U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2020–1092

Greater Everglades Priority Ecosystem Studies Program

Observed and Modeled Mercury and Dissolved Organic 

Carbon Concentrations and Loads at Control Structure S-12D, 

Florida Everglades, 2013–17
Cover. Photograph of U.S. Geological Survey monitoring station Tamiami Canal at S-12-D, near 

Miami, Florida (25454308040540), looking south downstream of S-12D structure. Photograph by 

Eduardo Patino, U.S. Geological Survey.
Observed and Modeled Mercury and 

Dissolved Organic Carbon Concentrations 

and Loads at Control Structure S-12D, 

Florida Everglades, 2013–17

By Amanda C. Booth, Brett A. Poulin, and David P. Krabbenhoft

Greater Everglades Priority Ecosystem Studies Program

Open-File Report 2020–1092

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2020

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Suggested citation:

Booth, A.C., Poulin, B.A., and Krabbenhoft, D.P., 2020, Observed and modeled mercury and dissolved organic carbon 

concentrations and loads at control structure S-12D, Florida Everglades, 2013–17: U.S. Geological Survey Open-File 

Report 2020–1092, 27 p., https://doi.org/10.3133/ofr20201092.

Associated data for this publication: 

Booth, A.B., 2020, Calculated mercury and carbon concentrations, USGS station 254543080405401: Tamiami Canal at 

S-12D Near Miami, Florida, 2013-2017: U.S. Geological Survey data release, https://doi.org/10.5066/P99L01UW.

ISSN 2331-1258 (online)
iii

Contents

Abstract�����������������������������������������������������������������������������������������������������������������������������������������������������������1

Introduction�����������������������������������������������������������������������������������������������������������������������������������������������������1

Purpose and Scope ������������������������������������������������������������������������������������������������������������������������������2

Description of the Study Area ������������������������������������������������������������������������������������������������������������2

Study Methods �����������������������������������������������������������������������������������������������������������������������������������������������4

Continuous Monitoring ������������������������������������������������������������������������������������������������������������������������4

Collection of Discrete Samples ����������������������������������������������������������������������������������������������������������5

Development of Empirical Models�����������������������������������������������������������������������������������������������������6

Analyses of Field and Laboratory Measurements �����������������������������������������������������������������������������������6

Channel Cross-Sectional Variability �����������������������������������������������������������������������������������������������������������8

Empirical Models �����������������������������������������������������������������������������������������������������������������������������������������14

Dissolved Organic Carbon Model ����������������������������������������������������������������������������������������������������14

Filter-Passing Total Mercury Model ������������������������������������������������������������������������������������������������14

Filter-Passing Methylmercury Model����������������������������������������������������������������������������������������������14

Particulate Total Mercury Model �����������������������������������������������������������������������������������������������������14

Particulate Methylmercury Model���������������������������������������������������������������������������������������������������14

Dissolved Organic Carbon Concentrations and Loads��������������������������������������������������������������������������15

Mercury Concentrations and Loads���������������������������������������������������������������������������������������������������������15

Comparison of Observed Loads to Calculated Loads����������������������������������������������������������������������������20

Comparison of Traditional Discrete Sampling to Surrogate Approach ����������������������������������������������22

Summary and Conclusions�������������������������������������������������������������������������������������������������������������������������23

Acknowledgments���������������������������������������������������������������������������������������������������������������������������������������23

References Cited������������������������������������������������������������������������������������������������������������������������������������������23

Appendixes 1–5.................................

Figures

1. Regional map showing S-12 control structures A–D that control the flow o

water from water conservation areas to Everglades National Park �����������������������������������3

2. Photograph showing water-control structure S-12D������������������������������������������������������������

3. Graph showing relation between temperature corrected fluorescenc

of chromophoric dissolved organic matter (fDOM), with the inner filter 

effect removed, and temperature corrected fDOM, without the inner filter 

effect removed ������������������������������������������������������������������������������������������������������������������������������5

4. Graphs showing comparisons between mean cross-sectional profile dat

and site monitor data for temperature, specific conductance, turbidity, and 

fluorescence of chromophoric dissolved organic matter ���������������������������������������������������12

5. Graphs showing comparisons between point sample and vertically-integrate

samples at point of highest flow for filter-passing total mercury, filter-passing 

methylmercury, particulate total mercury, and particulate methylmercury���������������������13

6. Graph showing calculated and observed concentrations of dissolved organi

carbon and flow��������������������������������������������������������������������������������������������������������������������������16

7. Graph showing cumulative loads of dissolved organic carbon for calenda

years 2014–16 ������������������������������������������������������������������������������������������������������������������������������16
iv

8. Graphs showing calculated and observed filter-passing total mercury 

concentrations and flow, and calculated and observed filter-passing 

methylmercury concentrations and flow ................................................................................17

9. Graph showing calculated and observed concentrations of particulate total 

mercury and flow, and calculated and observed particulate methylmercury 

concentrations and flow ............................................................................................................18

10. Graph showing cumulative loads of total mercury for 2014–16 calendar years ..............19

11. Graph showing cumulative loads of methylmercury for calendar years 2014–16 ...........19

12. Graphs showing comparisons between 1-minute loads calculated using 

observed concentrations and calculated concentrations ..................................................21

13. Graphs showing annual loads of mercury separated into fractions including 

particulate methylmercury, particulate inorganic divalent mercury, 

filter-passing methylmercury, and filter-passing inorganic divalent mercury—

calculated by the surrogate model approach and calculated using only 

observed samples .......................................................................................................................22

Tables

1. Maximum and minimum sensor values recorded during sampling events, and 

the maximum and minimum overall sensor data values recorded during the 

study period at S-12D ...................................................................................................................7

2. Number of observations, minimum, maximum, and mean concentrations of 

observed dissolved organic carbon, decadic absorption coefficient at 254 

nanometers, specific ultraviolet absorbance at 254 nanometers, specific 

conductance, fluorescence of chromophoric dissolved organic matter, 

temperature, turbidity, and flow data collected at S-12D from September 2013 

to December 2016 .........................................................................................................................7

3. Pearson correlation coefficients of observed dissolved organic carbon, 

decadic absorption coefficient at 254 nanometers, specific ultraviolet 

absorbance at 254 nanometers, specific conductance, fluorescence of 

chromophoric dissolved organic matter, temperature, turbidity, and flow data 

collected at S-12D from September 2013 to December 2016 ................................................8

4. Number of observations, minimum, maximum, and mean concentrations of 

observed filter-passing total mercury and methylmercury, particulate total 

mercury and methylmercury, dissolved organic carbon, decadic absorption 

coefficient at 254 nanometers, specific ultraviolet absorbance at 254 

nanometers, specific conductance, fluorescence of chromophoric dissolved 

organic matter, temperature, turbidity, and flow data collected at S-12D from 

September 2013 to December 2016 ...........................................................................................9

5. Pearson correlation coefficients of observed filter-passing total mercury 

and methylmercury, particulate total mercury and methylmercury, dissolved 

organic carbon, decadic absorption coefficient at 254 nanometers, specific 

ultraviolet absorbance at 254 nanometers, specific conductance, fluorescence 

of chromophoric dissolved organic matter, temperature, turbidity, and flow data 

collected at S-12D from September 2013 to December 2016 ..............................................10

6. Coefficient of determination, model standard error, and relative model bias 

for concentration models and calculated versus observed loads of dissolved 

organic carbon, filter-passing total mercury and methylmercury, particulate 

total mercury and methylmercury ............................................................................................20
v

Conversion Factors

U.S. customary units to International System of Units

Multiply By To obtain

Length

inch (in.) 2.54×107 nanometer (nm)

inch (in) 2.54×104 micrometer (μm)

inch (in) 25.4 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Volume

cubic foot (ft3) 28.32 cubic decimeter (dm3)

cubic foot (ft3) 0.02832 cubic meter (m3)

cubic inch (in.3) 0.01639 liter (L)

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

Mass

ton, short (2,000 lb) 0.9072 metric ton (t)

ton per day (ton/d) 0.9072 metric ton per day (t/d)

ton per year (ton/yr) 0.9072 metric ton per year (t/yr)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: °F 

= (1.8 × °C) + 32.

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: °C 

= (°F – 32) / 1.8.

Datum

Vertical coordinate information is referenced to the National Geodetic Vertical Datum of 1929 

(NGVD 29).

Elevation, as used in this report, refers to distance above the vertical datum.

Supplemental Information

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm 

at 25 °C).

Concentrations of chemical constituents in water are given in either milligrams per liter (mg/L) 

or nanograms per liter (ng/L).
vi

Abbreviations

α254 decadic absorption coefficient at 254 nanometers

DOC dissolved organic carbon

EPA U.S. Environmental Protection Agency

fDOM fluorescence of chromophoric dissolved organic matter

FHg(II)i filter-passing inorganic divalent mercury

FMeHg filter-passing methylmercury

FNU formazin nephelometric unit

FTHg filter-passing total mercury

Hg mercury

Hg(II)i inorganic divalent mercury

MeHg methylmercury

MSPE average model standard percentage error

NWIS National Water Information System

NWQL National Water Quality Laboratory

PHg(II)i particulate inorganic divalent mercury

PMeHg particulate methylmercury

PTHg particulate total mercury

QSE quinine sulfate equivalent

r pearson correlation coefficient

R2 coefficient of determination

RMSE root mean square error

SAID Surrogate Analysis and Index Developer

SC specific conductance

SUVA254 specific ultraviolet absorbance at 254 nanometers

THg total mercury

USGS U.S. Geological Survey
Observed and Modeled Mercury and Dissolved Organic 

Carbon Concentrations and Loads at Control Structure 

S-12D, Florida Everglades, 2013–17

By Amanda C. Booth, Brett A. Poulin, and David P. Krabbenhoft

Abstract

Mercury (Hg) has been a contaminant of concern for 

several decades in South Florida, particularly in the Florida 

Everglades. The transport and bioavailability of Hg in aquatic 

systems is intimately linked to dissolved organic carbon 

(DOC). In aquatic systems, Hg can be converted to methylmercury (MeHg), which is the form of Hg that bioaccumulates 

in food webs. The bioaccumulation of MeHg poses significant 

health risks to wildlife and humans. Fish consumption advisories triggered by elevated Hg levels first appeared in the 1980s 

in South Florida. Multiple structures regulate freshwater distribution to Everglades National Park, including S-12D. This 

report summarizes seasonal and annual concentration and load 

data from late September 2013 to April 2017 for the total of 

(1) filter-passing total mercury (FTHg), (2) filter-passing methylmercury (FMeHg), (3) particulate total mercury (PTHg), 

(4) particulate methylmercury (PMeHg) and, (5) DOC 

discharged through control structure S-12D. The loads of Hg 

fractions and DOC at control structure S-12D were determined 

by pairing discharge data with constituent concentrations 

estimated by empirical models based on surrogate in situ water 

quality measurements.

Calculated concentrations of DOC ranged from 

12.8 milligrams per liter (mg/L) to 27.9 mg/L with a mean 

of 18.8 mg/L during the study period. Annual loads of DOC 

ranged from 3,950 tons in 2015 to 10,900 tons in 2016. DOC 

loads increased linearly with an increase in flow, and the 

highest monthly DOC load of 1,630 tons was observed in 

February 2016.

Calculated concentrations of FTHg ranged from 0.35 to 

1.55 nanograms per liter (ng/L) with a mean of 0.85 ng/L 

during the study period. Calculated concentrations of FMeHg 

ranged from 0.06 ng/L to 0.24 ng/L with a mean of 0.14 ng/L 

during the study period. Generally, FTHg and FMeHg concentrations were lower during periods of decreased flow and 

higher during periods of increased flow. Calculated PTHg concentrations ranged from 0.09 ng/L to 4.19 ng/L with a mean of 

0.58 ng/L during the study period. Calculated PMeHg concentrations ranged from below the limit of detection <0.01 ng/L to 

0.29 ng/L with a mean of 0.03 ng/L during the study period.

Loads of Hg were often zero or lowest from November to 

May, owing to the lack of flow or low-flow conditions. FTHg 

and FMeHg loads increased linearly with an increase in flow 

and typically were highest from June to October. During periods of increasing flow or following changes in gate operations, 

PTHg and PMeHg constituted a greater percentage of the total 

Hg load. Annual loads of total Hg (filter-passing and particulate) ranged from 254 grams in 2015 to 658 grams in 2016. 

FTHg was the predominant contributor to the total Hg load. 

Information presented herein provides the first assessment 

of DOC and Hg loads to Everglades National Park through 

control structure S-12D using continuous in situ measurements 

of discharge and constituent surrogates and compares the surrogate model approach to loads calculated from monthly sampling. Analysis of calculated and observed loads demonstrates 

the significance of flow data on calculating constituent loads.

Introduction

Elevated levels of mercury (Hg) have long been a concern in the Florida Everglades. Fish consumption advisories 

issued in response to Hg contamination first appeared in South 

Florida in the 1980s. The predominant source of the Hg is wet 

deposition (Guentzel and others, 1995; Axelrad and others, 

2009; Coburn and others, 2016). While the emission of Hg in 

the United States has decreased by approximately 60 percent 

since 1990 (Wentz and others, 2014), fish consumption advisories in response to elevated concentrations of methylmercury 

(MeHg) still exist in many places throughout the country, 

including South Florida. MeHg bioaccumulates in organisms 

at the base of the aquatic food web and biomagnifies upward 

through trophic levels (Mason and others, 1996), posing major 

health risks to both humans and wildlife. Hg can adversely 

affect the cardiovascular, pulmonary, digestive, renal, immune, 

nervous, endocrine, and reproductive systems in humans (Rice 

and others, 2014).

In aquatic systems, anaerobic microorganisms can convert inorganic divalent mercury (Hg[II]i) to MeHg (Gilmour 

and others, 2013), which is the form of Hg that bioaccumulates in food webs. The production of MeHg from Hg(II)i 
2 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

varies spatially and temporally in the greater Everglades 

ecosystem and is influenced by temperature, oxygen levels, 

organic carbon, and sulfate (Aiken and others, 2011; Orem 

and others, 2011). Favorable conditions for the conversion of 

Hg(II)i to MeHg include lower dissolved oxygen concentration, higher concentrations of dissolved organic carbon (DOC) 

and sulfate, and higher temperatures (Wentz and others, 2014). 

Furthermore, wetting and drying of wetland sediments can 

also increase Hg(II)i methylation because of the oxidation of 

peat and subsequent release of sulfate upon rewetting (Wentz 

and others, 2014). Within Everglades National Park, a strong 

spatial dependence is observed in the concentration of MeHg 

in the aquatic food web (Rumbold and others, 2018), but questions remain regarding the processes controlling Hg delivery 

to the park.

The delivery of Hg and DOC to Everglades National 

Park is of keen interest, because MeHg can bioaccumulate in 

fish and Hg(II)i can be converted to MeHg within the park’s 

marshlands. Furthermore, this region of the Everglades ecosystem is an important conduit between the freshwater marshlands and tidal wetlands. Aside from influencing the transport 

and bioavailability of Hg, DOC can influence pH and controls 

several additional aquatic processes important to ecosystem 

health, including light penetration, and transport of hydrophobic compounds such as pesticides (Aiken and others, 2011). 

Previous studies demonstrated that measurements of fluorescence of chromophoric dissolved organic matter (fDOM) can 

be used to estimate concentrations of filter-passing total Hg 

(FTHg), filter-passing MeHg (FMeHg), and DOC in surface 

water, allowing for the computation of continuous timeseries data of concentrations (Bergamaschi and others, 2011; 

Bergamaschi and others, 2012a, Bergamaschi and others, 

2012b). Other studies have documented relations between 

suspended sediment and particulate Hg concentrations 

(Schoellhamer and others, 2007; Horowitz, 2009; Etheridge, 

2015). This report builds on previous studies by using fDOM 

to predict filter-passing constituents (FTHg, FMeHg, DOC) 

and turbidity data to estimate particulate total Hg (PTHg) and 

particulate MeHg (PMeHg) concentrations. Concentration 

data coupled with concurrent flow data were used to quantify 

constituent loads.

This study, conducted by the U.S. Geological Survey 

(USGS) as part of the Greater Everglades Priority Ecosystem 

Studies Program, developed site-specific surrogate models 

for calculating the concentrations and loads of Hg fractions 

(FTHg, FMeHg, PTHg, PMeHg) and DOC within flows 

passing through control structure S-12D. This report provides the first multiyear assessment of DOC and Hg loads to 

Everglades National Park through control structure S-12D 

using continuous in situ measurements (15-minute intervals). Long-term, continuous data can be used to describe the 

seasonal and annual variability of constituent concentrations, 

inform the understanding of Hg cycling and transport mechanisms within the greater Everglades landscape, and potentially provide insights into the effects of restoration efforts on 

Hg cycling.

Purpose and Scope

The primary purpose of this report is to summarize 

seasonal and annual concentration and load data from late 

September 2013 to early April 2017 for total Hg and MeHg 

concentrations (filter-passing and particulate fractions) and 

DOC at control structure S-12D. In addition, the report uses in 

situ data to document the development of site-specific surrogate models for quantification of total Hg and MeHg concentrations (filter-passing and particulate) and DOC. Data on flow, 

specific conductance (SC), temperature, turbidity, and fDOM 

were used to compute constituent concentrations and loads. 

All surrogate and discrete data used in the analyses are available in the USGS National Water Information System (NWIS) 

database (U.S. Geological Survey, 2019). Model archive 

summaries for each of the five models are described within the 

text and additional information is available in the appendixes. 

The 15-minute interval concentration data calculated from 

the models are provided by Booth (2020). Additionally, loads 

calculated using surrogate methods were compared to loads 

calculated using only observed concentrations.

Description of the Study Area

The construction of Tamiami Trail (U.S. 41) in the late 

1920s changed the natural freshwater flow to what is now 

Everglades National Park. To manage the flow of freshwater to 

the park, several control structures (pumps, gates, and culverts) were constructed. S-12D is one of several structures regulating freshwater flow from Water Conservation Area 3A and 

L-67 south to Everglades National Park (fig. 1). Water from 

Water Conservation Area 3A is released to the Shark River 

Slough in the park through the S-12 and S-333 structures. 

Water conservation areas were created to (1) provide flood 

control; (2) provide water for agricultural, municipal, and 

industrial uses, and for Everglades National Park; (3) enhance 

conditions favorable for groundwater recharge; (4) reduce saltwater intrusion; and (5) benefit Everglades wildlife and recreation. Historically, S-12D (fig. 2) has delivered 40 percent 

of the flow through the S-12 structures, and structures S-12A, 

S-12B, and S-12C have delivered 10, 20, and 30 percent, 

respectively, of the total flow (U.S. Army Corps of Engineers, 

2012). Restoration efforts are currently (2020) underway to 

mitigate many unintended water-related consequences of the 

Tamiami Trail (U.S. 41), including the construction of bridges 

to allow for improved hydrologic connectivity and water 

delivery schedules.

S-12D is on Tamiami Trail (U.S. 41), approximately 

30 miles (mi) west of Miami, 25–30 mi northeast of the tidal 

creeks of Everglades National Park, and 50 mi due north of 

Florida Bay (figs. 1 and 2). The S-12D structure consists of 

six vertical-lift gates designed to release freshwater at selected 

flow rates, controlled by the height of the gate openings. The 

four S-12 structures (S-12A, S-12B, S-12C, and S-12D) were 

designed to discharge a maximum of 32,000 cubic feet per 
Introduction 3

# # # ## A B C

D

80°

26°

25°

GULF OF MEXICO

Tamiami Trail

Shark River Slough

75

ATLANTIC OCEAN

FLORIDA

GULF OF MEXICO

ATLANTIC OCEAN

"

Base from U.S. Geological Survey and Wildlife Service digital data

North American Datum of 1983

Albers Conical Equal Area

WATER CONSERVATION 

AREA 2A

WATER 

CONSERVATION 

AREA 2B

WATER 

CONSERVATION 

AREA 3A

WATER 

CONSERVATION 

AREA 3B

Homestead

EXPLANATION

Water conservation areas

S-333

S-12

Everglades National Park

#

#

MONROE 

COUNTY

MIAMI-DADE 

COUNTY

BROWARD

COUNTY

COLLIER COUNTY

Map

area

Levee 67C

Levee 67A

0 5 10 KILOMETERS

0 5 10 MILES

Control structures

Miami

Florida Bay

Figure 1. S-12 control structures A–D that control the flow of water from water conservation areas to Everglades National Park.
4 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

second (ft3/s) combined; however, sedimentation and vegetation accumulation have decreased the capacity of the structures. No minimum discharge through the S-12 structures is 

required, but typically S-12A is closed from November 1 to 

July 14 and S-12B is closed from January 1 to July 14.

Study Methods

Various methods and procedures were followed to collect 

and analyze the data used to develop the empirical models to 

simulate concentrations and calculate loads of Hg and DOC 

at control structure S-12D. The study site, S-12D, was chosen 

because it typically has the highest flow volume of the S-12 

structures. Upstream and downstream water levels, flow 

volume, and several types of water-quality data were collected 

at the structure on a continuous (15-minute interval) basis. 

The water-quality data included measurements of specific 

conductance, temperature, turbidity, and fDOM. Data were 

transmitted hourly through the Geostationary Operational 

Environmental Satellite system and made available to the 

public on a near real-time basis. Discrete water samples for 

laboratory analyses of Hg and DOC were collected monthly 

from September 2013 to February 2017. Development of the 

models was guided by procedures described in Rasmussen and 

others (2009).

Continuous Monitoring

Water levels were continuously recorded at points just 

upstream (headwater) and downstream (tailwater) of the 

water-control structure, following USGS procedures outlined in Sauer and Turnipseed (2010). The magnitude of 

flow at S-12D, which was controlled by the height of the 

gate openings, was computed using one of two methods: 

(1) a submerged-orifice equation was applied when the gates 

were in the water, or (2) a stage-discharge rating was applied 

when the gates were out of the water. Both computation 

methods were developed and verified on the basis of historical discharge measurements made twice a month when 

water was flowing through the S-12D structure (Rantz and 

others, 1982; Sanders and Feaster, 2004). During this study 

period, discharge measurements were made with an acoustic 

Doppler current profiler twice a month to verify structure 

calibration ratings.

Water-quality data were collected by sensors in a YSI 

EXO multiparameter monitoring system from September 2013 

to April 2017 at a fixed point in the middle of the water column, at an elevation of approximately 4.7 ft above NGVD 29 

and near the west bank of the downstream pool of S-12D. 

Measurements included those for specific conductance, in 

microsiemens per centimeter at 25 degrees Celsius (μS/cm 

at 25 °C); temperature, in degrees Celsius (°C); turbidity, in 

formazin nephelometric units (FNU); and fDOM, in quinine 

Figure 2. Water-control structure S-12D. Photograph by Eduardo Patino, U.S. Geological Survey.
Study Methods 5

sulfate equivalents (QSE), the concentration of quinine sulfate 

dehydrate in parts per billion that results in an equivalent 

instrument response. The YSI EXO sensors were inspected for 

fouling approximately every 4 weeks, and calibration verifications were made every 8 weeks. Drift and fouling corrections 

were determined and applied using formulas provided in 

Wagner and others (2006).

fDOM data were corrected for temperature, turbidity, and 

inner filter effects as described in Downing and others (2012). 

The correction of fDOM for turbidity assumed the attenuation 

of light by International Humic Substances Society Elliot silt 

loam soil. Serial dilutions were performed to determine the 

inner filter effect using Caloosahatchee River water collected 

at S-79 (USGS site number 02292900; filtered at 0.45 micrometer (μm) on August 16, 2017) to span the range of fDOM 

measurements observed at S-12D during the study period. 

Data collected at S-12D on September 28, 2017, indicated that 

the Caloosahatchee water was representative of S-12D with 

respect to the inner filter effect (fig. 3).

Vertical profiles of water-quality sensor data were 

recorded across the sampling section to define vertical and 

horizontal stratification. Parameters recorded in the vertical 

profiles were the same as those for the site monitor sensor. 

At S-12D, measurements were made in vertical profiles at 

six locations in front of the midsection of each gate; the first

location matched the location where the site monitoring sensors were deployed. The data collected during measurements 

in the six vertical profiles were averaged to represent the mean 

channel conditions at the time of sampling. To ensure comparability between cross-sectional profiles and continuous site 

monitor readings, the same calibration verifications were used 

for the profile sensors and site monitor sensors. Raw fDOM 

values were used for comparison (that is, without correcting 

fDOM measurements for temperature, turbidity, or inner filter 

effect). The purpose of these raw fDOM data comparisons 

was to verify that continuous fDOM measurements at a single 

location of S-12D were representative of the entire channel.

Collection of Discrete Samples

All equipment used for the collection and transport of 

water samples for analysis of Hg was precleaned by the USGS 

Mercury Research Laboratory in Middleton, Wisconsin. 

Samples were collected using a USGS DH-81 sampler 

attached to a 1/2-inch-diameter wading rod. The DH-81 was 

configured with a US-D-95 cap, a nozzle, and a 1-liter bottle 

all made of polytetrafluoroethylene (Davis, 2005). Initially, 

two water samples were collected for Hg analysis: a point 

sample adjacent to the YSI EXO sensors and a single vertically 

0

50

100

150

200

250

300

350

400

Temperature corrected fluorescent dissolved organic

 matter (fDOM), in quinine sulfate equivalents (QSE), 

with inner filter effect removed

0 50 100 150 200 250 300

Temperature corrected fluorescent dissolved organic 

matter (fDOM), in quinine sulfate equivalents (QSE) 

y = 0.0022x2+ 0.7608x

R2

 = 0.999

S-79, Aug. 16, 2017

S-12D, Sept. 28, 2017

Polynomial line 

 (S-79, Aug. 16, 2017)

EXPLANATION

Figure 3. Relation between temperature corrected fluorescence of chromophoric 

dissolved organic matter (fDOM), with the inner filter effect removed, and temperature 

corrected fDOM, without the inner filter effect removed. The dashed blue line is the fit of 

all data using a second-order polynomial function.
6 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

integrated sample at the location of highest water velocity. 

Samples were placed on ice for transportation from the field 

to the processing laboratory, passed through a 0.7-μm quartz 

fiber filter to separate filter-passing and particulate Hg fractions, and shipped to the USGS Mercury Research Laboratory 

within 24 hours. Particulate samples were shipped on dry ice. 

Filtered waters were acidified to 1 percent volume-to-volume 

with concentrated ultraclean hydrochloric acid within 5 hours 

of collection and were stored and shipped in coolers at room 

temperature (U.S. Geological Survey, variously dated).

Samples were analyzed for total and methylmercury 

(filter-passing and particulate) at the USGS Mercury Research 

Laboratory. Laboratory personnel processed samples using 

techniques and methods documented in U.S. Environmental 

Protection Agency (EPA) Method 1631 (U.S. Environmental 

Protection Agency, 2002), DeWild and others (2002, 2004), 

and Olund and others (2004).

Surface-water samples for analyses of DOC, decadic 

absorption coefficient at 254 nanometers (nm) (α254), and specific ultraviolet absorbance at 254 nm (SUVA254) (Weishaar and 

others, 2003) were collected adjacent to the YSI EXO sensors in 

the same manner as the Hg samples. The SUVA254 was calculated by dividing the α254 by DOC concentration and is reported 

in units of liters per milligram carbon per meter (Weishaar and 

others, 2003). Samples were filtered through a 0.45-μm capsule 

filter into amber glass 2-liter bottles within 15 minutes of being 

collected and were stored immediately on ice. Samples for 

α254 measurement were analyzed at the USGS Organic Carbon 

Migration in Aquatic Environments, Project Laboratories 

Branch in Boulder, Colorado, using a spectrophotometer and 

a 1-centimeter quartz cuvette; sample spectra were measured 

with respect to a blank spectrum containing high-purity water. 

Samples were analyzed for DOC by wet chemical oxidation 

(Aiken, 1992) at the USGS Laboratory in Boulder, Colo., 

between September 2013 and September 2016. Samples collected between October 2016 and February 2017 were analyzed 

by high-temperature catalytic combustion at the USGS National 

Water Quality Laboratory (NWQL) in Denver, Colo. (Clescrei 

and others, 1998). Samples were collected and analyzed by 

both the USGS laboratory in Boulder and the NWQL on six 

instances (February 2, May 23, June 20, July 18, August 16, and 

September 19, 2016). Relative percent differences in DOC concentration from the split-sample analyses at the two laboratories 

ranged from −1.1 to 10.2 percent, with an average of 4.3 percent 

(n = 6). Although the results from the NWQL were slightly 

lower on average than those reported from the Boulder laboratory, the analyses from both laboratories were included in model 

development. For instances in which multiple samples were collected in a single day, the analysis for the first sample collected 

was the sole analysis used in model development, regardless of 

which laboratory analyzed the sample.

Development of Empirical Models

Five site-specific empirical models were developed to 

calculate concentrations of filter-passing and particulate total 

Hg and MeHg and DOC at control structure S-12D. Microsoft 

Excel and the USGS Surrogate Analysis and Index Developer 

(SAID) tool (Domanski and others, 2015) were used to make 

regression analyses of the concentrations for each constituent. 

Model archive summaries for each constituent are available in 

the appendixes. The distribution of residuals was examined for 

normality, and plots of residuals (the difference between the 

observed and calculated values) were examined for homoscedasticity (equal variance). In short, models were selected on 

the basis of residual plots, relatively high coefficient of determination (R2), and root mean square error (RMSE).

To develop empirical models for estimating long-term 

records of Hg and carbon concentrations that are comparable 

across sites and over time, instrument characteristics and 

site-specific environmental conditions need to be considered 

and multiple linear regressions employed (Rasmussen and 

others, 2009). The abbreviation fDOM refers to the fraction 

of chromophoric dissolved organic matter that fluoresces 

and is commonly used as a surrogate for DOC concentration 

(Spencer and others, 2007; Bergamaschi and others, 2012b; 

Pellerin and others, 2012). The use of specific conductance as 

a surrogate for DOC is also supported by the work of Curtis 

and Adams (1995) and that of Monteiro and others (2014). 

Temperature also was explored as a variable in all models to 

account for seasonal variability. Turbidity data were used as a 

proxy for particulate Hg concentration, applying documented 

techniques and methods on the use of turbidity for computing 

suspended sediment concentrations (Rasmussen and others, 

2009). The accuracy of these models is highly dependent on 

the range of variables used in their development and whether 

predictive variables are appropriately extrapolated when 

recorded values exceed those used in model development.

Analyses of Field and Laboratory 

Measurements

Large temporal variations in in situ measurements were 

observed at S-12D during the study period. Temperatures 

ranged from 15.9 °C on January 25, 2016, to 32.4 °C on 

August 25, 2015, and July 30, 2016. The values of fDOM 

ranged from 79.9 QSE on February 14, 2014, to 610.5 QSE 

on August 1, 2014. Specific conductance ranged from 

260 μS/cm at 25 °C on June 27, 2013, to 916 μS/cm at 

25 °C on September 22, 2015. Turbidity values ranged from 

zero FNU on several days throughout 2013 to 87 FNU on 

December 10, 2014. The maximum and minimum sensor data 

values recorded during model development and during the 

entire study period are presented in table 1.

Notably, the turbidity values used in development of 

the models ranged from 0.2 to 16.0 FNU (values measured 

during a sampling event), whereas the maximum recorded 

(sensor) value during the study period was 87.0 FNU. As is 

evident in recorded field data, periods of elevated turbidity 

were uncommon and of short duration (usually lasting less 

than 30 minutes). For example, daily mean values of turbidity for days when elevated turbidity was observed never 

exceeded 10 FNU. All recorded turbidity values greater than 

30 FNU were excluded from the calculations. The effect of the 
Analyses of Field and Laboratory Measurements 7

exclusion of values greater than 30 FNU on empirical models is considered negligible because of the low frequency of 

elevated turbidity measurements and the short duration of such 

periods of elevated turbidity. Samples containing elevated levels of turbidity were very rare at this site over the study duration and commonly were those collected immediately after a 

gate of the S-12D structure was opened. In an effort to capture 

a wide range of conditions for model calibration, a discrete 

sampling event was conducted on September 23, 2015, immediately after the gates of the S-12D structure were opened. 

Samples were collected on this date at 8:02 a.m., 8:19 a.m., 

and 9:41 a.m. eastern standard time. Because of the similarity 

in the laboratory-determined values and the field sensor data 

for the 8:02 a.m. and 8:19 a.m. samples, only the 8:02 a.m. 

sample was used in model development. The measurement 

collected at 9:41 a.m. eastern standard time was included in 

model development because there was a substantive change in 

field sensor data. This sample also had the highest turbidity of 

any sample collected (16 FNU).

Observed DOC concentrations ranged from 13.4 mg/L on 

July 18, 2016, to 25.8 mg/L on August 6, 2014 (table 2). These 

concentrations were highly correlated (Pearson correlation 

coefficient [r] greater than 0.8) with α254, specific conductance, 

and fDOM. Observed DOC concentration, however, was not 

strongly correlated with temperature, turbidity, or flow (r less 

than 0.2). Table 3 presents the r values and the number of 

observations, minimum, maximum, and mean values.

Table 1. Maximum and minimum sensor values recorded during sampling events, and the maximum and minimum overall sensor data 

values recorded during the study period at S-12D.

Data descriptor

Recorded during a sampling 

event and used in model 

development

Recorded at S-12D during 

the study period

Minimum turbidity (formazin nephelometric units) 0.2 0.0

Maximum turbidity (formazin nephelometric units) 16 87

Minimum temperature (degrees Celsius) 18.2 15.9

Maximum temperature (degrees Celsius) 30.4 32.4

Minimum specific conductance (microsiemens per centimeter at 25 degrees 

Celsius)

366 260

Maximum specific conductance (microsiemens per centimeter at 25 degrees 

Celsius)

796 916

Minimum fDOM (quinine sulfate equivalents) 118 80

Maximum fDOM (quinine sulfate equivalents) 538 611

Table 2. Number of observations, minimum, maximum, and mean concentrations of observed dissolved organic carbon (DOC), decadic 

absorption coefficient at 254 nanometers (α254), specific ultraviolet absorbance at 254 nanometers (SUVA254), specific conductance, 

fluorescence of chromophoric dissolved organic matter (fDOM), temperature, turbidity, and flow data collected at S-12D from 

September 2013 to December 2016.

Data descriptor Observations Minimum Maximum Mean

Dissolved organic carbon (milligrams per liter)31 13.4 25.8 19.1

Decadic absorption coefficient at 

254 nanometers, α254

30 0.34 0.78 0.55

SUVA254 (liters per milligram carbon per meter)30 2.2 3.5 2.9

Specific conductance (microsiemens 

per centimeter at 25 degrees 

Celsius)

31 366 796 541

fDOM (quinine sulfate equivalent) 31 119 538 289

Temperature (degrees Celsius) 31 18.2 30.4 25.8

Turbidity (formazin nephelometric 

units)

31 0.2 10.0 1.4

Flow (cubic feet per second) 31 0 1,160 398
8 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

Observed FTHg concentrations ranged from 0.48 ng/L 

on January 29, 2014, to 1.57 ng/L on September 23, 2015 

(table 4). These concentrations were most highly correlated with laboratory-derived α254 (r = 0.70) and fDOM 

(r = 0.59) (table 5). Observed FMeHg concentrations 

ranged from <0.06 ng/L on April 25, 2016, to 0.31 ng/L on 

September 23, 2015. FMeHg was most highly correlated 

with PTHg (r = 0.58 and turbidity (r = 0.57). Observed 

PTHg ranged from 0.10 ng/L on December 6, 2013, to 

6.47 ng/L on September 23, 2015. Observed PMeHg 

ranged from <0.003 ng/L on October 28, 2014, to 0.20 ng/L 

on September 23, 2015. Both PTHg and PMeHg were 

most strongly correlated with turbidity (r = 0.96 and 

r = 0.88, respectively).

Channel Cross-Sectional Variability

A total of 14 vertical sensor profiles (as described in 

the continuous monitoring section) were collected between 

September 11, 2013, and September 19, 2016. Raw fDOM 

values were used for comparison (that is, no corrections were 

applied for temperature, turbidity, or inner filter effect). Profile 

averages for fDOM measurements were within 10 percent 

(on average −4 percent) to the YSI EXO site monitor readings (n = 14), except for measurements on February 22 and 

July 18, 2016. Measurements at the continuous station on 

those dates were 17 and 24 percent lower, respectively, than 

the average for the channel profile data. The specific conductance data were also 17 and 19 percent lower on those dates, 

indicating that the variation between the continuous-recording 

station and the mean channel was not always consistent. The 

difference between the continuous station and mean crosssectional values for turbidity ranged from 0.2 to −0.6 FNU, 

with a mean difference of −0.2 FNU (fig. 4).

The differences between values recorded at the continuous station and the average of measurements for the channel 

sensor profiles and the differences between the paired point 

and vertically integrated samples were not uniformly biased 

for constituents of interest. Despite these differences, it was 

concluded that the station data were representative of the mean 

channel under most conditions. It is recognized that this may

Table 3. Pearson correlation coefficients (r) of observed dissolved organic carbon (DOC), decadic absorption coefficient at 254 

nanometers (α254), specific ultraviolet absorbance at 254 nanometers (SUVA254), specific conductance, fluorescence of chromophoric 

dissolved organic matter (fDOM), temperature, turbidity, and flow data collected at S-12D from September 2013 to December 2016.

Data descriptor

Dissolved organic 

carbon (milligrams per liter)

Decadic 

absorption 

coefficient at 

254 nanometers, α254

SUVA254

(liters per 

milligram 

carbon per 

meter)

Specific conductance (microsiemens per 

centimeter at 25 

degrees Celsius)

fDOM (quinine sulfate 

equivalent)

Temperature 

(degrees 

Celsius)

Turbidity 

(formazin 

nephelometric 

units)

Dissolved organic 

carbon (milligrams per liter)

Decadic absorption 

coefficient at 254 

nanometers, α254

0.871

SUVA254 (liters per 

milligram carbon 

per meter)

0.279 0.687

Specific conductance 

(microsiemens per 

centimeter at 25 

degrees Celsius)

0.834 0.671 0.171

fDOM (quinine sulfate equivalent)0.809 0.954 0.687 0.649

Temperature (degrees Celsius)0.036 0.099 0.105 -0.120 0.095

Turbidity (formazin 

nephelometric 

units)

0.151 0.096 -0.025 0.386 0.113 0.241

Flow (cubic feet per 

second)

0.086 0.411 0.565 -0.107 0.335 -0.092 -0.395
Channel Cross-Sectional Variability 9

not always hold true, particularly for turbidity, which is the 

sole predictive measurement used to model PTHg and PMeHg 

concentrations; this is a limitation of the study. The location 

of the point sensor for continuous in situ measurements was 

not directly in the path of highest flow velocity. Therefore, 

because the concentration of suspended particulate matter is 

expected to be positively correlated with flow velocity, values 

of suspended matter measured at the high-velocity point are 

also expected to be higher than those recorded at the point 

sensor. The calculated PTHg and PMeHg are expected to be 

representative of the location of the point sensor but may modestly underpredict the concentrations at the location of highest 

flow velocity.

During the period January 2013 through February 2015, 

between 12 and 15 pairs of vertically integrated samples were 

collected at the point of highest velocity at structure S-12D, 

and point samples were collected near the site monitor location 

for analyses of FTHg, FMeHg, PTHg, and PMeHg (fig. 5). 

The difference in value for FTHg between the paired point and 

vertically integrated samples ranged from −9 to +7 percent, 

with an average of −2 percent (n =14). The difference in value 

for FMeHg between the paired point and vertically integrated 

samples ranged from −22 percent (−0.02 ng/L) to +26 percent 

(0.05 ng/L), with an average of zero percent (n = 13). The difference in value for PTHg between the paired point and vertically integrated samples ranged from −50 to +8 percent, with 

an average of −10 percent (n = 15). The difference in value 

for PMeHg between the paired point and vertically integrated 

samples ranged from −55 to +27 percent, with an average of 

−7 percent (n = 14). The scatter plots in figure 5 show good 

agreement between the concentration of point samples and the 

concentration of vertically integrated samples for FTHg and 

FMeHg. For PTHg and PMeHg; however, the concentrations 

of point samples compared to the concentrations of vertically 

integrated samples showed more variability. The concentrations of point samples for PTHg and PMeHg were lower 

than concentrations of the vertically integrated samples at the 

location of highest flow, particularly when concentrations were 

higher, although no clear trends were observed with differences in flow rate.

Table 4. Number of observations, minimum, maximum, and mean concentrations of observed filter-passing total mercury (FTHg) 

and methylmercury (FMeHg), particulate total mercury (PTHg) and methylmercury (PMeHg), dissolved organic carbon (DOC), decadic 

absorption coefficient at 254 nanometers (α254), specific ultraviolet absorbance at 254 nanometers (SUVA254), specific conductance, 

fluorescence of chromophoric dissolved organic matter (fDOM), temperature, turbidity, and flow data collected at S-12D from 

September 2013 to December 2016.

Data descriptor Observations Minimum Maximum Mean

Filtered total mercury (nanograms per liter) 26 0.48 1.57 0.90

Filtered methylmercury (nanograms per liter) 26 <0.06 0.31 0.15

Particulate total mercury, (nanograms per liter) 26 0.10 6.47 0.76

Particulate methylmercury, (nanograms per 

liter)

26 <0.006 0.20 0.04

Dissolved organic carbon (milligrams per 

liter)1

31 13.4 25.8 19.1

Decadic absorption coefficient at 254 nanometers, α254130 0.34 0.78 0.55

SUVA254, (liters per milligram carbon per 

meter)1

30 2.2 3.5 2.9

Specific conductance (microsiemens per centimeter at 25 degrees Celsius)27 369 796 554

fDOM (quinine sulfate equivalents) 27 118 538 294

Temperature (degrees Celsius) 27 18.2 30.4 25.7

Turbidity (formazin nephelometric units) 27 0.2 16.0 2.1

Discharge (cubic feet per second) 27 0 1,160 359

1Dissolved organic carbon, decadic absorption at 254 nanometers and specific ultraviolet absorbance at 254 nanometers values were all collected on the same date as mercury constituents; however, the times varied by 2 hours or 

less. The field data (fDOM, temperature, turbidity, specific conductance, and discharge) in the table all correlate 

with the time the mercury (Hg) samples were collected.
10 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D Table 5. Pearson correlation coefficients (r) of observed filter-passing total mercury (FTHg) and methylmercury (FMeHg), particulate total mercury (PTHg) and methylmercury (PMeHg), dissolved organic carbon (DOC), decadic absorption coefficient at 254 nanometers (

α254), specific ultraviolet absorbance at 254 nanometers (SUVA254), specific 

conductance, fluorescence of chromophoric dissolved organic matter (fDOM), temperature, turbidity, and flow data collected at S-12D from September 2013 to December 2016.

Data descriptor

Filtered total mercury 

(nanograms 

per liter)

Filtered 

methylmercury 

(nanograms per 

liter)

Particulate 

total 

mercury 

(nanograms 

per liter)

Particulate 

methylmercury 

(nanograms 

per liter)

Dissolved 

organic 

carbon 

(milligrams 

per liter)

Decadic 

absorption 

coefficient at 

254 nanometers, 

α254

SUVA254

(liters per 

milligram 

carbon per 

meter)

Specific 

conductance 

(microsiemens per 

centimeter 

at 25 degrees 

Celsius)

fDOM 

(quinine 

sulfate 

equivalents)

Temperature 

(degrees 

Celsius)

Turbidity 

(formazin 

nephelometric 

units)

Filtered total mercury (nanograms 

per liter)

Filtered methylmercury (nanograms per liter) 0.69Particulate total 

mercury, (nanograms per liter)

0.55 0.58

Particulate 

methylmercury, (nanograms per liter)

0.24 0.50 0.79

Dissolved organic 

carbon (milligrams per liter)1

0.49 0.44 0.07 -0.06

Decadic absorption 

coefficient at 254 nanometers, α2541

0.70 0.40 0.16 -0.17 0.87

SUVA254, (liters per 

milligram carbon per meter)1

0.57 0.08 0.19 -0.25 0.28 0.69

Specific conductance (microsiemens per 

centimeter at 25 degrees Celsius)

0.20 0.36 0.11 0.28 0.80 0.64 0.22

fDOM (quinine 

sulfate equivalents)

0.59 0.37 0.15 -0.03 0.83 0.96 0.71 0.65
Channel Cross-Sectional Variability 11

Table 5. Pearson correlation coefficients (r) of observed filter-passing total mercury (FTHg) and methylmercury (FMeHg), particulate total mercury (PTHg) and methylmercury (PMeHg), dissolved organic carbon (DOC), decadic absorption coefficient at 254 nanometers (α254), specific ultraviolet absorbance at 254 nanometers (SUVA254), specific conductance, fluorescence of chromophoric dissolved organic matter (fDOM), temperature, turbidity, and flow data collected at S-12D from September 2013 to December 2016.—Continued

Data descriptor

Filtered total mercury 

(nanograms 

per liter)

Filtered 

methylmercury 

(nanograms per 

liter)

Particulate 

total 

mercury 

(nanograms 

per liter)

Particulate 

methylmercury 

(nanograms 

per liter)

Dissolved 

organic 

carbon 

(milligrams 

per liter)

Decadic 

absorption 

coefficient at 

254 nanometers, α254

SUVA254

(liters per 

milligram 

carbon per 

meter)

Specific 

conductance 

(microsiemens per 

centimeter 

at 25 degrees 

Celsius)

fDOM 

(quinine 

sulfate 

equivalents)

Temperature 

(degrees 

Celsius)

Turbidity 

(formazin 

nephelometric 

units)

Temperature (degrees Celsius) 0.42 0.38 0.21 0.29 0.09 0.14 0.06 -0.10 0.14Turbidity (formazin 

nephelometric units)

0.46 0.57 0.96 0.88 0.10 0.05 -0.05 0.23 0.09 0.23

Discharge (cubic 

feet per second)

0.37 -0.14 -0.21 -0.49 0.18 0.48 0.57 -0.04 0.37 -0.10 -0.33

1Dissolved organic carbon, decadic absorption at 254 nanometers and specific ultraviolet absorbance at 254 nanometers values were all collected on the same date as mercury constituents; however, the times varied by 2 hours or less. The field data (fDOM, temperature, turbidity, specific conductance, and discharge) in the table all correlate with the time the 

Hg samples were collected.
12 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

0

10

20

30

40

0 10 20 30 40

0

200

400

600

800

0 200 400 600 800

0

0.5

1.0

1.5

2.0

2.5

3.0

0 1 2 3

0

50

100

150

200

250

300

0 100 200 300

Site monitor temperature, in degrees Celsius Site monitor specific conductance, in microsiemens 

per centimeter at 25 degrees Celsius

Site monitor turbidity, in formazin nephelometric units Site monitor fluorescent dissolved organic 

matter, in quinine sulfate equivalents

Average profile temperature, 

in degrees Celsius

Average profile turbidity, in formazin

nephelometric units

Average profile fluorescent dissolved organic

matter (fDOM), in quinine sulfate equivalents

Average profile specific conductance, 

in microsiemens per centimeter 

at 25 degrees Celsius

A B

C D

Figure 4. Comparisons between mean cross-sectional profile data and site monitor data for, A, temperature, B, specific conductance, 

C, turbidity, and D, fluorescence of chromophoric dissolved organic matter (fDOM). The 1:1 line is shown in black.
Channel Cross-Sectional Variability 13

A B

C D

0

0.1

0.2

0.3

0

0.1 0.2 0.3

0

0.5

1.0

1.5

2.0

0 0.5 1.0

1.0

1.5 2.0

0

0.04

0.06

0.02

0.08

0.4 0 0.02 0.04 0.06

0.4

0.2

0.2

0.6

0.6

0.8

0.8

0.08

0

1.0

0

Particulate total mercury concentration in vertically 

integrated sample at point of highest 

velocity, in nanograms per liter

Filter-passing total mercury concentration in 

vertically integrated sample at point of highest

velocity, in nanograms per liter

Particulate methylmercury concentration in

vertically integrated sample at point of 

highest velocity, in nanograms per liter

Filter-passing methylmercury concentration in 

vertically integrated sample at point of 

highest velocity, in nanograms per liter

Particulate total mercury concentration in 

point sample, in nanograms per liter

Particulate methylmercury concentration in

point sample, in nanograms per liter

Filter-passing total mercury concentration in

point sample, in nanograms per liter

Filter-passing methylmercury concentration in 

point sample, in nanograms per liter

Figure 5. Comparisons between point sample and vertically integrated samples at point of highest flow for A, Filter-passing total 

mercury (FTHg), B, filter-passing methylmercury (FMeHg), C, particulate total mercury (PTHg), and D, particulate methylmercury 

(PMeHg). The 1:1 line is shown in black.
14 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

Empirical Models

Empirical models used to calculate Hg and DOC 

concentrations are described in this section of the report. 

Additional information on the models can be found in 

appendixes 1–5.

Dissolved Organic Carbon Model

The best predictors of DOC concentration were fDOM 

and SC, as represented in the following equation:

 DOC = (0.016 × SC)+ (0.0143 × fDOM)+ 6.26, (1)

where

DOC is dissolved organic carbon concentration, in 

milligrams per liter;

SC is specific conductance, in microsiemens per 

centimeter at 25 degrees Celsius; and

fDOM is fluorescence of chromophoric dissolved 

organic matter, in quinine sulfate 

equivalents.

The values of fDOM were corrected for temperature, 

turbidity, and inner filter effects. The regression model is 

based on 31 concurrent measurements of DOC, fDOM, 

and specific conductance (SC) on samples collected from 

September 11, 2013, to December 19, 2016. The coefficient of 

determination (R2) for the model is 0.819, the standard error is 

(RMSE) 1.32 and the average model standard percentage error 

(MSPE) is 6.94.

Filter-Passing Total Mercury Model

The best predictors of FTHg were temperature and 

fDOM, as represented in the following equation:

FTHg = (1.68 × 10−3 × fDOM)+

 (2.64 × 10−2 × T)− 0.293 , (2)

where

FTHg is filter-passing total mercury concentration, 

in nanograms per liter; and

T is temperature, in degrees Celsius.

The regression model is based on 25 concurrent 

measurements of FTHg, temperature, and fDOM collected from September 11, 2013, to December 19, 2016. 

The R2 for the model is 0.499, the RMSE is 0.227, and the 

MSPE is 25.9.

Filter-Passing Methylmercury Model

The best predictors of FMeHg were temperature and 

fDOM, as represented in the following equation:

FMeHg = (2.1 × 10−4 × fDOM)

 + (5.27 ×

10−3 × T)− 0.053  , (

where

FMeHg is filter-passing methylmercury concentration, 

in nanograms per liter.

The regression model is based upon 25 concurrent 

measurements of FMeHg, temperature, and fDOM 

collected from September 11, 2013, to December 19, 2016. 

The coefficient of determination is low for this model 

(R2 = 0.244), indicating that additional factors influence 

FMeHg concentration. Implications of model accuracy will 

be discussed later in the report. The RMSE for the model is 

0.0593 and the MSPE is 41.5

Particulate Total Mercury Model

The best predictor of PTHg was natural log transformed 

turbidity, as represented in the following equation:

ln (PTHg )= (0.701 × ln [Turbidity ] )− 0.906, (

where

PTHg is particulate total mercury concentration, in 

nanograms per liter; and

Turbidity is turbidity (YSI EXO model), in formazin 

nephelometric units.

The regression model is based on 26 concurrent 

measurements of PTHg and turbidity, collected from 

September 11, 2013, to October 26, 2016. The R2 for the 

model is 0.703, the RMSE is 0.522, and the MSPE is 54.6. 

A bias correction factor of 1.15 was applied to correct for 

retransformation.

Particulate Methylmercury Model

The best predictor of PMeHg was natural log transformed 

turbidity, as represented in the following equation:

ln (PMeHg)= (0.752 × ln [Turbidity ] )− 3.82, (

where

PMeHg is particulate methylmercury concentration, in 

nanograms per liter.

The regression model is based on 26 concurrent 

measurements of PMeHg and turbidity, collected from 

September 11, 2013, to October 26, 2016. Of the samples 

collected, concentrations of PMeHg in 8 percent (2 of the 26) 

were below the detection limit. Half of the detection limit was 

used for the censored data during the model development. The 

R2 for the model is 0.603, the RMSE is 0.7, and the MSPE is 

75.9. A bias correction factor of 1.23 was applied to correct for 

retransformation.
Mercury Concentrations and Loads 15

Dissolved Organic Carbon 

Concentrations and Loads

Calculated concentrations of DOC ranged from on 

February 14, 2014, to 27.9 mg/L on July 30, 2014 (fig. 6). 

The mean calculated DOC concentration for the study period 

was 18.8 mg/L, and the monthly average of DOC ranged 

from 14.3 mg/L in March 2017 to 23.3 mg/L in August 2014. 

The greatest range in DOC concentrations occurred at flows 

between 200 and 400 ft3/s. At flows higher than 400 ft3/s, the 

range in DOC concentrations decreased, as did the median and 

average concentrations.

Annual loads of DOC were 6,230 tons of organic carbon 

in calendar year 2014, 3,950 tons in calendar year 2015, and 

10,900 tons in calendar year 2016 (fig. 7). Calculated daily 

loads of DOC ranged from zero to 62 tons on February 7, 2016, 

and monthly loads were zero during periods of no flow 

(May 2014, April to August 2015, and January to March 2017). 

Monthly mean flow explained 97 percent of the variance in 

monthly DOC loads. DOC loads increased linearly with an 

increase in flow, and the highest monthly load (1,630 tons) 

occurred during February 2016. The high monthly load in 

February 2016 can be attributed to January 2016 being the wettest January in 100 years for most of Florida, with cumulative 

rainfall of 9.5 inches over Water Conservation Area 3A to the 

north of structure S-12D (Abtew and Ciuca, 2017).

Mercury Concentrations and Loads

Calculated concentrations of FTHg ranged from 

0.35 ng/L on January 27, 2014, to 1.55 ng/L on August 1, 2014 

(fig. 8A). The mean calculated FTHg concentration was 

0.85 ng/L over the duration of the study, and the monthly mean 

concentration of FTHg ranged from 0.51 ng/L in March 2017 

to 1.29 ng/L in August 2014. Calculated concentrations 

of FMeHg ranged from 0.06 ng/L on January 27, 2014, to 

0.24 ng/L on August 1, 2014 (fig. 8B). The mean calculated 

FMeHg concentration was 0.14 ng/L over the duration of the 

study. The differences between the observed and calculated 

FMeHg values appear to increase beginning in 2015, indicating additional influences on MeHg that are not accounted 

for in the model. The calculated monthly mean concentration of FMeHg ranged from 0.09 ng/L in January 2015 and 

March 2017 to 0.20 ng/L in August 2014. Calculated concentrations of FTHg were greater than 1.4 ng/L only for flows 

between 190 and 550 ft3/s, and calculated concentrations of 

FMeHg were greater than 0.2 ng/L only for flows between 

190 and 650 ft3/s. Annually, concentrations of FTHg and 

FMeHg were highest during August and September.

Calculated concentrations of PTHg ranged from 

0.09 ng/L on September 10, 2013, February 11, and 

April 25, 2016, to 4.19 ng/L on December 10 and 12, 2014, 

and February 24, 2015 (fig. 9A). The mean calculated PTHg 

concentration was 0.58 ng/L over the duration of the study. 

The monthly average of PTHg ranged from 0.28 ng/L in 

November 2013 to 1.31 ng/L in June 2014. Calculated 

concentrations of PMeHg ranged from <0.01 ng/L on 

September 10, 2013, February 11, and April 25, 2016, to 

0.29 ng/L on December 10 and December 12, 2014, and 

February 24, 2015 (fig. 9B). The mean calculated PMeHg concentration was 0.03 ng/L over the duration of the study. The 

monthly mean concentration of PMeHg ranged from 0.01 ng/L 

in November 2013 and March 2016 to 0.08 ng/L in June 2014. 

The maximum concentrations of PTHg and PMeHg varied 

throughout the year. The annual monthly mean concentrations of PTHg and PMeHG were highest in June 2014, 

August 2015, and December 2016. The monthly concentrations of both PTHg and PMeHg were lowest in months with 

low monthly mean flow.

Calculated daily loads of FTHg and PTHg ranged from 

zero to 4.2 g on February 21, 2016. Based on daily load calculations, PTHg accounted for 16–67 percent of the summation 

of filter-passing and particulate THg. These estimates over the 

entire study period are in good agreement with discrete observations where the observed PTHg accounted for 11–87 percent 

of the summation of filter-passing and particulate THg. 

Monthly loads of FTHg and PTHg were zero in May 2014, 

from April to August 2015, and from January to March 2017 

because there was no flow. The maximum monthly load of 

FTHg and PTHg was 85.6 g in September 2016. Annual 

loads of FTHg ranged from 154 g in 2015 to 455 g in 2016, 

and annual loads of PTHg ranged from 96 g in 2015 to 204 g 

in 2016 (fig. 10). As expected, the loads of FTHg and PTHg 

are often minimal during periods of low flow. Monthly mean 

flow explained 90 percent of the variance in monthly mean 

FTHg loads and 66 percent of the variance in monthly mean 

PTHg loads.

Calculated daily loads of FMeHg and PMeHg ranged 

from zero to 0.4 g on February 21, 2016. Based on daily load 

calculations, PMeHg accounted for 6–50 percent of the summation of filter-passing and particulate MeHg, which agrees 

well with discrete observations. Observed PMeHg ranged 

from 2 to 55 percent of the summation of filter-passing and 

particulate MeHg. Monthly loads of FMeHg and PMeHg 

were zero in May 2014, from April to August 2015, and 

from January to March 2017. The maximum monthly load of 

FMeHg and PMeHg was 11.2 g in September 2016. Annual 

loads of FMeHg ranged from 25.0 g in 2015 to 75.6 g in 2016, 

and annual loads of PTHg ranged from 5.7 g in 2015 to 11.7 g 

in 2016 (fig. 11). As expected, the loads were often zero or 

very low during periods of low flow. Monthly mean flow 

explained 91 percent of the variance in monthly mean FMeHg 

loads and 62 percent of the variance in monthly mean PMeHg 

loads. The summation of FMeHg and PMeHg accounted for 

8–15 percent (averaged 13 percent) of the daily mean of total 

Hg (summation of FTHg and PTHg).

When gates are initially opened or following changes in 

gate operations at S-12D, PTHg and PMeHg were a greater 

percentage of the Hg load. We attribute this observation to 
16 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

0

200

400

600

800

1,000

1,200

1,400

1,600

Date

Flow, in cubic feet per second

0

5

10

15

20

25

30

Dissolved organic carbon concentration, in milligrams per liter

9/5/2013 3/24/2014 10/10/2014 4/28/2015 11/14/2015 6/1/2016 12/18/2016

EXPLANATION

Calculated DOC

Observed DOC

Flow

Figure 6. Calculated and observed concentrations of dissolved organic carbon (DOC) and flow.

2014

Year

2015

2016

0

2,000

4,000

6,000

8,000

10,000

12,000

Tons of organic carbon

Month

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

EXPLANATION

Figure 7. Cumulative loads of dissolved organic carbon for calendar years 2014–16.
Empirical Models 17

Date

0

200

400

600

800

1,000

1,200

1,400

1,600

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Filter-passing total mercury concentration, in nanograms per liter Filter-passing methylmercury concentration, in nanograms per liter

Flow, in cubic feet per second Flow, in cubic feet per second

0

200

400

600

800

1,000

1,200

1,400

1,600

0

0.1

0.2

0.3

0.4

0.5

9/5/2013 3/24/2014 10/10/2014 4/28/2015 11/14/2015 6/1/2016 12/18/2016

A

B

EXPLANATION

Calculated FTHg

Observed FTHg

Flow

EXPLANATION

Calculated FMeHg

Observed FMeHg

Flow

Figure 8. A, Calculated and observed filter-passing total mercury (FTHg) concentrations and flow. B, Calculated 

and observed filter-passing methylmercury (FMeHg) concentrations and flow.
18 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

0

200

400

600

800

1,000

1,200

1,400

1,600

0

0.05

0.15

0.10

0.25

0.20

0.30

0.35

Flow, in cubic feet per second Flow, in cubic feet per second

0

200

400

600

800

1,000

1,200

1,400

1,600

0

1

2

3

4

5

6

7

Particulate methylmercury concentration, in nanograms per liter Particulated total mercury concentration, in nanograms per liter

9/5/2013 3/24/2014 10/10/2014 4/28/2015 11/14/2015 6/1/2016 12/18/2016

Date

A

B

EXPLANATION

Calculated PTHg

Observed PTHg

Flow

EXPLANATION

Calculated PMeHg

Observed PMeHg

Flow

Figure 9. A, Calculated and observed concentrations of particulate total mercury (PTHg) and flow. B, Calculated 

and observed particulate methylmercury (PMeHg) concentrations and flow.
Mercury Concentrations and Loads 19

Month

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

 Cumulative annual loads of total mercury, in grams

0

100

200

300

400

500

2014 filter-passing total mercury (FTHg)

2014 particulate total mercury (PTHg)

2015 filter-passing total mercury (FTHg)

2015 particulate total mercury (PTHg)

2016 filter-passing total mercury (FTHg)

2016 particulate total mercury (PTHg)

EXPLANATION

Figure 10. Cumulative loads of total mercury (Hg) for 2014–16 calendar years.

0

Month

10

20

30

40

50

60

70

80

Cumulative annual loads of methylmercury, in grams

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

2014 filter-passing methylmercury (FMeHg)

2014 particulate methylmercury (PMeHg)

2015 filter-passing methylmercury (FMeHg)

2015 particulate methylmercury (PMeHg)

2016 filter-passing methylmercury (FMeHg)

2016 particulate methylmercury (PMeHg)

EXPLANATION

Figure 11. Cumulative loads of methylmercury (MeHg) for calendar years 2014–16.
20 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

the resuspension of material deposited behind the gates during no-flow periods and during brief periods of higher flows, 

resulting in the transport of suspended materials downstream 

to Everglades National Park. This condition was demonstrated on June 24, 2014, and September 23, 2015, when 

multiple samples were collected under varying gate openings. 

Calculations of daily load indicated that 6 percent of PTHg 

was PMeHg.

Annual loads of total Hg (sum of filter-passing and particulate) were 419, 254, and 658 g in calendar years 2014, 2015, 

and 2016, respectively. FTHg was the predominant contributor 

to the total Hg load (65–69 percent annually). Annually, only 

6 percent of the PTHg load and 16 percent of the FTHg load 

was in the form of PMeHg and FMeHg, respectively.

Comparison of Observed Loads to 

Calculated Loads

One-minute loads were calculated using observed 

concentrations and calculated concentrations and compared 

(fig. 12). Linear regression comparison between calculated and 

observed 1-minute DOC loads, indicated that 99.5 percent of 

the variance has been accounted for, suggesting that the loads 

calculated using the surrogate approach are representative of 

the observed values. It is not surprising that the highest R2

for calculated versus observed loads was for the DOC comparison. The model to calculate DOC concentrations had the 

highest R2 and lowest relative concentration model bias of all 

calculated constituents (table 6).

Linear regression comparison indicated that the calculated loads accounted for 91.5 percent of the variation between 

calculated and observed FTHg 1-minute loads and 79.7 percent of the variation between calculated and observed FMeHg 

1-minute loads. The relative load model bias was highest for 

the 1-minute load FMeHg model; however, the FMeHg loads 

were still predicted with a reasonable degree of accuracy. This 

analysis indicates that the load calculations calculated using 

surrogate models for DOC, FTHg, and FMeHg are a good 

representation of observed conditions.

Linear regression comparison indicated that the calculated loads accounted for 61.7 percent of the variation between 

calculated and observed PTHg and 52.7 percent of the variation between calculated and observed PMeHg. While the 

PTHg and PMeHg concentration models had relatively high 

R2 values, they also had higher RMSE and relative concentration model bias values than the other models. Graphical 

comparison shows that calculated concentrations are a good 

representation of the observed loads most of the time; however, on several sampling dates, the difference between the 

observed and calculated values were substantial and are potentially undercalculating the load of PTHg.

This analysis shows that the need for flow data is essential for calculating accurate load data. Notably, the error associated with calculated and observed flow data was not assessed 

as part of this study. Measurements are typically made every 

2 weeks in order to maintain the maximum possible accuracy 

in flow record at this location; however, uneven distribution 

of flow, minimal elevation gradient, a lack of defined control, 

and variations in sedimentation and vegetation all decrease the 

accuracy of the calculated discharge at this location.

Table 6. Coefficient of determination (R2), model standard error (RMSE), and relative model bias for concentration models and 

calculated versus observed loads of dissolved organic carbon (DOC), filter-passing total mercury (FTHg) and methylmercury (FMeHg), 

particulate total mercury (PTHg) and methylmercury (PMeHg).

[Concentrations of DOC were calculated in milligrams per liter, and FTHG, FMeHG, PTHg, and PMeHg concentrations were calculated in nanograms per liter. 

Loads were calculated in tons per minute for DOC and grams per minute for FTHG, FMeHG, PTHg, and PMeHg]

Data descriptor

Concentration 

model coefficient 

of determination 

(R2)

Concentration 

model standard 

error (RMSE)

Relative concentration 

model bias

Calculated verses 

observed load coefficient of determination (R2)

Load model 

standard error (RMSE)

Relative load 

model bias

DOC 0.819 1.32 -0.012 0.995 0.0008 -0.003

FTHg 0.499 0.23 0.024 0.915 0.0002 0.039

FMeHg 0.244 0.10 0.029 0.797 0.0000 0.227

PTHg 0.703 0.52 -0.232 0.617 0.0003 -0.004

PMeHg 0.603 0.70 0.177 0.527 0.0000 -0.003
Comparison of Observed Loads to Calculated Loads 21

0 0.01 0.02 0.03 0.04 0.05

Observed load of dissolved organic

carbon, in tons per minute 

Calculated load of dissolved organic

carbon, in tons per minute

0 0.00001 0.00002 0.00003 0.00004 0.00005 0.00006 0.00007

Obsreved load of particulatemethylmercury,

in grams per minute 

Calculated load of particulatemethylmercury,

in grams per minute 

0 0.0005 0.0010 0.0015 0.0020

Observed load of particualtetotal mercury,

in grams per minute 

Calculated load of particulatetotal

mercury, in grams per minute

0 0.001 0.001 0.002 0.002 0.003 0.003

Observed load of filter-passing total 

mercury, in grams per minute

Calculated load of filter-passing total 

mercury, in grams per minute

0 0.00005 0.00010 0.00015 0.00020 0.00025

Observed load of filter-passing methylmercury, 

in grams per minute

Calculated load of filter-passing methylmercury, 

in grams per minute

0

0.01

0.02

0.03

0.04

0.05

0

0.00001

0.00002

0.00003

0.00004

0.00005

0.00006

0.00007

0

0.0005

0.0010

0.0015

0.0020

0

0.001

0.001

0.002

0.002

0.003

0.003

0

0.00005

0.00010

0.00015

0.00020

0.00025

A

B C

D E

Figure 12. Comparisons between 1-minute loads calculated using observed concentrations and calculated concentrations. A, 

Dissolved organic carbon (DOC), B, Filter-passing total mercury (FTHg), C, filter-passing methylmercury (FMeHg), D, particulate total 

mercury (PTHg), and E, particulate methylmercury (PMeHg). The 1:1 line is shown in black.
22 Observed and modeled mercury and dissolved organic carbon concentrations and loads at control structure S-12D

Comparison of Traditional Discrete 

Sampling to Surrogate Approach

An analysis was done to compare the surrogate model 

approach to traditional water-quality sampling approaches. 

For this exercise, the traditional water-quality sampling 

approach is defined as a single sample collected once a month 

that is assumed to represent the monthly mean concentration. The observed DOC concentrations were compared to 

the monthly mean surrogate calculated DOC concentrations, 

and the difference was greater than the maximum relative 

percent difference between concurrent observed and calculated concentrations (14 percent) during 22 percent of the 

study period, indicating that a single monthly sample of DOC 

is not always representative of the monthly conditions at the 

study area.

Hg loads were calculated using the surrogate model data 

and compared with loads calculated assuming the observed 

sample concentration was representative of the monthly mean 

(fig. 13). The latter approach used linear interpolations to estimate monthly concentrations for months when samples were 

not collected. In 2015, the Hg load predicted by the surrogate 

model is approximately 43 percent less than the load predicted solely on the basis of observed samples. Furthermore, 

the loads calculated from the surrogate model for 2014 and 

2016 were 12 percent greater than and 19 percent less than 

the loads calculated using solely observed samples. These 

discrepancies highlight the limitations of using only discrete 

samples to determine constituent loads and indicate that the 

surrogate model approach potentially has advantages. Whether 

or not these advantages are worth the cost of continuous sensor monitoring needed for surrogate model development will 

depend on project objectives.

0

100

200

300

400

500

600

700

800

900

2014 surrogate 

calculated

2014 observed 

samples

2015 surrogate 

calculated

2015 observed 

samples

2016 surrogate 

calculated

2016 observed 

samples

Particulate methylmercury (PMeHg)

Particulate inorganic divalent mercury (PHg(II)i)

Filter-passing methylmercury (FMeHg)

Filter-passing inorganic divalent mercury (FHg(II)i)

Annual loads of mercury, in grams

EXPLANATION

Figure 13. Annual loads of mercury, separated into fractions including particulate methylmercury (PMeHg), 

particulate inorganic divalent mercury (PHg[II]i), filter-passing methylmercury (FMeHg) and filter-passing 

inorganic divalent mercury (FHg[II]i)—calculated by the surrogate model approach and calculated using 

only observed samples.
References Cited 23

Summary and Conclusions

Mercury (Hg) has been a contaminant of concern in 

the Florida Everglades and South Florida since the 1980s 

because of the risks associated with bioaccumulation of 

methylmercury (MeHg) to both humans and wildlife. In 

aquatic systems, inorganic divalent mercury (Hg[II]i) can be 

converted to MeHg, the form of Hg that bioaccumulates in 

food webs. Dissolved organic carbon (DOC) is recognized as 

an important aqueous constituent that influences the transport 

and bioavailability of Hg to methylating organisms. This 

report summarizes the concentrations and loads, from late 

September 2013 to early April 2017, for total Hg and MeHg 

(filter-passing and particulate) and for DOC discharged 

through control structure S-12D, one of several structures that 

regulate freshwater flow to Everglades National Park.

Linear regression analyses were used to develop five 

site-specific models that were than used as surrogates to 

calculate concentrations and loads of DOC, filter-passing 

total mercury (FTHg), filter-passing methylmercury 

(FMeHg), particulate total mercury (PTHg), and particulate 

methylmercury (PMeHg) passing through S-12D at 15-minute 

intervals. To calculate DOC, the model inputs were fDOM and 

specific conductance. Temperature and fDOM were used as 

the model inputs to calculate FTHg and FMeHg. Turbidity was 

used as the sole model input to calculate PTHg and PMeHg.

Good agreement was demonstrated between observed 

and calculated concentrations of DOC at S-12D. The surrogate 

model revealed that concentrations varied over short periods 

of time. The annual loads of DOC to Everglades National Park 

through S-12D ranged from 3,950 tons in calendar year 2015 

to 10,900 tons in calendar year 2016. Monthly mean loads of 

DOC increased with increasing flow.

Reasonable agreement also was demonstrated between 

observed and calculated concentrations of FTHg, FMeHg, 

PHg, and PMeHg at S-12D. In general, FTHg and FMeHg 

concentrations were lower during periods of reduced flow 

and higher during periods of increased flow. For calculated 

daily loads of THg (filter-passing and particulate), FTHg 

constituted a greater portion than PTHg, though during early 

periods of high flow or immediately following gate openings at S-12D, the proportion of PTHg to FTHg increased. 

MeHg was present predominantly in the filter-passing fraction 

compared to the particulate fraction. The annual loads of total 

Hg (filter-passing and particulate) ranged from 254 grams 

in 2015 to 658 grams in 2016. Interannual variability in the 

annual loads of Hg were attributed to differences in flow 

through S-12D over the years of this study. Mercury transported into Everglades National Park through S-12D is primarily Hg(II)i. Methylmercury was approximately 6 percent 

of the total particulate fraction of Hg and 16 percent of the 

filter-passing fraction.

Information presented herein provides the first 

assessment of DOC and Hg concentrations and loads to 

Everglades National Park through control structure S-12D on 

the basis of continuous in situ measurements and documenting 

the changes in constituent concentrations and loads at a 

higher resolution than was previously possible. The models 

and load calculations allow for the evaluation of changes 

in DOC and Hg loads to the park in response to upgradient 

water and land management efforts. Analysis showed flow 

variability is more important than concentration variability 

for the purpose of computing loads. Furthermore, these data 

may inform the relative contribution of watershed source of 

Hg to Everglades National Park versus direct atmospheric 

deposition of Hg.

Acknowledgments

Gratitude goes to the Greater Everglades Priority Ecosystem Studies Program for providing the funding necessary 

to carry out the work described here. We also thank Sara 

Breitmeyer (U.S. Geological Survey) and the U.S. Geological Survey Mercury Research Laboratory for assistance with 

measurements of dissolved organic carbon and mercury 

fractions, respectively.

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Appendixes 1–5 27

Appendixes 1–5. Available at https://doi.org/10.3133/ofr20201092

Appendix 1. Model Archive Summary for Dissolved Organic Carbon 

Concentrations at Station 254543080405401: Tamiami Canal at S-12D Near 

Miami, Florida

Appendix 2. Model Archive Summary for Filtered Mercury Concentrations at 

Station 254543080405401: Tamiami Canal at S-12D Near Miami, Florida

Appendix 3. Model Archive Summary for Filtered Methylmercury Concentrations 

at Station 254543080405401: Tamiami Canal at S-12D Near Miami, Florida

Appendix 4. Model Archive Summary for Particulate Mercury Concentrations at 

Station 254543080405401: Tamiami Canal at S-12D Near Miami, Florida

Appendix 5. Model Archive Summary for Particulate Methylmercury 

Concentrations at Station 254543080404401: Tamiami Canal at S-12D Near 

Miami, Florida

For more information about this publication, contact

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U.S. Geological Survey

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