### TMD L and PLRG Modeling of the Lower St. Johns River Technical Report Series Volume 1:

Calculation of the External Load

By:
John Hendrickson, Environmental Scientist St. Johns River Water Management District
Nadine Trahan, GIS Anal yst Jones, Edmunds and Assoc.
Emily Stecker, Environmental Scientist BCI Engineers and Scientists
Ying Ouyang, Ph.D., Environmental Scientist St. Johns River Water Management District

May 200 22



### Table of Contents

INTRODUCTION AND APPROACH

................................ ................................ ................................ ........ 7 Project Area Description

................................ ................................ ................................ ........................... 8 Justification of the Conceptual Approach to the LSJ R External Load

................................ .................... 10 Distinction of Labile and Refractory Organic Nutrients and Carbon

................................ ...................... 12 Organic Matter Composition Effects on Bi odegradability And Nutrient Bioavailability

....................... 13 Objectives

................................ ................................ ................................ ................................ ................ 16
METHODS

................................ ................................ ................................ ................................ ................. 17 Separation of Labile and Refractory Organic Carbon, Nitrogen and Phosphorus

................................ .. 17
Overview

................................ ................................ ................................ ................................ ............. 17
Calculation of the External L oad For the Lower St. Johns River

................................ ............................ 27
Point Source Load Estimation

................................ ................................ ................................ ............. 28
Non

- Point Source Load Estimation

................................ ................................ ................................ ..... 29 Determination of the Upstream Load to the LSJR

................................ ................................ .............. 46 Determination of the Atmospheric Deposition Load

................................ ................................ ........... 53
RESULTS

................................ ................................ ................................ ................................ ................... 56 Tributary Organic Carbon and Nutrients

................................ ................................ ................................ . 56 Calibration Data Set Summary

................................ ................................ ................................ ............ 56
Watershed Model Calibration

................................ ................................ ................................ .............. 58 Point Source Organic Carbon and Nutrients

................................ ................................ ........................... 70 Upstream Concent rations of Organic Carbon and Nutrients

................................ ................................ ... 72 Reconstruction Of The Upstream Natural Background Load

................................ ................................ . 81 Natural Background Concentr ations of Small Order, Undeveloped Streams

................................ ...... 81 Role of Spring Inputs

................................ ................................ ................................ ........................... 85
Historic Data for the St. Johns River

................................ ................................ ................................ ... 89
Total LSJR Load Estimates

................................ ................................ ................................ ..................... 97 Atmospheric Deposition

................................ ................................ ................................ ........................ 105
DISCUSSION

................................ ................................ ................................ ................................ ........... 106
Literature Cited

................................ ................................ ................................ ................................ ......... 1133

FIGURES

1. The Lower St. Johns River Basin

................................ ................................ .................... 9

2. Comparison of Inorganic and Non

- Inorganic Nutrient Fractions for Black Creek and the Lower St. Johns R iver at Racy Point

................................ ................................ ....... 13

3. Rates of Exertion of BOD for Organic Substrates Typical of Northeast Florida Surface Waters

................................ ................................ ................................ ............................ 19

4. Tributary Water Quality Sampling Stations for Watershed Modeling Set

- up and Skill Assessment

................................ ................................ ................................ .................... 20

5. Organic Carbon: Nitrogen Ratio as a Functio n of the Percent Labile Organic Carbon

................................ ................................ ................................ ................................ ....... 25

6. Organic Carbon:Phosphorus Ratio as a Function of the Percent Labile Organic Carbon

................................ ................................ ................................ ........................... 26

7. Relationship Between PLSM Predicted Runoff:Observed Runoff Ratio and Measured Seasonal Whole Watershed Runoff Coefficient

................................ ............................ 34

8. Relative Position of the 1995

- 1999 Time Interval in the Historic Long Term Flow Record

................................ ................................ ................................ ........................... 35

9. Development of Hydrologic Correction Factors for the PLSM Runoff Coefficient

..... 38

10. Comparison of Original, Seasonal

- fixed and Long

- Term Rain Rat io Adjusted Runoff Coeffi cients

................................ ................................ ................................ ................... 39

11. Comparison of Original PLSM

- Predicted, Long

- Term Rain Ratio Adjusted and Observed Cumulative Discharge Curves for LSJRB Calibration Watersheds, 1995

- 99

................................ ................................ ................................ ................................ ....... 40

12. Monthly Mean Concentrations and 95% Confidence Intervals for Color and Total Organic Carbo n for 24 Unimpacted Blackwater Streams in Northeast Florida

............ 43

13. Watershed Model Input Areas for Nutrient Load Compilation

................................ .... 46

14. Tributaries Fo rming the Lower St. Johns River

................................ ............................ 48

15. Flow chart for differentiation of laboratory analytical fractions into CE

- QUAL

- ICM state variables for the lower St. Johns River upstream boundary a t Dunns Creek and Buffalo Bluff

................................ ................................ ................................ ................. 50

16. Comparison of Corrected Chlorophyll a and Algal Biovolume for Combined LSJR Freshwater Water Quality and Plankton Analysis, 1995

– 2001

................................ .. 524

17. Comparison of POC :Algal Biovolume (a) and POC:Total Chlorophyll a Ratios to Total Biovolume and Total Chlorophyll a Concentration for LSJR Freshwater Samples

................................ ................................ ................................ ................................ ....... 54

18. Relationship Between Refractory Dissolved Organic Carbon and Color for Blackwater Streams of the LSJR Basin

................................ ................................ ............................ 55

19. Comparison of Observed to Simulated Flow

- Weighted Concentrations of Carbon, Nitrogen and Phosphorus Forms for the December through March Season

................. 61

20. Comparison of Observed to Simulated Flow

- Weighted Concentrations of Carbon, Nitrogen and Phosphorus Forms for the April through July Season

............................. 63

21. Comparison of Observed to Simulated Flow

- Weighted Concentrations of Carbon, Nitrogen and Phosphorus Forms for the August through November Season

............... 65

22. Partitioned Nitrogen Concentrations at (a) Buffalo Bluff and (b) Dunns Cre ek, Dec. 1994

- Nov. 1999

................................ ................................ ................................ ........... 74

23. Partitioned Phosphorus Concentrations at (a) Buffalo Bluff and (b) Dunns Creek, 1995

– 1999

................................ ................................ ................................ ............................ 75

24. Partitioned Organic Carbon Concentrations at Buffalo Bluff and Dunns Creek, 1995

– 1999

................................ ................................ ................................ ............................... 76

25. Loads of Nitrogen Forms Entering the Lowe r St. Johns River at Buffalo Bluff and Dunns Creek, 1995

- 99

................................ ................................ ................................ ... 79

26. Loads of Phosphorus Forms Entering the Lower St. Johns River at Buffalo Bluff and Dunns Creek, 1995

- 99

................................ ................................ ................................ ... 80

27. Continuous Probability Density Functions for Total and Inorganic Nutrient Mean Concentr ations for Streams in Northeast Florida

................................ .......................... 84

28. Time

- Series Concentrations of Nitrate+Nitrite

- N and Orthophosphate

- P in Major Springs Discharging to the St. Johns River That Exhibit Nitrate+Nitrite Trends

......... 86

29. Comparison of Present Day and Predicted Natural Backg round Concentrations of Total Nitrogen and Total Phosphorus in the Lower St. Johns at Buffalo Bluff, 1995

- 99

................................ ................................ ................................ ................................ ....... 88

30. Population growth within the 14 Counties of the St. Johns River Basin, 1890

– 2000

................................ ................................ ................................ ................................ ....... 90

31. Comparison of Monthly Mean Water Quality Parameters for 1995

- 99 (solid boxes) to the Data Collected by Pierce (1947) in 1939

- 40 (open diamonds) for the St. Johns River near Buffalo Bluff

................................ ................................ ................................ 955

32. Comparison of Total and Bioavailable Nitrogen Forms in Runoff from Natural Forested and Mixed Urban/Commercial/Residential Watersheds

.............................. 1086



### TABLES

1. Tributary Water Quality Station Locations Employed in Determination of Labile and Refractory Organic Nutrients

................................ ................................ ........................ 22

2. Point Source Facilities Included in the Calculation of the Lower St. Johns River External Load

................................ ................................ ................................ ................ 23

3. Seasonal Runoff Coefficients for Application of the Pollution Load Screening Model to the LSJR Basin

................................ ................................ ................................ .......... 33

4. Seasonal Water Quality Coefficients Used in the PLSM to Predict Non

- Point Source Loads to the LSJR

................................ ................................ ................................ ......... 41

5. Total Organic, Labile Total Organic and Refractory Total Organic Carbon Land Use Category Concentration Coefficients

................................ ................................ ............ 45

6. Mean Total, Inorganic, and Calculated Labile and Refractory Organic Nutrient and Carbon Mean Annual Flow

- Weighted Concentrations for Tributaries sampled within the lower St. Johns River Basin

................................ ................................ ..................... 57

7. Pea rson Correlations, Slopes, and Confidence Intervals of the Slopes for Intercept

- Fit Regressions Between Calibration Station Measured Flow

- Weighted Concentrations and Contributing Area Modeled Runoff

- Weighted Concentrations

............................. 68

8. Summary of Point Source Mean Effluen t Water Quality Concentrations

.................... 71

9. Total Phosphorus Concentrations Determined for Selected Locations in St. Johns River Basin in 1952

................................ ................................ ................................ ....... 92

10. Summary of Mean Annual Loads to the Lower St. Johns River, 1995

......................... 98

11. Summary of Mean Annual Loads to the Lower S t. Johns River, 1996

......................... 99

12. Summary of Mean Annual Loads to the Lower St. Johns River, 1997

....................... 100

13. Summary of Mean Annual Loads to t he Lower St. Johns River, 1998

....................... 101

14. Summary of Mean Annual Loads to t he Lower St. Johns River, 1999

....................... 102

15. Summary of Overall Mean Annual Loads to the Lower St. Johns River, 1995

- 99

.... 1037



### INTRODUCTION AND APPROACH

Accelerated eutrophication arising from nutrient enrichment of estuaries represents one of the most significant water quality problems faced by near coastal waters worldwi de (National Research Council, 2000). Within the United States, part of this problem rests in the standards

based approach to water quality control, in which the potential harm incurred by sources is evaluated based upon effluent and near

- field concentrat ions of pollutants. In this approach, cumulative loads of substances, in particular nutrients, have been overlooked, with the result that receiving water assimilative capacities have been overwhelmed. This situation has increasingly lead water managers t o resort to the TMDL process (CWA Section 303(d)) as a means of eutrophication control. To address problems associated with accelerated eutrophication in the lower St. Johns River estuary (LSJR), both the Florida Department of Environmental Protection (FD EP) and the St. Johns River Water Management District (SJRWMD) are jointly executing a strategy for nutrient pollution control that fulfills their respective responsibilities for the establishment of TMDLs for impaired water bodies and stormwater PLRGs ( F. A.C . Chapter 62

- 40).

A generally accepted approach has evolved for addressing estuarine eutrophication in which the sources, magnitude and timing of the external nutrient load are linked to the effects of the receiving water body. Because of the tempor al and spatial disconnect between the entry of nutrient loads and the manifestation of eutrophication effects, and the need to predict the levels of expected improvement with various nutrient reduction strategies, dynamic water quality process models have become invaluable tools in estuarine nutrient management efforts. Such models “process” the external load in a time

- sequential fashion in a 2 or 3

- dimensional discretized grid that approximates the morphology of the water body. In the context of eutrophi cation, relevant “processes” are the biological processes photosynthesis and algal carbon fixation, community respiration (as both a loss of organic carbon and exertion of oxygen demand), and organic nutrient re

- mineralization, as well as physical processe s such as oxygen reaeration, substance advection, molecular dispersion, solar light absorption, and sedimentation.8

In this modeling approach to the establishment of nutrient pollution reductions, two large investigative efforts must be undertaken: one t o quantify the timing, magnitude, and spatial nature of the incoming nutrient load, referred to as the “external load”, and another to determine the effect of this load on the receiving water body. This report describes the first element of this
intricate undertaking for the Lower St. Johns River, that of the derivation of the external load.

Project Area Description

The St. Johns River is one of the largest blackwater rivers of the southeast U.S. The river is
located in northeast Florida and drains about 1/5 th of the state, encompassing a 9,562 square mile drainage area. The river is slow moving, with a slope of only 1.4 in/mi (Toth, 1993), and is essentially at sea level for its final 125 mi. The lower St. Johns is the estuarine portion of the
ri ver, formed at the confluence of the middle St. Johns and the Ocklawaha River, and encompassing a 2,750 square mile area (Figure 1). Within this reach, the St. Johns River is slightly more that 100 miles long and has a water surface area, including tribut ary mouths below head of tide, of 85,000 acres. The lower St. Johns can be differentiated into three riverine salinity and limnologic zones: a fresh tidal lacustrine zone which extends from the city of Palatka north to approximately the mouth of Black Cre ek; a predominantly oligohaline, lacustrine zone extending from the mouth of Black Creek northward to the Fuller Warren Bridge (I

- 95) in Jacksonville; and a mesohaline/polyhaline, riverine zone downstream to the mouth. The slow moving, lacustrine nature o f the river facilitates phytoplankton primary production, and spring and summer algal blooms in this nutrient

- rich river often exhibit chlorophyll a concentrations exceeding 100  g/L.

The southern portion of the lower basin is largely rural, with predom inant land uses in forestry and row crop agriculture. The northern portion of the basin is distinguished by the heavily urbanized cities of Jacksonville, Orange Park and Middleburg. Roughly three quarters (64 to 82 percent) of the basin’s highly develope d land uses (medium and high residential, high intensity commercial and industrial) drain to the oligohaline and mesohaline lower St. Johns. In contrast, 62 to 98 percent of the basin’s agricultural land uses drain to the fresh tidal reach.9

Figure 1. The Lower St. Johns River Basin.10

The existence of poor water quality in the LSJR has been identified in a number of reports dating back to at least 1947 (Florida State Board of Health 1947). Because of these problems, the
establishment of TMDLs and PLR Gs for the lower St. Johns River are a high priority, and an aggressive schedule has been established that seeks the identification of river assimilative capacity and general allocation to major sources by the end of 2002.

Comprehensive external nutrien t load assessments have been performed twice previously for the LSJR. In 1976, the firm of Atlantis Scientific (Atlantis Scientific, 1976), under authorization of the 1972 Clean Water Act Section 315, undertook a computation of the external load and
concl uded that point source comprised the majority of this load. Hendrickson and Konwinski (1998) also computed the external load to the river for 1993

- 94, and concluded that nitrogen and phosphorus were 2.5 and 6 times greater than natural background, with a ugmented nutrient loads (that load above natural background) approximately evenly split between point and nonpoint

sources.

Justification of the Conceptual Approach to the LSJR External Load

By virtue of its long term presence in the St. Johns River and its frequent project partnerships with the SJRWMD, the U.S. Army Corps Jacksonville District has brought valuable assistance to the river TMDL and PLRG development. This partnership has provided the assistance of the U.S. Army Engineer Research and De velopment Center (ERDC) at Vicksburg, MS, to assist in the examination of the nature of the interaction between river processes and the external load. To quantify this interaction, the ERDC has adapted its water quality model, CE

- QUAL

- ICM (Corps of Engine ers Water Quality Integrated Compartment Model), to the LSJR. CE

- QUAL

ICM (hereafter referred to as just ICM) was developed to study eutrophication processes in Chesapeake Bay (Cerco and Cole, 1994), however, as ICM simulates the fundamental processes
rel ated to algal (and plant) growth, death and decomposition, its robust model formulation is applicable to a wide range of water bodies and even wetlands. ICM differs from another widely used water quality model, WASP, in that it predicts eutrophication eff ects

– transparency loss, dissolved oxygen sags, and sedimentation

- through the use of a carbon budget, rather that11

relying on the input and internal formation of biochemical oxygen demand and chlorophyll a

. Because ICM allows for the distinction of carb on and nutrients compartmentalized within labile and refractory forms, it is in theory particularly useful for applications in blackwater river
estuaries.

Along with the mixture of inorganic and nutrient

- bearing organic substrates (such as animal and
hu man waste, industrial process effluents, and algae or algal detritus) that are the focus of anthropogenic nutrient enrichment, blackwater rivers and streams also exhibit significant nutrient content of natural origin. Large portions of this natural nutrie nt load, as much as 40 percent of the phosphorus, and over 90 percent of nitrogen, are contained within the organic fraction. Strong relationships between total organic carbon and color suggest that the majority of this natural, organic nutrient load is c ontained within colored, dissolved organic matter (CDOM) of terrestrial and riparian vascular plant origin . Although natural CDOM is generally believed to be resistant to microbial decomposition and largely unavailable for utilization by phytoplankton in typical estuarine residence times, these heterogeneous, humic substances contain a substantial amount of nitrogen (N) and phosphorus (P) in their structures (DeBusk et al, 2001), and hence the sheer volume of the material with respect to other OM pools dic tates its relevance be
considered.

With the capabilities of ICM come fairly rigorous requirements on the detail of the external nutrient and carbon load that must be input for model simulations. The most difficult of these determinations is the separati on of the external organic nutrient and carbon load into labile (easily decomposed and utilized) and refractory (slowly decomposed) components based upon readily available water quality monitoring data. This technique for separation needs to extend
also t o the river water quality monitoring calibration data set for ICM. In order to predict the changes in the external load with various nutrient reduction strategies, it is not sufficient to only characterize the incoming labile and refractory carbon and nut rient load; the relationship between land development and organic carbon and nutrient bioavailability must also be described. An additional relationship must be addressed between the concentration of colored dissolved organic matter (CDOM) and water colum n transparency in order for the appropriate functioning of the ICM light attenuation algorithm in the algal photosynthesis calculation.12

Distinction of Labile and Refractory Organic Nutrients and Carbon

It is generally understood that dissolved, inorg anic forms of nutrients (NO 2+3 , NH 4 , and PO 4 ), as well as some low molecular weight organic compounds such as urea, are immediately available for algal growth, while organic nutrient forms, which must first undergo desorption (if particulate bound), hydrol ysis, bacterial decomposition or photo

- decomposition (Bushaw et al. 1996) for inorganic nutrient regeneration and subsequent utilization by phytoplankton, are less readily available. Organic nutrient bio

- availability for aquatic primary production is depe ndent upon the utilization preference of the parent organic substrate by general microbial heterotrophs (DeBusk et al., 2001), which must first decompose this substrate in order to liberate mineral nutrient forms. With regard to organic carbon and nutrien t bioavailability, a general working hypothesis has evolved that partitions organic carbon and nutrients into two pools: a labile pool, that can be utilized in time frames relevant to water quality processes of interest in the receiving water, and a refra ctory pool, that is decomposed very slowly and essentially inert for relevant time frames (Wetzel, 1983). The bioavailablility of the organic nutrient pool represents an important issue in the assessment of nutrient enrichment in blackwater rivers of the southeast U.S. coastal plain, as much of the total phosphorus (TP) and most of the total nitrogen (TN) enters the river as an organic or non

- inorganic form (Figure 2).

Relatively little attention has been paid to differences in organic nutrient bioavai lability in assessments of external loads to eutrophic water bodies (Stepanauskas et al., 1998). This may be due to the predominance of inorganic nutrients in river flow to intensely studied temperate estuaries, leading most authors to not further differe ntiate the organic nutrient pool (Magnien et al. 1992; Goolsby et al. 2001) or even to distinguish it from the inorganic nutrient

- dominated total nutrient pool (Boynton et al. 1995; Jaworski et al. 1992; Valiela et al. 1992). This lack of
differentiation extends also to land use

- loading rates applied in watershed load indexing models (Hartigan et al., 1982; Adamus and Bergman, 1995; Harper, 1994; EPA, 1984), to the commonly used, process

- based watershed models such as HSPF, to agronomic field scale models such as GLEAMS, and to water quality process models such as WASP.13

Figure 2. Comparison of Inorganic and Non

- Inorganic Nutrient Fractions for Black Creek and

the Lower St. Johns River at Racy Point.

Organic Matter Composition Effec ts on Biodegradability And Nutrient Bioavailability

Research in aquatic microbiology has elucidated several patterns pertaining to organic matter utilization by general aerobic microbial heterotrophs, and concomitantly organic nutrient bioavailability. T wo generally accepted precepts form the basis of our understanding of the biodegradability of biogenic organic compounds, 1) recently produced, undecomposed OM is more biodegradable than material that has undergone diagenetic alteration through repeated
de composition cycles, and 2) OM produced by non

- structural aquatic plants and algae is more biodegradable than that produced by terrestrial, lingo

- cellulosic vascular plants. Carbohydrates,14

proteins, lipids, nucleic acids and pigments are decomposed in rela tively short time frames, while humic substances are less readily decomposed and in some cases essentially inert (Wetzel, 1983; Moran and Hodson, 1990; although this assertion is contradicted in the work of Volk et al., 1997,
who find similar utilization o f humic substances). The biodegradability of natural OM that occurs in aquatic systems and its bioavailability of incorporated C, N and P can be viewed as dependent largely upon two factors: 1) whether the material is allochthonous or autochthonous in
ori gin, and 2) whether or not the OM has undergone some degree of decomposition and diagenetic alteration prior to its entry to surface waters. Thus it generally holds that autochthonous OM is more labile than allochthonous OM, and that the humic fraction of DOM is
less bioavailable on a mole carbon than non

- humic DOM (Kaplan and Newbold 1995; Moran and Hodson, 1990; Moran et al., 1999). In their work on piedmont and blackwater river OM in the southeast U.S., Sun et al. (1997) demonstrate that the compositio nal changes that accompany diagenetic condensation relate directly to bioavailability, with blackwater stream OM appearing the most refractory per mole carbon. This assertion is in congruity with work that has shown
some forms of soil humus in the allocht honous organic carbon pool to be decades to hundreds of years old (Raymond and Bauer, 2001).

Surprisingly, this difference in OM bioavailability runs contrary to the “smaller is better” nutrient utilization paradigm, in that particulate organic nutrients in surface waters, in the form of algal cells or relatively undecomposed plant detritus, are generally more readily available than dissolved forms. Within the dissolved organic matter pool (<0.45  m diameter), high molecular weight organic compounds (> 10 ,000 nMW) also have been to found to be more bioavailable (Tranvik, 1990; Amon and Benner 1996; Gardner et al., 1996; Mannino and Harvey 2000) than low molecular weight DOM (< 1000 nMW). In the Amazon River, Hedges et al. (1994)
considered DOM to be the m ost profoundly degraded material, hence the most refractory, mobilized through a process of “selective solubilization”.

Also fundamental to the bioavailability of organic nutrients for primary production is whether or not OM decomposition will result in nutrient regeneration (e.g., an increase in water column inorganic nutrients) or nutrient immobilization to meet bacterial growth needs. Goldman et al. (1987) postulated that if the substrate C:N and C:P ratios are sufficiently low such that, when15

correct ed for carbon gross growth efficiency (the fraction of the total carbon decomposed that is retained as bacterial biomass), N and P remain in excess of bacterial growth needs, then these nutrients will be regenerated in the inorganic form and be potentially available for incorporation by phytoplankton. Because labile OM is high in proteins, amino acids and cellular metabolic organic compounds that exhibit relatively low C:N and C:P ratios, decomposition of labile substrates in the aquatic environment tends to lead to the regeneration of N and P. Conversely, substrates with a high C:N, such as aquatic humic OM (averaging 50:1 molar; Thurman, 1985), will tend to immobilize inorganic N and P (Mann, 1988; Strauss and Lamberti, 2000). Not surprisingly, organic substrates with high C:N ratios that typically exist in the aquatic environment exhibit low biological availability, and concomitantly a low likelihood that bacterial decomposition of this substrate can regenerate mineral nutrients for autotrophs (Bushaw et al., 1996). Because of these general differences, C:N and C:P ratios have been a commonly used proxy for bioavailability of OM.

No clear definition exists on what constitutes labile verses refractory, and whether or not the range between the two extremes exists as a continuum or as discrete states. Labile substrates have been described as those utilized within timeframes of one to two weeks (Sondergaard and Middelboe, 1995); as utilization through the exponential growth phase to the stationary p hase (approximately 2 days; Stepanauskas et al., 1999; approximately 4 days for DON of the Delaware River (Seitzinger and Sanders 1997)); or in

- situ bioreactor residence time (4 to 18 hours; Volk et al., 1997). The first order decay coefficient of 0.075 d ay - 1 used by ICM (Cerco and Cole, 1995) yields a duration of 9.2 days for 50% utilization of the original labile substrate, and 30 days for 90% utilization. Moran and Hodson (1989), in their investigation of fresh and salt marsh plant ligno

- cellulose, obs erved what appeared to be distinct rates of utilization, suggesting distinct, uniform chemical classes driving separate utilization rates. Similarly, Ogura (1975) determined that two distinct pools of dissolved organic compounds existed in most aquatic sy stems.

In various examinations of surface waters exhibiting a range of human impact, the biodegradable percent of the total OC pool has been found to vary between 1 and 86 percent (Sun et al., 1997). For most rivers dominated by allochthonous OC, the range is closer to between 7% to 25%16

(Sondergaard and Middelboe, 1995; Volk et al., 1997), with blackwater rivers exhibiting the lowest relative amounts of labile OC (Moran et al., 1999). Stepanauskas et al. (2000), in their study of Scandinavian rivers, estimated the percent of labile dissolved organic nitrogen (DON) as
between 19 and 55%.

Objectives The objectives of this report are 1) describe the approach to partitioning organic carbon and nutrients in the external load to the LSJR, for the purpose of distinguishing the relative bioavailability of these forms and hence the impact on eutrophication; 2) determine the relationship between land development factors and the relative bioavailability of carbon and nutrient forms in runoff at a watershed scale, f or the purpose of modeling the external carbon and nutrient load, and predicting the changes in this load with changes in land development patterns; 3) determine the load to the LSJR from all sources

– upstream, within basin point and non

point sources, and atmospheric sources

– in order to assess the relative effects of these sources on eutrophication; and 4) reconstruct the natural background load to the LSJR, for the purpose of putting present

day loading rates in perspective, and for gauging the baseline le vel of productivity of the LSJR in its pre

- development state.17



### METHODS

Separation of Labile and Refractory Organic Carbon, Nitrogen and Phosphorus

Overview

To partition labile and refractory organic carbon and nutrients in the external load calculat ion, a

two

- step empirical approach was developed. First, a conceptual model was developed relating the rate of oxygen consumption during decomposition to total organic carbon to determine overall decomposition rate, based upon partial decomposition rates for labile and refractory organic material already established within ICM. This model was then applied to a water quality data base of tributary sampling stations and point source effluents to partition labile and refractory organic carbon. A multiple re gression approach was employed to establish specific land use, labile and refractory organic carbon runoff concentrations, and these specific land use organic carbon concentrations are then applied to a watershed model to develop whole

- basin
labile and ref

ractory organic carbon loads. The second step partitioned organic N and P based upon the relative amounts of labile and refractory organic carbon. Again employing the tributary water quality monitoring and point source effluent data, C:N and C:P ratios w ere related to the percent of labile organic carbon, and this relationship used to predict refractory and labile C:N
and C:P ratios. These ratios were then used to sub

- divide the previously modeled organic nitrogen and non

- orthophosphate phosphorus (TP

- PO 4 ) loads into labile and refractory fractions.

Model for Organic Carbon Partitioning

Organic carbon found in surface waters is borne in a mix of organic matter from multiple sources. This complex mix of organic substrates is assumed to be composed of fractions that are readily available for microbial decomposition (labile) and relatively unavailable (refractory). The degree of organic carbon lability in oxygenated surface waters should, in theory, be reflected in carbonaceous biochemical oxygen de mand (CBOD), with labile substrates consuming more oxygen per mole of carbon in the test period (typically 5 days) than refractory substrates.18

Consumption of organic carbon by bacterial heterotrophs has generally been found to adhere to
first

- order expo nential decay. Chapra (1977) provides this relationship in the following form, in which the maximum amount of CBOD that can be exerted on a substrate, CBOD ultimate , is related to that amount consumed in time t, by the relationship:



### C t 

### = C o 

### (1

- e - kt 

### )

where C t is the oxygen (or carbon) consumed at time t, C o is the BOD ultimate , and k is the
substrate

- specific decomposition coefficient.

In practice, measurements of ultimate BOD are rarely performed, although total organic carbon, a frequently measured cons tituent , should in theory be related to ultimate BOD. The molar rate
of O 2 consumption per CO 2 production has typically been set at 1:1 in computations of community respiration (Wetzel and Likens, 1990; as per the respiratory quotient (RQ) of Strickland, 1960). In the computations here, a RQ of 1 mole O 2 consumed per 1 mole of OC consumed, or mass ratio of 2.67:1, was used.

Figure 3 demonstrates the theoretical rate of change in BOD exerted over time for various homogeneous categories of substrates com mon to surface waters of northeast Florida, and their associated decay coefficients. Algal biomass and domestic waste organic matter appear to be highly labile substrates, while pulp mill effluent and colored dissolved organic matter in runoff of native, undeveloped blackwater streams appear to be relatively refractory. Decay coefficients
established in the CE

- QUAL

- ICM model of 0.075 day - 1 for labile substrates and 0.001 day - 1 for refractory substrates appear representative of the range in decay coefficie nts for the aquatic organic substrates in Figure 3. In comparison, decay rates determined by Moran et al. (1999) for 5 rivers of the southeast U.S., expressed as first order decay coefficients, ranged from 0.003 day - 1 to 0.001 day - 1

.19

Figure 3

. Rates of Exertion of BOD for Organic Substrates Typical of Northeast Florida

Surface Waters. Model of the form Ct = Cu(1

- e

- Kt), adapted from Chapra (1997). Cu = ultimate BOD exertion, estimated from TOC from 2.67:1 mass ratio of O2 consumption to OC consumption and respiratory quotient =1. Algae: Determined from mean BOD data from lake Dora, FL; phytoplankton organic carbon determined form 50:1 carbon:chlorophyll a. Value should be considered the sum of algal respiration and bacterial decomposition. Secondar y WWTP effluent from a sampling of 23 point sources of the lower St. Johns River basin. Pulp and paper determined form a large mill in the lower St. Johns River basin. Native DOM developed from the mean of undeveloped blackwater streams in northeast Flor ida.

Determination of Labile and Refractory Organic Carbon

To partition labile and refractory organic carbon, tributary runoff and point source effluent water quality monitoring data collected between 1993 to 1999 within the lower St. Johns River basin were compiled to create a data base of BOD, nutrients and organic carbon. Tributary station descriptions and number of events sampled are included in Table 1, and the locations of these tributaries and their contributing areas are shown in Figure 4. Point sources are listed in Table 2. Stations were included in the analysis if the sample constituent suite included CBOD, total20

Figure 4

. Tributary Water Quality Sampling Stations for Watershed Model ing Set

- up and Skill

Assessment.21

organic carbon, total phosphorus, orthophosphate, total ammonia and total nitrate+nitrite nitrogen. In all, 789 samples were available for 28 surface water stations and 22 point sources.

Sample total organic carbon was considered to be the sum of carbon within labile substrates (labile total organic carbon, or LTOC) and refractory substrates (refractory total organic carbon, or RTOC), the proportions of which can be determined through the simultaneous expression of
their rates of decomposition, as indicated by oxygen consumption in the 5

- day biochemical oxygen demand (BOD 5 ) test. Using the rates of decomposition of the first

- order decay model of 0.075 day - 1 for labile substrates, and 0.001 day - 1 for refractory, a pair of equations for the simultaneous solution of labile and refractory portions can be set up in the form:



### (1) TOC t=5 

### = RTOC(1

- e - (0.001)*5 

### ) + LTOC(1

- e - (0.075)*5 

### ) (2) TOC t=  

### = RTOC(1

- e - (0.001)*  

### ) + LTOC(1

- e - (0.075)*  

### )

where RTOC = refractory total organic carbon, and LTOC = labile TOC. In equation (1), the moles of TOC decomposed at t=5 was assumed to be in unity (RQ = 1) with the moles of oxygen consumed (CBOD 5 ) and was converted to TOC consumed by dividing by 2.67. When all TOC is consumed, at t =  , (a nalogoug to ultimate BOD) the exponent term in parenthesis goes to zero,
and TOC  = RTOC + LTOC. The above paired equations were simplified for computation through the following steps:



### (1) (CBOD 5 

### /2.67) = RTOC*(0.005) + LTOC*(0.3127) (2) TOC = RTOC*(1) + LTOC*(1)

(1) 200*[(CBOD 5 

### /2.67) = RTOC*(0.005) + LTOC*(0.3127)]

(2) TOC = RTOC*(1) + LTOC*(1)

(1) CBOD 5 

### *74.906

- LTOC*(62.54) = RTOC (2) TOC

- LTOC = RTOC22

Table 1. Tributary Water Quality Station Locations Employed in Determination of Labile and Refra ctory Organic Nutrients23

Table 2. Point Source Facilities Included in the Calculation of the Lower St. Johns River External Load24

Solving these 2 equations for LTOC produces:



### LTOC = (CBOD 5 

### *74.906

- TOC)/61.54
And;
RTOC = TOC

– LTOC

In calculations, 2 of the 88 point source samples and 6 of the 702 tributary samples had CBOD 5 values that indicated decay rates less than 0.001 day - 1 ; conversely, 3 point source samples in the
data set exhibited CBOD 5 values that when converted to TOC exceeded the TOC at the maximum decomposition rate of 0.075 day - 1 . These values were omitted from subsequent
calculations.

Determination of Labile and Refractory Organic Nutrients

To determine labile and refractory organic nitrogen and phosphorus in tributary runoff and poi nt source effluents, the relationships between labile organic C content and organic C:N and C:P ratios were examined to partition organic nitrogen (TON = TKN

– NH 4 ) and non

- orthophosphate P (TNOP = TP

– PO 4 ) into these respective pools. In this partitioni ng scheme, it is assumed that the majority of nitrogen not accounted for in the separate analysis of inorganic nitrogen (NH 4 and NO X ) is in either dissolved or particulate organic matter. The same cannot be said for non

orthophosphate phosphorus forms, as a significant proportion of this analytical fraction may in the form of calcium or magnesium phosphates. For this reason, in fraction of total P not in orthophosphate is referred to as “total non

- PO 4 - phosphorus”, and abbreviated as TNOP.

Organic C:N a nd C:TNOP ratios for the tributary and point source data set were plotted against percent labile organic carbon [(LTOC/TOC)*100] to determine the relationship between proportional nutrient content and lability. One data point from stream runoff draining a large dairy and intensive pasture lands in which the TOC:TNOP was 4225:1 was omitted from this analysis. These log

– log plots (Figure 5 and 6 ) demonstrate significant partitioning of carbon to nutrient ratios based upon their content of labile organic c arbon, with samples high in labile organic carbon exhibiting low organic C:N and C:TNOP ratios.25

To determine the TOC:TON and TOC:TNOP for hypothetical, purely labile or refractory substrates, polynomial regressions of these log

– log relationships were solved for the TOC:TON
and TOC:TNOP values corresponding to the %LTOC = 0% a nd when %LTOC = 100% (Figures
5 and 6 ). This yielded an TOC:TON mass ratio of 37 for a completely refractory substrate, and a
ratio of 4.5 for a completely labile substrate. In t he case of non

- orthophosphate phosphorous, the
TOC:TNOP mass ratios obtained were 617 for refractory OM and 27 for labile.

Figure 5

. Organic Carbon:Nitrogen Ratio as a Function of the Percent Labile Organic Carbon.

for LSJR tributary and point source effluent samples; n = 763. Ratios in boxes identify the organic C:N for the hypothetical conditions of 0 and 100 percent labile organic carbon composition.26

Figure 6

. Organic Carbon:Phosphorus Ratio as a Function of the Percent Labile Organic

Carbon. for LSJR tributary and point source effluent samples; n = 727. Ratios in boxes identify the organic C:P for the hypothetical conditions of 0 and 100 percent labile organic carbon composition.

Determining refractory and labile nutrients directly as the product of the respective TOC:nutrient ratio x TON or TNOP would likely result in departures from the laboratory analytical determination of TON (TKN

- NH 4 ) and TNOP (TP

- PO 4 ). Because it is more plausible to constrain the sum of labile and refractory nutrient within the laboratory analytical determination, a proportional compartmentalization of nutrients was achieved by balancing labile and refractory forms within the already separated org anic carbon fractions. The form of this calculation was:27

LTON = {[LTOC/(TOC* 4.5 )]/[RTOC/(TOC* 37) + LTOC/(TOC*4.5 )]}*TON.

Following this calculation, RTON could be calculated by difference with the relationship

RTON = TON

– LTON,

or with the compl imentary partitioning equation of the form

RTON = {[ RTOC/(TOC*37)]/[RTOC/(TOC*37) + LTOC/(TOC*4.5 )]}*TON.

Similarly, TNOP was partitioned with the relationship

RTNOP = {[LTNOP/TOC*27)]/[RTNOP/(TOC*617)+LTNOP/(TOC*27 )]}*TNOP.

This approach also had the advantage in that it could be applied to the existing watershed modeling constituent breakdown, which provides TON and TNOP (Hendrickson and Konwinski, 1998). Thus, instead of deriving specific land use loading rates for LTON, RTON, LTNOP and RTNOP, i t was only necessary to establish new model specific land use water quality coefficients for LTOC and RTOC. From these, labile and refractory ON and NOP could be
determined outside the model framework.

Calculation of the External Load For the Lower St. Johns River

Several different statistical estimation approaches have been relied upon to calculate the external load to the LSJR. In general, the approach used is adapted to suit the inherent variability of the load source, and the monitoring data avail able for estimation, calibration and verification.28

Point Source Load Estimation

To perform the point source load estimation, six separate data sets were utilized to gain available information on concentration, flow, point of discharge and service are a. These data sets included 1) hard copy monthly operating report files maintained at the FDEP Northeast District Office; 2) NPDES electronic files obtained from FDEP Tallahassee; 3) Discharge quality data maintained by the Jacksonville Electric Authority ; 4) Fifth

- year synoptic surveys performed by FDEP Tallahassee or by contractor as part of permit renewal process or WQBEL studies; 5) a special 2 year sampling program conducted jointly by FDEP

- NED, SJRWMD and Duval County RESD; and 6) a GIS data base of locational information compiled by contractor. Table 2 lists the point source facilities included in the data base, their permitted volume, and location of entry into the WQ model grid.

Point source data were compiled into two files based on sampling f requency. The JEA data base in most cases contained daily data on flow and water quality concentration for the 5 largest facilities in Jacksonville, and these data were the core of one data set. Remaining facilities with monthly or quarterly reporting da ta were compiled into a second data base.

Data coverage for the JEA facilities was excellent, with almost complete daily coverage for the entire 1995 through 1999 time interval. Data coverage for the remaining facilities was fair to good, with data cove rage increasing through time as permit monitoring requirements increased to cover nutrients in effluent. The most serious data deficiency occurred for total organic carbon, and data from a short term, joint sampling program from 1995 through 1996 were hea vily relied upon to supply typical values for this constituent.

To calculate daily loads for facilities with monthly reporting data, mean monthly flow was multiplied by monthly grab sampling or flow composite water quality data when available.
General ly, monthly nutrient concentrations were not available, as quarterly nutrient sampling was typically the case for these facilities, so the mean of the sampling record was used. Some questions arose regarding data representation, as many nutrient values we re recorded in the FDEP WAFER data base as “monthly maximum value”. However, after conferring with FDEP

-29

NED staff, it was concluded that these data were invariably fixed

- interval grab sampling data, and could be used to generate representative mean values

.

Non

- Point Source Load Estimation

Unlike point source effluent loads, nonpoint source loads enter at so many locations and exhibit such large temporal variation that a direct monitoring approach is infeasible except for the largest, most significant inputs. At all other nonpoint entry points, statistical watershed modeling is relied upon to complete the external load budget.

Land development influences the delivery of water quality constituents to surface waters in two fundamental ways. Through fert ilization, lawn maintenance, manure spreading, septic tank operation, vehicular use, etc., nutrients and other pollutants are added to the land surface or to shallow groundwater in excess of natural land cover conditions (i.e., native forest, wetland).
Un like the situation that tends to predominate on developed lands, natural land covers are highly conservative of essential growth nutrients, and thus labile nutrient forms tend to be retained within these terrestrial ecosystems. In addition, the creation o f impervious surfaces, drainage development, and the destruction of near stream wetlands increases the amount of rainfall that ultimately ends up as runoff, thus increasing the pollutant exporting capability in developed landscapes. Thus, the process of n onpoint source pollution has both chemical and hydrologic components.

The watershed modeling approach used for the LSJR TMDL and PLRG development utilizes the relationship between land use development and alteration in water quality and quantity to perfo rm a spatial extrapolation of whole basin nonpoint source load. The formulation of this statistical model has its roots in the spreadsheet watershed load screening model, referred to as the Pollution Load Screening Model (acronym PLSM; Adamus and Bergman 1995), which utilizes a computer

- driven geographic information system framework to calculate constituent loads as the product of water quality concentration associated with certain land use practices, and runoff water volume associated with those same prac tices. The model’s nonpoint source30

pollutant export concentrations are specific to one of 15 different land use classes. Water quantity is determined through a hybrid of the SCS curve number method, and is the product of rain volumes and a coefficient (r eferred to as the runoff coefficient, or RC, with values ranging from 0 to 0.9) relating the propensity of various land use and soil hydrologic group combinations to generate runoff. The computational approach of the PLSM is similar to that of the Surface Water Management Model (SWMM) screening level tool.

In the initial application of the PLSM to the LSJRB, Hendrickson and Konwinski (1999) made 4 major modifications to the model’s original framework: 1) the model time step was shortened to seasonal, r ather than annual average loading rates, to account for seasonal differences in specific land use export concentrations and runoff quantity; 2) total nutrient forms were subdivided to provide orthophosphate and total inorganic nitrogen, and by difference, TON and TNOP; 3)
land

- use loading rates were adjusted to monitoring data collected within the LSJR basin using a linear multiple regression best

- fit approach based on contributing land

- use fractions in calibration watersheds; and 4) runoff coefficients wer e varied by season to account for intra

annual variation in rainfall and evapotranspiration patterns. In the original application, nonpoint source loads were predicted for the time period from 1993 to 1995, relying on land use information compiled from 1989

- 90.

In this application of the PLSM, the modifications described above were maintained, and in addition 1) LTOC and RTOC, were added to the specific land

- use loading rate water quality coefficients; and 2) runoff water quantity was varied based upo n deviations in the long term rainfall patterns. From the model output of labile and refractory organic carbon, LTON, RTON, LTNOP and RTNOP were differentiated based on the proportional nutrient ratio weighting described previously. A parameter referred to as the long

- term rain ratio (LTRR) was developed as a weighting factor to adjust the PLSM runoff coefficient based on antecedent watershed soil water conditions, and is described below. Two versions of the PLSM were utilized. One version
ran within AR C Info, and calculated loads directly as the sum within contributing areas of overlying grid coverage

- products of runoff and water quality concentration. A second version was run within Microsoft Excel, and calculates area

- weighted runoff coefficient and runoff

weighted concentration based upon the area within contributing watersheds of unique land us31

and soil hydrologic group combinations. Though different in computational approach, both models are theoretically identical and provided only slightly diffe rent results owing to the areal weighting applied to rainfall input data in the Excel model application.

Watershed NPS Hydrologic Set

- Up and Calibration

One of the principal deficiencies of the original PLSM hydrologic algorithm for predicting time

v arying load is its inability to account for short term changes in antecedent soil moisture conditions that lead to changes in the propensity for rainfall to generate runoff. To account for patterns in antecedent soil moisture associated with normal intra

- annual patterns in rainfall and evapotr anspiration, the previous PLSM application to the LSJR utilized a set of runoff
coefficients for each land use

- soil hydrologic group combination that varied by season (Table 3). While this adjustment helped to simula te seasonal runoff patterns, model responsiveness to long

term deviations from the normal seasonal rainfall pattern, for which model runoff coefficients were originally established, was still poor. The underlying nature of this insufficiency is readily
sh own in Figure 7, which plots the ratio of the PLSM

- predicted runoff to measured runoff to the
seasonal whole

- watershed water yield (the ratio of the measured runoff volume to the watershed seasonal incident rainfall volume). When watershed water yield is low, resulting from prevailing drier than normal conditions, the fixed runoff coefficient of the PLSM formulation over

- predicts measured volume (PLSM/observed > 1). When watershed yield is high due to high rainfall seasons, the opposite is observed (PLSM/ observed < 1). Because of the large range in flow conditions occurring in the 1995

- 99 TMDL/PLRG modeling time window (the relative ranking
of mean annual flow rates for the 1995

– 1999 is shown in Figure 8), it was critical to obtain a
better time

- varying estimate of runoff, and hence, load.

To adjust for this model insensitivity, a correction factor was developed based upon two concepts. First, as rainfall and runoff patterns deviate form the long term norm, the ratio of the fixed, PLSM runoff coeffi cient to the measured, basin yield (rain volume/runoff) varies in a predictable manner, as was shown in Figure 7. Because extended drought

- induced changes in
the rainfall

- runoff relationship may occur over several seasons, the calculation to devise what i s32

referred to as the long

- term rain ratio (LTRR) incorporated rain excess or deficit for one year (3 seasons) with the following equation:

LTRR = [RAIN CS /LT RAIN CS + (RAIN CS

- 1 /LT RAIN CS

- 1 )/2 +

(RAIN CS

- 2 /LT RAIN CS

- 2 )/3]/LT RAIN CS

where:
RAIN CS , RAIN CS

- 1 , and RAIN CS

- 2 = current season rain, the previous season’s rain, and rain of season prior to previous season; and LT RAIN CS , LT RAIN CS

- 1 , and LT RAIN CS

- 2 = 30

- year, long term mean rain for the corresponding seasons above (Rao et al., 1997).

Second, the rate of deviation of the ratio of PLSM runoff : Measured runoff with drought or wetness is a function of the degree of impervious surface area of the basin. Under extended durations of low rain, basins with a large degree of impervious surface area are l ess affected by dry soil moisture conditions, and as a result still return a relatively large amount of their rainfall
as runoff.33

Table 3. Seasonal Runoff Coefficients for Application of the Pollution Load Screening Model

to the LSJR Basin. Values repr esent the fraction of that produces runoff.34

Figure 7. Relationship Between PLSM Predicted Runoff:Observed Runoff Ratio and Measured

Seasonal Whole Watershed Runoff Coefficient. Seasonal Whole Watershed Runoff Coefficient = Incident Rainfall Volume/Runo ff Volume35

Figure 8

. Relative Position of the 1995

- 1999 Time Interval in the Historic Long Term Flow

Record. (From Hendrickson and Magley, 2002.)36

Figure 9 demonstrates this 2

- step analysis process. Analysis was confined to the 4 most reliable
flow gauging stations; the Deep Creek gauge, due to poor performance of the acoustic flow meter at this site, was excluded. The original model

- predicted, area

- weighted PLSM runoff coefficient for each season and year for these four calibration watersheds (60 seasonal values total) were placed into 4 runoff coefficient classes. The rate of change (slope) in the measured yield : PLSM predicted runoff coefficient ratio as a function of the LTRR 2 was determined for each of these classes through zero

- intercept sim ple linear regression (Figure 9(a)). As PLSM

predicted runoff coefficient class increases, the rate of change of the observed:PLSM runoff coefficient ratio, as LTRR increases, declines. Stated more simply, as watershed impervious surface area increases, t he degree to which variation in the long term rainfall pattern leads to deviations in the fixed, PLSM runoff coefficients, decreases. To account for lower response in watersheds with higher amounts of impervious surface, regression was again used to quant ify the rate of PLSM runoff coefficient deviation with changes in watershed impervious area, approximated by the original PLSM runoff coefficient (Figure 9(b)). This relationship was then integrated into an adjustment for the PLSM fixed, seasonal runoff c oefficients based on the
LTRR of the form:

Observed RC/PLSM RC = LTRR 2 *(0.3228*PLSM

- RC 0.6206 )

This relationship was multiplied through by the PLSM

- predicted runoff coefficient to derive a long term, rain

- adjusted runoff coefficient:

RUNOFF COEFFICIENT ad j = PLSM

- RC*[LTRR 2 *(0.3228*PLSM

- RC 0.6206 )]

Figure 10 compares the original and adjusted PLSM runoff coefficient and seasonal flow volume to the measured watershed yields and seasonal flows. The adjustment removes bias and improves precision in both the P LSM runoff coefficients and flows, moving slopes of the regressions between observed and simulated to near one and zero, respectively. The correlation coefficient improves from 0.12 to 0.43 in the case of runoff coefficient and from 0.59 to 0.80 in
the ca se of seasonal flow volume. Cumulative discharge curves for the seasons from December 1994 through November 1999 for the measured, original PLSM and PLSM

- adjusted discharge37

volumes (Figure 11) show that, with the exception of the South Fork of Black Creek , the long
term rain

- ratio adjustment of PLSM seasonal simulations greatly reduces the cumulative over

prediction of the measured flow.

Water Quality Set

- Up and Calibration

In the original application of the PLSM to the LSJR basin, specific land use w ater quality coefficients were developed for total nitrogen (TN), total inorganic nitrogen (TIN, or NO X + NH 4 ), total phosphorus (TP), orthophosphate (PO 4 ), biochemical oxygen demand (BOD), and total suspended solids (TSS). These coefficients, shown in Ta ble 4, were left unchanged from the earlier application, so the comparison of measured to simulated values here represents a skill assessment for these constituents. (The term skill assessment is used here, rather than verification , as the calculated flow

- weighted concentrations that are used to compare to watershed model simulations encompass the data used in the original calibration, collected from
1990

- 95, and newer data from 1996

- 2000.) New to this iteration of model development are total organic carbo n (TOC), labile total organic carbon (LTOC), and refractory total organic carbon (RTOC). To calculate the labile and refractory portions of organic nitrogen and non

- PO 4 - phosphorus, the proportioning equations described earlier were applied utilizing the s imulated LTOC or RTOC operating on the difference of simulated TN

- TIN and TP

- PO 4 . The use of the
simulated TON and TOP values assures that TIN+RTON+LTON and PO 4 +LTOP+RTOP will be equal to TN and TP.38

Figure 9

. Development of Hydrologic Correction Factor s for the PLSM Runoff Coefficient. (a)

Linear regressions relating changes in seasonal rainfall pattern, expressed as (LTRR) 2 , the ratio of observed watershed yield to PLSM predicted. (b) Relationship between the slope of the (LTRR) 2 and the watershed are a

- weighted runoff coefficient.39

Figure 10

. Comparison of Original, Seasonal

- fixed and Long

- Term Rain Ratio Adjusted

Runoff Coefficients (a) and Total Seasonal Discharge (b) for Flow Calibration Watersheds Within the LSJRB.40

Figure 11

. Comparison of Original PLSM

- Predicted, Long

- Term Rain Ratio Adjusted and

Observed Cumulative Discharge Curves for LSJRB Calibration Watersheds, 1995

- 99.41

Table 4. Seasonal Water Quality Coefficients Used In the PLSM to Predict Non

- Point Source

Loads to the LSJR. All Values represent flow

- weighted concentrations in mg/L.42

Following the calculation of LTOC and RTOC for the water quality data set, specific land use
flow

- weighted mean concentrations were developed following the procedure outlined in
Hendrickson and Konwinski (1998). Mean flow

- weighted concentrations for LTOC and RTOC were computed by sub

- dividing water quality samples for each season and station into low flow (samples collected within the 1

– 50 th percentile flow days) and high flow (samples col lected
within the 51

– 100 th percentile flow days) and multiplying mean concentrations within flow class by the proportion of total seasonal flow volume occurring within the class, and summing these two products. Seasonal, flow

- weighted concentrations wer e used to set specific loading rates, as observations from blackwater stream monitoring stations suggest that TOC concentrations in runoff change significantly due to season (Figure 12). Seasons used were not
the Julian seasons but modified seasons corres ponding to hydrologic and meteorological patterns of northeast Florida, and were comprised of a cool, moderately wet winter season from December through March characterized by regular frontal storm events; a hot, dry spring/summer from April through July; and a hot, wet summer/fall from August through November characterized by afternoon convective thunderstorms and tropical systems.

A multiple regression approach was then used to objectively assign seasonal, specific land

- use concentrations. Land uses w ere aggregated into four broad classes: 1) row crop agriculture, of which the majority was cabbage and potatoes on seepage

- irrigated flatwoods soils; 2) dairy, including associated feedlots, loafing areas, improved pastures and manure spray

- fields; 3)
medi um to high intensity residential, commercial and industrial; and 4) silviculture, native forest and wetlands. Multiple regression models for each season were set up in the form:

 1 *LU

- F R +  2 *LU

- F D +  3 *LU

- F U +  4 *LU

- F F = [TOC, LTOC, RTOC],43

Figure 12

. Monthly Mean Concentrations and 95% Confidence Intervals for Color and Total

Organic Carbon for 24 Unimpacted Blackwater Streams in Northeast Florida.44

where LU

- F R , LU

- F D , LU

- F U , and LU

- F F were the fractions of watershed area upstream of sampling stat ions in the aggregated row crop, dairy, urban and forested categories. Regression statistics were developed using Minitab Statistical Software. Determination of water quality coefficients in this manner generally produces concentrations lower than those determined from specific land use monitoring on small catchments of homogeneous land use, such as the type developed for the National Urban Runoff Program monitoring (U.S. EPA, 1984), presumably
due to instream assimilation and sedimentation that leads to an attenuation of concentration from source to mouth. However, such “implicitly attenuated” loading rates are viewed as superior for whole watershed determinations of constituent loads, as would be required for input to a receiving water body eutrophicati on model.

TOC, LTOC and RTOC concentrations within these aggregated land use categories were then disaggregated into a broader set of land uses corresponding to those employed in the PLSM framework of Adamus and Bergman (1995). The set of final water q uality coefficients for all
land uses is shown in Table 5.

In their development of the EFDC hydrodynamic model for the lower St. Johns, Sucsy and Morris (2002) identified 63 discrete entry points and their contributing areas for water volumes entering t he model grid (Figure 13), and these have been retained for the nonpoint source load entry points to ICM.45

Table 5. Total organic, labile total organic, and refractory total organic carbon land use category concentration coefficients. Aggregated class mo del coefficients in bold, with individual class concentrations estimated by the trend with land use intensity. RTOC concentrations determined from TOC

- LTOC. Values in parenthesis are regression model coefficients.46

Figure 13. Watershed Model Input Areas for Nutrient Load Compilation.47

Determination of the Upstream Load to the LSJR

Background

The upstream load is composed of the three large tributaries that make up the lower St. Johns: the middle St. Johns River, the Ocklawaha River, and Dunns Creek (Figure 14). These three tributaries make up approximately 61, 21, and 18 percent of the long

- term annual mean river discharge at Palatka. Because of autochthonous production in upstream lakes, the upstream load differs greatly from watershed load s that enter within the LSJR basin.

Because of the large amount of the entire LSJR flow that enters upstream (roughly 60%), a direct monitoring approach, featuring continuous measurement of discharge, and bi

- weekly collection of water quality samples, h as been used to determine its constituent load. This is in contrast to the watershed modeling approach that has been used to develop the downstream tributary load. Along with water quality monitoring, phytoplankton (algae) monitoring has also been perfor med at these inputs to determine the amounts and types of algae entering the LSJR, and how they change throughout the year and from year to year.

The presence of phytoplankton, and the preponderance of nutrients and carbon that is contained in phytoplan kton, is the single most important feature that separates the upstream inputs in the whole LSJR external load. This poses an additional level of difficulty in the compartmentalization of nutrients and carbon. Other tributaries to the LSJR (with the excep tion of Doctors Lake, which in the LSJR TMDL modeling is included within the model grid) contain insignificant amounts of algae, and distinguishing algal

- borne nutrients and carbon in the particulate labile organic pool has not been done in the calculation of the watershed nonpoint
source load.48

Figure 14. Tributaries Forming the Lower St. Johns River.

Approach to Upstream Boundary Nutrient and Carbon Partitioning

The combined incoming load from the middle and upper St. Johns and Ocklawaha River wa s determined from the LSJR sampling station at Buffalo Bluff, a location 11 miles downstream of the Ocklawaha River mouth. Direct monitoring was also used to characterize the load entering the LSJR from the Crescent Lake Basin, at the sampling location in Dunns Creek, just downstream of the Buffalo Bluff site. Both locations are instrumented with acoustic Doppler current profilers, capable of measuring velocity and discharge in conditions of reversing flows. Biweekly water quality monitoring has been per formed at these locations under two programs, with only slight differences in constituents. An ambient monitoring program, collecting samples during the second week of each month, includes BOD in its suite, necessary for the empirical separation of labile and refractory organic carbon. A second monitoring network samples these locations on the fourth week of the month, and includes the analysis of particulate organic49

carbon, and a complete plankton analysis featuring taxonomic classification, cell densiti es and cell biovolume for major algal taxonomic groups. Both programs analyze complete suites of chlorophyll and dissolved and total organic and inorganic nutrients and carbon.

The flow chart of Figure 15 illustrates the mapping of water quality chemic al analysis results to respective biological compartments of the LSJR WQ model. In this calculation cascade, one of the most important determinations that must be made (and that distinguishes this boundary determination from downstream tributary inputs) i s the content of the total nutrient and carbon load in phytoplankton. In this approach, the inorganic forms (for nitrogen or phosphorus) are distinguished from the organic forms directly through laboratory analysis as shown. Next, organic carbon is subdi vided into labile or refractory forms based upon the procedure described previously for tributary nonpoint sources, utilizing BOD converted to carbon units and the
established first

- order decomposition rates. Labile organic carbon and nutrients are then f urther subdivided into algal and non

- algal, under the assumption that all algal organic matter is labile. The determination of algal carbon content is described below, and algal nutrient composition was determined from this algal carbon estimate, assuming adherence to the Redfield stoichiometric

propo rtions of 106:16:1 C:N:P molar.50

Figure 15

. Flow chart for differentiation of laboratory analytical fractions into CE

- QUAL

- ICM state variables for the lower St.

Johns River upstream boundary at Dunns Creek and Buffalo Bluff.51

A data set of coincidently collected particulate organic carbon, nitrogen and phosphorus, chlorophyll a , and algal biovolume (Phlips and Cichra, 2001) were relied upon to determine algal
carbon content and con firm nutrient ratios. Direct measurements of particulate organic carbon using the high temperature combustion coulombmetric method were used to establish algal C:chlorophyll a and C:algal biovolume ratios. Estimation of POC by difference of TOC

– DOC
wit h measurements performed by the carbon gas analyzer were not used due to their much lower precision and poor fit with algal biomass. Coulombmetric POC data are limited in coverage, having been performed only once monthly from August 1998 to the present. Thus, to calculate TOC for subsequent compartmentalization to labile and refractory forms for dates on which POC was not determined, TOC was considered to be at least equal to DOC + algal POC, with algal POC determined either through algal biovolume or chl orophyll a , preferentially selecting biovolume data in the calculation when available. At most, TOC was considered to be equal to DOC + (total suspended solids  0.4), representing a situation in which all TSS were organic particles with a 40% composition by weight of carbon.

Comparison of algal biovolume to corrected chlorophyll a for LSJR freshwater sampling stations (Figure 16) shows a strong correlation between these two variables, as would be expected. Regression R 2 values for the relationship ran ge from 0.75 to 0.83 for the 6 stations examined, and intercepts are near zero. A similar correlation is obtained in regressions between uncorrected chlorophyll a and biovolume, with the exception that the intercept values are higher, suggesting the pres ence of chlorophyll

- containing algal detritus that is not accounted for in the
biovolume determination.52

Figure 16. Comparison of Corrected Chlorophyll a and Algal Biovolume for Combined LSJR

Freshwater Water Quality and Plankton Analysis, 1995

– 2001. P lankton taxonomy and biovolume determinations performed by Phlips and Cichra, 2001.

Both chlorophyll a and algal biovolume were strongly correlated to measurements of particulate organic carbon, though linear regression R 2 values were typically driven by a small number of large values in the data set associated with algal bloom events, when a large amount of the POC is expected to be in algal biomass. To help distinguish carbon to biomass relationships, sample C:chlorophyll and C:biovo lume ratios were viewed as boundary relationships, in which the approach of POC:biomass values the true C:biomass measure occurs as non

- algal carbon declines (Figure 17). In these boundary relationships, it can be seen that minimum C:chlorophyll ratios ap proach the commonly cited carbon:chlorophyll a ratio of 50:1, shown in red in Figure 17(a). In the case of the biovolume comparison, a similar boundary analysis yields an estimate
of 3.3  m 3 /ml per mg/L POC.53

To perform the partitioning of organic carb on when reliable TOC measurements were not available, RDOC was first derived through a relationship with color (Figure 18), and LDOC
determined as the DOC measured – RDOC color

- derived . LTOC and RTOC were then calculated by way of the carbon equivalency BO D decomposition

- derivation described in the watershed model coefficient development, using a TOC estimated by DOC + algal OC. For Dunns Creek, this approach suggested that a large fraction of the organic carbon pool is contained in RDOC. In the
case of t he Buffalo Bluff station, however, RDOC concentrations were lower, hence relatively large amounts of DOC were attributed to the LDOC partition. These high LDOC concentrations (mean = 3.7 mg/L; range = 0 to 6.1 mg/L) in most cases exceeded the calculated n on

- algal
LTOC concentration when non

- algal LTOC was calculated as TOC min – RTOC

– algal LTOC. To adjust for this inconsistency, LTOC concentration was increased such that non

- algal LTOC = LDOC, resulting in non

- algal LPOC = 0. In cases where the sum of L TOC + RTOC exceeded DOC + TSS*0.4 (TOC max ; occurring in 12 of the 51 sampling events in between Nov. 1996 through Dec. 1998), RTOC was adjusted downward.

Determination of the Atmospheric Deposition Load

The calculation of the atmospheric deposition loa d to the lower St. Johns River was performed by the private consulting firm Tetra Tech, Inc. (Pollman, 2003). Estimates were derived for total wet and dry deposition of nitrogen and phosphorus species. Wet nitrogen deposition
concentration was determined by the monthly mean of the 3 nearest National Atmospheric Deposition Program (NADP) sites to the LSJR, FL99 (Kennedy Space Center), FL03 (Bradford Forest), and GA09 (Okeefenokee Swamp). Volumes were computed as the product of the monthly Thiessen rainfal l polygon rainfall volumes (the same spatial rainfall data set as that used for watershed modeling) directly falling on the ICM grid surface area. Monthly dry deposition nitrogen flux estimates were developed using the method of Poor et al. (2001). Phosp horus wet deposition was determined by the volume weighted mean deposition estimates collected at the Lake Barco monitoring station, approximately 20 miles west of Palatka.54

Figure 17

. Comparison of POC:Algal Biovolume (a) and POC:Total Chlorophyll a Rat ios to

Total Biovolume and Total Chlorophyll a Concentration for LSJR Freshwater Samples. Red Lines in graph (a) correspond to a C:chlorophyll a of 50:1, and in graph (b) corresponds to C:biovolume of 0.0003 (for biovolume expressed as μm 3 /ml x 1000).55

Figure 18. Relationship Between Refractory Dissolved Organic Carbon and Color for

Blackwater Streams of the LSJR Basin. From Hendrickson et al., 2001.56



### RESULTS

Tributary Organic Carbon and Nutrients

Calibration Data Set Summary

Calculated seas onal flow

- weighted concentrations of organic carbon, nitrogen and phosphorus forms in field sampling data of tributary runoff are listed in Table 6. Organic carbon in samples was considered to be primarily allochthonous in origin. Of the 789 total sample s available, 570 were also analyzed for chlorophyll a . Of these, 82% had chlorophyll a concentrations less than 5

 g/L, suggesting low contributions to the total organic carbon pool of internally

- derived, autochthonous organic carbon. Of the 103 samples with chlorophyll a concentrations above 5

 g/L, 71 were obtained at on two tributaries, Sixmile and Peters Creeks, with sampling sites in quiescent, open embayments below the head of tide, where autochthonous production potential was high.

Using the BO D partitioning approach, labile total organic carbon (LTOC) was found to comprise a relatively small fraction of the TOC pool in stream runoff. This is not surprising, as southeast U.S. coastal plain blackwater streams are by nature high in refractor, col ored dissolved organic matter. The highest absolute concentrations of LTOC were found to occur in tributaries draining urbanized Jacksonville. These tributaries also exhibited lower TOC concentrations, presumably do to the increase in impervious surface area and the concomitant reduction in runoff leached through soils on the flow

- path to the stream, as well as a reduction in the amount of natural forest cover in the basin to supply organic detritus. Catchments with high contributions of dairy land

use e xhibited relatively high LDOC concentrations, but had calculated RDOC concentrations similar to undeveloped watersheds.57

Table 6. Mean Total, Inorganic, and Calculated Labile and Refractory Organic Nutrient and Carbon Mean Annual Flow

- Weighted Concen trations for Tributaries sampled within

the lower St. Johns River Basin. Sorted in order of increasing land use intensity. Values in boxes lacked BOD sampling data and were calculated using simulated values.58

Based on the empirical partitioning schem e derived here, organic N was on average evenly split between labile and refractory forms, while non

- orthophosphate P tended to predominate in labile forms. These proportions differed widely based upon the level and type of development within a sampling b asin. For undeveloped basins, with over 90% of their area in natural land covers or silviculture, LTON was on average 27% of TON. For these same basins, LTNOP was, at 51%, on average a higher proportion of TNOP. It should though be noted that these stre ams exhibited such low concentrations of TP that absolute LTNOP values are very low, and the precision of the partitioning approach should also be considered low. Conversely, for tributaries draining basins with over 30% of urban land area, LTON averaged 70% of TON, and LTNOP was on average 86% of TNOP. Despite relatively low TOC concentrations in comparison to streams draining undeveloped watersheds, urban development

- dominated watersheds exhibit some of the highest concentrations of LTOC, LTON, and LTNO P.

Streams draining agriculture

- dominated watersheds also exhibited proportionally high levels of LTOC, LTON and LTNOP. Within this category, several streams draining watersheds with large areas of dairy land use exhibited some extremely high concentra tions of TP and TN. The high levels of these nutrients were not consistent within any given stream, suggesting various unidentified dairy practices influence water quality in different ways. Bradley Creek and Governor’s Creek exhibited high TN levels, at 6.39 and 5.13 mg/L, respectively, with a large portion of this as nitrate+nitrite

- N, but had relatively low (for dairy runoff) TP concentrations. Another dairy

- influenced stream, Mill Log Branch, had elevated but much lower levels of TN, at 2 mg/L, while mean TP concentration was 1.36 mg/L! This lack of consistent pattern with respect to elevated nutrient concentrations led to difficulties in establishing model concentration parameters.

Watershed Model Calibration

Hydrologic and water quality coeff icients were calibrated separately and together (as load) to provide a relative sense of their individual and combined precision. Hydrologic calibration was59

discussed in the previous methods section, therefore only the quality and load calibration are

cov ered here. As TN, TIN, TP, and PO 4 were calibrated in the first LSJR development of the PLSM (Hendrickson and Konwinski, 1998) and remain the same in this application, the results
can be viewed as a verification of the established model coefficients. For the newly created constituents TOC, RTOC and LTOC, this comparison represents an assessment of the performance of the coefficient assignment process through the multiple regression approach.

Figures 19, 20 and 21 compare the seasonal measured and simul ated flow

- weighted concentrations of relevant water quality variables. Large differences are evident for monitoring stations downstream of dairy land uses (shown as open squares in Figures 19

- 21. Part of this appeared to be due to the inconsistencies i dentified in the previous section with regard to the appearance of N or P in runoff. However, the consistent under

- prediction suggests that either 1) water quality concentrations assigned to improved pasture and confined animal feeding zones are too low; 2) runoff coefficients assigned to these areas are too low; or 3) the area dedicated to dairy is under

- reported in the GIS land use layer. As the present PLSM nutrient concentrations assigned to improved pasture and concentrated feeding areas range from 3 .0

- 3.9 mg/L and 4.5

– 6.0 mg/L for TN and from 0.75

– 2.0 mg/L and 1.3

– 2.6 mg/L for TP, considerably greater that the 2.48 TN and 0.349 TP mg/L mean concentrations reported by Harper (1994) for central and south Florida, it does not appear at first tha t model concentrations are too low. After some inspection, it was discovered that the GIS land use data in many cases either incorrectly coded land cover associated with dairy, identifying intensive pasture or manure sprayfields as row crop
or miscellaneo us agriculture, or failed to account for it at all. Also, as the PLSM in its present form assigns the same runoff coefficients to pasture and dairy as undeveloped land use, this too may be a source of under

- representation. Darkened squares in Figures 19

- 21 identify these simulated values for these same watersheds derived by assigning all land are under the “miscellaneous agriculture” category to dairy, and increasing runoff coefficients to the same
values observed for the tri

- county agricultural area. T hese modifications appear to bring organic nutrient concentrations more in line with observed data, but still leave simulated inorganic nutrient concentrations (and hence, total nutrient) low. Because these manipulations are hypothetical, the sampling st ations within the three watersheds with substantial dairy60

development (Governors Creek, Mill Log Branch and Bradley Creek) are excluded from the quantitative calibration described below.61

Figure 19

. Comparison of Observed to Simulated Flow

- Weighted Conce ntrations of Carbon,

Nitrogen and Phosphorus Forms for the December through March Season. All units in mg/L. Open squares represent existing dairy watershed predictions, and solid squares represent miscellaneous agriculture added to dairy land and runoff coefficients increased to match tri

- county ag area row crop values.62

Figure 19 (cont.)63

Figure 20

. Comparison of Observed to Simulated Flow

- Weighted Concentrations of Carbon,

Nitrogen and Phosphorus Forms for the April through July Season. All units i n mg/L. Open squares represent existing dairy watershed predictions, and solid squares represent miscellaneous agriculture added to dairy land and runoff coefficients increased to match tri

- county ag area row crop values.64

Figure 20 (cont.)65

Figure 21

. Comparison of Observed to Simulated Flow

- Weighted Concentrations of Carbon,

Nitrogen and Phosphorus Forms for the August through November Season. All units in mg/L. Open squares represent existing dairy watershed predictions, and solid squares repres ent miscellaneous agriculture added to dairy land and runoff coefficients increased to match tri

- county ag area row crop values.66

Figure 21 (cont.)67

Two statistical tests were performed to characterize the level of agreement between the
calculated f

low

- weighted concentrations and the PLS model simulated, runoff

- weighted concentrations, and the results of these are shown in Table 7. Pearson product

- moment correlations were calculated to estimate general agreement between observed and simulated
values , and zero

- intercept regression slopes and confidence intervals, in which the observed
flow

- weighted concentration represented the independent variable, were calculated to assess potential bias in model water quality coefficients. Variables exhibiting the poorest correlations between the calculated flow weighted and simulated values include TPO 4 for the December

– March season, RTON for the April

– July

.68

Table 7. Pearson Correlations, Slopes, and Confidence Intervals of the Slopes for Intercept

- Fit Regre ssions Between Calibration Station Measured Flow

- Weighted Concentrations and Contributing Area Modeled Runoff

- Weighted Concentrations. Boxed confidence bounds indicate seasons and constituents for which agreement between measured (independent) and modeled (dependent) concentrations are significantly different than 1:1.69

season, RTON, RTNOP, LTOC and TSS for the August

– November season, and RTNOP for the overall, all season comparison. In all other comparisons, Pearson correlation p

- values are less

t han 0.05. In 22 of the possible 36 (3 seasons x 12 constituents) seasonal comparisons, the slope of the regression line relating the observed, flow

- weighted concentration to the simulated value was found to be significantly greater than or less than 1, su ggesting some bias in the model seasonal water quality coefficients. Many of the seasonal biases are compensating, and when all seasons are combined, 9 of the 12 constituent slope confidence intervals contain 1, and the remaining 3, TP, PO4 and LTOC, uppe r confidence bounds are close to 1. Seasonal oscillation in observed verses simulated slopes in several cases results from a few data points belonging to streams (principally Dog Branch and Deep Creek) draining the tri

- county agricultural area that
exhibi t much higher concentrations and thus heavily influence the regression line. When high flow samples from these streams, influential in the flow

- weighting calculation, were recorded
near the end of the December

– March WQ coefficient seasonal break, observ ed concentrations are high relative to model predictions, producing a observed x simulated slope of less that 1.

In most cases, the newly

- added model constituents for TOC, LTOC and RTOC perform well, however, it should be kept in mind that in the case of these constituents the same data set was used for assigning land use water quality coefficients and calibration, so statistics only reflect the skill in assigning coefficients.

To assess the accuracy of load prediction (the product of concentration a nd discharge volume), daily loads calculated at water quality sampling stations that also have established stream

- flow gauging installations were compared to watershed model simulated daily loads. Actual daily load was assumed to be equivalent to the prod uct of the instantaneous water quality sampling concentration and the daily discharge. Because water quality concentrations can vary within a day, this should be considered to be an estimate of daily load. Simulated daily load was
calculated as the seaso nal, watershed area

- weighted constituent concentration x seasonal water quantity x the daily flow fraction. Daily flow fraction is the fraction of the given daily flow of the total seasonal flow, and was the statistic used to disaggregate seasonal watersh ed model loads to daily loads for input to the LSJR water quality processes model.70

Three tributary stations in the LSJR meet the criteria of having both water quality and quantity monitoring, and comparison of their simulated and observed daily loads ar e shown in Figure 21. The plots demonstrate the tendency for baseflow conditions to be over

- sampled relative to stormflow or high

- flow conditions in fixed

- interval ambient monitoring data. This phenomena is manifested in the plots by many sampling points clustered near the origin, with few high

- flow (and thus high load) data points to discern the relationship between observed and simulated. Considering the uncertainty associated with estimating the true value of the daily load, and with fractionating a d aily load from the watershed model seasonal load, simulated and observed daily loads are in good agreement. Of particular interest are the dual data points of Figure 21 . Open diamonds represent the comparison using the North Fork’s daily flow fractions t o disaggregate the seasonal load, while the solid triangles represent the simulated daily load disaggregated with the daily flow fractions from the South Fork. This latter condition is a closer representation to the approach used for most sub

- basins, whic h did not have flow gauging stations, and were disaggregated with nearest

- neighbor daily flow fractions. In general, inorganic nutrient fractions appear to be more variable than total, and larger, homogeneous watersheds (exemplified by
South Fork Black Cr eek) have better agreement than heterogeneous watersheds with varying land uses and management that leads to variations in pollution runoff (exemplified by Deep Creek).

Point Source Organic Carbon and Nutrients

Mean point source effluent discharge vol umes and water quality concentrations for 1997

- 98 are summarized in Table 8. On average, 135.4 million gallons per day of treated domestic waste, and 59.9 million gallons per day of treated industrial waste were discharged during that time period, with th e majority of this entering the oligohaline and mesohaline portions of the river north of Julington Creek.71

Table 8. Summary of Point Source Mean Efflue nt Water Quality Concentrations72

In contrast to tributary surface waters, inorganic nutrients are the predominant form of domestic waste effluent TN and TP concentrations. Based upon the BOD

- partitioning of the organic nutrient fraction, domestic waste exhibits a high proportion in the labile form, on average 84%
for LTON and 87% for LTNOP.

Pulp and paper processing waste is the only industrial effluent (excluding dairy runoff) discharged directly to the lower St. Johns River. During the TMDL and PLRG water quality model calibration time period, three pulp and paper mills were operating within t he basin, however, since this time one has ceased operation (Jefferson Smurfit), and another, Georgia Pacific, has considerably reduced its effluent volume. Thus it should be understood that these statistics are in transition, and relevant only for calibr ation of the water quality model and for the starting point in subsequent load allocation calculations.

The most noteworthy characteristic of pulp and paper process waste is the very high levels of TOC. While largely refractory, LTOC concentrations are quite high, suggesting high potential oxygen demand. These effluents also appear to carry a large proportion of their organic nutrient forms in the labile fraction, with 78% of TON as LTON, and 88% of TNOP as LTNOP.

Upstream Concentrations of Organi c Carbon and Nutrients

Nutrients and organic carbon entering the lower St. Johns (Figure 14) exhibit an annual pattern in compartmentalization linked to algal production and upstream runoff. Lake George is the predominant hyro

- morphological feature that shapes the upstream load entering at Buffalo Bluff,
and the co

- occurrence of increased residence time and increased day length and temperature in the spring and early summer transforms the river from an allochthonous (external carbon

supplied) system to o ne which is principally autochthonous (internally

- generated carbon supply). This pattern results in a sequence of nutrient sequestration within the lake that starts with inorganic nitrogen disappearance in the spring, leading quickly to nitrogen

- fixing bl ue green algal dominance.73

Figures 2 2 , 2 3 , and 2 4 show the patterns in N, P and organic C form concentrations at Buffalo Bluff and Dunns Creek from 1995 through 1999. Algal organic carbon concentrations increase each year in May, and remain elevated th rough August. Peak algal organic carbon concentrations coincide with refractory organic carbon (largely composed of allochthonous colored dissolved organic matter) annual minimums. Annual peak algal organic carbon concentrations range between 2.4 mg/L ( June 1995) to 5.9 mg/L (July 1999) at Buffalo Bluff, and between 2.0 mg/L (July 1995) and 4.0 mg/L (May 1999) at Dunns Creek. Low inorganic N concentrations exiting Lake George and at Buffalo Bluff suggest a condition of low N stress on the algal communit y is a normal occurrence by late spring. If this is the case, then the drawdown
of non

- algal labile N that occurs in May and June may be an important regulator of algal growth
at this time.

By June of most years, evidence for blue

- green algal nitrogen fixation can be seen at Buffalo Bluff. These can be sees as peaks in non

- algal LTON, absent of flow events that would be expected to import allochthonous organic N. In 1996, the summer total Kjeldahl nitrogen peak is 2.05 mg/L, occurring on June 12; in 1 997, it is 2.32 mg/L on July 23; and in 1999, it is 2.8 mg/L on May 25. Peaks in LTON in Figure 2 2 near these events suggest either low C:N ratios due to luxury acquisition of nitrogen (in which case this should more correctly be attributed to the algal
N compartment), exudation of labile N by the actively

- growing N

- fixing community, or the presence of a large amount of algal detritus.74

Figure 22. Partitioned Nitrogen Concentrations at (a) Buffalo Bluff and (b) Dunns Creek, Dec.

1994

- Nov. 1999.75

F igure 2 3

. Partitioned Phosphorus Concentrations at (a) Buffalo Bluff and (b) Dunns Creek,

1995

- 1999.76

Figure 2 4

. Partitioned Organic Carbon Concentrations at Buffalo Bluff and Dunns Creek, 1995

- 1999.77

Algal organic carbon at Buffalo Bluff as a f raction of TOC peaks during the months of July and August, when algal biomass accounts for roughly 20 to 25% of water column TOC, and RTOC reaches its annual minimum (Figure 2 4 ). A decrease in allochthonous OC import, dilution with artesian spring flow, a nd perhaps photodecomposition, are suspected to be the principle factors leading to the annual summer decline in RTOC. On average, total labile OC (algal plus non

algal labile OC) represents 23 percent of the total organic carbon concentration entering th e lower St. Johns at Buffalo Bluff. In contrast, LTOC concentration was on average 10 percent of TOC exiting the Crescent Lake Basin (Dunns Creek).

While not shown in Figure 2 4 , the further partitioning of Buffalo Bluff LTOC into dissolved (LDOC) and p articulate (LPOC) forms resulted in assigning nearly all of the non

- algal labile carbon in the dissolved form, as a consequence of maintaining LDOC = DOC

– RDOC. It cannot be determined if this exists as an artifact of the carbon partitioning process, or represents a real phenomena. The presence of high concentrations of LDOC might be possible if the source of the LDOC was algal exudates associated with high levels of algal production. Between July and September, peak Buffalo Bluff LTOC (algal + non

- alga l) concentration ranges between 5 and 9 mg/L, exceeding the maximum concentrations calculated for any of the tributary sampling stations used in the development of watershed model water quality coefficients by 2

- fold. In comparison to the regression

- deter mined specific land

- use loading rate LTOC concentrations, the Buffalo Bluff LTOC is comparable to runoff from the highly urbanized tributaries of Jacksonville and northern Clay County. These high LTOC concentrations should be viewed in
context with the lo w dissolved oxygen concentrations in this reach of the lower St. Johns, which can exhibit frequent and prolonged excursions below 5 mg/L between July and October. From this analysis, it appears that the capability for generation of LTOC from river autocht honous production far exceeds that of terrestrial export in watershed runoff.

Incoming nutrients and organic carbon to the lower St. Johns River from Dunns Creek appears to resemble that of a large blackwater stream, with algal productivity in the upst ream Crescent Lake exerting less influence over carbon and nutrient partitioning that is observed for Lake George. RTOC concentrations of Dunns Creek are much higher than that of Buffalo Bluff, exceeding 3078

mg/L in the aftermath of the 1997

- 98 El Nino win ter. In March of 1998, a color measurement of 800 Pt Co units was recorded, however, this value stands alone as an outlier, and most color measurements during this period were between 400

– 500 Pt

- Co units

– still very highly colored. In the dry

- down fol lowing the El Nino 1997

- 98 winter, RTOC levels fall to a low of around 10 mg/L in September. Mean bio

- available TP (PO 4 + LTNOP) concentration of Dunns Creek is similar to that of Buffalo Bluff, 0.057 mg/L, compared to0.056 mg/L. Mean total bio

- availabl e nitrogen (TIN + LTON), at 0.732 mg/L, is somewhat less than that for Buffalo Bluff, which was 1.072 mg/L.

Figures 2 4

– 2 6 compare 1995

- 99 time series loads entering the LSJR from Buffalo Bluff and Dunns Creek. Because of the much greater flow from t he mid and upper St. Johns and Ocklawaha Rivers, the Buffalo Bluff load dominates the total upstream load to the LSJR. From April 1998 through November 1999, the net discharge from Dunns Creek was zero, resulting in
no net load to the LSJR.79

Figure 25. Loads of Nitrogen Forms Entering the Lower St. Johns River at Buffalo Bluff and

Dunns Creek, 1995

- 99.80

Figure 26. Loads of Phosphorus Forms Entering the Lower St. Johns River at Buffalo Bluff and

Dunns Creek, 1995

- 99.81

Reconstruction Of The Upst ream Natural Background Load

To provide the endpoint for the continuum in water quality responses to nutrient enrichment, it is necessary to estimate or reconstruct the characteristics of the natural background load. It should be emphasized that achievin g the natural background condition is not the objective of the TMDL

process per se , though it is assumed to be a desirable condition. Rather, the quantification of this condition anchors one end of the water quality impairment

– development continuum and allows for the interpolation to the point of water quality criteria adherence and hence tolerable anthropogenic load. Estimation of the natural background condition is also desirable as it helps in envisioning the estuarine ecosystem prior to human impact s, which can help define restoration goals and set realistic water quality targets.

To estimate the natural background load of nitrogen, phosphorus and organic carbon that enters the lower St. Johns from within the immediate (i.e., lower St. Johns) basi n, the watershed modeling approach use was followed, with all present

- day developed land used reverted to natural forest cover. Because similar modeling has not been performed for the Ocklawaha and St. Johns River basins upstream of the lower St. Johns, a nd perhaps more importantly, water quality modeling has not been performed to estimate the transformation of this load that would occur in the numerous, large upstream lakes, estimation of the upstream boundary natural background load is more difficult. I n the following analysis, the load delivered to the inlet of the lower St. Johns Basin is estimated through a simple source

- proportionality approach and by applying a Vollenweider model phosphorus settling term for Lake George. This estimate is then

corro borated by comparison to available historic water quality data.

Natural Background Concentrations of Small Order, Undeveloped Streams

Nitrogen, phosphorus and carbon forms engage in almost continuous flux in surface waters from
the moment of dissolutio n or detachment from the terrestrial environment. In streams contributing to the St. Johns River, the time between export from the terrestrial environment to82

the river’s predominantly lacustrine environment is relatively short (when compared to the river)
, on the order of hours to days. The lower light availability typical of stream environments limits photosynthesis, nutrient uptake, and autochthonous carbon accrual. The result is that the transformation of exported N, P and C is relatively low in flowin g water stream settings and is generally limited to sedimentation of larger, denser particles, bacterial decomposition of labile organic matter, dilution with lower concentration phreatic ground water within influent reaches, and abiotic gas exchange. Rate s of transformation associated with autochthonous production increase substantially when river systems become more lacustrine, and in such settings biological processes of assimilation and mineralization often dominate the speciation of N, P and C.

Asid e from development impact, three environmental gradients dominate the composition of tributary water quality in northeast Florida (Hendrickson 1993, unpublished data): stream order, watershed physiography, and annual cycles of temperature, rainfall and pla nt litter fall. The PLSM construct attempts to incorporate annual temporal patterns and watershed development. Stream order is implicitly handled by calibrating streams at their 2 nd to 4 th order reaches, the size typical of most contributing stream’s ter minus in the river. On a broader scale, stream water quality can vary based on physiographic features such as the topography of the landscape and the underlying parent material.

If one assumes that the continuum in concentration increase for these stre ams results largely from development intensification, it follows that some low percentile rank reflects the concentration resulting solely from natural watershed factors, such as parent material, native vegetation cover and endemic faunal communities. In recent work on the establishment of nutrient criteria (U.S. EPA, 2002) and identification of reference sites for developing bioassessment metrics, the 25 th percentile has become an accepted benchmark in this continuum. In both of these endeavors, the assu mption is that this point in the continuum represents a tolerable level of anthropogenic impact. Thus, implicit in the 25 th percentile concentration is some small impact, and true natural background concentrations of N and P should be considered
to be low er. The 25 th percentile concentration can therefore be considered to represent an upper range favoring low nonpoint source effect.83

The 25 th percentile rank (Figure 2 7 (a)) for streams of the type within the LSJR basin corresponds to 0.77 mg/L of total n itrogen (TN) for Pleistocene Ridge streams (streams that drain the higher, sandy ridges that predominate on the western banks of the St. Johns valley), and 0.88 mg/L TN for Atlantic Coast Flatwoods streams (streams draining the low

- lying region on the east ern banks of the St. Johns). For total inorganic nitrogen (TIN = NH 4 + NO 2+3 ), the 25 th percentile value for Pleistocene Ridge streams is 0.041 mg/L, and for Atlantic Coast Flatwoods streams is 0.058 mg/L. Total phosphorus (TP) and orthophosphate (PO 4 ), concentrations corresponding to the
25 th percentile for Pleistocene Ridge and Atlantic Coast Flatwoods streams are, respectively: 0.038 mg/L TP, 0.023 mg/L PO 4 , and 0.066 mg/L TP and 0.039 mg/L PO 4 (Figure 2 7 (b)). In comparison, concentrations assigned to undeveloped land uses (timberlands included) in watershed model development for the lower St. Johns River Basin (Hendrickson and Konwinski, 1998; Hendrickson et al. 2002), shown in Table 4, are similar, producing a somewhat lower concentrations of 0.7 mg/ L for TN and values of between 0.02 and 0.04 mg/L for TIN, but comparable and values between 0.05 to 0.07 mg/L for TP and between 0.03 and 0.05 mg/L for
PO 4 . Recent draft nutrient criteria developed by the EPA’s convened National Expert’s Workshop places criteria thresholds (considered upper acceptable levels and thus incorporating some anthropogenic contribution) for streams and rivers of this ecoregion (Region 12, northern peninsular Florida) at 0.9 mg/L for TN and 0.04 mg/L TP.84

Figure 27. Continuous Probability Density Functions for Total and Inorganic Nutrient Mean

Concentrations for Streams in Northeast Florida. ACF = Atlantic Coast Flatwoods Streams; PPR = Plio

- Pleistocene Ridge Streams; TN = Total Nitrogen; TIN = Total Inorganic N (NOX+NH4); TP = Total Phosphorus; PO4 = Orthophosphate.85

Role of Spring Inputs

Artesian spring flow represents a significant proportion of the baseflow of the St. Johns, comprising approximately 38% of the mean annual discharge at the basin’s inlet at Buffalo
Bluff . Thus, the influence of artesian spring N and P concentrations cannot be ignored in the present

- day and natural background levels of river and estuary productivity.

Today, many springs exhibit elevated concentrations of nutrients as a result of contami nation of deep Floridian ground water over the past half century. This contamination has occurred by several means, including percolation of surficial aquifer water that has been enriched through fertilization, land application of waste and nitrogen

- enric hed atmospheric deposition; stormwater drainage wells; and sinkhole inputs of eutrophic surface waters. In most cases this enrichment has resulted in increases in spring concentrations of the highly soluble and mobile nitrate

nitrogen form (Figure 28 ), th ough in some cases direct entry through drainage wells and sinkholes may have also resulted in elevated nitrogen and phosphorus concentrations (Figure
28 ). Trends in nutrient concentrations suggest that spring contamination via direct entry through

storm drain wells may be on the decline (as in the case of Rock and Wekiva Springs, Figure 28 ), while nitrate contamination through surficial aquifer downward percolation may still be increasing (as in the case of DeLeon, Blue, Silver and Gemini Springs). Pres umably, because of the tendency for phosphorus to be depleted in deep percolating groundwater from adsorbtion and assimilation in surface soils, downward percolation does not represent a significant pathway for phosphorus contamination.86

Figure 28. Time

- Series Concentrations of Nitrate+Nitrite

- N and Orthophosphate

- P in Major

Springs Discharging to the St. Johns River That Exhibit Nitrate+Nitrite Trends. Data From USGS, Odum (1953) and SJRWMD.87

Because of the absence of trends in orthophosphate concentr ations for major springs contributing to the St. Johns River (Wekiva and Rock Springs excluded), the present day, flow

- weighted orthophosphate concentration of 0.041 mg/L has been used to represent the natural background
concentration. For nitrate+nitrit e

- N, the 25 th percentile occurrence of all springs has been used, which corresponds to a concentration of 0.01 mg/L. Spring TP and TKN data are also available, though in lower numbers and temporal coverage. Spring

- flow mean TP concentration was found

to be 0.061 mg/L, while the mean concentration of dissolved TP was 0.04 mg/L, suggesting particulate P as a large component of non

- PO 4 - phosphorus. The mean concentration of total Kjeldahl nitrogen was 0.081 mg/L. Fourteen of the 35 available measurements fo r TKN were remarked as below the method detection limit of approximately 0.08 mg/L. Due to the uncertainty regarding the concentrations of TP and TKN, the relatively low concentrations, and the possibility for organic N and non

- PO4

- phosphorus to arise fro m spring source other that the artesian boil, the nitrate+nitrite and orthophosphate concentration have been used to represent total N and total P from artesian springs.

Using the 25 th percentile values of TN and TP observed for ridge and flatwoods tri butary runoff to represent background terrestrial flow concentrations, the 25 th and percentile concentration of spring NO 2+3 , and the mean flow

- weighted spring PO 4 concentration, natural background concentrations of TN and TP were projected for the St. Joh ns River at Buffalo Bluff (Figure 29 ). The proportions of surface and groundwater were determined through a relationship between present

- day refractory total organic carbon and the calculated ratio of terrestrial:artesian flow occurring during the 2 week period prior to the time of sampling. This approach was used to dampen the oscillations in discharge that occur at the Buffalo Bluff due to intermittent and extended reverse flows (Sucsy and Morris, 2001). TP concentration was reduced by the
sedimentatio n rate for Lake George by applying the Vollenweider calculation using the formulation of Chapra (1997). Calculated Lake George TP sedimentation rate was found to be 1.0 m/yr, and although unusually low, is in agreement with the sedimentation rate determi ned for Lake Apopka, another large, shallow sub

- tropical lake ( Coveney , 1997). The final natural background annual mean TP concentration delivered to Buffalo Bluff was determined to be 0.037 mg/L, for the hydrologic conditions existing from 1995

- 99. The actual 1995

- 99 mean TP concentration at Buffalo Bluff was 0.063 mg/L.88

Figure 29. Comparison of Present Day and Predicted Natural Background Concentrations of

Total Nitrogen and Total Phosphorus in the Lower St. Johns at Buffalo Bluff, 1995

- 99.89

To develop an estimate for natural background TN concentration, the terrestrial to spring flow ratio approach was again used, for both bioavailable (considered to be the sum of inorganic, labile organic and algal fractions) and refractory nitrogen partitions. Labile and refractory nitrogen concentrations were separately estimated to account for phytoplankton bio

- available nitrogen deficit that would presumably be made up through algal nitrogen fixation. Concentrations were calculated corresponding to each s ampling day of the bi

- weekly sampling
record between 1995

- 99. RTON was assumed to be the same as the present day concentration,
while time

- varying LTON was calculated using the present

- day RTON

- LTON relationship, reduced by the mean ratio of historic to p resent day LTON. Natural background estimated algal P was used to determine an algal ON based on Redfield stoichiometric equivalency (7.2:1 N:P mass ratio), and for days on which the labile N was less than algal ON, the difference was added
to the labile N concentration. Labile and refractory N concentrations for each day were then summed, to produce a mean 1995

- 99 natural background mean TN concentration of 0.687 mg/L. By comparison, the actual 1995

- 99 mean TN concentration at Buffalo Bluff was 1.54 mg /L. Mean natural background chlorophyll a concentration was estimated to be 14.0 mg/m 3 , while the
actual 1995

- 99 mean corrected chlorophyll a was 26.6 mg/m 3

.

Historic Data for the St. Johns River

Few water quality data exist for the St. Johns River prior to substantial levels of development. While the point at which the St. Johns River Basin became developed to a degree that it exerted a significant effect on water quality is difficult to ascertain, the sharp increase in the State’s population that began subsequent to World War II appears to correspond the earliest reports of water quality degradation (Figure 30 ). Population density within the basin upstream of Jacksonville remained relatively low until 1940. Between 1940 and 1950, population with in the basin increased by 39% (Fl. State Board of Health, 1951).90

Figure 30. Population growth within the 14 Counties of the St. Johns River Basin, 1890

–

2000. Data from Dietrich (1978) and University of Florida (2000). Significant events with likely impact on nutrient status identified.

By the time of the earliest, regular surface water quality monitoring efforts, beginning in the late 1960’s, water quality in the middle St. Johns River appears to have already declined. Moody (1970) attributed this decline in Lake George to the occurrence of regular, severe algal blooms. Principle factors leading to these algal blooms were believed to be: 1) upstream development and its concomitant nutrient enrichment through point and non

- point source pollutio n, and 2) aquatic weed spraying to eliminate floating water hyacinth. Moody notes that concentrations of blue

green algae were in 1967, “present in much greater numbers . . . “ than reported in an earlier study in 1939

- 40 (by E. Lowe Pierce (1947)). Two water quality sample events for Lake George, which were collected in July of 1967 and December of 1969, are contained in his report.91

The July sample contained 2.28 mg/L TN and 0.13 mg/L TP, while the December sample contained 1.3 mg/L TN and again, 0.13 m g/L TP.

Three data sets have been identified that can help in understanding the nutrient status and hence open water ecology of the St. Johns prior to substantial development. The first of these is a study conducted by E. Lowe Pierce (1947) reporting o n several aspects of the water quality and plankton in the St. Johns River at locations in the Ocklawaha River mouth, in the river upstream and downstream of the Ocklawaha from September 1939 to November 1940. The second is the work of Odum (1953) charact erizing the phosphorus concentrations of waters of the State. The third study was published by the Florida State Board of Health in1948 to determine the effect of untreated waste discharge in and around the City of Jacksonville.

The objective of the Fl orida State Board of Health study was to determine the effect of the discharge of untreated sewage in the vicinity of Jacksonville, and sampling stations were established in the St. Johns River near the city of Green Cove Springs to characterize upstream, background conditions. Two surveys were performed, the first conducted in May and June of 1945, and a second conducted from September 1945 to May of 1946. Though nutrient analysis was not performed, BOD and dissolved oxygen were examined and may be used to infer trophic status. Many of the reports’ sampling stations were located in Jacksonville, which at the time was already significantly impacted, principally from the discharge of raw sewage. However, comparing the report’s BOD data from the upstream, “un

- impacted” site near the Shands Bridge to present day concentrations at the same location suggests that water column biodegradable organic matter has increased over time. The May to June survey produced what was referred to a “highest average” (statisti cal methods are not explained in the report) of 0.83 mg/L; in comparison, the May

- June mean BOD concentration at Shands Bridge from 1996 to 2000 was 2.14 mg/L. The Sept. 1945 to May 1946 survey produced a highest average concentration of 0.82 mg/L, while the 1996

- 2000 average is 1.43 mg/L.

Under present

- day conditions in the LSJR, algal biomass is accounts for the majority of labile organic carbon in the river, and the relationship between BOD and chlorophyll a is highly significant, with chlorophyll a concentrations explaining over 70% of the variation in BOD (Chl

-92

a = 14.39*(BOD)

– 4.11; R 2 = 0.71). Based on this relationship, the 0.83 mg/L BOD measured in 1945 corresponds to a chlorophyll a of about 8 mg/m 3 . Converting the present day mean river
col or for this location to refractory organic nitrogen (assumes color has not changed; in reality, river color probably has declined somewhat due to basin development), and adding in the nitrogen content of algal biomass at 8 mg/m 3 chlorophyll a , a mean total organic nitrogen content of 0.48 mg/L can be calculated. With the inclusion of inorganic nitrogen, it would be expected from these BOD data that total nitrogen was in the neighborhood of 0.6 mg/L, comparable to the
reconstructed historic Buffalo Bluff me an TN concentration of 0.687 mg/L. It should be noted that Green Cove Springs is located downstream of where, even in 1946, potentially significant inputs of nutrients from the city of Palatka (20 miles upstream, at that time with a population of approxim ately 8,000) and the tri

- county agricultural area, may have occurred.

The Odum (1953) report to the Florida Geological Survey extensively surveyed orthophosphate and total phosphorus in surface waters around the State. Samples were collected at one tim e from many different locations, so annual trends cannot be inferred. These data for locations in the St. Johns River and its contributing streams are listed in Table 9. Because a significant amount of development had begun to occur at the time of this s tudy, these data must be viewed selectively for the potential of anthropogenic nutrient contamination. For locations that likely still represented unimpacted reaches of the lower St. Johns River in 1952 (assumed to be Lake George and Crescent Lake), these data suggest a concentration of total phosphorus of around 0.04 mg/L. Upstream of Lake Monroe, the Odum data suggests a St. Johns River that was remarkably low in phosphorus.

Table 9

. Total Phosphorus Concentrations Determined for Selected Locations i n St. Johns

River Basin in 1952. Data from Odum (1953).

Location Date Total P, mg/L Black Creek, Route 17 Aug. 9, 1952 0.04 Deep Creek, Hastings, Route 207 Jul. 14, 1952 0.54 Crescent Lake, Andalusia Jul. 19, 1952 0.03393

Doctor’s Lake, Route 17 Aug. 9, 1952 0.065 Lake George at Silver Glen Springs Aug. 14, 1952 0.044 Lake Monroe, Sanford Jun. 23, 1952 0.18 Ortega River, Route 21 Aug. 9, 1952 0.044 St. Johns R., Crows Bluff, Volusia Co. Sep. 3, 1952 0.117 St. Johns R., Palatka Jul 19, 1952 0.061 St. Johns R., Route 192 (Brevard Co.) Jun. 23, 1952 0.007 St. Johns R., Route 50 (Orange Co.) Jun. 23, 1952 0.015 St. Johns R., Green Cove Springs Jul. 16, 1952 0.119

The data of Pierce (1947), due to the length of his study and the comparatively larg e suite of measurements, provide compelling evidence of a river that was dramatically lower in nutrients and algal biomass. The graphs of Figure 3 1 compare the results of this study with the present day mean concentrations observed from 1995

- 99. In 1939

- 40, Pierce reported blue green algae (of the genera Anabaena
, Raphidiopsis and Microcystis ) ranging from too few to count for most months, to 36,000 cells/ml in August of 1940. In comparison, the annual mean peak blue

- green cell count exiting Lake George for 1997

- 2000 (Phlips and Cichra, 2001) was 518,893 cells/ml. The Pierce study also suggests a shift in the dominance of phytoplankton groups, with diatoms (primarily the genera Coscinodiscus and Melosira ) formerly making up a much greater relative portio n of the plankton.

The Pierce study also provides data on nitrogen forms throughout the year. (Unfortunately, analysis for phosphorus forms was not performed.) Due to some differences in methodology and uncertainties regarding sample handling and pre servation techniques, only total nitrogen concentrations are compared. Pierce reported mean annual TN as 0.41 mg/L in Little Lake George (upstream of the Ocklawaha mouth), and 0.37 mg/L at Welaka (downstream of the Ocklawaha), values that are roughly 1/4 t h of present day concentrations. The Pierce TN numbers are similar to the present day estimated mean concentration refractory total organic N at Buffalo Bluff, of 0.46 mg/L. Because RTON theoretically reflects a relatively constant natural supply of94

orga nic nitrogen, its concentration would be expected to remain constant, or perhaps even decrease due to development, over time.95

Figure 31. Comparison of Monthly Mean Water Quality Parameters for 1995

- 99 (solid boxes)

to the Data Collected by Pierce (1947 ) in 1939

- 40 (open diamonds) for the St. Johns River near Buffalo Bluff. Error bars on 1995

- 99 data represent 95% C.I. Pierce chlorophyll estimated from cell counts.96

The Pierce (1947) total N concentrations are low when compared to those reconstructed here, which suggest an annual mean concentration of 0.65

– 0.70 mg/L. Similarly, mean annual chlorophyll a estimated from cell densities in the Pierce work, at 2 mg/m 3 , are considerably lower than the reconstructed annual mean of 14 mg/m 3 . However, unacc ounted for in the water column measurements of these studies, but theoretically included in the runoff delivery approach, is the sequestration of nutrient in water hyacinth (Eichornia crassipes) . Water hyacinth, introduced to the St. Johns shortly before 1900, quickly spread through the river, and anecdotal accounts prior to 1940 (Rawlings, 193_) indicate widespread coverage. Annual reports to Congress on the progress of hyacinth control in the St. Johns (USACE Annual Reports to Congress, 1930

- 1967) ind icate that between 3000 to 13,000 acres of hyacinth were removed annually, suggesting that at least 5 to 10 % (based on the sum of lake surface areas of the St. Johns from Lake Winder through Little Lake George) of the rivers water surface area typically

m ay have been covered.

To estimate the potential sequestration of nitrogen in water hyacinth, literature values for organic matter dry weight and percent N and P content were combined with areal coverage estimates determined by the U.S. Army Corps of En gineers. Using an 8% areal hyacinth coverage estimate (U.S. Army Corps estimate from the only available comprehensive survey of hyacinth, conducted in November of 1948, of 9,500 acres (USACE, 1948)) and assuming 1000 g/m 2 organic matter, with 1% as N, 0.0 5% as P (Reddy and DeBusk, 1987; values represent minimum in the areal organic matter and nutrient content ranges, as would be expected for uncultivated hyacinth growing under natural and assumed nutrient

- limited conditions), an additional 0.27 mg/L N can be calculated as sequestered in hyacinth tissue. Adding this concentration to the Pierce mean TN concentration for the St. Johns below the Ocklawaha mouth produces a mean total N concentration of 0.64 mg/L.

Differences in analysis methodology, the rel atively short study durations, possibly different hydrologic conditions, and assumptions in the conversion procedures warrant that these estimates of background concentrations be used with caution. However, these studies suggest substantially
lower water column nitrogen and algal biomass, and marginally lower phosphorus concentrations prevailed in the pre

- development St. Johns River. If the concentrations of TN of 0.6 mg/L and97

TP of 0.04 mg/L are accepted as representative of water column (including float ing macrophytes) natural background levels, then nutrients flowing into the lower St. Johns River from the upper and middle St. Johns and Ocklawaha Rivers today appear to be elevated between 1.5 to 4 times above pre

- development conditions.

Total LS J

R L oad Estimates

With the estimated of upstream, natural background loads, it is possible to estimate the total incoming anthropogenic nutrient and organic carbon load to the Lower St. Johns River. Tables
10

- 15 summarize the mean annual loads from source categories to the LSJR by river ecozone from 1995 through 1999, and the over all average. When comparing this compilation of loads to that performed earlier by Hendrickson and Konwinski (1998) it should be noted that in this assessment, the portion of riv er identified as predominantly oligohaline has been moved upstream, to encompass all loads entering from Black Creek northward to the Ortega River. In the previous assessment, loads entering from Orange Park northward were considered to
contribute directl y to the oligohaline reach, and Black Creek loads were added to the freshwater river. The reason for the move was the prevailing drier conditions encountered during the 1995

- 99 assessment period, particularly for 1998 and 1999.

The separation of labile and refractory organic carbon and nutrients performed in this version of the external load provides a dramatically different conclusion on the relative sources of problem nutrients leading to algal blooms in the lower St. Johns. In the previous compilati on of the LSJR external load, Hendrickson and Konwinski (1998), in lieu of information on the utilization of organic nitrogen and non

- orthophosphate phosphorus forms by river algal communities, estimated the bioavailable load as that composed of only inorg anic nutrient (NH 4 , NO X , and PO 4 ) forms. The addition of labile organic forms to the “total bioavailable pool” of nutrients greatly increases the apparent influence of upstream loads. Accounting for labile organic forms also increases the relative import ance of urban nonpoint source runoff in potential contribution of problem nutrient loads

.98

Table 10. Summary of Mean Annual Loads to the Lower St. Johns River, 1995. All values in metric tons per year.99

Table 11. Summary of Mean Annual Loads to the L ower St. Johns River, 1996. All values in metric tons per year.100

Table 12. Summary of Mean Annual Loads to the Lower St. Johns River, 1997. All values in metric tons per year.101

Table 13. Summary of Mean Annual Loads to the Lower St. Johns River , 1998. All values in metric tons per year.

Total N Labile

TON Refractory

TON Total

Inorganic N Total P Labile TNOP Refractory

TNOP Total PO4 Total Organic C Labile

TOC Refractory

TOC Buffalo Bluff Total 8561.5 4942.4 3175.9 443.1 341.8 201.8 42.5 97.4 127323.1 21218.1 106105.0 Natural Background 4428.1 1189.7 3175.9 62.5 246.4 140.0 42.5 63.9 112873.9 6768.9 106105.0 Dunns Creek Total 971.2 217.6 681.9 71.7 51.3 15.8 15.9 19.7 21379.6 778.7 20600.9 Natural Background 813 .6 108.2 681.9 23.5 39.4 11.1 15.9 12.4 21216.6 615.7 20600.9 Upstream Total 9532.7 5160.0 3857.8 514.9 393.1 217.6 58.4 117.1 148702.7 21996.9 126705.9

Fresh Tidal NP Total 1652.2 480.2 935.0 237.0 222.9 53.8 31.1 138.0 44053.4 2272 .1 41781.4 Natural Nonpoint 1188.3 284.7 864.2 39.4 103.4 17.3 16.7 69.4 43976.7 1525.1 42451.6 Agriculture Contribution 350.3 111.7 93.9 144.7 92.2 27.3 11.9 53.0

- 257.6 256.3

- 513.9 Urban Contribution 110.4 92.5

- 21.4 39.4 25.8 13.4 2.8 9.6
- 817.6 443.8
- 1261.4 Other Nonpoint 3.1
- 8.7
- 1.7 13.6 1.5

- 4.2
- 0.2 5.9 1151.9 47.0 1105.0 Point Source 274.2 82.4 57.1 134.5 62.1 21.9 5.1 35.0 4154.4 582.3 3572.2

Oligohaline NP Total 1236.9 492.7 565.8 178.4 171.8 63.8 18 .4 89.6 28792.1 2331.6 26460.5 Natural Nonpoint 830.1 199.7 601.6 28.8 72.4 12.1 11.6 48.7 29623.8 1041.0 28582.8 Agriculture Contribution 35.9 17.9 9.5 8.5 8.7 5.9 2.4 0.5

- 51.2 53.9

- 105.1 Urban Contribution 374.4 282.0
- 33.2 125.6 90.6 50.4 6.3 33.9
- 1540.4 1200.2
- 2740.6 Other Nonpoint

- 3.5

- 6.9
- 12.1 15.4 0.1
- 4.5

- 1.9 6.5 759.8 36.5 723.3 Point Source 301.3 53.7 9.6 238.0 81.4 13.2 0.7 67.5 363.5 184.3 179.2

Meso

- Polyhaline NP Total 867.0 436.2 254.1 176.7 152.0 68.5 11.4 72.1 13343.1 1966.9 11376.3 Natural Nonpoint 426.5 109.6 299.9 17.0 37.7 6.5 5.7 25.5 14672.1 570.9 14101.2 Agriculture Contribution 38.3 7.3
- 1.5 32.5 11.8
- 3.1
- 1.1 16.1 29.8 49.8
- 20.0 Urban Contribution 404.1 315.7

- 40. 2 128.6 101.5 59.8 4.8 36.9
- 1741.7 1310.4
- 3052.1 Other Nonpoint

- 1.8 3.6
- 4.1
- 1.3 0.9 5.3 2.0
- 6.3 382.9 35.8 347.1 Point Source 1267.0 279.4 38.3 949.3 341.5 70.7 3.3 267.6 2468.4 1500.7 967.7

Total Atmospheric Dep

. 278.2 3.8 LSJRB Summary Total Natural Nonpoint 2444.9 594.0 1765.7 85.2 213.5 35.8 34.0 143.6 88272.7 3137.0 85135.7 Total Augmented Nonpoint 1311.2 815.2

- 10.8 506.8 333.2 150.3 26.8 156.1

- 2084.0 3433.6
- 5517.6 Total Point Source 1842.4 415.5 105.1 1321.8 485.0 105.8 9.1 370.1 6986.3 2267.2 4719.1

Grand Total 15409.4 6984.7 5717.8 2428.7 1428.5 509.5 128.4 786.9 241877.7 30834.6 211043.1

Notes: N= Nitrogen; P=Ph osphorus; C=Carbon. NP=Nonpoint Sources. LSJRB Summary sums loads for only the lower St. Johns Basin downstream of Dunns Cr eek.102

Table 14. Summary of Mean Annual Loads to the Lower St. Johns River, 1999. All values in metric tons per year.103

Table 15. Summary of Overall Mean Annual Loads to the Lower St. Johns River, 1995

- 1999. All values in metric tons per year.104

In the upstream nutrient load, total N and P in the 1995

- 99 load summary are similar to that of
1993

- 94, at 8,415 MT/yr TN and 370 MT/yr TP, compared to 7209 MT/yr TN and 317 MT/yr TP. Upstream TIN load from 1995

- 99 was considerably less than that in 1993

- 94, at 414 MT/yr compared to 639 MT/yr. Total PO 4 load was marginally less in 1997

- 98, at 73 MT/yr as compared to 99 MT/yr in 199 3

- 94. The generally lower upstream inorganic nutrient loads may be attributed to lower flow conditions that prevailed during the 1997

- 98 period, resulting in longer residence time in upstream lakes and greater incorporation of inorganic forms in algal
bio mass. Although the el Nino winter of 1997

- 98 resulted in one of the highest mean annual flow rates for the St. Johns River at the upstream Deland gauging station, this extremely wet interval was short, and immediately followed by one of the longest droug hts in Florida meteorological history. Hence the tendency for low algal standing stock associated with a high flow/short residence time hydrologic pattern was not seen in 1998.

Within

- LSJR basin anthropogenic loads are still predominantly attributabl e to point source discharges. From 1995

- 99, point sources accounted for 1,783 MT/yr of TN and 467 MT/yr of TP, as compared to the 1993

- 94 estimate of 1,886 MT/yr of TN and 530 MT/yr of TP. Applying the labile/refractory partitioning algorithm to point so urce effluents suggests that most of the organic nutrient portion of these effluents is in the bioavailable form.

Within

- LSJR basin nonpoint source loads are lower that that assessed for the 1993

- 94 time period, owing largely to the prevailing dry condi tion that decreased nonpoint source runoff in general. An additional factor leading to this decrease is a substantial decline in tri

- county row crop area. In the previous assessment, the within

- basin, nonpoint TN mean annual load was determined to be 3,5 28 MT/yr, with 1,243 MT/yr of this representing the above background load. In this assessment, the mean annual 1995

- 99 nonpoint TN load was determined to be 2,158 MT/yr, with 834 MT/yr of that the above

- background load. For TP, the 1993

- 94 nonpoint load was determined to be 665 MT/yr, with 468 MT/yr of this the above

- background load, while for
1995

- 99, the mean load was determined to be 360 MT/yr, with 243 MT/yr of this the augmented,
above

- background load. However, the relevance of the nonpoint source l oad as it relates to the contribution of bioavailable nutrients, is considerably increased in this assessment, owing to the distinction of labile organic pool. In the 1993

- 94 assessment, in which only the TIN load was105

considered to represent the “bioavail able” load, total anthropogenic bioavailable nonpoint load was assessed as 488 MT/yr of TIN and 248 MT/yr of PO 4 . With the inclusion of labile organic nutrient forms, the 1995

- 99 augmented nonpoint source bioavailable N load increases to 802 MT/yr. The 1 995

- 99 augmented nonpoint bioavailable P load was estimated as 223 MT/yr, less
than the estimated PO 4 load of 1993

- 94; however, the inclusion of the labile organic fraction represents a 97 percent increase over an estimate based upon PO 4 alone.

Atmosph eric Deposition

The estimate of directly

- intercepted atmospheric deposition load has been considerably reduced
in the 1995

- 99 total load assessment. Using three NADP gauges, rather than the one used in the
1993

- 94 assessment, including dry deposition, an d relying on tower measurements to determine the P load, Pollman and Roy (2003) provided data that demonstrates the directly

- intercepted rainfall load to the LSJR to be roughly 222 MT/yr of TN, of which most is inorganic N, and 3 MT/yr of P. Thus, direct atmospheric deposition represents only 2.8 percent of the total bioavailable N load and 0.5 percent of the bioavailable P load to the LSJR. The discrepancy between this estimate and the earlier estimate, which found atmospheric deposition to comprise
14.7 % of the bioavailable N load and 0.8% of the bioavailable P load can be attributed to two factors. The first is the inclusion of labile organic N and P in the assessment of bioavailable point and nonpoint source loads. The second can be traced to a units error in the Hendrickson and Konwinski (1998) assessment, in which atmospheric deposition NO 3 concentrations that were reported as mg/L NO 3 , were assumed to be as mg/L N. The result was a 443 percent overestimate in atmospheric N load.

Accounting for indirect deposition, that portion of wet deposition falling on terrestrial areas and entering surface waters un

- attenuated in runoff, can in some cases substantially increase the contribution of atmospheric nitrogen. Timpe (1999) concluded that 28% of the non

- point source nitrogen loading from mixed urban and residential watersheds in the Tampa Bay Watershed could be attributed to wet atmospheric deposition. Rushton (1997) found a significant correlation between precipitation nitrogen concentration and th e concentration of parking lot106

runoff for individual storm events in a study of wet detention pond effectiveness. Employing the wet deposition retention factors (the proportion of wet deposition that arrives in surface waters for various land covers) of Paerl et al. (2001), the 1995

- 99 mean annual indirect atmospheric deposition added by land areas in the lower St. Johns River basin is estimated at and additional 222 MT/year. This doubles the overall contribution of atmospheric nitrogen to the
total N lo ad of the LSJR to 5.5 percent. However, an arguably more relevant way to view the contribution is to view it in the context of the urban nonpoint nitrogen pollution. If atmospheric nitrogen deposition has roughly doubled over the past 150 years due to an thropogenic inputs (Pollman, pers. comm.), then 84 MT per year of the indirect atmospheric deposition that is exported from urban areas can well be viewed as anthropogenic atmospheric contribution. This represents 15 percent of the anthropogenic labile ni trogen that enters the lower St. Johns River in

an average year.



### DISCUSSION

In order to develop ecologically relevant nutrient budgets for estuaries, that portion of the organic nutrient pool that is biologically available must be quantified (Seitzing er and Sanders, 1997). The results of this analysis suggest that a potentially wide range in bioavailability may exist in the organic carbon and nutrient pools of tributary runoff from different land uses. For the watersheds employed in the calibration o f PLSM coefficients, LTOC was on average 10% of TOC, ranging from 4% in waters draining undeveloped, forested streams to 54% for a highly urbanized streams. In a study of mixed urban/ag/forested watersheds in southeast Pennsylvania, Volk et al. (1997) fou nd bioavailable DOC to constitute on average 25 % of DOC. As southeast US blackwater streams would be expected to exhibit higher levels of RDOC, the values obtained here appear consistent with that study. Due to the tendency for labile organic matter to possess higher relative amounts of N and P, the fractions of labile nutrient within total organic nutrient forms are higher than labile carbon fractions, on average 50% for LTON and 75% for LTNOP. LTON ranged from 28% of TON for largely undeveloped waters heds to 78% for urbanized streams. Due to the very low levels of TNOP observed in undeveloped watershed runoff, it cannot be said with certainty what percent is composed of LTNOP, although it appears that concentrations are probably less than 10  g/L. Fo r urbanized streams, TNOP was found to be107

92% composed of LTNOP. Thus it can be seen that anthropogenic nutrient loading, due to its relatively greater amount of labile nutrient content, has a proportionally greater effect on eutrophication than would be surmised by the absolute increase in total nutrient (Figure 3 2 ).108

Figure 32. Comparison of Total and Bioavailable Nitrogen Forms in Runoff from Natural

Forested and Mixed Urban/Commercial/Residential Watersheds. Due to the higher relative amounts of lab ile nutrients in developed landscapes, deleterious nutrient load often exceeds that which would be inferred by absolute increases in nutrients alone. Reductions in refractory organic nutrient

- bearing colored dissolved organic matter may also increase alga l productivity by increasing transparency.109

The addition of organic carbon and nutrient forms provides the necessary enhancement to our understanding of the external load to permit the development of hypothesis related to the natural and altered produ ctivity states of the LSJR estuary, in particular as it relates to the TMDL response variables: dissolved oxygen, chlorophyll a and turbidity. The analysis here suggests that in its natural state, the LSJR saw considerably lower levels of labile organic carbon and
nutrients. The un

- altered condition of forested landscapes and contiguous riparian areas also probably resulted in a river that was more darkly colored. Based on 1995

- 99 annual averages, the present anthropogenic load of total labile nitrogen of 6,407 MT/yr represents a 374 percent increase over the natural background labile nitrogen load of 1,720 MT/yr. For labile phosphorus, the 1995

- 99 mean annual anthropogenic load of 820 MT/yr represents a 297 percent increase over the natural background load of 276 MT/yr. One would assume that this degree of loading over background would result in much higher levels of internal algal production, although the amount of this increased production cannot be determined from the annual average
external load a lone. With regard to the pre

- development oxygen regime that the estuary experienced, not only would lower levels of algal production have a positive effect, but the lower incoming load of LTOC would also be expected to result in lower imported BOD.

A significant, seasonal component exists in the concentrations of organic carbon forms that occur in runoff, and such seasonal effects have been observed elsewhere. In Scandinavian boreal forests, DON concentrations were observed to increase as spring snow melt

- induced water table rise brings water table to surface (Stepanauskas et al., 2000), and Leff and Meyer (1991), in an examination of DOC patterns in the Ogeechee River in Georgia, found concentrations to increases with increasing flow. With regard to bioavailability, Moran et al. (1999) found the highest per mole C rates of utilization in August for the Satilla River in Georgia. McLatchey and Reddy (1998) attribute such seasonal changes as largely due to the annual pattern in soil temperature and soi l redox changes owing to saturation and decomposition. With declining free energy (redox potential) there is decreased OM decomposition, hence greater OM export; greater P mobilization; higher substrate quality of the exported organic matter; and enhanced NH 4 mobilization. Other important factors may include the timing of forest litterfall, the timing of110

temperatures leading to litter decomposition, or the occurrence of leaching rains following
litterfall.

In the examination of multiple regression

- deve loped land use loading rates, it is clear (and perhaps intuitive) that intensification in land use, in particular associated with urbanization, leads to an increase in the concentration and export of labile organic matter and a decrease in the refractory f

orm. While the increase in labile organic matter is clearly detrimental, the implications of a decrease in RTOC are less clear. RTOC is assumed to be synonymous with colored dissolved organic matter, and the effects of CDOM on aquatic systems are to incr ease color, decrease pH, as well as to possibly exert some antibacterial properties. Higher prevailing color levels in the lower St. Johns River has been suggested to favor the dominance of the low

light adapted native submersed grass Vallisneria american a (Dean Dobberfuhl, pers. communication) allowing it to fix carbon at lower light levels and thus out compete phytoplankton for nutrients.

While the high concentrations calculated for LTOC in dairy runoff are probably not surprising, the relatively hig her concentrations calculated for urban runoff are. Possible sources in the urban and residential environment could include malfunctioning septic tanks, sanitary sewer line leaks, pump station overflows, animal feces, grass clippings, industrial waste or perhaps even hydrocarbons. The implications of this are that while urban runoff tends to exhibit moderate levels of organic nutrient concentrations, it is likely that much of this is in the labile fraction and hence potentially more detrimental to receivi ng waters. The differentiation and inclusion of labile organic carbon and nutrients into the total bioavailable pool increases the relative harm that this form of land use development perpetrates over what was previously believed.

The greatest chang e in our understanding of the relative sources of bioavailable nutrients to the lower St. Johns that comes with the inclusion of labile carbon and nutrients is the apparent huge contribution of the upper and middle St. Johns. Based on only inorganic nutri ent concentrations, Hendrickson and Konwinski (1998) estimated its contribution to be much lower, providing only
20% of TIN and 11% of PO 4 . With the inclusion of labile organic N and P, the upper and middle
St. Johns can be seen to contribute 61% of the w hole LSJR bioavailable N and 28% of111

bioavailable P. Much of this bioavailable N is produced internally through atmospheric nitrogen fixation by blue

- green algae in Lake George (Paerl, 2002; Phlips and Cichra, 2001) during long
residence times that favor t he depletion of bioavailable N stores. Thus, while nitrogen control has been one of the most widely touted management strategies in the oligohaline and mesohaline portions of the LSJR to reduce the potential for nuisance algal blooms in this nitrogen

- limi ted estuary, the most successful approach to reducing nitrogen in the upstream load delivered to the estuary may well be reduction in phosphorus load to the upper and middle St. Johns River.

The discovery of the potential sequestration of the greater part of the historic bioavailable nitrogen (and presumably also phosphorus) supply to the St. Johns by water hyacinth ( Eichhornia crassipes ) necessitates a fundamental re

- definition of the “natural background” load to the LSJR. Even prior to the introduc tion of water hyacinth, Bartram (1792) noted in his travels of Florida in 1774, in the reach of the LSJR between Green Cove Springs and Palatka, vast quantities of the floating aquatic plant water lettuce ( Pistia stratiotes ). He wrote:

“I set sail on ear ly, and saw, this day, vast quantities of Pistia stratiotes

. . . forming most delightful green plains, several miles in length, and in some places a quarter of a mile in breadth”

Accounts such as this indicate that vast areas of floating aquatic plants existed in the pre

- colonial LSJR, and that river ecology was based upon the substantial utilization of nutrients by macrophytes, rather than phytoplankton

.

Beginning in 1949, chemical aquatic weed spraying with 2,4

- D began in an effort to eradicate

wate r hyacinth. It was not until the late 1960’s that the exotic weed, by this time wildly out of control due no doubt by the increased nutrient load to which the river was now subjected, was reduced to manageable levels. Today, hyacinth is kept in check thr ough continuous aquatic weed spraying. This undertaking, along with continued urban, industrial and agricultural development within the basin, has undoubtedly transformed the river into the phytoplankton

dominated system that it is today. The characteriz ation of the incoming, natural background load to the LSJR as phytoplankton

- dominated should be viewed as a device to establish a common112

currency for evaluating nutrient enrichment and control efforts against established, phytoplankton chlorophyll

- a based standards. The apparent importance of water lettuce in the

pre

- colonial LSJR suggests that a balanced ecosystem restoration approach should not only address nutrient reduction to reduce the severity of algal blooms, but should also assess to the
role of f loating and submersed, rooted aquatic macrophytes in maintaining water quality and organism health and diversity.113



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