Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 2 Coastal Waters 

 

 

Technical Support Document for

U.S. EPA’s Proposed Rule for Numeric

Nutrient Criteria for Florida’s Estuaries, 

Coastal Waters, and South Florida Inland

Flowing Waters 

Volume 2: Coastal Waters 

November 30, 2012 
Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 2 Coastal Waters 

Disclaimer 

This document supports the U.S. Environmental Protection Agency’s (hereafter EPA or the 

Agency) numeric nutrient criteria proposed on November 30, 2012, pursuant to section 303(c)(4) 

of the Clean Water Act (CWA) (Title 40 of the Code of Federal Regulations [CFR] section 

131.43). The information provided herein does not substitute for the CWA or EPA’s regulations; 

nor is this document a regulation itself. Thus, this document cannot and does not impose any 

legally binding requirements on EPA, states, authorized tribes, the regulated community, or any 

other party, and might not apply to a particular situation or circumstance. 

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Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 2 Coastal Waters 

Contents 

1. Derivation of EPA’s Numeric Nutrient Criteria for Coastal Waters .............................. 1
 

1.1. Introduction ...................................................................................................................... 1
 

1.2. Deriving Numeric Criteria to Protect Designated Uses in Florida Coastal Waters ......... 2
 

1.2.1.	 Nutrient-Sensitive Biological Endpoints and Water Quality Targets Used 

to Derive Numeric Nutrient Criteria .....................................................................3
 

1.2.2.	 Chlorophyll a ........................................................................................................5
 

1.2.3.	 Using Satellite Remote Sensing to Detect Chlorophyll a.....................................6
 

1.3. Delineation of Coastal Waters.......................................................................................... 7
 

1.4. Data Selection and Preparation ...................................................................................... 10
 

1.4.1.	 Data Selection .....................................................................................................10
 

1.4.2.	 Field Data for Satellite Validation ......................................................................10
 

1.4.3.	 Data Preparation..................................................................................................14
 

1.4.4.	 K. brevis Data Preparation ..................................................................................16
 

1.5. Analytical Approach for Numeric Nutrient Criteria Derivation .................................... 17
 

1.5.1.	 Using Reference Analysis to Maintain Designated Use Support .......................17
 

1.5.2.	 Accounting for the Occurrence of K. brevis Blooms in Criteria
 

Derivation ...........................................................................................................19
 

1.6. Calculation of Numeric Nutrient Criteria for Chlorophyll a.......................................... 21
 

1.6.1.	 Satellite ChlorophyllRS a Data Evaluation..........................................................21
 

1.6.2.	 Options for Calculating Criteria..........................................................................25
 

1.6.3.	 ChlorophyllRS a Annual Geometric Mean Concentration...................................26
 

1.7. References ...................................................................................................................... 34
 

Appendix A: A Review of Reference Conditions in Florida’s Coastal Waters 

Appendix B: Supporting Documentation for Remote Sensing Methods 

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Figures 

Figure 1-1. Factors that influence upwelling light leaving the sea surface. ....................................7
 

Figure 1-2. 76 FDEP WBIDs originally considered for satellite remote sensing analysis 

extending to 3 nautical miles. Numbers are coastal WBIDs from FDEP 

IWR Run 39. .................................................................................................................8
 

Figure 1-3. Fixed coastal segments used in satellite remote sensing analysis. Coastal 

segments were delineations proposed in this approach to develop numeric 

chlorophyll a criteria for the (A) Florida Panhandle, (B) West Florida Shelf, 

and (C) Atlantic Coast. Numbers are coastal segment numbers ranging from 

1 through 74..................................................................................................................9
 

Figure 1-4. Field data locations used to validate satellite remote sensing analysis of 

chlorophyllRS a. Dark circles indicate the station data used to compare 

chlorophyll a to satellite remote sensing observations of chlorophyllRS a. ................11
 

Figure 1-5. Enlarged view of coastal segment #22 near Tampa Bay. The dotted white 

line defines the coastal segment polygon. As an example, ROI would not 

include any chlorophyllRS a bins (#1) that were on the polygon line or a 

vertex. Bins masked by SeaDAS standard quality control flags (#2), 

including stray light contamination and land, had no value and were not 

included in the computation. ChlorophyllRS a bins completely within coastal 

segment polygon (#3) were included in the computation. .........................................16
 

Figure 1-6. Number of K. brevis events greater than 50,000 cells/L flagged in each 

segment.......................................................................................................................17 

Figure 1-7. Annual geometric mean for the Florida Panhandle, West Florida Shelf, and 

Atlantic Coast from 1998-2009. Linear regression indicated that the slope is 

not significantly different from zero in the Florida Panhandle (p = 0.42), 

West Florida Shelf (p = 0.34), and Atlantic Coast (p = 0.76). ...................................18
 

Figure 1-8. (A) A time-series of SeaWiFS satellite remote sensing chlorophyllRS a 

observations for the coastal segment adjacent to Tampa Bay. ChlorophyllRS 

a data were flagged when K. brevis cell counts exceed 50,000 cells/L (solid 

circles). (B) The cumulative distributions of SeaWiFS chlorophyllRS a were 

calculated using all the data (gray solid line) and using only the unflagged 

data (dashed black line)..............................................................................................20
 

Figure 1-9. SeaWiFS observations of chlorophyllRS a compared to in situ chlorophyll a 

from all stations. The gray dashed line is a 1:1 fit, and the black line is the 

regression slope. Plots are presented in log space, but regression coefficients 

have been converted to linear space to represent a linear regression formula 

of y = slope * x + intercept. ........................................................................................21
 

Figure 1-10. MODIS observations of chlorophyllRS a compared to in situ chlorophyll a 

from all stations. The gray dashed line is a 1:1 fit, and the black line is the 

regression slope. Plots are presented in log space, but regression coefficients 

have been converted to linear space to represent a linear regression formula 

of y = slope * x + intercept. ........................................................................................22
 

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Figure 1-11. MERIS observations of chlorophyllRS a compared to in situ chlorophyll a 

from all stations. The gray dashed line is a 1:1 fit, and the black line is the 

regression slope. Plots are presented in log space, but regression coefficients 

have been converted to linear space to represent a linear regression formula 

of y = slope * x + intercept. ........................................................................................22
 

Figure 1-12. Coastal segment bottom depths and PAR integrated satellite penetration 

depths, based on KdRSPAR regression in Figure 1-13. ..............................................23 

Figure 1-13. SeaWiFS observations of KdRSPAR compared to KdPAR from stations 

within the 3-nautical-mile coastal segments (A) and for all the stations (B). 

The gray dashed line is a 1:1 fit, and the black line is the regression slope. 

Plots are presented in log space, but regression coefficients have been 

converted to linear space to represent a linear regression formula of y = 

slope * x + intercept. ..................................................................................................24
 

Figure 1-14. Regression between coastal segment median bathymetry and minimum 

chlorophyllRS a showing the influence of bottom reflectance on 

chlorophyllRS a. ..........................................................................................................25
 

Figure 1-15. 71 coastal segments for which coastal criteria were derived. ...................................28
 

Figure 1-16. The chlorophyllRS a annual geometric mean of each coastal segment from 

MODIS and MERIS were regressed against SeaWiFS to adjust each satellite 

sensor to a similar response. .......................................................................................30
 

Figure 1-17. Top: Example of annual geometric means from SeaWiFS and adjusted 

MODIS and adjusted MERIS data for the coastal segment adjacent to 

Perdido Bay, which are based on the regression coefficients from Figure 

1-16. Bottom: Example of annual geometric means from SeaWiFS and 

adjusted MODIS and adjusted MERIS data after applying the normalization 

factor...........................................................................................................................31
 

Figure 1-18. Percent difference in the 90th percentile calculation compared against the 

90th percentile calculation using only SeaWiFS. (A) Difference when the 

SeaWiFS record was used from 1998 to 2002 and the data from 2003 to 

2009 was replaced with the MODIS record. (B) Difference when the 

SeaWiFS record was used from 1998 to 2002 and the data from 2003 to 

2009 was replaced with the MERIS record................................................................32
 

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Tables 

Table 1-1. Chlorophyll a threshold values at the 90th percentile of associated
 

concentrations established by several eutrophication assessment frameworks............6
 

Table 1-2. Number of observations in IWR Run 40 since 1990 for chlorophyll a, TN, 

and TP for coastal water bodies .................................................................................10
 

Table 1-3. Candidate chlorophyllRS a criteria for each coastal segment calculated on the 

basis of the 90th percentile of the annual geometric means with and without 

K. brevis......................................................................................................................27
 

Table 1-4. MODIS and MERIS normalization factors based on SeaWiFS from 2003 to 

2009 ............................................................................................................................33
 

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Abbreviations and Acronyms 

CDOM chromophoric dissolved organic matter 

ChlorophyllRS a remotely sensed chlorophyll a 

CWA Clean Water Act 

DO dissolved oxygen 

EPA U.S. Environmental Protection Agency 

FDEP Florida Department of Environmental Protection 

FWRI Fish and Wildlife Research Institute 

IWR Impaired Water Rule 

Kd light extinction 

KdPAR light extinction from photosynthetically active radiation 

KdRSPAR PAR integrated satellite penetration depth 

L liter 

μM micromoles 

MERIS Medium Resolution Imaging Spectrometer 

MODIS Moderate Resolution Imaging Spectroradiometer 

NASA National Aeronautics and Space Administration 

PAR photosynthetically active radiation 

ppt parts per thousand 

ROI region of interest 

SeaDAS SeaWiFS Data Analysis System 

SeaWiFS Sea-viewing Wide Field-of-view Sensor 

TN total nitrogen 

TP total phosphorus 

TSD Technical Support Document 

VIIRS Visible Infrared Imager Radiometer Suite 

WBID water body identification number 

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Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

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1. Derivation of EPA’s Numeric Nutrient Criteria for Coastal Waters 

1.1. Introduction 

The U.S. Environmental Protection Agency (EPA) is proposing to establish numeric nutrient 

criteria for Florida’s coastal waters. For the purposes of this rulemaking, coastal waters are 

defined by EPA as marine waters up to 3 nautical miles from shore as consistent with Clean 

Water Act (CWA) jurisdiction. EPA’s definition of coastal waters excludes estuaries, which are 

generally semi-enclosed waters connected to the open sea, defined at the seaward margin by the 

natural limits of the semi-enclosed basin. This definition is consistent with the Florida 

Department of Environmental Protection (FDEP) defining open coastal waters as “gulf or ocean 

waters that are not classified as estuaries1 or open ocean waters”2 (Section 62-303.200, F.A.C.). 

Information on deriving numeric nutrient criteria within estuarine waters, including the coastal 

waters in the Big Bend region, is in Volume 1 of the Technical Support Document (TSD). 

Information regarding Florida’s derivation of numeric nutrient criteria for all other coastal waters 

in south Florida can be found at 62-302.532 and 62-302.351, F.A.C. 

This volume provides information on data sources, approaches, and statistical analyses used to 

derive the proposed coastal numeric chlorophyll a criteria to protect designated uses. In line with 

the objective of the CWA to restore and maintain the chemical, physical, and biological integrity 

of the nation’s waters [33 United States Code (U.S.C.) 1251 101(a)], state water quality criteria 

must protect the designated uses determined by the state. Therefore, EPA interpreted FDEP’s 

narrative nutrient criteria of causing no imbalance to natural populations of aquatic flora and 

fauna as equivalent to protecting against harmful increases in nutrients and the resulting adverse 

effects to natural populations of aquatic flora and fauna. After a review of potential indicators of 

aquatic system condition, phytoplankton was chosen as a nutrient-sensitive, biological endpoint 

of aquatic life use conditions in coastal waters. Variability in phytoplankton biomass and 

production or change in the natural balance of coastal waters can be measured through 

concentrations of chlorophyll a. Satellite remote sensing was identified as a scientifically 

defensible and reliable way to obtain data to derive coastal criteria and to monitor coastal trends 

in chlorophyll a over time. EPA has employed a reference condition approach verified by a 

review of the water quality conditions in these waters to ensure that the selected period of record 

(1998–2009) is supportive of designated uses and protective numeric criteria values for Florida. 

The following sections describe the steps pertaining to the delineation of coastal waters, data 

selection and preparation, and analytical approaches explored by EPA for coastal numeric 

criteria derivation. Finally, a discussion of calculating the numeric chlorophyll a criteria 

according to an annual geometric mean approach that considers the 90th percentile to prevent a 

harmful increase in nutrients is provided. Proposed numeric nutrient criteria are presented in 

Section 1.6. While EPA proposes a lead option for criteria derivation, several alternate 

computation approaches have been provided for public comment, such as the inclusion or 

exclusion of Karenia brevis data in analysis. After careful scientific consideration and review, 

1 FDEP defines estuary as “predominantly marine regions of interaction between rivers and nearshore ocean waters, where tidal
 

action and river flow mix fresh and salt water. Such areas include bays, mouths of rivers, and lagoons.”
 2 FDEP defines open ocean waters as “all surface waters extending seaward from the most seaward natural 90-foot (15-fathom)
 

isobaths.”
 

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EPA proposes deriving chlorophyll a criteria at the 90th percentile of all annual geometric means 

of screened data from 1998 to 2009. The criteria would have a duration and frequency of 

exceedance of no more than one in three years. EPA is not proposing to derive TN and TP 

criteria for Florida’s coastal waters because of lack of sufficient field monitoring data for TN and 

TP; however, EPA believes that the chlorophyll a criteria should provide the necessary 

protection of these waters because chlorophyll a is a sensitive biological parameter that would 

serve as a warning signal to the State that excess nutrients might be causing adverse impacts to 

natural populations of aquatic flora and fauna. Coastal numeric chlorophyll a criteria values 

would apply to areas in the Florida Panhandle between the Alabama border and St. Joseph Bay, 

the West Florida Shelf from Anclote Bay to Rookery Bay, and Atlantic Coast segments from 

Biscayne Bay to the Georgia border. Detailed descriptions of the characteristics of those coastal 

areas are presented in Volume 2, Appendix A. 

1.2.	 Deriving Numeric Criteria to Protect Designated Uses in Florida Coastal 

Waters 

Florida’s current narrative criterion reads, in part, that “in no case shall nutrient concentrations of 

a body of water be altered so as to cause an imbalance in natural populations of aquatic flora or 

fauna” [Subsection 62-302.530 (47)(b), F.A.C.]. Given those expectations for state numeric 

criteria under the CWA and in accordance with the court decision on EPA’s December 2010 

rule,

3

 EPA interpreted FDEP’s narrative nutrient criterion of causing no imbalance as equivalent 

to the prevention of a harmful increase in levels of nutrients. 

In its decision,4 the court, in discussing numeric criteria translating Florida’s narrative criterion, 

stated that “the right target was a criterion that would identify a harmful increase in a nutrient 

level – an increase that, in the language of Florida’s narrative criterion, would create an 

‘imbalance’ in flora and fauna.” Order at 63. Upon review of the latest scientific knowledge, 

EPA has identified nutrient-sensitive biological endpoints relevant to particular estuarine and 

coastal systems. EPA determined that maintenance of seagrasses, maintenance of balanced algal 

populations, and maintenance of aquatic life are three sensitive biological endpoints, which can 

be measured by water clarity (as it relates to light levels sufficient to maintain historic depth of 

seagrass colonization), chlorophyll a, and DO, respectively, and appropriately used in derivation 

of numeric nutrient criteria that protect the State’s designated uses from harmful increases in 

nitrogen and phosphorus concentrations. 

EPA’s approach to deriving numeric nutrient criteria is consistent with FDEP’s approach to 

interpreting its narrative nutrient criterion and deriving numeric thresholds at the State level. 

FDEP has approached the derivation of numeric nutrient criteria in much the same way as EPA 

by aiming to prevent adverse effects to natural populations of aquatic flora and fauna.5 

3 Case 4:08-cv-00324-RH-WCS, February 18, 2012.
 4 Case 4:08-cv-00324-RH-WCS, February 18, 2012.
 5 State of Florida Numeric Nutrient Criteria Development Plan, Prepared by: Bureau of Assessment and Restoration Support,
 

Division of Environmental Assessment and Restoration, Florida Department of Environmental Protection, Tallahassee, FL,
 

March 2009; Technical Support Document: Development of Numeric Nutrient Criteria for Florida Lakes and Streams. Florida
 

Department of Environmental Protection, Standards and Assessment Section, June 2009; Technical Support Document:
 

Development of Numeric Nutrient Criteria for Florida Lakes, Spring Vents and Streams. Florida Department of Environmental
 

Protection, Standards and Assessment Section, 2012.
 

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The CWA requires water quality standards, consisting of designated uses of the waters and water 

quality criteria based on such uses, to restore and maintain the chemical, physical, and biological 

integrity of the nation’s waters. To derive numeric criteria to protect designated uses, it is 

important to consider the CWA goal of restoring and maintaining biological integrity. This is 

important because the State’s numeric criteria must serve the purposes of the CWA and 

ultimately restore and maintain water quality integrity. The concept of integrity is commonly 

defined as “the capability of supporting and maintaining a balanced, integrated, adaptive 

community of organisms having a species composition, diversity, and functional organization 

comparable to that of the natural habitat of the region” (Frey 1977). EPA uses an operational 

definition of integrity as “the ability of an aquatic community to support and maintain a 

structural and functional performance comparable to the natural habitats of a region” (Frey 1977; 

Karr and Dudley 1981). 

As required by 40 Code of Federal Regulations (CFR) Part 131.11, water quality criteria must be 

based on sound scientific rationale, must contain sufficient parameters or constituents to protect 

the designated use, and, for multiple use designations, must support the most sensitive use. 

Because the state has already interpreted what it means to protect designated uses from nutrient 

pollution through its narrative criterion statement, EPA is proposing to prevent the harmful 

increase of nutrient levels by ensuring that the numeric values developed will protect nutrientsensitive aquatic floral and faunal species. 

For Florida’s coastal waters, EPA will derive numeric criteria to maintain consistency with 

establishing criteria for Florida’s estuarine and inland flowing waters. EPA is approaching the 

interpretation of biological integrity in coastal waters by characterizing the existing condition of 

natural populations of aquatic flora and fauna on the basis of the best available information. The 

coastal waters of Florida are a unique collection of physically and ecologically diverse 

environments. Some of those areas might have experienced changes from historical conditions as 

a result of physical modifications to inland environments in adjacent estuarine waters. However, 

such modifications do not necessarily preclude support of balanced populations of flora or fauna 

or the use of a reference condition approach to derive numeric nutrient criteria for coastal waters 

(see Section 1.5.1). 

1.2.1.	 Nutrient-Sensitive Biological Endpoints and Water Quality Targets Used to 

Derive Numeric Nutrient Criteria 

EPA researched and identified a range of indicators to detect the ecological well-being of coastal 

waters on the basis of their appropriateness for deriving numeric criteria. EPA evaluated the 

available literature on biological communities in Florida coastal waters to identify assemblages 

that could be used to assess and reflect the effect of nutrients on the region’s coastal natural 

population of aquatic flora and fauna. A general conceptual model of eutrophication that links 

the relationship between nutrients and biological responses was also referenced to determine 

impacts on both aquatic life and human uses, and how those impacts could be captured by 

relevant indicators (see the Introduction to the Technical Support Document for U.S. EPA’s 

Proposed Rule for Numeric Nutrient Criteria for Florida’s Estuaries, Coastal Waters, and 

Southern Inland Flowing Waters). The findings from the literature and review of the conceptual 

model were both used to identify indicators of ecosystem health throughout the region, some of 

which are also used to monitor the progress of current preservation and restoration efforts in the 

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coastal waters of the south Florida region. Out of several indicators, phytoplankton was 

identified as an integral part of Florida coastal ecosystems and a sensitive, quantifiable indicator 

of nutrient impacts for which sufficient data existed (see USEPA 2010; Appendix C). Other 

indicators lacked a sufficient amount of available data and evidence on the concentrations of 

nutrients that would result in adverse ecological impacts. 

Phytoplankton was therefore chosen as the nutrient sensitive biological endpoint to evaluate 

aquatic life conditions in Florida coastal waters. Moreover, the selection of chlorophyll a as an 

indicator of phytoplankton biomass is supported by EPA guidance, which advocates the use of 

response variables in coastal waters (Gibson et al. 2000). In reference to the general conceptual 

model of eutrophication, it was determined that elevated nutrient inputs could result in a 

substantial rise in chlorophyll a concentrations, promoting the development of highly colored 

waters. This increased phytoplankton biomass can lead to both decreased light availability and 

greater organic decomposition in coastal waters. These outcomes can be ecologically significant, 

as chlorophyll a exhibits an exponential relationship to light extinction (Kd) and, thus, small 

changes in Kd can greatly decrease oxygen in surface water. Given these ecological outcomes 

from enhanced nutrient inputs, both recreation and aquatic life support in coastal waters may be 

jeopardized. Coastal eutrophication, and the subsequent increase in phytoplankton biomass that 

limits light availability in the water column, may ultimately lead to blooms of gelatinous 

zooplankton (stinging and non-stinging jellyfish). Despite the inconclusive nature of the exact 

cause of increasing jellyfish populations in the literature, academic evidence currently suggests 

that areas with high nutrient levels and disturbance experience a shift in the phytoplankton 

community away from diatoms toward flagellates and jellyfish (Richardson et al. 2009). 

Moreover, the low dissolved oxygen (DO) levels and decreased light availability in coastal 

waters caused by eutrophication create an aquatic habitat that favors jellyfish survival and 

reproduction over fish (Purcell et al. 2007). The competitive advantage of jellyfish in these 

environments may lead to a regime shift in ecosystem structure and population outbreaks. 

Numerous deleterious consequences including beach closures and reductions of commercial 

fisheries harvest can be a result of these shifts, negatively impacting designated uses in coastal 

environments. Therefore, chlorophyll a itself is a strong, reliable indicator of harmful biological 

change and community shifts caused by nutrient pollution in coastal waters. 

Eutrophication assessment frameworks in Europe and the U.S, such as the Oslo-Paris 

Commission “Common Procedure,” Water Framework Directive of the European Union, 

Assessment of Estuarine Trophic Status in the U.S., and the Marine Strategy Framework 

Directive used by the European Commission capture the various available classification systems 

and approaches for assessing nutrient pollution impacts and status in marine waters. These 

assessment frameworks are based on measuring the deviation from a defined baseline, or 

reference condition, and then assessing current conditions against standards to assign a level of 

eutrophication or overall ecological status to coastal waters. They also collectively support the 

use of phytoplankton as an indicator of the direct effects of nutrient pollution, as an immediate 

biological response to excess nutrients identified as an increase in primary production (Devlin et 

al. 2007, 2011). While greater levels of primary production are observed and measured by an 

escalation in chlorophyll a concentrations, nutrients are measured primarily as a causative factor 

of eutrophication. Thus, nutrients are measured initially to determine whether waters are 

impacted by nutrient pollution and chlorophyll a concentrations are measured to gather evidence 

on the effects of nutrient pollution. Phytoplankton change can capture the direct adverse or 

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harmful effects of nutrient pollution through examining bloom frequency, a rise in biomass, and 

change in species composition (OSPAR Commission 2005, 2008). Furthermore, these 

frameworks recommend using chlorophyll a as a statistic that can effectively measure changes in 

primary production and serve as an indication of ecological state via concentrations measured at 

the 90th percentile. Under these assessments, elevated chlorophyll a concentrations are 

considered a primary symptom of water quality degradation associated with nutrient pollution, or 

strong indicator of the “onset” of eutrophication (Bricker et al. 2003; European Commission 

2003; Ferreira et al. 2011). Because these assessments aim to identify where an undesirable 

disturbance to aquatic life and water quality from eutrophication is present, it is highly likely that 

adverse effects will appear above the 90th percentile of chlorophyll a concentrations. While mean 

levels of chlorophyll a are also recorded, the participating parties concluded that a chlorophyll a 

concentration at the 90th percentile is a more appropriate statistic to establish that waters with 

concentrations exceeding this value are already experiencing the adverse effects of 

eutrophication; placing them in a more degraded status category due to evidence of undesirable 

disturbance (Bricker et al. 1999, 2003; CEFAS 2008; Ferreira et al. 2011; OSPAR Commission 

2005, 2008). It is after these status thresholds are exceeded that the frameworks require specific 

management actions be taken to address nutrient pollution. 

1.2.2. Chlorophyll a 

As a proxy of phytoplankton biomass, chlorophyll a has been widely used as an indicator of 

aquatic system condition (Boyer et al. 2009; Steele 1962). Phytoplankton chlorophyll a 

concentrations have been routinely measured in much of Florida since the late 1980s. The coastal 

waters of Florida are generally oligotrophic. 

Chlorophyll a is well studied in aquatic ecology as a measure of variability in biomass (Gameiro 

et al. 2004; Huot et al. 2007). The chlorophyll a content of phytoplankton and periphyton is not 

constant, changing over time in response to growth and resources including light and nutrient 

concentrations. Because chlorophyll a is a measure of phytoplankton biomass, it can be used to 

infer a harmful increase in nutrients that would lead to adverse ecological effects within coastal 

waters. Excess phytoplankton biomass is correlated with nuisance algae blooms, shading 

seagrasses, and causing shifts in aquatic species composition or primary producers. Historical 

dominance of Florida marine systems by seagrasses and corals is indicated by paleoecological 

studies and historical accounts. Because both of those habitats are autotrophic, they require light. 

Phytoplankton is a component of the coastal marine environment that influences the amount of 

available light. 

Chlorophyll a also serves as an indicator of species shift in primary producers, which can alter 

the base of food webs. As a result of competitive differences among multiple species, as nutrient 

concentrations increase, the species composition, diversity, and abundance of different floral 

species are changed from what is expected of natural habitats in the region (Hutchinson 1959; 

Tilman 1977, 1981, 1985). Such alterations result in harmful or adverse increases in nutrients 

that comprise the aquatic ecosystem. For example, chlorophyll a has been shown to be 

responsive to overall nutrient conditions in Florida. Increases in nitrogen for the western part of 

Florida Bay have been associated with blooms (Tomas et al. 1999). In eastern Florida Bay and 

southern Biscayne Bay, sensitivity to phosphorus levels was exhibited in 2006 by a multiyear 

bloom thought to be caused by a unique combination of natural and anthropogenic factors 

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Framework Delineation of Waters Threshold Information 

Oslo-Paris Commission 

“Common Procedure” 

Waters are classified according to salinity gradients 

as: 

Estuarine 0–20 PSU 

Coastal 30–34.5 PSU 

Offshore >34.5 PSU 

Statistical Measure: Threshold as growing season 

90th percentile chlorophyll a: 

Threshold: 15 μg/L, 

>15 μg/L = threshold exceeded indicating a 

“Problem Area” 

WFD–UK National Report Estuaries, coastal, offshore waters delineated 

based on salinity range. 

Estuarine: All areas 0–<30.0 PSU 

Coastal: Irish Sea 30–34.0 PSU 

North Sea 30–34.5 PSU 

Offshore: Irish Sea >34.0 PSU 

North Sea >34.5 PSU 

Statistical Measure: Mean March–September 

chlorophyll a concentrations at the 90th percentile 

were used as thresholds corresponding to areas 

that are nutrient enriched: 

Estuarine: 15 μg/L
 

Coastal: 15 μg/L
 

Offshore: 10 μg/L
 

ASSETS (Assessment of Estuaries and coastal waters determined by salinity Statistical Measure: Chlorophyll a annual 90th 

Estuarine Trophic Status) zones for the eutrophic condition rating. percentile concentrations to determine the level of 

eutrophication are listed as: Tidal 0–0.5 PSU 

Mixing 0.5–25 PSU Hypereutrophic: >60 μg/L 

Seawater 25 PSU High: >20, ≤60 μg/L 

Medium: >5, ≤20 μg/L 

Low: >0, ≤5 μg/L 

Note: PSU = practical salinity units; μg/L = micrograms per liter 

 

 

Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 2 Coastal Waters 

(Rudnick et al. 2007). To prevent “undesirable disturbance” caused by eutrophication, several 

eutrophication assessment frameworks mentioned above in Section 1.2.1 have established 

chlorophyll a threshold values at the 90th percentile of associated concentrations across their 

specific timeframes of analysis. These values are captured in Table 1-1 below. 

Phytoplankton species generally exhibit increases in growth rates with a rise in the availability of 

limiting resources, such as nutrients (reviewed in Wetzel 2001). There is also an abundance of 

scientific literature on the correlative effect of nutrients on both the alteration of species 

composition and increased biomass across the range of aquatic ecosystems (Schindler 1990; 

reviewed in Wetzel 2001 and Kalff 2002). Thus, the overall biomass of the entire primary 

producer assemblage can increase as the growth rates increase with nutrients, even while the 

presence and abundance of species are altered from what is expected in the absence of nutrient 

pollution (Elser et al. 2007; Schindler 1990; Smith and Schindler 2009; reviewed in Wetzel 2001 

and Kalff 2002). Because the effect of nutrients on species composition is correlated with overall 

system productivity or biomass, chlorophyll a can be considered an indicator of a harmful 

increase in nutrient concentrations. 

Table 1-1. Chlorophyll a threshold values at the 90th percentile of associated concentrations established by 

several eutrophication assessment frameworks 

1.2.3. Using Satellite Remote Sensing to Detect Chlorophyll a 

Ocean color satellites are capable of detecting optical components (Figure 1-1), such as 

chlorophyll a, and their respective concentrations in the water column according to the 

wavelengths of light they individually reflect back to the sensor. The sensor measures the 

remotely sensed chlorophyll a (chlorophyllRS a) signal by detecting the water leaving radiance in 

selected wavelengths of light per unit area. The signal read by the sensor is then used to derive 

quantitative information on the substances in the water and their concentrations in the near6 
 

 

 

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surface layers of the ocean. Therefore, it is important to distinguish between light effects of 

water and phytoplankton from that of dissolved or suspended matter in the cumulative 

chlorophyllRS a signal. While there is a difference in nomenclature in field measurements for 

coastal waters— between water column chlorophyll a and the satellite response (chlorophyllRS 

a), EPA believes that the latter is a suitable measure for use in numeric criteria development and 

subsequent assessments. For Florida coastal waters, relevant signal components to consider are 

that of bottom reflectance, chromophoric dissolved organic matter (CDOM), and potentially 

turbidity (insoluble particulate matter) because such waters might experience greater levels of 

mixing and particulate matter due to their proximity to shore. 

Figure 1-1. Factors that influence upwelling light leaving the sea surface. 

CDOM and bottom reflectance were both identified as possible interferences affecting the 

relationship between chlorophyll a and chlorophyllRS a in coastal waters (Schaeffer et al. 2011). 

While CDOM interference is quasi-conservative with river discharge from neighboring estuaries, 

bottom reflectance is a function of both bathymetry and seagrass presence. Seagrass is a specific 

type of bottom reflectance. Areas with seagrasses were not included in this analysis because the 

noise from the interference was greater than the signal from phytoplankton chlorophyll a. Both 

bottom reflectance and CDOM interferences are additive to the chlorophyllRS a value in each 

coastal segment, setting the lower limit for the chlorophyllRS a response (Schaeffer et al. 2011). 

To read more about the approaches taken to ensure that interferences by bottom reflectance and 

CDOM were considered in the derivation of numeric criteria, see Section 1.6.1. 

1.3. Delineation of Coastal Waters 

Florida’s coastal waters were first delineated into three regions: Florida Panhandle between the 

Alabama border and St. Joseph Bay, the West Florida Shelf from Anclote Bay to Rookery Bay, 

and the Atlantic Coast segments from Biscayne Bay to the Georgia border (Figure 1-2). This 

delineation into three regions was suggested by Tomlinson et al. (2004). The delineation process 

was based on both physical factors and optical properties of the coastal areas, aside from 

jurisdictional considerations of the CWA. 

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In line with the Science Advisory Board recommendation to extend beyond the statutory 

3-nautical-mile-limit for delineation purposes, EPA used data out to 4 nautical miles to 

determine differences between physical characteristics of coastal waters. Optical properties that 

were considered relevant components of the satellite chlorophyll a signal are CDOM and bottom 

reflectance, which vary according to location and depth. To account for inter-regional variability 

in CDOM and bottom reflectance, the coastal regions were further subdivided on the basis of the 

FDEP’s water body identification numbers (WBIDs), which start at the land margin and extend 

seaward, to 3 nautical miles, which is the CWA jurisdictional boundary (see Figure 1-2). Further 

evaluation of the available data and water quality characteristics among the 76 WBIDs indicated 

that WBIDs 8062 and 8125 had decreased satellite data availability compared to the other coastal 

WBIDs. Therefore, sets of neighboring WBIDs in the West Florida Shelf (WBID 8062 and 

WBID 8063) and the Atlantic Coast (WBID 8124 and WBID 8125) were aggregated because the 

geometric means of all available data were within +/–5 percent of each other. The nearby coastal 

WBIDs 8061 and 8126 were not included in the aggregated pair because there was greater than a 

10 percent difference in the geometric mean of all available data. 

Figure 1-2. 76 FDEP WBIDs originally considered for satellite remote sensing analysis extending 

to 3 nautical miles. Numbers are coastal WBIDs from FDEP IWR Run 39. 

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EPA also extended the seaward boundary of the WBID to 4 nautical miles so satellite data bins 

out to approximately 3 nautical miles would be included in the analysis. The extension of the 

extra nautical mile was necessary to ensure satellite data bins that approached the 3 nautical mile 

limit were included in the criteria analysis. More details on the binning process, data extraction, 

and justification for the seaward boundary extension are presented below in Section 1.4.3. 

The refined delineation was designated as coastal segments that were used for numeric nutrient 

criteria derivation. This process lead to a final total of 74 fixed coastal segments (Figure 1-3): 

Florida Panhandle has 17 segments, West Florida Shelf has 19 segments, and Atlantic Coast has 

38 segments. 

EPA is proposing coastal criteria using a different method for the area between St. Joseph Bay 

and Anclote Bay because coastal seagrass coverage is available and extremely high colored 

dissolved organic carbon exports from rivers (Morey et al. 2009) would confound derivation of 

chlorophyllRS a. 

Figure 1-3. Fixed coastal segments used in satellite remote sensing analysis. Coastal segments were delineations 

proposed in this approach to develop numeric chlorophyll a criteria for the (A) Florida Panhandle, (B) West 

Florida Shelf, and (C) Atlantic Coast. Numbers are coastal segment numbers ranging from 1 through 74. 

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Chlorophyll a,
 

Region Years Uncorrected Chlorophyll a TN TP
 

Florida Panhandle 1990–2010 311 241 180 156 

Florida West Shelf 1990–2009 865 498 1,862 1,932 

Atlantic Coast 1990–2008 51 16 50 73 

 

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1.4. Data Selection and Preparation 

1.4.1. Data Selection 

EPA is proposing the use of three data sources to derive and validate numeric nutrient criteria for 

coastal waters: (1) chlorophyll a field observations for satellite validation, (2) satellite remote 

sensing data to derive numeric criteria, and (3) K. brevis data to flag bloom events. Traditional 

field samples account for 1,648 chlorophyll a observations from 1998 to 2010, averaged over 

12 years to result in approximately 137 samples for the entire coastal area per year. In contrast, 

satellites can provide measures of chlorophyll a in each coastal segment typically more than 

20 times per year. This section describes both the data preparation for the various categories of 

data and how each source was used in the process of criteria derivation. Data used in criteria 

computations can be viewed using HDFView, a publically available program that will permit 

viewing of the files containing the chlorophyll a, sample number, KdPAR, Latitude, and 

Longitude products. HDFView can be downloaded from http://www.hdfgroup.org/hdf-javahtml/hdfview/. A comprehensive image analysis package called the Sea-viewing Wide Field-ofview Sensor (SeaWiFS) Data Analysis System (SeaDAS) and an Interactive Data Language 

(from Exelis Visual Information Solutions, Inc.) license are required to process, display, and 

analyze ocean color data; they are available to the public at http://seadas.gsfc.nasa.gov/. 

1.4.2. Field Data for Satellite Validation 

Table 1-2 presents data from FDEP’s Impaired Waters Rule (IWR) Run 40 over a 20-year period 

from 1990 to the present for three coastal regions. 

Table 1-2. Number of observations in IWR Run 40 since 1990 for chlorophyll a, TN, and TP for coastal water 

bodies 

Monitoring data (Figure 1-4) used for satellite validation were from the Northeastern Gulf of 

Mexico project, obtained from the National Oceanographic and Atmospheric Administration 

(NOAA) National Oceanographic Data Center; the Ecology and Oceanography of Harmful Algal 

Blooms project; the Fish and Wildlife Research Institute (FWRI); Mote Marine Laboratory; 

SeaWiFS Bio-optical Archive and Storage System (Werdell and Bailey 2002; Werdell et al. 

2003); and FDEP’s IWR Run 40 database. 

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Figure 1-4. Field data locations used to validate satellite remote sensing analysis of chlorophyllRS a. Dark circles 

indicate the station data used to compare chlorophyll a to satellite remote sensing observations of 

chlorophyllRS a. 

Satellite Remote Sensing 

Florida’s coastal waters have not been monitored comprehensively via sampling (see Table 1-2); 

however, satellite remote sensing provides the necessary data to represent conditions in these 

waters. While monitoring sample observations are temporarily infrequent and spatially sparse, 

satellite measures of chlorophyllRS a provide temporal and spatial observations that are not 

otherwise possible. Satellite remote sensing currently uses low, earth-orbiting satellites to derive 

ocean color products on a global scale (see O’Reilly et al. 1998, Appendix C) and at frequent 

revisit intervals. Remote sensing has most commonly been used for open ocean applications. 

However, the satellites are also useful for applications in near-coastal waters (Lunetta et al. 2009; 

Werdell et al. 2009). 

Regarding binning considerations, in a lognormal distribution, the sample mean tends to 

underestimate the population mean when the sample size is small (because of outliers). Thus, 

small samples with large variances make the sample mean a poor estimator of the population 

mean. The objective is to determine the optimal number of bins that will decrease the variance 

and error around the sample mean. Also, binning helps account for time gaps due to clouds, 

sunlight, and other factors in the data collected by the satellite. Ocean color satellites capture 

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daily images with pixels having a nominal ~0.7 mi (1.1 km) spatial resolution at nadir (the point 

directly below the sensor). Pixels are then assigned into predefined data bins when generating 

8-day composites. Data bins are pre-mapped locations on a sinusoidal equal-area projection, in 

which products are both spatially and temporally averaged. An 8-day binning period is 

considered a standard approach (Campbell et al. 1995) to collect data. This is consistent with of 

the orbit repeat period of 16 days for Moderate Resolution Imaging Spectroradiometer (MODIS) 

and SeaWiFS, which helps alleviate a temporal measure bias when choosing a typical 7-day 

binning period. Operational binning methods were tested for 1 day, 8 days, 1 month, and 1 year 

in several studies; however, the 8-day composite is an appropriate compromise between filling in 

the bins with data and a temporal span that can capture short-term dynamics. The act of binning 

helps overcome the issue of missing data (Campbell et al. 1995). An 8-day binning period 

represents one week, leaving 46 weeks per calendar year with the last week containing 5 or 

6 days. Thus, the 8-day composite is a compromise between filling in the bins with data and a 

temporal span that can capture short-term dynamics. 

SeaWiFS6

 is assumed to be the standard having the longest data record (from 1997 to the 

present) and was used to derive numeric criteria that will capture long-term variability in coastal 

waters. Thus, MODIS and Medium Resolution Imaging Spectrometer (MERIS) annual 

geometric means were adjusted on the basis of the chlorophyllRS a regressions with SeaWiFS. 

K. brevis Data 

K. brevis is a harmful algal bloom dinoflagellate that frequently occurs in the coastal waters of 

Florida, and produces blooms that have significant implications for human health and aquatic life 

(Steidinger et al. 1998). K. brevis is commonly called red tide in Florida. While most blooms 

occur along the Gulf Coast of Florida, K. brevis blooms can also affect Florida’s Atlantic Coast. 

This organism produces brevetoxin, a potent neurotoxin that can cause respiratory irritation 

(at 103

 cells/L) through aerosolization, neurotoxic shellfish poisoning through consumption of 

contaminated shellfish (at > 5 × 103 cells/L), and large fish kills (at 1 to 2.5 × 105 cells/L). 

Mortalities of shore birds and manatees have also been linked to K. brevis blooms (Landsberg 

2002). 

Laboratory and field research over several decades has shown that K. brevis, an athecate 

dinoflagellate, is highly adapted to conditions in the Gulf. The organism is particularly suited to 

coastal salinities (33–36 parts per thousand [ppt]), light conditions and the temperature regime of 

the West Florida Shelf region (Tester and Steidinger 1997). Karenia spp. have been part of the 

Gulf of Mexico’s ecosystem for at least 125 years and probably longer (Steidinger 2009). The 

earliest reports by European explorers in 1530 described discolored waters and the presence of 

phycotoxins (Magana et al. 2003). More accounts of blooms and their effects were found 

between 1844 and 1936 (FWRI 2011). By the mid-1900s significant problems with fish kills, 

aerosolized toxins, and discolored waters prompted studies that led to the identification of the 

causative organism. However, because of inconsistencies in sampling locations, it remains 

unclear whether there is a trend in the frequency of K. brevis blooms (FWRI 2012). 

6 SeaWiFS and MERIS are no longer operational; however, VIIRS and MODIS are currently functional. 

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Before 1973, K. brevis blooms were thought to initiate inshore around passes because their 

effects are visible in those areas (Steidinger et al. 1998). The present understanding of bloom 

dynamics, however, indicates that initiation occurs in the West Florida Shelf about 18–74 km 

(11–46 mi) offshore in 12- to 37-m water depth. About 70 percent of outbreaks take place in late 

summer to fall associated with meanders of the Loop Current in the Gulf. Approximately 17 

percent of blooms occur from winter to spring. Following 2–8 weeks of growth, the bloom 

moves across the shelf toward the coast where the distribution is influenced by winds and alongshore currents. The bloom can become patchy and concentrated, but it can also spread and cause 

effects over a large portion of the Gulf Coast. Blooms that affect the Atlantic Coast of Florida 

and other states farther north (e.g., North Carolina) come from Gulf populations that are 

entrained into and transported by the Loop Current through the Straits of Florida then into the 

Gulf Stream proper (Tester et al. 1991; Walsh et al. 2009). Tester et al. (1991) noted a 22- to 54­

day transit time from the Gulf to North Carolina. In some cases, eddies containing cells can 

break off from the Loop Current and move toward Texas (Steidinger 2009). 

The ability of K. brevis to survive and out-compete other phytoplankton species has been the 

subject of much research. The organism is slow-growing and shade-adapted compared to other 

phytoplankton (e.g., diatoms). Nevertheless, it survives in low-nutrient waters of the West 

Florida Shelf and can produce high-density, monospecific blooms. In addition to its nutrient 

physiology and other adaptations described below, the organism could have other characteristics 

that allow it to succeed. For instance, the organism’s motility and vertical migration behavior 

allow it to take advantage of subsurface nutrient pools. Brevetoxins might depress grazing. 

Allelopathic substances released by K. brevis could inhibit competitive species. Evidence of 

mixotrophy has been found and could allow the organism to feed on bacteria and other organic 

particles to supplement its nutritional requirements. Steidinger (2009) suggests that nutrient 

concentrations in the West Florida Shelf are low despite potentially high estuarine flux and 

coastal inputs as microscopic and macroscopic inshore plants remove the nutrients effectively in 

the estuaries before the nutrients can reach 18–74 km offshore; moreover, water from the 

Mississippi River plume loses most of its nutrient load by the time it reaches Tampa Bay. When 

nutrients become available, Steidinger (2009) proposes that growth rate does not change 

drastically. Instead, Vargo et al. (2008) suggest that high cell densities might have been 

transported from other areas or concentrated by physical means (e.g., tides and currents). 

One research team has suggested that a significant increase in nutrient inputs to the nearshore 

waters of West Florida correlates with an increased abundance of K. brevis (Brand and Compton 

2007). Their analysis was based on comparing K. brevis abundance to distance off shore. Other 

researchers have proposed that the release of dissolved organic nitrogen and remineralization of 

nutrients from dead fish can lead to initiation, growth, and maintenance of K. brevis blooms on 

the West Florida and Texas shelves (Walsh et al. 2006; Walsh and Steidinger 2001). 

Two recent reviews (Anderson et al. 2008; Vargo 2009) summarize the current state of the 

knowledge regarding nutrient pollution and K. brevis blooms on the West Florida Shelf. Vargo 

(2009) states, “there is no single hypothesis that can account for blooms.” Of approximately 

24 hypotheses, 7 are related to rainfall or riverine flux (or both), 6 focus on benthic flux in one 

form or another, 7 involve hydrodynamics or are unrelated to the benthos or land sources, and 

4 are based on chemical interactions or allelopathy. Nutrient sources for the growth and 

maintenance of the blooms include a wide array of sources including atmospheric deposition, 

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nitrogen fixation, riverine inputs, benthic fluxes (e.g., from groundwater or remineralized diatom 

blooms), zooplantonkton excretion, and even organic matter from the decaying fish killed by 

K. brevis toxins. Anderson et al. (2008) also state that “clear evidence to support hypotheses 

about increased bloom frequency and biomass on the West Florida Shelf is still not yet 

available.” 

Acknowledging the potential for K. brevis blooms to increase coastal chlorophyll a 

concentrations, EPA also evaluated an approach for addressing K. brevis blooms within the 

context of a reference condition approach to numeric criteria development. Weekly K. brevis cell 

counts for the entire state of Florida were acquired from FWRI. EPA performed an analysis with 

K. brevis and one without K. brevis bloom data included in the criteria derivation for 

chlorophyll a (see Section 1.5.2). 

1.4.3. Data Preparation 

Use of Monitoring Sample Observations 

Minimum metadata requirements for monitoring sample observations were date, time, latitude, 

longitude, and chlorophyll a or light extinction from photosynthetically active radiation 

(KdPAR). Latitude and longitude data rounded to the nearest degree or one-half degree were not 

used. Where multiple samples of chlorophyll a at different depths existed, the most surface 

(shallowest) sample was kept. The field observation sampling times were compared to the 

SeaWiFS, MODIS, and MERIS overpass times. Samples falling within a +/–3 hour time window 

were retained for further analysis (Bailey and Werdell 2006). Most of the retained chlorophyll a 

data were from laboratory filter extracted values. The Northeastern Gulf of Mexico data set used 

some fluorometric values, but the fluorometry data were adjusted on the basis of a regression 

with in situ extracted chlorophyll a. Therefore, all in situ chlorophyll a data were based on 

extracted values. For calculating KdPAR, all data were excluded for depths less than 5.6 ft 

(1.7 m), roughly the height of the conductivity, temperature, and depth apparatus. The log10 of 

photosynthetically active radiation (PAR) was plotted as a function of depth. Any regression of 

log10(PAR) against depth that returned an R2 greater than 0.8 was retained for satellite validation. 

Field monitoring data included more than 5,500 chlorophyll a measurements, which were 

reduced to 1,947 after screening. 

Use of Remote Sensing Data 

EPA is proposing the use of SeaWiFS, MODIS, and MERIS satellites to provide measurements 

of chlorophyllRS a (satellite-derived chlorophyll a). SeaWiFS provides a historical time-series of 

chlorophyllRS a back to 1997, whereas MODIS and MERIS data collection began in 2002. 

Satellite ocean color data were obtained from the National Aeronautics and Space 

Administration’s (NASA’s) Ocean Color Web (Feldman and McClain 2010) and the European 

Space Agency’s MERIS Catalogue and Inventory website. SeaWiFS, MODIS, and MERIS 

provided daily images with pixels having a nominal ~0.7 mi (1.1 km) spatial resolution. 

SeaWiFS data (reprocessing R2009) temporally spanned between September 14, 1997, and 

January 1, 2010. MODIS data (reprocessing R2009.1) temporally spanned between August 7, 

2002, and January 1, 2010. MERIS reduced resolution data (2nd reprocessing) temporally 

spanned between April 29, 2002, and January 1, 2010. Imagery spatially covered between 

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31.0 to 23.0°N and 88.0 to 79.0°W. The SeaDAS version 6.1 was used to process data that met 

all standard quality control flags from level-1 to level-3 8-day composites (see Volume 2, 

Appendix B for more information on SeaDAS and the data products that were used). 

SeaWiFS, MODIS, and MERIS derived chlorophyll a (chlorophyllRS a) were validated against 

field chlorophyll a measurements using the native resolution of the sensor. The closest satellite 

pixel corresponding to a monitoring chlorophyll a measurement that occurred within +/–3 hours 

of the satellite overpass was selected. Because satellite navigation might not be accurate to the 

pixel, a 3×3 box of pixels was selected with the center of the pixel box corresponding to a 

monitoring chlorophyll a measurement. Both the average chlorophyllRS a concentration and 

standard deviation was calculated from the 3×3 box if the satellite viewing angle was less than 

60° and solar zenith angle was less than 75°. In addition, 50 percent of the pixels within the 3×3 

box had to be unflagged. The coefficient of variation of the nine pixels was also determined. If 

the coefficient of variation was greater than 10 percent, the sample was excluded because of 

indicated spatial inhomogeneity. Satellite matchups were evaluated as described, which is from 

the method of Bailey and Werdell (2006) with a geometric mean (Type II) linear regression 

between the 3×3 pixel box extraction of satellite data and the corresponding monitoring sample 

measurement. The default ocean color chlorophyll a algorithms were selected because they were 

universal algorithms that could be applied to locations throughout Florida, and they were 

algorithms packaged within the SeaDAS Level 2 Processing program (l2gen) so future 

processing could easily be completed in SeaDAS (Schaeffer et al. 2011). 

ChlorophyllRS a values within coastal segments were extracted by matching segment polygon 

vertex coordinates with corresponding satellite image pixel and line values on 8-day composites 

within SeaDAS (Schaeffer et al. 2011). The satellite image pixel and line locations were used to 

build a polygon using the 8-day array with Interactive Data Language. Values from the 8-day 

array were then averaged if bins were completely contained within the coastal segment polygon 

using Interactive Data Language’s region of interest (ROI, Figure 1-5). If bins fell on or outside 

the coastal segment polygon, they were excluded from further analysis. EPA coastal segment 

seaward boundaries were delineated at 4 nautical miles to avoid exclusion of bins up to the 

3 nautical mile limit. A single nautical mile is approximately equivalent to 1.85 km or almost 

two 1 km by 1 km satellite data bins. The ROI should then include one of the additional data bins 

from the extra 1.85 km extension. This one additional data bin would be on the landward side, 

proximal to the 3 nautical mile limit. If EPA had kept the seaward delineation at 3 nautical miles, 

the ROI would more likely only include data from land to approximately 2 nautical miles. EPA 

acknowledges bins adjacent to land are masked in this process, but it considers the response from 

the ROI to represent the trend for the entire delineated coastal segment. Advection, mixing events, 

coastal upwelling, and other physical dynamics are expected to result in a homogeneous and 

parallel response, between the masked area and averaged bins in the ROI, for each coastal segment. 

Averages were calculated from the beginning of the satellite mission until January 1, 2010. 

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Figure 1-5. Enlarged view of coastal segment #22 near Tampa Bay. The dotted white line defines the coastal 

segment polygon. As an example, ROI would not include any chlorophyllRS a bins (#1) that were on the polygon 

line or a vertex. Bins masked by SeaDAS standard quality control flags (#2), including stray light contamination 

and land, had no value and were not included in the computation. ChlorophyllRS a bins completely within coastal 

segment polygon (#3) were included in the computation. 

1.4.4. K. brevis Data Preparation 

The FWRI data were combined into 8-day periods that matched the satellite dates. Satellites 

detect K. brevis blooms when cell counts are above 50,000 cells/L (Heil and Steidinger 2009; 

Stumpf et al. 2003). If a single count greater than 50,000 cells/L occurred at any point during the 

8-day period, it was retained for flagging. The FWRI K. brevis cell count data were then spatially 

matched to the coastal segments in ArcGIS. The chlorophyllRS a data were flagged in any coastal 

segment for the 8-day period that had a count greater than 50,000 cells/L. In addition, the same 

segment was flagged one week before and after a bloom was detected, unless data indicated 

lower counts, to provide a temporal buffer as blooms were transported along the coast (Schaeffer 

et al. 2011). The number of flag occurrences in each coastal segment is presented in Figure 1-6. 

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Figure 1-6. Number of K. brevis events greater than 50,000 cells/L flagged in each segment. 

1.5. Analytical Approach for Numeric Nutrient Criteria Derivation 

EPA proposes a reference condition approach for coastal numeric criteria development that uses 

data from satellite remote sensing. Few monitoring data from field samples are available for 

Florida’s coastal waters; however, a robust data set is available from satellite remote sensing that 

can be used (Schaeffer et al. 2011). 

EPA had previously recommended (e.g., USEPA 2001) that a percentile of water quality 

measurements in a sample of minimally disturbed water bodies, which currently are supporting 

designated uses, could serve as a basis for setting criteria for water quality in similar water 

bodies. Therefore, EPA is considering using a reference condition approach when current water 

quality conditions are supporting balanced natural populations of aquatic flora and fauna to 

protect against harmful/adverse impacts on aquatic ecosystems. Further discussion of the 

reference condition approach as applied to coastal waters in Florida is discussed in Section 1.5.1 

below. 

1.5.1. Using Reference Analysis to Maintain Designated Use Support 

A reference condition approach involves computing criteria on the basis of water quality 

conditions present in reference water bodies that are known to be supporting or protecting the 

designated uses. The reference condition could be based on data collected in the past, when the 

water body was determined to be minimally disturbed by nitrogen or phosphorus pollution 

(historical reference condition) or from a similar water body that was determined to be minimally 

affected by nitrogen or phosphorus pollution (comparative reference condition). Therefore, EPA 

is proposing to base the reference condition approach for Florida’s coastal waters on water 

quality conditions during times when those waters support designated uses in order to maintain 

these conditions and protect these waters from eutrophication. 

Several steps were taken to ensure that the data used in criteria derivation and analyses were 

representative of waters that are supporting designated uses. EPA conducted a review of water 

quality information, using CWA section 303(d) listings for nutrients, chlorophyll a, and DO; 

identified coastal segments adjacent to 303(d) listed estuarine segments; consulted available 

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scientific literature; and evaluated satellite data trends in chlorophyll a from 1998 to 2009. Listed 

coastal segments, in addition to those adjacent to 303(d) listed estuarine waters, were screened 

out of the analysis during the period of designated use impairment. The review of 303(d) listed 

coastal WBIDs resulted in five coastal segments being screened out during the period of 

designated use impairment for DO (8106, 8108, 8110, 8115/Indian River) and nutrients and 

chlorophyll a (8037 Waccasassa [not in EPA coastal segments]). Thirteen coastal segments 

adjacent to impaired estuaries were removed prior to analysis. After removing segment data 

when designated use impairments were documented, the remaining data set of reference waters 

are representative of waters with water quality that support designated uses. This new data set 

can be used in criteria derivation on the basis of a reference condition approach. Screening 

impairments did not result in the complete removal of the period of record. Differences were 

observed when comparing the entire data record to the screened record. Most coastal segment 

criteria values decreased when the screen was applied, with three coastal segments resulting in a 

22 percent decrease. In addition, the satellite period of record was examined for chlorophyll a 

trends in the data that would indicate an increase in chlorophyll a or periods of high 

concentration. Analysis of the SeaWiFS chlorophyllRS a annual geometric means for all three 

coastal regions (Florida Panhandle, West Florida Shelf, and Atlantic Coast) showed no trend 

from 1998 to 2009 (Figure 1-7). Individual linear regressions were conducted, which indicated 

that the slope of the regression lines were not significantly different from zero (p > 0.05), within 

95 percent confidence. Therefore, the reference condition could be based on recent water quality. 

While the literature does raise some questions of whether anthropogenic nutrient inputs 

contribute to K.brevis blooms, the findings are inconclusive as discussed above. Because the 

requirements for using a reference condition approach were met under this review process, EPA 

affirmed that the selected period of record supports designated uses in Florida coastal waters. 

Greater detail on the findings of these analyses is in Volume 2, Appendix A. 

Figure 1-7. Annual geometric mean for the Florida Panhandle, West Florida Shelf, and Atlantic Coast from 1998-

2009. Linear regression indicated that the slope is not significantly different from zero in the Florida Panhandle 

(p = 0.42), West Florida Shelf (p = 0.34), and Atlantic Coast (p = 0.76). 

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1.5.2. Accounting for the Occurrence of K. brevis Blooms in Criteria Derivation 

In the proposed approach, EPA is considering excluding from the statistical distribution of 

affected coastal segments any chlorophyllRS a measurements taken during K. brevis bloom 

events. Removing K. brevis has some advantages and is consistent with recommendations from 

the Science Advisory Board (USEPA-SAB 2011). K. brevis cell abundance observations from 

FWRI could be used to exclude the chlorophyll a signature that resulted from the onset of 

K. brevis blooms in the chlorophyllRS a distributions (Figure 1-8). That option can also be 

viewed as a precautionary action in regards to whether the blooms are representative of nutrient 

pollution and impaired designated uses. Although Vargo (2009) concludes that “there is no 

single hypothesis that can account for blooms,” removing data collected during bloom events 

could ensure that the data points used to derive the statistical distribution of reference conditions 

would represent conditions that are supporting balanced natural populations of aquatic flora and 

fauna. This option is also in line with present public concern regarding the health effects of these 

blooms. 

Retaining K. brevis in the analysis is an alternative approach that might have merit because 

blooms are potentially a natural event in coastal waters. It is important to clarify that K. brevis is 

typically found in background concentrations of 1,000 cells/L, or less, year round (Geesey and 

Tester 1993). Historical documentation suggests that K. brevis is also a part of the natural floral 

community of the Gulf of Mexico. Historical records reported observations of K. brevis blooms 

in the Gulf of Mexico before the 1950s (Steidinger 2009; Walsh et al. 2009) and it is still unclear 

if K. brevis blooms have increased in frequency or biomass (Anderson et al. 2008). In addition, it 

is possible that not all K. brevis blooms are detected and flagged. While FWRI sampling is 

robust, covering the vast spatial area remains a challenge (Heil and Steidinger 2009). No 

reporting method currently covers all coastal waters out to 3 nautical miles. In addition, 

including K. brevis in the criteria distributions would allow the data to reflect any bloom 

increases over time. An alternative approach, therefore, is to regard K. brevis blooms as a natural 

part of the reference condition and not exclude flagged observations. 

Analyses of cumulative distributions show that they were minimally disturbed by inclusion or 

removal of observations affected by K. brevis. K. brevis blooms contributed to variability in 

chlorophyllRS a, but they typically initiate 43 mi (70 km) offshore (Tester and Steidinger 1997). 

Even though coastal segment #22 near Tampa Bay was heavily influenced by K. brevis, the 90th 

percentile decreased by only 12 percent when observations flagged for K. brevis were removed 

(Figure 1-8B). Because of the uncertainty of K. brevis impact on Florida coastal waters, EPA is 

proposing numeric nutrient criteria without these data included. However, EPA is requesting 

public comment on whether these data should be included in the data set used to derive numeric 

coastal criteria. 

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Volume 2 Coastal Waters 

Figure 1-8. (A) A time-series of SeaWiFS satellite remote sensing chlorophyllRS a observations for the coastal 

segment adjacent to Tampa Bay. ChlorophyllRS a data were flagged when K. brevis cell counts exceed 50,000 

cells/L (solid circles). (B) The cumulative distributions of SeaWiFS chlorophyllRS a were calculated using all the 

data (gray solid line) and using only the unflagged data (dashed black line). 

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1.6. Calculation of Numeric Nutrient Criteria for Chlorophyll a 

1.6.1. Satellite ChlorophyllRS a Data Evaluation 

A series of regressions were conducted to ensure that chlorophyllRS a and monitoring sampled 

chlorophyll a demonstrate similar responses over time. Monitoring sample data included more 

than 5,500 chlorophyll a observations. Chlorophyll a and SeaWiFS chlorophyllRS a (Figure 1-9) 

were significantly correlated (slope = 1.08, R2 = 0.86, p < 0.01, N = 133). Chlorophyll a and 

MODIS chlorophyllRS a (Figure 1-10) were significantly correlated (slope = 1.19, R2 = 0.64, 

p < 0.01, N = 128). Chlorophyll a and MERIS chlorophyllRS a (Figure 1-11) were also 

significantly correlated (slope = 0.97, R2 

= 0.67, p < 0.01, N = 99). The statistically significant 

regressions from SeaWiFS, MODIS, and MERIS chlorophyllRS a with monitoring sampled 

observation chlorophyll a indicated the default ocean color chlorophyllRS a algorithm provides a 

similar response as seen in the field observation data set. Therefore, the default ocean color 

chlorophyllRS a algorithm could be used universally in the identified coastal segments. No 

regional tuning or corrections were applied to the default chlorophyllRS a algorithms on the basis 

of those regressions. Additionally, only monitoring data from surface samples (< 6.6 ft [2 m]) 

were regressed against the chlorophyllRS a because there was no consistent sampling 

methodology with regard to depth among the different data sets. Satellite ocean color 

chlorophyllRS a more accurately represents a measure of the geometric mean of chlorophyllRS a 

within the first optical depth of the water column. That discrepancy could explain some of the 

scatter around the regression lines. 

Figure 1-9. SeaWiFS observations of chlorophyllRS a compared to in situ chlorophyll a from all stations. The gray 

dashed line is a 1:1 fit, and the black line is the regression slope. Plots are presented in log space, but regression 

coefficients have been converted to linear space to represent a linear regression formula of y = slope * x + 

intercept. 

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Figure 1-10. MODIS observations of chlorophyllRS a compared to in situ chlorophyll a from all stations. The gray 

dashed line is a 1:1 fit, and the black line is the regression slope. Plots are presented in log space, but regression 

coefficients have been converted to linear space to represent a linear regression formula of y = slope * x + 

intercept. 

Figure 1-11. MERIS observations of chlorophyllRS a compared to in situ chlorophyll a from all stations. The gray 

dashed line is a 1:1 fit, and the black line is the regression slope. Plots are presented in log space, but regression 

coefficients have been converted to linear space to represent a linear regression formula of y = slope * x + 

intercept. 

To ensure that the satellite chlorophyllRS a signal is representative of the chlorophyll a 

concentration in coastal waters, other components in the signal had to be taken into account. 

Turbidity might be a concern within estuaries and near sediment-laden rivers. However, rivers in 

Florida are typically sediment poor (Schaeffer et al. 2011), such that chlorophyll a along the 

West Florida Shelf explained 87 percent of particulate backscatter (Cannizzaro et al. 2008). 

Because CDOM and bottom reflectance were also identified as possible interferences affecting 

the relationship between chlorophyll a and chlorophyllRS a in coastal waters, the proposed 

approach used non-parametric statistics and focused on the upper quartiles of the distribution, so 

that these interferences were unlikely to affect derivation of numeric criteria (Schaeffer et al. 

2011). Bottom reflectance was reduced by incorporating the stray light contamination flag, 

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Volume 2 Coastal Waters 

which identified nearshore bins where reflected light from land enters the satellite’s field of 

view. To identify interference of bottom reflectance on chlorophyllRS a, the distributions of 

bathymetry and integrated satellite penetration depth were examined in each coastal segment 

(Schaeffer et al. 2011, Figure 1-12). Median depth of each coastal segment was calculated using 

295-ft (90-m) resolution bathymetry data (NOAA National Geophysical Data Center). PAR 

integrated satellite penetration depths were calculated as the inverse of light attenuation 

(KdRSPAR). Figure 1-13 shows the regression of SeaWiFS KdRSPAR against field light 

attenuation (KdPAR) (Schaeffer et al. 2011). 

Figure 1-12. Coastal segment bottom depths and PAR integrated satellite penetration depths, based on KdRSPAR 

regression in Figure 1-13. 

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Volume 2 Coastal Waters 

Figure 1-13. SeaWiFS observations of KdRSPAR compared to KdPAR from stations within the 3-nautical-mile 

coastal segments (A) and for all the stations (B). The gray dashed line is a 1:1 fit, and the black line is the 

regression slope. Plots are presented in log space, but regression coefficients have been converted to linear 

space to represent a linear regression formula of y = slope * x + intercept. 

Differences in penetration depths were expected between the single bands (i.e., 443, 490, 510, 

and 555) and the PAR integrated response, because the PAR spectrum changes with depth in the 

water column due to different absorption rates at each wavelength. The red wavelengths 

attenuate rapidly with depth, and the blue wavelengths penetrate deeper into the water column 

(Kirk 1994). PAR integrated satellite penetration depth was used here as a general indicator of 

the penetration depth from the ocean color chlorophyll a bands. Mean penetration depth was 

consistently shallower than median water depth in each Florida Panhandle coastal segment. The 

deepest penetration depths (90th percentile) were rarely greater than the shallowest water depths 

(10th percentile) in the Florida Panhandle. Similarly, the Atlantic Coast exhibited little overlap 

between the deepest penetration depths and shallowest water depths. In the West Florida Shelf, 

mean penetration depth was deeper than the median water depth in 10 percent of the coastal 

segments. Coastal segments with median bathymetry shallower than 82 ft (25 m) exhibited 

bottom reflectance interference (Figure 1-14) described by an exponential decay function 

(chlorophyllRS a = 1.17 × exp(-0.14x), where x is the depth in meters; R2 = 0.62, p < 0.01, N = 76) 

(Schaeffer et al. 2011). 

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Volume 2 Coastal Waters 

Figure 1-14. Regression between coastal segment median bathymetry and minimum chlorophyllRS a showing the 

influence of bottom reflectance on chlorophyllRS a. 

Seagrass also affects chlorophyllRS a, representing a special case of bottom reflectance. 

However, seagrasses were not present in coastal waters in the Florida Panhandle, Atlantic Coast, 

and most of the West Florida Shelf (Carlson and Madley 2007). 

1.6.2. Options for Calculating Criteria 

The annual geometric mean of each coastal segment was calculated for each of the 12 years 

(1998–2009), then the 90th percentile of the annual geometric means both with and without 

K. brevis were determined. In addition to supporting the use of chlorophyll a as a reliable 

indicator, several European and U.S. eutrophication assessment frameworks, such as the OsloParis Commission “Common Procedure,” Water Framework Directive of the European Union, 

Assessment of Estuarine Trophic Status in the U.S., and the Marine Strategy Framework 

Directive used by the European Commission also support the use of the 90th percentile mean 

chlorophyll a concentration in marine waters. These frameworks are applied over broad scales 

(Arctic Waters, the Greater North Sea, the Celtic Sea, the Bay of Biscay, and Iberian Coast, and 

the Wider Atlantic), and are often interrelated regarding their assessment methodologies and 

eutrophication classification categories. While some differences exist between the frameworks in 

data analysis time frames and spatial coverage of ocean waters, they share similar justifications 

for using the 90th percentile as a recognized statistic (Devlin et al. 2007, 2011). Chlorophyll a 

typically exhibits periodicity and sporadic change, and consequently tends to be asymmetrically 

distributed with spikes in concentration. Establishing criteria based on the 90th percentile values 

can better encompass the spread of data by capturing variation over time and space, resulting in 

greater statistical confidence (Bricker et al. 2003; Foden et al. 2010). For example, the United 

Kingdom uses the 90th percentile in their modified Oslo-Paris Commission framework to capture 

a more representative spread of data because there is high confidence the growing season period 

used in their analysis includes elevated spring bloom values (CEFAS 2008). These frameworks 

also assert the calculation of chlorophyll a must be based on commonly observed peaks rather 

than outliers to adequately capture variation representative of system conditions (Bricker et al. 

2003). Application of the percentile based approach provides a more robust analysis for 

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calculating chlorophyll a criteria, such that there is less of a risk that a system will be wrongly 

classified, or in this case, less risk the criteria will be underprotective. Despite their differences in 

assessment time frames, spatial scales, and statistical measures, the frameworks identify the 90th 

percentile as a representation of chlorophyll a concentration that above which eutrophication is 

considered ecologically problematic (Ferreira et al. 2011). This is captured in Oslo-Paris 

Commission, where the 90th percentile represents the boundary between a “Non-Problem Area” 

meeting reference conditions and “Problem Area” where management intervention is needed 

(Devlin et al. 2007, 2011; OSPAR Commission 2005). In addition to accounting for large spatial 

and temporal variation, the 90th percentile also helps allow for natural variability and cloud cover 

to be considered in the detection of chlorophyll a concentrations via remote sensing (Park et al. 

2010). In a study performed by Park et al. (2010), a threshold map was defined as the 90th 

percentile of previous years’ chlorophyll a concentration data to accommodate regional 

differences in typical chlorophyll a levels, with separate maps for MODIS and MERIS to 

account for sensor-specific bias. 

Using the 90th percentile reflects EPA’s confidence that the coastal data set used to derive the 

criteria is supportive of designated uses and balanced natural populations of aquatic flora and 

fauna. Selection of the 90th percentile also supports the confidence that satellite chlorophyllRS a 

measures have captured an expected dynamic range of responses for each coastal segment. The 

criteria will apply on a segment-specific and annual basis, with a frequency of exceedance of no 

more than once in a three year period. 

1.6.3. ChlorophyllRS a Annual Geometric Mean Concentration 

The geometric mean is preferred to the arithmetic mean because the geometric mean is a better 

estimator of the central tendency when the data are log-normally distributed, as is commonly the 

case for chlorophyll a. EPA is proposing to calculate the annual geometric mean by coastal 

segment for 71 coastal segments.

7

 Only the SeaWiFS data from 1998 to 2009 were used to 

calculate criteria. Criteria values were selected as the 90th percentile from the reference period 

annual geometric mean values for each segment (Table 1-3; a map showing the location of each 

segment is provided for reference in Figure 1-15). EPA calculated the 90th percentile using a 

z-statistic in contrast to a Student’s t-statistic. Although these test statistics yield similar results 

with large sample sizes, they represent different assumptions regarding the distribution of the 

variable being sampled from the data. The z-statistic should follow a standard, normal 

distribution. Thus, the z-statistic is used when the sample size is large, such that the sample 

variance is a good estimation of the population variance under the Central Limit Theorem, in 

addition to the condition of random sample selection. In comparison, a t-statistic is appropriate 

when the sample size is small, such that the population parameter cannot be properly estimated 

because sufficient uncertainty exists around the sample variance. Because the population 

standard deviation is unknown in this case, the sample standard deviation is computed to 

determine the standard error of the mean. The t-statistic follows a Student’s t-distribution, which 

compensates for the decreased confidence in the sample standard deviation estimate with 

allowing degrees of freedom. 

7 Criteria for three segments in the West Florida Shelf region were derived using procedures in Volume 1 of the TSD. 

26 
90th Percentile 90th Percentile 

ChlorophyllRS a ChlorophyllRS a 

(mg/m3

) (mg/m3) 

Coastal K. brevis K. brevis Coastal K. brevis K. brevis 

Segment Location included flag Segment Location included flag 

1 Alabama border 2.41 2.41 37 0.24 0.24 

2 Pensacola Pass 2.54 2.57 38 0.21 0.21 

3 1.44 1.44 39 0.21 0.21 

4 1.16 1.16 40 0.20 0.20 

5 1.07 1.06 41 0.20 0.20 

6 1.04 1.04 42 0.21 0.21 

7 1.14 1.14 43 0.25 0.25 

8 Choctawhatchee Pass 1.23 1.23 44 0.57 0.57 

9 1.08 1.08 45 St. Lucie Inlet 1.08 1.08 

10 1.09 1.09 46 1.42 1.42 

11 1.11 1.11 47 1.77 1.77 

12 1.18 1.18 48 1.55 1.55 

13 1.46 1.45 49 1.44 1.44 

14 St. Andrews Pass 1.75 1.74 50 1.53 1.53 

15 St. Joseph Pass 2.75 2.75 51 1.31 1.31 

16 2.39 2.39 52 1.40 1.40 

17 Southeast St. Joseph 3.47 3.47 53 1.80 1.80 

18 4.09 3.96 54 Canaveral Bight 2.74 2.73 

19 Tampa Bay Pass 4.75 4.45 55 2.33 2.33 

20 3.42 3.37 56 2.28 2.28 

21 3.34 3.25 57 2.06 2.06 

22 3.06 2.95 58 1.92 1.92 

23 3.00 2.79 59 1.76 1.76 

24 2.98 2.98 60 1.72 1.72 

25 3.26 3.24 61 2.04 2.04 

26 Charlotte Harbor 4.58 4.55 62 1.94 1.94 

27 4.31 4.22 63 1.86 1.86 

28 3.71 3.67 64 1.95 1.95 

29 4.17 4.16 65 2.43 2.41 

30 5.62 5.70 66 2.78 2.76 

31 4.47 4.54 67 2.81 2.80 

32 4.03 4.03 68 3.48 3.45 

33 Fort Myers 4.58 4.61 69 Nassau Sound 3.71 3.69 

34 Biscayne Bay 0.92 0.92 70 3.82 3.78 

35 0.26 0.26 71 Georgia border 4.24 4.22 

36 0.26 0.26 

Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 2 Coastal Waters 

Table 1-3. Candidate chlorophyllRS a criteria for each coastal segment calculated on the basis of the 90th 

percentile of the annual geometric means with and without K. brevis 

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Figure 1-15. 71 coastal segments for which coastal criteria were derived. 

The proposed coastal criteria are defined as a specific concentration of SeaWiFS chlorophyllRS a 

as a magnitude as well as by a frequency and duration of allowable exceedances that ensure 

protection of the designated use. EPA proposes that for a given water body, the SeaWiFS 

chlorophyllRS a content must not exceed the applicable criterion concentration more than once in 

a 3-year period. EPA is proposing criteria duration of a year, in which sampled nutrient 

concentrations are summarized as annual geometric means, because annual average 

concentrations are directly related to annual nutrient loading to the water body. EPA has 

determined that such a frequency of exceedances would result in acceptable effects on 

designated uses because it would allow water bodies enough time to recover from occasionally 

elevated levels of nitrogen and phosphorus concentrations. 

Because the SeaWiFS and MERIS satellites are no longer operational, EPA has developed an 

approach that can use operational satellites, such as MODIS, for future analysis. It is 

recommended that compliance with chlorophyllRS a criteria be assessed using similar satellite 

data and algorithms (Schaeffer et al. 2011). Doing so will mitigate problems associated with 

using the default ocean color algorithms close to the coast because interferences and overestimations are expected to be constant. Despite the loss of SeaWiFS, other satellites will 

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continue to be available. The Visible Infrared Imager Radiometer Suite (VIIRS) was launched on 

October 28, 2011, and has similar wavebands as MODIS. As an alternative for MERIS, the ESA 

Sentinel-3 is scheduled to launch in 2013. 

EPA has set criteria that are based on SeaWiFS and is providing the following method for future 

assessment/analysis of coastal criteria using MODIS and MERIS. First, MODIS and MERIS 

annual geometric means were adjusted using the chlorophyllRS a regressions with SeaWiFS 

(Figure 1-16) to merge multi-mission ocean color satellites. MODIS had a linear response with 

SeaWiFS (slope = 1.13, R2

 = 0.84, N = 507). MERIS had a cubic polynomial response with 

SeaWiFS (R2 = 0.93, N = 518). The result of the annual geometric means in coastal segment #1 

for SeaWiFS, adjusted MODIS, and adjusted MERIS are presented in Figure 1-17 (top graph). 

Although all three sensors have similar trends, the magnitude of the response is different. The 

next step was to normalize the magnitude of the response from MODIS and MERIS on the basis 

of SeaWiFS. A new calculation of the 90th percentile from each coastal segment was calculated 

using the period from 2003 to 2009: the record of overlap from all three missions. The new 90th 

percentile was calculated from each individual sensor; SeaWiFS, adjusted MODIS, and adjusted 

MERIS data. The difference between the SeaWiFS percentile and adjusted MODIS or adjusted 

MERIS percentiles was applied as a normalization factor (Table 1-4) for each annual geometric 

mean in each coastal segment. The result is demonstrated in Figure 1-17 (bottom graph) where 

the magnitude of the response is now normalized in coastal segment #1. EPA ran a series of tests 

always using SeaWiFS data from 1998 to 2002 and either keeping the SeaWiFS record from 

2003 to 2009 or replacing it with available MODIS (2003–2009) or MERIS (2003–2009) data. 

The differences between the criteria at the SeaWiFS only 90th percentile compared to 

SeaWiFS/MODIS (Figure 1-18A) or SeaWiFS/MERIS (Figure 1-18B) is less than 7 percent, 

suggesting that MODIS and MERIS (although not presently operational) could be used for future 

analysis of criteria. Only coastal segment #26 had a difference of 30 percent because of minimal 

data retrieval from MODIS within that segment. 

To use MODIS and VIIRS in the future, an annual geometric mean would be calculated for each 

coastal segment using the default chlorophyll a algorithm. The annual geometric mean would be 

adjusted on the basis of the respective chlorophyllRS a regressions with SeaWiFS. Next, the 

corresponding normalization factor would be applied to the adjusted annual geometric mean. The 

adjusted and normalized value could be compared against the set criteria values from SeaWiFS. 

Either MODIS, VIIRS, or a combination of both sensors could be used in the future. NASA 

reprocessing events do occur occasionally, which could improve the response of chlorophyllRS a 

from the satellite. To address the issue of NASA satellite reprocessing events, EPA recommends 

that an annual geometric mean be calculated with data before and after the reprocessing event. 

The percent difference resulting from the reprocessing event could be added to the normalization 

factor. Finally, water quality managers might wish to convert from satellite-derived 

chlorophyllRS a criteria to in situ chlorophyll a. If this is the case, validation efforts and 

algorithms for each coastal segment would be necessary. 

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Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 2 Coastal Waters 

Figure 1-16. The chlorophyllRS a annual geometric mean of each coastal segment from MODIS and MERIS 

were regressed against SeaWiFS to adjust each satellite sensor to a similar response. 

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Figure 1-17. Top: Example of annual geometric means from SeaWiFS and adjusted MODIS and adjusted MERIS 

data for the coastal segment adjacent to Perdido Bay, which are based on the regression coefficients from Figure 

1-16. Bottom: Example of annual geometric means from SeaWiFS and adjusted MODIS and adjusted MERIS data 

after applying the normalization factor. 

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(A) 

(B) 

Figure 1-18. Percent difference in the 90th percentile calculation compared against the 90th percentile calculation 

using only SeaWiFS. (A) Difference when the SeaWiFS record was used from 1998 to 2002 and the data from 

2003 to 2009 was replaced with the MODIS record. (B) Difference when the SeaWiFS record was used from 1998 

to 2002 and the data from 2003 to 2009 was replaced with the MERIS record. 

32 
 

Normalization Factors Normalization Factors 

Segment MODIS MERIS Segment MODIS MERIS 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

0.496 

0.864 

0.383 

0.251 

0.188 

0.250 

0.275 

0.299 

0.182 

0.334 

0.266 

0.358 

0.408 

0.608 

0.802 

-0.050 

0.152 

-0.557 

-0.339 

-1.038 

-1.618 

-0.966 

-2.658 

-1.277 

-1.163 

-0.427 

-0.676 

-0.206 

0.134 

-0.182 

-1.626 

-0.886 

0.184 

0.250 

-0.030 

-0.024 

-0.626 

-0.172 

-0.194 

-0.246 

-0.195 

-0.003 

-0.016 

-0.054 

-0.004 

-0.041 

-0.031 

-0.026 

-0.102 

-0.043 

0.549 

0.103 

1.278 

-1.018 

-0.221 

-0.518 

-0.040 

0.085 

-0.406 

-0.544 

-0.344 

-0.261 

-0.020 

-0.828 

-0.472 

0.431 

0.736 

0.463 

-0.266 

0.440 

-0.036 

-0.004 

37 

38 

39 

40 

41 

42 

43 

44 

45 

46 

47 

48 

49 

50 

51 

52 

53 

54 

55 

56 

57 

58 

59 

60 

61 

62 

63 

64 

65 

66 

67 

68 

69 

70 

71 

-0.030 

-0.055 

-0.027 

-0.023 

-0.026 

-0.038 

-0.050 

-0.104 

0.023 

0.313 

0.101 

0.155 

0.125 

0.019 

-0.115 

0.095 

0.078 

0.443 

0.060 

0.013 

0.095 

-0.163 

0.216 

-0.488 

-0.655 

-0.613 

-0.762 

-0.339 

-0.326 

-0.386 

-0.927 

-1.336 

-1.365 

-0.378 

-0.146 

-0.013 

-0.007 

0.020 

0.030 

0.006 

0.020 

-0.013 

0.019 

0.080 

0.303 

0.274 

0.290 

0.519 

0.480 

0.299 

0.647 

0.443 

0.975 

0.458 

0.284 

0.206 

-0.040 

-0.053 

-0.069 

-0.086 

-0.122 

-0.353 

-0.468 

-0.138 

-0.319 

-0.325 

-0.458 

-0.117 

-0.464 

-0.610 

Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

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Table 1-4. MODIS and MERIS normalization factors based on SeaWiFS from 2003 to 2009 

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1.7. References 

Anderson, D.M., J.M. Burkholder, W.P. Cochlan, P.M. Glibert, C.J. Gobler, C.A. Heil, R.M. 

Kudela, M.L. Parsons, J.E.J. Rensel, D.W. Townsend, V.L. Trainer, and G.A. Vargo. 

2008. Harmful algal blooms and eutrophication: Examining linkages from selected 

coastal regions of the United States. Harmful Algae 8(1):39-53. 

Bailey, S.W., and P.J. Werdell. 2006. A multi-sensor approach for the orbit validation of ocean 

color satellite data products. Remote Sensing of Environment 102:12-23. 

Boyer, J.N., C.R. Kelble, P.B. Ortner, and D.T. Rudnick. 2009. Phytoplankton bloom status: 

Chlorophyll a biomass as an indicator of water quality condition in the southern estuaries 

of Florida, USA. Ecological Indicators 9(6, Supplement 1):S56-S67. 

Brand, L.E., and A. Compton. 2007. Long-term increase in Karenia brevis abundance along the 

southwest Florida coast. Harmful Algae 6(2):232-252. 

Bricker, S.B., C.G. Clement, D.E. Pirhalla, S.P. Orlando, and D.R.G. Farrow. 1999. National 

Estuarine Eutrophication Assessment: Effects of Nutrient Enrichment in the Nation’s 

Estuaries. National Oceanic and Atmospheric Administration. Available at: 

http://coastalscience.noaa.gov/publications/eutro_report.pdf. Accessed April 2012. 

Bricker, S.B., J.G. Ferreira, and T. Simas. 2003. An integrated methodology for assessment of 

estuarine trophic status. Ecological Modelling 169:39–60. 

Campbell, J.W., J.M. Blaisdell, and M. Darzi. 1995. Level-3 SeaWiFS Data Products: Spatial 

and Temporal Binning Algorithms. In SeaWiFS Technical Report Series, ed. S.B. 

Hooker, E.R. Firestone, and J.G. Acker. National Aeronautics and Space Administration, 

Greenbelt, MD. 

Cannizzaro, J.P., K.L. Carder, F.R. Chen, C.A. Heil, and G.A. Vargo. 2008. A novel technique 

for detection of the toxic dinoflagellate, Karenia brevis, in the Gulf of Mexico from 

remotely sensed ocean color data. Continental Shelf Research 28:137-158. 

Carlson, P.R., and K. Madley. 2007. Statewide summary for Florida. In Seagrass Status and 

Trends in the Northern Gulf of Mexico: 1940-2002., ed. L. Handley, D. Altsman, and 

R. DeMay. U.S. Environmental Protection Agency, Washington, DC. 

CEFAS. 2008. Common Procedure for the Identification of the Eutrophication Status of the 

OSPAR Maritime Area, UK National Report. Centre for the Environment, Fisheries, and 

Aquaculture Science, Lowestoft, UK. Available at: 

http://www.cefas.defra.gov.uk/media/167074/ospar_uk%20national%20report_16july07. 

pdf. Accessed April 2012. 

Devlin, M., S. Bricker, and S. Painting. 2011. Comparison of five methods for assessing impacts 

of nutrient enrichment using estuarine case studies. Biogeochemistry 106(2):177-205. 

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Volume 2 Coastal Waters 

Devlin, M.J., S. Painting, and M. Best. 2007. Setting nutrient thresholds to support an ecological 

assessment based on nutrient enrichment, potential primary production and undesirable 

disturbance. Marine Pollution Bulletin 55(1–6):65-73. 

Elser, J.J., M.E.S. Bracken, E.E. Cleland, D.S. Gruner, W.S. Harpole, H. Hillebrand, J.T. Ngai, 

E.W. Seabloom, J.B. Shurin, and J.E. Smith. 2007. Global analysis of nitrogen and 

phosphorus limitation of primary production in freshwater, marine, and terrestrial 

ecosystems. Ecology Letters 10: 1135-1142 

European Commission. 2003. Common Implementation Strategy for the Water Framework 

Directive (2000/60/EC). European Commission, Working Group 2.4—COAST. 

Luxembourg. 

http://circa.europa.eu/Public/irc/env/wfd/library?l=/framework_directive/guidance_docu 

ments/guidancesnos5scharacteri/_EN_1.0_&a=d. Accessed April 2012. 

Feldman, G.C., and C.R. McClain. 2010. Ocean Color Web, SeaWiFS and MODIS Reprocessing 

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