conductivity are evaluated separately, as are samples for laboratoryanalysis. A “Valcode” of 0 is assigned if the sample is considered invalid, allowing the deletionof these data from the database. In addition to the rigorous evaluation of each sample, the samelaboratory, the Central Analytical Laboratory at the Illinois State Water Survey., has been analyzingsamples since the inception of the program in 1978. Procedural changes have been few, welldocumented, and effects on data quantified.
IX. DATA PROCESSING
Parameters to be downloaded from the Environmental Protection Agency’s STORET (STOrage andRETreival) Database were first identified through an all-state inventory process which providessummary data on the number of observations by parameters. Parameters with less than twentyobservations for the entire state were not retrieved. Parameters requested appear in Table 2.Retrievals were not restricted by agency and selected stations were categorized as “ambient” andeither “stream”, “ocean”, “estuary”, or “spring”. Geographic boundaries for the retrieval includedLevy, Marion, Citrus, Hernando, and Pasco counties. Stations retrieved are (or were) maintainedby the following agencies:
21FLSWFD Southwest Florida Water Management District11COEJAX U.S. Army Corps of Engineers21FLA Florida Department of Environmental Protection21FLKWAT Florida LAKEWATCH112WRD U.S. Geological Survey21FLSUW Suwannee River Water Management District
The positional data of stations were reviewed and data from some stations were combined whereclearly justified both by description and by latitude and longitude of the individual stations. Somepositions appear unreliable based on the text descriptions. If in doubt, station data were notcombined. Two locations were clearly represented by multiple stations and were combined aslisted in Table 3. Data from combined stations were reviewed for evidence of step trends beforeproceeding with temporal trend analyses.
WAC005C1 WACCASASSA RIVER @SR-24 21FLSUW02313500 WACCASASSA RIVER NR OTTER CREEK 112WRD23020006 WACCASASSA RIVER @SR-24 21FLAWACCA324 WACCASASSA RIVER NR OTTER CREEK COMBINEDWEEKI-1 WEEKI WACHEE RIVER; TRIANGULAR SEAWALL SECTION 21FLSWFDFLO0098 WEEKI WACHEE RIVER AT ROGERS PARK 21FLSWFDWEEKIROG WEEKI WACHEE RIVER AT ROGERS PARK COMBINED
Stations were only retrieved if the data extended at least over a three year period and had at leastten observations. Stations which met this and the other project criteria appear in Table 4. Thirty-two locations were distributed as follows: five in the Waccasassa River and tributaries, two in theRainbow Springs and River, eleven in the Withlacoochee River/Lake Rousseau/Cross Florida BargeCanal region, two in the Crystal River, three in the Homosassa River and tributaries, two in theChassahowitzka River, two in the Weeki Wachee River, three in the Pithlachascotee River, andtwo on the Anclote River.
Obviously miskeyed data were deleted from the STORET retrieval (pH of 0.00, temperature of0.2°C or 53.0°C). All data with remark codes were reviewed and those data where the remarkcodes indicated quality assurance problems were also deleted. Acceptable remark codes do notaffect data quality and were used as retrieved (Table 5). Censored values reported as less than themethod detection limit (“U” remark code) were converted to a value of one-half of the detectionlimit, considered appropriate for data which are not necessarily log-normal in distribution(USAEWES, 1995).
Parameter values were transformed as appropriate to provide the largest number of data pointspossible per parameter category. Nitrogen and phosphorus species reported as ionic concentrations(NO,, PO,, etc.) were transformed to elemental concentrations (NO3-N, PO4-P, etc.) for both totaland dissolved parameters. Total nitrogen quantities were computed from nitrate, nitrite, and totalKjeldahl nitrogen; organic nitrogen was computed from total Kjeldahl nitrogen and ammonium-nitrogen concentrations. Fahrenheit temperatures were converted to centigrade. Dissolved oxygen,pH, and conductivity observations were compiled from field observations and were supplementedwith laboratory determinations in the absence of field data.
Multiple observations at a station on a single date were reviewed. When chemistry values wereavailable from multiple depths at a station, values were averaged if collected within one hour ofone another. If chemistry values were available for a single depth while in situ data existed as adepth profile, only the in situ data from the depth of the chemistry sample were retained. Theresulting STORET database consisted of 34 stations, 47 parameters, and 2,172 records.
LAKEWATCH data were obtained from the ongoing LAKEWATCH Program for the stationssampled on the Crystal and Withlacoochee Rivers. Four of the five Withlacoochee LAKEWATCHstations were contained within the STORET data set but an additional year of sampling was
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Table 4. Station designations, descriptions, locations, and responsible entity, as retrieved from the EPA STORET data base.
WACCASASSA RIVER NR GULF HAMMOCKWACCASASSA RIVER @ SR 326WACCASASSA RIVER/OTTER CREEK/FRESHWATER REF SITEWACCASASSA RIVER AT GULF HAMMOCK AT US 19TEN MILE CREEK AT LEBANON STATION AT US 19RAINBOW SPRINGS NEAR DUNNELLONBLUE RUN AT DUNNELLONWITHLACOOCHEE R FLA 200 E OF HOLWITHLACOOCHEE RIVER NR HOLDERLAKE ROUSEAU ABOVE DAMWITHLACOOCHEE RIVER AT SR 41BYPASS CANAL AT INGLES LOCKWITHLACOOCHEE R AT INGLIS DAM NR DUNNELLONWITHLACOOCHEE R BL INGLIS DAM NR DUNNELLONBARGE CANAL ABOVE INGLIS LOCK NR INGLISBARGE CANAL AT INGLIS LOCK NR INGLISWITHLACOOCHEE R BYPASS CHANNEL NR INGLIS FLAWITHLACOOCHEE R BYPASS CH BEL STR NR INGLISCRYSTAL RIVER NR CRYSTAL RIVERA01 CRYSTAL RIVER STATION #lHOMOSASSA RIVER AB HALLS RIVERHALLS RIVER ABOVE HOMOSASSA RIVERANCLOTE CRYS R - CHAN#74 HOMOSASSA R AB GULF/MEXCHASSAHOWITZKA RIVER NEAR HOMOSASSAANCLOTE CRYS R - CHASSAHOW R AB GULF OF MEXICOWEEKI WACHEE SPRINGS NR BROOKSVILLEWEEKI WACHEE RIVER AT ROGERS PARKPITHLACHASCOTEE RIVER NR FIVAY JUNCTIONPITHLACHASCOTEE RIVER NR NEW PORT RICHEYPITHLACHASCOTEE RIVER NEAR RICHEY LAKESANCLOTE RIVER NR ODESSAANCLOTE RIVER NR ELFERS
STORET remark codes considered acceptable for inclusion in the data base.“K” and “U” values transformed to one half the numerical value.
Value reported is the mean of two or more determinations.
Indicates field measurement.
Value is between the method detection limit (MDL) and the practicalquantitation limit (PQL)
Actual value is known to be less than the value given. (Less than themethod detection limit .)
Actual value is known to be greater than the value given.
Value reported is less than the criteria of detection.
Indicates material was analyzed for but not detected. (Less themethod detection limit. )
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available directly through the program. All Withlacoochee LAKEWATCH stations are below LakeRousseau and sampling began in 1990. Seven stations have been sampled on the Crystal River,primarily in the Kings Bay region, since 1992. (A newer LAKEWATCH program withmeasurements in the adjacent canal systems had an insufficient time period for analysis under thecriteria of this project.) Nitrogen and phosphorus data were converted from µg/l to mg/l forconsistency with the STORET data base. Secchi depths were deleted when reported as equal toor greater than the overall water column depth. A total of 12 stations, 1,043 records, and 3,291individual parameter values resulted, and included the only chlorophyll data available.
The National Atmospheric Deposition Program/National Trend Network (NADP/NTN) rainfallquantity and quality data were available on-line for the Verna Wellfield (Sarasota) and BradfordForest (Starke) sites. These two sites are outside of, but bracket, the study area and were deemeduseful for depicting long-term trends in rainfall quality for the region. The sites have been inoperation since 1983 and 1978, respectively, and form an excellent long-term record. Data werecensored if NADP/NTN quality assurance procedures indicated a compromised sample. In additionto the concentration data, major ion data were also converted to weekly loads using collectedrainfall amounts.
Trend analyses on surface water and rainfall quality and loads were conducted with a non-parametric LOWESS procedures (Locally WEighted Scatterplot Smooth; Cleveland, 1979). Thisrobust smoothing procedure computes general tendencies of data with respect to the selectedindependent variable (or forcing function), describing the relationship between nitrogenconcentration and rainfall, for example, while not requiring either linearity of relationship ornormality of residuals. Iteration procedures in computing the smoothed curve reduce the impactof outlier data, particularly at the extremes of the independent data distributions. The procedureis also resistant to periodicity, missing and censored data, and serial correlation.
Once the influence of selected forcing functions is evaluated in the LOWESS smooth, residualsfrom the curve are then evaluated against time to detect temporal trends. Residuals can be testedeither through lineal regression or non-parametric methods, with a non-parametric method requiredwhen residuals data do not fit a bivariate normal distribution or otherwise deviate from requiredassumptions. Kendall’s tau or the Mann-Kendall test is applicable to data with skewed distributionsor outliers and will detect any monotonic correlation in addition to linear relationships. As aresult, non-parametric tests are particularly suited to environmental data and especially to theanalysis of large data sets where the appropriateness of each linear model cannot be examined.Serial correlation, however, cannot be present for computed P-values to be correct.
For the assembled data sets, dependent variables fitted through LOWESS procedures included allnutrient and major ion parameters available. As exogenous variables or forcing functions, theindependent variables used were a series of cumulative rainfalls, fraction of year (season),temperature, conductivity, and groundwater elevations, where applicable. For rainfall quality, pHwas also used. Again, at least ten data points were required for each analysis.
As an example of the technique (Figure 3), conductivity data are plotted against date (Figure 3A)for time series which may or may not exhibit trend. However, it is reasonable to expect someseasonal variation in conductivity; decreases during the wet season when low-conductivity rainfall
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Figure 3. An example of conductivity against time (3A), LOWESS smooth of the conductivityagainst fraction of the year (3B), and analysis of residuals from the LOWESS smoothagainst time (3C) to determine trend. Linear representation only, as significance of trendswas detected with non-parametric rank-correlation analyses.
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is more evident in surface drainage systems. Any temporal trends may be obscured by the highvariability in the data set, or spurious trends could perhaps be produced by a temporal bias insampling (more frequent sampling during the wet season in later years, for example).
Accordingly, the same conductivity data are replotted in Figure 3B against fraction of year(analogous to season). In this instance, fraction of year is considered to be an independent variablewhich may account for some of the observed temporal variability in the conductivity data. TheLOWESS procedure is used to compute a smoothed, best-fit curve (Figure 3B), the result of whichis quite analogous to computing seasonal averages. As expected, decreases in conductivity appearassociated with the rainfall from winter frontal systems (year fraction 0.1 to 0.2) and with summerconvective rainfall (year fraction 0.6 to 0.8).
Residuals (observed conductivity data less the LOWESS computed smoothed value) are thencomputed for all data points in Figure 3B. Residuals are then plotted against the original samplingdate (Figure 3C) and examined for trend. Significance of trend in the residual values can beevaluated either parametrically via linear regression, or non-parametrically via Kendall’s tau. Inthe example in Figure 3C, there is an increasing trend in conductivity residuals with date. Thisdoes not mean that raw conductivity. values are increasing with time, but that, within expectedseasonal fluctuations in conductivity, more recent values are higher than previous data.Alternatively stated, in Figure 3B, more recent data tend to be located above the LOWESS curve,while older data generally fall below it.
Other forcing functions control water quality and typically complicate trend analyses. In particular,rainfall and runoff contain and transport many water quality parameters of interest. The recurringquestion in any trend analysis is whether the observed trend in nitrogen, for instance (Figure 4A)is due to long-term climactic changes in rainfall amounts or is due to anthropogenic impacts. Inthis example, total kjeldahl nitrogen data are replotted against 60 day cumulative totals and a fairlycoherent pattern observed (Figure 4B) of generally increasing total kjeldahl nitrogen with increasingrainfall amounts, although the relationship is not very linear.
Total Kjeldahl nitrogen residuals from the LOWESS smooth against date (Figure 4C), have muchless overall variation than the original data and indicate that a substantial amount of nitrogenvariation is correlated with rainfall. The increasing trend noted for residuals with date againindicate that more recent data fall above the LOWESS curve in Figure 4B and further imply that,for a given set of rainfall conditions, total Kjeldahl nitrogen concentrations have increasedsignificantly over time. There are no statements implied here regarding the frequency ordistribution of rainfall amounts.
For cumulative rainfall amounts, daily rainfalls for the period of record were obtained fromSWFWMD’s rainfall monitoring network. Surface water stations were assigned to the threenearest rainfall stations (Table 6). In some cases, stations representing a large drainage area wereassigned stations. from a broader region. Stations with a known dependence on groundwaterdischarge were assigned the stations within or near the groundwater recharge area. Sampling datesfor the surface water quality data were used to enter the three rainfall records and computecumulative rainfall totals at each rainfall station. The three rainfall totals were then averaged. Aminimum of 85 percent completeness for a station’s rainfall record was required before it would
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Figure 4. An example of a parameter against time (4A), LOWESS smooth of the parameter againstan explanatory independent variable (4B), and analysis of residuals from the LOWESSsmooth against time (4C) to determine trend. Linear representation only, as significanceof trends was detected with non-parametric rank-correlation analyses.
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Table 6. Surface water stations and assignment to rainfall stations for average cumulativerainfall.
Station Description Rainfall Stations
WACCASASSA RIVER NR GULF HAMMOCK 1 2 4WACCASASSA RIVER @ SR 326 1 2 4WACCASASSA RIVER/OTTER CREEK/FRESHWATER REF SITE 1 2 4WACCASASSA RIVER AT GULF HAMMOCK AT US 19 1 2 4TEN MILE CREEK AT LEBANON STATION AT US 19 1 2 4WACCASASSA RIVER NR OTTER CREEK 1 2 4RAINBOW SPRINGS NEAR DUNNELLON 2 3 5BLUE RUN AT DUNNELLON 2 3 5WITHLACOOCHEE R FLA 200 E OF HOL 6 7 8WITHLACOOCHEE R.BASIN/STOKE’S FERRY/FRESHWAT 6 7 8WITHLACOOCHEE RIVER NR HOLDER 6 7 8LAKE ROUSEAU ABOVE DAM 5 6 7WITHLACOOCHEE RIVER AT SR 41 5 6 7BYPASS CANAL AT INGLES LOCK 4 5 6WITHLACOOCHEE R AT INGLIS DAM NR DUNNELLON 4 5 6WITHLACOOCHEE R BL INGLIS DAM NR DUNNELLON 4 5 6BARGE CANAL ABOVE INGLIS LOCK NR INGLIS 4 5 6BARGE CANAL AT INGLIS LOCK NR INGLIS 4 5 6WITHLACOOCHEE R BYPASS CHANNEL NR INGLIS FLA 4 5 6WITHLACOOCHEE R BYPASS CH BEL STR NR INGLIS 4 5 6CRYSTAL RIVER NR CRYSTAL RIVER 9 10 11A01 CRYSTAL RIVER STATION #1 9 10 11HOMOSASSA RIVER AB HALLS RIVER 12 13 14HALLS RIVER ABOVE HOMOSASSA RIVER 12 13 14ANCLOTE CRYS R - CHAN#74 HOMOSASSA R AB GULF/MEX 12 13 14CHASSAHOWITZKA RIVER NEAR HOMOSASSA 13 14 15ANCLOTE CRYS R - CHASSAHOW R AB GULF OF MEXICO 13 14 15WEEKI WACHEE SPRINGS NR BROOKSVILLE 16 17 18WEEKI WACHEE RIVER AT ROGERS PARK 16 17 18PITHLACHASCOTEE RIVER NR FIVAY JUNCTION 19 20 21PITHLACHASCOTEE RIVER NR NEW PORT RICHEY 20 21 22PITHLACHASCOTEE RIVER NEAR RICHEY LAKES 19 20 21ANCLOTE RIVER NR ODESSA 21 22 23ANCLOTE RIVER NR ELFERS 21 22 23WITHLACOOCHEE RIVER - ABOVE BARGE CANAL 4 5 6WITHLACOOCHEE RIVER - ABOVE US 19 4 5 6WITHLACOOCHEE RIVER - BELOW US 19 4 5 6WITHLACOOCHEE RIVER - AT YANKEETOWN 4 5 6WITHLACOOCHEE RIVER - BELOW YANKEETOWN 4 5 6CRYSTAL RIVER - CONFLUENCE OF SALT RIVER 9 10 11CRYSTAL RIVER - CEDAR COVE 9 10 11CRYSTAL RIVER - NORTH BUZZARD ISLAND 9 10 11CRYSTAL RIVER - THREE SISTERS SPRING 9 10 11CRYSTAL RIVER - SE BAY 9 10 11CRYSTAL RIVER - SW BAY 9 10 11CRYSTAL RIVER - INDIAN RIVER CANAL 9 10 11
be included in the average. If one rainfall station was incomplete, the remaining two rainfallstations were used to produce the average cumulative rainfall value. Cumulative rainfall totalswere computed for 3-day, 7-day, 14-day, 30-day, 60-day, and 90-day periods. Rainfall data wereavailable for all surface water quality stations except for the Waccasassa River at Gulf Hammock,Blue Run at Dunnellon, and the Pithlachascotee River near Richey Lakes where the period ofrecord for all three rainfall stations did not encompass the water quality sampling dates.
Fraction of year was used rather than season to allow time of year to be treated as a continuousvariable. The quantity was computed as the decimal fraction of the calendar year (day of yeardivided by 365.
Data from stations known to have substantial groundwater influence were similarly smoothedagainst groundwater elevation data (as a surrogate for discharge measurements) before testingresiduals for temporal trend. Groundwater data from representative wells were obtained fromSWFWMD. Stations were assigned up to four wells (Table 7) for testing, including wells whichrepresented both regional Floridan groundwater levels (Homosassa #1, Weeki Wachee Deep,ROMP TR 18-3 Suwannee/Avon Park and Upper Avon Park) and coastal, tidally-influencedelevations (ROMP TR 19-2 Ocala, Homosassa #3) (Yobbi and Knochenmus, 1989; Yobbi, 1992).Well assignments for this project were not to imply that groundwater elevations in the assignedwells control discharge at the given surface water station, but were used only as general indicatorsof annual patterns of groundwater. Well elevations used for statistical testing were computed asthe average elevation within the seven days prior to the surface water sampling, and as a result donot include any diurnal or tidal variations. A total of 344 groundwater elevation data points wasmatched to surface water quality observations.
For the NADP/NTN rainfall concentration and weekly loading data, independent variables includedfield conductivity, pH, total weekly rainfall, and fraction of year. Table 8 lists all potentialindependent variables to which parametric data were fit before performing residuals analyses.
X. RESULTS AND DISCUSSION BY REGION
Sufficient data existed to perform LOWESS procedures on 393 station-independent variablecombinations, or a total of 5,974 parameter-independent variable combinations. Residuals fromLOWESS smoothing were assessed against time non-parametrically. For visualization, graphicsof results depict a linear fit through residuals as a function of time, but significance was determinedby the non-parametric Kendall’s tau coefficient of rank correlation.
Tables 9-11 summarize the significance of Kendall’s tau of the LOWESS residuals against time.In addition to level of significance, the direction of the slope of the parameter with respect to timeis indicated. In viewing the results, one should consider not only the significance of eachcorrelation, but also the number of independent variables with similar trends for that parameter.Where one parameter is increasing with respect to time against a number of independent variables,trends in other parameters should be examined for intelligible patterns. Mixed trends against time(both positive and negative slopes for the same parameter against differing independent variables)imply that at least one of the independent variables does not reliably covary with the parameter.
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Table 7. Well designations of groundwater elevation data used for indications of discharge
at stations with substantial groundwater influences.
Location
Rainbow Springs near Dunnellon 6 10Blue Run at Dunnellon 6 10
Crystal River near Crystal River 3 2 9 5A01 Crystal River Station #1 2 3
Homosassa River above Halls River 4 5Homosassa River above the Gulf of Mexico, Channel Marker #74 4 5 10Halls River above Homosassa River 4 5 10
Chassahowitzka River near Homosassa 1 5 9Chassahowitzka River above the Gulf of Mexico 1 5 9
Weeki Wachee Springs near Brooksville 11 12 7 8Weeki Wachee River Rogers Park 11 12 7 8
W1 W2 W3 W4
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Table 8. Independent variables to which water quality parameters at each station were smoothedprior to performing temporal trend analysis on residuals.
Table 10. The Crystal River and Withlacoochee River stations collected by the LAKEWATCHProgram. Results of trend analyses on LOWESS residuals against time (Kendall’stau). Direction of slope indicated following significance: ” *** ” = p < 0.001," ** " = p<0.01, “*” = p <0.05, “ns” = not significant, ‘/’ = not analyzed sincen< 10.
Table 11. Rainfall quantity and quality collected by NADP/NTN near Starke and Sarasota, FL. Results of trend analyses on LOWESS residualsagainst time (Kendall’s tau). Direction of slope indicated following significance: “***” = p<0.001, “**” = p<0.01,"*" = p<0.05, “ns” = not significant, ‘/” = not analyzed since n< 10.
Additionally, where there are strong temporal trends in raw data, and little effect of independentvariables on parameter values, LOWESS smoothing and residuals analysis may not appreciablyreduce sample variation, and yet trends will still be evident due to the overriding trend present inthe original data.
Waccasassa River
For the Waccasassa River watershed, five stations had sufficient data for analysis (Figure 4A).While data collection at one station ceased in the late 1970s, others are currently being sampled,with the most recent data available through August 1996. Among the differing time periods, fewoverriding trends were apparent for the entire region. By station, the most apparent trends werefor the Waccasassa River/Otter Creek/Freshwater Reference Site which has experienced decreasingammonium-nitrogen and total phosphorus (Appendix Figure IB-1) between 1975 and 1995. Trendsin total phosphorus are present in both raw and LOWESS residuals data. Waccasassa River at SR326 evidenced consistent increasing dissolved oxygen and decreasing ammonium-nitrogen between1987 and 1994. Waccasassa River at Gulf Hammock at US 19 recorded consistently decreasingpH, increasing color, with some evidence of decreasing nitrate-nitrite-nitrogen (AppendixFigure IB-2), nitrate-nitrogen, and ammonium nitrogen between 1989 and 1996. For the region,when temporal trends in nutrient species were significant, they reflected decreasing concentrations.
Rainbow River
Two stations were identified for the Rainbow River region (Figure 4B). Rainbow Springs and BlueRun at Dunnellon had data available through 1989 and 1981, respectively. Regional rainfall datawere not readily available to perform LOWESS procedures on the Blue Run data, but both stationsexpressed several strong trends. For Rainbow Springs, increasing nitrate-nitrite-nitrogen and totalnitrogen concentrations were apparent against all independent variables, including well elevationdata. Total phosphorus evidenced some increases over time, as well, while silicates and hardnessdecreased with respect to many independent variables. Water quality at the Blue Run station,where trends existed, generally exhibited decreasing nitrogen concentrations through 1981.
At Rainbow Springs, increasing nitrate-nitrogen concentrations (Appendix Figure IB-3) have beenobserved by others in the same and in more extensive data sets although there are questions as tothe consistency of sampling location. LOWESS analysis confirms this result in a way whichreduces the relative weight of the later, more concentrated samples. A more restricted data setfrom the late 1980s to present, however, also indicates increasing concentrations (Jones et al.,1995; Mr. Gregg Jones, personal communication).
Withlacoochee River
For the remaining stations on the mainstem of the Withlacoochee (Figures 4B and 4C), there areno consistent regional trends apparent for the differing time periods represented. Again, however,individual stations display strong trends in a variety of parameters. By far the largest data set isfor the USGS NASQAN (National Stream Quality Accounting Network) station, WithlacoocheeRiver near Holder, with data current in STORET through 1990. (The station was discontinued in1995 [USGS, 1996]) Strong trends exist at this location for a number of parameters, including
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decreasing dissolved oxygen, increasing nitrate-nitrogen (Appendix Figure IB-4), total Kjeldahlnitrogen (Appendix Figure IB-5), nitrite-nitrogen, total phosphorus (Appendix Figure IB-6) andortho-phosphate. There is no trend in time of day of sampling.
Another large data set exists through 1989 at Withlacoochee River FLA 200, east of Holder. Thisdata set has no trends in nutrient concentrations, although strongly decreasing in pH andconductivity. Earlier data, through 1977, from Withlacoochee River at and below the Inglis Damnear Dunnellon, the Barge Canal above and at the Inglis Lock near Inglis, and in the bypasschannel below the structure, have no overall trends. By station, increases in chloride wereobserved above and at the Inglis Lock on the Barge Canal. Some increases in nitrite-nitrogen inthe Withlacoochee River at the Inglis Dam were recorded. As the nitrite concentrations weretypically low and many data censored (less than method detection limits) the magnitude of thechange is quite small. The Withlacoochee Bypass Channel near Inglis, with data through 1983,exhibits strong trends of increasing ortho-phosphate concentrations against almost all independentvariables. Decreasing nitrite nitrogen again represents a minimal although significant change, withdecreases appearing to represent a change in reporting format, from <0.01 to 0.00 for valuesbelow the detection limit.
The remaining three stations on the Withlacoochee River, Lake Rousseau above the dam, at SR41, and in the Bypass canal at the Inglis lock, have water quality data through 1990. Again, noregional patterns predominate, but Lake Rousseau has experienced increases in temperature(Appendix Figure IB-7), chloride and ammonium-nitrogen (Appendix Figure IB-8), while at thesame time recording a decrease in total dissolved solids. The Withlacoochee at SR 41 has recordedincreases in temperatures and dissolved oxygen, with decreases in nitrite-nitrogen, and to a lesserextent, in iron. The bypass canal at the Inglis Lock has observed increases in temperature togetherwith some decrease in nitrate-nitrite-nitrogen.
The LAKEWATCH data consist of monthly data from March 1990 through December 1996 fromfive stations on the Withlacoochee River at approximately 2.5 km intervals from above the BargeCanal to below Yankeetown. While the parameter suite is restricted to total nitrogen, totalphosphorus, chlorophyll, and Secchi depths, the data are remarkable in that all five stations haveconsistent and highly significant trends of increasing nitrogen (Appendix Figures IB-9 and IB-10)and phosphorus (Appendix Figures IB-11 and IB-12) against all independent variables. Trends donot coincide with the dates of any changes in analytical methods. There are indications ofincreasing chlorophyll concentrations at the station above the Barge Canal (AppendixFigure IB-13), coupled with decreasing Secchi depths (Appendix Figure IB-14), althoughchlorophyll in relation to Secchi depths indicate that color or suspended solids also contribute todecreased water clarity. There are no significant trends in chlorophyll or Secchi depth at thestation between the Barge Canal and US 19. Below US 19 and at the stations above and belowYankeetown, however, Secchi depths are decreasing (less water clarity). No trends are seen forchlorophyll at these station which indicates that suspended solids or color may be primarilyresponsible for the decreasing water clarity.
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Crystal River
Crystal River data consisted of records from two STORET stations and seven ongoingLAKEWATCH stations (Figure 5A). At the Crystal River near Crystal River station, trends from1966 through 1978 indicated increasing nitrate-nitrite-nitrogen (Appendix Figures IB-15 and IB-16)and nitrate-nitrogen together with decreasing organic nitrogen and no significant trend in totalnitrogen. Some evidence of increasing total and ortho-phosphorus was observed (AppendixFigure IB-17). From 1989 through 1996, however, there were no observed trends in organicnitrogen, ammonium nitrogen, ortho- or total phosphorus, or major ions at the A01 Crystal RiverStation #1. There were insufficient nitrate-nitrogen data to evaluate at this station.
The Crystal River LAKEWATCH stations provided data from 1992 through early 1997 fromstations in and around Kings Bay, downstream to the confluence with the Salt River. There arestation differences, but regionally both total nitrogen and total phosphorus (Appendix Figure IB-18)have decreased. Total phosphorus has decreased against all independent variables at all stations.Total nitrogen decreases were noted primarily at Three Sisters Spring, SE Kings Bay, Cedar Cove(Appendix Figure IB-19), and north of Buzzard Island.
Using data from Romie (1990), a large decrease in nutrient concentrations was observed at selectedstations following the removal of a wastewater treatment plant discharge from the Cedar Cove areain 1992 (Bishop and Canfield, 1995). The continued declines in nutrient concentrations can reflectthe more gradual remobilization of nutrients from the organic content of the sediments, reportedto be highest in the Cedar Cove and western areas of Kings Bay (Belanger et al., 1993). Theorganic content of sediments in the Bay appear to be from algae and other aquatic plants, whileorganics at a canal station have a more terrestrial origin (Belanger et al., 1993).
Despite the decrease in nutrients, however, significant chlorophyll increases were exhibited at theCedar Cove station (Appendix Figure IB-20), and to a lesser extent, at the SW Kings Bay station.No chlorophyll trends were observed elsewhere. Aquatic vegetation control efforts continueprimarily with mechanical harvesting, and complicate the linkage between chlorophyll and watercolumn nutrients.
The overall clarity and shallow depths prevented trend analysis on Secchi depths at some stations.A decrease in water clarity was observed at the Salt River station (Appendix Figure IB-21) but wasnot accompanied by increasing chlorophyll.
Homosassa River
Three stations in the Homosassa River and Halls River had sufficient data for analysis, from 1992through late 1996 (Figure 5B). In the Homosassa River, above the Halls River, increases in totalKjeldahl, organic (Appendix Figure IB-22), and total nitrogen were seen. Nitrate-nitrite-nitrogen,total phosphorus, and major ion concentrations were not significantly increasing over this four yearperiod. No trends were observed for the Halls River station. Decreases in some of the majoranions at the most downstream station on the Homosassa River could be an artifact of tidalvariation.
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Figure 5. Station locations of data analyzed for trend: Waccasassa region (5A), RainbowRiver (5B), and Withlacoochee River (5C).
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Chassahowitzka River
Two stations provided information on the Chassahowitzka River system (Figure 5B). The earlierdata set, 1964 through 1978, has recorded decreases in stage with respect to rainfall, implying thatfor given rainfall amounts, pool elevation has declined. The spring does experience tidal changesin elevation, however, which could produce spurious trends, and tidal data would be needed toresolve the validity of this trend. Also evident are indications of increases in both ortho- and totalphosphorus.
The more recent data, 1992 through 1996, show some increases in total Kjeldahl (AppendixFigure IB-23) and organic nitrogen, with indications of decreased chloride concentrations. Dataare not numerous at this station, and water quality of the discharge is also known to be tidallyaffected (Yobbi, 1992; Dixon and Estevez, 1996). Again, tidal phase of sampling could accountfor the apparent temporal difference in chloride of approximately 600 mg/l over the samplingperiod, and a decreased saline influence would also be consistent with increased nitrogenconcentrations. There appears no consistent temporal trend in calcium:chloride ratio, implying thatchloride trends may be an artifact of sampling times.
Weeki Wachee River
For the Weeki Wachee River, an extensive data set exists for the spring itself, from 1961 through1995 (Figure 5C). Clear and highly significant trends of nitrate-nitrogen (Appendix Figures IB-24and IB-25), and as a result, of total nitrogen exist against almost all independent variables.Increases in conductivity and decreases in temperature are also significant, but since deseasonalizedtrends of temperature against fraction of year are not significant, this result may be spurious.
Pithlachascotee River
The Pithlachascotee River watershed included three stations with sufficient data for temporal trendanalysis (Figure 6A). The two stations near Fivay Junction and near New Port Richey, have largerdata sets from 1964 through 1995, while the station near Richey Lakes operated from 1964 through1972. The earlier data near Richey Lakes exhibited increases in both inorganic and totalphosphorus species. At the Fivay and New Port Richey stations, strong trends of decreasing pHwere observed against all independent variables. To a lesser extent, increasing conductivity anddecreasing total phosphorus (Appendix Figure IB-26) were recorded at both stations. Decreasedstage and dissolved oxygen (Appendix Figure IB-27) and increased color and temperature(Appendix Figure IB-28) were also apparent at Fivay Junction. At New Port Richey, decreaseddissolved oxygen and ortho-phosphorus, and increased total Kjeldahl, organic, and total nitrogen(Appendix Figure IB-29) were present with several independent variables. Trends were alsopresent for some major ions, potassium and fluoride. Trends in stage at New Port Richey werespurious, reflecting a change in gage datum.
Anclote River
For the Anclote River, stations near Elfers and Odessa were assessed (Figure 6A). Near Odessa,no trends were evident except for a slight indication of decreasing total phosphorus between 1988
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Figure 6. Station locations of data analyzed for trend: Crystal River (6A), Homosassa andChassahowitzka Rivers (6B), and Weeki Wachee River (6C).
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and 1994. Downstream near Elfers, however, a number of trends are evident. Nutrients areclearly increasing, for nitrate-nitrite-nitrogen, nitrate-nitrogen, total Kjeldahl (AppendixFigure IB-30), and total nitrogen, as well as for ortho- and total phosphorus. Dissolved oxygenis decreasing against all independent variables, and major ions have changed as well, withincreasing conductivity (Appendix Figure IB-31), sodium, potassium, and chloride, and decreasedfluoride concentrations. Trends in major ion concentrations implies a changing’ proportion ofgroundwater at the site.
Rainfall Quality
The NADP/NTN data base for rainfall quality is extensive. The Bradford Forest site (near Starke,FL) to the north of the study area has been sampled from 1978 to present with more than 800observations. The Verna Wellfield site to the south near Sarasota has more than 600 data points,collected since 1983. Data were subjected to LOWESS analysis, both as concentrations and asloads, against fraction of year, rainfall amounts, conductivity, and pH to look for temporal trends.At both sites (Figure 7B), pH is decreasing with time against all independent variables (AppendixFigure IB-32). Procedural changes in sample handling of laboratory pH samples do not accountfor the differences observed in the field pH data illustrated here. Similarly, concentrations ofcalcium, magnesium, sodium, potassium, chloride, and sulfate are also decreasing at both sites,with no significant trends in conductivity of samples. Although the Bradford Forest site shows notrend, at the Verna Wellfield site near Sarasota, both nitrate-nitrogen and ammonium-nitrogenconcentrations are increasing.
Similar trends exist at each station when loads are examined. At Bradford Forest, there weresignificant trends of decreasing calcium, magnesium, potassium, sodium and sulfate weeklyloadings. Both nitrate-nitrogen and ammonium-nitrogen loads (Appendix Figures IB-33 and IB-34)were increasing with respect to rainfall. At the Verna Wellfields, decreases in magnesium andpotassium were accompanied to a lesser extent by decreases in calcium. Ammonium-nitrogen load(Appendix Figure IB-35), however, was increasing with respect to all independent variables andnitrate-nitrogen was increasing with respect to conductivity and pH.
Summary of Trends
Where trends in nutrients were significant, they were generally increasing. Increases wereobserved both for spring runs and in watershed rivers such as the Anclote and WithlacoocheeRivers. In particular, the LAKEWATCH program, with the continuous and long-term sampling,documents increasing nutrients and declining water clarity at all stations in the lower WithlacoocheeRiver. Increasing nitrogen concentrations in spring discharges, as observed by other investigations,were similarly observed in the data sets examined for this project. Exceptions to increasingnutrient trends were observed at the Crystal River/Kings Bay LAKEWATCH stations and for somecomparatively recent data from the Waccasassa River basin. According to LAKEWATCHsamples, nutrients (especially phosphorus) in the Crystal River/Kings Bay area are declining, withsome stations increasing in chlorophyll. The rainfall concentrations and loading of nitrogen fora given weekly rainfall amount are increasing in some locations.
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Figure 7. Station locations of data analyzed for trend: Pithlachascotee and Anclote Rivers(7A), NADP rainfall sites (7B).
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Data Gaps
Several monitoring efforts exist or are underway which have been designed for the purpose ofestablishing a baseline and documenting change (Table 12). Water quality sampling of selectedspring vents and vegetation mapping and monitoring in the Rainbow River, Kings Bay, and WeekiWachee River are examples. For the region as a whole, however, existing data can be describedas fragmented, with insufficient information to determine baselines or depict trends in manyimportant ecological components. Many efforts were designed to examine impacts other than fromnutrient loads, and so are of limited usefulness in this investigation. Older analytical data alsosometimes suffer from different and perhaps non-comparable methodologies, a lack of meta-data,and a lack of quality assurance by today’s standards, lessening the credence given to detectedtrends.
Notable gaps in data coverage for the region include phytoplankton, macroalgae, and epiphytes ofthe estuarine and near-coastal regions of the various rivers. Existing mapping of coastal seagrassesis insufficient to reflect species-level changes, and, with a few exceptions, is generally lacking inthe estuarine portions of the various rivers. Water quality of the near-coastal regions is, for themost part, quite dated. Ongoing mapping of aquatic vegetation focuses on selected exotic species.Particular geographic regions that are data-poor for the purposes of this project include theHomosassa and Chassahowitzka Rivers. The information most consistently available is for waterquality at the various spring vents. Flow information is not available for many systems due to thecomplexity of discharge from multiple spring vents. Data on forcing functions are generallylacking and should be included in all new investigations. With the exception of LAKEWATCHstations, there are few regions with more than one or two stations with contemporary data, whichhave been sampled consistently and for a sufficient period to determine spatial trends. Again,except for LAKEWATCH stations, there are few long-term stations downstream from any springdischarge to evaluate spatial gradients and water quality impacts.
XI. MONITORING RECOMMENDATIONS
Based on existing data, trends, and ongoing work, generalized monitoring guidelines can berecommended for future data collection efforts. Results from such monitoring efforts willeventually be used to support both the establishment of resource-based targets and monitoring ofprogress toward management goals.
Projected Use of Monitoring Data
Investigations of natural systems to determine nutrient impacts can be designed with increasinglevels of sophistication, broadly categorized into four areas: 1) establishing an original or baselinecondition, 2) documenting the presence or absence of change to monitor trends, 3) identifyingforcing functions to link observed-trends to nutrients, and 4) developing a mechanistic/ecologicalpredictive tool to forecast impacts or select restoration targets. Data requirements, complexity, andexpense increase between each category, so planned data analyses and expected products shouldbe identified clearly before monitoring begins.
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Table 12. Existing data, by region, suitable for baseline or as initiating data for status andtrend analyses.
Region 1 2Category
3 4 5 6
Rainbow SpringsRainbow RiverWithlacoochee River
(freshwater)(estuarine)(coastal)
Crystal Springs GroupKings BayCrystal River
(freshwater)(estuarine)(coastal)
Homosassa SpringsHomosassa River
(freshwater)(estuarine)(coastal)
Chassahowitzka SpringsChassahowitzka River
(freshwater)(estuarine)(coastal)
Weeki Wachee SpringsWeeki Wachee River
(freshwater)(estuarine)(coastal)
123456***
Some information available, dated or species-level sensitivity could be improvedInsufficient information available
Water quality at springs, in riverMacroalgae near springs, in riversRooted macrophytes near springs, in riversWater quality of estuarine, near-coastal areasPhytoplankton, macroalgae, epiphytes of estuarine, coastal areasRooted macrophytes of estuarine, coastal areasSampling program in processSampling program in process, limited usefulnessUseful initial survey, need additional seasonal samplings
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Monitoring of a single aspect of a particular spring (water quality, for instance) can establish abaseline and document change. Monitoring of additional systems which are affected by waterquality (phytoplankton, submerged aquatic vegetation [SAV], etc.) can perform the same functionand provide status information on ecologically and socially important components/habitats of thewaterbody. Change can be examined between as few as two samplings separated by any periodof time, although the power of the two samplings to detect true change at an appropriate level ofinterest will be limited by the variation inherent in the system. Depiction of small scale changeswill also require high station frequency or density.
A broader variety of data and larger data sets will be required before changes can be linked withcertainty to increasing nutrient loads. The success of this empirical approach will depend on thesize and amenability of the data set to empirical statistical analysis; the completeness with whichforcing functions can be identified and quantified, and the underlying strength of relationships.Once the data set is of sufficient size and relationships can be demonstrated, parameters of littlesignificance can be eliminated, reducing monitoring requirements in subsequent years to thatnecessary to demonstrate status and trends of the resource.
With the empirical approach, predictions outside of the measurement conditions are not advisable.Numerical, resource-based management targets are available from this method, provided that thedata encompass the desired conditions of the resource (i.e., to maintain present day or baselineconditions) and that forcing functions (such as flow) remain similar. For monitoring to documentchange and to identify relationships with forcing functions, qualitative predictions of ecologicalchange can employ the conceptual model discussed above to look for probable impacts fromnutrients or management actions.
To predict quantitative changes due to nutrient loading or to set numerical loading targets, nutrient-or energy-based ecological models which document the pools and transfer rates within the systemare useful. A conceptual model (Figure 2), is quantified by measuring or estimating the size ofeach model component and the transfer rate between components for each parameter (nitrogen,carbon, phosphorus, etc.) of interest.
The approach is data-intensive and generally is coupled with continued monitoring and smallerscale experimental testing to support predictions, to isolate selected transfer rates, and to supporttheoretical hypotheses. Some pools or transfers are poorly understood. Many are technically verydifficult to quantify, are highly variable, or require estimates of net results from a number ofcomponents together. Additional forcing functions or structural controls add complexity. Datagathered to quantify reservoirs or transfers are of limited transferability between individual rivers.
While the data requirements for ecological modeling are fairly inclusive, some selectivity ispossible in monitoring for trend or in empirically demonstrating relationships of ecologicalcondition to nutrient additions. Systems or rates likely to be sensitive to increased nutrient loads,and with the highest power to detect change, can be identified using the conceptual model andexisting knowledge of the waterbody.
Within any data set, the probability of detectable relationships will depend on how direct aconnection exists between the response system being measured and the increased nutrient load, as
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well as the number and magnitude of other forcing functions. Generally, effects will be mostreadily observed in the water column at the source of the increasing load, i.e., in springdischarges. The more distant (in time, space, or trophic level) the measured parameter is from thespring discharge, the more variables will need to be considered to examine relationships and toattribute any changes to increased nutrient loads with certainty. Responses of submergedmacrophytes in particular are complicated by the continued invasion of exotic species (andsubsequent human control efforts) which may be coincident rather than in response to increasednutrient loads.
The strategic data categories and generalized geographic locations recommended to depict theimpacts of increased nitrogen loading from coastal springs are those used for the data inventoryprocess (from highest to lowest utility): 1) water quality at springs, in rivers, 2) macroalgae nearsprings, in rivers, 3) rooted macrophytes near springs, in rivers, 4) water quality of estuarine,near-coastal areas, 5) phytoplankton, macroalgae, epiphytes of estuarine and coastal areas, and6) rooted macrophytes of estuarine, near-coastal areas.
Exact details of sampling and parameters within each category will vary geographically and bywaterbody. Specific monitoring plans must recognize the differences in drainage characteristics,hydrology, and geomorphological controls between the individual rivers. For example, monitoringof inorganic nutrient species may be sufficient in spring discharges, but estuarine locations willrequire determinations of total or particulate nutrients as well. It may be appropriate to measureselected rates, such as community metabolism or phytoplankton primary production, in some areasof a particular river and not in others.
For drift algae, rooted macroalgae, or seagrasses, quantitative species level information (as percentcover, biomass, or other numerical assessment) is required in order to identify the magnitude ofany trophic changes resulting from nutrient additions. Information on phytoplankton (species orcommunity) abundance is also desirable but trends can also be detected in frequent chlorophyllvalues. Algal, phytoplankton, or seagrass species with distinct seasonal patterns in biomass shouldalso either have seasonal samplings to identify seasonal range in parameters, or should minimallyoccur at reproducible periods. Responses of freshwater SAV, in particular, are complicated bythe continued invasion of exotic species which may be coincident with, rather than in response to,increased nutrient loads. Mapping efforts, and trends between subsequent efforts, can providesuperior information for a waterbody on the spatial extent of vegetation or water quality changesthat is not usually possible without a large number of discrete sampling points. Again, differenceswill exist between sampling programs for the individual rivers. For some rivers and biologicalsystems (SAV off of the mouth of the Withlacoochee, for example), it may not be possible todistinguish impacts of spring discharge from surface drainage loadings.
The hierarchy of data categories described above is designed for a specific purpose, to examinethe impacts of increased nutrient discharges from coastal springs. There are other nutrient sourcesto the region, both internal and external to the Coastal Basin, including surface water dischargeswith associated domestic and non-point source loadings, atmospheric loadings, and riverinedischarges from upgradient regions such as from the Suwannee River. Monitoring designed toexamine status and trends of valued ecosystem components in response to regional perturbations
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also is of value, although it is not reflected in the generalized monitoring design or in the orderof data categories recommended here.
Recommended Approach
The strategic objectives for SWFWMD is to identify and prevent adverse impacts due to increasednitrogen loadings from coastal springs. Based on proximity to the spring discharge and aconceptual model of nutrient transformations within a riverine system, the resources most likelyto reflect adverse impacts from increased nutrient loads are:
Resources
1. Water quality at springs, in river2. Macroalgae near springs, in rivers3. Rooted macrophytes near springs, in rivers4. Water quality of estuarine, coastal areas5. Phytoplankton, macroalgae, epiphytes of estuarine, coastal areas6. Rooted macrophytes of estuarine, coastal areas
To protect these resources adequately (and others which depend upon them), information is neededto complete one or more of the following objectives or approaches, in order of increasinginformation, time, and budgetary requirements:
Methods to establish status and trends, to identify parameter linkages, to predict impacts, or toselect loading goals in the absence of baseline conditions are varied, but for each resource selected,can generally be evaluated by:
For resources, approaches, and methods, data inventory reveals that more information is availablefor items 1 than for any other. In other words, there are spring water quality data and single riversurveys, which can be used for baseline purposes. There have been, however, no efforts to test
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rooted macrophyte responses to nitrogen loads in the estuarine and coastal regions of the springscoast.
Based on selection of approaches, the recommendation is to address the resources, in the orderidentified, with the variety of methods to obtain standardized information for the Rainbow, Crystal,Homosassa, Chassahowitzka, Weeki Wachee, and Aripeka systems. The Chassahowitzka hasexisting information in resource areas 1-6, and we recommend that SWFWMD use theChassahowitzka region as a demonstration area for measurement systems. Afterwards, theremaining spring-fed systems can be added in turn, depending on data gaps. In time the ensembleof spring runs should be bracketed by at least one northern and one southern river with minimalspring discharge (i.e., the Waccasassa and Pithlachascotee or Anclote Rivers).
In more detail, the recommended approach to monitoring of the spring-fed rivers of the CoastalBasin is, at a minimum, to perform sufficient sampling in the rivers of interest to establish abaseline for future comparisons. For regions with no existing baseline, results from the initialeffort can serve as a resource protection target. Measured systems should include the resourceslisted above. A portion of this information already is available for some categories in a numberof waterbodies. Any new monitoring of other rivers or springs should be performed reasonablysynoptically to permit comparative assessments between rivers. If baseline information is available,repetition of measurements should be with comparable techniques. If more than one river is beingsampled, sampling design should balance the advantages of repeating an older, perhaps lessquantitative or rigorous, technique against the value received through comparative studies of avariety of regions with a single, more quantitative approach.
Variations in the sampling plan may exist between the different rivers (and different regions ofrivers) to recognize the differences of size, geomorphology, surface water drainage, and flow thatexist between waterbodies. Even if a baseline effort is all that is funded, sufficient data on flow,nutrient loadings, tides, salinity, and other forcing functions should be accumulated to permitempirical investigations in the future. Extreme events (storm tides, severe freezes) should also berecorded to interpret long-term patterns.
At a higher level of support, a repetition of a baseline effort will permit a minimal trenddetection effort. Again, forcing functions should be identified and quantified to permit futureempirical analyses. Any trends observed will not be solely attributable to changes in nutrientloadings, but a record of forcing functions will allow qualitative links between resource trends andnutrients to be discussed. A continuation of the monitoring effort over a number of years willpermit eventual empirical analysis and the possible statistical link between nutrient loadings andresource trends. The level of effort required to develop a quantitative ecological model is notrecommended at this time due to the initial data and continued monitoring requirements.
Specific projects should be designed to answer questions about resource status and trends inselected areas and regions. At the springs and in the spring runs, water quality, macroalgae, androoted macrophytes should be quantified and mapped. Inorganic nutrients will be the most usefulnear spring vents, while downstream measurements should include organic concentrations andwater column chlorophyll, as well. Mineral content (conductivity or other analyses) assist ininterpretation. Impacts on SAV are expected to be reflected in species replacement as well as
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biomass, and so mapping and measurements should be conducted at the species scale. As therivers are a gradient of many forcing functions (salinity, clarity, nutrients, etc.), station andsampling density should be sufficient to allow statistical testing between the categories and locationsof interest. Baseline data should be correlated against nutrient loadings, rather than concentrationalone. As a result, flow measurements will also be required. Community metabolismmeasurements should reflect the net response of the spring runs to nutrient loads.
Secondarily, similar investigations should be conducted in the estuarine portions of the rivers.Water quality measurements (for the purpose of identifying spring discharge impacts) should berestricted to estuarine and near-coastal waters. Chlorophyll and light attenuating substances shouldbe addressed. Community metabolism may again be of interest. Phytoplankton species-levelinformation could reflect nutrient-related changes. Measurements and mapping of macroalgae andSAV should also be made at the species level, and epiphytic coverage of seagrasses should beinvestigated at least from the standpoint of attenuation. Epiphytic coverage and SAV in near-coastal areas will be difficult to link definitively to spring discharge, except for selected rivers.
Duration of Economic Support
For any sampling program undertaken, planned sampling frequency, sample density, andreasonable estimates of expected variation in the sampled system should be used to estimate themagnitude of change which will be detectable, given the projected number of samplings. Poweranalyses, sampling costs, and reasonable estimates of the duration of economic support which maybe available to the program should be incorporated into project design at the initial stages.
Summary
To assess the impacts of increased nutrient loading from coastal springs, efforts should successivelybe directed to: 1) establishment of baseline conditions; 2) repeated measurements for detectionof trends; and 3) eventual empirical analysis in which resource status is linked to forcingfunctions, including nutrients. Systems selected for measurement should be those with the mostdirect spatial and ecological links to nutrients. Expected impacts from nutrient increases shouldbe high in proportion to normal variation. Ecological systems selected for measurement shouldbe well described so that forcing functions other than nutrients can be adequately identified andmeasured. Attention should be focused on the geographic regions considered as data-poor.
Trend analyses in this and other reports have demonstrated that nitrogen concentration and loadare increasing significantly in the coastal springs of west-central Florida. Although not the subjectof this report, other studies attribute such increases to changing land uses within the groundwaterrecharge areas of each spring. In light of a) lag times between recharge and spring discharge, andb) an accelerating rate of land conversions east of US 19, many-fold increases in future nitrogenconcentration and load are reasonable expectations. Data of the kinds outlined in this report arecritical to documenting the present-day fate and effect of nitrogen in coastal ecosystems. The samedata will be needed to guide ecosystem management and restoration efforts as waters beingrecharged today and over the last 10-50 years begin to reach these waterbodies.
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XII. LITERATURE CITED
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Bachmann, R.W. and D.E. Canfield, Jr. 1996. Use of an alternative method for monitoring totalnitrogen concentrations of Florida lakes. Hydrobiologia. 323: 1-8.
Bass, D.G. and D.T. Cox. 1985. River habitat and fishery resources of Florida. Chapter In:W.J. Seaman, Jr. (ed.). Florida aquatic habitat and fishery resources. Florida Chapter ofAmerican Fisheries Society.
Belanger, T.V., H. Heck, M. Sohn, and P.R. Sweets. 1993. Sediment mapping and analysis inCrystal River/Kings Bay and Lake Panasoffkee. Report to the Southwest Florida WaterManagement District, Brooksville, FL.
Bigelow, D.S. 1984. Instruction Manual - NADP/NTN Site Selection and Installation, NationalAtmospheric Deposition Program. Natural Resource Ecology Laboratory, Colorado StateUniversity, Fort Collins, CO.
Bigelow, D.S. and S.R. Dossett. 1988. Instruction Manual - NADP/NTN Site Operation.National Atmospheric Deposition Program. Natural Resource Ecology Laboratory,Colorado State University, Fort Collins, CO.
Cambridge, M.L. and A.J. McComb. 1984. The loss of seagrasses in Cockburn Sound, WesternAustralia. I. The time course and magnitude of sea-grass declines in relation to industrialdevelopment. Aquatic Botany. 20:229-243.
Cleveland, W.S. 1979. Robust locally weighted regression and smoothing scatter-plots. J. Amer.Statistical Assoc. 74:368(829-836).
Cleveland, W.S., S.J. Devlin and E. Grosse. 1988. Regression by local fitting: Methods,properties, and computational algorithms. J. Econometrics. 37:87-114.
Coffin, J.E. and W.L. Fletcher. 1996. Water resources data, Florida, water year 1995.Volume 3B. Southwest Florida surface water. U.S. Geological Survey, Report No. U.S.Geological Survey-WDR-FL-95-3A.
Coffin, J.E. and W.L. Fletcher. 1997. Water resources data, Florida, water year 1996.Volume 3B. Southwest Florida surface water. U.S. Geological Survey, Report No. U.S.Geological Survey-WDR-FL-96-3A.
Dixon, L.K. and A. Nissanka. 1997. Coastal nitrate assessment: Data synthesis. Draft Reportto the Southwest Florida Water Management District - Surface Water Improvement andManagement Division. Mote Marine Laboratory Technical Report No. 502. Sarasota, FL.
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Dixon, L.K. and E.D. Estevez. 1996. Biogeochemical indicators of trophic status in a relativelyundisturbed shallow water estuary. Mote Marine Laboratory Draft Technical Report toU.S. Fish and Wildlife Service, Air Quality Branch, Denver, CO.
Dixon, L.K. and E.D. Estevez. 1997. Biogeochemical indicators of trophic status in a relativelyundisturbed shallow water estuary. Report to U.S. Fish and Wildlife Service - Air QualityBranch. Mote Marine Laboratory Technical Report No. 518. Sarasota, FL.
Dixon, L.K., S. Murray, J.S. Perry, P.J. Minotti, M.S. Henry, and R.H. Pierce. 1996.Assessment of bulk atmospheric deposition to the Tampa Bay watershed. Report to theTampa Bay National Estuary Program. Mote Marine Laboratory Technical ReportNo. 488. Sarasota, FL.
Durako, M.J., J.A. Browder, W.L. Kruczynski, C.B. Subrahmanyam, and R.E. Turner. 1985.Saltmarsh habitat and fishery resources of Florida. Chapter In: W.J. Seaman, Jr. (ed.).Florida aquatic habitat and fishery resources. Florida Chapter of American FisheriesSociety.
Estevez, E.D., L.K. Dixon, and M.J. Flannery. 1991. West Coast Rivers of Peninsular Florida.Chapter 12 In: R.J. Livingston (ed.). The Rivers of Florida. Springer-Verlag, NY.
Franklin, M.A. and P.E. Meadows. 1995. Water resources data, Florida, water year 1995.Volume 4. Northwest Florida. U.S. Geological Survey Water-Data Report FL-95-4.
Hine, A.C. and D.F. Belknap. 1986. Recent geological history and modern sedimentaryprocesses of the Pasco, Hernando, and Citrus counties coastline: West-central Florida.Florida Sea Grant Report 79. Gainesville, FL.
Joyner, B .F. 1973. Nitrogen, phosphorus, and trace elements in Florida surface waters, 1970-1971. U.S. Geological Survey Open-File Report.
Jones, G.W. and S.B. Upchurch. 1993. Origin of nutrients in ground water discharging fromLithia and Buckhorn Springs. Ambient Ground-Water Quality Monitoring Program,Southwest Florida Water Management District, Brooksville, FL.
Jones, G.W. and S.B. Upchurch. 1994. Origin of nutrients in ground water discharging from theKing’s Bay springs. Ambient Ground-Water Quality Monitoring Program, SouthwestFlorida Water Management District, Brooksville, FL.
Jones, G.W., S.B. Upchurch, and K.M. Champion. 1995. Origin of Nitrate in Ground WaterDischarging from Rainbow Springs, Marion County, Florida. Ambient Ground-WaterQuality Monitoring Program. Southwest Florida Water Management District..
Jones, G.W., S.B. Upchurch and K.M. Champion. 1997. The water quality and hydrology ofHomosassa, Chassahowitzka, Weeki Wachee, and Aripeka spring complexes with emphasis
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in increasing nitrate concentrations. Draft report prepared by the Ambient Ground-WaterQuality Monitoring Program, Southwest Florida Water Management District.
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APPENDIX IA
Citations of Data Summaries
APPENDIX IB
SELECTED ILLUSTRATIONS OF LOWESSBASED TREND ANALYSES
Appendix Figure IB-1. Waccasassa River/Otter Creek/Freshwater Reference site. Totalphosphorus as a function of 90-day cumulative rainfall. Decreasingtrend with time, p=0.005.
IB-1
Appendix Figure IB-2. Waccasassa River at Gulf Hammock at US 19. Nitrate-nitrite nitrogenas a function of year fraction. Decreasing trend with time, p=0.009.
IB-2
Appendix Figure IB-3. Rainbow Springs near Dunnellon. Nitrate-nitrite nitrogen as a functionof 3-day cumulative rainfall. Increasing trend with time, p=0.001.
IB-3
Appendix Figure IB-4. Withlacoochee River near Holder. Nitrate-nitrogen as a function of 60-day cumulative rainfall. Increasing trend with time, p=0.020.
IB-4
Appendix Figure IB-4. Withlacoochee River near Holder. Nitrate-nitrogen as a function of 60-day cumulative rainfall. Increasing trend with time, p =0.020.
IB-4
Appendix Figure IB-5. Withlacoochee River near Holder. Total Kjeldahl nitrogen as afunction of conductivity. Increasing trend with time, p =0.011.
IB-5
Appendix Figure IB-6. Withlacoochee River near Holder. Total Phosphorus as a function of30-day cumulative rainfall. Increasing trend with time, p=0.022.
IB-6
Appendix Figure IB-7. Lake Rousseau above dam. Temperature as a function of year fraction.Increasing trend with time, p=0.049.
IB-7
Appendix Figure IB-8. Lake Rousseau above dam. Ammonium nitrogen as a function of yearfraction. Increasing trend with time, p=0.049.
IB-8
Appendix Figure IB-9. LAKEWATCH Withlacoochee River. Total nitrogen as a function of90-day cumulative rainfall. Increasing trend with time, p=0.000.
IB-9
Appendix Figure IB-10. LAKEWATCH Withlacoochee River. Total nitrogen as a function ofyear fraction. Increasing trend with time, p=0.000.
IB-10
Appendix Figure IB-11. LAKEWATCH Withlacoochee River. Total phosphorus as a functionof 30-day cumulative rainfall. Increasing trend with time, p = 0.000
IB-11
Appendix Figure IB-12. LAKEWATCH Withlacoochee River. Total phosphorus as a functionof 14-day cumulative rainfall. Increasing trend with time, p=0.000.
IB-12
Appendix Figure IB- 13. LAKEWATCH Withlacoochee River. Chlorophyll a as a function ofyear fraction. Increasing trend with time, p=0.004.
IB-13
Appendix Figure IB- 14. LAKEWATCH Withlacoochee River: Secchi depth as a function of 90-day cumulative rainfall. Decreasing trend with time, p=0.000.
IB-14
Appendix Figure IB- 15. Crystal River near Crystal River. Nitrate-nitrite nitrogen as a functionof year fraction. Increasing trend with time, p=0.000.
IB-15
Appendix Figure IB-16. Crystal River near Crystal River. Nitrate-nitrite nitrogen as a functionof 7-day cumulative rainfall. Increasing trend with time, p =0.004.
IB-16
Appendix Figure IB-17. Crystal River near Crystal River. Total phosphorus as a function of 3-day cumulative rainfall. Increasing trend with time, p =0.015.
IB-17
Appendix Figure IB- 18. LAKEWATCH Crystal River - Indian River Canal. Total phosphorus asa function of 90-day cumulative rainfall. Decreasing trend with time,p=0.002.
IB-18
Appendix Figure IB-19. LAKEWATCH Crystal River - Cedar Cove. Total nitrogen as a functionof year fraction. Decreasing trend with time, p = 0.003.
IB-19
Appendix Figure IB-20. LAKEWATCH Crystal River - Cedar Cove. Chlorophyll-a as a functionof 60-day cumulative rainfall. Increasing trend with time, p = 0.005.
IB-20
Appendix Figure IB-21. LAKEWATCH Crystal River - Confluence of Salt River. Secchi depthas a function of 3-day cumulative rainfall. Decreasing trend with time,p=0.028.
IB-21
Appendix Figure IB-22. Homosassa River above Halls River. Organic nitrogen as a function ofyear fraction. Increasing trend with time, p =0.023.
IB-22
Appendix Figure IB-23. Chassahowitzka River above the Gulf of Mexico. Total Kjeldahlnitrogen as a function of 60-day cumulative rainfall. Increasing trendwith time, p=0.018.
IB-23
Date
Well 1 Level (ft)
Year
Appendix Figure IB-24. Weeki Wachee Springs near Brooksville. Nitrate-nitrite nitrogen as afunction of well 1 level. Increasing trend with time, p =0.000.
IB-24
Year
Appendix Figure IB-25. Weeki Wachee Springs near Brooksville. Nitrate-nitrite nitrogen as afunction of 3-day cumulative rainfall. Increasing trend with time,p=0.000.
IB-25
Appendix Figure IB-26. Pithlachascotee River near Fivay Junction. Total phosphorus as afunction of 60-day cumulative rainfall. Decreasing trend with time,p=0.031.
IB-26
Appendix Figure IB-27. Pithlachascotee River near Fivay Junction. Dissolved oxygen as afunction of 90-day cumulative rainfall. Decreasing trend with time,p=0.039.
IB-27
Appendix Figure IB-28. Pithlachascotee River near Fivay Junction. Temperature as a functionof year fraction. Increasing trend with time, p=0.004.
IB-28
Appendix Figure IB-29. Pithlachascotee River near New Port Richey. Total nitrogen as afunction of 90-day cumulative rainfall. Increasing trend with time,p=0.001.
IB-29
Appendix Figure IB-30. Anclote River near Elfers. Total Kjeldahl nitrogen as a function of 60-day cumulative rainfall. Increasing trend with time, p=0.001.
IB-30
Appendix Figure IB-31. Anclote River near Elfers. Conductivity as a function of year fraction.Increasing trend with time, p =0.000.
IB-31
Appendix Figure IB-32. Verna Wellfield Site - Sarasota. pH as a function of year fraction.Decreasing trend with time, p=0.000.
IB-32
Appendix Figure IB-33. Bradford Forest Site - Starke. Nitrate nitrogen load as a function ofweekly rainfall. Increasing trend with time, p = 0.001.
IB-33
Appendix Figure IB-34. Bradford Forest Site - Starke. Ammonium nitrogen load as a functionof weekly rainfall. Increasing trend with time, p = 0.032.
IB-34
Appendix Figure IB-35. Verna Wellfield Site - Sarasota. Ammonium nitrogen load as a functionof weekly rainfall. Increasing trend with time, p = 0.001.
