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 

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

Executive Summary 

This report describes the application of the water quality model, CE-QUAL-ICM, to the 

Lower St. Johns River for calculating nutrient load reductions. The results of this study 

provide recommended nutrient Pollution Load Reduction Goals for the Lower St. Johns 

River. We also calculate the assimilative capacity of the river 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 capacity was calculated separately for both the freshwater 

and predominantly marine reaches of the Lower St. Johns River. For the freshwater 

reach, assimilative capacity was based on achieving reductions of chlorophyll-a. The 

recommended freshwater target requires that 40 Pg 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 indicate negative ecological effects are associated with 

concentrations of chlorophyll-a greater than 40 Pg L-1. Because of the association 

between algal organic matter loading and detrimentally low levels of dissolved oxygen, 

assimilative capacity of the marine segment was based on preventing harmful dissolved 

oxygen sags. The recently proposed guidance by EPA for the protection of aquatic 

organisms from persistent low dissolved oxygen 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 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. 

The recommended Pollution Load Reduction Goal required to meet the dissolved oxygen 

target in the marine reach is about 350 metric tons per year of total nitrogen. The 

assimilative capacity, and recommended TMDL, is about 1470 metric tons of total 

nitrogen per year.

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

Chapter 1. Overview of Report

This report describes the use of a water quality model to calculate nutrient reduction 

goals for the Lower St. Johns River. Nutrient 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 chlorophyll-a levels. In the 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 nutrient cycling and algal growth. Chapter 3 

describes model input files that 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 chlorophyll-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 possible. Finally, in 

Chapters 6 and 7 the assimilative capacity of the 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 Maximum Daily Loads and Pollution Load 

Reduction Goals, respectively. 

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Sucsy 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-quality model designed for the study of 

eutrophication in fresh and marine systems (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 serve as a tool for calculating 

nutrient load reductions that would achieve target 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 undertaken 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 Pollution 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 selection 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 additional features are also summarized here. 

Several other reports provide details about CE-QUAL-ICM and its application to the 

LSJR. The LSJR model application was developed and calibrated by the U.S. Army 

Corps of Engineers (USACE), Waterways Experiment Station. 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 describes 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-QUAL-ICM (Cerco and Cole, 1995) provides 

additional, detailed model documentation. The sediment diagenesis sub-model is fully 

described by DiToro and Fitzpatrick (1993). 

The purpose of this report is to summarize the principle rates and constants used for the 

final model application to the LSJR. The reports described above provide the foundation 

for this report. Brief descriptions of kinetics algorithms are 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 reference 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 within the context of a rigorous process aimed 

 4 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

at developing Total Maximum Daily Loads (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 40Pg L-1. 

c. Turned nitrogen fixation by cyanobacteria 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 cyanobacteria off entirely for Years 1 and 2 (1995 

and 1997) based on the results of skill tests 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 variables. 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 two 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 Organic Nitrogen [LPON] 

19 Refractory Particulate Organic Nitrogen [RPON] 

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

20 Total Phosphate [PO4] 

21 Labile Dissolved Organic Phosphorus [LDOP] 

22 Refractory Dissolved Organic Phosphorus [RDOP] 

23 Labile Particulate Organic Phosphorus [LPOP] 

24 Refractory Particulate Organic 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, Group 2 as freshwater 

diatoms, and Group 3 as general marine phytoplankton. Because marine diatoms 

dominate the biomass of the marine phytoplankton community, characteristics of this 

group were used to parameterize Group 3. 

Phytoplankton Kinetics 

Phytoplankton kinetics are described in the model by rates and constants that effect algal 

growth, algal respiration, settling, and predation. The conservation equation for algae is 

as follows: 

PRB

z

RG W

t

B a  ̧  1

·  ̈

©

§

w

w  ww

B = algal biomass (mg C L-1). 

G = growth (d-1). 

R = respiration (d-1). 

Wa = settling velocity (m d-1). 

PR = predation (g C m-3 d-1). 

Growth 

Growth (G) is modeled as a temperature-dependent maximum growth rate (Gmax) 

multiplied by the minimum value of the light and nutrient limitations. Nutrient 

limitations include phosphorus and nitrogen for all three algal groups with silica added 

for diatoms. Growth is calculated as follows: 

 6 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

G G Minimum> @ Light Nitrogen Phosphorus Silica it it it it max *

lim , lim , lim , lim

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

Maximum Growth Rate

Maximum growth rate (Gmax) is calculated as the ratio of maximum production, Pmax, (g 

C g-1 Chl d-1) and carbon-to-chlorophyll ratio, C:Chl, modified by temperature: 

)(

:

max

max Tf ChlC

P G 

e Tf ToptTKTg )(1 )( 2

  when T d Topt

e Tf ToptTKTg )(2 )( 2

  when T > Topt

Gmax for each algal group is thus defined by five parameters, Pmax, C:Chl, KTg1, KTg2, 

and Topt. The values of these parameters used for the LSJR application are shown in 

Table 2.1. 

Table 2.1. Parameters that define maximum algal growth rate. 

Parameter Units Cyanobacteria Freshwater 

Diatoms 

Marine 

Diatoms 

Pmax gC g-1 Chl d-1 300 350 350 

C:Chl gC g-1Chl 0.02 0.02 0.02 

Topt °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: 

I Ik

I FI 2 2  

Ik = Pmax/D. 

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

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

Nitrogen Limitation

Nitrogen limitation (NL) is calculated using a Monod function: 

DINKHN

DIN

NL

 

DIN = the concentration of dissolved inorganic nitrogen (mgL-1). 

KHN = the half-saturation coefficient for nitrogen uptake. 

Silica Limitation

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

DSiKHSi

DSi

SiL

 

DSi = the concentration of dissolved bioavailable silica (mgL-1). 

KHSi = the half-saturation coefficient for silica uptake. 

Phosophorus Limitation

Phosphorus limitation (PL) is modeled using Droop kinetics, where the limitation is a 

function of internal phosphorus cell quota. 

Q

Q PL

0 1 

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

Q0 = the minimum cell quota. 

Phosphorus uptake (Pup) is modeled as a function of dissolved phosphate using a Monod 

function. 

4

4 max POKHPPO

Pup V

 

Vmax = the maximum phosphorus uptake rate (gP g-1 algal C d-1). 

KHP = the half-saturation concentration for phosphorus uptake (mg L-1). 

 9 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

The nutrient limitation terms, then, are defined by six parameters, D, KHN, KHSi, KHP, 

Q0, and Vmax. Values used in the LSJR application 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 

Q0 g P g-1 algal C 0.015 0.010 0.010 

Vmax 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 temperature (T) and salinity (S). The respiration 

equation is as follows: 

BMGespR e Stox 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 changed by SJRWMD for the LSJR application to 

that developed by Hamilton and Hertzfeld (Centre for Water Research, Univ of Western 

Australia) for the model CAEDYM (Computational Aquatic Ecosystem Dynamics 

model). This formulation for salt toxicity of freshwater algae is as follows: 

max )(

)1(2)1( )1( 1 2

2 2

S Sopt

S SSopt Sopt Stox



 xxxx  x  E E E

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

Sopt

S SSopt Stox 2

2  )1(2)1( xxxx  E E E

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Sucsy 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 (Wa) 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 = BPR·B2

), where 

BB

PR is a temperature-dependent constant defined for each algal group. The increasing 

rate of grazing with increased algal biomass achieved by this formulation appeared 

contradictory to observed zooplankton densities in the LSJR (Phlips and Cichra, 2002; 

Hendrickson et al., 2004). When algal biomass as chlorophyll-a exceeded levels of 

approximately 40 Pg L , zooplankton numbers were instead found to be significantly 

reduced. The reduction in mesozooplankton (copepods 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 incorporated into chlorophyll-a criteria 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 

 11 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

predation at high levels of cyanobacteria biomass. The formulation for predation on 

cyanobacteria is as follows: 

Bc e BcCYc

CYc

BHcBtot

Bc BPRcPR TrefTKTPR )( ( )()  x x xx

  

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 cyanobacteria 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 (this formula is identical for both fresh and 

marine diatoms) is as follows: 

Bd e BcCYd

CYd

BHdBtot

Bd BPRdPR TrefTKTPR )( ( )()  x x xx

  

BPRd = the maximum predation rate on diatoms (d-1). 

Bd = the diatom biomass (g C m-3). 

BHd = the half saturation for diatom biomass on predation (g C m-3). 

Cyd = the lower limit of cyanobacteria suppression 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 algorithm 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 in growth calculated with the new (higher) Nlimitation term and the old N-limitation term determines the amount of new nitrogen 

 12 
Sucsy 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( DINKHN KHNFIX

DIN KHNFIX 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 fraction 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-fixation was not used at all 

during 1995 and 1997. N-fixation 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 seasonal cyanobacteria bloom and are accompanied 

by an equally sharp drop in dissolved oxygen. 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 grazing. 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 community. The homogenous physiological state 

could result in senescence of the population as phosphorus stores are depleted. This 

hypothesis led SJRWMD staff to add a senescense algorithm to the model that depletes 

cyanobacteria biomass at a rate that is functionally 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 the 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 coding only) by a term that increases predation 

rate. The senescence formulation introduces two coefficients, Qmin and Sr, as follows: 

Q

QSr

Q

Sr Q SrSnes

min

2

min

2 )1( x x  )1(2 x    

Sr = the maximum multiplier on predation. 

 13 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Qmin = the phosphorus cell quota, below which, senescence occurs. 

For the LSJR application Sr = 500 and Qmin = 0.02. Note that Qmin is greater than Q0

(0.015) the minimum cell quota used in the calculation of phosphorus limitation. 

Phytoplankton Stoichiometry 

Phosphorus:Carbon ratios (phosphorus cell quotas) are calculated within the model 

constrained by the minimum cell quota as defined 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 through respiration or predation, 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, phytoplankton is a significant source of new 

LDOC.) For the LSJR application, the distribution coefficients are uniform among the 

three algal groups, but differ depending on whether the loss is by respiration or predation. 

The distribution coefficients for respiration are shown in Table 2.5 and for predation in 

Table 2.6. 

Table 2.5. Distribution coefficients for partitioning internal algal nutrients to external 

nutrient compartments when algal biomass 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 coefficients for partitioning internal algal nutrients to external 

nutrient compartments when algal biomass 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 

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

Dissolution and Mineralization Rates 

The nutrient cycles in CE-QUAL-ICM are generally governed by a series of first-order 

reaction rates for the dissolution of particulate 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 ammonium, 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: 

FTMNL e TrmnlTKTMNL )( 

KTMNL=0.092 °C-1. 

Trmnl = 20 °C. 

Carbon 

The following six coefficients determine 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 are a function of algal 

biomass and nutrient availability. For example, the mineralization rate of labile dissolved 

organic phosphorus is governed by the following formula: 

BKdpa POKHp

KHp Kldop Kldp x x



 lg 4

Kldop = the mineralization rate of labile 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: 

 15 
Sucsy 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 mineralization 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 oxygen, and temperature as follows: 

max )(

3

3 2

e ToptTKTNT NT

KHnnt NH

NH

KHont DO

DO NT x x  x

  

The following eleven coefficients, then, determine the rate of nitrogen cycling: 

Khont = half-saturation of DO required for nitrification = 1.0 mg L-1

KHnnt = half-saturation of NH3 required 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 correction 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 the rate of silica cycling: 

Kpbs = particulate biogenic silica dissolution rate = 0.03 d-1

Wpbs = settling velocity of particulate biogenic silica = 0.25 m d-1

Dissolved Oxygen 

Dissolved oxygen is removed by algal respiration, heterotrophic respiration and 

nitrification, and added by algal production (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 

 16 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

2.67 g O2 g-1 C. Transformation of NH3 to NOx by nitrification is similarly related to 

dissolved oxygen by an oxygen-to-nitrogen mass ratio equal to 4.33 g O2 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 function of temperature and salinity) 

Saturation dissolved oxygen is calculated as follows: 

005866.01665.0() 00009796.0 ) 80655.10054258.038217.05532.14 ( 2 2 T T S DOs  T  T  x  

Kr is modeled as a function of wind speed, W, (m s-1), slightly modified by temperature 

(T) and salinity (S) as follows: 

5.1 )( )002.00233.054.0( 5.1

Kr AREAR x W x  ST

AREAR has a value of 0.08 throughout the model domain with the exception of the 

narrow shipping channel between Jacksonville (Acosta Bridge) and Mayport where 

AREAR has a value of 0.04. 

Light Attenuation 

Light attenuation is modeled using the Beer-Lambert Law , I(z) = Io*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 chlorophyll 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: 

Kess  x ISS  xVSS  15.006.008.03.0 x DOC

 17 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Chapter 3. Water Quality Model Input Files 

Applying CE-QUAL-ICM to the LSJR required specification of boundary conditions and 

loads for the periods simulated. We simulated 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 dissolved oxygen were specified as concentrations. Details of 

the calculation of the external load can be found in Hendrickson et al, 2002. 

Meteorologic boundary data included solar radiation, 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 three 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 definitions used for the hydrologic estimates of 

watershed loads (Hendrickson et al., 2004). In that study, loads were 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 fraction 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 observations of wind speed, air temperature, and relative humidity. 

These variables, together with observed solar 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 angle with minimum values occurring 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, 

 18 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

presumably because of variability in cloud cover. Note, for example, that June 1998 

received a relatively high amount of PAR as compared to the 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 each model 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 period 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 McCutcheon (1999). This method expresses the net 

heat transfer to the water body as: 

TT )( H n Ce e  s

where Ce = the coefficient of heat exchange (Watts m-2 °C-1), Te = the equilibrium 

temperature (°C), and Ts = the surface water temperature (°C). Ce and Te are calculated 

from a simple heat budget using observations of air temperature, relative humidity, solar 

radiation, and wind. 

Te ranged from 1 to 38 °C over the four-year model period. 90% of the values for Ce

were between 20.5 and 58.6 Watts m-2 °C-1. 

 19 
Sucsy 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 concentration for marine diatom carbon were 

used for 1995, 1997, and 1998. Marine phytoplankton carbon for 1999, however, was 

estimated from observations of diatom cell counts on the incoming tide at the river 

mouth. 

Monthly concentrations specified at the ocean boundary are summarized in Table 3.2. 

For the sake of brevity, certain 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 variables specified at the 

ocean boundary. The italicized entry is for 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 watersheds enters the LSJR through Buffalo Bluff. 

A smaller, secondary inflow enters the upstream boundary through Dunns Creek. Timevarying concentrations of water quality variables were specified at these upstream flow 

boundaries. The specified concentrations were derived from observations collected by 

the SJRWMD at approximately two-week intervals. Summary statistics for the primary 

water quality variables specified at Buffalo Bluff are listed in Table 3.3. 

Some of the variables listed in Table 3.3 are correlated with 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. 

 20 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Total organic carbon is dominated by RDOC 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 generally quite high indicating 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 fraction 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 similar 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 for 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 represent ocean temperature was justified because 

 21 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

of the similarity between concurrent measurements at Buffalo Bluff and Dames Point, a 

station with ocean influence. Water temperature 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 the 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 example, 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 identical (low) values observed in Black Creek, 

except for tributaries entering from the tri-county agricultural area. The latter tributaries 

have generally higher conductivities 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 concentration specified as boundary conditions for the 63 

tributaries were constant in time. East bank tributaries generally have more silica than 

west bank tributaries. Particulate and dissolved 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 provided 4-month seasonal loads based on 

rainfall, land-use, and soil type. Constituents calculated by the hydrologic model were: 

total suspended solids (TSS), labile and refractory total organic carbon (LTOC and 

RTOC), inorganic nitrogen (TIN), labile and refractory organic nitrogen (LTON and 

RTON), inorganic phosphorus (PO4), and labile and refractory organic phosphorus 

(LTOP and RTOP). Documentation of the watershed loads and the methods used for 

distinguishing labile and refractory organic nutrients and carbon are provided in 

Hendrickson et al. (2004). 

Seasonal loads were disaggregated to daily values using observed discharge hydrographs. 

This method of disaggregating the seasonal loads makes the concentrations of 

 22 
Sucsy 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 load was converted to algal 

compartments (cyanobacteria and freshwater diatoms). Finally, ISS was calculated from 

TSS less an estimate of volatile suspended solids. 

The coefficients used to fractionate organic tributary loads into dissolved and particulate 

forms are shown in Table 3.4. 

Table 3.4. Partition coefficients used to fractionate organic tributary 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 additional algal carbon added to the model was 

removed from the LPOC pool to balance TOC. The addition of algal carbon implicitly 

increases algal nitrogen by 0.175 g N g-1 C. This additional nitrogen was removed from 

TPON. Finally, algal phosphorus was explicitly added to the model using the Redfield 

ratio of 0.025 g P g-1 C. The additional algal phosphorus was removed from the LPOP 

pool. 

Volatile suspended solids (VSS) were estimated 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 watershed. A turbid plume of mostly colloidal 

inorganic material entered the LSJR through Rice Creek. This event increased turbidity 

in affected portions of the river for approximately two months. Because this material 

remained in suspension, treating it as ISS, 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 

 23 
Sucsy 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. The additional loads added to the Rice Creek 

tributary on April 28, 1997 as a result of the 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 the effect of a mine spill on that day. 

Constituent Additional Load Due to 

Mine Spill (kg) 

LDOC 26 

RDOC 2.66 x 106

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 reports, synoptic surveys performed by the 

Department of Environmental Protection, monitoring 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 included (a) ISS, (b) all species (labile/refractory; 

dissolved/particulate) of organic carbon, nitrogen, and phosphorus, and (c) inorganic 

nitrogen and phosphorus. When point source effluent monitoring data lacked the 

constituent detail necessary for describing the 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. 

 24 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Atmospheric Deposition 

Atmospheric deposition of nitrogen and phosphorus is observed at several locations 

throughout northeast Florida. These observations were used to estimate direct 

atmospheric load to the LSJR. The calculation of atmospheric deposition loads 

observations are discussed Pollman and Roy (2003). 

The atmospheric deposition stations report both total nitrogen and total phosphorus (as 

the sum of both wet and dry deposition). Total phosphorus was entered into the model as 

dissolved inorganic phosphorus (PO4). Total nitrogen was fractionated into ammonium 

(25%), NOx (60%), and dissolved organic nitrogen (15%). 

The contribution of nutrients to the LSJR by direct atmospheric deposition is relatively 

small. As an areal rate, atmospheric deposition of total nitrogen was estimated as about 

0.002 g m-2 d-1. The reasonableness of this estimate is established by comparison with 

the 0.0026 g m-2 d-1 calculated for the Danish coast (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 Characteristics 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 and least in the middle reach. Point sources 

predominate in the marine reach while non-point 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 segments of the river and were developed by 

FDEP for the purpose of defining impairment for different segments within the context of 

Florida’s application of the TMDL evaluations. 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 nitrogen and the lower plot shows total 

phosphorus. Loads are represented by vertical bars and the fraction of shading 

corresponds to the fractional point source contribution within each WBID. The load 

shown for the non-point sources is the anthropogenic load only. These figures thus show 

load totals in excess of natural background loads. 

 25 
Sucsy 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 loadings to this portion of the river. 

Figure 3.1. Water Basin IDs (WBIDs) defined for the LSJR. WBIDs are defined by 

FDEP for application of TMDL regulations. Here they serve as a convenient 

segmentation. 

 26 
Sucsy 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. 

 27 
Sucsy 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 targets 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 present focus on chlorophyll-a and dissolved 

oxygen means that the water quality model must 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 productivity, 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. Cerco (2003) showed that simulated light attenuation coefficients 

for this system compare well with a limited set of observed data, although further 

research regarding the best functional relationship 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, continuous 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. 

 28 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 4.1. Locations of stations where water quality data were available for comparison 

with simulated results. Water quality data 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 domain 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, representing the lower marine reach. Palatka and 

Racy Point typically have the highest chlorophyll-a levels in the LSJR. Talleyrand 

represents the marine reach where low dissolved oxygen is a concern. Piney Point 

 29 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

represents an important transition zone between oligohaline and marine waters. Although 

dissolved oxygen levels are generally not overly suppressed at Piney Point, 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 symbol. The vertical scales of the plots are 

the same for common parameters. This choice was made to illustrate spatial variability 

between stations and the 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 shown 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 (Figure 4.3a), is quite similar to water 

temperature at Palatka, a station that showed 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 stations (Figure 4.3a) to the upper stations 

(Figure 4.3b). Figure 4.3a shows an increase 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 stations 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 nutrient concentrations do not sufficiently predict 

nutrient limitation, they are, at least, necessary conditions for limitation. As such, 

predicting periods when inorganic nutrient concentrations are low is an important model 

skill. “Low” nutrient concentration is here taken to mean below the half-saturation 

constants for algal growth and nutrient 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 generally higher in the 

marine reach than in the freshwater reach and the model captures this variability well 

(Figures 4.5a and 4.5b). Inorganic phosphorus concentrations at Racy Point became 

particularly low and limiting to algal growth 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 silica 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. 

 30 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 4.2a. Simulated (line) and observed (square) water temperature at Talleyrand and 

Piney Point. 

 31 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 4.2b. Simulated (line) and observed (square) water temperature at Racy Point and 

Palatka. 

 32 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Point. 

 33 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

 34 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Talleyrand and Piney Point. 

 35 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 4.4b. Simulated (line) and observed (square) dissolved inorganic nitrogen at Racy 

Point and Palatka. 

 36 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

and Piney Point. 

 37 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

and Palatka. 

 38 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Piney Point. 

 39 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Palatka. 

 40 
Sucsy 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). Total nitrogen concentrations below 0.8 mg L-1

were never observed at Racy Point and Palatka, while values below this level occurred 

frequently at Talleyrand. While the model closely simulated total nitrogen in the 

freshwater reach (Figure 4.7b), the model 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 influence 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 3rd 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 concentrations at Racy Point and Palatka during 

the summer of 1999. The nitrogen fixation algorithm was needed to explain these high 

total nitrogen levels. 

Total phosphorus concentrations generally increase 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 tends to slightly underestimate total phosphorus in the water 

column. 

Chlorophyll-a and dissolved oxygen were the parameters of concern for the TMDL and 

PLRG calculations. Chlorophyll-a concentrations are greatest in the upper freshwater 

reach and exhibit frequent blooms there (Figure 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 spatial variability in chlorophyll-a, and bloom 

durations and peaks quite well. Simulated chlorophyll-a did not attain 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 attributed to spatial sub-grid 

scale patchiness of chlorophyll-a in the river and increased nitrogen fixation rates during 

periods of intense blooms. Variations in the C:chlorophyll-a ratio during episodes of 

phosphorus limitation or phytoplankton community stationary phase may also account for 

the inability of the model’s fixed ratio to capture peaks. 

 41 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Piney Point. 

 42 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Palatka. 

 43 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 4.8a. Simulated (line) and observed (square) total phosphorus at Talleyrand and 

Piney Point. 

 44 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Palatka. 

 45 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Piney Point. 

 46 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Palatka. 

 47 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

The model captures annual-temporal and spatial trends in dissolved 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 model simulates this effect (Figure 4.10b). 

The low dissolved oxygen event during the summer of 1997 at Palatka was partially 

attributed to export of low dissolved oxygen waters that formed 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 oxygen 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 concentration 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 increasing algal respiration 

due to both salt toxicity for freshwater species and freshwater toxicity for marine species. 

Contributing factors to persistent, low dissolved 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. 

 48 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Piney Point. 

 49 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Palatka. 

 50 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Chapter 5. Natural Background 

We calculated natural background loads to the LSJR to estimate, by difference, the 

anthropogenic fraction of the present day loads. The estimation of background loads was 

necessary for this study because load reductions were aimed at reducing anthropogenic 

loads only. In addition, knowledge of the background load allowed us to estimate 

changes in the relative composition of loads as nutrients are reduced. For example, 

reduction of anthropogenic nitrogen disproportionately 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 the undeveloped (assumed forested) condition. 

Any additional loads caused by land-use changes, such as urbanization or farming, are 

the anthropogenic loads. We tested the response of the river to the natural background 

loads using the water quality model. These tests 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 conditions that would have existed prior to 

human influence. Two important factors affecting water quality have not been 

considered here. First, morphologic changes 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 indicate that floating macrophytes, without human 

intervention, would likely constitute much of the primary production within the LSJR. 

Thus, it should be understood the projections of natural background productivity based 

upon chlorophyll-a should not imply the LSJR was a phytoplankton-dominated system. 

Rather, these should be viewed as projections of productivity potential, if all growth 

physically and biologically possible were performed by phytoplankton. 

Natural Background Input Loads 

Natural background loads of nitrogen, phosphorus, suspended solids, and organic carbon 

were estimated for all tributaries and upstream sources entering the LSJR. Ocean 

boundary concentrations were also estimated for natural background conditions. We did 

not alter salinity or silica boundary conditions. Also meteorologic and hydrodynamic 

conditions remained the same. 

 51 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Ocean Boundary 

Constant concentrations used to represent 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 varied monthly. Saturation 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 quality 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 estimated at both Buffalo Bluff and Dunns 

Creek (Hendrickson, 2004b). Salinity, RDOC, POC, D.O., and Total Silica remained 

identical or nearly identical to present day concentrations. Algal carbon, LDOC, total 

nitrogen, and total phosphorus were reduced considerably over present day levels. 

Summary statistics for the natural background concentrations 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 fraction 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 

 52 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Tributary Loads 

Tributary loads under natural background conditions 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. Total phosphorus load reduction under natural 

background conditions was relatively greater than total nitrogen, decreasing from 380 to 

120 metric tons yr-1. Total organic carbon load increased slightly under natural 

background conditions, from 49,500 to 51,000 metric tons yr-1. Increase in total organic 

carbon is attributed to greater 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 calculated using estimated natural background 

tributary loads, upstream flow concentrations, 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 reduction of both total nitrogen and total phosphorus 

at both stations (Figures 5.1 and 5.2). Dissolved 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 under natural background 

loads (Figure 5.4). Peak chlorophyll-a concentrations are reduced from values exceeding 

80 Pg L-1 to values less than 20 Pg 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 the present day scenario (Figure 5.5). 

 53 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 5.1. Present day (black) and natural background (red) simulated total nitrogen at 

Talleyrand and Racy Point. 

 54 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 5.2. Present day (black) and natural background (red) simulated total phosphorus 

at Talleyrand and Racy Point. 

 55 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 5.3. Present day (black) and natural background (red) simulated dissolved silica at 

Talleyrand and Racy Point. 

 56 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 5.4. Present day (black) and natural background (red) simulated chlorophyll-a at 

Talleyrand and Racy Point. 

 57 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 5.5. Present day (black) and natural background (red) simulated dissolved oxygen 

at Talleyrand and Racy Point. 

 58 
Sucsy 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 freshwater reach of the LSJR, assimilative 

capacity is based on achieving reductions of chlorophyll-a. Two chlorophyll targets are 

examined here. The first target requires that mean annual chlorophyll-a concentration not 

exceed 20 Pg 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 Pg L-1 of 

chlorophyll-a be exceeded no more than 10% of the time. This second target is based on 

site-specific analyses of zooplankton diversity and abundance that indicate negative 

ecological effects are associated with concentrations of chlorophyll-a greater than 40 Pg 

L-1. Because this latter target is supported by site-specific data, SJRWMD recommends 

this target be used for the assimilative capacity calculation. Assimilative capacity of the 

freshwater segment, then, is defined as the maximum nutrient loading that is insufficient 

to support chlorophyll concentrations greater than 40 Pg L-1 more than 10% of the days 

within a long-term averaging period. Deviations 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 (Figure 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, however, WBIDs G and H are not listed as 

impaired and so not included in this analysis. Simulated chlorophyll-a was spatially 

averaged within each WBID to be consistent with the methodology used by FDEP for 

defining impairment. 

Table 6.1. Water Body ID Segments contained within the freshwater segment of the 

LSJR. 

WBID Code Segment Description 

(north to south) 

Surface Area 

(km2) 

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 

 59 
Sucsy 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 water 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 40Pg L-1 chlorophyll-a was 

estimated at each of the 11 stations for each of four years. These statistics were estimated 

by interpolating monthly observations of chlorophyll-a to daily intervals 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 assumption 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 simulations and experience of the environmental 

scientists maintaining the water quality monitoring network indicate that this assumption 

is reasonable. 

Jan Feb Mar Apr May Jun Aug Sep Oct Nov Jul

1999

Chlorophyll (Pg L-1)

150

100

50

0

Racy Point

Avg = 50.2

% > 40 = 41.9

Figure 6.2. Cubic spline interpolation of observed chlorophyll-a at Racy Point, within 

WBID K. Annual average chlorophyll and the percentage of days exceeding 40 PgL-1

were estimated from the 365 interpolated values. The observed values are indicated with 

an asterisk.

 60 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Annual average chlorophyll-a for each station is shown in Table 6.2 and the percentage 

of days exceeding 40 Pg 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 statistics decrease markedly from 

upstream to downstream. A pronounced inter-annual variability of chlorophyll-a levels 

within the freshwater reach contrasts with the relative lack of inter-annual variability in 

the oligo- and mesohaline segments. 

Table 6.2. Annual average chlorophyll-a (Pg 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 the 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 40PgL-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 oligo- 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

 61 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Table 6.3 shows that 40 Pg L-1 chlorophyll-a was exceeded for more than 10% of the 

days for 3 of the 4 simulations years. The range of this statistic 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 correlated with discharge. Year 1 (1995) was a 

relatively wet year with lower chlorophyll-a levels than Year 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 discharge for each year was desired to place the four 

simulation years within a long-term context. An exact determination of probability of 

discharge in the LSJR is difficult, however, because of the relatively short records at the 

two main-stem flow gauges. (Measurements at Buffalo Bluff 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 LSJR discharge. The nearest long-term flow 

gauge upstream of Buffalo Bluff is located at DeLand in the Middle 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 continuous 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 the probabilities, or 

annual recurrence intervals, are reported in Table 6.4. 

Table 6.4. Recurrence intervals of maximum and 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 

 62 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Year 1, when chlorophyll targets were met, 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 occurred, 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 discharge during Year 2 (2.7 yr low), while local 

tributary flow was quite high during this period (15.7 yr high). 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 Basin 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 DeLand 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 less than the mean annual low discharge. 

These four years, then, were slightly drier than average. This 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 chlorophyll 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 for each set of observed and simulated values. 

These statistics are (a) r2

, 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 observed and simulated values would have r2

 = slope = 

1.0 and rms = intercept = 0.0. 

The r2

 values for annual average chlorophyll are all quite high (0.86-0.97). This statistic 

indicates that the model simulates the downstream decline in chlorophyll well. Figures 

6.3-6.6 further illustrate this point. These 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 slopes 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). 

 63 
Sucsy 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 simulated 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 Pg L-1 . 

 64 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 6.3. Comparison of observed and simulated average annual chlorophyll during 

Year 1. The line indicates a perfect 1:1 fit. 

Figure 6.4. Comparison of observed and simulated average annual chlorophyll during 

Year 2. The line indicates a perfect 1:1 fit. 

 65 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 6.5. Comparison of observed and simulated average annual chlorophyll during 

Year 3. The line indicates a perfect 1:1 fit. 

Figure 6.6. Comparison of observed and simulated average annual chlorophyll during 

Year 4. The line indicates a perfect 1:1 fit. 

 66 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Table 6.6 compares observed and simulated values of the percentage of days that 40 Pg 

L-1 was exceeded at each station for each year. The correlation between simulated and 

observed values is quite good for Year 2-4 (r2

 = 0.74-0.95). However, the correlation for 

Year 1 is poor due to the large 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 chlorophyll (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 Pg L-1. Even a small overprediction of chlorophyll, 

then, would result in a large increase in the percent exceedence of the threshold without 

having a large effect on the average chlorophyll. Figure 6.7 compares observed 

chlorophyll with the time-series of simulated chlorophyll at Federal Point to illustrate 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 Pg 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 largely a result of the model underprediction at the 

farthest upstream station. Although not as dramatic as Year 1, the percent errors for Year 

3 are high. For example, the model underpredicts the percent exceedence by 55.7% at 

Watson I. during Year 3. An examination of the time-series plot (Figure 6.8) shows that, 

again, the discrepancy is largely a result of the inherent difficulty in determining percent 

 67 
Sucsy 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 were calculated for percent exceedence of the chlorophyll-a 

target for Years 2-4 in the freshwater segment. 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 Pg L-1 more than 10% of the time that 

year, no reduction was required for that year. The r2

 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. 

 68 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 6.8. Simulated and observed chlorophyll at Watson I. during Year 3. Observed 

values are indicated by the squares and simulated values by the continuous black line.

 69 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 6.9. Observed and simulated percent exceedence of 40 Pg 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 segment 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 attain the chlorophyll targets. Because the 

freshwater section exhibits both nitrogen and phosphorus limitation (complicated by the 

ability of cyanobacteria to fix atmospheric nitrogen), both phosphorus and nitrogen 

reductions were treated identically in this analaysis. 

The input loads to the freshwater segment consist of five point sources, 16 tributaries, and 

two upstream inflows. The five point sources are as follows: 

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Sucsy 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 organic carbon loads to the freshwater segment of the LSJR 

are summarized in Tables 6.8-6.11. Both existing and natural background loads are 

shown. The difference between existing 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 years. 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 considerably between years because of their 

inextricable link to hydrologic variability. For example, the annual contribution of total 

nitrogen from the Middle Basin (through Buffalo 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 m3

s

-1 in Year 2 

and Year 1, respectively) of 0.44. 

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Sucsy 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 (mi2) 

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 

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

Table 6.8 goes here.

 73 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

 Table 6.9 goes here.

 74 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

 Table 6.10 goes here.

 75 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

 Table 6.11 goes here.

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

Insensitivity of Freshwater Response 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- and 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 the oligo/mesohaline segment 

x Point sources entering the freshwater segment 

x Non-point sources entering the freshwater segment 

x Upstream sources entering the lower basin through Buffalo Bluff 

For each test, a response was calculated for annual average chlorophyll and percent 

exceedence of 40 Pg L-1 chlorophyll in the freshwater segment. 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 expressed as the ratio of the percent response of the 

target and the percent decrease in the given load, similar to a condition number as 

described by Chapra (1997). 

Table 6.12 contains the results of the sensitivity tests for average annual chlorophyll and 

Table 6.13 contains the results for percent exceedence of 40 Pg L-1. The results in both 

Tables 6.12 and 6.13 show that sources entering the oligo- and mesohaline segments have 

an insignificant effect on chlorophyll targets in the freshwater segment. This result 

means that the assimilative capacity calculation 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 

Fresh 

NonPoint 

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 

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

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

Upstream 

Sources 

Oligo/meso 

Point 

Sources 

Oligo/meso 

Non-Point 

Sources 

Fresh 

Point 

Sources 

Fresh 

NonPoint 

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 interpolation for calculating the minimum percent 

reduction required to meet the chlorophyll targets. The minimum percent reduction was 

calculated within each of the five WBIDs. An example calculation is shown in Table 

6.14 for average annual chlorophyll in Year 2. 

Table 6.14. Percent exceedence of 40 Pg L-1 chlorophyll by WBID during Year 3 for 

existing condition and various percent reduction levels. Percent reductions are applied to 

anthropogenic loads. The last column shows the percent reduction required 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 calculations for all years are summarized in Table 

6.15, below. The results shown are percent anthropogenic load reductions 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 the chlorophyll target within all WBIDs. 

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

Table 6.15. Target reductions required to maintain chlorophyll targets throughout the 

freshwater segment of the LSJR for each of four model years. 

Year Target reduction to maintain 

average chlorophyll 

 < 20Pg L-1

Target reduction to maintain 

exceedence of 40Pg L-1 chlorophyll < 

10% of time 

1 6 0*

2 47 17

3 22 28

4 74 63 *

This reduction based on observations that showed no violations, not on model simulation. 

Assimilative Capacity by Year 

The percent load reductions shown in Table 6.15 can be used together with the values of 

load shown in Tables 6.8-6.11 to determine 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 equivalent to “Total Maximum Daily Load”, or TMDL. (The use 

of TMDL and PLRG here are for convenience 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 Pg L-1 for more than 10% of the time throughout 

the freshwater section. All values 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 assimilative capacity. 

The large variability of assimilative capacity is because low-flow years, such as Year 4, 

have a smaller assimilative capacity than high flow years, such as Year 1. This 

sensitivity to hydrologic variability should be considered for design of treatment facilities 

and implementation of Best-Management-Practices, 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, treatment design that is intended to handle the worst-case 

 79 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

scenario is expected to meet the same treatment efficiency at all times. Alternatively, 

under this interpretation, treatment efficiency for a wet interval may be much lower. 

Reductions Required to Meet Chlorophyll Targets For Long-term Average Conditions 

The recommended chlorophyll target does not allow exceedence of 40 Pg 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 source treatment efficiency improves, so 

stormwater retention facilities 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 trophic transfer of primary production. For 

example, favorable zooplankton populations 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 perterbation 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 percentages 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 Pg L-1 is shown in 

Table 6.18. 

Table 6.17. Long-term average chlorophyll by WBID for existing load and various 

percent reduction levels. Percent reductions are applied to anthropogenic loads. 

Chlorophyll values in columns 2-6 are Pg L-1. The last column shows the percent 

reduction required of all sources to attain average chlorophyll of 20 Pg 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 

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Sucsy 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 

 81 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Table 6.18. Long-term average percent exceedence of 40Pg 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 required of all sources 

so that 40 Pg 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 Recommended Reduction Goals 

Based on the preceding results, the recommended reduction of anthropogenic sources of 

both nitrogen and phosphorus to the freshwater segment of the LSJR is 30%. The longterm 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 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 with the effects of 

eutrophication. Increased understanding of other important biological components of the 

system could, in the future, require additional load reductions. 

As discussed previously, a target based on exceedence criteria of chlorophyll 

concentration greater than 40Pg L-1 is best supported by biological analyses. However, 

basing the assimilative capacity on maintaining a long-term average chlorophyll-a 

concentration of less than 20Pg L-1 would require 43% reduction of anthropogenic loads 

(Table 6.17). 

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Sucsy and Hendrickson Nutrient Reduction to LSJR 

Chapter 7. Assimilative Capacity of the Marine Section of 

the LSJR 

The marine section of the LSJR encompasses the area from the river mouth to a point 

near the entrance to Doctors Lake. This section 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 oligohaline reaches is regarded to be near 

Acosta Bridge where a sharp change in both breadth 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. 

 83 
Sucsy 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 available 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 summer 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 rates due to a transition between freshwater and marine algal 

communities. 

 84 
Sucsy 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 episodes of hypoxia in the marine LSJR. The 

applicability of this index for the LSJR is discussed in Hendrickson et al. (2004a). This 

index accounts for varying durations that can 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 associated with a given 

level. For example, at a dissolved oxygen level of 3.5 mg L-1 the maximum duration is 

10 days; at 4.5 mg L-1 the maximum continuous duration increases to 26 days. 

Harm occurs when the dissolved oxygen remains at a level for longer than the maximum 

duration. If the dissolved oxygen 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 potentially harmful level for one-half the maximum 

duration (5 days instead of 10 days). 

This concept of a harmful dose is extended to a time-series of daily dissolved oxygen 

values to calculate the dissolved oxygen index 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 simulated for 1999. Results are compared with 

observed values from three continuous monitoring sites available in the mesohaline and 

oligohaline reaches. The monitoring sites are located 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 dissolved 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 simulated daily values was better at Dames Point 

and Acosta Bridge (Figure 7.4 and 7.5) than at Buckman Bridge (Figure 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 remained above the critical threshold at this 

location (Figure 7.3). Dissolved oxygen dropped below the critical threshold most 

frequently at Dames Point, farthest downstream (Figure 7.5). 

 85 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 7.3 Comparison of observed and simulated daily-averaged dissolved oxygen at 

Buckman Bridge during 1999. The vertical line indicates when observed values are at 

potentially harmful levels. 

 86 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

Figure 7.4. Comparison of observed and simulated 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 simulated 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 algal 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 chlorophyll levels in the marine reach. 

(b) Reducing total nitrogen alone to the marine section was equivalent to reducing 

both nitrogen and phosphorus. 

These results indicated that proposed load reduction to the freshwater reach could benefit 

the marine reach. For this reason, the assimilative capacity of the marine reach was 

calculated by first assuming a 30% reduction 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 examine the response of dissolved oxygen to two 

“non-nutrient” factors. The factors examined were the direct input of organic carbon 

 87 
Sucsy and Hendrickson Nutrient Reduction to LSJR 

(CBOD) load and low dissolved oxygen waters entering the mainstem of the river 

through the tributaries. The sensitivity of the model to organic carbon load was 

examined by comparing the effect of a 50% reduction in organic carbon to an equivalent 

percent reduction of total nitrogen. 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 levels set to zero. The sensitivity scenarios 

were run for 1999 since that was the year used for setting the TMDL for the marine 

segment. We compared dissolved oxygen values 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 believe this result is because oxygen sags in the 

marine segment are at present primarily a result 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 processes for reducing CBOD load, particularly those 

loads associated with wastewater from sewage plants. 

Although the N-reduction:Trib-concentration ratio shown in Table 7.1 does not represent 

equal percent reductions, the values do show that direct tributary influence by dilution of 

low dissolved oxygen waters is relatively small 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 this portion of the river, 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 made for both the maximum daily 

and seasonally-averaged deviation in dissolved 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 dissolved 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 then 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. 

 88 
Sucsy 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 index although WBIDs AC still all exceed the oxygen criteria. 

With an additional 25% reduction of anthropogenic 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 percentage of anthropogenic nitrogen load 

reduction to the marine section was estimated as 22%. 

Table 7.2. Dissolved oxygen index averaged by WBID for (a) existing 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 dissolved 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 proportion of the total nitrogen load to the marine section, the 

anthropogenic load is also a large proportion of the total load. 

 89 
Sucsy 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 reduction required to meet the dissolved oxygen target is about 

350 metric tons per year of total 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. 

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