WATER QUALITY TARGET REFINEMENT PROJECT

Task 3: Water Clarity Target Development

Interim Report 3

Prepared for:

Charlotte Harbor National Estuary Program

1926 Victoria Avenue

Fort Myers, FL 33901-3414

Prepared By:

Janicki Environmental, Inc.

1155 Eden Isle Drive NE.

St. Petersburg, Florida 33704

February, 2010
1.0 INTRODUCTION: ..............................................................................1

1.1 Purpose...........................................................................................1

1.2 Location...........................................................................................1

1.3 Background .....................................................................................1

1.4 Objective .........................................................................................5

2.0 METHODS.........................................................................................5

2.1 Target Exceedances........................................................................6

2.2 Observed vs Predicted Kd................................................................7

2.3 Tuning the Model Fit........................................................................8

3.0 RESULTS..........................................................................................8

3.1 Target Exceedances........................................................................9

3.2 Predicted vs Observed Kd ................................................................11

3.2.1 Graphical Comparison.............................................................11

3.2.2 Residual Analysis.....................................................................16

3.3 Model Tuning.................................................................................19

4.0 DISCUSSION ..................................................................................21

5.0 MANAGEMENT RECOMMENDATIONS .........................................23

5.1 Target Identification: .....................................................................25

5.2 Assessment:..................................................................................30
1

1.0 INTRODUCTION:

1.1 Purpose

The Charlotte Harbor National Estuary Program (CHNEP) is in the process of 

developing criteria to evaluate management actions using the extent of seagrass in 

estuarine waters as one indicator of estuarine health. These seagrass protection and 

restoration “targets” are being developed to meet objectives set forth in the CHNEP 

Comprehensive Conservation and Management Plan (CCMP). This project helps 

implement the following CCMP Quantifiable Objectives 

• FW-1: Native submerged aquatic vegetation should be maintained and restored 

to a total extent and quality no less than caused by natural variation, and 

• WQ-2: Develop and meet site-specific alternative criteria that are protective of 

living resources for dissolved oxygen, chlorophyll a , turbidity, total suspended 

solids, salinity and pesticides.

Therefore, the targets represent management level indicators to track seagrass extent 

through time to evaluate the effectiveness of management actions in the watershed to 

minimize watershed impacts on estuarine seagrasses. Seagrasses are known to be 

primarily limited by the quantity and quality of light available for photosynthesis; 

therefore, water clarity targets are needed to monitor a significant factor in determining 

the health and productivity of seagrass in Charlotte Harbor and associated estuarine 

waters. 

1.2 Location 

The Charlotte Harbor Estuarine system is located in southwest Florida (Figure 1) and 

includes 224,000 acres (350 square miles) of estuaries downstream from a 3,008,000 

acre (4,700 square mile) watershed. The CHNEP is a partnership of citizens, elected 

officials, resource managers and commercial and recreational resource users working 

to improve the water quality and ecological integrity of the greater Charlotte Harbor 

Watershed. A cooperative decision-making process is used within the program to 

address diverse resource management concerns in the study area.

1.3 Background

A cornerstone of any effective resource monitoring program is the development, 

selection, and use of ecological indicators appropriate for the spatial and temporal 

scales defined for the assessment (Smith et al., 2006). The Charlotte Harbor National 

Estuary Program (CHNEP) has developed a resourced-based approach to derive water 

quality targets. The goal of the approach was to achieve a water clarity that will allow 

seagrasses to grow to their maximum observed depth (CHNEP 2006; Corbett and Hale 

2006). The CHNEP has specified a minimum of 25% of the subsurface light reaching 
2

the maximum depth at which seagrasses occur in the greater Charlotte Harbor region 

based upon annual fixed seagrass transect monitoring and biennial aerial mapping 

throughout the region. The seagrass depth targets are then segment-specific, based on 

a segmentation scheme developed for the Coastal Charlotte Harbor Monitoring Network 

(CCHMN: Figure 2); an inter-agency monitoring network that samples water quality 

using a stratified, probabilistic sampling design (CHNEP 2006; Corbett and Hale 2006).

Figure 1. Map of the Charlotte Harbor National Estuary Program study area.
3

Figure 2. Segmentation scheme used by the Coastal Charlotte Harbor Water Quality 

Monitoring Network.

Water quality and light availability are linked through a locally-derived optical model 

from McPherson and Miller (1994) that relates the concentrations of colored dissolved 

organic material (CDOM), turbidity and chlorophyll a to the light attenuation coefficient 

(Kd) (see CHNEP 2006; Corbett and Hale 2006). The coefficient Kd defines the 

exponential decay rate in downwelling irradiance as a function of depth as established 

by the Lambert-Beer law (Kirk 1994). The combination of depth targets and the 

requirement for a minimum of 25% subsurface irradiance at those depths, allows for the

calculation of the CHNEP segment specific targets for the diffuse light attenuation 

coefficient, Kd which are provided for convenience in Table 1. 
4

Table 1. Charlotte Harbor National Estuary Program seagrass depth targets and 

corresponding target light attenuation coefficients (Kd) (adapted from Table 4 in CHNEP 

2006).

Segment Seagrass Depth Target 

(meters)

Kd (m-1)

Lemon Bay 2.0 0.7

Cape Haze 1.9 0.7

East Wall 1.4 1.0

West Wall 1.4 1.0

Tidal Myakka 0.9 1.6

Tidal Peace 1.0 1.4

Tidal Caloosahatchee 1.0 1.4

Bokeelia 2.4 0.6

Pine Island Sound 2.2 0.6

Matlacha Pass 2.0 0.7

San Carlos Bay 2.2 0.6

Estero Bay 1.6 0.9

The use of the light attenuation coefficient for setting management related water quality 

targets has been popularized as a management tool for translating light requirements 

into water quality targets capable of supporting seagrass communities (Gallegos and 

Kenworthy 1996; Dixon and Kirkpatrick 1999; Gallegos 2001; Steward et. al. 2005;

Smith et. al. 2006). Several resource managers have found it useful for restoration and 

protection efforts to estimate Kd using linear combinations of commonly collected water 

quality constituents (e.g., Gallegos 2001; Biber et al. 2008). These efforts are supported 

by Lorenzen (1972) who partitioned absorption and scattering components of the water 

column into four parts: water, phytoplankton, dissolved matter and non-chlorophyll 

suspended matter. However, it is widely recognized that partitioning of light attenuation 

into linear components is only an approximation because the distribution and availability 

of light is not a linear function of partial attenuation component concentrations (Kirk 

1994). It is therefore imperative that linear light attenuation models be validated using 

empirical data whenever possible to ensure the representativeness of the model in 

predicting the light attenuation coefficient Kd. 

The study area for the CCHMN encompasses a wide range of water body types 

differentially affected by freshwater inputs, tidal passes, circulation and other factors. 

While Kd targets differ by segment, the equation used to predict Kd does not: the same 

equation is used for all segments. Gordon (1989) classified water bodies as either Case 

I, where optical properties are governed primarily by phytoplankton; or Case II waters 

where optical properties are primarily governed by CDOM (Gallegos 2001). While both 

Case I and Case II waters were found to obey the Lambert –Beer law, linear equations

predicting Kd have been found to rely on different sets of coefficients at threshold Kd

values of 0.27m-1 (Duarte et. al. 2007). 
5

1.4 Objective

The development of resource based water quality targets has been an important 

accomplishment for the CHNEP and an important step in developing objective 

environmental assessment tools that are both accurate and useful to managers and 

policy makers who must present complex information to the general public. The validity 

and reliability of resource based targets are critical to the long term success of those 

management programs that utilize them. Implementing a statistically robust water 

quality monitoring program that collects data on water quality constituents used in the 

optical model (i.e., the McPherson and Miller 1994) and also collecting empirical 

estimates of light attenuation has allowed the CHNEP to test the validity and reliability of 

the optical model now that several years of empirical data have been collected. In this 

study, we assess the optical model performance by comparing model predictions to 

empirically collected light attenuation data.

2.0 METHODS

Currently, the CHNEP evaluation strategy for the light requirements of seagrass uses 

the water quality data collected by the CCHMN as inputs into the optical model to derive 

Kd; however, the CCHMN also measures Kd empirically. The instrument used for 

measuring Photosynthetically Active Radiation (PAR) was a LI-COR Quantum®

consisting of one sensor to measure irradiance above the waters surface and another to 

measure irradiance at various depths through the water column starting just below the 

waters surface and increasing at intervals of approximately 0.2m to a depth close to the 

bottom. Light data are not collected at depths shallower than 0.75 meters. Very recently 

(i.e. since 2008), the program has switched to using two underwater sensors to avoid 

the confounding effects of cloud cover. 

The study area is divided by two water management district jurisdictions. The dividing 

line between the Southwest and South Florida Water Management District lies at 

approximately at the southern end on Boca Grande Pass at latitude 26.42.00. The 

South Florida Water Management District requires the use of a spherical light sensor 

that measures light captured from any angle while the SWFWMD requires a 2π cosine 

sensor that only measures downwelling irradiance. Irrespective of these differences, the 

calculation of Kd is then derived by defining a slope of the natural log-transformed subsurface irradiance values as a function of depth to obtain an average change in 

irradiance with change in depth. This method, based on the Lambert Beer law, was 

adapted from the equation for downward irradiance described by Kirk (1994).

Kd = [-Ln(Ed(z1)/Ed(z2))] / z2-z1 (1)

Where: Ed = downward irradiance at depth d and Z= depth below waters surface in 

meters.
6

The optical model predictions used by Corbett and Hale (2006) were based on the 

McPherson and Miller equation (1994):

Kd = 0.014*C2 + 0.062* C3 + 0.049*C4 + 0.30 (2)

where Kd equals the light extinction coefficient, C2 is water color in Pt-Co units, C3 is 

turbidity in NTU and C4 is chlorophyll a in micrograms per liter. Given this equation, the 

minimum Kd value is 0.30 and the positive coefficients determine that as concentration 

of any constituent increases from zero, the Kd value increases. Increasing Kd values 

correspond to decreasing water clarity meaning less light is available to seagrasses at 

depth. 

Since both empirical and model based estimates of Kd were available, this presented 

the opportunity to evaluate the McPherson and Miller (1994) model performance relative 

to the empirical data on light attenuation collected concurrently with the water quality 

data. The analysis was restricted to the model domain from McPherson and Miller’s 

(1994) optical model (i.e. Kd values <= 3.0 m-1

) which corresponds to the range of 

targets developed by Corbett and Hale (2006). The data were further screened to 

eliminate data collected prior to 10am and after 2pm to reduce the influence of solar 

angle on the empirical Kd estimate. Where possible the empirical Kd data were further 

subset to eliminate data where there was a large degree of uncertainty in the slope 

estimate (r2

<0.70). These steps were taken to obtain a set of data where the observed 

Kd was thought to reflect the true condition with increased confidence. Once these data 

were refined, the associated water quality data were used in the optical model to 

produce a model-based (predicted) Kd value. The predicted and observed Kd could then 

be compared to evaluate how well the optical model predicted the observed Kd. The 

analysis assumed that the observed Kd is without error and represents the “true” light 

attenuation in the water column. 

Model performance was evaluated in three ways:

1. Modeled and empirical estimates of Kd were compared in the context of 

percent exceedances of the targets derived by Corbett and Hale (2006).

2. Differences between modeled and empirical data were described across 

the range of Kd values using statistics.

3. Attempts were made to improve model performance where modeled and 

empirical data did not agree.

2.1 Target Exceedances

To examine the difference in target exceedances, contingency tables (2X2) were 

constructed to examine the exceedance frequencies predicted by the model compared 

to exceedance frequencies calculated using the empirical Kd data. McNemars test 

(Stokes et al. 2000) was used to assess agreement between measures of light 

attenuation. Since at each sampling location, water quality data were collected (used in 
7

the model predictions) and Kd was also measured, the test statistic is constructed based 

on the fact that the observations are matched (i.e., measured on the same experimental 

unit). The test statistic considers only those measurements that were discordant with 

respect to predicting Kd exceedances (i.e., the model predicts an exceedance when the 

observed data suggest a non-exceedance or vice versa). The test statistic follows a chi 

square distribution and therefore a statistic for one degree of freedom greater than 

3.841 (alpha=0.05) equates to a significant result. 

2.2 Observed vs Predicted Kd

To evaluate model predictions across the range of the Kd targets within the study area, 

descriptive plots were generated comparing model predictions and observed values for 

each segment. Simple linear regression analysis was used to test agreement of 

modeled and empirical data by testing the null hypothesis that the intercept was not

different from zero and that the slope was not different from 1. Reject of either null 

hypothesis suggests disagreement between the observed and modeled Kd. The 

difference between modeled and observed Kd was also quantified using the prediction 

error (i.e. residuals). The residuals were calculated by subtracting the observed Kd (О) 

from the predicted Kd (φˆ ).

Residual = ( ) φ φ ˆ − 

 

If the model accurately reflected the observed condition then the expectation is that the 

residuals would be centered on zero. Prediction bias was calculated as the average 

difference between the predicted and observed Kd. Residuals were also plotted as a 

function of water quality constituents to examine if prediction bias was a function of 

constituents both inside and outside the model equation.

Bias =

The Mean Square Error (MSE) is a statistic that estimates the precision of the model in 

capturing the true Kd value. Smaller MSE values correspond to increased precision 

indicating better reliability. 

MSE = 2 2 Bias +σ error

Where:

 σ2 error = variance of the residuals 

Since the magnitude of the light attenuation coefficient varied by segment, a coefficient 

of variation was calculated by dividing the square root of the MSE by the mean of the 

observed Kd to compare the dispersion relative to the observed segment mean Kd.

While no specific criteria were specified for determining acceptance or rejection of the 

model based on these statistics it was helpful to compare the values among segments 

to determine their relative validity and reliability with respect to model predictions. 

(3)

(4)

(5)

( ) ˆ

N

∑ φ φ−
8

2.3 Tuning the Model Fit

Attempts were also made to improve the model fit to the observed data in segments 

where model predictions and observed data did not agree. Segment specific 

regressions were attempted using the optical model parameters as well as total nitrogen 

(mg/L), total phosphorus (mg/L) and total suspended solids (mg/L). The segment 

specific regressions were applied using a stepwise regression that selects order of 

inclusion to the regression model based on the relative contribution to the overall Rsquare statistic and retains only variables that contribute to the model with statistical 

significance at alpha< 0.05 (SAS Institute Inc. 2006). Data were also aggregated 

monthly and segment specific regression equations derived of the monthly averages of 

the water quality constituents to examine the effects of averaging data on model 

predictions.

3.0 RESULTS

The sample size and period of record for the water quality data used in this effort is 

listed by segment in Table 2. Between 200 and 300 observations were analyzed in each 

segment coinciding with an approximate 7 year time period between 2002 and 2008.

The table is divided into the upper and lower portions of the study area designated as 

Upper Charlotte Harbor and Lower Charlotte Harbor, respectively. Segment average

observed Kd estimates ranged from 0.94 in Lemon Bay and East Wall to 2.44 in the 

Tidal Caloosahatchee River (Table 3). The segment average Kd values translate to an 

average depth at which 25% of the subsurface irradiance reached the bottom from 1.47

m and 0.47 m in Lemon Bay and Tidal Caloosahatchee, respectively. Observed 

segment average water quality concentrations for optical model parameters as well as 

other relevant potential attenuators of light in estuarine environments are also provided 

in Table 3.
9

Table 2. Water quality data period of record and number of observations for each 

segment of the Coastal Charlotte Harbor Monitoring Network.

Segment Number of 

Observations

Period of Record

Upper Charlotte Harbor

Cape Haze 257 Oct 2002 – Jul 2008

East Wall 311 Oct 2002 – Jul 2008

Lemon Bay 279 Oct 2002 – Jun2008

Tidal Myakka 289 Oct2002- Jul 2008

Tidal Peace 304 Oct2002- Jul 2008

West Wall 321 Oct2002- Jul 2008

Lower Charlotte Harbor

Bokeelia 382 Apr2002 – Nov2008

Estero Bay 248 Sep 2003- Jul 2008

Matlacha Pass 331 Mar2002- Jul 2008

Pine Island Sound 234 Sep2003- Jul 2008

San Carlos Bay 190 Mar2002 – Jul 2008

Tidal Caloosahatchee 229 May2002- Jul 2008

Table 3. Grand averages for water quality parameters relevant to light attenuation collected 

by the Coastal Charlotte Harbor Monitoring Network by segment.

Segment

Kd

(m-1)

Color

(PCU)

Turbidity

(NTU)

Chla 

(μg/l)

TP 

(mg/l)

TN 

(mg/l)

TOC 

(mg/l)

TSS 

(mg/l)

Upper Charlotte 

Harbor

Lemon Bay 0.94 13.34 4.18 5.13 0.13 0.58 5.05 22.67

East Wall 0.94 36.25 1.85 5.50 0.17 0.66 8.10 14.53

Cape Haze 1.01 25.98 2.87 6.09 0.14 0.68 6.50 22.13

West Wall 1.20 48.82 2.34 6.93 0.23 0.78 9.81 14.25

Tidal Myakka 2.04 84.33 4.07 8.76 0.26 1.01 14.21 13.40

Tidal Peace 2.07 79.67 4.35 10.45 0.41 1.08 13.86 12.68

Lower Charlotte 

Harbor

Bokeelia 1.02 20.49 1.33 4.06 0.08 0.43 3.34 15.10

San Carlos Bay 1.07 13.00 1.76 9.57 0.05 0.45 5.53 14.88

Pine Island 1.21 14.23 2.14 4.85 0.04 0.55 5.24 16.11

Estero Bay 1.53 25.73 3.12 5.16 0.05 0.59 6.39 16.33

Matlacha Pass 1.91 22.42 1.35 5.35 0.07 0.47 8.84 15.23

Caloosahatchee 2.44 68.69 3.84 9.49 0.12 1.04 12.50 8.44

3.1 Target Exceedances

Both model predictions and observed Kd frequently exceeded the Kd targets established 

by Corbett and Hale (2006). Overall exceedance frequencies ranged from 40% to 82% 
10

for observed Kd and 44% to 78% for model predictions (Table 4). During the wet season 

(i.e., July through November) water quality was only sufficient ca. 25% of the time in any 

segment. In Upper Charlotte Harbor, a significant difference in expected exceedance 

proportions between observed and predicted Kd was found in Cape Haze, Tidal Peace 

and West Wall (Table 5). 

Table 4. Percent of the model predictions that exceeded targets values by 

segment and season.

Segment Percent Target Exceedance

Dry Wet Overall

Upper Charlotte Harbor

East Wall 20 79 44

Cape Haze 71 88 78

Lower Lemon Bay 70 77 73

Tidal Myakka 44 72 53

Tidal Peace 49 81 60

West Wall 46 89 62

Lower Charlotte Harbor

Bokeelia 43 64 53

Estero Bay 35 73 51

Matlacha Pass 43 61 49

Pine Island Sound 62 90 74

San Carlos Bay 44 68 53

Tidal Caloosahatchee 51 75 60
11

Table 5. Light attenuation (Kd) target exceedance percentages using the observed 

and modeled data. Analysis was limited to data where observed Kd) was less than 3. 

A ** indicates significant difference at alpha=0.05 based on McNemars test.

Upper Charlotte Harbor Percent Exceedance

Segment Observed Predicted

East Wall 40 44

Cape Haze ** 65 78

Lemon Bay 75 73

Tidal Myakka 47 53

Tidal Peace ** 49 60

West Wall ** 49 62

Lower Charlotte Harbor Percent Exceedance

Segment Observed Predicted

Bookelia** 75 53

Estero Bay** 75 51

Matlacha Pass** 82 49

Pine Island Sound 78 74

San Carlos Bay** 82 53

Tidal Caloosahatchee** 75 60

The model predicted a larger proportion of target exceedances than the observed Kd

data in these segments. These differences were principally driven by differences in the 

wet season exceedance frequencies. In Lower Charlotte Harbor, Pine Island Sound 

was the only segment where observed and predicted exceedance frequencies were not 

significantly different. Under prediction of observed Kd was prevalent with dry season 

predictions principally responsible for differences between observed and modeled 

exceedances in Lower Charlotte Harbor. 

3.2 Predicted vs Observed Kd

3.2.1 Graphical Comparison

A graphical comparison of the predicted vs observed Kd is provided for Upper Charlotte 

Harbor segments (Figure 2) and for Lower Charlotte Harbor segments (Figure 3). The 

solid line in the plots represents a 1:1 relationship indicating the line of perfect 

agreement between predicted and observed values. The broken line is a regression line 

comparing the observed and predicted Kd. In all segments the regression analysis 
12

suggested a significant difference in both intercept (from zero) and slope estimate 

(different from 1) indicating that the prediction errors were dependent on where along 

Figure 2. Predicted vs observed Kd plots for Upper Charlotte Harbor segments.
13

Figure. 3. Predicted vs observed Kd plots for Lower Charlotte Harbor segments.
14

the range of empirical Kd the model value was estimated. In all segments, lower values 

of Kd were over predicted by the model while higher values were under predicted. 

Especially noticeable was that many observed measurements were near or below 

0.30m-1, the minimum light attenuation predicted by the model. The frequency of values 

below 0.30 ranged from 2% of the observations in San Carlos Bay to 11% of the 

observations in East Wall. The average of the segment minima observed Kd value over 

the study period was 0.11m-1. 

The average contribution of each parameter to the model predictions varied among 

segments and between seasons (Figure 4). The highest percent contribution of the 

intercept to the total Kd in the dry season was Pine Island, San Carlos Bay, Bokeelia 

and Lemon Bay. These segments tended to have the lowest percent contribution of 

color and the highest percent contribution of chlorophyll, especially in the wet season. 

Matlacha Pass had a relatively consistent contribution of color across seasons but 

increased influence of chlorophyll in the wet season. 
15

Figure. 4. Percent contribution of optical model constituents to the total light attenuation in each 

segment in the dry season (Top) and wet season (bottom). Model constituents from bottom to top 

are: Intercept, Color (PCU), Turbidity (NTU), and Chlorophyll a (μg/l).

0%

20%

40%

60%

80%

100%

Intercept Color Turbidity Chla

0%

20%

40%

60%

80%

100%

Bokeelia

Cape Haze

East Wall

Lower Lemon Bay

Tidal Myakka

Tidal Peace

West Wall

Caloosahatchee

Estero Bay

Matlacha Pass

Pine Island

San Carlos Bay
16

3.2.2 Residual Analysis

Analysis of the residuals suggested that when the residual deviation was over 50% of 

the observed light attenuation, chlorophyll had the highest average relative contribution 

to the total Kd (Figure 5). Conversely, samples where model predictions were less than 

50% of the observed Kd had the lowest percent contribution of chlorophyll and the 

highest percent contribution of the intercept. Turbidity contributed nearly identically

irrespective of the prediction errors. Examination of the residuals plotted against water 

quality and field parameters suggested that the time of sample, total depth, nitrogen and 

phosphorus concentrations and turbidity exhibited little influence on prediction errors in 

either upper Charlotte Harbor segments (Figure 6.) or lower Charlotte Harbor segments 

(Figure 7.). However, higher chlorophyll and color values tended to correspond with 

over prediction of the model. Most notable was that positive prediction errors increased 

with increasing chlorophyll a concentrations above ca. 10 μg/l and trended in an 

exponential manner as chlorophyll concentrations increased above ca. 30 μg/l. 

Figure 5. Contributions of model constituents to the total light attenuation when prediction errors are at 

least 50% greater than the observed Kd (Over), at least 50% less than observed Kd (Under), or with 50% of 

the observed Kd (Avg). The exact proportion is inset in the histograms.

0.28 0.27 0.35

0.35 0.31

0.31

0.14 0.13

0.14

0.24 0.29 0.20

0%

20%

40%

60%

80%

100%

Avg Over Under

Intercept Color Turbidity Chla
17

Figure 6. Prediction errors plotted as a function of water quality parameters in Upper Charlotte 

Harbor. The x axis for chlorophyll a (μg/l) and color (PCU) are plotted on a log base 2 scale for 

clarification.
18

Figure 7. Prediction errors plotted as a function of water quality parameters in Lower Charlotte 

Harbor. The x axis for chlorophyll a (μg/l) and color (PCU) are plotted on a log base 2 scale for 

clarification.
19

Bokeelia, Cape Haze, Pine Island Sound and Lower Lemon Bay had the highest 

reliability with respect to model predictions and San Carlos Bay, Matlacha Pass, Tidal 

Peace and Tidal Myakka were the least reliable (Figure. 8). Bokeelia, Cape Haze, Pine 

Island Sound and Lemon Bay are largely influenced by coastal gulf waters and less 

influenced by direct freshwater inputs than other segments. Conversely, the Tidal 

Peace, and Tidal Myakka are largely influenced by freshwater inflows and had the 

highest average concentrations of total phosphorus, total nitrogen and total organic 

carbon. San Carlos Bay is a dynamic estuarine environment heavily influenced by both 

the Caloosahatchee River and the Gulf of Mexico.

3.3 Model Tuning

Segment-specific models fit the observed data better than the McPherson and Miller 

equation in 6 of the 12 segments including East Wall, Tidal Myakka, Tidal Peace, West 

Wall San Carlos Bay and Tidal Caloosahatchee (Table 6.). Interestingly, in none of 

these segments was chlorophyll a significant contributing factor to prediction of 

observed Kd. In some cases, total phosphorus was a significant contributor to the final 

segment specific model. However, only in West Wall was the R2 greater than 0.50 

indicating that, in the majority of the segments, less than half of the variation in Kd was 

explained by the model. Aggregating the data monthly increased the fit to the observed 

data in many segments but there remained substantial disagreement when compared to 

optical model predictions based on aggregated water quality data. Further, chlorophyll 

was no longer a significant predictor of light attenuation in any segment when averaging 

segment specific monthly values.
20

Figure 8. Mean Square Error (MSE) of the predicted and observed Kd (top) and coefficient of 

variation (CV; bottom) for each segment in the CHNEP study area.
21

Table 6. Comparison of Coefficient of Determination (R2) for optical model, 

segment specific regression model and aggregated (monthly averaged) 

regression model on observed light attenuation (Kd). For empirical 

regression results, constituents explaining a significant portion of the 

variation in Kd are provided and key is provided as footnote. 

Segment Predicted vs 

Observed

R2

Segment 

Specific 

Regression 

Monthly 

Averaged 

Segment 

Specific 

Regression

Cape Haze 0.26

Color, Turb, TP 

R2 = 0.25

Color

R2 = 0.40

East Wall 0.26

Color, Turb 

R2 = 0.37

Color, Turb

R2 = 0.51

Lemon Bay 0.17

Color, Chla, Turb 

R2 = 0.16

Color

R2 = 0.07

Tidal Myakka 0.07

Color, Turb 

R2 = 0.29

Color

R2 = 0.45

Tidal Peace 0.14

Color, TP 

R2 = 0.46

Color, TP

R2 = 0.68

West Wall 0.27

Color, Turb, TP

R2 =0.56

Color,Turb, TP

R2 = 0.74

Bokeelia 0.28

Color, Chla, Turb 

R2 =0.29

Color

R2 = 0.49

Estero Bay 0.13

Color, Turb 

R2 =0.14

Turb

R2 = 0.09

Matlacha Pass 0.02 NS NS

Pine Island 

Sound 0.12

Turb 

R2 =0.02 NS

San Carlos Bay 0.02

Color, TP 

R2 =0.22

Color, TP

R2 = 0.35

Tidal 

Caloosahatchee 0.24

Color, Turb, TN 

R2 =0.32

Color, TN

R2 = 0.47

NS = not significant (alpha=0.05)

Color=Color (PCU), Turb=turbidity (NTU), Chla = Chlorophyll a (μg/l), TP = Total Phosphorus 

(mg/l)

4.0 DISCUSSION

The water quality targets developed by the CHNEP are an important contribution to 

identifying thresholds in water quality that may affect the health and productivity of 

valued natural resources in the Charlotte Harbor Estuary. Corbett and Hale (2006) 

established segment specific Kd targets and used the empirical data to estimate 

threshold values for water quality constituents that contribute to light attenuation in 

these segments as a management strategy. One component of this management 

strategy is simply to assess the frequency of Kd target exceedances and establish 

criteria to determine whether that frequency is sufficiently low to allow for seagrass 
22

success. From this perspective, our analysis has shown that in East Wall, Lemon Bay, 

Tidal Myakka and Pine Island Sound the model may serve this purpose adequately as 

there was good agreement between modeled and observed Kd at the target values. 

However, this does not suggest that the optical model and observed data agreed across 

the range of Kd. In fact, in most segments there was substantial disagreement between 

modeled and observed estimates of Kd. Many segments in the Upper Charlotte Harbor 

region exhibited model over-prediction bias. These observations tended to occur when 

chlorophyll contributed a higher relative proportion to the total Kd than other 

observations. Conversely, under prediction bias occurred when the contribution of the 

intercept was highest and the contribution of chlorophyll was lowest. This suggests that 

some factor outside the model domain may be contributing to light attenuation in these 

circumstances. 

McPherson and Miller (1994) estimated that, on average, 55% of the total light 

attenuation was due to non-chlorophyll suspended matter (NSM) in Charlotte Harbor. 

However, the optical model equation does not directly include NSM. Inclusion of 

turbidity in the model was intended to represent a scatter coefficient. However, turbidity

represented only 19% of the total contribution to light attenuation on average 

(McPherson and Miller 1994). The scattering property of estuarine waters is

conditional, based on the concentrations of the various water quality constituents in the 

water at the time of the sample. In the optical model, that portion of the contribution of 

non-chlorophyll suspended matter not accounted for by turbidity was partitioned into the 

intercept based on subtraction and is therefore a constant despite NSM having been 

documented to vary substantially spatially and seasonally in Charlotte Harbor (Dixon 

and Kirkpatrick, 1999). These factors are likely to be in part responsible for the under 

prediction of light attenuation, especially in the dry season when color and chlorophyll a 

values tend to be lower but NSM may be higher due to winds, tides and vessel traffic. 

Bowers and Binding (2006) suggested that reflectance of suspended particles increases 

multiplicatively with turbulence. Lawson et. al., (2007) suggested wind driven sediment 

suspension is the dominant source of light attenuation in shallow coastal lagoons. Much 

of the Charlotte Harbor study area is an integral part of Florida’s of the Intracoastal 

Waterway (ICW) providing an inshore route for vessel traffic. High vessel traffic, 

especially those utilizing the ICW as an inshore route to southern cruising grounds 

during the winter may also contribute to the prediction errors by re-suspending 

sediments into the water column. 

While this exercise assumed that the observed Kd was measured without error, that is 

likely not the case. Kirk (1994) describes major sources of measurement error of 

underwater irradiance as: wave action; atmospheric condition, and perturbation of the 

light field by the vessel. While efforts were made to account for these various factors 

affecting in situ measurements of light, there is no way to eliminate these factors. Wave 

action can focus the light field causing rapid temporal intensity changes as the waves 

pass through the measurement field. The fluctuations are greater in shallow waters than 

deep waters. However, residual plots suggested that total sample depth did not have a 

systematic effect on prediction errors in this study. Neither did the time of the sample or 

the salinity gradient to a large extent. Rather, it appeared that chlorophyll and color 
23

values were principally responsible for observation where over prediction was prevalent. 

The probabilistic nature of the sampling design likely also contributed to the prediction 

errors. Many sampling programs measuring light attenuation empirically use fixed 

station monitoring programs with station locations along the deep edge of a seagrass 

transect. Fixed locations eliminate one source of variability between measurements

relative to probabilistic designs though they are less well generalized to describe a 

given water body considered for management actions. Phytoplankton is a well 

documented attenuator of light (Kirk 1994) but results of this study suggest that the 

McPherson and Miller optical model may overweight the contribution of chlorophyll to 

the prediction of light attenuation in the Charlotte Harbor study area when chlorophyll a 

concentrations are greater than 10 μg/l. The observed under prediction is speculated to 

come from some unmeasured quantity including the potential for multiplicative 

interaction among water quality constituents in the water column. 

5.0 MANAGEMENT RECOMMENDATIONS

The CHNEP has established a foundation of objective, science-based, decision making 

tools for use as indicators of estuarine health and is furthering its goals under its 

Comprehensive Conservation and Management Plan to protect and restore water 

quality through rigorous analysis of its management tools. This study has furthered 

those objectives by assessing the optical model performance relative to empirical 

estimates of light attenuation. The extent of seagrass in the CHNEP study area is an 

exceptionally valuable natural resource and a primary focus of the CHNEP 

Comprehensive Conservation and Management Plan. The continued focus on science 

based management tools for seagrass will help to ensure the protection of these vital 

resources as anthropogenic pressures increase. This analysis suggests that more 

efforts are needed to understand the interactions between attenuators of light in these 

waters before the optical model can be confidently used to set management level 

criteria for individual water quality constituents in the Charlotte Harbor study area.

In light of the uncertainty in linking water quality and light attenuation in Charlotte Harbor 

estuarine waters, we sought to derive water clarity targets that were relevant to the 

observed conditions affecting seagrass within each Harbor segment without explicitly 

identifying the light requirements of seagrass. The following reasoning was used to 

derive the proposed water clarity targets:

• Mechanistic relationships between ambient/antecedent water quality conditions 

and the living resource requirements of seagrass are not fully understood.

• Stability in measures of seagrass areal extent over recent history suggests that 

either:

1. Ambient water quality conditions are sufficient for success of seagrasses 

(as expressed as areal extent) within the segment, 
24

2. seagrasses are stressed by ambient/antecedent water quality conditions 

but the seagrasses have not expressed the stressor - response 

relationship as a change in areal extent, or

3. Ambient/antecedent water quality conditions are not relevant to the 

success of seagrasses (as expressed as areal extent), or 

The preponderance of evidence suggests that water quality, explicitly water clarity, is a 

limiting factor in determining the depth distribution of seagrass. Therefore, the third

argument is dismissed. The second alternative is plausible but the targets identified by 

this study are intended to make inferences regarding the extent of seagrass within a 

segment and not on:

• Speciation of seagrass within a bed

• Density of seagrass within a bed

• The quality of seagrasses within a bed

These measures of seagrass condition are better estimated through other monitoring 

efforts. 

Based on the above, the following water clarity targets are proposed based on the 

assumptions that;

• changes in the areal extent of seagrasses are related to changes in water quality 

even if the mechanistic relationships are not fully understood, and

• improving water clarity will result in an increase in the areal extent of seagrasses 

given other factors are not limiting.

Given the definitions above, the seagrass targets identified in Task 2 of this scope of 

work (provided for convenience in Table 7) were used to establish the “target” water 

clarity conditions for the Harbor segments. Based on the seagrass targets, the following 

decision rules were applied:

• If the protection target was chosen as the segment specific seagrass target, 

then the water clarity target established will be a “hold the line” strategy to 

maintain ambient conditions experienced over the recent areal surveys.

• If the restoration target was chosen as the segment specific seagrass target, 

then the water clarity target will be an “Improvement” strategy measured as an 

“improving” trend in for light attenuation.

The tidal tributary seagrass targets including the Tidal Peace, Tidal Caloosahatchee, 

Tidal Myakka and Dona and Roberts Bay were not to be considered as management 
25

targets based on the subcommittee opinion that the influence of tanic river waters 

reduced the ability to capture the bottom profile of these segments with aerial 

photography as well as the local observations of sparse but substantial coverage of 

seagrass in areas previously characterized by aerial photography as being devoid of 

seagrass. Despite the lack of seagrass targets, the “Protection” strategy was 

applied to water clarity in the tidal tributary segments.

Table 7. Draft CHNEP Seagrass Targets

Harbor Segment

Baseline, 

adjusted 

(B)

Mean Annual 

Extent all 

years (A)

Protection 

Target

Restoration 

Target

Total 

Target Target Range

Dona and Roberts Bay 112 91 91 21 112 70-124

Upper Lemon Bay 880 1,009 1,009 1,009 949-1,175

Lower Lemon Bay 2,882 2,502 2,502 380 2,882 2,396-2,597

Tidal Myakka River 344 456 456 456 331-539

Tidal Peace River 975 384 384 591 975 295-573

West Wall 2,106 1,907 1,907 199 2,106 1,676-2,121

East Wall 3,898 3,465 3,465 433 3,898 3,275-3,591

Cape Haze 5,670 6,998 6,998 6,998 6,709-7,464

Lower Charlotte Harbor 2,964 3,342 3,342 3,342 3,101-3,520

Pine Island Sound 23,757 26,837 26,837 26,837 25,941-29,204

Matlacha Pass 9,315 7,582 7,582 1,733 9,315 6,055-7,619

San Carlos Bay 3,118 4,372 4,372 4,372 3,709-5,376

Tidal Caloosahatchee 93 87 87 6 93 2-103

Estero Bay 3,662 3,071 3,071 591 3,662 2,393-3,409

TOTAL 59,776 62,103 62,103 3,954 66,057 N/A

5.1 Target Identification:

A unifying evaluation strategy was conceptually defined to establish and assess water 

clarity targets for both the protection and the restoration targets. The concept is based 

on two points in the distribution of light attenuation measurements between 2003 and 

2007 that corresponded to the targets identified by CHNEP 2006 and in Corbett and 

Hale (2006) as well as the grand average value between 2003-2007. For future 

reference we call this the “benchmark” distribution. For example, in Pine Island Sound, 

CHNEP identified a Kd target of 0.6 (1/m) and the grand average Kd between 2003 and 

2007 was 0.98 (1/m) as shown in the cumulative distribution function (CDF) plots of 

Figure 8. These two points along the benchmark distribution correspond to the 28th and 

58th percentile of the empirical distribution of Kd between 2003-2007. Therefore, we 

expect that ca.30% of the Kd values in any year would be below 0.60m-1 and ca. 60% 

of the values would be below 0.98m-1. If for example, only 15% of the values were 

below 0.60m-1 and only 40% of the values were below 0.98 for a particular years worth 

of data then light attenuation is higher and water clarity is reduced. The converse is also 
26

true. This can be tested statistically to provide confidence that these differences are not 

due to chance. 

Figure 8. Cumulative distribution of Kd values in Pine Island Sound between 2003 and 2007 with 

the CHNEP target value denoted as gray dot and the grand average denoted as a black dot. 

Plots for the CHNEP target values and the grand average are provided for each 

segment in Figures 9-11 and the seagrass aerial extents are inset in each figure. 

Generally, the CHNEP targets occurred near the 30th percentile and the grand average 

occurred between the 60th and 70th percentile of the benchmark distribution. Exceptions 

were West Wall and East Wall where the CHNEP targets were very similar to the grand 

average.
27

Figure 9. Cumulative Kd distributions (2003-2007) for Harbor segments with Restoration targets. 

Benchmark values on distributions are identified by gray dot (CHNEP target) and black dot (Grand 

average value). Histograms displaying extent of seagrass for each survey year are inset. Upper 

Lemon Bay targets based on data from 1998-2007.
28

Figure 10. Cumulative Kd distributions (2003-2007) for Harbor segments with Protection targets. 

Benchmark values on distributions are identified by gray dot (CHNEP target) and black dot (Grand 

average value). Histograms displaying extent of seagrass for each survey year are inset.
29

Figure 11. Cumulative Kd distributions (2003-2007) for Harbor Tidal River segments. Benchmark 

values on distributions are identified by gray dot (CHNEP target) and black dot (Grand average 

value). Histograms displaying extent of seagrass for each survey year are inset.

Based on the observation that these two points generally aligned with the 30th and 70th

percentiles of the benchmark period distribution, these were chosen as points to 

evaluate each annual years worth of water clarity data. The rationale of choosing these 

particular points along the distribution included:

• choosing consistent endpoints among segments would achieve internal 

consistency in reporting among segments;

• The 30th and 70th percentiles were in general proximity to the CHNEP targets and 

the historic averages, respectively;

• the 30th and 70th percentiles are not likely to be effected by extreme 

observations, and

• the 30th and 70th percentiles are likely to vary independently of one another 

relative to points closer together on the distribution curve.

The curve between the 30th and 70th percentile values tended to be fairly flat in the CDF 

plots for both the restoration and protection targets indicating that relatively large 
30

changes in the exceedance percentiles could be accomplished with small changes in 

the Kd value. This is an important consideration. Since Kd represents an exponential 

loss of light, small decreases in the Kd value result in exponential increases in light 

penetration to the seagrasses. This strategy is then aimed at maximizing the return on 

improving water clarity conditions in terms of the increasing the duration of acceptable 

light penetration to protect and restore seagrass areal extents in each Harbor segment. 

These “benchmark points” for each Harbor segment, identified from the distribution of 

measurements collected over the benchmark time period are provided in Table 8. 

Table 8. Segment specific empirical light attenuation Kd (m-1) values for the 30th

percentile and 70th percentile based on data collected between 2003-2007. 

Segment 30th Percentile 70th Percentile

Bokeelia 0.58 1.16

Cape Haze 0.63 1.15

Dona and Roberts Bay 0.64 1.04

East Wall 0.64 1.16

Estero Bay 0.91 1.58

Lower Lemon Bay 0.73 1.12

Matlacha Pass 0.73 1.63

Pine Island Sound 0.64 1.1

San Carlos Bay 0.73 1.16

Tidal Caloosahatchee 1.58 2.93

Tidal Myakka 1.3 2.27

Tidal Peace 1.06 2.4

Upper Lemon Bay ** 0.73 1.18

West Wall 0.73 1.36

** Note: Upper Lemon Bay data are from the 1998-2007 time period.

5.2 Assessment:

The binomial test (Wackerly et. al., 1996) will be used to evaluate each year’s water 

quality data at the benchmark points on the distribution. For example:

• If more than 30% of the Kd measurements are below the 30th percentile 

benchmark with statistical significance (alpha=0.05), then the water clarity is 

considered to be improving and is assigned a value of positive 1. 

• If less than 30% of the values are below the 30th percentile benchmark with 

statistical significance (alpha=0.05), then the water clarity is considered to be 

degrading and is assigned a value of negative 1. 

• Otherwise the value is 0.
31

This scoring is performed on both endpoints (i.e. the 30th and 70th percentile).

The sum of these score will be used as the basis for the assessment of water clarity for 

each Harbor segment. The distribution of potential scores ranges from -2 to 2.

For the protection targets the following grading system will be used:

Green = scores greater than -1 Stable

Yellow = a score of -1 or less Caution

Red = a score of less than -1

 for consecutive years Degrading

For the restoration targets the following grading system will be used:

Green = scores greater than 1 Improving towards target

Yellow = scores between -1 and 1 Caution

Red = a score of less than -1 Degrading

As an example, consider the Bokeelia segment (Figure 12). The benchmark distribution 

is defined by the solid black line with the benchmark points represented at the 30th and 

70th percentiles. Overlaid on this figure are the distributions for 2003 and 2007. The 

distribution for 2003 was above the benchmark distribution and the specific value used 

to assess the 30th percentile was exceeded a larger percentage of the time (i.e., there 

were fewer observations at or below the 30th percentile value). This difference in 

frequency was statistically significant and therefore was given a score of negative one. 

Conversely, in 2007 there were significantly more than 30 percent of the values below 

the 30th percentile value and the score was positive one. Evaluation at the 70th

percentile values suggested that the exceedance frequencies were not significantly 

different from the benchmark period and were therefore given a score of zero.
32

Figure 12. Benchmark distribution of light attenuation in Bokeelia with data from 2003 and 2007 

overlaid providing an example of the scoring algorithm.

An example of the scoring algorithm applied each year of the benchmark period applied 

to Cape Haze and Pine Island Sound is provided in Figure 13. The figures are 

constructed so that the benchmark period is a solid black line and each individual years 

data are overlaid with broken line of different colors. The scores for each benchmark are 

provided as an inset table and the resulting grade is given for the cumulative scores for

each year. In Cape Haze, 2003 had significantly fewer observations below the P30 

value and in 2004 both the P30 and P70 values occurred less frequently as well 

resulting in negative scores and a grade of “degrading” condition because the score 

was negative one or less for consecutive years for this protection target. Conversely in 

2007 nearly 60 percent of the observations were below the P30 threshold, indicating a 

higher frequency of good water clarity conditions. For this year, both the P30 and P70 

were improved compared to the benchmark time period and consequently the grade is 

stable water clarity for that year. In Pine Island Sound, water clarity was stable over the 

entire time period and therefore all grades are green in color. The importance of stability 

in water clarity condition may be an extremely important characteristic for the long term 

success of seagrasses. Pine island Sound has far more seagrass than any other 

segment and also has the most consistent water clarity over the benchmark period. 
33

Figure 13. Cumulative distribution of light attenuation for benchmark period (solid black line) with 

benchmark points and each individual years data from 2003-2007. Scores are inset. 
34

Based on this grading system, the restoration targets have more stringent water quality 

criteria than the protection targets. Stability in scores relative to the benchmark period is 

considered sufficient for the protection targets but not for the restoration targets. 

Therefore, scores between -1 and 1 are given a “caution” score. These scores can be 

related to changes in seagrass over time by either adding the scores between the 

biennial seagrass surveys, or evaluating each of the benchmark scores (i.e., the 30th

and 70th percentile) separately. It should be noted that the binomial test used to score 

the water clarity data relies on the assumption that samples are independent and the 

design of the CCHMN network conforms to this assumption. The test result also relies 

on the sampling frequency such that dramatic changes to the sampling design may 

affect the power of the binomial test to distinguish a significant result.

Based on the above assessment, it is recommended that in 2012, the scoring method

be re-assessed to ensure that the benchmark period remains relevant to the expected 

ambient water quality conditions in each Harbor segment. A change in the method used 

to collect information on light attenuation occurred recently and it will be important to 

ensure that this change in method did not affect the distribution of values. The grading 

system should also be evaluated at this time to determine the sensitivity of the grading 

cutpoints relative to changes in water clarity and seagrass conditions and consideration 

should be given to using the identical scoring algorithm to assess the modeled Kd

estimates from McPherson and Miller (1994) and compared those scores to the 

observed values. 

The fourth task of this project is to update the nutrient loading estimates for all sub 

basins of the CHNEP study area. These pollutant loading estimates will be an important 

contribution to the future development numeric nutrient criteria for the estuarine portions 

of the CHNEP study area. Numeric nutrient criteria will establish target loadings 

affecting water quality in the estuaries, again using seagrass as a principal natural 

resource requiring protection. The combination of the water clarity targets developed in 

this report, pollutant loading estimates identifying major contributing sources to the 

Harbor, and the development of numeric nutrient criteria will contribute to the effective 

management of the CHNEP study area to help achieve the goals set forth in the 

CHNEP’s Comprehensive Conservation and Management Plan.
35

6.0 References

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model in the North River, North Carolina (Albemarle-Pamlico Sound): A tool to 

evaluate water quality impacts on seagrass. Estuaries and Coasts v31:177-191.

Bowers, D.G. and C. E. Binding 2006. The optical properties of mineral suspended 

particles: A review and synthesis. Estuarine Coastal and Shelf Science v67:219-

230 

Charlotte Harbor National Estuary Program (CHNEP) 2006. Numerical Water Quality 

Targets for Lemon Bay, Charlotte Harbor and Estero Bay, Florida. Technical 

Report: 06-3. 2006

Corbett, C. and J. Hale 2006. Development of water quality targets for Charlotte Harbor, 

Florida using Seagrass light requirements. Florida Scientist v69: p36-50.

Dixon, L. K. and G. J. Kirkpatrick. 1999. Causes of light attenuation with respect to 

seagrasses in upper and lower Charlotte Harbor, Report to Southwest Florida 

Water Management District, SWIM Program and Charlotte Harbor National 

Estuary Program. Available from the Southwest Florida Water Management 

District, SWIM Program, Tampa, FL and Charlotte Harbor National Estuary 

Program, Fort Myers, FL.

Gallegos, C.L. and W. J. Kenworthy 1996. Seagrass depth limits in the Indian River 

Lagoon: Application of an optical water quality model. Estuarine Coastal Shelf 

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Gallegos, C.L. 2001. Calculating optical water quality targets to restore and protect 

submerged aquatic vegetation: overcoming problems in partitioning the diffuse 

attenuation coefficient for photosynthetically active radiation. Estuaries 

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Gordon, H. R. 1989. Can the Lambert-Beer equation be applied to the diffuse 

attenuation coefficient of ocean water? Limnology and Oceanography v34:1389-

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Kirk, J.T. 1994. Light and Photosynthesis in Aquatic Ecosystems. Cambridge University 

Press, New York, NY.

Lawson, S. E., P. L. Wiberg, K. J. McGlathery, and D. C. Fugate 2007. Wind-driven 

sediment suspension controls light availability in a shallow coastal lagoon. 

Estuaries and Coasts. V30(1):102-112.

McPherson, B.F. and R.L. Miller. 1994. Causes of light attenuation in Tampa Bay and 

Charlotte Harbor, Southwestern Florida. Water Resources Bulletin. V30(1): 43-53 
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Smith, L. M., V. D. Engle, and J. K. Summers 2006. Assessing water clarity as a 

component of water quality in Gulf of Mexico estuaries. Environmental Monitoring 

and Assessment v115:291-305

Statistical Analysis Systems (SAS®) 2008. Version 9.1. Cary, North Carolina.

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Steward, J.S., R. W. Virnstein, L. J. Morris and E. F. Lowe. 2005 Setting seagrass 

depth, coverage, and light targets for the Indian River Lagoon system, Florida. 

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Stokes, M.E., C.S. Davis, and Gary G. Koch. 2000. Categorical Data Analysis using the 

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