APPENDIX J SURFACE WATER SALINITY CONTROLS REPORT

Transcription

1 APPENDIX J SURFACE WATER SALINITY CONTROLS REPORT

2 OROP Surface Water Salinity Controls SURFACE WATER SALINITY CONTROLS IN SUPPORT OF TAMPA BAY WATER S OPTIMIZED REGIONAL OPERATIONS PLAN Prepared for: Tampa Bay Water 2535 Landmark Drive, Suite 211 Clearwater, Florida Prepared by: David Wade 1, Anthony Janicki 1, and Judy Fouts 2 1 Janicki Environmental, Inc Eden Isle Drive NE St. Petersburg, Florida Ayres Associates 8875 Hidden River Parkway Suite 200 Tampa, Florida February 2002 Janicki Environmental, Inc.

3 OROP Surface Water Salinity Controls ii ACKNOWLEDGMENTS This report was prepared for Tampa Bay Water under Contract to Ayres Associates, Inc. Mr. Subrata Bandy, P.E. was Project Manager. The authors wish to express their gratitude to Mr. Bandy, and Ms. Susan Janicki and Ms. Michele Winowitch of Janicki Environmental, Inc. for their contributions to this report. Janicki Environmental, Inc.

4 OROP Surface Water Salinity Controls iii EXECUTIVE SUMMARY Tampa Bay Water adopted a Master Water Plan in 1997 that identified surface waters including the Tampa Bypass Canal (TBC), the Hillsborough River, and the Alafia River as significant sources of additional water supply. The TBC/Hillsborough River and the Alafia River projects are part of an integrated system referred to as the Enhanced Surface Water System (ESWS). The ESWS is designed to manage and optimize withdrawals, conveyance, and storage of surface water supply. Water use permits (WUPs) issued by the Southwest Florida Water Management District in 1999 for the TBC/Hillsborough River and Alafia River Water Supply Projects specify withdrawal schedules that vary with available flows (i.e., withdrawals increase with increasing flows up to a permitted maximum, no withdrawals below a designated low flow). To address concerns regarding potential adverse impacts to aquatic ecosystems and recreational attributes associated with these water bodies, the WUPs also required a comprehensive Hydrobiological Monitoring Program (HBMP). In addition, WUPs for the surface water supply projects require incorporation of withdrawals into Tampa Bay Water s Optimized Regional Operations Plan (OROP). The OROP was originally developed to manage groundwater withdrawals to maximize utilization of multiple source areas while minimizing environmental impacts. Currently, the OROP utilizes an integrated hydrologic simulation model and an optimization model to manage the 11 Northern Tampa Bay wellfields operated by Tampa Bay Water. This management process is implemented under the Consolidated Water Use Permit for the 11 wellfields; the wellfields are managed as an integrated regional water supply system through the development of an optimized production schedule every two weeks. Both the TBC/Hillsborough River and Alafia River surface water withdrawal projects are scheduled to come on-line in Although WUP conditions specify withdrawal rates that vary with available flows, the inclusion of specific indicators, such as salinity change, as an OROP constraint was recommended during development of the HBMP. For this reason, both flow and salinity criteria for the surface water withdrawals will be incorporated into the OROP. The approach used to develop salinity criteria and recommendations for implementation into the OROP are provided in this report. The overall purpose of this project was to develop a simple to measure and biologically meaningful measure of the influence of the surface water withdrawals on the salinity conditions of the water bodies of concern. This salinity control will be used as one of the response variables for prioritizing withdrawals from the ESWS. Janicki Environmental, Inc.

5 OROP Surface Water Salinity Controls iv There were two parallel approaches taken to develop the salinity controls. The first approach was to establish salinity controls based on historical salinity observations. The second approach was to establish salinity controls based on the literature-based salinity tolerances of important (i.e., abundant and widely distributed) biota. Sufficient data exist to implement the first approach immediately, and HBMP biological data are currently being collected to support the second approach. The tasks completed for this project included reviewing and summarizing the biological communities within the study area and their salinity preferences / tolerances; selecting a salinity control area for each of the three water bodies; developing a model for estimating future salinity based on current salinity, and future surface flow; and recommending a salinity control approach. The salinity control areas were selected as: the HBMP stratum HR-2 in the Hillsborough River, the HBMP stratum AR-4 in the Alafia River, and the HBMP stratum MB-2 in the Tampa Bypass Canal/Palm River/McKay Bay. The authors, in consultation with the OROP Technical Advisory Committee, have presented specific recommendations for implementation of the salinity controls. The recommendations were based on three important and simultaneous needs: to provide a working set of salinity controls that can be implemented immediately based on historical salinity data, to provide a detailed process for implementing a set of salinity controls in the future based on biological salinity tolerance/preference data, to provide recommendations for additional monitoring of the salinity conditions during the OROP operations to protect the natural resources and to optimize opportunities for water withdrawals when they occur. We recommend implementation of the salinity controls based on historical data starting in Year 1, and we recommend continued use of the historical based controls until sufficient biological data become available from the HBMP to implement the Janicki Environmental, Inc.

6 OROP Surface Water Salinity Controls v salinity controls based on biological data. We expect sufficient biological data from the HBMP sampling effort when the salinity conditions in the waterbodies return to conditions representative of the longer-term historical period from 1987 to We recommend a salinity control operational process be implemented every 2 weeks. Tampa Bay Water staff will provide the optimal minimum streamflow rates to the plant operators for each of the three waterbodies. If unexpected storm events provide additional freshwater resources during the 2-week period, then the plant operators will be able to withdraw greater amounts of water than initially expected. The plant operators will be constrained by withdrawal schedules produced by the OROP. We recommend that Tampa Bay Water staff use daily salinity monitoring data to provide an additional level of assurance that the OROP system is providing both a suitable level of protection to the waterbodies (i.e. compared to historical salinity controls), and optimal utilization of the freshwater resources. The best available resources for monitoring the daily ambient salinity conditions are the continuous recorders currently installed in the waterbodies. We recommend that the OROP salinity controls for surfacewater withdrawals have design enhancements, performance evaluations, and operational reporting on an annual basis as part of the Tampa Bay Water OROP annual reporting. We have recommended a specific set of enhancement opportunities expected to arise in Year 1. Janicki Environmental, Inc.

7 OROP Surface Water Salinity Controls vi TABLE OF CONTENTS Acknowledgments Executive Summary ii iii 1.0 Background Approach and Objectives Biological Communities and Salinity Preferences/Tolerances Flow and Salinity Relationships Salinity Control Areas Salinity Controls Recommendations References 28 Janicki Environmental, Inc.

8 OROP Surface Water Salinity Controls Background Tampa Bay Water adopted a Master Water Plan in 1997 that identified surface waters including the Tampa Bypass Canal (TBC), the Hillsborough River, and the Alafia River as significant sources of additional water supply. The TBC/Hillsborough River and the Alafia River projects are part of an integrated system referred to as the Enhanced Surface Water System (ESWS). The ESWS is designed to manage and optimize withdrawals, conveyance, and storage of surface water supply. Water use permits (WUPs) issued by the Southwest Florida Water Management District in 1999 for the TBC/Hillsborough River and Alafia River Water Supply Projects specify withdrawal schedules that vary with available flows (i.e., withdrawals increase with increasing flows up to a permitted maximum, no withdrawals below a designated low flow). To address concerns regarding potential adverse impacts to aquatic ecosystems and recreational attributes associated with these water bodies, the WUPs also required a comprehensive Hydrobiological Monitoring Program (HBMP). In addition, WUPs for the surface water supply projects require incorporation of withdrawals into Tampa Bay Water s Optimized Regional Operations Plan (OROP). The OROP was originally developed to manage groundwater withdrawals to maximize utilization of multiple source areas while minimizing environmental impacts. Currently, the OROP utilizes an integrated hydrologic simulation model and an optimization model to manage the 11 Northern Tampa Bay wellfields operated by Tampa Bay Water. This management process is implemented under the Consolidated Water Use Permit for the 11 wellfields; the wellfields are managed as an integrated regional water supply system through the development of an optimized production schedule every two weeks. Both the TBC/Hillsborough River and Alafia River surface water withdrawal projects are scheduled to come on-line in Although WUP conditions specify withdrawal rates that vary with available flows, the inclusion of specific indicators, such as salinity change, as an OROP constraint was recommended during development of the HBMP. For this reason, both flow and salinity criteria for the surface water withdrawals will be considered for incorporation into the OROP. The approach used to develop salinity criteria and the recommendations for implementation into the OROP are provided in this report. Janicki Environmental, Inc.

9 OROP Surface Water Salinity Controls Approach and Objectives The overall purpose of this project was to develop a simple to measure and biologically meaningful measure of the influence of the surface water withdrawals on the salinity conditions of the water bodies of concern. This salinity control will be used as one of the response variables for prioritizing withdrawals from the ESWS. The study area for the water bodies of concern for this project was operationally defined as the Lower Hillsborough River, the Alafia River, and the Tampa Bypass Canal/ Lower Palm River/ McKay Bay system. There were two different but parallel approaches taken to develop the salinity controls for this project. The first approach was to establish salinity controls based on historical ambient salinity observations. It is reasonable to expect that the historical salinity conditions in the water bodies of concern provided a suitable environment for the biota observed in the water bodies. The second approach was to establish salinity controls based on the literature-based salinity tolerances of important (i.e., abundant and widely distributed) biota. Our overall approach was to develop salinity controls following both of these parallel approaches. Either one of the two approaches may provide an effective management tool for integration into the OROP, but it is probable that following both of these approaches will be even more effective. This is especially true, given that the biological data needed to implement the second approach are relatively sparse at this time. Over the next two to three years, there will be an abundance of data from the HBMP and other programs to identify the important biota in each of the waterbodies. To develop these parallel approaches the following objectives of this project were identified: To review and summarize the biological community spatial distributions within the study area and the salinity preferences/tolerances of these organisms; To select a salinity control area for each of the three water bodies, as the geographic region within each water body with the greatest salinity variation with respect to flow; Janicki Environmental, Inc.

10 OROP Surface Water Salinity Controls 3 To quantify flow-salinity relationships and develop a predictive model for estimating future salinity based on current salinity, and future surface flow; To recommend a salinity control approach and criteria and begin the process of working with the OROP Technical Advisory Committee to incorporate initial salinity controls into the OROP. The salinity controls developed for this report were established using the best available data at the time the project was completed. These data are expected to be augmented in the future with a great deal of valuable information from the ongoing Tampa Bay Water Hydrobiological Monitoring Program (HBMP). The salinity controls developed by this project were designed to be established and maintained using data from the HBMP, following the approach and specific methods developed in this report. Data currently being collected and reported by the HBMP will be available for use in future implementation and maintenance of the approach developed for this project. Janicki Environmental, Inc.

11 OROP Surface Water Salinity Controls Biological Communities and Salinity Preferences/Tolerances The Tampa Bay Water HBMP program is currently collecting information on the presence, absence, and abundance of biota within the three water bodies comprising this project. The biological communities within each of these water bodies are summarized in this Section. To date, HBMP biological data have been reported for benthic organisms, ichthyoplankton, juvenile fish, adult fish, and vegetation for April through September of Figures 3-1, 3-2, and 3-3 present the HBMP sampling and reporting strata for each of the three water bodies. At the time of the preparation of this report, adult fish data were available from May through September of 2000, ichthyoplankton data were available from April through September of 2000, and benthic organism sampling data were available from June through September. For the purposes of this report, the dominant organisms reported in these HBMP data were summarized by HBMP stratum. A population measure of species dominance relative to the biological community of interest was calculated. It should be noted that the April-September 2000 sampling period occurred within a severe drought period that began in The salinity conditions were unusual even relative to the typically dynamic conditions found in the Tampa Bay and other estuaries. Thus, the biological data collected during that period are certainly influenced by those low flow conditions. As more data become available it may be found that the dominant organisms and base spatial and temporal distributions may vary appreciably from that which was observed during the June-September 2000 sampling period. Species A population-based measure of species dominance was computed for each species and life stage within each water body and each HBMP strata using the April to September 2000 HBMP data. The methods used were similar to those reported for other biological community studies (Karlen and Grabe, 1996; Windell, 1971). This information provides a biologically meaningful measure of species dominance that can be aggregated and reported at a variety of differing spatial and temporal scales (e.g., by month, by wet season/dry season, by year, by waterbody). Janicki Environmental, Inc.

12 OROP Surface Water Salinity Controls 5 For each species, percent species composition (C) was first calculated as: C i= 1 = species I I A J Ai i= 1 j= 1 species, i, j where Aspecies,i = the abundance of the species/life stage in the ith sample, Ai,j = the abundance of the jth species/life stage in the ith sample, I = the total number of biological samples collected, and J = the total number of species/lifestages observed within this biological group (i.e., biological groups were defined based on sampling gear as benthic sampling, ichthyoplankton sampling, fish sampling). relative occurrence (O) was calculated as: O species = n species I where nspecies = the number of samples in which the species/life stage was found, and I = the total number of samples collected. Finally, the dominance measure (D) was calculated as the product of the percent species/life stage composition (C) and percent relative occurrence (O) for each species/life stage: D C = species species O species as the product of a percent composition metric and a relative occurrence measure of each species/life stage. Janicki Environmental, Inc.

13 OROP Surface Water Salinity Controls 6 When more data are available from future HBMP sampling, the species dominance measures may be aggregated over seasons, years, and water bodies using a geometric mean: J = exp ln j= 1 ( ) Dspecies Dspecies, j Where Dspecies,j = species dominance in the jth spatial and temporal unit to be aggregated over (e.g., month in the case of the calculation of an annual mean), and J = the number of spatial and temporal units to be aggregated over. Appendix A presents the results of this analysis, and it lists each species and life stage within a HBMP stratum ordered by the dominance measure as calculated from the April to September 2000 HBMP data. The species listed at the top of each section of this table were the most dominant species observed, and the species listed at the bottom of each section of this table were the least dominant species. Salinity Preferences/Tolerances Salinity preferences and tolerances for the biota found in the study area of this project were obtained from the literature in order to relate the salinity controls for the OROP to biologically meaningful targets (pers. comm. King Engineering, Inc., 2001, pers. comm. Dr. Tom Frazier, University of Florida, PBS&J Alafia Assessment (PBS&J,1998a), PBS&J McKay Bay Assessment, (PBS&J,1998b)). These data are applied and presented in Section 6 of this report. The species inhabiting the water bodies in the study area range from estuarine organisms able to withstand large fluctuations in salinity to freshwater species that inhabit the upstream reaches of the water bodies. The salinity preference and tolerance information was integrated into the salinity controls presented in Section 6 of this report. As more salinity tolerance information is collected through the ongoing HBMP program, this information can be further updated and integrated into the OROP salinity controls developed for this project. Janicki Environmental, Inc.

14 Figure 3-1. The study area and HBMP sampling strata for the Lower Hillsborough River.

15 Figure 3-2. The study area and HBMP sampling strata for the Alafia River.

16 Figure 3-3. The study area and HBMP sampling strata for the Tampa Bypass Canal/ Lower Palm River/ McKay Bay system.

17 OROP Surface Water Salinity Controls Flow and Salinity Relationships The purpose of this task was to quantify flow-salinity relationships within the study area and develop a predictive model for estimating future salinity based on current salinity in the receiving waters of Tampa Bay, and future surface water flow. The predictive models were developed to be applied in two ways. First, the models were used to select a salinity control area for each of the three water bodies, as the geographic region within each water body with the greatest salinity variation with respect to flow. Second, the models were integrated into the salinity control process to provide a predictive capability to the salinity controls. Multiple regression procedures were used to select well-fit and robust predictive models of salinity using the best available data. The OROP salinity controls were developed in consideration of the fact that the predictive models developed for this project will likely be replaced in the near future with more precise hydrodynamic models being developed for the Lower Hillsborough River and the Alafia River. The Southwest Florida Water Management District (SWFWMD) is currently conducting dam release experiments to calibrate and refine an existing hydrodynamic model of the Lower Hillsborough River (SWFWMD Laterally Averaged Model of Flow for Estuaries - LAMFE model), and the SWFWMD is also developing a similar model for the Alafia River. When completed, these models could potentially be integrated into the OROP salinity control framework developed by this project. Salinity Data Sources The best available salinity data to date were the ambient water quality data collected by the Environmental Protection Commission (EPC) of Hillsborough County at stations (see Figure 4-1) in the water bodies of interest as follows: In the lower Hillsborough River, the receiving water salinity was characterized by two candidate independent variables, the mean salinity from all Hillsborough Bay stations and the salinity at Station 2. The salinity responses within the Lower Hillsborough River were characterized by the salinity at Stations 105 and 137. In the Alafia River, the receiving water salinity was characterized by two candidate independent variables, the mean salinity from all Hillsborough Bay stations and the salinity at Station 8. The salinity responses within the Alafia River were characterized by the salinity at Stations 74 and 153. Janicki Environmental, Inc.

18 OROP Surface Water Salinity Controls 8 In the Tampa Bypass Canal/Palm River/McKay Bay system the receiving water salinity was characterized by two candidate independent variables, the mean salinity from all Hillsborough Bay stations and the salinity at Station 52. The salinity responses within the system were characterized by the salinity at Stations 58, 109, and 110. The most complete salinity data from the 1974 to 2001 period of record were compiled. At the time of preparation of this report, salinity data were available up to and including November of The periods of record for salinity observations for each station and depth are presented in Table 4-1: Table 4-1 Periods of Record for Salinity Observations for Selected Fixed Stations of the EPC of Hillsborough County Ambient Water Quality Monitoring Program. Waterbody EPCHC Station Number Starting Dates of Relatively Continuous* Monthly Sampling Last Date for which Data were Acquired for this Report Surface Mid-depth Bottom All Depths Hillsborough 105 Jan 1987 Jan 1974 Jan 1987 Nov 2001 River 137 Jan 1987 Sep 1979 Jan 1987 Nov 2001 Alafia 74 Feb 1976 Jan 1974 Feb 1976 Nov 2001 River 153 Sep 1999 Sep 1999 Sep 1999 Nov 2001 TBC/Palm 58 Feb 1976 Feb 1976 Feb 1976 Nov 2001 River/McKay 109 Jan 1987 Jan 1974 Jan 1987 Nov 2001 Bay 110 Jan 1987 Jan 1974 Jan 1987 Nov 2001 Hillsborough Bay 2 May 1978 Jan 1974 May 1978 Nov Jun 1977 Jan 1974 Jun 1977 Nov Feb 1976 Jan 1974 Feb 1976 Nov 2001 * Occasional gaps exist in the monthly sampling after these dates. However, the sampled months are relatively continuous. Janicki Environmental, Inc.

19 OROP Surface Water Salinity Controls 9 In order to maximize the number of consecutive non-missing monthly salinity observations for surface and bottom measurements, data prior to 1987 were excluded from the flow and salinity regression analyses. Station 153 is a relatively new station, and data were only available starting in September of Streamflow Data Sources United States Geological Survey stream flow data were compiled for the 1987 to most recently available time period corresponding to the salinity data. The flow data were included from the sum of Sulphur Springs gaged flow and Hillsborough River Reservoir Dam gaged flow for the Lower Hillsborough River up to September of 2000, and the Alafia River at Bell Shoals Road flow for the Alafia River up to November of To be consistent with the data used for water withdrawal permitting, the Alafia River at Bell Shoals Road flow was calculated as: FlowBell Shoals Rd = FlowLithia x [(335 m m 2 )/335 m 2 ] + FlowLithia Springs SWFWMD provided the S160 structure gaged flow data for the Tampa Bypass Canal/Palm River/McKay Bay system up to December of Flow Salinity Relationships Bivariate plots of salinity and streamflow are presented for each of the EPC stations in Figures 4-2 through Although the salinities in the water bodies are affected greatly by tidal stage, tidal currents, and surface winds, the salinity in each waterbody responds in a decreasing manner to increasing stream flow. The most upstream areas respond more rapidly to increasing flows, and will eventually become predominantly freshwater under high stream flow conditions. Using the salinity and streamflow data as described above, least-squares multiple regression procedures were used to fit predictive equations for future salinity for each waterbody and EPC station. The fundamental concept of all of these models was that salinity in each water body in the next calendar month is a function of the stream flow to the water body expected in the next calendar month and the receiving water body salinity in the current month. The models differed in the functional form of the selected dependent and independent variables. Janicki Environmental, Inc.

20 OROP Surface Water Salinity Controls 10 Prior to model fitting, outlying data points were removed. This was done to provide a better predictive model for the majority of the salinity observations, and to conservatively focus the model fitting on lower flow times when withdrawals are more likely to affect the salinity distributions. Data vectors where flows were greater than 1000 cfs were removed from the Alafia River data, and data vectors where flows were greater than 500 cfs were removed from the Tampa Bypass Canal data. No outliers were removed from the Hillsborough River data. The outlying data points are included for review with the rest of the complete data sets in Figures 4-2 through The candidate response variables for these regressions included salinity, logtransformed salinity, and squared salinity. The candidate predictor variables included: mean salinity in Hillsborough Bay across all depths at month t, log-tranformed mean salinity in Hillsborough Bay across all depths at month t, squared mean salinity in Hillsborough Bay across all depths at month t, salinity at the closest receiving water station in Hillsborough Bay for surface (0.3 m below surface) or bottom (0.3 m above bottom) measurements at month t, log-transformed mean salinity at the closest receiving water station in Hillsborough Bay for surface or bottom measurements at month t, squared mean salinity at the closest receiving water station in Hillsborough Bay for surface or bottom measurements at month t, expected stream flow to the water body at month t+1, log-transformed expected stream flow to the water body at month t+1, and squared expected stream flow to the water body at month t+1. All log-tranformed quantities were coded by adding 100 prior to transformation. The selected regression models, the resulting goodness of fit measures, and F values are presented in Table 4-2. Complete statistical details from each selected regression model are presented in Appendix B. Janicki Environmental, Inc.

21 OROP Surface Water Salinity Controls 11 The regressions fit the observed data moderately well, and the probability of greater F values for the ANOVAs were highly significant < all cases. The least acceptable fit was for Alafia River Station 153, which had a very limited amount of data relative to the other stations (see Table 4-1). The variable that explained most of the variation in next month s salinity value in the salinity control area was next month s streamflow to the salinity control area. The slopes for the streamflow parameter are all highly significantly different from zero at significance levels ranging from 0.05 to The current month s salinity in the receiving waterbody downstream of each salinity control area explained an additional smaller portion of the variation in salinity in the salinity control area, and most of the slopes for this additional variable were significantly different from zero. There were three exceptions, where the slopes for this additional explanatory variable were close to zero. These were for Station 74 surface, and Station 153 surface and bottom. For these stations, the receiving water body salinity does not explain a notable portion of the variation in salinity in the salinity control areas. As previously discussed, the SWFWMD hydrodynamic models are expected to provide a greater level of precision in the estimates for future OROP salinity controls. The integration of the hydrodynamic models should be considered while balancing the desire for a readily implementable salinity control for the OROP system. Janicki Environmental, Inc.

22 OROP Surface Water Salinity Controls 12 Table 4-2. Selected regression models for salinity-flow relationships. (Note: Models selected for implementation in salinity control process are shown in boldface type, and are discussed in Section 5.0 of this report). River Station Model Form Hillsborough Surface Probability of >F Value 105 lnst+1=α + βshb,t + β lnft+1 < lnst+1=α+βlnshb,t +β lnft+1+β I < R 2 Alafia 74 lnst+1=α + βlnsrw,t + β lnft+1 < lnst+1=α + βlnsrw,t + β lnft+1 < TBC/Palm 58 lnst+1=α + βsrw,t + β lnft+1 < River/McKay 109 St+1=α + βsrw,t + β lnft+1 < Bay 110 lnst+1=α + βlnsrw,t + β lnft+1 < Bottom Hillsborough Alafia 105 lnst+1=α + βlnshb,t + β lnft+1 < lnst+1=α + βlnsrw,t + β Ft+1 < lnst+1=α + βsrw,t + β lnft+1 < lnst+1=α + βsrw,t + β lnft+1 < TBC/Palm 58 lnst+1=α + βsrw,t + β lnft+1 < River/McKay 109 lnst+1=α + βlnsrw,t + β Ft+1 < Bay 110 (St+1) 2 =α + βsrw,t + β Ft+1 < Key to symbols: St+1 SRW,t SHB,t Ft+1 I = Predicted next month s salinity at the monitoring station = Current month s salinity in the receiving water body immediately downstream. = Current month s salinity in Hillsborough Bay = Predicted next month s flow to river system = (lnshb,t)(lnft) interaction term α,β,β,β = Parameters estimated for each station and depth using ordinary-leastsquares regression. Janicki Environmental, Inc.

23 Figure 4-1. The Environmental Protection Commission of Hillsborough County ambient water quality monitoring stations of interest in the study area.

24 Surface Salinity (ppt) MONTHLY SURFACE SALINITY VS FLOW Hillsborough River - Station Data Source: EPC of Hillsborough County Hillsborough River Dam Flow + Sulphur Springs Flow (cfs) Figure 4-2

25 Surface Salinity (ppt) MONTHLY SURFACE SALINITY VS FLOW Hillsborough River - Station Data Source: EPC of Hillsborough County Hillsborough River Dam Flow + Sulphur Springs Flow (cfs) Figure 4-3

26 Surface Salinity (ppt) MONTHLY SURFACE SALINITY VS FLOW Alafia River - Station Data Source: EPC of Hillsborough County Alafia River Flow at Bell Shoals Rd. (cfs) Figure 4-4

27 Surface Salinity (ppt) MONTHLY SURFACE SALINITY VS FLOW Alafia River - Station Data Source: EPC of Hillsborough County Alafia River Flow at Bell Shoals Rd. (cfs) Figure 4-5

28 Surface Salinity (ppt) MONTHLY SURFACE SALINITY VS FLOW Tampa Bypass Canal - Station Data Source: EPC of Hillsborough County Tampa Bypass Canal Flow at Structure 160 (cfs) Figure 4-6

29 Surface Salinity (ppt) MONTHLY SURFACE SALINITY VS FLOW Tampa Bypass Canal - Station Data Source: EPC of Hillsborough County Tampa Bypass Canal Flow at Structure 160 (cfs) Figure 4-7

30 Surface Salinity (ppt) MONTHLY SURFACE SALINITY VS FLOW McKay Bay - Station Data Source: EPC of Hillsborough County Tampa Bypass Canal Flow at Structure 160 (cfs) Figure 4-8

31 Bottom Salinity (ppt) MONTHLY BOTTOM SALINITY VS FLOW Hillsborough River - Station Data Source: EPC of Hillsborough County Hillsborough River Dam Flow + Sulphur Springs Flow (cfs) Figure 4-9

32 Bottom Salinity (ppt) MONTHLY BOTTOM SALINITY VS FLOW Hillsborough River - Station Data Source: EPC of Hillsborough County Hillsborough River Dam Flow + Sulphur Springs Flow (cfs) Figure 4-10

33 Bottom Salinity (ppt) MONTHLY BOTTOM SALINITY VS FLOW Alafia River - Station Data Source: EPC of Hillsborough County Alafia River Flow at Bell Shoals Rd. (cfs) Figure 4-11

34 Bottom Salinity (ppt) MONTHLY BOTTOM SALINITY VS FLOW Alafia River - Station Data Source: EPC of Hillsborough County Alafia River Flow at Bell Shoals Rd. (cfs) Figure 4-12

35 Bottom Salinity (ppt) MONTHLY BOTTOM SALINITY VS FLOW Tampa Bypass Canal - Station Data Source: EPC of Hillsborough County Tampa Bypass Canal Flow at Structure 160 (cfs) Figure 4-13

36 Bottom Salinity (ppt) MONTHLY BOTTOM SALINITY VS FLOW Tampa Bypass Canal - Station Data Source: EPC of Hillsborough County Tampa Bypass Canal Flow at Structure 160 (cfs) Figure 4-14

37 Bottom Salinity (ppt) MONTHLY BOTTOM SALINITY VS FLOW McKay Bay - Station Data Source: EPC of Hillsborough County Tampa Bypass Canal Flow at Structure 160 (cfs) Figure 4-15

38 OROP Surface Water Salinity Controls Salinity Control Areas The predictive salinity-flow models were used to select a salinity control area for each of the three water bodies, as the geographic region within each water body with the greatest salinity variation with respect to flow following the two steps described below. Salinity variation was operationally defined as the difference between the salinity predicted for the 75 th percentile of streamflow and the salinity predicted for the 25 th percentile of stream flow. This range was chosen to be broad enough to accurately represent the relative magnitude in the range of salinity responses to flow, but not so broad as to be unduly influenced by outlying data points. In the first step, the 25 th percentile and 75 th percentile of streamflow were calculated from the 1987 to 2000 time period for which the regression model domains were constructed. For the Alafia River, the temporal model domains for Stations 74 and 153 were different because data were available for different months for these two stations. Since the time period for data collection for station 153 was relatively limited, the full flow record time period from Station 74 was applied to this station to compute the percentiles. The results are as follows: Table 5-1 Interquartile-range statistics for each waterbody. Water Body 25 th ile of Flow 75 th ile of Flow Lower Hillsborough River 31 cfs 284 cfs Alafia River 123 cfs 352 cfs TBC/Palm R./McKay Bay 38 cfs 132 cfs Janicki Environmental, Inc.

39 OROP Surface Water Salinity Controls 14 In the second step, the predictive salinity-flow models developed in Section 4.0 were applied to each 25 th percentile and 75 th percentile of streamflow presented above to estimate the salinity value that would correspond to this typical range of flows as follows: Table 5-2 Salinities estimated for 25 th and 75 th percentiles of flow. Water Body Lower Hillsborough River Station 105 Surface Station 105 Bottom Station 137 Surface Station 137 Bottom Salinity Estimate Corresponding to The 25 th ile of Flow 4.1 ppt 4.5 ppt 13.4 ppt 24.1 ppt Salinity Estimate Corresponding to The 75 th ile of Flow 1.8 ppt 2.0 ppt 5.9 ppt 20.6 ppt Alafia River Station 74 Surface Station 74 Bottom Station 153 Surface Station 153 Bottom 19.2 ppt 26.3 ppt 5.0 ppt 14.7 ppt 10.3 ppt 23.6 ppt 0.8 ppt 5.6 ppt TBC/Palm R./McKay Bay Station 58 - Surface Station 58 - Bottom Station Surface Station Bottom Station Surface Station Bottom 24.1 ppt 27.4 ppt 21.8 ppt 26.5 ppt 21.0 ppt 26.1 ppt 21.3 ppt 26.2 ppt 19.5 ppt 25.2 ppt 18.4 ppt 24.9 ppt Janicki Environmental, Inc.

40 OROP Surface Water Salinity Controls 15 Based on the results of these two steps, the geographic region (i.e., EPC station and its corresponding HBMP strata) within each water body with the greatest salinity variation with respect to flow was selected as the OROP salinity control area. The salinity control areas were selected as: the HBMP stratum HR-2 that encompasses EPC Station 137 in the Lower Hillsborough River (see Figures 3-1 through 3-3, and Figure 4-1), the HBMP stratum AR-4 encompasses EPC Station 153 in the Alafia River, and the HBMP stratum MB-2 that encompasses EPC Station 58 in the Tampa Bypass Canal/Palm River/McKay Bay system. The predicted (for observed surface flows) and observed salinity values for 1987 to 2000 are presented for each of the selected salinity control areas in Figures 5-1 through 5-6. The OROP salinity controls were developed with the assumption that as more HBMP water quality data become available under a wider range of natural rainfall and flow conditions; the analysis described above can be repeated using the HBMP strata as candidate salinity control areas in a manner similar to that employed in this study using data from individual sampling sites. Janicki Environmental, Inc.

41 Surface Salinity (ppt) 30 Lower Hillsborough River - HBMP Stratum HR2 Observed vs. Predicted Salinity Date Observed Surface Salinity (ppt) Predicted Surface Salinity (ppt) (Breaks in line indicate periods of no reported observations) Figure 5-1

42 Bottom Salinity (ppt) 30 Lower Hillsborough River - HBMP Stratum HR2 Observed vs. Predicted Salinity Date Observed Bottom Salinity (ppt) Predicted Bottom Salinity (ppt) (Breaks in line indicate periods of no reported observations) Figure 5-2

43 Alafia River - HBMP Stratum AR4 Observed vs. Predicted Salinity Surface Salinity (ppt) Date Observed Surface Salinity (ppt) Predicted Surface Salinity (ppt) (Breaks in line indicate periods of no reported observations) Figure 5-3

44 Bottom Salinity (ppt) 30 Alafia River - HBMP Stratum HR4 Observed vs. Predicted Salinity Date Observed Bottom Salinity (ppt) Predicted Bottom Salinity (ppt) (Breaks in line indicate periods of no reported observations) Figure 5-4

45 Surface Salinity (ppt) 40 Tampa Bypass Canal/Palm River/McKay Bay - HBMP Stratum MB Observed vs. Predicted Salinity Date Observed Surface Salinity (ppt) Predicted Surface Salinity (ppt) (Breaks in line indicate periods of no reported observations) Figure 5-5

46 Bottom Salinity (ppt) 40 Tampa Bypass Canal/Palm River/McKay Bay - HBMP Stratum MB Observed vs. Predicted Salinity Date Observed Bottom Salinity (ppt) Predicted Bottom Salinity (ppt) (Breaks in line indicate periods of no reported observations) Figure 5-6

47 OROP Surface Water Salinity Controls Salinity Controls The biological and physical data were integrated into specific quantitative salinity controls. Following the project objectives (Section 2), a salinity control was defined as a relatively simple to measure and biologically meaningful measure of the influence of surfacewater withdrawals on the salinity conditions of a water body. Principal Definitions A salinity control - was mathematically defined as a monotonic curve representing the increasing level of adverse influence on a surface water body as a function of increasing salinity. For the OROP, these curves have also been termed weighting functions. Figure 6.1 represents a conceptual diagram of a salinity control. Figure 6-1 Conceptual diagram of a salinity control. Greater Influence Large Deviation of Influence on a Waterbody Lesser Influence Less Saline Small Deviation More Saline Salinity in Waterbody As ambient salinity conditions within a waterbody are increased, a salinity control provides a relative measure of the ecological influence on the waterbody. The measure of influence then provides early warning information to the OROP optimization model, which can be used to prioritize which waterbodies from which to withdraw fresh surfacewater. In addition to the OROP system, surfacewater permit schedules provide further protective limits. A small deviation - was defined as a point on the salinity control above which salinity conditions are approaching the upper range of salinity requirements for the biota of the waterbody. The shallow-sloped interval to the left of the small deviation provides an early warning system for prioritizing surfacewater withdrawal sources among the three waterbodies if all withdrawals are still well below the small deviation points. A large deviation - was defined as a conservatively established point on the salinity control above which salinity values are greater than those expected to be required Janicki Environmental, Inc.

48 OROP Surface Water Salinity Controls 17 for the biota of the waterbody. The interval between a small deviation and large deviation provides a very sensitive exponential-shaped early warning system. As discussed previously, the salinity controls were developed following two parallel approaches, a historical data-based approach, and a biological data-based approach. It is expected that the historical data-based approach will be implemented into the OROP surfacewater procedures first, and that the biological data-based approach will be implemented in subsequent years when additional biological data are reported from the HBMP monitoring program (see recommendations in Section 7). Salinity Controls Based on Historical Data The first set of salinity controls were based on the historical salinity conditions in the salinity control areas. The first benefit of this approach was that a long-term dataset of salinity data were available from the EPCHC (Table 4-1) for each of the three waterbodies. The second benefit of this approach was that the monthly and depth specific resolution of the dataset would assist in maintaining: the dynamic nature of salinity fluctuations in the estuarine waterbodies, the salinity regime variation between the three water bodies, the salinity regime variation over seasons of the year, and the salinity regime variations between water depths. Separate salinity controls were developed for each combination of waterbody, month of the year, and depth (surface/bottom). The monthly specific salinity controls were quantified by selecting a small deviation point and large deviation point (as discussed in Figure 6-1) in order to provide an early warning system before changing salinity conditions beyond the historical conditions for a particular month. Typically, a 5% upper tail of a distribution would be used to distinguish a conservative point at which a salinity value would become not representative of the historical conditions. Thus, one could set the large deviation point at the 95 th percentile of historical salinity conditions for a particular month. In order to provide an even more conservative early warning system, the large deviation point was defined as the 90 th percentile of historical salinities. Finally, in order to provide the early warning salinity control information to prioritize surface water withdrawals (within surfacewater withdrawal permit limits), the small deviation point was defined as the 75 th percentile of historical salinities. The EPCHC salinity data were used for this initial implementation of the historical data based salinity controls. The 90 th and 75 th percentile of salinity in the salinity Janicki Environmental, Inc.

49 Table 6-1 Monthly Salinity Controls Based on Historical Data River=Lower Hillsborough River HBMP Stratum=HR2 Month 75th ile of Surface Salinity (ppt) 90th ile of Surface Salinity (ppt) 75th ile of Bottom Salinity (ppt) 90th ile of Bottom Salinity (ppt) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

50 Table 6-2 Monthly Salinity Controls Based on Historical Data River=Alafia River HBMP Stratum=AR4 Month 75th ile of Surface Salinity (ppt) 90th ile of Surface Salinity (ppt) 75th ile of Bottom Salinity (ppt) 90th ile of Bottom Salinity (ppt) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

51 Table 6-3 Monthly Salinity Controls Based on Historical Data River=Palm River/Tampa Bypass Canal/McKay Bay HBMP Stratum=MB Month 75th ile of Surface Salinity (ppt) 90th ile of Surface Salinity (ppt) 75th ile of Bottom Salinity (ppt) 90th ile of Bottom Salinity (ppt) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

52 OROP Surface Water Salinity Controls 18 control areas from the 1987 to 2001 time period are presented in Tables 6-1, 6-2 and 6-3. The percentiles are presented by month and water depth. The mathematical weighting functions for the salinity controls will be implemented based on the large deviation and small deviation values presented in Tables 6-1, 6-2, and 6-3. Thus, separate weighting functions will be integrated into the OROP surfacewater withdrawal optimization for each combination of waterbody, month, and depth (surface/bottom). The measure of influence on a waterbody ω d,t at a particular depth (d) in a particular month (t) is used as a weight in the OROP optimization model to prioritize which waterbodies to withdraw surface water from first, and is defined as: where ω = d, t ω C C d, t U, d, t C L, d, t C d, t ω d,t = the measure of influence on a waterbody at depth (d) and month (t), ω = an OROP inflection parameter defined by the Tampa Bay Water OROP staff as 1 for these surfacewater withdrawal penalty functions, Cd,t = the regression model predicted salinity at depth (d) and month (t), CU,d,t = large deviation value (eg., 90 th percentile) at depth(d) and month(t), and CL,d,t = small deviation value (eg., 75 th percentile) at depth (d) and month (t). Figure 6-2 presents an example of the salinity control that will result for the AR4 stratum, bottom salinity, in July. The complete set of salinity control parameters are listed in Tables 6-1, 6-2, and 6-3. Figure 6-2 Example salinity control for AR4 Stratum, Bottom Salinity, in July. W AR4 Stratum - July Expected Salinity (ppt) Janicki Environmental, Inc.

53 OROP Surface Water Salinity Controls 19 Salinity Controls Based on Biological Data Following a similar logical approach, the second set of salinity controls were based on the magnitude of the deviations from biologically important salinity values for the dominant species in the salinity control areas. The benefits of this approach are: it is directly supported by the HBMP sampling efforts, it uses the available salinity tolerance and preference information for all species and lifestages observed (see below for a detailed discussion of how salinity preference and tolerance data were prioritized), and it allows for seasonal salinity regimes by basing salinity controls on monthlyspecific species and lifestages dominance. As previously discussed, the major constraint to initially implementing the biological approach is that the HBMP biological data have been reported for a very limited period of unusually dry conditions. It is expected that the biological approach will be implemented in subsequent years when additional HBMP biological data have been reported (see recommendations in Section 7). The biologically important salinity values were computed using the following steps. In the first step, the salinity tolerance data for organisms / lifestages found in the study area (see Section 3.0) were assigned to the organisms / lifestages ranked by the dominance measure (D) within each salinity control area. Salinity tolerances for organisms collected in the benthic sampling and adult fish sampling were applied to the bottom salinity control values, and salinity tolerances for ichthyoplankton sampling were applied to the surface salinity controls. In the second step, the large and small deviation values were defined to specify the salinity controls. Separate salinity controls will eventually be developed for each combination of waterbody, month of the year, and depth. Due to the biological data constraints at this time, salinity controls were developed for each waterbody for the entire dataset reported by the HBMP biological sampling to date. The specific salinity controls were quantified by selecting a small deviation point and large deviation point (as discussed in Figure 6-1) in order to provide an early warning system prior to changing salinity conditions beyond the salinity tolerances for a particular assemblage of species and lifestages. Typically, a 5% upper tail of a distribution would be used to distinguish a conservative point at which a salinity value would become not representative of the tolerable/preferred conditions. Thus, one could set the large deviation point at 95 percent of the distance from the lower Janicki Environmental, Inc.