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An Analysis the Relationships of Freshwater Inflow and Nutrient Loading with Chlorophyll Values and Primary Production Rates in the Lower Peace River Southwest Florida Water Management District

July 2014

An Analysis the Relationships of Freshwater Inflow and Nutrient Loading with Chlorophyll Values and Primary Production Rates in the

Lower Peace River

Prepared for

Southwest Florida Water Management District

2379 Broad Street Brooksville, FL 34604-6899

Prepared by

4030 West Boy Scout Blvd. - Suite 700 Tampa, FL 33607

July 2014

Southwest Florida Water 1 Relationship between inflows and primary production Management District in the lower Peace River/upper Charlotte Harbor July 2014

An Analysis the Relationships of Freshwater Inflow and Nutrient Loading with Chlorophyll a Values and Primary Production Rates in the Lower Peace River

1.0 Introduction The overall object of this project was to statistically analyze available long-term data to determine if better relationships could be developed between chlorophyll a (and related primary production estimates) and seasonal variations in freshwater inflow to the lower Peace River and upper Charlotte Harbor estuarine system. The source of the chlorophyll a/ primary production data utilize in these analyses was obtained from monitoring elements conducted in conjunction with the ongoing, long-term lower Peace River estuarine Hydrobiological Monitoring Program (HBMP). The HBMP was initially implemented by General Development Utilities (1976-1990) and subsequently continued by the Peace River Manasota Regional Water Supply Authority (1991-present). Additional data combined with the HBMP information was obtained from a number of additional sources, including: Daily USGS flow data that was used to determine preceding gaged freshwater estuarine

inflows from relative tributary sites (USGS Peace River at Arcadia, Joshua Creek at Nocatee, Horse Creek near Arcadia, and Shell Creek near Punta Gorda).

Water ages under varying flow conditions were calculated at two kilometer spatial intervals along the lower river/upper harbor HBMP monitoring transect based on modeled outputs provided by Southwest Florida Water Management District (District). The District was able to provide this information based on its existing hydrodynamic model for the lower/river upper harbor estuary system.

Watershed water quality information from a number of sources was used to estimate nutrient loading rates coming from the Peace River at Arcadia, Joshua Creek at Nocatee, Horse Creek near Arcadia, and Shell Creek near Punta Gorda basins (District, FDEP, Peace and Shell Creek HBMPs).

Daily solar radiation over the 1983-2011 time interval of HBMP data collection was derived combining information from a number of regional sources (District, EQL, Mote Marine, University of Florida IFAS).

The District’s primary interest in supporting this effort centers on expanding and potentially identifying probable sources of temporal/spatial variability in chlorophyll biomass/primary production beyond that previously presented in HBMP reports, and initial effort during the establishment of the existing MFL for the lower Peace River.

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1.1 Chlorophyll a Biomass (Primary Production) Data Sources The HBMP monitoring elements originally designed in 1976 in coordination with District staff sought to provide answers to specific questions initially raised during the beginning of Water Use permitting process for the Peace River water treatment facility. However, the HBMP was never conceived as a rigid monitoring program and since its inception has maintained a flexible design (adaptive management) that has been periodically restructured (with District concurrence) based on updated findings and identified data needs. Historically, the major monitoring elements associated with assessing direct relationships with temporal variations in freshwater inflows have had the longest histories of monitoring. An early and continuing HBMP objective has been the development of a comprehensive understanding of temporal and spatial variability in phytoplankton biomass (as measured by chlorophyll a) within the lower Peace River/upper Charlotte Harbor estuarine system. Development of a conceptual understanding of the associated relationships between phytoplankton biomass/production and freshwater inflows and has been established as a fundamental HBMP goal towards developing an overall understanding of other key interrelated biological communities and physical processes within the estuary, including secondary production and nutrient cycling.

Phytoplankton production generally represents an immediately available food resource, unlike some other sources of estuarine production, such as those often associated with seagrass, mangrove and fresh and salt marsh habitats. In these later instances much of the derived available estuarine food resource becomes available only through longer term secondary processes. Of the various inputs into the Charlotte Harbor estuarine system, phytoplankton production represents both the largest single component of primary production, and a food resource directly accessible to many Figure 1.2 - Importance of the Location of Estuarine Chlorophyll Maxima

Figure 1.1 - Lower Peace River/upper Charlotte Harbor estuarine system

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filter/detrital feeding organisms. Further, due to the relatively very short generation times involved, phytoplankton biomass/production in other estuarine systems has been shown to be effective in demonstrating ephemeral, seasonal and longer term changes in upstream freshwater inflows and water quality characteristics. Thus, phytoplankton biomass (production) represents a highly integrated estuarine component that can provide information on both direct and predictive secondary impacts of external influences relative to seasonal and longer term temporal variations in estuarine freshwater inflows. Data from two long-term HBMP monitoring elements were incorporated in this study to provide the underling information used to assess spatial/temporal variability of chlorophyll a biomass in the lower river/upper harbor estuarine system relative to seasonal patterns in freshwater inflows (and other available parameter estiamtes). This monitoring data includes information gathered both from “fixed” long-term sites along the lower Peace River, and analogous data collected at “moving” isohaline bases sampling locations.

Historically, the HBMP water quality monitoring design included the monthly collection of in situ physical measurements of water column profile characteristics at a number of “fixed” station locations along the lower Peace River and in upper Charlotte Harbor. Between 1976 and 1990, the Environmental Quality Laboratory (EQL) collected sub-surface and near-bottom water quality data at five of these “fixed” locations (Figure 1.3) as part of background monitoring for General Development Corp. Under the 1996 Water Use Permit’s expansion of the monitoring program, monthly surface and bottom water chemistry data collections were again initiated at these same five previous fixed sampling locations utilized in the EQL monitoring. Combining the historic EQL background and with the more recent HBMP monitoring information gathered since 1996 provided sub-surface physical in situ water quality and water chemistry data (including chlorophyll a) for the 1976-1989 and 1996-2012 time intervals from these five fixed water quality sampling locations, which was utilized as the basis for the presented analyses.

In June 1983, the EQL undertook monthly monitoring of phytoplankton biomass, estimated primary productivity (C14 uptake) and water quality measurements at four salinity-based “moving” isohalines as part of General Development’s lower Peace River/Charlotte Harbor general background monitoring programs. The selection of the salinity-based sampling zones was originally established base on a literature review of known spatial estuarine differences among the major plankton groups. The selected isohalines were:

Figure 1.3 “Fixed” and river kilometer isohaline based “moving” HBMP monitoring elements

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Oligohaline Conditions = 0 psu (defined as upstream of 500 us/cm conductivity) Lower Mesohaline = 5-7 psu Upper Mesohaline = 11-13 psu Upper Brackish = 20-22 psu

The four monthly isohaline sampling locations in this HBMP study element thus represent non-fixed sites, such that the relative monthly spatial location of each along the HBMP monitoring transect (Figure 1.3) is largely dependent upon the preceding amounts of both shorter, and longer term freshwater inflows to the estuarine system. An explicit element of the 1996 HBMP renewal was the development of standardized station descriptors to be applied across all program elements. As part of a required morphometric study, the “mouth” (River Kilometer zero) of the lower Peace River was defined using U. S. Geological Survey standardized protocols as an imaginary line extending from Punta Gorda Point to Hog Island (Figure 1.3). All current on-going and historic monitoring data, including the monthly “fixed” and “moving” isohaline based sampling, have been cross-referenced to this “River Kilometer” identification system.

This “moving” isohaline-based water quality sampling program element was incorporated into the HBMP in 1987 in conjunction with other program modifications made during a renewal of the Facility’s Water Use Permit. The monthly “moving” isohaline based sampling HBMP study element has been included as part of the HBMP since that time. The initial objective of the isohaline based monitoring HBMP program element was to develop a thorough understanding of the temporal/spatial processes controlling phytoplankton production within Charlotte Harbor. The ultimate objective was to further quantify the estuary's immediate and long-term responses to both seasonal and long-term changes in freshwater flows and nutrient inputs, including both nutrient loadings (nitrogen) and increased water color (which influences light availability).

Between June 1983 and December 1999, statistically comparable levels of phytoplankton 14C fixation rates were measured monthly at each of the four salinity-based isohaline locations. Although, direct in situ measurements of phytoplankton carbon uptake rates are no longer measured, monthly determinations continue to be made at the four isohaline locations for phytoplankton biomass (chlorophyll a), physical water quality parameters, water column light extinction, and near surface measurements for the same series of chemical constituents used in the fixed station HBMP design.

In addition to overall estimates of phytoplankton production, carbon uptake rates were determined for three separate size fractions: 1) greater than 20 microns; 2) 5 to 20 microns; and 3) less than 5 microns. The results of this long-term HBMP study presented in previous HBPM reports clearly showed the quick response of phytoplankton production to brief pulses of relatively nitrogen rich freshwater into the estuary during the early spring. These results further supported the extreme importance to other components of the estuarine food-web of early spring/summer flows to the estuary during the start of the typical summer wet-season.

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1.2 Study Tasks/Results This final summary report represents the last of six tasks defined in the project’s scope of work. The summaries outline and discuss the results of each of the preceding first five project tasks before finally specifically addressing how phytoplankton chlorophyll a biomass and estimated rates of primary production in various reaches of the study area were found to vary as a function of seasonal changes in the rate of freshwater inflow. More specifically how such identified relationships with factors such as color, water age, and nutrient loading are affected by preceding intervals of freshwater inflows. The principle objectives, approaches and findings for each of the first five project tasks are initially presented prior to presenting the final summary conclusions relative to the interactions of freshwater inflows relative to the observed spatial/temporal estuarine chlorophyll biomass. More complete, thorough presentations of each of the initial five project tasks were separately provided to the District in the form of technical memos. Those complete technical memos, containing more detailed descriptions, tables, and graphics are attached at the end of this document in their entirety. 2.0 Task 1 Update inflow - nutrient concentration relationships and compute nutrient loading rates for freshwater tributary sites and HBMP station 18. This task included obtaining and utilizing available USGS/District/FDEP and HBMP information to update inflows and nutrient concentration information for each of the lower Peace Rivers upstream freshwater tributaries (Peace River at Arcadia, Joshua Creek at Nocatee, Horse Creek near Arcadia, and Shell Creek near Punta Gorda) as well as for the HBMP site at RK 30.7 (Station 18). These data utilized to assess relationships between nutrients and flows, as well as compute nutrient loading rates for each of the lower Peace River four primary tributaries. Specific elements within this task included:

Establishing the statistical characteristics and relationships of nutrient concentrations with flow within each basin tributary, emphasizing total, organic, and inorganic species of nitrogen and phosphorus, as well as water color.

Determining correlations and regressions of nutrient levels with flow in order to assess developing methodologies for development of predictive daily/monthly nutrient loading rates.

Utilize alternative methodologies to assess nutrient loadings estimates and assess/determine a preferred method for establishing the most appropriate nutrient loading rate estimates to be applied in assessing the response of chlorophyll (production) responses to freshwater inflows.

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Compared and contrasted upstream watershed nutrient loading rates with those determined for RK 30.7.

2.1 Methods Water quality and freshwater inflow data were compiled for the most downstream freshwater stations on the main tributaries to the Peace River, and the Peace River at Arcadia station. The list of stations is: Peace River at Arcadia (USGS 02296750) Horse Creek near Arcadia (USGS 02297310) Joshua Creek near Nocatee (USGS 02297100) RK 30.7 (Peace River HBMP Station 18) Shell Creek (Shell Creek HBMP Station 3 immediately upstream of the dam) Data are available for the following water quality constituents: • Total Nitrogen (TN) • Total Kjeldahl Nitrogen (TKN) • Nitrate+Nitrite (N23) • Total Phosphorus (TP) • Orthophosphate (OP) • Color Summary statistics of were calculated at each station for each constituent. Color is measured in platinum cobalt units (PCU) which do not correspond to a cumulative loaded mass. Therefore nutrient loads are not calculable for color in terms of an absolute mass of color. The freshwater inflow record was reviewed using the Univariate Procedure in SAS to produce ranges and histograms of flow distributions. Time series plots of flow and nutrient concentrations were generated using the Statistical Analysis System (SAS) SGplot Procedure. Correlations between nutrients and flow were generated using the SAS Corr Procedure. These correlations were generated using data within the interdecile range of flows for each station in order to remove unusually high or low flow conditions from the analysis. Correlations were further generated separately for all seasons combined, each season separately (Mar-May, Jun-Oct, Nov-Feb), and finally each of these iterations were analyzed using both raw and natural log transformed flow data. Multiple methodologies were employed to generate nutrient loading estimates. The first method uitilized monthly average concentrations for each constituent calculated from the data for each year and month where sample data were available (1983-2011). For months in which sample data was not available the average for that month over the entire available record was used in lieu of the missing data. These values were combined with the daily flow data and the daily nutrient load was calculated by multiplying the concentration with the daily flow value. Alternatively, the second method calculated nutrient load for each day when a nutrient sample

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was collected. The resulting dataset of loads based on field measurements was then used to generate a model to fill in the load estimates for days on which nutrient samples were not taken. Models were generated using a variety of transformations during this effort (using the Reg Procedure and the NLIN Procedure in SAS). The final method utilized was very similar to the second, except that unique models were generated for each of the three designated seasons, rather than for all seasons combined. 2.2 Results The Task 1 Technical Memo presents tabular results and summary graphics. Statistically significant (p

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3.0 Task 2 Prepare a data base of chlorophyll a, primary production, and nutrient and color data for HBMP stations and other data sources in the study area. Prepare basic statistical summaries, including seasonal variations and simple, univariate relationships with freshwater inflow. At the time of the original project scoping, it was presumed that the solar insolation data were available in a single format. On further investigation, however, this was found not to be the case. Solar insolation data were identified from four sources extending from 1983 to recent, using differing units of measure:

SWFWMD WMIS data base (1984 -2012, in various units) Environmental Quality Lab (1983 – 1998, in Einsteins per day). UF IFAS (2005-2012, in watts/m2) Mote Marine Lab (2004- 2012, in watts/m2)

Task 2 was therefore modified to include development of statistical relationships among these four datasets to determine the best option(s) to compile the best complete data base for solar insolation as possible for the period 1983 to 2011. This task further included preparing an overall summary data base of HBMP chlorophyll a/ primary production, nutrient, physical, and color information. In addition the task included preparing basic statistical summaries, as well as assessing seasonal variations and univariate relationships with freshwater inflows. Specific elements within this task included:

Construction of a summary data base(s) for all data for nutrients, physical parameters, color, chlorophyll a and primary production (C14 uptake) information collected in the lower Peace River and upper Charlotte Harbor.

Prepare basic statistical and graphical summaries of the data that characterize the seasonal variations, spatial distributions, and univariate relationships of variables that may influence chlorophyll a with freshwater inflow in the study area.

Present graphical analysis of the relationships between these variables, flow and chlorophyll.

3.1 Methods/Results – Develop Standardized Solar Insolation Database A series of analyses were conducted in order to develop a standardized dataset of estimated daily light (watts/m2) over the 1984-2012 time interval for the area of the lower Peace River/upper Charlotte Harbor. The goal was utilize ambient daily light data, added with other variables in modeling/assessing the relative importance of the key factors that influence the seasonal/spatial variability of chlorophyll patterns in the estuarine system.

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The four identified available data sets of daily incident solar radiation were reviewed and assessments made of available options towards developing standardized data measured in Watts over the long-term 1984-2012 period. Individually each of these data sets presents its own difficulties. The two IFAS (University of Florida) are limited to just the relatively recent years, and thus were only applicable in checking and providing estimates for any missing District data for the recent overlapping periods. In comparison, both the District and EQL data sets provide daily light measurements covering periods of much longer duration. However, each of these two long-term data sets have unique issues that need to be addressed prior to development on a single comprehensive 1984-2012 light data set. A complete discussion of the specific mythologies utilized is presented in Technical Memo Task 2A at the end of this report.

Figure 3.1 – Statistical comparison of District solar radiation data in Watts/m2 with overlapping EQL data measured in photosynthetic active radiation (PAR). This calculated regression was then used to convert the older EQL data to watt/m2, which was then combined with the newer District data collected at the Facility. The average of daily light values collected at the two regional IFAS sites were used to provide estimates to any missing observations in the more recent District data. These data were then checked against available Mote Marine data for overlapping intervals. The final resulting estimated daily light data set over the 1983-2012 time interval is depicted in Figure 3.2.

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Figure 3.2 – Final measured and estimated long-term solar data in watts/m2 3.2 Methods/Results – Master Data Set & Statistical Summaries The development of a Master Data Base included not only combining the long-term subsurface physical in situ and chemical water quality information from the “fixed” and “moving” HBMP study elements, but also incorporating additional information from a variety of sources. These included: 1. Ambient solar radiation (in Watts) – The methodology for deriving this information has been presented above. The resulting ambient light levels were then matched to each of the sampling observations in the Master Data Set for the same day, as well as determining average solar radiation for the preceding 3, 5, 10, 15, 30 and 60 days. 2. River Segment – In order to be able to assess the seasonal response of chlorophyll spatially along the lower river, the “fixed” and “moving” HBMP sampling events were grouped into five river segments created by dividing the river kilometer distances between the fixed monitoring sites (Figure 1.3 above). 3. Upstream Gaged Flows – Upstream gaged flow estimates were added to the Master Data Base for each observation. For observations within the three most upstream river segments, the

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combined USGS gaged flows for the Peace River at Arcadia, Joshua Creek at Nocatee and Horse Creek near Arcadia were utilized. Same day combined flows, as well as averages for the preceding 3, 5, 10, 15, 30 and 60 day intervals were incorporated. Similar information was added for the two most downstream river segments after incorporating flow data from Shell Creek near Punta Gorda. 4. Flow Percentiles – Upstream gaged flows for each of the observations in the Master Data Set were further divided into one of four categories based on the statistical distribution of flows. Minimum to the 25th percentile 25th to 50th percentile 50th to 75th percentile 75th percentile to Maximum 5. Water Age – The District provided output data from its hydrodynamic model relative to projected water age along the lower river/upper harbor. This data was provided in 2 kilometer intervals under various upstream flow conditions. Modeled water age is defined as the time it takes 50% of the particles released at the head of the estuary to move past different points in the lower river. Under Task 3 (see below) water age was calculated for the date and river kilometer location of each “moving” or “fixed” sampling event. Water age was calculated for samples collected upstream of the Shell Creek confluence as a function of combined upstream gaged Peace River flow, while the water age of samples collected downstream of the confluence, were calculate based on both upstream Peace River and Shell Creek flows. 6. Upstream Nutrient Loading – As previously dicussed, available historical water quality and flow information for the upstream Peace River and Shell Creek were combined and utilized to determine monthly nutrient loadings from following sites. Peace River at Arcadia (USGS/District/FDEP data) Joshua Creek at Nocatee (USGS/District data) Horse Creek near Arcadia (USGS/District data) Shell Creek near Punta Gorda (USGS/Shell Creek HBMP data) Station 18 (Peace River HBMP data) 7. District Seasonal Blocks – The date (including accounting for leap-years) of each sampling event in the Master Data Set was used to make associations/divisions relative to each of the three defined seasonal blocks used in the District MFL for the lower Peace River. Block 1 – April 20 to June 25 Block 2 – October 27 to April 19 Block 3 – June 26 to October 26 Following development of the Master Data Base, a series of basic statistical and graphical summaries were conducted. These included analyses of key parameters expected to be utilized in characterizing the influences of freshwater inflows on observed variations in the

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seasonal/spatial distributions of chlorophyll levels in the lower river/upper harbor estuarine system. Task 2B attached at the end of this report presents graphical summary univariate plots for each of the parameters listed below. The statistical distributions of flows and solar irradiation are shown for each of the defined District seasonal blocks. Four differing alternative univariate distributions are shown for each of the other parameters. These alternatives include: 1. Overall by river segment 2. By river segment, for each of the three District seasonal blocks 3. By river segment, and by each of the four flow percentiles designations 4. By flow percentile, for each of the defined five river segments Initial Parameters Analyzed 5-Day PHJ Flow (cfs) 5-Day PHJS Flow (cfs) 5-Day Solar Irradiance (Watts) Water Age (days) Upstream N23 Load (kg/day) Upstream TKN Load (kg/day) Upstream TN Load (kg/day) Upstream TP Load (kg/day) Upstream OP Load (kg/day) Salinity (psu) Temperature (C) Dissolved Oxygen (mg/l) Light Extinction Coefficient Color (CPU) Nitrite/Nitrate (mg/l) Total Kjeldahl Nitrogen (mg/l) Total Nitrogen (mg/l) Orthophosphorus (mg/l) Chlorophyll a (ug/l) Carbon Uptake (mg/m3/hr) Carbon Uptake (mg/m3/E) The analyses presented in Task 2B further present, by river segment, these selected parameters versus combined 5-day average gaged upstream flows, using combined gaged flow upstream of the Peace River Water Facility for the three upstream river segments and incorporating flows from Shell Creek for the most downstream segments. The following presents examples of Water Age and chlorophyll a univariate plots relative to divisions of flow percentiles.

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Figure 3.3 – Calculated water age under low and moderate upstream flows for each river segment.

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Figure 3.4 – Differences in chlorophyll spatial variability under moderate and high freshwater inflow percentiles.

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4.0 Task 3 Merge water age values generated by District with chlorophyll a and primary production values from Task 2. The complete Task 3 write-up and appendices is contained in Appendix C. The timing of Task 3 preceded the final development of the Master Data Set under Task 2, and the development of the associated statistical summaries. Specific elements within this task included:

Application of estimates water age at two kilometer intervals supplied by the District, which utilized output from the hydrodynamic model of the lower Peace River and upper Charlotte Harbor estuary, based on sixteen differing rates of combined upstream freshwater inflow.

These data were then used to identify non-linear (curvilinear) equations (typically power functions) that predicted water age at each location as a function of upstream freshwater inflow within each river segment.

Separate sets of equations were developed for water age computed as a function of Peace River flow, as well as a function of flow from the Peace River and Shell Creek.

Theses equations were then used for computation of water age using the actual flow records of the river for each chlorophyll a or primary production sample.

Relationships of water age with chlorophyll a in various reaches of the Peace River were determined, and analyses were conducted to determine if critical rates of water age might influence chlorophyll a concentrations in the lower river.

4.1 Task 3 Methods The District ran hydrodynamic models for the Lower Peace River and Upper Charlotte Harbor to produce water age estimates in two kilometer segments over a wide range of typical rates of freshwater inflow. The model was run over 1,054 hours for even river kilometers from river kilometer 0-30 (i.e. 0, 2, 4, 6, etc.), and under the following flow conditions: 40 cfs 79 cfs 115 cfs 144 cfs 172 cfs 204 cfs 241 cfs 282 cfs

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356 cfs 472 cfs 690 cfs 1,042 cfs 1,652 cfs 3,006 cfs 4,621 cfs 8,974 cfs

In these analyses water age was defined as the estimated time taken for 50% of the particles released at the head of the model area to reach each designated river kilometer. In addition to the models for the lower Peace River the District ran the hydrodynamic model for Shell Creek. A second dataset of hydrodynamic model output was created by merging the lower Peace River and Shell Creek outputs and calculating a weighted average for the water age values for the river kilometer segments below the confluence with Shell Creek. Water age estimates at river kilometers 0 and 2 were not used when flows were below 116 cfs, and estimates at river kilometer 4 when flows were below 40 cfs. These exclusions were the result of predicted water ages outside the duration range (greater than 1,054 hours) for which the hydrodynamic model was run. Statistical models were next developed using the SAS NLIN Procedure to predict water age at each individual location used in the District hydrodynamic model. The NLIN method was used after initial linear models indicated that a model using a power function provided the overall best fit. Analyses indicated that the optimum results came from applying multiple statistical models for different flow ranges at each location (particularly under very low flows). Ultimately, appropriate flow break points for each location were determined by running alternative statistical models for all flows over 100 cfs. The weighted water age equations were used for the river kilometer locations downstream of the Shell Creek confluences (RK 0-14), while Peace River water age equations were used for locations further upstream. Water age estimates based on same day flows were generated by applying these equations to the flow record and daily water age estimates were generated for each 2 kilometer location in the river from river kilometer 0-30. To further improve these estimates relative to real world conditions, the median water age in the river was calculated for each day in the record. For each date in the record the preceding day(s) lag average flow term was calculated with the median water age for same day flow as the lag term. Water age estimates were then recalculated using these lag average terms instead of the same day flows. 4.2 Task 3 Results Univariate relationships between these water age estimates and the measured chlorophyll a concentration data were then reviewed graphically. Analyses were conducted for all samples, as well as separately for “fixed” and “moving” sampling sites. Relationship between chlorophyll a and the predicted water age determinations were assessed graphically. Complete statistical and

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graphical analyses are included in the complete, comprehensive Task 3 report attached to this report. The graphical analyses of chlorophyll a versus predicted water age (see below) failed to show consistent strong, distinctive patterns within any of the tested two kilometer intervals along the lower river transect. The patterns that are apparent are not consistent in identifying a well-defined range of water ages at which high chlorophyll a concentrations occur. Overall, the higher observed chlorophyll a concentrations generally coincide with the lower half of the water age range at any given location. This consistency provides added support for the concept that the largest chlorophyll a concentrations are most likely to occur in the lower (but not the lowest) part of the range of calculated water ages spatially along the lower river. The lack of strong patterns in the presented analyses suggest that further improvement may require a multivariate modeling approach (see Task 5). The following series of figures depict generalized spatial difference among the relationships between water age and chlorophyll along the lower river upstream of the US41 Bridge (see Figure 1.3 above). As this series of figures shows, moving upstream along the lower river the is a distinct relationship between declining water age and higher measured chlorophyll levels.

Figure 4.1 Comparison of chlorophyll a measurements and water age at river kilometer 6

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Figure 4.2 Comparisons of chlorophyll a measurements and water age at river kilometers 24 & 30

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5.0 Task 4 Examine relationships between the approximate location of maximum chlorophyll a concentration as a function of freshwater inflow. The objective of this task was to develop a statistical based model to predict the location of the chlorophyll maximum (location of the highest chlorophyll concentration) as a function of combined gaged freshwater inflow. The complete Task 4 write-up and appendices is contained in Appendix D. 5.1 Task 4 Methods Initial analyses showed that data from the fixed stations were less useful in the development of such models, and therefore the final modeling efforts primarily focused on the 1983-2011 HBMP moving station data. The location of the highest chlorophyll sample for each monthly sampling date was identified, and resulting locations were merged with flow data. Lag flow terms for 5, 10 and 15-day average flow of the combined gaged flows upstream of the Peace River Facility were calculated for each sample date. Two alternative statistical approaches were evaluated. First linear regressions were applied using the SAS REG Procedure, in addition power models were developed using the SAS NLIN Procedure. The resulting final models were evaluated in terms of statistical significance, explanatory power and model fit with the data. For the nonlinear modeling a proxy for R2, known as the pseudo-R2 was calculated applying the following equation:

Pseudo-R2 = 1 - SS(Residual)/SS (TotalCorrected)

5.2 Task 4 Results The results of the linear regression modeling exhibited poor model fits (Figure 5.1) having R2 values typically less than 0.18, and exhibiting distinct residual patterns. In comparison, both the fit and statistical power of the nonlinear equations were far stronger. The observed best fit came from using the 5-day lag average flow term, which produced a pseudo-R2 of 0.85 and a good model fit (Figure 5.2). The final selected best-fit statistically based model to predict the location of the chlorophyll maximum was determined to be:

Location of chlorophyll maximum (km) = 56.3 * 5-day avg flow-0.2616

It should be noted that the residuals for this model are quite large, with predictions potentially being off by as much as 10 km for any individual measurements. This is understandable given the number of factors that can affect chlorophyll blooms, such as nutrient concentration, species of primary producer, temperature, light, and many others. Given the large area of coverage, and the small number of sampling point available each month, this model is fairly powerful for assessing the impact of near term flows on the location of the maximum chlorophyll concentration. It should be noted that spatially intense, monthly in situ data chlorophyll monitoring was begun by the Authority in the spring of 2013 (see discussion in Task 6). This new study is specifically

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being done to address questions of how freshwater inflows influence monthly peaks in chlorophyll levels in the lower river/upper harbor estuarine system. However, it will be a number of years before sufficient data has been gathered to support similar statistical analyses.

Figure 5.1. Model fit plot for the linear regression of the river kilometer location

of the chlorophyll maximum using 5-day average flows

Figure 5.2 Model fit plot for the power regression of the river kilometer location

of the chlorophyll maximum with 5-day average flow

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6.0 Task 5 Multivariate analyses of relations of freshwater inflow related parameters with chlorophyll a in various reaches of the Lower Peace River and Charlotte Harbor This task included using graphical and multivariate analytical procedures to assess and determine if specific relationships could be established seasonally between estuarine chlorophyll a levels and upstream gaged freshwater inflows (and other measured physical/chemical parameters) in differing reaches of the lower Peace River/upper Charlotte Harbor estuary. Note: The complete write-up of Task 5 contained in Appendix E includes methods, results, as well as summary conclusions. The presented methods and results summarized here include some specific conclusions revealed by the presented graphical/statistical analyses, while other relative to the influences of freshwater inflows on the temporal/spatial distribution of phytoplankton densities in the lower river/upper harbor are contained below under Task 6. 6.1 Task 5 Methods/Results Based on a series of preliminary analyses initially conducted during the construction of the project’s master database, the decision was made (in coordination with District staff) to evaluate phytoplankton chlorophyll/primary production both over the length of the HBMP monitoring transect, as well as by combining the data from the “moving” and “fixed stations into five spatially grouped river segments. In addition to the physical/chemical water quality parameters measured in conjunction with phytoplankton biomass/production, a number of other potential metrics were included as part of the graphical/statistical analyses. These included: Same day combined flows upstream of each designated river segment, as well as

averages for the preceding 3, 5, 10, 15, 30 and 60 days. Flows were further grouped into four divisions based on percentiles of long-term averages.

Computed water age (Task 3). Same day and averaged solar radiation for the preceding 3, 5, 10, 15, 30 and 60 days.

Estimated upstream nutrient loading rates (see Task 1).

Divisions based on District seasonal blocks. 6.1.1 Statistical Testing of Correlations An initial preliminary step conducted prior to undertaking more detailed multivariate graphically/statistically procedures was to run simple correlation matrices between the phytoplankton biomass/production metrics and the other potentially interacting measured physical/chemical parameters. Correlations were run for:

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For the entire lower river/upper harbor (combining all five river segments) Within each of the river segments individually For each of the river segments, within each of the three District designated seasonal

blocks The following summarizes some of the major conclusions drawn from correlation analyses of phytoplankton biomass/production with preceding periods of freshwater inflows, solar radiation and upstream nutrient loadings. An important finding was that there were relatively strong correlations (R) between measured levels of chlorophyll biomass and calculated rates of phytoplankton production (C14 uptake). The lowest correlations (~ 0.4) occurred within the most downstream upper harbor segment (< RK 2.1), with the correlations between phytoplankton biomass and production progressively increasing upstream to above 0.9 in the most upstream river segment (> RK 27.1). This observation means that most of the spatial/temporal patterns described for chlorophyll biomass can also be generally applied (with some exceptions) to phytoplankton production. It is not surprising that phytoplankton biomass and production would be more closely linked in the upstream segments, which are often characterized by shorter water ages as residence times decline with increasing freshwater (nutrient) inflows. Correspondingly, the upper harbor often experiences higher water ages, which can result in the accumulation of higher phytoplankton biomass (chlorophyll), quickly leading to reduce nutrient (inorganic nitrogen) levels. Under such conditions senescent cells can begin to dominate, resulting in the observed lower rates of carbon uptake per unit of phytoplankton chlorophyll biomass. When the river segments were combined, chlorophyll levels and preceding intervals of freshwater inflow showed very small overall correlations (R Value < 0.1). This finding is in line with other graphical analyses that indicate an initial positive chlorophyll response to increasing freshwater inflows and then a marked decline as water age decreases (increased flushing) and increasing water color reduces light levels in the water column (higher extinction coefficients). Since correlation analyses are based on linear relationship, and the observed relationships between chlorophyll and upstream freshwater flows are highly non-linear, the overall lack of fit should be expected. When analyzed over the full range of observed flows, chlorophyll/productivity levels were found to be only positively correlated to the various tested preceding flow intervals within the two most downstream river segments (reflecting nutrient inputs), while being slightly negatively correlated in the more upstream reaches of the lower river (reflecting the interactions of decreasing residence time and negative influences of increasing higher water color). Interestingly, when correlations between flows and phytoplankton biomass/production within the river segments were further analyzed by the seasonal MFL blocks, much stronger correlations with flows were observed with flows in Block 1 (April 20 to June 25). The R value for the correlation between chlorophyll and 30-day average upstream flow was 0.57, and corresponding 0.93 for carbon uptake. Again, correlations with freshwater inflows (even during Block 1) were found to progressively decline among the river segments moving upstream.

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Preceding levels of solar radiation were generally found to be, by themselves, poorly (R < 0.1-0.2) correlated with measured levels of both chlorophyll biomass and productivity. A noted exception being within the most upstream river segment, where correlations (R) with averages over longer preceding intervals were above 0.4. This observation coincides with other analyses that found the highest chlorophyll levels in the most upstream segment seasonally occurred during periods characterized by lower water ages during the spring; which is often the seasonal period of maximum daily light, since increasing cloud cover during the summer notably reduces levels of solar radiation. The correlation analyses indicated that the various measures of nutrient loadings were relatively strong correlated among themselves, since they are all calculated from and strongly influenced by seasonal variations in freshwater inflows. By themselves, these loading rates were found to be poorly (R < 0.1-0.2) correlated with measured levels of either chlorophyll biomass or production. Since nutrient loading rates were directly calculated from flows, it isn’t surprising that the phytoplankton metrics initially show positive responses with increasing nutrient loadings only to decline with decreasing water age and increasing water color. These analytical results further indicated the following interesting correlations among the phytoplankton metrics and physical/chemical water quality parameters. Stronger R values (> 0.5) were observed between chlorophyll levels and Kjeldhal and

total nitrogen concentrations. This isn’t surprising since both these water quality parameters include measure organic nitrogen, and thus can be viewed in part as alternative measures of phytoplankton biomass. This is supported by the observation that the correlations between these nitrogen measures and phytoplankton carbon uptake rates were far weaker. The correlations (0.4 to 0.7) between water color and water quality characteristics such as measures of nitrogen, phosphorus and silica suggest the potential for water color to serve as a conservative proxy for nutrient loadings.

Within the most upstream river segment (> RK 27.1), there was a noticeable change in the observed increase in the R values between measures of carbon uptake, water temperature, phosphorus, iron and silica. The later three parameters are all characteristic of greater groundwater influences, which increase during warmer spring dry-season conditions. The period is also characterized by longer water ages (greater residence time) and often the peak seasonal period of phytoplankton growth in this upper reach of the lower river.

Finally correlation analyses were used to evaluate and select the most appropriate preceding interval(s) for flows, light, and nutrient loadings to be applied in subsequent graphical/statistical analyses within each of the fiver river segments. 6.1.2 Simple Graphical Analyses A series of simple x-y graphical were made first to assess seasonality by plotting chlorophyll levels versus the day of the year, for each of the five river segments. Next plots were made to

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assess the potential changes in chlorophyll levels relative to available physical/chemical parameters. The following patterns generalize patterns were depicted in the resulting figures. Seasonality The highest levels of chlorophyll in the downstream upper harbor segment < RK 2.1)

were observed to generally occur during the fall, after summer wet-season flows have delivered large amounts of differing forms of nitrogen to the upper harbor. Seasonally, the phytoplankton maxima in the upper harbor takes place as freshwater inflows decline and tidal dilution and sunlight reduce water color (increasing the depth of the photic zone).

In the middle zones of the lower river (RK 10.8 to 19.5) high phytoplankton densities occur throughout the year depending on the timing and magnitude of preceding flows and the combined influences on both water color and residence time.

In the most upstream river segment (> RK 27.1) the occurrence of high chlorophyll levels

typically seasonally occurs under the spring dry-season conditions (high light and temperatures), when relatively low freshwater inflows provide enough nitrogen to support increasing production, and resident still times remain relatively long.

Figure 6.1 Plots of chlorophyll in the most upstream and downstream river segments show strong differences in the spatial seasonal occurrences in peak phytoplankton densities

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Chlorophyll Response That within the two most downstream river segments, increasing upstream flows

generally stimulated chlorophyll levels up to 2500 cfs (30-day average of Peace River and Shell Creek).

Within the middle segments of the lower river, chlorophyll levels initially increase with higher upstream freshwater inflows, and then past some level chlorophyll levels begin to decline. This decline coincided with both declining water age and increasing color.

The level of flow needed to reduce chlorophyll levels occurs under increasingly lower flows moving upstream among the river segments. A key factor in explaining this observed pattern can be seen in comparing observed chlorophyll levels with the calculated water ages within differing reaches of the lower river. Maximum chlorophyll levels generally occur under relatively similar lower water ages (5-10 days). However decreasing lower amounts of inflow create such conditions moving upstream along the HBMP monitoring transect. Thus in the middle reaches of the lower river chlorophyll levels generally rapidly decline under both lower water ages (due to increased flushing) and under low flow conditions characterized by higher water ages (lower nutrient inputs).

Similarity, the highest chlorophyll a phytoplankton biomass levels within each of the segments interestingly occur around similar water color concentrations. Again, the amount of upstream flow needed to increase color decreases moving upstream due to smaller volumes and the decreasing influences of lower color tidal harbor waters.

Combined these results suggest that relatively similar levels of water age and color, mediated by freshwater inflows, establish the physical conditions necessary to stimulate chlorophyll densities within each of the river segments. There are however obviously a large number of other interactions and confounding conditions that can potentially influences chlorophyll levels within the lower Peace River/upper Charlotte Harbor estuary.

6.1.3 Graphical Analyses of Chlorophyll Relative to Potential Influences SAS Scatter and interpolated Surface Response graphical approaches were then prepared to help better understand and evaluate potential temporal/spatial interactions of multiple factors on phytoplankton chlorophyll densities within and among differing reaches of the lower Peace River estuary. Both these SAS graphical methods involved producing three dimensional (3-D) representations of the response of chlorophyll (z axis) to a pair of parameters along the x and y axes.

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6.2 Chlorophyll scatter and response surface plots of water age versus water color RK

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Table 6.1 summarizes the selected eighteen pairs of parameter interactions that were utilized to develop the 3-D scatter and response surface plots created to assess the interactions of a range of physical/chemical conditions, relative to chlorophyll levels in the lower river/upper harbor estuarine system.

Table 6.1 – Matrix of 3-D Scatter and Response Surface Plots

Z (Vertical) Axis Y (Left to right) Axis X (front to back) Axis

Chlorophyll Water Color Water Temperature

Chlorophyll Water Color Time (Day of Year)

Chlorophyll Water color Water age (Modeled)

Chlorophyll Water Color Salinity

Chlorophyll Water Color NOx concentration

Chlorophyll Water Age (Modeled) Water Temperature

Chlorophyll Water age (Modeled) NOx concentration

Chlorophyll Upstream Gaged Flow (30-Day Avg.) Water Temperature

Chlorophyll Upstream Gaged Flow (30-Day Avg.) Salinity

Chlorophyll Upstream Gaged Flow (30-Day Avg.) Time (Day of Year)

Chlorophyll Upstream Nitrite/Nitrate Loading Water Temperature

Chlorophyll Upstream Total Nitrogen Loading Water Temperature

Chlorophyll Salinity Temperature

Chlorophyll Salinity Upstream Total Nitrogen Loading

Chlorophyll Salinity Water Age (Modeled)

Chlorophyll Salinity NOx concentration

Chlorophyll Upstream Gaged Flow (30-Day Avg.) NOx concentration

Chlorophyll NOx concentration Water Temperature The 3-D scatter and corresponding 3-D response graphics for each of the eighteen analyses in Table 6.1 are contained in the write-up of Task 5 in Appendix E. In addition to the 3-D chlorophyll response surface graphic shown in Figures 6.2 above, an additional series of similar graphics 3-D chlorophyll response surface graphics comparing selected parameter pairs are presented below in Figures 6.3 through 6.5. In these figures the RK < 2.1 segment was selected as representative of the upper harbor, while the RK 10.8 to 19.5 segment was chosen to depict parameter interactions in this often seasonally highly productive middle reach of the lower river. Based on these figures the following findings and contrasts between these two segments are indicated relative to the differing influences on chlorophyll biomass of the shown parameters.

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Southwest Florida Water 28 Relationship between inflows and primary production Management District in the lower Peace River/upper Charlotte Harbor

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Figure 6.3 Chlorophyll versus: 1) water color and water temperature, and 2) water color and day of year

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Figure 6.4 Chlorophyll versus: 1) water age and water temperature, and 2) 30-day flow and temperature

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Figure 6.5 Chlorophyll versus: 1) salinity and water color, and 2) water color and nitrite/nitrate levels

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Upper Harbor – River Segment Downstream of RK 2.1 This river segment covers an area of the upper harbor extending both downstream and upstream of the USGS defined river’s mouth. Seasonally, this upper harbor region of the estuary experiences two distinct periods of increasing phytoplankton biomass. The first chlorophyll increase occurs during the spring under physical conditions characterized by lower water temperatures (22-25 oC), low water color (75-150 PCU), lower to intermediate preceding freshwater inflows, and estimated water ages in the range of 5 to 20 days. Such conditions are characterized by relatively brief periods of increased freshwater inflow, which are annually not uncommon both during the early spring prior to the typical summer wet-season. In other instance, the observed spring phytoplankton bloom in the upper harbor probably simply reflects the uptake of winter recycled inorganic nitrogen in response to increasing light/temperature levels. The spring peak in phytoplankton in the upper harbor is characteristically much smaller in both magnitude and duration that the more distinct second seasonal peak that generally occurs in the fall as freshwater inflows dramatically decline. During the fall phytoplankton increase water temperatures (30-35 oC), and water color (125-225 PCU) are decidedly higher than observed during the spring bloom, and estimated water ages are typically notably longer (10-45 days). This difference in water ages between the seasonal phytoplankton increases is reflected by the distinctly higher salinities observed during the fall period. During both these seasonal periods of phytoplankton biomass increases, corresponding measures of inorganic nitrite/nitrate nitrogen in the surface waters are shown to rapidly decline to near detection levels as chlorophyll levels increase. Figure 6.5 clearly shows the potential difficulties inherent in using actual inorganic nitrogen concentrations in predicting chlorophyll levels. Middle Lower River – Segment RK 10.8 to 19.5 Early in the HBMP monitoring this reach of the lower river was identified as an important area of phytoplankton production that often seasonally experiences marked increases in phytoplankton biomass. Subsequently, the importance of this area of the lower river has been supported by District/Authority supported studies of both larval fish/zooplankton and benthic invertebrates. Like the upper harbor, this region of the lower river seasonally experiences two distinct periods of increased phytoplankton growth (chlorophyll a). The first occurs during the early summer as freshwater inflows begin to increase. The initial seasonal increase in this reach of the lower river notably begins both later in the year and is of greater magnitude/duration than the earlier spring bloom in the upper harbor. Accordingly this initial increase occurs under conditions of higher water color (150-250 PCU), and estimated water ages between 10 and 25 days. The graphic in Figure 6.4 clearly show that this initial increase in phytoplankton biomass ends as flows increase and corresponding water ages decrease. As in the upper harbor, the fall phytoplankton increase in the lower river is typically of both much greater magnitude and duration that the initial early summer peak in this reach of the river. However, the fall peak in the lower river occurs noticeably later in the year than that in the upper harbor, as water ages extend out to greater lengths. Both the early summer and later fall increases in phytoplankton biomass in this region of the lower river occur under conditions of intermediate salinities (12-20 psu). The early summer chlorophyll increase is initially stimulated by increasing flows, while the fall phytoplankton blooms responds to decreasing water color and increasing residence time.

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Under both conditions inorganic nitrogen levels are at first relatively high, but as shown in Figure 6.5 ambient levels dramatically decline as phytoplankton biomass increases. 6.1.4 Multivariate Statistical Analyses Based on the above analyses, a series of differing multivariate statistical methods were tested to determine if additional information could be determine to further differentiate the degree of influence seasonal freshwater inflows variations and interactions among other identified influencing factors have on lower Peace River estuarine phytoplankton biomass/production. Principal Component Analysis (PCA) Principal component analysis technically simply provides new artificial variables (Factors) resulting from optimally-weighted linear combinations derived from an initial much large number of observed variables. Conceptually the objective of PCA analysis is to reduce the number of independent variables, with the first component capturing most of the observed variance, the second component the next greatest variance, and so on until all the modeled variance has been accounted. Typically in PCA analysis most of the observed variance among the original parameters can be accounted for by a small number of new Factors. These principal components (Factors) can then be used independently as predictor variables or criterion during subsequent analyses. PCA was first run for the entire data set, and then for each of the previously used individual river segments. Complete results are provided in Appendix E. A number of differing alternative groupings of potential parameters were tested and evaluated in an iterative process for the entire monitoring transect, as well as for each of the five utilized river segments. Although there were some differences among the river segments relative to parameter loadings, the first three Factors generally contained the following: Factor 1 – this initial Factor (which by definition accounted for the largest portion of the observed variance) loaded with variables related to variations in freshwater inflows. This included a term for lagged flows, water age, water color, and combined upstream nutrient loading rates. In the majority of the more downstream river segments the variability of water age and salinity also loaded into the first Factor. Factor 2 – the second Factor accounted for the observed variability due to solar radiation and water temperature. In the most upstream river segment salinity also loaded into this second Factor. This is probably due to the fact that when most of the estuary is experiencing the greatest salinity variability as freshwater flows increase, there is little if any variability in salinity in this upper reach of the river. Thus, upstream of RK 27.1 PCA doesn’t show salinity loading with flows on the first Factor. Factor 3 – this Factor primarily accounted for the day of the year (although within some river segments another parameter was included). As discussed, phytoplankton increases (blooms)

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within the river segments tend to follow repeatable seasonal patterns with the largest increases occurring toward the end of the year. As Figure 6.6 indicates, plotting Factor 1 (flow related) scores against Factor 2 (light and temperature) scores shows few differences among the sampling events within the combined five river segments. However, when Factor 1 scores are plotted against Factor 3 (day of year) scores the observations among the river segments undergo marked sequential separation. This suggests that the physical conditions that characterize differing Factor 1 scores aren’t entirely unique to any particular segment of the lower river estuary. These flow mediated physical conditions (water age, color, etc.) however do characterize different segments of the lower river estuary during slightly different temporal intervals (Factor 3). This concept was further support by plotted against Factor 1 and Factor 2 scores. The resulting graphic indicated that when plotted against Factor 1, the seasonal peaks in chlorophyll levels among the five river segments show little separation. However, again when plotted against Factor 3, well defined chronological, temporal differences were apparent in the timing of chlorophyll increases among the five river segments. Thus the PCA Factors can be used to show that there are clear temporal differences in relative to how the flow related components (water age, color, nutrient loading) spatially influence phytoplankton biomass along the lower Peace River estuarine system. Corollary analyses of primary production (carbon uptake rates) using PCA failed to show similar temporal/spatial relationships.

Figure 6.6 Division of river segments relative to Factor 1 and 3 scores,

and chlorophyll versus Factor 3 scores SAS RSREG and STEPWISE Analyses The final applied multivariate statistical methodologies used to analyze phytoplankton chlorophyll biomass and primary production in the lower peace river/upper Charlotte Harbor estuary were the combined application of the SAS RSREG and STEPWISE analytical

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procedures. The RSREG procedure is a particularly useful application that be applied to initially screen and test for the both linear and quadratic significance of individual parameters, as well as for potential interactions among parameters. The interactions of chlorophyll a levels along the entire monitoring transect and within each of the five river segments were tested against a variety of potential available parameters. The results of the RSREG procedure were evaluated and potential significant terms, power functions, and interactions were then incorporated into the SAS STEPWISE procedure in order to further test the potential development of additional multivariate modeling. Method and results of these analyses are presented in Appendix E. The results of these combined multivariate procedures indicated a very high degree of variation in temporal/spatial responses of phytoplankton chlorophyll a and primary production (carbon uptake) relative to corresponding physical/water quality parameter metrics. This observed high degree of natural variation made additional development of predictive statistical models (beyond the results of the applied stepwise regression) of questionable value, since the resulting R2 of these initial derived models among the river segments ranged from approximately 0.06 to 0.14 for chlorophyll, and 0.12 to 0.29 for primary production. Further iterative attempts using linear and nonlinear terms failed to produce particularly useful statistical models that could be predictably applied to estimate chlorophyll biomass within segments of the river to flow related terms with any meaningful statistical rigor. As previously described the differing applied graphical analyses and multivariate PCA analyses indicated district seasonal flow related temporal/spatial patterns in chlorophyll biomass along the lower river estuary. However, the high observed degree of annual variation makes development and practical application of effective, specific statistically based predictive models, which can then be used to assess the potential magnitude and timing of existing and future water withdrawals, extremely problematic. This however begs the question whether the influences/causes of this high degree of annual variation in phytoplankton biomass/production within the lower river estuary be assess in any systematic manner. 6.1.5 Factors Influencing High Degree of Observed Variances in Phytoplankton Population Dynamics along the lower Peace River Estuary The results of the multivariate PCA analyses and graphical analyses clearly indicated that the timing and distribution of observed increases in phytoplankton biomass within the lower river/upper harbor estuarine system are primarily mediated through the timing and magnitude of seasonal flows. The question then becomes how do annual variations in freshwater inflow patterns (beyond the conceptual dry-season, wet-season periods) influence the timing, magnitude and spatial distribution of observed chlorophyll levels/patterns? To assess such potential influences two sets of graphics were created for the each of the years between 1984-1989 and 1997-20011. These specific time intervals were selected since only within these years were monthly chlorophyll data collected from both the “moving” and “fixed” HBMP monitoring.

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1. Plots of annual flows for each of these years were created to depict the seasonal timing and magnitude using the combined upstream daily flows from the three USGS gages upstream of the Peace River Water Treatment Facility.

2. Corresponding 3-D surface plots were then constructed of chlorophyll levels (z axis)

relative to river kilometer (y axis) and time of year (x axis) for each of these years. The paired graphics among the selected years were then compared and contrasted to further assess the potential influences that the timing/magnitude of freshwater inflows have on corresponding temporal/spatial peaks (or lack thereof) in phytoplankton biomass within the lower river/upper harbor estuary. The graphical pairs of annual flows plots and constructed 3-D chlorophyll response graphics are presented in Appendix E for each of the selected twenty-one years where both monthly “fixed” and “moving” chlorophyll data were available along the HBMP monitoring transect. Figures 6.7 through 6.9 depict these paired graphics for six of these years, which were chosen to depict some of the wide degree of variation exhibited both by seasonal freshwater inflow, as well as in the corresponding temporal/spatial responses of lower river estuarine phytoplankton populations. In contrasting and making comparisons among these annual graphical pairs it is important to keep in mind the primary factors (shown by the preceding graphical/statistical analyses) associated with freshwater inflows that affect estuarine chlorophyll levels. As these figures (and the additional in Appendix E) indicate that there are general overall temporal/spatial seasonal patterns in annual estuarine chlorophyll levels. However, among years there is a tremendous amount of actual variability. This variability is primarily driven by the specific physical/water quality conditions created both by immediate and preceding longer term patterns in freshwater estuary inflows, which vary dramatically in both timing and magnitude among years. As a result, the development of accurate and predictable simple statistical models of the relationships between freshwater inflows (and other physical/chemical parameters/ interactions) is probably realistically unattainable.

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Figure 6.7 Comparisons of the timing and magnitude of freshwater inflows and the relative response in estuarine phytoplankton biomass for 1997 and 2000

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7.0 Task 6 Data synthesis, interpretation The overall goal of the study’s final task was to summarize the findings of the presented analyses, specifically addressing how phytoplankton chlorophyll a biomass and estimated rates of primary production within differing reaches of the study area seasonally vary as a function of natural variability in the rate of freshwater inflow. Specifically how any identified relationships with factors such as color, water age, and nutrient loading might be affected by preceding seasonal freshwater inflows? A corollary goal was to determine at a conceptual level if reductions in flow might stimulate higher phytoplankton production in some reaches of the estuary, while at the same time reducing chlorophyll levels in others; and if such relationships might vary seasonally. Finally, again conceptually, assess what influences existing and permitted Peace River Facility withdrawals might have on these phytoplankton patterns. 7.1 Temporal/Spatial Influences of Freshwater Inflows The graphical and PCA analyses and conclusions presented in the preceding section describe the observed general seasonal temporal/spatial patterns in phytoplankton chlorophyll levels within the lower river/upper harbor estuary. The following outlines the relationship between freshwater inflow influences (water age, color and nutrient loading) and these observed general patterns. The limiting nutrient in the lower Peace River/upper harbor estuarine system is nitrogen,

and given the right combination of physical factors (primarily light and residence time) chlorophyll levels can rapidly increase reducing inorganic nitrogen forms to near laboratory detection limits during periods of low freshwater inflows.

There are extended areas of the lower river/upper harbor where organic material

delivered by previous wet-season high river flows settles into the upper sediments. Seasonally, especially early during the spring dry-season, the regeneration of nitrogen from these sediments can sustain relatively brief increases in phytoplankton biomass. However, such blooms are generally of relatively short duration and magnitude.

Such early spring increases in phytoplankton densities, which occur in both in the upper harbor and middle lower river segments typically coincide with increasing light intensity, higher water temperatures and seasonally longer water ages.

In other instances spring increases in chlorophyll biomass within regions of the lower river/upper harbor estuary often occur in response to late winter/early spring relatively brief periods of freshwater inflows that are typically related to the passage of cold fronts.

Exceptions to these general patterns occur during/following periodic El Niño events

when large sustained phytoplankton winter/spring phytoplankton blooms often occur in the upper harbor.

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The dry spring (low freshwater inflows) also typically corresponds with the seasonal interval of maximum chlorophyll densities in the upper most reaches of the HBMP monitoring transect. Within this portion of the lower river, seasonally low flows deliver a continuing supply of sufficient inorganic nitrogen, while physical conditions characterized by long resident times, relatively low water color, seasonally near maximum light levels and increasing water temperatures provide ideal conditions to support phytoplankton blooms. The low, but continuous input of new nitrogen often results in very high phytoplankton blooms that are often sustained until early in the start of the summer wet-season, when residence times rapidly decline and color levels increas.

By contrast, in the lower river/upper harbor reaches of the estuary, once inorganic nitrogen levels have been depleted during the dry-season, chlorophyll densities decline rapidly until summer increasing flows deliver additional nitrogen from the upstream tributary watershed basins.

Early in the summer freshwater inflows are often initially lower and far more intermittent

than those occurring during the later summer months. The inorganic nitrogen carried to the estuary by such lower, early summer flows typically results in a series of sequential phytoplankton blooms that chronologically move downstream from the lower river into the upper harbor. At the same time, increasing flows raise water color and decrease residence time in most upstream reaches of the river bringing an end the spring bloom conditions.

These initial summer increases in phytoplankton biomass in the lower river/upper harbor

often coincide with relatively high water color (150-250 PCU). Eventually however increasing freshwater inflows further increase water color reducing the availability of light in the water column), while simultaneously progressively reducing residence times. Under sustained higher flow conditions the blockage of sufficient sunlight by high water color limits photosynthesis to a very narrow portion of the upper water column. The corresponding reduction in residence times under higher flows also becomes a limiting factor in sustaining higher phytoplankton densities. These limiting factors to the buildup of chlorophyll levels initially begin upstream, and then sequentially influence more downstream segments of the estuary as summer flows increase.

In some years, the onset of summer wet-season starts abruptly with rapid, sustained

increases in freshwater inflows. Under such conditions the occurrences of early summer chlorophyll blooms in the lower river/upper harbor estuary are noticeably minimal or absent.

Often freshwater inflows decline fairly abruptly in the fall following the end of the

summer wet-season as drier air moves down from the north. As flows decrease, residence times in the upper harbor and lower river increase, and water color declines (as the combined result of dilution and sunlight degradation.) In the fall, the combination of physical conditions (increasing residence time and lower color) necessary to sustain phytoplankton growth return. Following summer flows, large amounts of available

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nitrogen that have accumulated and are available to support phytoplankton growth. These conditions typically results in large, sustained chlorophyll increases initially beginning in the harbor and then progressively moving upstream into the lower river following the end of wet-season flows. These falls chlorophyll increases in both the upper harbor and lower river are characteristically of both greater magnitude and duration than the phytoplankton blooms observed during the spring/early summer.

The length and intensity of such fall blooms varies depending on amount of flow during the summer, the timing of the fall decline in freshwater inflows and when winter cold fronts begin to drop water temperatures.

Alternatively, when high freshwater inflows flows extend from the fall into the early

winter, estuarine residence times remain low, water color high, and the typically observed fall phytoplankton increases are limited or absent.

At some point (often in Southwest Florida late in November to mid December) the normal summer thermal/salinity stratification in the estuary begins to break down due to the combined influences of declining flows and/or declining water temperatures. More frequent cold fronts then begin mixing the water column. These physical changes, combined with rapidly dropping water temperatures, leads to rapid declines in phytoplankton densities throughout the estuary.

7.1.2 Potential Influences of Freshwater Withdrawals on Estuarine Phytoplankton Levels The analyses presented in Task 5 provided insight into the primary factors influencing the seasonal timing and relative locations of seasonal phytoplankton increases in the estuary. However, the applied graphical and statistical multivariate techniques failed to result in applicable predictive models that could then be used to assess potential temporal/spatial changes in chlorophyll levels due to withdrawals from the river. The proximate cause of the lack of fit of such models being that the spatial and temporal frequency of sampling (although covering more than 30 years) wasn’t sufficient to account for the high variances in the data resulting from the variable nature of freshwater inflows within and among the years. Phytoplankton densities at any given time and location are the result of not only the immediate combination of physical/water quality characteristics, but are also highly influenced by similar preceding conditions. The PCA analyses indicated that most important of these (water age, water color, and nutrient loadings) are all mediated through changes in the timing and magnitude of inflows, which are highly naturally variable. The question then becomes, conceptually, do current (or future) freshwater withdrawals have the potential to influence the existing variability observed in the spatial/temporal distribution and magnitude of phytoplankton densities in the lower Peace river/upper Charlotte Harbor estuarine system? Conceptually, freshwater withdrawals have the potential to influence chlorophyll levels primarily through one of three major mechanisms. 1. Decreasing water color

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2. Reducing nutrient loadings 3. Changing residence times Table 7.1 summarizes the District adopted MFL for the lower Peace River, while Table 7.2 indicates the Peace River Facility’s 2011 revised permitted withdrawals schedule.

Table 7.1 Final Adopted District Lower Peace River MFL Schedule

(based on combined USGS gaged flow at three gages upstream of the Peace River Facility)

Block Allowable Percent Reduction in Flow

Block 1 (April 20th – June 25th) 16% Block 2 (October 27th – April 19th) 16% if flow < 625 cfs 29% if flow > 625 cfs Block 3 (June 26th – October 26th) 16% if flow < 625 cfs 38% if flow > 625 cfs

Table 7.2

April 2011 Revised Authority Lower Peace River Withdrawal Schedule

Block Allowable Percent Reduction in Flow Block 1 (April 20th – June 25th) 16% if flow is above 130 cfs

Block 2 (October 27th – April 19th) 16% if flow is > 130 cfs 28% if flow > 625 cfs Block 3 (June 26th – October 26th) 16% if flow is > 130 cfs 28% if flow > 625 cfs

Decrease Water Color – the analyses presented in Task 5 indicated that peak ear

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