Sucsy and Hendrickson Nutrient Reduction to LSJR



# Calculation of Nutrient Reduction Goals for the Lower St. Johns River by Application of CE-QUAL-ICM, a Mechanistic Water Quality Model

Peter Sucsy

and

John Hendrickson

Department of Water Resources St. Johns River Water Management District
Palatka, Florida

August 2003

1Sucsy and Hendrickson Nutrient Reduction to LSJR



## Executive Summary
This report describes the appl ication of the water quality model, CE-QUAL-ICM, to the Lower St. Johns River for calculating nutrient lo ad reductions. The results of this study provide recommended nutrient Pollution Load Reduction Goals for the Lower St. Johns River. We also calculate the assimilativ e capacity of the ri ver and thus provide recommendations for Total Maximum Daily Loads.

Assimilative capacity is an estimate of the maximum pollutant supply rate that can be tolerated by an aquatic system and still achieve the designated uses ( F.A.C. 62-302) of the water body. Assimilative cap acity was calculated separate ly for both the freshwater and predominantly marine reaches of the Lower St. Johns River. For the freshwater reach, assimilative capacity was based on ach ieving reductions of chlorophyll-a. The recommended freshwater target requires that 40 P g L -1 of chlorophyll-a be exceeded no more than 10% of the time. This target is based on site-specific analyses of zooplankton diversity and abundance that i ndicate negative ecological e ffects are associated with concentrations of chlo rophyll-a greater than 40 P g L -1 . Because of the association between algal organic matter loading and detr imentally low levels of dissolved oxygen, assimilative capacity of the marine segmen t was based on preventing harmful dissolved oxygen sags. The recently proposed guidance by EPA for the protection of aquatic organisms from persistent low dissolved oxyge n episodes in Virginian Province estuaries was used for the Lower St. Johns River estuary.

The recommended reduction for the freshwater reach is 30% reduction of anthropogenic loads of both total phosphorus and total nitrogen based on average hydrologic conditions. The long-term average assimilative capacity (TMDL) for total nitrogen is about 8440 metric tons yr -1 , and for total phosphorus is about 500 metric tons yr -1 . The mean annual Pollution Load Reduction Goal (PLRG) is about 1560 metric tons of total nitrogen and 85 metric tons of total phosphorus. Of consid eration is that these recommended load reductions do not meet the chlorophyll targets in all years, but would allow this target to be met over the long-term.

The recommended Pollution Load Reduction Goal required to meet the dissolved oxygen target in the marine reach is about 350 metr ic tons per year of total nitrogen. The assimilative capacity, and recommended TMDL , is about 1470 metric tons of total nitrogen per year.

2Sucsy and Hendrickson Nutrient Reduction to LSJR



## Chapter 1. Overview of Report

This report describes the use of a water qu ality model to calculate nutrient reduction goals for the Lower St. Johns River. Nutrie nt reduction loads are estimated that will alleviate defined symptoms of eutrophication in the LSJR. In the freshwater reach the objective was to limit excessively high chloroph yll-a levels. In th e marine reach the objective was to prevent excessive periods of low dissolved oxygen.

Chapter 2 summarizes the important model processes along with selected rates and constants that control the kinetics of nut rient cycling and algal growth. Chapter 3 describes model input files th at define the primary boundary forcings to the model. These include point and non-point loads, upstream inflows, ocean conditions, and meteorological conditions. Chapter 4 summarizes model calibration results for inorganic and total nutrients, and the target parameters of chlo rophyll-a and dissolved oxygen. Chapter 5 shows model results under natural background (all forest) conditions to show how much improvement to water quality is at least theoretically pos sible. Finally, in Chapters 6 and 7 the assimilative capacity of th e LSJR is estimated. Chapter 6 deals with the upper freshwater reach and Chapter 7 with the lower marine reach. Both chapters present assimilative capacity results in terms of load, and also recommend percent load reductions to correspond with Total Ma ximum Daily Loads and Pollution Load Reduction Goals, respectively.

3Sucsy and Hendrickson Nutrient Reduction to LSJR



## Chapter 2. Rates and Constants Used for the Application of CE-QUAL-ICM to the Lower St. Johns River

Introduction CE-QUAL-ICM is a mechanistic water-qua lity model designed for the study of eutrophication in fresh and marine system s (Cerco and Cole, 1995). This model was applied to the Lower St. Johns River (LSJR) to help facilitate the understanding of eutrophication processes within that basin, and also to serv e as a tool for calculating nutrient load reductions that would achieve ta rget values for chlorophyll-a and dissolved oxygen. The set-up and initial calibration of this model by the U.S. Army Corps of Engineers Engineer Research Development Center, and the further development and application of the model by the SJRWMD, was unde rtaken as part of an overall effort to assist the U.S. Environmental Protection Agency and the Florida Department of Environmental Protection to establish Total Maximum Daily Loads (TMDLs; FCWA Section 303(d)), as well as to identify Po llution Load Reduction Goals for the basin as mandated in F.A.C . 62-40, for nutrients entering the LSJR.

Applying CE-QUAL-ICM to a system requires se lection of many rates and constants to simulate the kinetics of nutrient cycles and phytoplankton growth. This document summarizes the rate values selected for application of the model to the LSJR. The application of CE-QUAL-ICM to the LSJR also required the addition of a few sitespecific model features. These additiona l features are also summarized here.

Several other reports provide details about CE-QUAL-ICM and its application to the LSJR. The LSJR model app lication was developed and cal ibrated by the U.S. Army Corps of Engineers (USACE), Waterways Experiment Sta tion. Their report (Cerco, 2003) thoroughly documents algorithms used for water column kinetics, data sets used for the modeling, the linkage between the hydrodynamic and water quality model, and model calibration. The report also describe s some new model features, the N-fixation algorithm, Droop kinetics for the effect of phosphorus on algal growth rate, and light attenuation. A user’s guide to CE-Q UAL-ICM (Cerco and Cole, 1995) provides additional, detailed model documentation. Th e sediment diagenesis sub-model is fully described by DiToro and Fitzpatrick (1993).

The purpose of this report is to summarize th e principle rates and constants used for the final model application to the LSJR. The reports described above provide the foundation for this report. Brief descrip tions of kinetics algorithms ar e repeated here to provide the reader context for the rates and constants presented. The addition of these algorithms also allows this report to be read without requiring refe rence to the previous reports.

The St. Johns River Water Management District (SJRWMD) was the principle agency responsible for using the model to calculate load reductions that would meet specific water quality targets. This work was done w ithin the context of a rigorous process aimed

4Sucsy and Hendrickson Nutrient Reduction to LSJR

at developing Total Maximum Daily Lo ads (TMDLs). SJRWMD made a few modifications to the model to help achieve specific goals of the TMDL process. The principle modifications were as follows: a. Adjusted carbon:chlorophyll ratio from 30:1 to 50:1 in accordance with our estimates of this ratio based on field data. b. Added a Monod-like functional relationship between algal biomass and predation rate that accounts for an observed leveling of zooplankton biomass when total chlorophyll exceeds 40 P g L -1

. c. Turned nitrogen fixation by cyanobacter ia off within the oligo- and mesohaline segments and after September 1 to reflect lack of observed N-fixation in these areas and during these times. d. Turned nitrogen fixation by cyanobacter ia off entirely for Years 1 and 2 (1995 and 1997) based on the results of skill te sts for predicting chlorophyll targets. e. Added a simple senescence algorithm that triggers a collapse of a cyanobacteria bloom at the onset of phosphorus limitation to better simulate this phenomenon during the dry summer of 1999.



### Model State Variables The LSJR application uses 29 state variable s. The version of CE-QUAL-ICM used for this application allows 32 state variables. A complete list of available state variables, with acronyms in brackets, follows below (units are mg L -1 except where noted). The LSJR application omits the tw o zooplankton groups and urea.

1 Temperature [T] (°C) 2 Salinity [S] (ppt) 3 Inorganic Suspended Solids [ISS] 4 Algal Group 1 (mg L -1 Carbon) 5 Algal Group 2 (mg L -1 Carbon) 6 Algal Group 3 (mg L -1 Carbon) 7 Zooplankton Group 1 (mg L -1 Carbon) 8 Zooplankton Group 2 (mg L -1 Carbon) 9 Labile Dissolved Organic Carbon [LDOC] 10 Refractory Dissolved Organic Carbon [RDOC] 11 Labile Particulate Organic Carbon [LPOC] 12 Refractory Particulate Organic Carbon [RDOC] 13 Ammonium [NH3] 14 Nitrite+Nitrate [Nox] 15 Urea 16 Labile Dissolved Organic Nitrogen [LDON] 17 Refractory Dissolved Organic Nitrogen [RDON] 18 Labile Particulate Or ganic Nitrogen [LPON] 19 Refractory Particulate Or ganic Nitrogen [RPON]

5Sucsy and Hendrickson Nutrient Reduction to LSJR

20 Total Phosphate [PO4] 21 Labile Dissolved Orga nic Phosphorus [LDOP] 22 Refractory Dissolved Organic Phosphorus [RDOP] 23 Labile Particulate Or ganic Phosphorus [LPOP] 24 Refractory Particulate Or ganic Phosphorus [RPOP] 25 Particulate Inorganic Phosphorus 26 Chemical Oxygen Demand 27 Dissolved Oxygen [D.O.] 28 Particulate Silica [Psi] 29 Dissolved Silica [Dsi] 30 Algal Phosphorus Group1 31 Algal Phosphorus Group 2 32 Algal Phosphorus Group 3

The algal carbon groups (state variables 4-6) are generalized in the model and can be used to represent different taxonomic groups of phytoplankton. The LSJR model application defines Group 1 as freshwater blue-green algae, Gr oup 2 as freshwater diatoms, and Group 3 as general marine phytoplankton. Because marine diatoms dominate the biomass of the marine phytopl ankton community, characteristics of this group were used to parameterize Group 3.



### Phytoplankton Kinetics

Phytoplankton kinetics are described in the m odel by rates and constants that effect algal growth, algal respirati on, settling, and predat ion. The conservation equation for algae is as follows:

PRB

z W RG t B

a   ̧ 1 ·  ̈ © §
w w

 w w

B = algal biomass (mg C L -1 ). G = growth (d -1 ). R = respiration (d -1 ). W a = settling velocity (m d -1 ). PR = predation (g C m -3 d -1 ).

Growth Growth (G) is modeled as a temperat ure-dependent maximum growth rate (G max ) multiplied by the minimum value of the li ght and nutrient limitations. Nutrient limitations include phosphorus and nitrogen fo r all three algal groups with silica added for diatoms. Growth is calculated as follows:

6Sucsy and Hendrickson Nutrient Reduction to LSJR


## > @ Silica Phosphorus Nitrogen Light Minimum G G it it it it lim lim lim lim max , , , *

7Sucsy and Hendrickson Nutrient Reduction to LSJR

Maximum Growth Rate

Maximum growth rate (G max ) is calculated as the ratio of maximum production, P max , (g C g -1 Chl d -1 ) and carbon-to-chloroph yll ratio, C:Chl, modified by temperature:

)(
: max

max Tf ChlC

P G

e ToptTKTg Tf )(1

)( 2

 when T d T opt

e ToptTKTg Tf )(2

)( 2

 when T > T opt

G max for each algal group is thus defined by five parameters, P max , C:Chl, KTg1, KTg2, and T opt . The values of these parameters used for the LSJR application are shown in Table 2.1.

Table 2.1. Parameters that de fine maximum algal growth rate. Parameter Units Cyanobacteria Freshwater Diatoms Marine Diatoms P max gC g -1 Chl d -1 300 350 350 C:Chl gC g -1 Chl 0.02 0.02 0.02 T opt °C 35 25 25 KTg1 °C -2 0.005 0.004 0.004 KTg2 °C -2 0.004 0.006 0.006

Light Limitation

Light limitation (FI) is calculated as:

Ik I I FI
2 2 

Ik = P max / D

. D = the initial slope of the production/irradiance curve.

8Sucsy and Hendrickson Nutrient Reduction to LSJR

Nitrogen Limitation

Nitrogen limitation (N L ) is calculated using a Monod function:

DINKHN DIN N L 

DIN = the concentration of dissolved inorganic nitrogen (mgL -1 ). KHN = the half-saturation coefficient for nitrogen uptake.

Silica Limitation

Silica limitation (Si L ) is also calculated using a Monod function:

DSiKHSi DSi Si L 

DSi = the concentration of disso lved bioavailable silica (mgL -1 ). KHSi = the half-saturation coefficient for silica uptake.

Phosophorus Limitation

Phosphorus limitation (P L ) is modeled using Droop kinetics, where the limitation is a function of internal phosphorus cell quota.

Q Q P L 0 1 

Q = the phosphorus cell quota (gP g -1 algal C). Q 0 = the minimum cell quota.

Phosphorus uptake (P up ) is modeled as a function of dissolved phosphate using a Monod function.

4 4

max POKHP PO V P up 

V max = the maximum phosphorus uptake rate (gP g -1 algal C d -1 ). KHP = the half-saturation concentration for phosphorus uptake (mg L -1 ).

9Sucsy and Hendrickson Nutrient Reduction to LSJR

The nutrient limitation terms, then, are defined by six parameters, D , KHN, KHSi, KHP, Q 0 , and V max . Values used in the LSJR applic ation for each algal group are shown in Table 2.2.

Table 2.2. Parameters that define limitations to algal growth rate. Parameter Units Cyanobacteria Freshwater Diatoms Marine Diatoms

D g C g -1 Chl (E m -2 ) -1 3.15 8.00 8.00 KHN mg L -1 0.02 0.015 0.015 KHSi mg L -1 N/A 0.05 0.05 KHP mg L -1 0.00075 0.003 0.003 Q 0 g P g -1 algal C 0.015 0.010 0.010 V max g P g -1 algal C d -1 0.006 0.009 0.009

Respiration Algal respiration, R, is simulated as the sum of photo-respiration and basal metabolism. Basal metabolism is a function of temperatur e (T) and salinity (S). The respiration equation is as follows:

Stox e BMGespR TRTKTB x x x   )( Pr

Presp = the fixed fraction of production needed for photo-respiration. BM = the metabolic rate at reference temperature (TR). Stox = the salt toxicity multiplier.

Salt Toxicity

The formulation of salt toxicity was cha nged by SJRWMD for the LSJR application to that developed by Hamilton and Hertzfeld (Cen tre for Water Research, Univ of Western Australia) for the model CAEDYM (Com putational Aquatic Ecosystem Dynamics model). This formulation for salt toxicity of freshwater algae is as follows:



## )( max )1( )1(2)1( 1 2 2 2

Sopt S Sopt SSopt S Stox



##  x xxxx 

  E E E

The corresponding formulation for freshwater toxicity of ma rine algae is as follows:

Sopt SSopt S Stox 2 2 )1(2)1( xxxx   E E E

10Sucsy and Hendrickson Nutrient Reduction to LSJR

In the above formulas, S = salinity (ppt) and E , Sopt, and Smax are specified model coefficients.

Respiration, then, is defined by seven parameters for each algal group, Presp, BM, KTB, TR, E , Sopt, and Smax. The values used for the LSJR application are shown in Table 2.3.

Table 2.3. Parameters that define algal respiration, including salt (freshwater) toxicity. Parameter Units Cyanobacteria Freshwater Diatoms Marine Diatoms Presp dimensionless 0.25 0.25 0.25 BM d -1 0.05 0.05 0.05 KTB °C -1 0.0322 0.0322 0.0322 TR °C 35 20 20 E dimensionless 500 100 100 Sopt ppt 5 5 20 Smax ppt 36 36 Not applicable

Algal Settling Velocity Algal settling velocity (W a ) was specified as zero for cyanobacteria and 0.1 m d -1 for fresh and marine diatoms.

Predation The formulation for predation was altered by SJRWMD. The original formulation in CEQUAL-ICM related predation to the square of the algal biomass (PR = B PR ·B 2 ), where B

B

PR

is a temperature-dependent constant de fined for each algal group. The increasing rate of grazing with increased algal biom ass achieved by this formulation appeared contradictory to observed zooplankton densitie s in the LSJR (Phlip s and Cichra, 2002; Hendrickson et al., 2004). When algal biom ass as chlorophyll-a exceeded levels of approximately 40 P g L , zooplankton numbers were in stead found to be significantly reduced. The reduction in mesozooplankton (c opepods and cladocerans) feeding rates and numbers in cyanobacteria-dominated blooms has been documented in a number of studies (Fulton and Paerl, 1987; Paerl et al. (2001); Demott and Moxter, 1991), and the concept has been incorporat ed into chlorophyll-a criter ia for Chesapeake and algal production formulations (CBP, 2000; Welch, 1980) . This reduced grazing pressure is believed to be an important factor in the steep peaks in algal biomass observed in the freshwater LSJR. -1

The modified formulation for predation uses a Monod-like term that causes a leveling of predation at high values of total algal biomass and a term that further suppresses

11Sucsy and Hendrickson Nutrient Reduction to LSJR

predation at high levels of cyanobacteria biomass. The formulation for predation on cyanobacteria is as follows:

e Bc BcCYc CYc
BHcBtot Bc BPRcPR TrefTKTPR )( )() (

 xx x x  

BPRc = the maximum predation rate on cyanobacteria (d -1 ). Bc = the cyanobacteria biomass (g C m -3 ). Btot = the total algal biomass (g C m -3 ). BHc = the half saturation for cyanob acteria biomass on predation (g C m -3 ). Cyc = the lower limit of cyanobacteria suppression (g C m -3 ). KTPR = a temperature coefficient (°C -1 ). Tref = the reference temperature (°C).

A similar formula for predation on diatoms (thi s formula is identical for both fresh and marine diatoms) is as follows:

e Bd BcCYd CYd
BHdBtot Bd BPRdPR TrefTKTPR )( )() (

 xx x x  

BPRd = the maximum predation rate on diatoms (d -1 ). Bd = the diatom biomass (g C m -3 ). BHd = the half saturation for diat om biomass on predation (g C m -3 ). Cyd = the lower limit of cyanobacteria s uppression of diatom predation (g C m -3 ).

Table 2.4 lists the values of the parameters that define algal predation for the LSJR application.

Table 2.4. Parameters that define algal predation. Parameter Units Cyanobacteria Freshwater Diatoms Marine Diatoms BPR d -1 0.3 2 2 BH g C m -3 0.25 1.5 1.5 CY g C m -3 0.2 0.1 0.1 KTPR °C -1 0.069 0.069 0.069 Tref °C 20 20 20

Nitrogen Fixation The importance of nitrogen fixation on the nutrient budget of the LSJR became apparent during model development. An N-fixation al gorithm for cyanobacteria was added to the model by the model developers (Cerco, 2003). This algorithm increases the value of the N-limitation term (thereby decreasing N-limitation and increasing growth rate) at low concentrations of DIN. The difference ingrowth calculated with the new (higher) Nlimitation term and the old N-limitation term determines the amount of new nitrogen

12Sucsy and Hendrickson Nutrient Reduction to LSJR

added to the system by stoichiometry. The altered N-limitation term adds two coefficients and is as follows:

) ( ) ( )1( KHNFIX DINKHN KHNFIX DIN FNFIX DINKHN DIN FNFIX      x x

FNFIX = the fraction of N-fixers. KHNFIX = the half-saturation concentration for the reduction in N-fixation at higher values of DIN.

The N-fixation algorithm requires specification of the fraction of N-fixers within the cyanobacteria population. At present the fracti on of N-fixers is temporally constant. The model cannot dynamically simulate the fraction of N-fixers (a reasonable limitation given our present lack of understanding of this phenomenon). Because of this limitation, SJRWMD only employed this model feature during periods and within areas where Nfixation was known to have occurred a priori . As a result, N-fixati on was not used at all during 1995 and 1997. N-fixati on was used during 1998 and 1999, but was restricted to the portion of the model domain upstream of Black Creek. When N-fixation was used, FNFIX was set at 0.292 and KHNFIX at 0.01 mg L -1

.

Cyanobacteria Senescence Cyanobacteria biomass in the upper freshwater zone of the LSJR occasionally exhibited a sudden, precipitous decline. These population crashes particularly occurred during dry years following the onset of the first seas onal cyanobacteria bloom and are accompanied by an equally sharp drop in dissolved oxyge n. For example, observed cyanobacteria biomass (as carbon) at Racy Point dropped from over 5 to 0.5 mg L -1 between June 1 and July 1, 1999 in the absence of events such as washout or gr azing. A possible explanation of this phenomenon (Phlips, personal comm .) is that the physiological state of the cells in the bloom population is uncharacteristically homogenous, both because of similar age and mono-specific composition of the comm unity. The homogenous physiological state could result in senescence of the population as phosphorus stores are depleted. This hypothesis led SJRWMD staff to add a senescen se algorithm to the model that depletes cyanobacteria biomass at a rate that is func tionally related to phosphorus cell quota. The algal carbon lost to senescence is treated in a manner similar to predation loss, by transferring algal carbon to th e labile organic carbon pool. In this way, the associated consumption of dissolved oxygen occurs by microbial heterotrophic activity following cell death and not as an immediate result of cell respiration. Loss by senescence is functionally handled (for convenience in codi ng only) by a term that increases predation rate. The senescence formulation introduces two coefficients, Q min and Sr, as follows:

Q QSr Q Q Sr SrSnes

min 2 min 2 )1(2 )1( x x x 

  

Sr = the maximum multiplier on predation.

13Sucsy and Hendrickson Nutrient Reduction to LSJR

Q min = the phosphorus cell quota, below which, senescence occurs.

For the LSJR application Sr = 500 and Qmin = 0.02. Note that Q min is greater than Q 0 (0.015) the minimum cell quota used in th e calculation of phosphorus limitation.

Phytoplankton Stoichiometry Phosphorus:Carbon ratios (phosphorus cell quot as) are calculated within the model constrained by the minimum cell quota as de fined above. Nitrogen:Carbon ratios are fixed at 0.175 g N g -1 C and Silica:Carbon ratios are fixed at 0.8 g Si g -1 C.

Phytoplankton Distribution Coefficients When algal biomass loss occurs, either thr ough respiration or pred ation, nutrients are transferred from internal algal compartments to external mineral and organic nutrient compartments. The division of the internal nutrients to the external compartments is determined by distribution coefficients that are fixed fractions of the exiting internal nutrients. (In this way, for example, phyt oplankton is a significant source of new LDOC.) For the LSJR application, the di stribution coefficients are uniform among the three algal groups, but differ depending on whether the loss is by respiration or predation. The distribution coefficients fo r respiration are shown in Ta ble 2.5 and for predation in Table 2.6.

Table 2.5. Distribution coeffi cients for partitioning internal algal nutrients to external nutrient compartments when algal biom ass decreases due to respiration. Inorganic Labile Dissolved Organic Labile Particulate Organic Refractory Dissolved Organic Refractory Particulate Organic Nitrogen 0.35 0.20 0.10 0.25 0.10 Phosphorus 0.75 0.20 0.05 0.00 0.00

Table 2.6. Distribution coeffi cients for partitioning internal algal nutrients to external nutrient compartments when algal bi omass decreases due to predation. Inorganic Labile Dissolved Organic Labile Particulate Organic Refractory Dissolved Organic Refractory Particulate Organic Nitrogen 0.30 0.25 0.10 0.25 0.10 Phosphorus 0.50 0.30 0.10 0.07 0.03 Carbon 0.00 0.30 0.00 0.70 0.00

14Sucsy and Hendrickson Nutrient Reduction to LSJR



### Dissolution and Mineralization Rates The nutrient cycles in CE-QUAL-ICM are gene rally governed by a series of first-order reaction rates for the dissolution of particulat e material and re-mineralization of organic material. For example, particulate organic nitrogen is hydrolyzed to dissolved organic nitrogen, the dissolved organic nitrogen is re-mineralized to a mmonium, and ammonium finally nitrified to NOx. Particulate species also have specified rates of settling in the water column.

Temperature Effect on Nutrient Cycling All dissolution and mineralization rates are governed by the same temperature-dependent correction factor:

e FTMNL TrmnlTKTMNL )(



KTMNL=0.092 °C -1

. Trmnl = 20 °C.

Carbon The following six coefficients dete rmine the rate of carbon cycling: KLPOC = dissolution rate of LPOC to LDOC = 0.075 d -1

. KRPOC = dissolution rate of RPOC to LDOC = 0.0025 d -1

. KLDOC = respiration rate of LDOC by heterotrophs = 0.075 d -1

. KRDOC = respiration rate of RDOC by heterotrophs = 0.001 d -1

. Wlpoc = settling velocity of labile particles = 0.25 m d -1

. Wrpoc = settling velocity of refractory particles = 0.25 m d -1

.

Phosphorus
Dissolution and mineralization rates for labile organic phosphorus ar e a function of algal biomass and nutrient availability . For example, the mineraliza tion rate of labile dissolved organic phosphorus is governed by the following formula:

BKdpa POKHp KHp Kldp Kldop x x   lg 4

Kldop = the mineralization rate of la bile dissolved organic phosphorus. Kldp = the minimum mineralization rate. KHp = the half-saturation concentration for algal uptake. PO4 = the dissolved phosphate concentration. Kdpalg = a constant relating labile mineralization rate to biomass. B = the total algal biomass.

The following eight coefficients, then, determine the rate of phosphorus cycling:

15Sucsy and Hendrickson Nutrient Reduction to LSJR

KLDP = 0.1 d -1 KDPALG = 0.2 d -1 KRDP =mineralization rate of RDOP to PO4 = 0.01 d -1 KLPP = minimum dissolution of LPOP to LDOP = 0.075 d -1 KLPALG = 0.4 d -1 KRPP = dissolution rate of RPOP to LDOP = 0.005 d -1 Wl = settling velocity of labile particles = 0.25 m d -1 Wr = settling velocity of refractory particles = 0.25 m d -1

Nitrogen
Rates of dissolution and mine ralization of nitrogen forms are independent of algal biomass. However, the nitrogen cycle adds an additional inorganic transformation of ammonium (NH3) to nitrite+nitrate (NOx). This nitrification rate is modeled as a function of ammonium, dissolved oxy gen, and temperature as follows:

max )( 3 3 2 NT e ToptTKTNT NH KHnnt NH
DO KHont DO NT x x x   

The following eleven coefficients, then, de termine the rate of nitrogen cycling: Khont = half-saturation of DO requi red for nitrification = 1.0 mg L -1 KHnnt = half-saturation of NH3 requ ired for nitrification = 1.0 mg L -1 Ntmax = maximum nitrification rate at optimal temperature = 0.4 g N m -3 d -1 Topt = optimal temperature for nitrification = 30 °C KTNT = temperature correc tion constant = 0.0045 °C -1 KLDN = mineralization rate of LDON to NH3 = 0.025 d -1 KRDN =mineralization rate of RDON to NH3 = 0.0025 d -1 KLPN = dissolution rate of LPON to LDON = 0.0375 d -1 KRPN = dissolution rate of RPON to LDON = 0.0025 d -1 Wl = settling velocity of labile particles = 0.25 m d -1 Wr = settling velocity of refractory particles = 0.25 m d -1

Silica
Two coefficients determine th e rate of silica cycling: Kpbs = particulate biogenic si lica dissolution rate = 0.03 d -1 Wpbs = settling velocity of par ticulate biogenic silica = 0.25 m d -1

Dissolved Oxygen Dissolved oxygen is removed by algal resp iration, heterotro phic respiration and nitrification, and added by algal producti on (photosynthesis) and reaeration. Algal respiration, heterotrophic respiration, and photosynthesis are also sources and sinks of DOC and are equated to dissolved oxygen by an oxygen-to-carbon mass ratio equal to

16Sucsy and Hendrickson Nutrient Reduction to LSJR

2.67 g O 2 g -1 C. Transformation of NH3 to NOx by nitrification is similarly related to dissolved oxygen by an oxygen-to-nitrog en mass ratio equal to 4.33 g O 2 g -1 N.

Reaeration is proportional to the difference in instream DO and saturation DO as follows:

) ( )( DODOs

z Kr
dt DOd

 ' x

where,

Kr = reaeration coefficient (m d -1 ) DOs = saturation dissolved oxygen (a f unction of temperature and salinity)

Saturation dissolved oxygen is calculated as follows:

) 00009796.0 005866.01665.0() 80655.1 ( 0054258.038217.05532.14 2 2 T T S T T DOs 

    x

Kr is modeled as a function of wind speed, W, (m s -1 ), slightly modified by temperature (T) and salinity (S) as follows:

)002.00233.054.0( 5.1 

## )( 5.1 ST W AREAR Kr  x x

AREAR has a value of 0.08 throughout the model domain with the exception of the narrow shipping channel between Jacksonvi lle (Acosta Bridge) and Mayport where AREAR has a value of 0.04.

Light Attenuation Light attenuation is modeled usi ng the Beer-Lambert Law , I(z) = I o *exp(-Kess*z). The attenuation coefficient (Kess) is modeled as a function of volatile suspended solids (VSS), inorganic suspended solids (ISS), and dissolved organic carbon (DOC). Attenuation by phytoplankton chlo rophyll is included in VSS and attenuation by color in DOC. VSS is calculated as 2.5*(Algal Carbon+LPOC+RPOC). The light attenuation coefficient, Kess, (m -1 ) is then:

DOC VSS ISS Kess x x x   

15.006.008.03.0

17Sucsy and Hendrickson Nutrient Reduction to LSJR



## Chapter 3. Water Quality Model Input Files

Applying CE-QUAL-ICM to the LSJR require d specification of boundary conditions and loads for the periods simulated. We simu lated four years for this study: 1995, 1997, 1998, and 1999. Concentration time-series were specified for the upstream flow boundaries at Buffalo Bluff and Dunns Creek, and for the ocean boundary. Contributions from tributary and point sources were specified as mass loads (kg d -1 ), except water temperature, salinity, and disso lved oxygen were specified as concentrations. Details of the calculation of the external load ca n be found in Hendrickson et al, 2002. Meteorologic boundary data included solar radi ation, wind speed, day length, equilibrium temperature and a coefficient of heat exchange (the latter two parameters describe airwater heat flux). Atmospheric deposition of nitrogen and phosphorus were based on observed monthly loading rates from the th ree nearest National Atmospheric Deposition Program monitoring station concentrations and the volumes of the combined LSJR Thiessen rainfall monitoring network for the river’s water surface (Pollman and Roy, 2003).

For this study a model year is offset one month from the calendar year. The model year is defined to begin on December first of the previous calendar year. For example, the simulation period for 1995 is December 1, 1994 to November 30, 1995. This convention is followed to be consistent with seasonal de finitions used for the hydrologic estimates of watershed loads (Hendrickson et al., 2004). In that study, loads we re calculated in 4- month blocks with the winter season defined as December-March.



### Meteorologic Boundary Data Meteorologic data were specified at daily intervals and include daily solar radiation as PAR (E m -2 d -1 ), daily wind speed (m s -1 ), day length as fracti on of day, equilibrium temperature (°C), and coefficient of heat exchange (Watts m -2 °C -1 ). Values of PAR for 1996-99 were observed by USGS. Values of PAR for 1994-5 were estimated from observed daily cloud cover at the Jacksonville Naval Air Station. Jacksonville Naval Air Station also provided observati ons of wind speed, air temperat ure, and relative humidity. These variables, together with observed so lar radiation, were used to calculate equilibrium temperature and the coefficient of heat exchange (Martin and McCutcheon, 1999).

Solar Radiation Solar radiation was input to the model as daily PAR. These data are summarized in Table 3.1 as monthly-averaged values PAR. Ranges of PAR closely follow the annual variation of maximum sun angl e with minimum values occurr ing near the winter solstice and maximum values occurring near the summer solstice. Monthly-average values of PAR ranged from 18.2 E m -2 d -1 in December 1995 to 52.9 E m -2 d -1 in June 1998. The summer months of May-July tend to exhibit the greatest inter-annual variability of PAR,

18Sucsy and Hendrickson Nutrient Reduction to LSJR

presumably because of variability in cloud cover. Note, for example, that June 1998 received a relatively high am ount of PAR as compared to th e four-year average for that month, while PAR in June 1999 was relatively low compared to the four-year average.

Table 3.1. Summary of PAR values for eachmodel year. Values shown are average PAR (E m -2 d -1 ) for the month. (PAR was input to model as daily values.) Month 1995 1997 1998 1999 4-year Avg Dec 18.2 22.1 18.3 20.2 19.8 Jan 21.5 22.8 23.2 24.8 23.1 Feb 25.0 26.8 28.8 30.8 27.9 Mar 33.1 35.4 40.4 40.2 32.3 Apr 43.3 41.1 43.4 42.2 42.5 May 47.8 45.2 48.2 45.8 46.8 June 45.7 45.0 52.9 41.6 46.3 July 46.4 46.7 48.4 49.4 47.7 Aug 43.8 42.7 42.7 43.1 43.1 Sept 32.8 37.9 33.0 33.8 34.4 Oct 27.5 28.3 35.2 28.5 29.9 Nov 25.8 24.5 24.8 24.9 25.0 Year Average 34.2 34.9 36.7 35.5 35.3

Wind Speed Median hourly wind speed in the LSJR basin is about 3.6 m s -1 with a slightly higher median value (3.9) in winter than summer (3.3 ). A discussion of inter-annual variability of wind speed and direction for the peri od 1995-8 is provided in Sucsy and Morris (2002). Daily wind speeds were input to the model. Wind speed was used to estimate reaeration rates and surface heat exchange.

Equilibrium Temperature and Coefficient of Heat Exchange Water temperature is simulated in CE-QUAL-ICM by the equilibrium temperature method as described by Martin and McCutche on (1999). This method expresses the net heat transfer to the water body as:

)( TT

e C e H s n 

where C e = the coefficient of heat exchange (Watts m -2 °C -1 ), T e = the equilibrium temperature (°C), and T s = the surface water temperature (°C). C e and T e are calculated from a simple heat budget using observations of air temperature, relative humidity, solar radiation, and wind.

T e ranged from 1 to 38 °C over the four-year model period. 90% of the values for C e were between 20.5 and 58.6 Watts m -2 °C -1

.

19Sucsy and Hendrickson Nutrient Reduction to LSJR



### Ocean Boundary Water quality variables were specified as concentrations at the ocean boundary. The concentrations are monthly-averaged values from observations collected seaward of the LSJR mouth. Average values of ocean c oncentration for marine diatom carbon were used for 1995, 1997, and 1998. Marine phytoplankton carbon for 1999, however, was estimated from observations of diatom ce ll counts on the incomi ng tide at the river mouth.

Monthly concentrations specified at the ocean boundary are summarized in Table 3.2. For the sake of brevity, certai n water quality variables are combined. For example, the sum of LDOC and RDOC is listed simply as DOC. The italicized row shows the values of diatom carbon specified for 1999 only. The previous row shows phytoplankton carbon specified for the other model years.

Table 3.2. Monthly concentrations (mg L -1 ) of water quality variab les specified at the ocean boundary. The italicized entry is fo r the 1999 year only and substitutes for the previous row entry. Dec Jan Feb Mar Apr May June July Aug Sep Oct Nov ISS 6.1 1.8 29.4 50.2 21.6 12.6 17.7 13.8 12.0 4.7 21.4 37.8 Diatoms (1995-8) 0.10 0.11 0.15 0.16 0.26 0.67 0.67 0.37 0.38 0.39 0.26 0.09

Diatoms (1999) 0.09 0.13 0.16 0.16 0.36 0.98 0.34 0.39 0.36 0.42 0.09 0.12

DOC 5.2 4.0 2.5 2.8 2.6 3.0 1.7 2.7 4.0 3.0 3.5 3.2 POC 1.3 1.8 1.4 1.0 0.6 0.7 0.6 0.6 0.8 0.8 0.7 0.8 TN 0.58 0.40 0.42 0.29 0.47 0.53 0.41 0.38 0.35 0.49 0.46 0.55 TP 0.104 0.118 0.040 0.051 0.068 0.096 0.088 0.103 0.161 0.125 0.066 0.112 TSi 1.94 0.87 0.43 0.50 0.50 0.13 0.26 0.31 0.38 0.77 0.38 0.83 D.O. 7.74 8.87 7.85 7.64 7.07 6.98 6.84 6.02 5.60 5.78 7.12 7.46



### Upstream Flow Boundary The majority of inflow from upstream watershe ds enters the LSJR through Buffalo Bluff. A smaller, secondary inflow enters the upstream boundary through Dunns Creek. Timevarying concentrations of water quality variab les were specified at these upstream flow boundaries. The specified concentrations were derived from observations collected by the SJRWMD at approximately two-week inte rvals. Summary statistics for the primary water quality variables specified at Buffa lo Bluff are listed in Table 3.3.

Some of the variables listed in Table 3.3 are correlated wi th river discharge. For example, salinity is inversely correlated with discharge because a greater percentage of the flow is from high conductivity groundwater during periods of low discharge; whereas RDOC (color) is positively correlated with discharge.

20Sucsy and Hendrickson Nutrient Reduction to LSJR

Total organic carbon is dominated by RD OC in this highly-colored system. Cyanobacteria tend to dominate the algal carbon pool and are primarily responsible for nuisance algal blooms. TN:TP ratios are genera lly quite high indica ting the likelihood of phosphorus limitation for waters entering at this boundary.

Table 3.3. Summary statistics for primary water quality variables observed at Buffalo Bluff, 1995-99. Concentrations are all mg L -1 . The TN:TP ratios in the last row are unitless. Diatom carbon includes a small fr action of other taxonomic groups, primarily green algae. Variable Mean Standard Deviation Minimum Maximum

Salinity 0.47 0.13 0.20 0.81 ISS 4.89 3.55 0.62 17.9 Cyanobacteria Carbon 1.03 1.12 0.011 5.32

Diatom Carbon 0.42 0.26 0.064 1.54 LDOC 2.86 1.62 0.00 6.33 RDOC 11.5 4.64 4.60 23.7 POC 2.57 1.56 0.00 6.36 Total N 1.56 0.37 0.87 2.87 Total P 0.062 0.024 0.016 0.170 D.O. 7.31 1.24 4.28 9.61 Total Si 3.17 1.89 0.00 8.46 TN:TP 29.0 13.7 6.8 114.8

Summary statistics for concentrations of water quality variables entering the LSJR from Dunns Creek (not shown) are generally sim ilar to the values listed in Table 3.3 for Buffalo Bluff. Notable differences between mean values at Dunns Creek and Buffalo Bluff are as follows:

x ISS was lower at Dunns Creek (2.7 mg L -1 )

x Cyanobacteria carbon was lower at Dunns Creek (0.6 mg L -1 )

x RDOC was higher at Dunns Creek (20.0 mg L -1 )

x Total phosphorus was higher at Dunns Creek (0.083 mg L -1 ).



### Tributary Boundaries

Water Temperature Water temperature was specified at each model boundary with the exception of point source inflows. Temperature was specified at 82 grid cells including the ocean boundary (17 cells), tributary inflows ( 63 cells), Dunns Creek, and Buffalo Bluff. Observed water temperature at Buffalo Bluff was used fo r both the Buffalo Bluff and Dunns Creek boundary. In addition these data were also applied to the ocean cells. The use of mainstem temperature observations to repres ent ocean temperature was justified because

21Sucsy and Hendrickson Nutrient Reduction to LSJR

of the similarity between concurrent measurements at Buffalo Bluff and Dames Point, a station with ocean influence. Water temper ature was set identically for all tributaries based on weekly median values from 8 tributary monitoring locations.

Salinity and Dissolved Oxygen Specified values of dissolved oxygen for th e 63 tributary inflows were derived from observed values at four locations: Black Creek, Rice Creek, Deep Creek, and Ortega River. Un-sampled tributaries assumed the observed values from one of these base locations. Northern urban tributaries were associated with Ortega River, tributaries entering from the tri-county agricultural area were associated with Deep Creek, and all other tributaries with the exception of Rice Creek were associated with Black Creek. Rice Creek was treated independently because dissolved oxygen is generally lower in Rice Creek than neighboring creeks. For ex ample, average dissolved oxygen exiting Rice Creek was 5.1 mg L -1 (1993-2000) while average dissolved oxygen exiting Black Creek was 6.0 mg L -1 over the same period.

For salinity, all tributaries were assigned iden tical (low) values observed in Black Creek, except for tributaries entering from the tri-county agricultural area. The latter tributaries have generally higher conductiviti es than surrounding watersheds due to the influence of irrigation water high in dissolved solids, so observations from Deep Creek were used. Black Creek salinity was estimated to be approximately 0.04 ppt, while Deep Creek salinity ranged from 0.2 to 1.0 ppt.

Tributary Silica
Particulate and dissolved silica concentrati on specified as boundary conditions for the 63 tributaries were constant in time. East ba nk tributaries generally have more silica than west bank tributaries. Particulate and di ssolved silica concentrations for west bank tributaries were 2.34 and 3.48 mg L -1 (as Si), respectively, and concentrations for east bank tributaries were 5.46 and 8.12 mg L -1

.

Tributary Loads Watershed loads input to the water quality model were previously calculated using a hydrologic model. The hydrologic model pr ovided 4-month seasonal loads based on rainfall, land-use, and soil type. Constituen ts calculated by the hydrologic model were: total suspended solids (TSS), labile and re fractory total organic carbon (LTOC and RTOC), inorganic nitrogen (TIN), labile and refractory organi c nitrogen (LTON and RTON), inorganic phosphorus (PO4), and lab ile and refractory organic phosphorus (LTOP and RTOP). Documentation of the watershed loads and the methods used for distinguishing labile and re fractory organic nutrients and carbon are provided in Hendrickson et al. (2004).

Seasonal loads were disaggregat ed to daily values using obs erved discharge hydrographs. This method of disaggregating the seasona l loads makes the concentrations of

22Sucsy and Hendrickson Nutrient Reduction to LSJR

constituents constant over the 4-month season with the load varying in direct proportion to the daily discharge.

Watershed loads provided by the hydrologic model were also partitioned into forms required by the water quality model. Both labile and refractory forms were further partitioned into dissolved and particulate forms and inorganic nitrogen was partitioned into ammonium and NOx, based upon the mean proportions observed in water quality monitoring data. In addition a small fraction of nutrient lo ad was converted to algal compartments (cyanobacteria and freshwater di atoms). Finally, ISS was calculated from TSS less an estimate of vol atile suspended solids.

The coefficients used to fractionate organic tributary loads into di ssolved and particulate forms are shown in Table 3.4.

Table 3.4. Partition coefficients used to fract ionate organic tributar y loads into dissolved and particulate forms. Nutrient Dissolved Fraction Particulate Fraction LTOC 0.72 0.28 RTOC 0.93 0.07 LTON 0.70 0.30 RTON 0.90 0.10 LTOP 0.30 0.70 RTOP 0.30 0.70

TIN was assumed to exist in dissolved form (DIN) only. 33% of the DIN was input as ammonium and 67% as NOx.

Algal carbon was linearly related to LTON by regression with a constant of proportionality of 0.343. For simplicity, the total algal carbon was partitioned equally between cyanobacteria and diatoms. The addi tional algal carbon added to the model was removed from the LPOC pool to balance TOC. The addition of al gal carbon implicitly increases algal nitrogen by 0.175 g N g -1 C. This additional nitrogen was removed from TPON. Finally, algal phosphorus was explici tly added to the mode l using the Redfield ratio of 0.025 g P g -1 C. The additional algal phosphor us was removed from the LPOP pool.

Volatile suspended solids (VSS) were estima ted as (Total Algal Carbon + TPOC)*2.5. VSS was subtracted from TSS to obtain ISS.

On April 28, 1997 a large rainfall event caused the collapse of a levy holding the tailings from a mining operation in the Rice Creek wate rshed. A turbid plume of mostly colloidal inorganic material entered th e LSJR through Rice Creek. Th is event increased turbidity in affected portions of the river for approxi mately two months. Because this material remained in suspension, treating it as I SS, with a settling velocity of 0.75 m d -1 , was inappropriate. Instead, we entered it as an equivalent weight of RDOC because of its

23Sucsy and Hendrickson Nutrient Reduction to LSJR

similar characteristics of no settling, very low rate of decomposition, and effective reduction of light in the water column. Th e additional loads added to the Rice Creek tributary on April 28, 1997 as a result of th e mine spill event are shown in Table 3.5.

Table 3.5. Additional load (kg) added to Rice Creek load on April 28, 1997. The additional load was added to simulate th e effect of a mine spill on that day.

Constituent Additional Load Due to Mine Spill (kg) LDOC 26 RDOC 2.66 x 10 6
LPOC 611 RPOC 445,438 NH3 17,126 NOx 27 LDON 25 RDON 1830 LPON 582 RPON 43,208 PO4 20 LDOP 0 RDOP 594 LPOP 1 RPOP 14,028



### Point Sources Nutrient loads from 36 point sources were provided to the model using observed water quality data from monthly operating repor ts, synoptic survey s performed by the Department of Environmental Protection, m onitoring data from the SJRWMD, and from other special studies (Hydroqual, 1996). These data were provided as concentrations and flow. We subsequently converted the input to mass load (kg d -1 ). Observations were made at monthly intervals for 31 sources and at daily intervals for 5 sources. Details regarding the point source loads are documented in Hendrickson et al. (2004).

Loads from point sources in cluded (a) ISS, (b) all sp ecies (labile/refractory; dissolved/particulate) of or ganic carbon, nitrogen, and phosphorus, and (c) inorganic nitrogen and phosphorus. When point source effluent monitoring data lacked the constituent detail necessary for describing th e full state variable suite of CE-QUAL-ICM, best available data from time intervals outside the modeling window were used. Coverage was typically the poorest for TOC and dissolved/particulate fractionation. Because point source flows are small relative to the river flow, constituents (dissolved oxygen, temperature, and conductivity) that would primarily influence the river by dilution were omitted. Point sources were also assumed to be negligible sources of silica.

24Sucsy and Hendrickson Nutrient Reduction to LSJR



### Atmospheric Deposition Atmospheric deposition of nitrogen and phos phorus is observed at several locations throughout northeast Florida. These observa tions were used to estimate direct atmospheric load to the LSJR. The calcu lation of atmospheric deposition loads observations are discussed Pollman and Roy (2003).

The atmospheric deposition stat ions report both total nitrog en and total phosphorus (as the sum of both wet and dry deposition). To tal phosphorus was entered into the model as dissolved inorganic phosphorus (PO4). Total nitrogen was fractionated into ammonium (25%), NOx (60%), and dissolv ed organic nitrogen (15%).

The contribution of nutrients to the LSJR by direct atmospheric deposition is relatively small. As an areal rate, atmospheric deposit ion of total nitrogen was estimated as about 0.002 g m -2 d -1 . The reasonableness of this estima te is established by comparison with the 0.0026 g m -2 d -1 calculated for the Danish co ast (Asman and Larsen, 1996).

This loading rate results in an annual total nitrogen load by direct atmospheric deposition to the LSJR of about 0.3 million kg yr -1 . This value is small compared to the approximately 4 million kg yr -1 delivered by point and non-point sources.



### Spatial Characteristic s of Nutrient Loads
Nutrient loads to the LSJR are distributed unevenly along the river’s length. Loads are greatest at both ends of the lower river a nd least in the middle reach. Point sources predominate in the marine reach while non-poi nt sources tend to predominate in the freshwater reach.

We totaled nutrient loads by Water Basin ID to illustrate the spatial distribution of loads. Water Basin IDs (WBIDs) define unique segm ents of the river and were developed by FDEP for the purpose of defining impairment fo r different segments within the context of Florida’s application of the TMDL evalua tions. The LSJR was segmented into 13 WBIDs, labeled A through M in Figure 1. WBIDs A-F encompass the marine reach and WBIDs G-M the freshwater reach. The WBIDs are of fairly uniform length, with the exception of WBID G. These segments provide a reasonable segmentation, then, for examining spatial variation in nutrient load.

Annual nutrient loads to each WBID for 1997 are shown in Figure 2. The segment marked “U” represents nutrient load entering the top of WBID M from upstream sources. The upper plot in the figure shows total n itrogen and the lower plot shows total phosphorus. Loads are represented by vert ical bars and the fraction of shading corresponds to the fractional point source co ntribution within each WBID. The load shown for the non-point sources is the anthropog enic load only. Thes e figures thus show load totals in excess of natural background loads.

25Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 2 shows the relative minimum in nutrient load within WBIDs G-J. Nutrient load to the freshwater reach is predominately to WBIDs M-K.

The marine reach (WBIDs A-F) have generally greater loading rates. Note in particular the predominance of point source loadi ngs to this portion of the river.

Figure 3.1. Water Basin IDs (WBIDs) defi ned for the LSJR. WBIDs are defined by FDEP for application of TMDL regulati ons. Here they serve as a convenient segmentation.

26Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 3.2. Total annual nitrogen load ( upper) and total annual phosphorus load (lower) for each Water Basin ID (WBID) in the LSJR. Values are for 1997. Bars represent total load to each WBID and shaded areas the fraction of the load from point sources.

27Sucsy and Hendrickson Nutrient Reduction to LSJR



## Chapter 4. Calibration Results
The purpose of simulating water quality in the St. Johns River with CE-QUAL-ICM was to develop a tool that could be used to calculate nutrient reductions that meet defined water quality targets. The water quality ta rgets established for the PLRG and TMDL process in the LSJR focus on chlorophyll-a, in the upper freshwater reach, and dissolved oxygen, in the lower marine reach. The pr esent focus on chlorophyll-a and dissolved oxygen means that the water quality model mu st accurately simulate algal dynamics. Chlorophyll-a levels are a direct result of algal growth in the water column and dissolved oxygen is inextricably linked to algal produc tivity, respiration, and decay. As such, comparisons of simulated and observed water quality parameters shown in this chapter focus on those parameters that most directly control phytoplankton dynamics. These parameters are (a) the physical conditions of temperature and salinity and (b) the principle controlling nutrients of nitrogen, phosphorus, and silica.

Light, flushing, and grazing (Phlips et al., 2000 ) can also be important controlling factors for algae in estuaries. Cerc o (2003) showed that simulated light attenuation coefficients for this system compare well with a lim ited set of observed data, although further research regarding the best functional rela tionship between light attenuation and model state variables is ongoing. The model’s ability to accurately simulate flushing has been established by comparison with long-term, c ontinuous salinity records (Sucsy and Morris, 200x), opportunistic simulation of a turbid mine spill (Sucsy), and comparison of a simulated passive tracer with a field dye study (ECT, 200x). Grazing rates are less well known for the LSJR and simulated rates were guided by previous applications of CEQUAL-ICM (Cerco, 19xx), literature values (Tetra Tech, 1985) and qualitative relationships between zooplankton and algae biomass.

28Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.1. Locations of stations where wate r quality data were available for comparison with simulated results. Water quality da ta were observed at monthly intervals.

Ten synoptic water quality stations were used for comparison with model results (Figure 4.1). These ten stations cover the model dom ain of the LSJR and were generally sampled at monthly intervals over the simulation period. For the sake of brevity, time-series plots at four of these stations are discussed in this chapter, and shown below.

The four stations selected were Palatka and Racy Point, representing the upper freshwater reach, and Piney Point and Talleyrand, represen ting the lower marine reach. Palatka and Racy Point typically have the highest chlo rophyll-a levels in the LSJR. Talleyrand represents the marine reach where low di ssolved oxygen is a concern. Piney Point

29Sucsy and Hendrickson Nutrient Reduction to LSJR

represents an important transition zone be tween oligohaline and marine waters. Although dissolved oxygen levels are generally not overl y suppressed at Piney Po int, the export of algal biomass from this area to the downstream marine reach is a principle cause of low dissolved oxygen downstream.

In all the time-series plots presented below, simulated values are represented by a solid black line and observed values by a square symb ol. The vertical scales of the plots are the same for common parameters. This choice was made to illustrate spatial variability between stations and th e models ability to capture this variability (see, for example, salinity variability shown in Figures 4.3a and 4.3b).

Water temperature at the four stations is show n in Figures 4.2a and 4.2b. The plots show that water temperature exhibits little spatial variability in the LSJR. Water temperature at Talleyrand, often dominated by ocean water (F igure 4.3a), is quite similar to water temperature at Palatka, a station that s howed no influence by ocean water during the simulation period (Figure 4.3b). Simulated and observed values of water temperature are nearly identical.

Salinity varied considerably from the lower st ations (Figure 4.3a) to the upper stations (Figure 4.3b). Figure 4.3a shows an increa se of salinity at both Talleyrand and Piney Point during a protracted drought period that began in mid-1998 and persisted through the fall of 1999. Wet conditions during the 1997-8 “El Nino” winter resulted in depressed salinity at both stat ions during this time. Racy Point and Palatka remained fresh even through the drought periods (Figure 4.3b). Simulated salinity closely matches observed values.

Dissolved inorganic nutrients of nitrogen, phosphorus, and silica affect algal growth rates as presented in Chapter 2. Although nutrien t concentrations do not sufficiently predict nutrient limitation, they are, at least, n ecessary conditions for limitation. As such, predicting periods when inorgani c nutrient concentrations ar e low is an important model skill. “Low” nutrient concentration is here taken to mean below the half-saturation constants for algal growth and nut rient uptake listed in Table 2.

The model proved to be a good predictor of low nutrient concentration, as shown in Figures 4.4 through 4.6. Inorganic phosphorus concentrations are ge nerally higher in the marine reach than in the freshwater reach and the model captures this variability well (Figures 4.5a and 4.5b). Inorganic phosphor us concentrations at Racy Point became particularly low and limiting to algal grow th during both summers of 1998 and 1999. Again, the model correctly simulates this important limitation (Figure 4.5b).

Dissolved silica concentrations tended to be greater in the freshwater reach than the marine reach and this variability was captured by the model (Figures 4.6a and 4.6b). The model also captured the low dissolved sili ca observed at both Racy Point and Palatka during the spring of 1998 (Figure 4.6b). At Talleyrand potentially limiting concentrations of silica occurred during the spring of 1998 and 1999 (Figure 4.6a) and these low silica events are captured by the model.

30Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.2a. Simulated (line) and observed (square) water temperat ure at Talleyrand and Piney Point.

31Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.2b. Simulated (line) and observed (square) water temperatur e at Racy Point and Palatka.

32Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.3a. Simulated (line) and observed (square) salinity at Talleyrand and Piney Point.

33Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.3b. Simulated (line) and observed (s quare) salinity at Racy Point and Palatka.

34Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.4a. Simulated (line) and observed (square) dissolved inorganic nitrogen at Talleyrand and Piney Point.

35Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.4b. Simulated (line) and observed (s quare) dissolved inorgani c nitrogen at Racy Point and Palatka.

36Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.5a. Simulated (line) and observed (square) inorganic phosphorus at Talleyrand and Piney Point.

37Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.5b. Simulated (line) and observed (square) inorganic phosphorus at Racy Point and Palatka.

38Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.6a. Simulated (line) and observed (square) dissolved silica at Talleyrand and Piney Point.

39Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.6b. Simulated (line) and observed (square) dissolved silica at Racy Point and Palatka.

40Sucsy and Hendrickson Nutrient Reduction to LSJR

Total nitrogen tends to be somewhat higher in the freshwater reach of the river than in the marine reach (Figures 4.7a and 4.7b). Tota l nitrogen concentrations below 0.8 mg L -1 were never observed at Racy Point and Palatk a, while values below this level occurred frequently at Talleyrand. While the model closely simulated total nitrogen in the freshwater reach (Figure 4.7b), the mode l tended to underestimate total nitrogen downstream (Figure 4.7a). The relatively few ocean chemistry data upon which the ocean boundary is based may have had some infl uence in the underestimation of nitrogen in the marine reach. The greatest mismatch occurred in the winter, however, a period of least concern. Simulated and observed values were much closer during the summer periods of greatest algal production, that is, the 3 rd quarters of 1998 and 1999.

Temporal variability of total nitrogen was less at Piney Point than Racy Point and the model captures this phenomenon well. Note, in particular, that the model correctly simulates particularly high total nitrogen con centrations at Racy Point and Palatka during the summer of 1999. The nitrogen fixation al gorithm was needed to explain these high total nitrogen levels.

Total phosphorus concentrations generally in crease from the upper freshwater reach to the lower marine reach (Figures 4.8a and 4.8b). The model captures this spatial variability well. The model te nds to slightly underestimate total phosphorus in the water column.

Chlorophyll-a and dissolved oxygen were the pa rameters of concern for the TMDL and PLRG calculations. Chlorophyll-a concentra tions are greatest in the upper freshwater reach and exhibit frequent blooms there (Fig ure 4.9b). Chlorophyll-a concentrations are considerably lower and temporally less variable in the marine reach (Figure 4.9a). The model simulated mean chlorophyll-a, the spa tial variability in ch lorophyll-a, and bloom durations and peaks quite well. Simulated ch lorophyll-a did not atta in the level of the highest observed chlorophyll-a values in the freshwater reach. The inability of the model to simulate short durations of high chlorophyll-a intensity is at tributed to spatial sub-grid scale patchiness of chlorophyll-a in the rive r and increased nitrogen fixation rates during periods of intense blooms. Variations in the C:chlorophyll-a ra tio during episodes of phosphorus limitation or phytoplankton community stationary phase ma y also account for the inability of the model’s fixed ratio to capture peaks.

41Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.7a. Simulated (line) and observed (square) total nitrogen at Talleyrand and Piney Point.

42Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.7b. Simulated (line) and observed (square) total nitrogen at Racy Point and Palatka.

43Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.8a. Simulated (line) and observed (square) total phosphor us at Talleyrand and Piney Point.

44Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.8b. Simulated (line) and observed (square) total phosphorus at Racy Point and Palatka.

45Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.9a. Simulated (line) and observed (square) chlorophyll- a at Talleyrand and Piney Point.

46Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.9b. Simulated (line) and observed (square) chlorophyll-a at Racy Point and Palatka.

47Sucsy and Hendrickson Nutrient Reduction to LSJR

The model captures annual-temporal and spat ial trends in dissol ved oxygen quite well. Annual and spatial trends in dissolved oxygen are largely a function of oxygen solubility driven by patterns in temperatre and salinity. Palatka tends to exhibit somewhat lower dissolved oxygen than Racy Point and the m odel simulates this effect (Figure 4.10b). The low dissolved oxygen event during the su mmer of 1997 at Palatka was partially attributed to export of low dissolved oxygen waters that fo rmed in the narrow channel upstream of Palatka. The simulation, however, does not capture the low dissolved oxygen episode that followed the July 1999 bloom crash.

Persistent low dissolved oxyge n tends to occur during summer in the marine reach. Dissolved oxygen at Talleyrand, for example, was below 5 mg L -1 for 2-3 months in each of the 1998 and 1999 summers (Figure 4.10a). The model correctly simulated the dissolved oxygen sags below saturation concentr ation at Talleyrand. The model explains these dissolved oxygen sags as responses to algal biomass entering this deep, narrow section. Algal biomass probably is imported to this section primarily from the upstream oligohaline reach, although import of marine phytoplankton blooms from the coastal ocean could contribute as well. Unfavorable horizontal salinity gradients probably exacerbate the dissolved oxygen sags in this reach by rapidly increa sing algal respiration due to both salt toxicity for fres hwater species and freshwater toxicity for marine species. Contributing factors to persistent, low dissolv ed oxygen levels in this reach are discussed more fully in Hendrickson et al. (2004), and overall, the model captures these excursions above and below saturation concentration well.

48Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.10a. Simulated (line) and observed (square) dissolved oxygen at Talleyrand and Piney Point.

49Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 4.10b. Simulated (line) and observed (square) dissolved oxygen at Racy Point and Palatka.

50Sucsy and Hendrickson Nutrient Reduction to LSJR



## Chapter 5. Natural Background
We calculated natural background loads to th e LSJR to estimate, by difference, the anthropogenic fraction of the present day load s. The estimation of background loads was necessary for this study because load reduc tions were aimed at reducing anthropogenic loads only. In addition, knowledge of th e background load allowed us to estimate changes in the relative composition of loads as nutrients are reduced. For example, reduction of anthropogenic nitrogen disproportio nately reduces bioavailable nitrogen, so that the relative proportion of bioavailable to total nitrogen also decreases as loads are reduced. Certain water quality loads, for example refractory dissolved organic carbon, increase between present day and natural conditions.

Natural background loads are defined here as the watershed loads that result when all land-uses within the basin are reverted to th e undeveloped (assumed fo rested) condition. Any additional loads caused by land-use changes, such as urbanization or farming, are the anthropogenic loads. We tested the response of the ri ver to the natural background loads using the water quality model. These te sts illustrate the range of water quality improvements possible through load reduction. These tests also show whether water quality targets are reasonable. For example, the tests clearly showed that dissolved oxygen in the marine reach and chlorophyll-a in the freshwater reach met water quality targets under natural background loads.

We caution the reader not to infer that the model results calculated using natural background loads represent water quality condi tions that would have existed prior to human influence. Two important factors affecting water qual ity have not been considered here. First, morphologic change s in the marine reach due to dredging and channelization for navigation have probably increased both tidal mixing and the upstream intrusion of salinity into the LSJR. Second, implicit in the modeling is that all primary production is undertaken by phytoplankton. Historical accounts, and more recent invasions by exotic aquatic weeds indicat e that floating macrophytes, without human intervention, would likely cons titute much of the primary production within the LSJR. Thus, it should be understood the projections of natural backgr ound productivity based upon chlorophyll-a should not imply the LSJR was a phytoplankton-dominated system. Rather, these should be viewed as projections of productiv ity potential, if all growth physically and biologically possible were performed by phytoplankton.



### Natural Background Input Loads Natural background loads of nitrogen, phosphor us, suspended solids, and organic carbon were estimated for all tributaries and ups tream sources entering the LSJR. Ocean boundary concentrations were also estimated for natural background conditions. We did not alter salinity or silica boundary cond itions. Also meteorologic and hydrodynamic conditions remained the same.

51Sucsy and Hendrickson Nutrient Reduction to LSJR

Ocean Boundary Constant concentrations used to repres ent ocean boundary conditions under natural background conditions are shown in Table 5.1, below. Dissolved oxygen at the ocean boundary was set at saturation levels and va ried monthly. Satu ration dissolved oxygen ranged from a low of 6.5 mg L -1 in June to a high of 8.2 mg L -1 in December.

Table 5.1. Constant values of water quali ty parameters applied at ocean boundary for natural background scenarios Value ISS 16.4 Diatoms 0.11 DOC 1.04 POC 0.59 TN 0.38 TP 0.042 TSi 0.53 D.O. Saturation

Upstream Boundary Natural background concentrations were es timated at both Buffalo Bluff and Dunns Creek (Hendrickson, 2004b). Salinity, RDOC , POC, D.O., and Total Silica remained identical or nearly identical to present da y concentrations. Alga l carbon, LDOC, total nitrogen, and total phosphorus were reduced considerably over present day levels. Summary statistics for the natural background c oncentrations defined at Buffalo Bluff are shown in Table 5.2. Values at Dunns Creek were similar.

Table 5.2. Summary statistics for primary water quality variables observed at Buffalo Bluff, 1995-99. Concentrations are all mg L -1 . The TN:TP ratios in the last row are unitless. Diatom carbon includes a small fr action of other taxonomic groups, primarily green algae. Variable Mean Standard Deviation Minimum Maximum

Cyanobacteria Carbon 0.44 0.29 0.00 0.96

Diatom Carbon 0.26 0.17 0.00 0.75 LDOC 0.53 0.15 0.23 0.88 Total N 0.69 0.23 0.41 1.52 Total P 0.037 0.004 0.026 0.050 TN:TP 18.1 4.8 11.1 41.0

52Sucsy and Hendrickson Nutrient Reduction to LSJR

Tributary Loads Tributary loads under natural background conditi ons were calculated using a hydrologic model. Total nitrogen loads decreased from about 2300 to 1400 metric tons yr -1 between present day and natural background. Tota l phosphorus load reduction under natural background conditions was relativel y greater than total nitrogen, decreasing from 380 to 120 metric tons yr -1 . Total organic carbon load in creased slightly under natural background conditions, from 49, 500 to 51,000 metric tons yr -1 . Increase in total organic carbon is attributed to gr eater refractory organic carbon loads from undeveloped compared with developed watersheds.


### Natural Background Simulations Results of simulations using the natural background loads are compared with simulations using present day loads below. Comparison plots are shown for total nitrogen, total phosphorus, dissolved silica, chlorophyll-a, and dissolved oxygen. Comparisons are made at two locations. Racy Point represents changes typical of the freshwater reach and Talleyrand represents changes typical of the marine reach.

The natural background results were calcu lated using estimated natural background tributary loads, upstream flow concentrati ons, and ocean boundary concentrations as described previously. Point source loads were also reduced to zero. Atmospheric loads were unaltered for these simulations.

The results clearly show the marked reducti on of both total nitrogen and total phosphorus at both stations (Figures 5.1 and 5.2). Disso lved silica, on the other hand, increases in response to nutrient load reduction because of the attendant decrease in diatom production (Figure 5.3).

Chlorophyll-a levels decreased dramatically at Racy Point un der natural background loads (Figure 5.4). Peak chlorophyll-a concentrations are reduced from values exceeding 80 P g L -1 to values less than 20 P g L -1 . Already low chlorophyll-a values at Talleyrand were further reduced under natural background loads.

Dissolved oxygen increased under natural background loads. Dissolved oxygen at Talleyrand exceeded 5.5 mg L -1 throughout the simulation period, thus eliminating the persistent summer oxygen sags found in th e present day scenario (Figure 5.5).

53Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 5.1. Present day (bl ack) and natural back ground (red) simulated total nitrogen at Talleyrand and Racy Point.

54Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 5.2. Present day (black) and natura l background (red) simulated total phosphorus at Talleyrand and Racy Point.

55Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 5.3. Present day (bl ack) and natural back ground (red) simulated dissolved silica at Talleyrand and Racy Point.

56Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 5.4. Present day (black) and natura l background (red) simulated chlorophyll-a at Talleyrand and Racy Point.

57Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 5.5. Present day (black) and natura l background (red) simulated dissolved oxygen at Talleyrand and Racy Point.

58Sucsy and Hendrickson Nutrient Reduction to LSJR



## Chapter 6. Assimilative Capacity of the Freshwater Section of the LSJR

Chlorophyll Targets

Assimilative capacity is an estimate of the loading required to meet a desirable concentration level (Chapra, 1997). For the fr eshwater reach of the LSJR, assimilative capacity is based on achieving reductions of chlorophyll-a. Two chlorophyll targets are examined here. The first target requires th at mean annual chlorophyll-a concentration not exceed 20 P g L -1 . The target is adopted from the recently promulgated F.A.C. 62-303 criteria for impairment in rivers. The second target requires that 40 P g L -1 of chlorophyll-a be exceeded no more than 10% of the time. This second target is based on site-specific analyses of z ooplankton diversity and abundanc e that indicate negative ecological effects are associated with con centrations of chlorophyll-a greater than 40 P g L -1 . Because this latter target is supporte d by site-specific data, SJRWMD recommends this target be used for the assimilative capac ity calculation. Assimilative capacity of the freshwater segment, then, is defined as the maximum nutrient loading that is insufficient to support chlorophyll concen trations greater than 40 P g L -1 more than 10% of the days within a long-term averaging period. Devi ations from the chlorophyll standard for impairment of freshwaters for various nutrient loading rates are presented for comparison.

CE-QUAL-ICM was used to evaluate the response of the river relative to these targets for each of four years and within five WBIDs (F igure 3.1) in the freshwater reach. The locations of the five WBIDS are described in Table 6.1, below, along with the code name assigned by FDEP, and approximate surface area. The freshwater reach was previously defined as encompassing WBIDs G-M, how ever, WBIDs G and H are not listed as impaired and so not included in this anal ysis. Simulated chlorophyll-a was spatially averaged within each WBID to be consis tent with the methodology used by FDEP for defining impairment.

Table 6.1. Water Body ID Segments contained w ithin the freshwater segment of the LSJR.

WBID Code Segment Description (north to south) Surface Area (km 2 ) 2213I Black Creek to Sixmile Creek 47.9 2213J Sixmile Creek to Tocoi 44.9 2213K Tocoi to Federal Point 46.6 2213L Federal Point to Rice Creek 22.6 2213M Rice Creek to Dunns Creek 22.2

59Sucsy and Hendrickson Nutrient Reduction to LSJR



### Observed Chlorophyll-a

A water quality monitoring station is located within each of the five WBIDS described above (Figure 4.1). An additional six wa ter quality stations are located farther downstream in the oligo- and mesohaline reaches of the river. Annual average chlorophyll-a and the percentage of days that exceeded 40 P g L -1 chlorophyll-a was estimated at each of the 11 stati ons for each of four years. These statistics were estimated by interpolating monthly observations of chlo rophyll-a to daily inte rvals using a cubic spline. An example of an interpolated time-series is shown in Figure 6.2. The correctness of this method is based on the a ssumption that the monthly sampling interval is not aliased (that is, the principle time-scale of the variability is of the order of several weeks). Both the water quality model simu lations and experience of the environmental scientists maintaining the water quality mon itoring network indicate that this assumption is reasonable.

Jan Feb Mar Jul Jun Aug Sep Oct Nov Apr May

1999Chlorophyll (Pg L-1) 150

100

50

0 Racy Point Avg = 50.2 % > 40 = 41.9

Figure 6.2. Cubic spline interpolatio n of observed chlorophyll-a at Racy Point, within WBID K. Annual average chlorophyll and the percentage of days exceeding 40 P gL -1 were estimated from the 365 interpolated valu es. The observed values are indicated with an asterisk.

60Sucsy and Hendrickson Nutrient Reduction to LSJR

Annual average chlorophyll-a fo r each station is shown in Table 6.2 and the percentage of days exceeding 40 P g L -1 is shown in Table 6.3. Stations are listed in order from upstream to downstream. The first five stations are located in the freshwater segment and correspond to WBIDS M-I, respectively. Both statistic s decrease markedly from upstream to downstream. A pronounced inter-an nual variability of chlorophyll-a levels within the freshwater reach c ontrasts with the relative lack of inter-annual variability in the oligo- and mesohaline segments.

Table 6.2. Annual average chlorophyll-a ( P g L -1 ) in the LSJR during the period of model simulation. The first five stations are located in the freshwater reach. The stations in the oligo- and mesohaline segment of th e river are indicated by shading.

Station WBID Year 1 Year 2 Year 3 Year 4 Palatka 2213M 18.0 29.0 33.6 42.7 Federal Pt 2213L 18.6 29.6 22.1 42.1 Racy Pt 2213K 18.0 28.2 25.5 50.2 Watson I. 2213J 14.6 24.1 27.4 19.9 Picolata 2213I 13.9 20.2 25.0 16.5 Hibernia 2213G 10.9 11.7 14.7 11.2 Mandarin 2213F 10.6 9.6 11.5 7.9 Piney Pt 2213E 10.6 8.6 8.3 7.9 Talleyrand 2213D 6.9 5.1 5.1 6.1 Fulton 2213B 3.2 3.3 4.1 4.2

Table 6.3. Observed percentage of days exceeding 40 P gL -1 chlorophyll-a in the LSJR during the period of model simulation. The first five stations are located in the freshwater reach. The stations in the o ligo- and mesohaline segment of the river are indicated by shading.

Station WBID Year 1 Year 2 Year 3 Year 4 Palatka 2213M 6.5 17.3 28.3 39.3 Federal Pt 2213L 1.7 13.5 14.8 44.7 Racy Pt 2213K 8.5 13.2 16.8 41.9 Watson I. 2213J 9.1 9.9 25.5 10.8 Picolata 2213I 8.8 8.3 15.7 0.0 Hibernia 2213G 0.0 0.0 5.9 0.0 Mandarin 2213F 0.0 0.0 2.5 0.0 Piney Pt 2213E 0.0 0.0 0.0 0.0 Talleyrand 2213D 0.0 0.0 0.0 0.0 Fulton 2213B 0.0 0.0 0.0 0.0

61Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.3 shows that 40 P g L -1 chlorophyll-a was exceeded for more than 10% of the days for 3 of the 4 simulations years. The range of this statisti c was quite wide among years. During Year 1 chlorophyll-a levels exceeded the threshold for about 9% of the days, while in Year 4 the threshold was exceeded for over 40% of the days. These patterns are consistent with the observation that chlorophyll levels in the freshwater segment of the LSJR are inversely correlate d with discharge. Year 1 (1995) was a relatively wet year with lowe r chlorophyll-a levels than Y ear 4 (1999), a relatively dry
year.

Hydrologic Variability Given the extent that chlorophyll-a levels are sensitive to hydrologic variability, quantification of the probability of discharg e for each year was desired to place the four simulation years within a long-term context. An exact determina tion of probability of discharge in the LSJR is difficu lt, however, because of the relatively short records at the two main-stem flow gauges. (Measurements at Buffalo Bluf f began in June 1990, while measurements at Main St. Bridge began in August 1996.) To overcome this difficulty, we used long-term gauges as surrogates for LS JR discharge. The nearest long-term flow gauge upstream of Buffalo Bluff is located at DeLand in the Middl e Basin and measures the majority of flow from the Upper and Middle Basins to the LSJR. This gauge was chosen as a surrogate for upstream flow. A gauge located on the South Fork of Black Creek, the largest tributary to the LSJR, was chosen as the surrogate for local tributary flow. Both gauges have been in conti nuous operation since 1940. Probabilities of occurrence were calculated for both maximum and minimum discharges averaged over the season of high algal growth (April-August) . The inverse of th e probabilities, or annual recurrence intervals, ar e reported in Table 6.4.

Table 6.4. Recurrence intervals of maximum a nd minimum seasonally-averaged (AprilAugust) discharge at DeLand and the South Fork of Black Creek during the years of model simulation. DeLand is a surrogate for flow entering the Lower Basin from the Middle Basin and Black Creek is a surrogate for local tributary flow. Recurrence intervals were developed from 50-year discharge records.

Recurrence of Maximum Seasonal Discharge (Years) Recurrence of Minimum Seasonal Discharge (Years) Year DeLand Black Ck DeLand Black Ck 1 3.3 3.9 1.4 1.3 2 1.6 15.7 2.7 1.1 3 4.2 1.3 1.3 4.2 4 1.1 1.0 7.9 21.0

62Sucsy and Hendrickson Nutrient Reduction to LSJR

Year 1, when chlorophyll targets were me t, was indeed a high flow year for both upstream (3.3 yr high) and local tributary (3.9 yr high) flow. In contrast, Year 4, when large deviations from the chlorophyll targets o ccurred, was a dry year (7.9-21.0 yr lows). Years 2 and 3 show mixed wet/dry seasonality for upstream and local flows. Upstream flow was close to the average minimum discha rge during Year 2 (2.7 yr low), while local tributary flow was quite high during this period (15.7 yr hi gh). Year 3 exhibited the opposite pattern with higher than average flow from the Middle Basin (4.2 yr high), but lower than average flow from the tributaries (4.2 yr low). Year 3 included the “El Nino” winter of 1997-8 when high winter flows from the Middle Basi n lagged the local tributary flows as the Lower Basin lapsed into a protracted drought.

The recurrence intervals for these four years based on a 4-year seasonally-averaged discharge are relatively moderate. The DeLa nd discharge was a 2.79-year low discharge event, and the Black Creek discharge was a 2. 31-year high discharge event. The tributary flows, then, were close to the mean annual high discharge for a 4-year seasonal average, while the upstream flows were somewhat le ss than the mean annua l low discharge. These four years, then, were sl ightly drier than average. Th is result implies that using these four years to represent long-term averaged conditions for the purpose of examining chlorophyll-a response to load reduction would be conservative for the resource.



### Comparison of Observed and Simulated Chlorophyll

Observed and simulated annual average chlor ophyll for each year along with a statistical summary of the fit are compared in Table 6.5. The table shows both observed (O) and simulated (S) average annual chlorophyll at each station along with the absolute percent error. Error statistics were generated fo r each set of observed and simulated values. These statistics are (a) r 2 , the square of the correlation coefficient, (b) root-mean-square (rms) error, (c) the slope of the linear regression line, and (d) the intercept of the linear regression line. A perfect fit of obser ved and simulated values would have r 2 = slope = 1.0 and rms = intercept = 0.0.

The r 2 values for annual average chlorophyll are a ll quite high (0.86-0.97) . This statistic indicates that the model simulates the downs tream decline in chlo rophyll well. Figures 6.3-6.6 further illustrate this point. Th ese figures compare observed and simulated average annual chlorophyll for each year.

Figures 6.4-6.6 show that the model tended to underpredict average annual chlorophyll in the farthest upstream stations during Years 2-4 by about 10- 20%. The diagonal line on each figure represents the 1:1 line of perfect fit. This 1:1 line serves as a guide to show areas of under- or over-prediction. The underprediction at these upstream stations during these years also causes the regression line slope s to be less than one. For example, the regression line slope of 0.63 during Year 3 is largely a result of the underprediction of chlorophyll at Palatka (Table 6.5).

63Sucsy and Hendrickson Nutrient Reduction to LSJR

In general, the model successfully predicted annual average chlorophyll both in regards to spatial variability within the river and inter-annual variability.

Table 6.5. Comparison of observed and simu lated annual average chlorophyll.

Year 1 Year 2 Year 3 Year 4 Station O S %

error O S %

error O S %

error O S %

error Palatka 18.0 19.9 10.6 29.0 26.7 -7.9 33.6 21.2 -36.7 42.7 34.9 -18.3 Federal Pt 18.6 20.1 8.1 29.6 26.1 -11.8 22.1 21.9 -0.9 42.1 41.3 -1.9 Racy Pt 18.0 19.6 8.9 28.2 25.1 -11.0 25.5 22.4 -24.5 50.2 40.1 -20.1 Watson I. 14.6 15.2 4.2 24.1 20.8 -13.7 27.4 20.7 -22.8 19.9 25.0 25.6 Picolata 13.9 12.9 -7.2 20.2 18.2 -9.9 25.0 19.3 9.5 16.5 21.6 30.9 Hibernia 10.9 10.0 -8.3 11.7 13.6 16.2 14.7 16.1 9.5 11.2 16.0 42.9 Mandarin 10.6 8.5 -19.8 9.6 10.7 13.6 11.5 12.6 9.6 7.9 11.4 44.3 Piney Pt 10.6 8.4 -20.8 8.6 9.7 10.7 8.3 10.8 30.1 7.9 9.2 16.5 Talleyrand 6.9 5.7 -17.4 5.1 5.4 9.7 5.1 6.0 17.7 6.1 4.3 -29.5 Fulton 3.2 2.9 -9.4 3.3 3.0 5.4 4.1 3.0 -26.8 4.2 2.5 -40.5

r 2 0.97 0.98 0.86 0.92

rms error 1.33 1.89 3.51 4.2 Slope 1.2 0.83 0.63 0.79 Intercept -2.8 1.85 4.3 4.1 O = observed value, S = simulated value in P g L -1

.

64Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 6.3. Comparison of observed and simula ted average annual chlorophyll during Year 1. The line indica tes a perfect 1:1 fit.

Figure 6.4. Comparison of observed and simula ted average annual chlorophyll during Year 2. The line indica tes a perfect 1:1 fit.

65Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 6.5. Comparison of observed and simula ted average annual chlorophyll during Year 3. The line indica tes a perfect 1:1 fit.

Figure 6.6. Comparison of observed and simula ted average annual chlorophyll during Year 4. The line indica tes a perfect 1:1 fit.

66Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.6 compares observed and simulated va lues of the percentage of days that 40 P g L -1 was exceeded at each station for each y ear. The correlation between simulated and observed values is quite good for Year 2-4 (r 2 = 0.74-0.95). However, the correlation for Year 1 is poor due to the la rge overprediction of percent exceedence in the freshwater segment. This mismatch for Year 1 is interesting because Year 1 produced the best fit among the four years for average annual chlo rophyll (Table 6.5). The reason for this incongruity is that observed peak chlorophyll in the freshwater segment during that year was very close to the threshold of 40 P g L -1 . Even a small overprediction of chlorophyll, then, would result in a larg e increase in the percent exceed ence of the threshold without having a large effect on the average chlo rophyll. Figure 6.7 compares observed chlorophyll with the time-series of simulated chlorophyll at Fede ral Point to i llustrate this point. Note how the choice of the target threshold affects the difference between simulated and observed percent exceedence in this case.

Table 6.6. Comparison of observed and simulated percent exceedence of 40 P g L -1 chlorophyll.

Year 1 Year 2 Year 3 Year 4 Station O S %

error O S %

error O S %

error O S %

error Palatka 6.5 23.1 255.4 17.3 19.5 12.7 28.3 15.9 -43.8 39.3 37.7 -18.3 Federal Pt 1.7 20.6 1111.8 13.5 17.3 28.2 14.8 17.9 21.0 44.7 41.3 -1.9 Racy Pt 8.5 18.7 120.0 13.2 15.7 18.9 16.8 15.1 -10.1 41.9 38.8 -20.1 Watson I. 9.1 10.2 12.1 9.9 9.1 -8.1 25.5 11.3 -55.7 10.8 16.8 55.6 Picolata 8.8 6.3 -28.4 8.3 2.7 -67.5 15.7 9.9 -36.9 0.0 21.6 Not applicable Hibernia 0.0 0.0 Not applicable 0.0 0.0 Not applicable 5.9 6.9 17.0 0.0 10.5 Not applicable Mandarin 0.0 0.0 Not applicable 0.0 0.0 Not applicable 2.5 0.8 -68.0 0.0 9.1 Not applicable Piney Pt 0.0 0.0 Not applicable 0.0 0.0 Not applicable 0.0 0.0 Not applicable 0.0 0.0 Not applicable Talleyrand 0.0 0.0 Not applicable 0.0 0.0 Not applicable 0.0 0.0 Not applicable 0.0 0.0 Not applicable Fulton 0.0 0.0 Not applicable 0.0 0.0 Not applicable 0.0 0.0 Not applicable 0.0 0.0 Not applicable

r 2 0.37 0.92 0.74 0.95

rms error 4.93 1.49 4.0 3.4 Slope 1.39 1.13 0.58 0.85 Intercept 3.1 -0.57 1.39 3.80 O = observed value, S = simulated value.

Rms error is reasonable for all years (Table 6.6). The slope of the regression line was poor (0.58) for Year 3, but this was largel y a result of the model underprediction at the farthest upstream station. Although not as drama tic as Year 1, the percent errors for Year 3 are high. For example, the model underp redicts the percent exceedence by 55.7% at Watson I. during Year 3. An examination of the time-series plot (F igure 6.8) shows that, again, the discrepancy is largely a result of the inherent difficulty in determining percent

67Sucsy and Hendrickson Nutrient Reduction to LSJR

exceedence when peak values are close to the target threshold. Figure 6.8 shows that the model simulated chlorophyll-a reasonably well.

Figure 6.7. Simulated and observed chlorophyll-a at Federal Point during Year 1. Observed values are indicated by the squares and simulated values by the continuous black line.

Linear regression statistics we re calculated for percent exceedence of the chlorophyll-a target for Years 2-4 in the freshwater se gment. This analysis was limited to the freshwater segment since prediction within that segment is the focus of this analysis. Simulated values for Year 1 are not included in this plot. Since the observed data clearly show that chlorophyll-a levels did not exceed 40 P g L -1 more than 10% of the time that year, no reduction was requi red for that year. The r 2 for these data was 0.75 and rms error was 5.1%. The slope (0.76) and intercept (3.5) of the linear regression line do not indicate any unreasonable bias. Observed and simulated values used for the analysis are shown in Figure 6.9.

68Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 6.8. Simulated and observed chlorophy ll at Watson I. during Year 3. Observed values are indicated by the squares and simu lated values by the continuous black line.

69Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 6.9. Observed and simulated percent exceedence of 40 P g L -1 chlorophyll for 5 stations in freshwater segment. Year 1 values were excluded from this plot

.



### Calculation of Assimilative Capacity

Summary of Nutrient Loads Assimilative capacity of the freshwater segm ent of the LSJR was calculated by making equal percent reductions of anthropogenic loads entering the freshwater segment. The reduced load scenarios were tested using the water quality model to determine the percentage of nutrient reduction required to at tain the chlorophyll targets. Because the freshwater section exhibits both nitrogen and phosphorus limitation (complicated by the ability of cyanobacteria to fix atmospheri c nitrogen), both phosphorus and nitrogen reductions were treated identically in this analaysis.

The input loads to the freshwater segment consis t of five point sources, 16 tributaries, and two upstream inflows. The five point sources are as follows:

70Sucsy and Hendrickson Nutrient Reduction to LSJR

a. City of Palatka Waste-Water Treatment Facility (WWTF) b. Georgia Pacific Corporation, Palatka c. City of Hastings WWTF d. South Green Cove Springs WWTF e. Green Cove Springs WWTF

The two upstream inflows, entering through Buffalo Bluff and Dunns Creek, and the 16 tributaries are listed, in order of basin area, in Table 6.7 along with the approximate area of each basin.

Nitrogen, phosphorus, and organi c carbon loads to the freshw ater segment of the LSJR are summarized in Tables 6.8-6.11. Both existing and natural background loads are shown. The difference between existi ng and natural background loads is the anthropogenic load that results from human activities. The anthropogenic load represents the portion of the existing load that can be reduced to meet the chlorophyll targets.

Point source loads are nearly constant among year s. For example, the annual load of total nitrogen from point sources ranged from 271- 304 metric tons over the four years. Nonpoint source loads, however, vary consid erably between years because of their inextricable link to hydrologic variability. For example, the annual contribution of total nitrogen from the Middle Basin (through Bu ffalo Bluff) ranged from a low of 4,453 metric tons in Year 2, to a high of 11,569 metric tons in Year 1. The ratio of these loads, at 0.38, is nearly equal to the ratio of the mean discharges (91 and 207 m 3 s -1 in Year 2 and Year 1, respectively) of 0.44.

71Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.7. Tributaries entering the freshwater segment of the LSJR arranged by basin

area.

Basin Name Basin Area (mi 2 ) Middle Basin (Buffalo Bluff) 6580 Dunns Creek (Crescent Lake) 585 Rice Creek 238 Sixmile Creek 119 Deep Creek 68 Mill Branch 66 Governor’s Creek 38 Tocoi Creek 35 Moccasin Branch 35 Clarkes Creek 31 Dog Branch 23 McCullough Creek 23 Cedar Creek 22 Mason Branch 16 Unnamed Creek 11 Moccasin Creek 4 Kendall Creek 4 Orange Grove Branch 1.5

72Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.8 goes here.

73Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.9 goes here.

74Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.10 goes here.

75Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.11 goes here.

76Sucsy and Hendrickson Nutrient Reduction to LSJR

Insensitivity of Freshwater Re sponse to Downstream Loads Before load reduction tests for the freshwater segment were made, sensitivity tests were conducted to determine the extent that loads entering the oligo- a nd mesohaline segments affect chlorophyll in the freshwater segment. These tests were made by independently reducing loads from five groups of sources by 50%. The five groups of sources were as follows:

x Point sources entering the oligo/mesohaline segment

x Non-point sources entering th e oligo/mesohaline segment

x Point sources entering the freshwater segment

x Non-point sources entering the freshwater segment

x Upstream sources entering the lo wer basin through Buffalo Bluff

For each test, a response was calculated fo r annual average chlorophyll and percent exceedence of 40 P g L -1 chlorophyll in the freshwater se gment. These tests were made for each of two years, where 1998 represents a high-flow (wet) year and 1999 represents a low-flow (dry) year. Results were expresse d as the ratio of the percent response of the target and the percent decreas e in the given load, simila r to a condition number as described by Chapra (1997).

Table 6.12 contains the results of the sensitivity tests fo r average annual chlorophyll and Table 6.13 contains the results for percent exceedence of 40 P g L -1 . The results in both Tables 6.12 and 6.13 show that sources enteri ng the oligo- and mesohaline segments have an insignificant effect on chlorophyll targets in the freshwater se gment. This result means that the assimilative capacity calculati on for the freshwater segment does not need to consider load reductions to the lower portions of the river.

Table 6.12. Condition numbers for annual average chlorophyll in freshwater segment.

Upstream Sources Oligo/meso Point Sources Oligo/meso Non-Point

Sources Fresh Point Sources FreshNonPoint Sources Wet Year 0.750 0.002 0.002 0.052 0.086

Dry Year 0.852 0.006 0.000 0.240 0.060

77Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.13. Condition numbers for exceedence of 40 P g/L in freshwater segment.

Upstream Sources Oligo/meso Point Sources Oligo/meso Non-Point

Sources Fresh Point Sources FreshNonPoint Sources Wet Year 0.598 0.000 0.000 0.046 0.116

Dry Year 0.240 0.000 0.000 0.120 0.174

Percent Reductions Required to Meet Targets By Year
Load reduction tests were conducted for each year by making equal percent reductions of anthropogenic nitrogen and phosphorus loads to the freshwater segment at 25%, 50%, 75%, and 100% reduction levels. The response of chlorophyll to load reductions was remarkably linear, allowing linear interpol ation for calculating the minimum percent reduction required to meet the chlorophyll ta rgets. The minimum percent reduction was calculated within each of the five WBIDs. An example calculation is shown in Table 6.14 for average annual ch lorophyll in Year 2.

Table 6.14. Percent exceedence of 40 P g L -1 chlorophyll by WBID during Year 3 for existing condition and various percent reduction le vels. Percent reductions are applied to anthropogenic loads. The last column shows the percent reduction re quired of all sources to attain prevent exceedences above 10%.

WBIDS Existing 25% 50% 75% Natural Target Reduction (%) 2213-I 11.1 7.7 3.5 0.5 0.0 8.1 2213-J 11.4 7.9 5.3 1.1 0.0 10.0 2213-K 14.9 10.3 7.3 0.2 0.0 27.5 2213-L 17.4 10.3 3.6 0.0 0.0 26.1 2213-M 16.1 10.0 0.9 0.0 0.0 25.0

The results of the assimilative capacity calcula tions for all years are summarized in Table 6.15, below. The results shown are percen t anthropogenic load re ductions required to meet each chlorophyll target for the given year . The percent reductions in Table 6.15 are based on the reduction required to meet th e chlorophyll target within all WBIDs.

78Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.15. Target reductions required to ma intain chlorophyll ta rgets throughout the freshwater segment of the LSJR for each of four model years.

Year Target reduction to maintain average chlorophyll < 20 P g L -1 Target reduction to maintain exceedence of 40 P g L -1 chlorophyll < 10% of time 1 6 0 *
2 47 17 3 22 28 4 74 63

* This reduction based on observations that show ed no violations, not on model simulation.

Assimilative Capacity by Year The percent load reductions show n in Table 6.15 can be used together with the values of load shown in Tables 6.8-6.11 to determin e the mass load reductions that would be required to meet the targets in each year, as well as the maximum mass load that meets the targets. The mass load reductions define a “Pollution Load Reduction Goal”, or PLRG, and the maximum mass load defines the assimilative capacity. Assimilative capacity is essentially equivale nt to “Total Maximum Daily Load”, or TMDL. (The use of TMDL and PLRG here are for convenien ce in describing the concept and do not imply recommendation as a final value.)

Table 6.16. Existing load, assimilative capacity (TMDL), and load reduction (PLRG) required to prevent exceedences of 40 P g L -1 for more than 10% of the time throughout the freshwater section. All valu es are in metric tons per year. Total Nitrogen Total Phosphorus Year Existing TMDL PLRG Existing TMDL PLRG 1 14162 14162 0 916 916 0 2 6823 6176 647 441 399 42 3 13623 11835 1788 731 647 84 4 5415 3278 2137 778 182 95

Table 6.16 illustrates that the load required to meet the chlorophyll targets in any given year depends on hydrologic variability, resulting in a large range in as similative capacity. The large variability of assimilative capacity is because low-flow years, such as Year 4, have a smaller assimilative capacity than hi gh flow years, such as Year 1. This sensitivity to hydrologic variability should be considered for design of treatment facilities and implementation of Best-Management-Pr actices, which should emphasize greater treatment efficiency under dry conditions. It should be noted that such BMP design in this case would be fundamentally different that the concept of “worst-case” (i.e., dry year) design. Typically, tr eatment design that is intended to handle the worst-case

79Sucsy and Hendrickson Nutrient Reduction to LSJR

scenario is expected to meet the same treatment efficiency at all times. Alternatively, under this interpretation, trea tment efficiency for a wet interval may be much lower.

Reductions Required to Meet Chlorophy ll Targets For Long-term Average Conditions The recommended chlorophyll target does not allow exceedence of 40 P g L -1 chlorophyll for greater than 10% of the time. At present, it is proposed that this target be applied to long-term average conditions rather than the annual average condition. The application of the target to long-term average conditions recognizes several mitigating factors. First, as conditions become drier, non-point s ource treatment efficiency improves, so stormwater retention facilitie s designed for 30 % removal will probably achieve greater removal under drier, “worst-case” conditions. This improvement in treatment efficiency also applies in point sources that rely on land application, reuse, wet-weather discharge, or retention basin configurations.

Second, the implementation of a 30% reduction that achieves the goal of reducing blooms of greater than 40 μg/L chlorophyll-a to less than 10% of the long term recognizes uncertainty that exists in the ultimate tr ophic transfer of primary production. For example, favorable zooplankton populati ons may rebound following the cessation of severe blooms, so that long-term impact on fisheries may be minimal; and that occasional severe algal blooms may be a natural perterba tion within the system, or an unavoidable perturbation in a system now managed to be free of floating aquatic plants.

As was previously discussed, the four model years happen to provide a reasonable approximation to long-term average conditions . In addition, using these years to represent a long-term response to load reduction is conservative and provides an implicit margin of safety for the resource.

The long-term average response of the system to various percentage s of load reduction are shown in Table 6.17 and Table 6.18 for each WBID. Results for average chlorophyll are shown in Table 6.17 and results for percent exceedence of 40 P g L -1 is shown in Table 6.18.

Table 6.17. Long-term average chlorophyll by WB ID for existing load and various percent reduction levels. Percent reducti ons are applied to anthropogenic loads. Chlorophyll values in columns 2-6 are P g L -1 . The last column shows the percent reduction required of all sources to attain average ch lorophyll of 20 P g L -1

.

WBIDS Existing 25% 50% 75% Natural Target Reduction (%) 2213-I 17.4 13.7 10.8 8.2 5.7 0.0 2213-J 21.3 16.7 13.0 9.7 6.6 7.1 2213-K 26.5 20.9 16.4 12.2 8.2 30.0

80Sucsy and Hendrickson Nutrient Reduction to LSJR

2213-L 27.7 22.7 18.6 14.4 10.0 41.5 2213-M 25.8 22.1 19.2 15.7 11.8 43.1

81Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 6.18. Long-term average percent exceedence of 40 P g L -1 chlorophyll by WBID for existing load and various percent reduction levels. Percent reductions are applied to anthropogenic loads. The last column shows the percent reduction re quired of all sources so that 40 P g L -1 is exceeded no more than 10% of the time over a long-term period.

WBIDS Existing 25% 50% 75% Natural Target Reduction (%) 2213-I 5.6 3.2 1.3 0.1 0.0 0.0 2213-J 9.8 4.4 2.1 0.3 0.0 0.0 2213-K 16.8 9.9 5.1 0.6 0.0 24.6 2213-L 19.7 11.1 5.6 0.3 0.0 30.0 2213-M 18.2 10.2 3.7 0.1 0.0 25.8

Assimilative Capacity and R ecommended Reduction Goals Based on the preceding results, the recomme nded reduction of anthropogenic sources of both nitrogen and phosphorus to the freshwater segment of the LSJR is 30%. The longterm average assimilative capacity (TMDL) fo r total nitrogen is a bout 8440 metric tons
yr -1 , and for total phosphorus is about 500 metric tons yr -1 . The mean annual Pollution Load Reduction Goal (PLRG) is about 1560 metr ic tons of total ni trogen and 85 metric tons of total phosphorus.

Of consideration is that these recommended load reductions do not meet the chlorophyll targets in all years, but would allow this target to be met over the long -term. In addition, these reductions only address nuisance algal levels associated w ith the effects of eutrophication. Increased understanding of other important biologi cal components of the system could, in the future, require additional load reductions.

As discussed previously, a target base d on exceedence criteri a of chlorophyll concentration greater than 40 P g L -1 is best supported by biolog ical analyses. However, basing the assimilative cap acity on maintaining a long- term average chlorophyll-a concentration of less than 20 P g L -1 would require 43% reduc tion of anthropogenic loads (Table 6.17).

82Sucsy and Hendrickson Nutrient Reduction to LSJR



## Chapter 7. Assimilative Capaci ty of the Marine Section of the LSJR

The marine section of the LSJR encompasse s the area from the river mouth to a point near the entrance to Doctors Lake. This sec tion covers the lower 60 km of the river and is more formally described by SJRWMD as the mesohaline and oligohaline reaches. The line of division between the mesohaline and olig ohaline reaches is regarded to be near Acosta Bridge where a sharp change in both br eadth and depth also occurs (Figure 7.1). The marine section contains six WBIDs, A-F, as shown in Figures 7.1 and 7.2.

Figure 7.1. Mesohaline reach of the LSJR, extending from the mouth to Acosta Bridge. This reach includes WBIDs A-D. Continuous dissolved oxygen data are available at Dames Point and Acosta Bridge.

83Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 7.2. Oligohaline reach of the LSJR . This reach includes WBIDs E and F. Continuous dissolved oxygen data are availabl e at Acosta Bridge and Buckman Bridge.

Assimilative capacity of the marine segment was based on preventing harmful dissolved oxygen sags that occur primarily downstream of Acosta Bridge. We found that reduction of nitrogen to both the mesohaline and oligohaline reaches alleviate oxygen sags in this portion of the river. Load reductions were based on the year 1999 alone because that was the worst-case year available. Dissolved oxygen sags during that su mmer resulted in a fish kill event near Jacksonville.


### Dissolved Oxygen Targets The effects of eutrophication in the marine section are manifested as persistent low dissolved oxygen in the summer periods (see, for example, Figure 4.10a). The mesohaline reach, WBIDs A-D in Figure 7.1, is most affected. The lower dissolved oxygen in the mesohaline reach as compared to the oligohaline reach is probably due to several factors. Factors of greatest importance seem to be (a) reduced reaeration rates caused by a small surface area to volume ratio, (b) higher salinity, and (c) higher algal respiration and senescence rate s due to a transition between freshwater and marine algal communities.

84Sucsy and Hendrickson Nutrient Reduction to LSJR

The persistent low-dissolved oxygen impairment index described in Ambient Aquatic Life Water Quality Criteria for Dissolved Oxygen (Saltwater): Cape Cod to Cape Hattaras was used to evaluate detrimental epis odes of hypoxia in the marine LSJR. The applicability of this index fo r the LSJR is discussed in Hendrickson et al. (2004a). This index accounts for varying durations that ca n be tolerated by estuarine organisms at different dissolved oxygen levels. Dissolved oxygen levels greater or equal to 4.8 mg L -1 are considered always acceptable. On the other extreme, dissolved oxygen levels at or below 2.3 mg L -1 are considered immediately harmful. For dissolved oxygen levels between these two levels, there is a maximum allowable duration asso ciated with a given level. For example, at a disso lved oxygen level of 3.5 mg L -1 the maximum duration is 10 days; at 4.5 mg L -1 the maximum continuous durat ion increases to 26 days.

Harm occurs when the dissolved oxygen rema ins at a level for longer than the maximum duration. If the dissolved oxyge n reaches a potentially harmful level, but for less than the maximum duration, than a “harmful dose” can be defined. For example, if dissolved oxygen remained at 3.5 mg L -1 for 5 days, the “harmful dose” would be 5/10 or 0.5, since the dissolved oxygen remained at a potential ly harmful level for one-half the maximum duration (5 days instead of 10 days).

This concept of a harmful dos e is extended to a time-ser ies of daily dissolved oxygen values to calculate the dissolved oxygen inde x for a dissolved oxygen event. An event is any period of time when the dissolved oxygen drops and remains below 4.8 mg L -1 . For example, if over 5 days the dissolved oxygen were 4.5, 3.5, 3.0, 3.5, 4.5, then the “dose” for each day would be 0.04, 0.10, 0.18, 0.01, and 0.04, and the total dose or dissolved oxygen index for the event would be 0.37. Since the index for this event is less than one, the event would not be considered harmful because the event did not result in a full “harmful dose”. Index values of 1.0 or greater, then, represent harmful events.



### Comparison of Observed and Simulated Dissolved Oxygen Daily-averaged dissolved oxygen was simulate d for 1999. Results are compared with observed values from three continuous mon itoring sites available in the mesohaline and oligohaline reaches. The monitoring sites are lo cated at Dames Point, Acosta Bridge, and Buckman Bridge (Figures 7.1 and 7.2). Comparisons of simulated and observed dissolved oxygen are shown in Figures 7.3- 5, below, along with summary statistics.

In general, observed and simulated dissolv ed oxygen values compare well. Average relative error was less than 10.5% and rms error about 0.6 mg L -1 at all three stations. The correlation between observed and simulate d daily values was better at Dames Point and Acosta Bridge (Figure 7.4 and 7.5) than at Buckman Bridge (Fig ure 7.3). However, Buckman Bridge was located where dissolved oxygen values were always above the critical threshold of 4.8 mg L -1 so that predictive capability was less important there. Simulated dissolved oxygen correctly remain ed above the critical threshold at this location (Figure 7.3). Dissolved oxygen dr opped below the critical threshold most frequently at Dames Point, farthest downstream (Figure 7.5).

85Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 7.3 Comparison of observed and si mulated daily-averaged dissolved oxygen at Buckman Bridge during 1999. Th e vertical line indicates wh en observed values are at potentially harmful levels.

86Sucsy and Hendrickson Nutrient Reduction to LSJR

Figure 7.4. Comparison of observed and si mulated daily-averaged dissolved oxygen at Acosta Bridge during 1999. The vertical line indicates when observed values are at potentially harmful levels.

Figure 7.5. Comparison of observed and si mulated daily-averaged dissolved oxygen at Dames Point during 1999. The vertical line indicates when observed values are at potentially harmful levels.



### Sensitivity to Nutrient Reductions In the early stages of this study, sensitivity tests were run to establish the relative importance of different nutrient sources on al gal production in the marine section. The results indicated that:

(a) Equivalent percent load reductions to the freshwater and marine reaches had about equal effect on average chloro phyll levels in the marine reach. (b) Reducing total nitrogen alone to the ma rine section was equivalent to reducing both nitrogen and phosphorus.

These results indicated that proposed load re duction to the freshwater reach could benefit the marine reach. For this reason, the assi milative capacity of the marine reach was calculated by first assuming a 30% reducti on of both nitrogen and phosphorus to the freshwater reach. Further tests were then made making additional stepped reductions of total nitrogen to the marine reach.

Additional sensitivity tests were run to ex amine the response of dissolved oxygen to two “non-nutrient” factors. The factors examined were the di rect input of organic carbon

87Sucsy and Hendrickson Nutrient Reduction to LSJR

(CBOD) load and low dissolved oxygen wate rs entering the mainstem of the river through the tributaries. Th e sensitivity of the model to organic carbon load was examined by comparing the effect of a 50% re duction in organic car bon to an equivalent percent reduction of total nitr ogen. Dissolved oxygen levels in the tributaries were also compared to a 50% reduction in total nitrogen , but for the tributaries we ran an extreme scenario with all tributary dissolved oxygen leve ls set to zero. The sensitivity scenarios were run for 1999 since that was the year us ed for setting the TMDL for the marine segment. We compared dissolved oxygen va lues throughout the marine segment for the summer period when the lowest dissolved oxygen occurred. This summer period was defined as June through September.

Results of these analyses are summarized in Table 7.1. These results show that total nitrogen reduction is about twice as effective in increasing oxygen levels as an equivalent percent reduction of organic carbon. We belie ve this result is because oxygen sags in the marine segment are at present primarily a re sult of excess algal production. As point source loads are relatively high in this portion of the river, this result also illustrates the effectiveness of present treatment processe s for reducing CBOD load, particularly those loads associated with wastewater from sewage plants.

Although the N-reduction:Tr ib-concentration ratio shown in Table 7.1 does not represent equal percent reductions, the valu es do show that direct tri butary influence by dilution of low dissolved oxygen waters is relatively sma ll compared with nitrogen reduction. The effect of tributaries on main -stem dissolved oxygen occurs at short time-scales (1-2 days). At the more relevant seasonal time scales, that is those processes affecting the persistent summer dissolved oxygen sags in th is portion of the rive r, inflow of low dissolved oxygen waters from tributaries is insignificant.

Table 7.1. Relative effect on dissolved oxygen of reducing organic carbon load and tributary dissolved oxygen levels. Effect of reducing each variables is shown relative to the effect of reducing total nitrogen. Comparisons are ma de for both the maximum daily and seasonally-averaged deviation in di ssolved oxygen over the summer period.
N-reduction:OC-reduction N-reduction:Trib-concentration Maximum daily D.O. change 2.4 5.2 Seasonally-averaged D.O. change 1.9 30.7



### Assimilative Capacity Calculations The assimilative capacity calculations for the marine reach were relatively straightforward, and the results are summarized in Table 7.2. First, the model was run for the 1999 model year under the existing loads. Simulated time-series of daily-averaged dissolved oxygen within each model cell were reduced to only one time-series for each WBID by first vertically-averaging dissolv ed oxygen in each cell column, and then spatially-averaging over each WBID. The maximum dissolved oxygen index, based upon the EPA Guidance (U.S.EPA, 2000), was th en calculated for each of the six timeseries. The resultant dissolved oxygen indices for each WBID are shown under column “existing” in Table 7.2. Under existing loads WBIDs A-C exceeded the oxygen criteria.

88Sucsy and Hendrickson Nutrient Reduction to LSJR

The next simulation tested the effect of applying a 30% reduction of anthropogenic nutrient load to the freshwater reach alone. The results, displayed in the next column in Table 7.2, show modest improvement to the dissolved oxygen i ndex although WBIDs AC still all exceed the oxygen criteria.

With an additional 25% reduction of anthr opogenic nitrogen to the marine section all WBIDs meet the oxygen criteria. At a 50% reduction of anthropogenic nitrogen the dissolved oxygen criteria in all WBIDs approaches zero, meaning that the dissolved oxygen levels are almost always above the target threshold of 4.8 mg L -1

. Using linear interpolation, the minimum pe rcentage of anthropogenic nitrogen load reduction to the marine sec tion was estimated as 22%.

Table 7.2. Dissolved oxygen index averaged by WBID for (a) ex isting loads, (b) 30% reduction of loads to freshwater reach, (c) as b but with an additional 25% reduction of total nitrogen to the marine section, (d) as b but with an additional 50% reduction of total nitrogen to the marine section. A diss olved oxygen index greater than 1.0 is an exceedence of the oxygen criteria.

WBID Existing 30% Fresh Reduction Additional 25% Marine Reduction Additional 50% Marine Reduction A 1.32 1.24 0.21 0.08 B 1.99 1.86 0.84 0.10 C 1.09 0.95 0.45 0.02 D 0.34 0.18 0.08 0.00 E 0.00 0.00 0.00 0.00 F 0.00 0.00 0.00 0.00

Total nitrogen loads to the marine section are summarized in Table 7.3. Because point sources contribute a large proporti on of the total nitr ogen load to the marine section, the anthropogenic load is also a larg e proportion of the total load.

89Sucsy and Hendrickson Nutrient Reduction to LSJR

Table 7.3. Contributions of total nitrogen to the marine section of the LSJR. Values are metric ton per year. Category Existing Natural Background Anthropogenic

Point sources 1427 --- 1427 Non-point sources 393 242 151 Totals 1820 242 1578

The anthropogenic load reducti on required to meet the dissolved oxygen target is about 350 metric tons per year of to tal nitrogen. This value is the recommended Pollution Load Reduction Goal for the marine reach. The assimilative capacity, and recommended TMDL, is about 1470 metric tons of total nitrogen per year.



## References
Asman, W.A.H. and S. E. Larsen. 1996. Atmospheric Processes, in Eutrophication in Coastal Marine Systems, eds. B. B. Jorgenson and K. Richardson, American Geophysical Union.

C.B.P, 2000. Proposed chlorophyll-a criter ia for Chesapeake Bay. Chesapeake Bay Website.

Cerco, C. F. 2003. Final Report for App lication of CE-QUAL-IC M to the Lower St. Johns River, Florida (draft).

Cerco, C. F. and T. Cole, 1995. User’s Guide to the CE-QUAL-ICM Three-Dimensional Eutrophication Model. Tech. Rept. EL-95- 15, March 1995. U.S. Army Corps of Engineers, Vicksburg, MS.

Chapra, S. C. 1997. Surface Water Quality Modeling. McGraw-Hill Companies, Inc. New York.

DiToro, D. M. and J. J. Fitzpatrick. 1993. Chesapeake Bay Sediment Flux Model. Contract Report EL-93-2, June 1993. Prepar ed for Chesapeake Bay Program Office, U.S. Environmental Protection Agency and U.S. Army Engineer District, Baltimore.

DeMott, W.R.and F. Moxter. 1991. Foraging on cyanobacteria by copepods: responses to chemical defenses and resource abundance. Ecology 72: 1820-1834.

Fulton III, R.S., and H.W. Paerl. 1987. Toxic an d inhibitory effects of the blue-green alga Microcystis aeruginosa on herbivorous zooplankton. J. Plankton Res. 9(5): 837-855.

Hendrickson, J.C., Lowe, E.F., Dobberfuhl, D., Sucsy, P. and D. Campbell. 2004a. Characteristics of Accelerated Eutrophicati on in the Lower St. Johns River Estuary and Recommendations of Targets for the Achiev ement of Water Quality Goals to Fulfill

90Sucsy and Hendrickson Nutrient Reduction to LSJR

TMDL and PLRG Objectives. Draft Tec hnical Memorandum, St. Johns River Water Management District. Palatka, Florida.

Hendrickson, J.C., N. Trahan, E. Gordon, E. and Y. Ouyang. 2004b. Calculation of the External Nutrient Load to the Lower St . Johns River Estuary. Draft Technical Memorandum, St. Johns River Water Mana gement District. Palatka, Florida.

Hydroqual, 1996. Evaluation of Effect on Water Quality of Proposed Relocation of Georgia Pacific Mill Discharge to the St. Johns River. Volume 1: Water Quality Analysis. Prepared for Georgia Pacific Corp. Palatka Division by Hydroqual, Inc., Mahwah, NJ.

Martin, J. L. and S. C. McCutcheon. 1999 Hydrodynamics and Transport for Water Quality Modeling. Lewis Publishers, New York.

Paerl, H.W., M.F. Piehler, W.W. Carmichael, J. Dyble, P.H. Moisander, J. Leonard and A.Waggener. 2002. Phytoplankton and zoopl ankton in the St. John’s River System: Factors affecting community structure and fu nction. Year 2 Final Report to St. Johns River Water Management District under Contract No. SD154RA.

Paerl , H.W., R.S. Fulton, III, P. H Moisander and J Dyble. 2001. Harmful Freshwaer Algal Blooms, with and Emphasis on Cyanobaceria. The Scientific World 1: 76-113.

Phlips, E.J., M. Cichra, F.J. Aldridge, J. Jembeck, J. Hendrickson and R. Brody. 2000. Light availability and vari ation in phytoplankton standi ng crops in a nutrient-rich blackwater river. Limnol. Oceanogr . 45(4): 916-929.

Phlips, E.J. and M. Cichra. 2002. Spatial and Temporal Patterns in the Plankton Community of the Lower St. Johns River. Final Report to the St. Johns River Water Management District, Contract No. 97W165.

Pollman, C.D. and S. Roy. 2003. Examination of Atmospheric Deposition Chemistry and Its Potential Effects On The Lower St. Johns Estuary. Final Report, SJRWMD Contract No. SE706AA. Tetra Tech, Inc., Gainesville, FL.

Sucsy, P. V. and F. W. Morris. 2002. Calibration of a Three-Dimensional Circulation and Mixing Model of the Lower St. Johns Rive r. Department of Water Resources, St. Johns River Water Management District, Palatka, Florida.

Welch, E. B. 1980. Ecological Effects of Wa ste Water. Cambridge University Press, New York.

91