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 , 2012Technical 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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### 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 Design ated 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 Chlorophyll RS a Data Evaluation

.......................................................... 21

1.6.2. Options for Calculating Criteria

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

1.6.3. Chlorophyll RS a Annual Geometric Mean Concentration

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

1.7. References

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

Appendix A : A Review of Reference Conditio ns 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 fro m 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. Numbe rs are coastal segment numbers ranging from 1 through 74..................................................................................................................9

Figure 1

- 4. Field data locations used to validate satellite remote sen sing analysis of chlorophyll RS a . Dark circles indicate the station data used to compare chlorophyll a to satellite remote sensing observations of chlorophyll RS 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 chlorophyll RS a bins (#1) that were on the polygon li ne 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. Chlorophyll RS 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 chlorophyll RS a observations for the coastal segment adjacent to Tampa Bay. Chlorophyll RS

a data were flagged when K. brevis cell counts exceed 50,000 cells/L (solid circles). (B) The cumulative distributions of SeaWiFS chlorophyll RS 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 chlorophyll RS 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 chlorophyll RS 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 chlorophyll RS 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 r egression 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 Kd RS PAR regression in Figure 1-13. ..............................................23

Figure 1 -13. SeaWiFS observations of Kd RS PAR 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 ar e 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 chlorophyll RS a showing the influence of bottom reflectance on chlorophyll RS a. ..........................................................................................................25

Figure 1

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

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

MODIS and MERIS were regressed against SeaWiFS to adjust each satellite

se nsor to a similar response. .......................................................................................30

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

f actor. ..........................................................................................................................31

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

90 th 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 90 th 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 chlorophyll RS a c riteria for each coastal segment calculated on the

basis of the 90 th 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

Chlorophyll RS 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 Impai red Water Rule

K d l ight extinction

KdPAR light extinction from photosynthetically active radiation

Kd RS PAR 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 photosynthetic ally active radiation

ppt parts per thousand

ROI region of interest

SeaDAS SeaWiFS Data Analysis System

SeaWiFS Sea

- viewing Wide Field -of-view Sensor

TN t otal nitrogen

TP total phosphorus

TSD Technical Support Document

VIIRS Visible I nfrared Imager Radiometer Suite

WBID water body identification number

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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 exclude s estuaries, which are generally semi

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

natural limits of the semi

- enc losed basin. This definition is consistent with the Florida Department of Environmental Protection (FDEP) defin ing open coastal waters as “gulf or ocean

waters that are not classified as estuaries 1 or open ocean waters ” 2 ( Section 62-303.200, F.A.C. ).

Infor mation 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 a ll 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 in dicators 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 . S atellite remote sensing was identified as a scientifically

defen sible 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 t he selected period of record (1998–2009) is supportive of designated uses and protective numeric criteria values for Florida.

Th e following sections describe the steps pertaining to the delineation of coastal waters, data selection and preparation, and ana lytical approaches explored by EPA for coastal numeric criteria derivation. Finally, a discussion of calculating the numeric chlorophyll a criteria according to a n annual geometric mean approach that considers the 90 th percentile to prevent a

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

S ection 1.6. While EPA proposes a lead option for criteria derivation

, several alternate computation approaches have be en 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 90 th 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 pr ovide 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 m ight 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 Atlant ic 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 c urrent 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 expec tations 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 la nguage 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 s ystems. 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 concentrati ons.

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 A ssessment 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, Stan dards 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 w ater quality standards, consisting of designated uses of the waters and water quality criteria based on such use s, to restor e and maint ain 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

i mportant 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 com munity 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 des ignated uses from nutrient pollution through its narrative criterion statement, EPA is proposing to prevent the harmfulincrease of nutrient levels by ensuring that the numeric values developed will protect nutrientsensitive aquatic floral and faunal spec ies.

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 cha racterizing 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 ar eas m ight 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 fau na or the use of a reference condition approach to derive numeric nutrient criteria for coastal waters (see S ection 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 cri teria . 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 indicato rs ( 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 conceptua l

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 wat ers . 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 av ailability and greater organic decomposition in coastal waters. These outcomes can be ecologically significant,

as chlorophyll a exhibits an exponential relationship to light extinction (K d ) and, thus, small changes in K d 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 ec osystem 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 Pro cedure,” Water Framework Directive of the E uropean 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 ob served 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 chloro phyll 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 sp ecies 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 measure d at

the 90 th 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 e ffects will appear above the 90 th 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 90 th 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 wide ly 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 (Gam eiro 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 shift s in aquatic species composition or primary producers. Historical

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

studies an d 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 sh ift 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 f rom 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

res ponsive to over all nutrient conditions in Florida. Increases in nitrogen for the w estern part of

Florida B ay 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

5Framework Delineation of Waters Threshold Information

Oslo

- Paris Commission

“Com mon Procedure” Waters are classified according to salinity gradients

as :

E stuarine 0–20 PSU

C oastal 30–34.5 PSU

O ffshore > 34.5 PSU Statistical Measure: Threshold as growing season

90 th 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 rang e.

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 90 th percentile
were used as thresholds corresponding to areas

that are nutrient enriched :

Estuarine : 15 μ g/L

Coastal : 15 μ g/L

Offshore : 10 μ g /L

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

Estuarine Trophic Status) zones for the eutrophic condition rating

. percentile concentrations to determine the level of
eutrophication are listed as: T idal 0–0.5 PSU

M ixing 0.5–25 PSU Hypereutrophic : >60 μg/ L

S eawater 25 PSU High : >20, ≤60 μg/ L

Medium : >5, ≤20 μg/ L

Low : >0, ≤5 μg/ L

Note: PSU = practical salinity units; μ g/L = micrograms per liter Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria,

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(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 valu es at the 90 th percentile of associated concentrations across their specific timeframes of analysis. These values are captured in Table 1-1 below.

Phyt oplankton s pecies 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 90 th 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 (chlorophyll RS 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 derivequantitative information on the substances in the water and their concentrations in the near6Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria,

Volume 2 Coastal Waters

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

coastal waters

— between water column c hlorophyll a and the satellite response (chlorophyll RS

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 potential ly 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 s ea surface

.

CDOM and bottom reflectance were both identified as possible interferences affecting the relationship between chlorophyll a and chlorophyll RS 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 chlorophyll RS a v alue in each coastal segment, setting the lower limit for the chlorophyll RS 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 S ection 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 W est Florida S helf from Anclote Bay to Rookery Bay,

and the Atlantic Coast segments from Biscayne Bay to the Georgia border ( Figure 1-2 ). Th is 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 reflectan ce , the coastal regions were further subdivided on the basis of the

FDEP ’s w ater b ody 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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E PA 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 include d 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 . Th is 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 c arbon exports from rivers ( Morey et al. 2009) would confound derivation of chlorophyll RS a

.

Figure 1-3

. F ixed 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

.

9Chlorophyll 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

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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 v alidation, (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 approximatel y 137 samples for the entire coastal area per year. In contrast,

satellites can provide measure s of chlorophyll a in each coastal segme nt 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 wil l permit viewing of the files containing the chlorophyll a , sample number, KdPAR , Lat itude, and Longitude products. HDFView can be downloaded from http://www.hdfgroup.org/hdf- java

html/hdfview/

. A comprehensive image analysis package called the Sea

- viewing Wide Field

- of

view 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 W aters R ule (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 a l. 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 chlorophyll RS a

. Dark circles
indicate the station data used to compare chlor ophyll a to satellite remote sensing observations of
chlorophyll RS 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 chlorophyll RS 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 la rge 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 p redefined 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 (Campbel l et al. 1995) to collect data. Th is 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 a n 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, t he 8 -day composite is a compromise between filling in the bins with data and a temporal span that can capture short- term dynamics.

SeaWiFS 6 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 chlorophyll RS a regressions with SeaWiFS.

**K. brevis**  D ata

K. brevis is a harmful algal bloom dinoflagellate that frequently occurs in the coastal waters o f Florida, and produc es 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 b looms can also affect Florida’s Atlantic Coast. This organism produces brevetoxin, a potent neurotoxin that can c ause respiratory irritation (at 10 3 cells/L) through aerosolization, neurotoxic shellfish poisoning through consumption of contaminated shellfish (at > 5 × 10 3 cells/L), and large fish kills (at 1 to 2.5 × 10 5 cells/L).

Mortalities of shore birds and manatees have also been linked to K. brevis blooms (Landsberg 2002).

L aboratory 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 discol ored 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 i t can also spread and cause effects over a large portion of the Gu lf 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 ).

Th e 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 w aters 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 characteristic s that allow it to succeed. For instance, the organ ism’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 s tate 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 exti nction 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 w as 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 chlo rophyll a data were based on

extracted values. For calculatin g KdPAR, all data were excluded f or depths less than 5.6 ft (1.7 m), roughly the height of the conductivity, t emperature, and depth apparatus. The l og 10 of photosynthetic ally active radiation ( PA R) was plotted as a function of depth. Any regression of log 10 (PAR) against depth that returned an R 2 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 chlorophyll RS a (satellite

- derived chlorophyll a ). SeaWiFS provides a histori cal time

- series of chlorophyll RS 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) an d 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 (2 nd reprocessing) temporally spanned between April 29, 2002, and January 1, 2010. Imagery spatiall y 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 (chlorophyll RS 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 chlorophyll RS 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 pi xels 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 mat chups were evaluate d 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).

Chlorophyll RS 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 Se aDAS (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 avoi d exclusion of bins up to the

3 nauti cal 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 parall el 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 chlorophyll RS a bins (#1) that were on the polygon
line or a vertex. Bins masked by SeaDAS standard qua lity control flags ( #2), including stray light contamination
and land, had no value and were not included in the computation. Chlorophyll RS a bins completely within coastal segment polygon (#3) were included in the computation.

**1.4.4.** K. brevis **Data Preparation**

T he 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 chlorophyll RS 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. F ew 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 S ection 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 wat er quality conditions present in reference water bod ies that are known to be supporting o r 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 tho se 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;

identifie d 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

whe n designated use impairm ents were documented , the remaining data set of reference waters are representative of waters with water quality that s upport designated uses . This new data set

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

im pairments 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 chlorophyll RS a annual geometric mean s 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 d oes 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 a ny chlorophyll RS 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 chlorophyll RS 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 m ight have merit becau se 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 Steidinge r 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. A n 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. brev is blooms contributed to variability in chlorophyll RS 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 90 th
percentile decreased by only 12 percent when observations flagged for K. brevis were removed ( Figure 1-8 B). Because of the uncertainty of K. brevis im pact on Florida coastal waters, EPA is proposing numeric nutrient criteria without the se data included . However, EPA is request ing public comment on whether these data should be included in the data set used to derive numeric

coastal criteria.

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Figure 1-8 . (A) A time

- series of SeaWiFS satellite remote sensing chlorophyll RS a observations for the coastal
segment adjacent to Tampa Bay. Chlorophyll RS a data were flagged when K. brevis cell counts exceed 50,000

c ells / L (solid circles). (B) The cumulative distributions of SeaWiFS chlorophyll RS 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 Chl orophyll**  a

**1.6.1. Satellite Chlorophyll**  **RS** a **Data Evaluation**
A series of regressions were conducted to ensure that chlorophyll RS 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 chlorophyll RS a ( Figure 1-9 ) were significantly correlated (slope = 1.08, R 2 = 0.86, p < 0.01, N = 133 ). Chlorophyll a and MODIS chlorophyll RS a ( Figure 1-10 ) were significantly correlated (slope = 1.19, R 2 = 0.64, p < 0.01, N = 128). Chlorophyll a and MERIS chlorophyll RS a ( Figure 1-11 ) were also significantly correlated (slope = 0.97, R 2 = 0.67, p < 0.01, N = 99 ). The statistically significant regressions from SeaWiFS, MODIS, and MERIS chlorophyll RS a with monitoring sampled observation chlorophyll a indicated the default ocean color chlorophyll RS a algorithm provides a similar response as seen in the field observation data set. Therefore, the default ocean color chlorophyll RS a algorithm could be used universally in the identified coastal segments. No regional tuning or corrections were applied to the default chlorophyll RS a algorithms on the basis of those regressions. Additionally, only monitoring data from surface samples (< 6 .6 ft [2 m]) were regressed against the chlorophyll RS a because there was no consistent sampling methodology with regard to depth among the different data sets. Satellite ocean color chlorophyll RS a more accurately represents a measure of the geometric mea n of chlorophyll RS 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 chlorophyll RS 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 reg ression formula of y = slope * x +
intercept.

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Figure 1- 10 . MODIS observations of chlorophyll RS 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 . M ERIS observations of chlorophyll RS 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 li near space to represent a linear regression formula of y = slope * x +
intercept.

To ensure that the satellite chlorophyll RS 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 chlorophyll RS a in coastal waters, the propos ed 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 t he stray light contamination flag,

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which identified nearshore bins where reflected light from land enters the satellite’s field of view. To identify interference of bottom reflectance on chlorophyll RS a , the distributions of bathymetry and integrated satel lite 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 (Kd RS PAR). Figure 1-13 shows the regression of SeaWiFS Kd RS PAR against field light attenuation (KdPAR) (Schaeffer et al . 2011).

Figure 1- 12

. Coastal segment bottom depths and PAR integrated satellite penetration depths, based on Kd RS PAR
regression in Figure 1- 13

.

23Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria,

Volume 2 Coastal Waters

Figure 1- 13 . SeaWiFS observations of Kd RS PAR 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 (90 th percentile) were rarely greater than the shallowest water depths (10 th percentile) in the Flori da 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

s egments. Coastal segments with median bathymetry shallower than 82 ft (25 m) exhibited bottom reflectance interference ( Figure 1-14 ) described by an ex ponential decay function (chlorophyll RS a = 1.17 × exp (- 0.14 x ) , where x is the depth in meters; R 2 = 0.62, p < 0.01, N = 76) (Schaeffer et al . 2011).

24Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria,

Volume 2 Coastal Waters

Figure 1- 14

. Regression between coastal segment median bathymetry and minimum chlorophyll RS a showing the
influence of bottom reflectance on chlorophyll RS a.

Seagrass also affects chlorophyll RS a , representing a special case of bottom reflectance.
However, seagrasses w ere not present in coastal waters in the F lorida Panhandle, A tlantic C oast

, and most of the W est Florida S helf (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 90 th 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 reliableindicator, 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 Europe an Commission also support the use of the 90 th 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 90 th 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 concen tration. Establishing criteria based on the 90 th 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 Un ited Kingdom uses the 90 th 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 90 th
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 90 th 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 Commi ssion 2005). In addition to accounting for large spatial and temporal variation, the 90 th 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 90 th
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 90 th 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 90 th percentile also supports the confidence that satellite chlorophyll RS 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 onc e in a three year period.

**1.6.3. Chlorophyll**  **RS** 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 lo g-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 90 th 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 90 th 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 free dom.

7 Criteria for three segments in the West Florida Shelf region were derived using procedures in Volume 1 of the TSD.

2690 t h Percentile 90 th Percentile
Chlorophyll RS a Chlorophyll RS a (mg/m 3 ) (mg/m 3 )

Coastal K. brevis K. brevis Coastal K. brevis K. brevis

Segment Location included flag Segment Location included flag

1 A labama border 2.41 2.41 37 0.2 4 0.2 4

2 Pensacola Pass 2.54 2.5 7 38 0.2 1 0.2 1

3 1.44 1.44 39 0.21 0.21

4 1.16 1.16 40 0.2 0 0.2 0

5 1.07 1.0 6 41 0.20 0.20

6 1.04 1.04 42 0.2 1 0.2 1

7 1.14 1.14 43 0.2 5 0.2 5

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 1.4 5 49 1. 44 1. 44

14 St. Andrew s Pass 1.75 1.7 4 50 1. 53 1. 53

15 St. Jo s e ph Pass 2.75 2.75 51 1. 31 1. 31

16 2.39 2.39 52 1. 40 1. 40

17 Southeast St. Jo s e ph 3.47 3.47 53 1. 80 1. 8 0

18 4.09 3.96 54 Canaveral Bight 2.74 2.73

19 Tamp a Bay Pass 4.75 4. 4 5 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 3.2 4 61 2.04 2.04

26 Charlotte Harbor 4.58 4.5 5 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 4.1 6 65 2.43 2.4 1

30 5.62 5. 70 66 2.78 2.7 6

31 4. 47 4. 54 67 2.81 2.8 0

32 4.03 4.03 68 3.48 3.4 5

33 Fort Myers 4.58 4. 61 69 Nassau Sound 3.71 3. 69

34 Biscayn e Bay 0.92 0.92 70 3.82 3. 78

35 0.2 6 0.2 6 71 G eorgia border 4.24 4.2 2

36 0.26 0.26 Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria,

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Table 1 -3 . Candidate chlorophyll RS a criteria for each coastal segment calculated on the basis of the 90 th

percentile of the annual geometric means with and without K. brevis

27Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria,

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Figure 1- 15

. 71 coastal segments for which coastal criteria were derived.

The proposed coastal c riteria are defined as a specific concentration of SeaWiFS chlorophyll RS 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 chlorophyll RS 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 su ch 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 satell ites 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 chlorophyll RS a criteria be assessed using similar satellite data and algorithms ( Schaeffer et al. 2011) . Doing so will mitigate problems associated withusing 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 chlorophyll RS a regressions with SeaWiFS ( Figure 1-16 ) to merge multi

- mission ocean color satellites. MODIS had a linear response with SeaWiFS (slope = 1.13, R 2 = 0.84, N = 507). MERIS had a cubic polynomial response with SeaWiFS (R 2 = 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 SeaW iFS. A new calculation of the 90 th percentile from each coastal segment was calculated using the period from 2003 to 2009: the record of overlap from all three missions. The new 90 th
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 90 th percentile compared to

SeaWiFS/MODIS ( Figure 1-18 A) or SeaWiFS/ME RIS ( Figure 1-18 B) 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 chlorophyll RS 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 chlorophyll RS 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 m ight wish to convert from satellite -derived chlorophyll RS a criteria to in situ chlorophyll a . If this is the case, validation efforts and algorithms for each coastal segment would be necessary.

29Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria,

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Figure 1- 16 . The chlorophyll RS a annual geometric mean of each coas tal segment from MODIS and MER IS

were regressed against SeaWiFS to adjust each satellite sensor to a similar response

.

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

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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.

31Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria,

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(A)

(B)

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

32Normalization 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,

Volume 2 Coastal Waters

Table 1-4 . MODIS and MERIS normalization factors based on SeaWiFS from 2003 to 2009

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