Characterizing mercury concentrations and fluxes in a Coastal

Plain watershed: Insights from dynamic modeling and data

H. E. Golden,1 C. D. Knightes,2 P. A. Conrads,3 G. M. Davis,2 T. D. Feaster,4

C. A. Journey,3 S. T. Benedict,4 M. E. Brigham,5 and P. M. Bradley3

Received 8 July 2011; revised 9 November 2011; accepted 11 November 2011; published 26 January 2012.

[1] Mercury (Hg) is one of the leading water quality concerns in surface waters of the

United States. Although watershed-scale Hg cycling research has increased in the past two

decades, advances in modeling watershed Hg processes in diverse physiographic regions,

spatial scales, and land cover types are needed. The goal of this study was to assess Hg

cycling in a Coastal Plain system using concentrations and fluxes estimated by multiple

watershed-scale models with distinct mathematical frameworks reflecting different system

dynamics. We simulated total mercury (HgT, the sum of filtered and particulate forms)

concentrations and fluxes from a Coastal Plain watershed (McTier Creek) using three

watershed Hg models and an empirical load model. Model output was compared with

observed in-stream HgT. We found that shallow subsurface flow is a potentially important

transport mechanism of particulate HgT during periods when connectivity between the

uplands and surface waters is maximized. Other processes (e.g., stream bank erosion,

sediment re-suspension) may increase particulate HgT in the water column. Simulations

and data suggest that variable source area (VSA) flow and lack of rainfall interactions with

surface soil horizons result in increased dissolved HgT concentrations unrelated to DOC

mobilization following precipitation events. Although flushing of DOC-HgT complexes

from surface soils can also occur during this period, DOC-complexed HgT becomes more

important during base flow conditions. TOPLOAD simulations highlight saturated

subsurface flow as a primary driver of daily HgT loadings, but shallow subsurface flow

is important for HgT loads during high-flow events. Results suggest limited seasonal

trends in HgT dynamics.

Citation: Golden, H. E., C. D. Knightes, P. A. Conrads, G. M. Davis, T. D. Feaster, C. A. Journey, S. T. Benedict, M. E.

Brigham, and P. M. Bradley (2012), Characterizing mercury concentrations and fluxes in a Coastal Plain watershed: Insights from

dynamic modeling and data, J. Geophys. Res., 117, G01006, doi:10.1029/2011JG001806.

1. Introduction

[2] Mercury (Hg) is one of the leading water quality

concerns in surface waters of the United States [U.S.

Environmental Protection Agency, 2005]. Atmospheric

deposition is a primary source of Hg to watersheds. As such,

transformations of atmospherically derived Hg on the landscape and subsequent fluxes to surface waters are wellrecognized drivers of Hg concentrations in aquatic habitats

[Bradley et al., 2011; Brigham et al., 2009; Glover et al.,

2010; Grigal, 2002; Hurley et al., 1995]. The consequences

of increased Hg in surface waters results from microbial

communities in anoxic environments (e.g., benthic sediments

and wetlands) that methylate inorganic Hg to produce

methylmercury (MeHg), a potent neurotoxin that bioaccumulates and biomagnifies in aquatic food webs [Bloom,

1992; Krabbenhoft et al., 1999], and subsequent ingestion

of Hg-contaminated fish by humans [Clarkson and Magos,

2006; Mergler et al., 2007] and piscivorous wildlife [e.g.,

Burgess and Meyer, 2008; Evers et al., 2008].

[3] The number of watershed-scale Hg cycling studies has

substantially increased in the past two decades, providing

important insights on Hg inputs, outputs, and processes

through empirically based approaches. Previous studies

focused primarily on small forested catchments of northern climates or boreal regions [Grigal, 2002] or specific

catchment-scale or surface water processes including, for

example, Hg mobilization and watershed transport [Bushey

et al., 2008; Demers et al., 2010; Mason and Sullivan,

1998] and in-stream cycling [Brigham et al., 2009]. Recent

trends in Hg exposure science and risk management, however,

emphasize a comprehensive understanding of Hg cycling

1

Ecological Exposure Research Division, Office of Research and

Development, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA. 2

Ecosystems Research Division, Office of Research and Development,

U.S. Environmental Protection Agency, Athens, Georgia, USA. 3

South Carolina Water Science Center, U.S. Geological Survey,

Columbia, South Carolina, USA. 4

South Carolina Water Science Center, U.S. Geological Survey,

Clemson, South Carolina, USA. 5

Minnesota Water Science Center, U.S. Geological Survey, Mounds

View, Minnesota, USA.

This paper is not subject to U.S. copyright.

Published in 2012 by the American Geophysical Union.

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, G01006, doi:10.1029/2011JG001806, 2012

G01006 1 of 17
processes for a variety of physiographic regions, spatial

scales, and land cover types. Research in this area requires

an approach that can both spatially and temporally link the

information gained from such studies focusing on different

components of the watershed Hg cycle and predict future

changes in these processes.

[4] Watershed models are primary tools for ecological risk

management; however, watershed Hg modeling is a relatively recent research focus area. There are currently few

spatially explicit watershed-scale models that focus on a

wide range of Hg cycling processes from the landscape to

surface water habitats, particularly for stream systems in the

geographically extensive Coastal Plain region. While valuable and abundant, most available literature on Hg cycling in

watersheds explore empirical relationships between Hg and

various chemical, hydrologic, and geomorphic correlates.

Spatially explicit numerical watershed models, however, can

(1) improve spatial and temporal linkages between environmental processes and subsequent water quality when

observational studies are limited and (2) provide a mathematical approach for predicting the impact of current and

future changes in climate, land cover, or resource management on watershed Hg cycling [National Research Council,

2007]. Likewise, empirical watershed models directly link

important landscape variables to watershed fluxes using data

to provide a strong modeling foundation.

[5] Applying a variety of models, which emphasize

different watershed Hg processes, allows simultaneous

assessment of the reasonableness of each model’s underlying conceptual framework and the quality of the respective mathematical simulations. Thus, employing multiple

process-based and empirical approaches concurrently promotes understanding of overall watershed Hg dynamics

above that achievable by individual modeling efforts or

monitoring alone. This is especially critical for Coastal

Plain systems, where elevated fish Hg burdens are common

[Bradley et al., 2010; Brumbaugh et al., 2001; Glover et al.,

2010; Scudder et al., 2009] but where limited understanding

of watershed Hg cycling exists.

[6] The purpose of this study was to explore Hg cycling

within a small Coastal Plain watershed (McTier Creek) by

simulating daily and seasonal total Hg (HgT) concentrations

and fluxes using multiple watershed-scale models with distinct mathematical frameworks that emphasize different

system dynamics. Modeling HgT processes is an important

first step toward advancing the science of watershed MeHg

dynamics. As such, we aim to explore the value in using a

multiple model and data approach for understanding watershed Hg cycling. Our assessments are based on the range of

available data; thus, we do not attempt to extend model

simulations beyond these boundaries. Further, identifying

the limitations of each model for HgT simulations (i.e.,

knowing which Hg dynamics are not represented by the

simulations) was considered a fundamental step toward

improved understanding Hg cycling in Coastal Plain watersheds. A secondary goal was to identify current needs in the

science of watershed-scale mercury modeling.

[7] We applied two spatially explicit mechanistic models

of watershed-scale Hg cycling, an empirical daily Hg load

model, and a regression-based water quality flux estimator

model for seasonal variations in Hg load estimates, to better

understand Hg processes in this Coastal Plain system. The

Grid-Based Mercury Model (GBMM) is a spatially explicit

mechanistic that emphasizes surface runoff, particulate production, and sediment delivery as primary drivers of HgT

fluxes. Therefore, GBMM simulations provided insight on

the particulate fraction of Hg, suggesting that shallow subsurface flow and subsequent discharge are potentially

important controls on the mobilization of particulate HgT

during periods of high upland-to-stream connectivity. The

Visualizing Ecosystems for Land Management Assessment

(VELMA) model for Hg (VELMA-Hg) is the second spatially explicit mechanistic we employed. VELMA-Hg

associates HgT fluxes with multisoil layer hydrologic transport along with carbon, nitrogen, and mercury cycling. As a

result of the carbon cycling module housed in its structure,

the VELMA-Hg model afforded insights on DOC-bound Hg

cycling in the study watershed. For example, VELMA-Hg

simulations revealed that DOC-complexed HgT becomes

more important during base flow conditions compared to

high-flow events. We also applied a recently developed

empirical Hg load model (TOPLOAD), which is linked to

the TOPMODEL hydrologic framework, in order to simulate daily and seasonal HgT loadings from the watershed and

identify flow components contributing to Hg in the stream.

TOPLOAD simulations suggested that saturated subsurface

flow is the primary component driving daily HgT loadings,

but shallow subsurface flows are important contributors to

HgT loads during high-flow events. We used observed data

to further understand watershed-scale daily HgT dynamics

and to gauge the accuracy of the models’ daily HgT flux

estimates. This approach allowed a comparison of statistical

relationships and conceptualizations of HgT dynamics

among existing empirical and mechanistic models and data,

which are used as tools to characterize Hg cycling in a

mesoscale, Coastal Plain watershed. Finally, we compared

seasonal load estimates at the watershed outlet for each of

the three models to outputs from S-LOADEST, a regressionbased water-quality constituent flux estimator, which suggested that dissolved fraction of HgT is dominant compared

to the particulate fraction of HgT, both seasonally and daily.

2. Methods

2.1. Site Description

[8] McTier Creek watershed falls within the Sand Hills

Region of the Upper Coastal Plain physiographic province

in South Carolina (Figure 1) and is described in detail in

Feaster et al. [2010]. The study site covers the approximately 79 km2 drainage area above U.S. Geological Survey

stream gage 02172305 (McTier Creek near New Holland,

South Carolina), and modeling outputs were assessed at the

gage station. The climate of the McTier Creek watershed is

subtropical humid, and the average annual mean air temperature is approximately 18°C (http://www.dnr.sc.gov/climate/

sco/ClimateData/countyData/county_aiken.php/), based on

climate data for period from 1945 to 2008. Land cover within

the study area is approximately 49% forest, 21% grassland/

herbaceous, 16% agriculture, 8% wetland, 5% developed,

and about 1% open water, using the 2001 National Land

Cover Database [Homer et al., 2004].

[9] The Sand Hills is a topographically distinctive region

of the Upper Coastal Plain and is characterized by features

ranging from broad, nearly flat ridges to rolling hills (up to

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600 ft above sea level) [Marshall, 1993]. The two major

geologic units in the Sand Hills, the Cretaceous age Middendorf Formation and the Eocene age Pinehurst Formation

are derived from depositional activity from fluvial systems

dating 90 mya, and Pinehurst sediments are marine in origin

dating approximately 45 mya, respectively [Bennett and

Patton, 2008].

[10] The hydrology of McTier Creek watershed is

described in detail elsewhere [Bradley et al., 2010; Feaster

et al., 2010]. It is important to note, however, that the

coarse-grained sandy sediments of the Coastal Plain exhibit

efficient vertical recharge and low surface runoff [Atkins

et al., 1996; Campbell and Coess, 2010]. Discharge from

the shallow flow system represents 72–100% of the total

groundwater discharge to South Carolina Coastal Plain

streams [Atkins et al., 1996; Campbell and Coess, 2010].

The gradient and the direction of shallow groundwater flow

are toward the stream channel during normal to low-flow

conditions, with active groundwater/surface water exchange

limited to wetlands and channel margins [Bradley et al.,

2010]. The gradient and the direction of shallow groundwater flow remain toward the stream channel during flood

conditions, but with increased area for groundwater/surface

water exchange [Bradley et al., 2010].

Figure 1. Study site: McTier Creek Watershed, South Carolina, USA.

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2.2. Data Compilation

[11] Observed streamflow data for the study were from the

U.S. Geological Survey gage 02172305, McTier Creek near

New Holland, South Carolina, starting from the inception of

the gage (13 June 2007) through 30 September 2009. These

data are used as a baseline for runoff estimates from each

model. Observed HgT (filtered and particulate) (analysis

based on DeWild et al. [2002]; Olson et al. [1997]; Olund

et al. [2004]), dissolved organic carbon (DOC) (analysis

based on Aiken et al. [1992]), and suspended sediment concentrations (SSC) (analysis based on Shreve and Downs

[2005]) were sampled 41 times at the stream gage during the

June 2007 to September 2009 study period using collection

and analyses procedure as described in Lewis and Brigham

[2004].

[12] Of the samples collected at the study site, 20% were

quality assurance quality control (QAQC) samples. The

QAQC samples included seven field blanks for Hg and DOC

species related to field collection procedures. Filtered HgT

concentrations in field blanks ranged from 0.07 to 0.29 ng L1

and averaged 0.13 ng L1 (0.07 standard deviation). These

levels were considered negligible because they represented

less than 5% of the environmental sample concentrations, on

average. Particulate HgT concentrations in field blanks consistently were below the laboratory reporting level (0.07 ng

L1

). DOC concentrations in field blanks ranged from 0.2 to

0.7 mg L1 and averaged 0.4 mg L1 (0.2 standard deviation). These levels were considered negligible because they

represented less than 7% of the environmental sample concentrations, on average. Two replicates had relative percentage

differences (RPDs) of 10% and 11% for HgT.

2.3. Watershed Hg Modeling

[13] The analysis period for each model’s simulation was

from 13 June 2007 through 30 September 2009, based on

the beginning of the period of record for McTier Creek at

New Holland, South Carolina. Table 1 provides a descriptive

list of each model applied in this study. We used a 30 m grid

cell resolution for the two spatially explicit models, GBMM

and VELMA-Hg, which totals over 86,000 grid cells contributing to the watershed-scale analysis. Each model was

calibrated using the available observed flow and collected

HgT data during the period of analysis. Final calibrated

output is discussed in section 3.

2.3.1. Grid-Based Mercury Model (GBMM)

[14] The Grid-Based Mercury Model was designed to

simulate the daily fluxes and mass balances of water, sediment, and Hg from each GIS raster grid cell to the watershed

outlet using a spatially explicit, process-based model structure [Dai et al., 2005]. Spatial input layers to the model

include a digital elevation map (DEM), soil types and distributions, and land cover data. Daily fluxes of water, sediment, and Hg from each grid cell are routed through the

watershed tributary networks to assessment points along

stream channels.

[15] GBMM performs a simple water balance and estimates a modified National Resource Conservation Service

(NRCS) curve number (NRCS-CN) for each grid cell on a

daily basis. The mass balance of Hg at the watershed outlet

is simulated using the equation:

dCs

dt 1⁄4 L

Vs

 ð Þ Kr þ Kl þ Kro þ Ke * Cs ð1Þ

L 1⁄4 Lp for pervious surfaces ð2Þ

L 1⁄4 Lf þ Ld for forested areas ð3Þ

Vs 1⁄4 Ac * zd; ð4Þ

where Cs is the concentration of Hg in watershed soils

(mg m3

), L is the Hg load (mg d1), Lp is the Hg atmospheric deposition load on pervious land (mg d1

), Lf is the

Hg atmospheric deposition load on forest land (mg d1

), Ld

is the litter decomposition Hg load on forestland (mg/d), Kr is

the reduction rate constant (d1

) where reduced Hg is

assumed to immediately volatilize and is considered a loss

from the watershed, Kl is the leaching loss constant (d1

),

Kro is the runoff loss constant (d1

), Ke is the erosion

loss constant (d1

), Vs is the watershed soil volume (m3), Ac

is the grid area (m2), and zd is the watershed soil mixing

depth (m).

Table 1. Summary of Models Applied in McTier Creek Watershed During the Simulation Period, 13 June 2007–30 September 2009

Model Model Type Key Drivers of Hg Transport Primary Advantages Primary Scientific Limitations

TOPLOAD-H Semi-distributed

load model

Flow components of

TOPMODEL and

mass balance daily

Hg accounting

Simple, empirical framework; links

to TOPMODEL hydrology

to identify flow components

contributing to in-stream Hg

No simulations of watershed

Hg cycling and

transformations

VELMA-Hg Spatially explicit,

process-based

Dissolved organic carbon

(DOC) within a four-layer

soil framework with lateral

and vertical movement of

water and chemicals

Spatially explicit; captures some

Hg transformations;

multilayer soil framework for

hydrology; coupled biogeochemical

cycles (Hg, DOC, N)

Limited in particulate

Hg simulations

GBMM Spatially explicit,

process-based

Surface soil erosion and in-stream

deposition with spatially

explicit modified curve

number hydrology

Spatially explicit; captures some Hg

transformations; simulates particulate

Hg fate and transformations

Limited in dissolved

Hg simulations

S-LOADEST Empirical load

model

Fitted regression curve Simple, empirical framework;

models seasonal load variations

Limited accurate estimates of daily

loads in this watershed; no

simulations of Hg watershed

cycling and transformations

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[16] The current version of GBMM is structured to partition HgT between its solid and aqueous forms in the soil

system. Thus, by calibrating the partition coefficient (Kd) in

the model (i.e., the ratio of the equilibrium concentration in

the soil particle to the concentration in soil water), GBMMsimulated HgT dynamics are largely associated with particulate production and movement from soils to surface waters.

[17] Details of GBMM’s equations and sensitivity analysis

are previously published [Golden and Knightes, 2011].

GBMM has been applied and validated in multiple settings

in the southeastern United States, including the Piedmont

Physiographic Province [Golden and Knightes, 2011;

Golden et al., 2010] and Coastal Plain Province [Dai et al.,

2005; Feaster et al., 2010]. Final input parameters for

McTier Creek watershed are found elsewhere for the

hydrology module [Feaster et al., 2010]. Parameters for the

sediment and Hg modules are detailed in the auxiliary

material of this paper.1

2.3.2. The Visualizing Ecosystems for Land

Management Assessment Model for Hg (VELMA-Hg)

[18] VELMA is a spatially distributed ecohydrological

model that simulates soil water infiltration and redistribution, evapotranspiration, surface and subsurface runoff, carbon (C) and nitrogen (N) cycling in plants and soils, and the

transport of dissolved organic carbon (DOC), dissolved

inorganic nitrogen (DIN), and dissolved organic nitrogen

(DON) from the terrestrial landscape to streams [Abdelnour

et al., 2011]. Recently, a Hg cycling submodule was added

to the existing VELMA framework, resulting in a new version of VELMA: VELMA-Hg. Following previous studies

that associate HgT with DOC movement [e.g., Brigham

et al., 2009], particularly in forested settings, we here

closely associate VELMA-Hg mercury dynamics in the

model with the fate and transport of DOC in the watershed.

[19] VELMA-Hg uses a distributed soil column framework to simulate the lateral and vertical movement of water,

heat, and nutrients within the soil (see Figure S1 in the

auxiliary material). The modeling domain of VELMA-Hg is

a three-dimensional matrix covering the topographical surface (x–y) and four soil layers (z). The soil column model

consists of three coupled submodels: (1) a hydrological

model that simulates vertical and lateral movement of water

within the soil, including a variable source area (VSA) flow

component similar to TOPMODEL [Wolock, 1993] and

losses of water from the soil and vegetation to the atmosphere, (2) a soil temperature model that simulates daily

ground soil layer temperatures from surface air temperature

(SAT) and snow depth and, (3) a biogeochemistry model that

simulates C, N, and Hg dynamics. The soil column model is

then placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other

through the downslope lateral transport of water. Surface

and subsurface lateral flow are routed using a multiple flow

direction method. A DEM is used to determine flow direction and compute flow contribution area. Required input

data includes: air temperature, precipitation, soil texture, soil

depth, and DEM. We used air temperature and precipitation

values from Feaster et al. [2010].

[20] The net equations governing changes in Hg concentration are solved for each soil column (see below) within

the catchment using a forward Euler finite difference

approximation. The change in soil mercury areal density,

CHgII or CMeHg (g m2

) for a given time step is:

dCHgII

dt 1⁄4 LT;HgII þ ksdm þ Jin  ksr  ksm  Jout ð5Þ

dCMeHg

dt 1⁄4 LT;MeHg þ ksm þ Jin  ksdm  Jout; ð6Þ

where total LT, HgII is the Hg(II) load (g m2 d1], ksdm is the

demethylation source (equation (5)) or sink (equation (6))

(g m2 d1

), Jin is the daily soil Hg(II) (equation (5)) or

MeHg (equation (6)) influx (g m2 d1

), ksr is the reduction

sink (g m2 d1

), ksm is the methylation sink (equation (5))

or source (equation (6)) (g m2 d1

), Jout is the daily soil

Hg(II) (equation (5)) or MeHg (equation (6)) outflux (g m2

d1), and LT,MeHg is MeHg deposition (g m2 d1). Further

details of the VELMA-Hg model, including model equations

and a list of other final calibrated input parameters and initial

concentrations, are given in the auxiliary material.

2.3.3. TOPLOAD

[21] TOPMODEL is a physically based, semi-distributed

watershed model that simulates the hydrologic fluxes in

a watershed [Beven and Kirkby, 1979]. Application of

TOPMODEL to the McTier Creek watershed is discussed

elsewhere [Feaster et al., 2010]. TOPMODEL does not

include a mass balance algorithm for evaluating waterquality loads. Therefore, we developed a mass balance

algorithm and applied it to the surface and subsurface flow

components simulated by TOPMODEL, resulting in a load

model that assesses the daily mass fluxes for a given constituent (HgT). Details of the TOPLOAD model are also

available elsewhere [Benedict et al., 2011]. In this investigation, we apply the TOPLOAD-H variation of our TOPMODEL load model, which utilizes simulated surface and

subsurface flow components from TOPMODEL and assigns

each flow component a HgT concentration. TOPLOAD-H

also includes a groundwater partitioning algorithm, specifically, upper subsurface (unsaturated) flow and lower subsurface (saturated) flow [Hornberger et al., 1994]. Thus,

daily HgT flux estimates from TOPLOAD-H provide a

simple method for users to quickly evaluate the relative

influence of different flow contributions of surface water

HgT on the basis of TOPMODEL-simulated daily flow. The

general equation for both TOPLOAD-H is as follows:

LOAD 1⁄4 Qsub1Csub1 þ Qsub2Csub2... þ QsubnCsubn þ Qqinf Cqinf

þ QqimpCqimp þ QqsripCqsrip þ Qqof Cqof ; ð7Þ

where, LOAD represents the estimated watershed load of a

given constituent; Qsub1, Qsub2,..., and Qsubn, represent the

TOPMODEL subsurface flow as distributed across the

number of soil zones, n; Qqinf, Qqimp, Qqsrip, and Qqof represent the flow associated with the respective TOPMODEL

surface flow components; and Csub1, Csub2,..., Csubn, Cqinf ,

Cqimp, Cqsrip, and Cqof represent the constituent concentration associated with the respective flow component.

TOPLOAD-H is essentially a mixing model and the fluxes

1

Auxiliary materials are available in the HTML. doi:10.1029/

2011JG001806.

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can be evaluated by assigning different concentrations to the

various flow components. Here, we assign a daily 5 ng L1

HgT concentration to each TOPMODEL flow component

for TOPLOAD-H load calculations. This value represents

the average daily concentration for all flows (i.e., low, high,

and base flow) on the basis of field measurements from the

41 sampling days during the simulation period and expert

judgement. While each basin will have unique considerations

in the assignment of constituent concentrations associated

with the various surface and subsurface flow components, the

studies by Hornberger et al. [1994] and Robson et al. [1992]

provide practical insights on how this assignment can be

accomplished.

2.3.4. LOAD ESTimator (S-LOADEST) Model

[22] We used a regression-based modeling program,

S-LOADEST, to model seasonal estimates of HgT loads.

The strength of the S-LOADEST estimated HgT flux (particulate + filtered) regression model for the period of record is

relatively strong (R2 = 0.89, p < 0.0001), based on the 41 day

sampling period for our study analysis. S-LOADEST is a

U.S. Geological Survey “plug-in” version of LOADEST

(LOAD ESTimator) (David Lorenz, U.S. Geological Survey,

personal communication, 2010) for S+® software (TIBCO

Spotfire Co., Palo Alto, Calif.). The original LOADEST is a

FORTRAN program that has been used extensively for estimating constituent loads in streams and rivers [Runkel et al.,

2004]. The rating curve approach takes into account flow

and seasonal dependence of constituent concentrations, which

is not always adequately accounted for by other approaches

(e.g., linear interpolation between sampling dates).

[23] Instantaneous constituent loads are computed by the

following equation:

LHg 1⁄4 CHg * Qi * Cl:; ð8Þ

where LHg is the Hg species (or other constituent of interest)

load at the time of sampling in milligrams per day, CHg is the

concentration of the Hg species (or other constituent of

interest) in ng/L; Qi is the instantaneous stream discharge at

the time of sampling in cubic feet per second; and Cl is a unit

conversion factor (2.447). The model selected for this study

accounted for streamflow and seasonality [Cohn et al., 1992;

Helsel and Hirsch, 1992] with the daily load as:

L 1⁄4 bo þ b1LnQ þ b2LnQ2 þ b3sin 2ð Þþ pT b4cos 2ð Þð pT 9Þ

where L is the natural logarithm (Ln) of the estimated load

in milligrams per day; Q, log of the mean daily stream

discharge in cubic feet per second; T, centered time in decimal years; sin, sine; cos, cosine; p, pi; bn, estimated coefficients for each variable. For the constituent of interest, the

formulated regression model was used to estimate loads over

a selected time interval (estimation period) of October 2004

to September 2009. Mean load estimates, standard errors,

and 95% confidence intervals were developed on a seasonal

and annual basis. Validation of model output included

examination of model residuals to ensure linear fit, uniform

scatter around the fit, normality of distribution, and linearity

with all explanatory variables [Helsel and Hirsch, 1992].

This rating curve approach has recently been applied in

multiple surface water systems [Brigham et al., 2009;

Journey et al., 2011].

3. Results

3.1. Runoff

[24] Direct comparisons between modeled GBMM and

TOPMODEL runoff, including calibration and validation

results at a nested stream gage within McTier Creek watershed, have been presented elsewhere [Feaster et al., 2010].

The addition of VELMA-Hg generated a new set of comparison statistics (Table 2). The mean and standard deviation

of simulated daily flows for the study period were relatively

similar among observed data and the three models. Some

slight variations were evident. VELMA-Hg’s multilayered

hydrologic model represented a minor improvement over

GBMM’s flow simulations; however, the Nash-Sutcliffe for

VELMA hydrology is substantially lower than that of

TOPMODEL and GBMM (Table 2), which potentially

reflects VELMA-Hg’s over predictions during high-rainfall

events.

[25] Simulations suggest that daily flow from each of the

three models is fairly well matched to the distribution in

observed flow conditions (Figure 2). Differences in the performances of the hydrologic models can be seen in the

extremes of the flow distribution curve. VELMA underestimated flows except for high flows greater than 1.0 m3 s

1

,

which are overestimated. GBMM accurately simulated the

occurrence of average flows 10%–80% of the time but

underestimated both high and low flows. TOPMODEL accurately simulated flows 5%–80% of the time but overestimated

low flows. VELMA simulations regularly peaked above

observed flow during high-flow periods (Figure 3). However,

at or below base flow (approximately 0.4 m3 s1

), VELMA

daily flows were well below observed flow and exhibited rapid

Table 2. Summary Statistics for Daily Hydrologic Simulations in McTier Creek Watershed During the Simulation

Period, 13 June 2007–30 September 2009a

Simulated Runoff (m3 s

1

)

Observed GBMM TOPMODEL VELMA

Mean (m3 s

1

) 0.61 0.62 0.61 0.59

Maximum (m3 s

1

) 4.62 6.57 3.68 6.55

Minimum (m3 s

1

) 0.07 0.04 0.14 0.08

Range (m3 s

1

) 4.54 6.54 3.54 6.47

Standard Deviation (m3 s

1

) 0.55 0.51 0.49 0.76

RMSE — 0.35 0.29 0.16

R2 — 0.54 0.64 0.57

NS — 0.50 0.64 0.20

a

RMSE is the root mean squared error, and NS is the Nash-Sutcliffe efficiency index [Nash and Sutcliffe, 1970].

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groundwater recession (Figure 3). Groundwater recession for

both TOPMODEL and GBMM was often slower than

observed stream recession (Figure 3).

3.2. HgT

3.2.1. Daily HgT Concentrations

[26] Average observed filtered Hg (HgF,OBS) concentrations comprised over two-thirds of the average HgT,OBS

(particulate + filtered) concentrations (Table 3). An exception to this trend involved some peak flow events, such as

those in August 2008 and October 2008 (Figure 3), when

observed particulate Hg (HgP,OBS) concentrations exceeded

those of HgF,OBS. All observed HgT concentrations (HgF,

OBS, HgP,OBS, and HgT,OBS) increased rapidly during January and February 2009, a period at or near base flow conditions preceded by three strong peaks in the hydrograph in

December 2008 (Figure 3). GBMM HgT (HgT,GBMM) and

VELMA HgT (HgT,VELMA) daily concentrations for the

sampling days (n = 41) were approximately 93% of the HgT,

OBS concentrations. However, this estimate varies widely

ranging from 13% to 367% of observed HgT concentrations

on any given day. Among observed data (n = 41), HgT,OBS

concentrations exhibited statistically significant relationships with streamflow, observed dissolved organic carbon

(DOCOBS) concentrations, and observed total suspended

sediment concentrations (TSSOBS; Table 4). HgT,OBS concentrations were also correlated with HgF,OBS (r = 0.88) and

HgP,OBS (r = 0.62) concentrations (both p < 0.0001; see

Figures S2–S5 in the auxiliary material for scatterplots of

all statistical relationships). HgP,OBS concentrations were

significantly related to DOCOBS and TSSOBS concentrations

(Table 4), and HgF,OBS concentrations were significantly

related to flow (Table 4).

[27] Average HgT,GBMM concentrations and HgP,OBS

concentrations were similar on the days sample data were

collected (1.18 ng L1 and 1.27 ng L1

, respectively, n = 41;

Table 3); however, the standard deviation of HgT,GBMM

concentrations was larger than that of HgP,OBS. HgT,GBMM

concentrations were closest to total (filtered (HgF,OBS) +

HgP,OBS) observed daily HgT (HgT,OBS) concentrations

when sampling was conducted during peaks in the hydrograph (e.g, August, October, and December 2008 and May

and July 2009; Figure 3). Daily HgT,GBMM concentrations

were significantly correlated with simulated sediment

(TSSGBMM), TSSOBS, and HgP,OBS concentrations in addition to streamflow (Table 4; see also Figures S2–S5 in the

auxiliary material).

[28] Average HgT,VELMA and HgT,OBS concentrations

were similar (3.54 ng L1 and 3.92 ng L1

, respectively)

and much higher than HgT,GBMM and HgP,OBS concentrations (Table 3 and Figure 3). Surprisingly, HgT,VELMA concentrations often matched HgP,OBS concentrations rather

than HgF,OBS (Figure 3), which is supported by the statistically significant relationship between DOCOBS and HgP,OBS

(Table 4) and leads to multiple interpretations elucidated in

section 4.2 of the Discussion. HgT,VELMA concentrations

were significantly correlated with DOCOBS concentrations,

HgP,OBS concentrations, and simulated streamflow (Table 4).

HgT,VELMA concentrations were inversely related to

VELMA-Hg DOC (DOCVELMA) concentrations (Table 4);

Figure 2. Flow duration curves of simulated (GBMM, VELMA-Hg, and TOPMODEL) and observed

flow conditions during the simulation period (13 June 2007–30 September 2009).

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however, this relationship was strongest when streamflow

was above base flow conditions (r = 0.87, p < 0.0001).

In fact, at or below base flow conditions the relationship

between DOCVELMA and HgT,VELMA concentrations is relatively weak and positive (r = 0.25, p < 0.0001). Clusters of

simulated DOC and HgT pairs showing this change in relationship further suggest a change in the DOC-HgT dynamics

in the model under different flow regimes (see scatterplots in

Figures S2–S5 in the auxiliary material). The relationship of

simulated HgT to streamflow resulted in overall increases in

Figure 3. Simulated daily runoff and HgT concentrations. OBS, observed streamflow; HgT,OBS, total

(filtered + particulate) observed mercury; HgF,OBS, observed filtered mercury; and HgP,OBS, observed

particulate mercury.

Table 3. Mean and Standard Deviation of Observed and Simulated Runoff, Total Mercury (HgT) Concentrations, and HgT Fluxes for the

Simulation Period, 13 June 2007–30 September 2009

RunoffGBMM

(m3 s

1

)

RunoffVELMA-Hg

(m3 s

1

)

RunoffTOPMODEL

(m3 s

1

)

RunoffOBS

(m3 s

1

)

Averagea 0.56 0.68 0.70 0.72

Std Deva 0.40 0.55 0.51 0.79

HgT,GBMM (ng L1

) HgT,VELMA (ng L1) HgT,TOPLOAD (ng L1)

b HgP,OBS (ng L1

) HgF,OBS (ng L1) HgT,OBS (ng L1)

Averagea 1.18 3.54 5.00 1.27 2.64 3.92

Std Deva 2.01 2.75 0.00 0.44 0.93 1.17

HgT,GBMM

(mg km2 d1)

HgT,VELMA

(mg km2 d1)

HgT,TOPLOAD

(mg km2 d1)

b

HgP,OBS

(mg km2 d1)

HgF,OBS

(mg km2 d1)

HgT,OBS

(mg km2 d1)

Averagea 434 4440 3740 1200 2520 3660

Std Deva 862 7310 2700 1840 3940 5310

a

Statistics calculated from days when observed data are available, n = 40 and 41.

b

TOPLOAD model is assigned a constant daily concentration for each of TOPMODEL’S flow components. HgP,OBS, observed particulate Hg; HgF,OBS,

observed filtered Hg; HgT,OBS, HgP,OBS + HgF,OBS.

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HgT,VELMA concentrations with increased discharge (Figure 3).

However, at streamflow greater than 1.5 m3 s

1

, minimal

increases occurred.

[29] Concentration values for the TOPLOAD-H model did

not vary across the simulation period; a single daily HgT

concentration was assigned for each TOPMODEL flow

component (5 ng l1

).

3.2.2. Daily HgT Fluxes

[30] HgT,OBS fluxes followed trends in the hydrograph relatively closely (Figure 4); however, increases in fluxes in

January and February 2009 correspond with variations in HgT

concentrations rather than flow conditions. HgF,OBS fluxes are

higher than HgP,OBS fluxes during every measurement period

except January and February 2009. Among observed data

(n = 41), HgT,OBS, HgF,OBS, and HgP,OBS fluxes are each

significantly correlated with streamflow, DOCOBS, and

TSSOBS (Table 5). HgT,OBS fluxes are also significantly correlated with HgF,OBS (r = 0.96) and HgP,OBS (r = 0.81), both

p < 0.0001. Scatterplots of the statistical relationships

among the variables are shown in Figures S2–S5 of the

auxiliary material.

[31] Average HgT,GBMM flux simulations (434 mg km2

d1) were low compared to average HgT flux estimates from

the other models (Table 3), which resulted from GBMM

underestimates of low flow conditions (Figure 2). Although

average daily HgT,GBMM fluxes are typically lower than

observed HgT fluxes, HgT,GBMM fluxes peak above observed

fluxes in December 2009 and March 2009 (Figure 4) and

follow the trend of simulated flow conditions during that

period. HgT,GBMM fluxes exhibited a strong positive statistically significant relationship with HgP,OBS fluxes, TSSGBMM

fluxes, and streamflow and were correlated to a lesser extent

with HgT,OBS and HgF,OBS fluxes (Figure 4).

[32] Average daily HgT,VELMA fluxes (4438 mg km2 d1

)

were higher than all other modeled estimates (Table 3).

VELMA-Hg fluxes peaked much higher and more regularly

than HgT,GBMM and TOPLOAD HgT (HgT,TOPLOAD) estimates and typically surpassed HgT,OBS as well (Figure 4).

Spikes in the HgT,VELMA fluxes typically corresponded with

peaks in the hydrograph (Figure 4). HgT,VELMA fluxes were

significantly related to streamflow, DOCOBS, and HgT,OBS,

HgP,OBS, and to a lesser extend HgF,OBS (Table 5).

[33] The TOPLOAD-H model produced HgT fluxes higher

than HgT,GBMM for the simulation period but lower than HgT,

VELMA. A salient feature and the goal in this TOPLOAD-H

study application, however, was its use as a tool to assist in

determining which hydrologic components contribute to daily

HgT fluxes. The HgT,TOPLOAD simulations suggested that total

daily HgT fluxes are largely driven by saturated subsurface

flow conditions (TLOAD-lower, Figure 5). However, infiltration excess overland flow (TLOAD-infiltration, Figure 5),

precipitation on water surfaces (TLOAD-riparian, Figure 5),

TOPMODEL overland flow (flow just below the surface

soil), and, in a more limited capacity, flow from impervious

surfaces, regulated peaks in daily HgT fluxes. TOPLOAD-H

simulated these peaks in HgT fluxes with greater accuracy

when HgT concentrations were increased (from 5 ng L1 to

15 ng L1

) for the flow component that represents direct precipitation on water (TLOAD-riparian) (see Figure S6 in the

auxiliary material).

3.2.3. Seasonal HgT Concentrations

[34] Average HgT,OBS concentrations did not exhibit a

strong seasonal trend, although the two winter seasons had

the highest average concentrations (Figure 6). LOADEST

total and filtered HgT concentrations were typically higher

than LOADEST particulate (HgP,LOADEST) and HgT,GBMM

concentrations for each season. Total LOADEST HgT

(HgT,LOADEST) concentrations averaged across seasons were

strongly driven by trends in HgF,OBS (Figure 6) and often

matched the average HgT,OBS as well, the latter which reflects

the structure of the load model (i.e., using observed concentration and streamflow to estimate fluxes). As a result,

LOADEST produced the highest HgT concentrations in

the winter, similar to observed concentrations. VELMA-Hg

simulations were variable and showed limited similarity to

the trends of the other modeled or observed data. GBMM

Table 4. Correlation Coefficients and Significance Levels Between Observed Total Hg (HgT,OBS) Concentrations, Observed Particulate

Hg (HgP,OBS) Concentrations, Observed Filtered Hg Concentrations (HgF,OBS), GBMM-Simulated HgT Concentrations (HgT,GBMM),

VELMA-Hg HgT Concentrations (HgT,VELMA), and Other Observed and Simulated Variablesa,b

HgT,OBS HgF,OBS HgP,OBS FlowOBS DOCOBS TSSOBS

FlowOBS 0.35* 0.31* 1.00*** 0.67***

DOCOBS 0.33* 0.61** 0.67*** 1.00*** 0.54**

TSSOBS 0.35* 0.84*** 1.00***

HgT,OBS HgF,OBS HgP,OBS HgT,GBMM TSSOBS TSSGBMM FlowOBS FlowGBMM

HgT,GBMM 0.65*** 1.00*** 0.60*** 0.87*** 0.30* 0.36***

FlowGBMM 0.35* 0.31* 0.23*** 0.74*** 1.00***

TSSGBMM 0.35* 0.84*** 0.87*** 0.48** 1.00*** 0.56*** 0.23***

HgT,OBS HgF,OBS HgP,OBS HgT,VELMA DOCOBS DOCVELMA FlowOBS FlowVELMA

HgT,vELMA 0.58*** 1.00*** 0.62*** 0.55** 0.31* 0.64***

FlowVELMA 0.44** 0.64*** 0.64*** 0.88*** 0.60*** 0.64*** 1.00***

DOCVELMA 0.46** 0.32* 0.49** 0.55** 0.54** 1.00*** 0.39* 0.60***

a

DOCOBS, observed dissolved organic carbon concentrations; TSSOBS, observed total suspended solids concentration; FLOWGBMM, GBMM-simulated

streamflow; TSSGBMM, GBMM-simulated total suspended solids; FLOWVELMA, VELMA-simulated streamflow; DOCVELMA, VELMA-simulated

dissolved organic carbon concentrations. b

Significance levels (p values) are indicated as follows: a single asterisk (*) is p < 0.05, a double asterisk (**) is p < 0.01, and a triple asterisk (***) is

p < 0.0001. All relationships that include observed data, n = 41; all relationships among modeled data only, n = 841.

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Figure 4. Simulated daily runoff and HgT fluxes. OBS, observed streamflow; HgT,OBS, total (filtered +

particulate) observed mercury; HgF,OBS, observed filtered mercury; and HgP,OBS, observed particulate

mercury.

Table 5. Correlation Coefficients and Significance Levels Between Observed Total Hg (HgT,OBS) Fluxes, Observed Particulate Hg

(HgP,OBS) Fluxes, Observed Filtered Hg Fluxes (HgF,OBS), GBMM-Simulated HgT Fluxes (HgT,GBMM), VELMA-Hg HgT Fluxes

(HgT,VELMA), and Other Observed and Simulated Variablesa,b

HgT,OBS HgF,OBS HgP,OBS FlowOBS DOCOBS TSSOBS

FlowOBS 0.95*** 0.94*** 0.71*** 1.00*** 0.64*** 0.51**

DOCOBS 0.61*** 0.58*** 0.51** 0.64*** 1.00*** 0.40*

TSSOBS 0.63*** 0.41** 0.92*** 0.51** 0.40* 1.00***

HgT,OBS HgF,OBS HgP,OBS HgT,GBMM TSSOBS TSSGBMM FlowOBS FlowGBMM

HgT,GBMM 0.62*** 0.41** 0.89*** 1.00*** 0.78*** 0.84*** 0.55*** 0.75***

FlowGBMM 0.63*** 0.62*** 0.46** 0.75*** 0.30* 0.72*** 0.74*** 1.00***

TSSGBMM 0.76*** 0.63*** 0.81*** 0.84*** 0.57*** 1.00*** 0.78* 0.72***

HgT,OBS HgF,OBS HgP,OBS HgT,VELMA DOCOBS DOCVELMA FlowOBS FlowVELMA

HgT,vELMA 0.59*** 0.38* 0.86*** 1.00*** 0.49** 0.51** 0.97***

FlowVELMA 0.69*** 0.50** 0.88*** 0.97*** 0.71*** 0.17*** 0.47** 1.00***

DOCVELMA 0.58*** 0.70*** 0.38* 1.00*** 0.76*** 0.17***

a

DOCOBS, observed dissolved organic carbon fluxes; TSSOBS, observed total suspended solids fluxes; FLOWGBMM, GBMM-simulated streamflow;

TSSGBMM, GBMM-simulated total suspended solids fluxes; FLOWVELMA, VELMA-simulated streamflow; DOCVELMA, VELMA-simulated dissolved

organic carbon fluxes. b

Significance levels (p values) are indicated as follows: a single asterisk (*) is p < 0.05, a double asterisk (**) is p < 0.01, and a triple asterisk (***) is

p < 0.0001. All relationships that include observed data, n = 41; all relationships among modeled data only, n = 841.

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seasonally averaged HgT concentrations were consistently

low, but generally followed the trend of HgP,LOADEST concentrations from Autumn 2007 through Winter 2009.

3.2.4. Seasonal HgT Fluxes

[35] Trends in observed seasonal HgT,OBS fluxes were

similar to that of seasonally averaged HgT,OBS concentrations; however, HgT fluxes were elevated during autumn

2008 when streamflow conditions were highest during the

simulation period (Figure 6). Tukey Studentized Range tests

determined that winter HgT,OBS and HgF,OBS fluxes were

significantly higher (p < 0.05) than other seasons. The HgF,

LOADEST fluxes drive the curve of the HgT,LOADEST fluxes;

however, HgT,TOPLOAD followed a similar trend, with the

exception of Spring 2009 when TOPMODEL overestimates

runoff conditions (Figure 4). The HgT,LOADEST and HgT,

TOPLOAD estimates exhibited the highest seasonal fluxes

among modeled HgT estimates. HgT,GBMM and HgP,LOADEST

fluxes followed a similar pattern during each season of our

analysis period. The HgT,VELMA seasonal flux simulations

exhibited a similar trend as all other observed and simulated

HgT fluxes from Spring 2007 through Winter 2008. Following this, HgT,VELMA fluxes matched observed fluxes

relatively well in Summer 2008 and Spring 2009 but exhibited minimal seasonal trends during the simulation period.

4. Discussion

[36] Applying three models (GBMM, VELMA-Hg,

TOPMODEL/TOPLOAD-H) with differing underlying

structures and representative processes, in addition to

observed data, affords a greater number of tools for understanding of Hg cycling and loadings beyond the dynamics

and assumptions of single model simulations or monitoring

alone. The particular emphasis of this discussion focuses on

what can be learned about Hg fate and transport and Hg

modeling at the watershed scale in McTier Creek by variations in simulation output from these diverse models and

data. Insight is gained not only when models perform satisfactorily but also when simulations fail to align closely with

observed data or produce reasonable results despite a

potentially mischaracterized conceptualization of particular

system dynamics in the model equations. In this way,

although the structure of mechanistic models is generally

not modified when new data become available (i.e., unlike

Figure 5. Daily TOPLOAD output for the simulation period. HgT,OBS, HgF,OBS, and HgP,OBS represent

observed combined (filtered + particulate) HgT, observed filtered Hg, and observed particulate Hg, respectively. TLOAD-total is the cumulative predicted daily HgT output. Source contributions include unsaturated shallow subsurface flow (TLOAD-lower); saturated shallow subsurface flow (TLOAD-upper);

flow from riparian areas (TLOAD-riparian); total subsurface flow (TLOAD-subsfc); flow from impervious surfaces (TLOAD-impervious); infiltration excess overland flow, above and/or over saturated soils

(TLOAD-infiltration); and subsurface flow within fine upper layer of wetted soil (TLOAD-overland).

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empirical models), they provide valuable information and

approaches to evaluate different conceptualizations of

watershed processes. Therefore, this discussion adheres to

the following guidelines: (1) observed data and mechanistic

model results are mutually informative, (2) model parameters are not forced beyond realistic values in an effort to

match simulation output to observed values (i.e., some

potential model output error is allowed to maintain reasonable parameterization of the model), (3) the potential contribution of processes that are not explicitly represented in

an individual model is always acknowledged.

4.1. Daily Streamflow as the Transport

Mechanism for HgT

[37] Daily runoff estimates from each model were compared to elucidate HgT transport characteristics at McTier

Creek watershed. This complementary modeling approach

highlighted multiple explanations for water transport and

interacting dynamics within the watershed’s hydrologic

system, particularly during peak flow periods. For example,

GBMM runoff simulations matched the daily observed

hydrograph, particularly peaks in the hydrograph, relatively

well (Figures 2 and 3). Therefore, if GBMM were implemented alone to understand the hydrologic and Hg dynamics

of the watershed, simulations would suggest that during

high-rainfall events, the system responds rapidly via

infiltration-excess overland flow, the hydrological mechanism driving GBMM. This interpretation would, in turn,

affect the analysis of Hg transformation and transport

dynamics in the watershed. However, when GBMM results

were analyzed alongside VELMA and TOPMODEL simulations, the three models exhibited similar trends in the daily

hydrograph and matched daily observed runoff relatively

well despite their diverse underlying structures (Figure 2;

Table 2). Moreover, observed and simulated hydrographs of

all three models reflected hydroperiods typical of Coastal

Plain streams (Figure 2), exhibiting lower flows from June

through October and higher flows from November through

March [Hupp, 2000].

[38] Similar results for three models with different conceptualizations of primary rainfall-runoff mechanisms (e.g.,

infiltration-excess overland flow with GBMM and VSA

contributions using VELMA and TOPMODEL) suggest a

“modified” interpretation of peak flow hydrology — and

potential Hg transport — in McTier Creek watershed. The

infiltration-excess overland flow of GBMM and the VSA

contributions of VELMA and TOPMODEL (along with

analogous flow components in numerous other models),

Figure 6. Simulated seasonal HgT concentrations and flux estimates. HgT,OBS is the seasonally averaged observed HgT, and the error bars represent one standard deviation of the mean. HgT,LOADEST,

HgF,LOADEST, and HgP,LOADEST represent simulations from S-LOADEST combined (filtered + particulate) HgT, S-LOADEST filtered Hg, and S-LOADEST particulate Hg, respectively.

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originated as conceptualizations of water transport in upland

systems [Hupp, 2000]. Coastal Plain hydrologic dynamics

are typically not well represented in current hydrological

models [e.g., Lu et al., 2006]. However, a rapid runoff

response in McTier Creek following rainfall events also may

be attributed to shallow groundwater flow path(s) with a

kinetic (temporal) response similar to that of higher gradient

water transport in upland systems. The flashy character

(rapid rising limb of the hydrograph) typically associated

with lower permeability, higher gradient upland systems also

could occur in permeable Coastal Plain sediments if subsurface water storage is restricted by a shallow subsurface

impermeable zone (e.g., bedrock). This “modified” conceptualization of the hydrologic framework is consistent with

empirical observations of shallow bedrock in the upper

reaches of McTier [Feaster et al., 2010] along with a highly

permeable surficial aquifer, rapid vertical infiltration, limited

surface runoff and continuous shallow subsurface discharge

of groundwater toward the stream throughout the McTier

watershed.

4.2. Daily HgT: Insights for Characterizing HgT

Processes in Coastal Plain Watersheds

[39] Given that existing watershed HgT models represent

diverse conceptualizations of drivers that influence in-stream

Hg concentrations and fluxes, applying multiple models

concurrently can improve the understanding of overall

watershed Hg dynamics above that achievable by individual

modeling efforts or monitoring alone. As such, each model

provides insights into a particular set of Hg cycling processes, specifically: (1) GBMM elucidated, in part, particulate HgT fate and transport in the watershed, (2) VELMA-Hg

simulations represented what can be learned about the dissolved fraction of HgT based on current conceptualizations

in watershed HgT modeling, (3) TOPLOAD model outputs

reflected hydrologic sources in the system that contribute to

HgT at the watershed outlet, and (4) LOADEST revealed

patterns (or lack thereof) of seasonal variations in HgT at the

watershed outlet. Details of interpretations gained from each

model and their relationship of simulations to observed data

follow.

[40] Two particular findings from GBMM simulations

partially explained patterns in particulate HgT in the watershed. First, HgT,GBMM flux simulations exhibited a statistically significant relationship with simulated and observed

streamflow, especially for flows above 1.5 m3 s

1 (Figure 4).

This pattern is consistent with the above “modified” conceptualization of the hydrologic framework in which a highly

responsive (flashy), shallow groundwater flow path(s)

becomes increasingly important to HgT transport under highflow conditions. This finding is also in line with a previously

published conceptual model of Coastal Plain hydrology

depicting increased floodplain to stream connectivity during

high-flow events [Bradley et al., 2010].

[41] Second, GBMM simulations revealed the importance

of erosional processes for Hg transport during high-flow

events and the need for additional interpretations of in-stream

particulate Hg dynamics. For example, in McTier Creek

watershed, HgF,OBS typically comprised 80–85% of the HgT,

OBS concentrations during base flow conditions, as expected

in watersheds with a high proportion of forested land

[Munthe et al., 2007]. During peak flow periods, however,

the particulate contribution to total HgT concentrations often

exceeded that of HgF,OBS. Within these periods, GBMM

simulations of daily HgT fluxes followed the trend of HgP,

OBS, and both HgT,GBMM and HgP,OBS (Figure 4) were highly

correlated with TSSOBS and TSSGBMM. However, GBMM

fluxes were also three times lower than those of observed

data (Table 3). This finding reflects simulated low flows from

GBMM during precipitation events and suggests that mobilization of Hg via erosion of surface sediment and subsequent

deposition in surface waters, primary transport mechanisms

in GBMM, are important; however, these are not the primary

drivers of particulate HgT dynamics in McTier Creek during

high-flow conditions. Other processes, such as stream bank

erosion and benthic sediment re-suspension, may potentially

lead to increased particulate HgT in the water column but are

not explicitly modeled in the current version of GBMM and

are not distinguishable by the current data set. GBMM

modeling results, therefore, point to the need for (1) further

monitoring to determine the relative importance of in-stream

and landscape sources of particulate HgT during high-flow

events and (2) inclusion of stream bank erosion and benthic

sediment re-suspension processes in watershed Hg models.

[42] VELMA-Hg relies strongly on the interactions

between HgT and DOC to drive dissolved HgT dynamics.

While GBMM places limited emphasis on DOC as a key

factor influencing HgT concentrations and fluxes, VELMAHg simulations highlight the relevance of DOC in McTier

Creek watershed. The link between HgT and DOC is particularly important here, as previous studies suggest that DOC

dynamics are a primary regulator of HgT concentrations and

fluxes in Coastal Plain watersheds [Barringer et al., 2010;

Guentzel, 2009]. As such, the statistical relationship between

HgT,VELMA concentrations and DOCOBS concentrations versus VELMA-simulated DOC (DOCVELMA) concentrations

reveal two different, yet complementary, insights about dissolved HgT fate and transport in McTier Creek and highlight

that the DOC-HgT relationship is much more complex than a

simple linear model could convey.

[43] As HgT,VELMA increases, DOCVELMA is predicted to

decrease. This is especially apparent for simulations of highflow events, as evidenced by (1) the strengthened inverse

relationship between DOCVELMA concentrations and HgT

above base flow conditions of approximately 0.4 m3 s

1

(i.e., R2 changes from 0.30 with combined flow conditions

to 0.75 at flows above base flow) and (2) a distinct trend

in small clusters of these two modeled constituents at

DOC concentrations above approximately 15 mg L1 (see

Figure S6 in the auxiliary material). Because of the variations in the HgT-DOC relationship, the correlation between

HgT,VELMA and DOCVELMA fluxes was not significant

(Figure 4).

[44] This statistically inverse relationship between

DOCVELMA and HgT,VELMA is imposed by (1) low DOC

concentrations and significant HgT concentrations in rainfall

and (2) the model structure, namely the dominance of VSA

flow in the model during high-precipitation events. Hg is

more concentrated in precipitation than in surface waters.

According to VELMA-Hg, during precipitation events the

Hg-concentrated rain falls onto the watershed, and VSA

runoff in the VELMA-Hg model becomes the dominant flow

process. As originally conceived, this flow mechanism

would rapidly flush precipitation falling on saturated areas

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from the landscape to surface waters, affording minimal

infiltration and limited contact with the surface soil system.

This rapid runoff process, combined with direct precipitation

of rainwater on open water surfaces, would effectively

bypass potential interactions with DOC and flushing of

DOC-bound Hg in the soil system. As such, HgT,VELMA

concentrations could increase without a corresponding

increase in DOCVELMA concentrations (Figure 3). However,

HgT,VELMA, HgT,OBS, and DOCOBS concentrations increased

concomitantly, suggesting a direct relationship between

(1) streamflow and HgT,VELMA concentrations, in addition

to streamflow and HgT,VELMA fluxes (Figure 4), and

(2) DOCOBS and HgT,VELMA concentrations (Figure 3), in

addition to DOCOBS and HgT,VELMA fluxes (Figure 4). The

increase in HgT,VELMA concentrations was particularly

strong when runoff increased slightly above base flow

(Figure 3).

[45] The differences in the relationship between

HgT,VELMA concentrations and DOCVELMA versus DOCOBS

suggest that there are additional Hg dynamics at work in the

watershed that are not captured by the VELMA-Hg model.

For example, DOC removal from the upper soil horizons

often increases when connectivity between the uplands and

surface waters is maximized [Boyer et al., 1997]. The collective increase of streamflow, DOCOBS, HgT,OBS, and HgT,

VELMA indicates DOC flushing continues to occur in the

system even during high-precipitation events. This disparity

between model prediction and observation is consistent with

the “modified” conceptualization of the VELMA-Hg VSA

flow component in Coastal Plain environments and a highly

responsive shallow subsurface flow component, which

would continue to supply DOC from the sediment to the

stream even during high flow. Increased DOC flushing from

highly organic floodplain surface soils near the stream due to

vertical groundwater discharge under flood conditions also

is consistent with continued DOC supply at high flow

[Bradley et al., 2010]. The good agreement between

VELMA-Hg and observed Hg and DOC data at low flows

suggests that the supply of DOC-complexed HgT from the

shallow groundwater system predominates under base flow

conditions, when rapid runoff from VSAs and DOC flushing

potential from upland soils is minimized.

[46] The TOPLOAD model provides insights on source

flow contributions of HgT. Based on TOPLOAD simulations, saturated subsurface flow drives the daily curve of the

HgT loading (TLOAD-lower; Figure 5). This is supported

by earlier interpretations of McTier Creek watershed’s

Coastal Plain system [Bradley et al., 2010]. However, above

base flow TOPLOAD attributes HgT loads primarily to

shallow subsurface flows (TLOAD-upper under non-flood

conditions; TLOAD-upper and TLOAD-infiltration under

flooded conditions) (Figure 5).

[47] TOPLOAD simulations were also applied to evaluate

the hypothesis, based on VELMA simulations, suggesting

that when HgT concentrations are increased in the model

flow component that represents direct precipitation on water

(Qrip; results in higher concentrations in precipitation compared to surface waters), peaks in HgT,TOPLOAD are higher

than those from simulations where direct rainfall HgT concentrations are equal that of all other subsurface components

(see Figure S6 in the auxiliary material). The close relationship of HgT,VELMA concentrations and fluxes with HgP,OBS

(Figure 3 and 4), for example, supports this conceptualization

of ready transport of precipitation derived dissolved HgT to

the stream during rainfall events.

4.3. Insights From Seasonal Variations in HgT

Concentrations and Fluxes

[48] Strong seasonal trends in the simulations and data

were not evident for the two-year study period with the

exception of higher than average HgT,OBS and HgF,OBS

based on observed data only. LOADEST and TOPLOAD

simulations exhibited the most explicit seasonal pattern of

HgT concentrations or fluxes among the four simulations;

however, these LOADEST estimates were based on a linear

statistical relationship between observed and predicted

fluxes with sine curves assigned to the model for estimating

fluxes. Thus, the seasonal pattern may partially represent the

structure of the regression model rather than the “true” HgT

concentration and flux dynamics. Because HgT,LOADEST

concentrations mirrored trends in HgF,LOADEST concentrations, seasonal results reflected daily results, suggesting that

the dissolved fraction of HgT is largely regulating average

seasonal HgT concentrations in McTier Creek watershed.

Further, HgT,VELMA seasonal simulations are typically

slightly higher than HgP,LOADEST concentrations and seasonal HgT,GBMM concentrations, which also confirmed that

the dissolved fraction of HgT remains most important in this

watershed at a seasonal time scale.

4.4. Advancing Current Watershed Modeling

Approaches for Understanding HgT Fate

and Transport

[49] Multiple model and data results suggest that based on

the current state of the science of watershed scale Hg modeling, a multimodel, complementary approach to understanding the dissolved HgT dynamics in McTier Creek

watershed is desirable. Ultimately, we suggest development

of a spatially explicit watershed Hg model that incorporates,

at minimum, both erosion and DOC complexation to

explicitly simulate HgT loadings to surface waters. This is

consistent with empirical models developed in other stream

ecosystems [e.g., Brigham et al., 2009; Yin and Balogh,

2002]. However, if only one of these processes dominates

a system, then GBMM or VELMA-Hg may perform well as

a stand-alone model. We suggest that (1) GBMM would be

most reliably applied as the primary watershed fate and

transport model in catchments with highly erodible land,

because HgT transport would most likely be highly associated with sediment transport in these settings [Hurley et al.,

1995; Munthe et al., 2007], and (2) VELMA-Hg would be

more suitable for forested catchments where DOC is the

primary driver of HgT in-stream concentrations and watershed fluxes.

[50] Additional processes, however, are required in the

models to fully capture HgT dynamics using watershed-scale

simulation and include: (1) Hg methylation processes for

estimating MeHg, (2) sulfate (SO4

2) cycling, which is

known to influence methylation of Hg and fluxes of MeHg

from watersheds [Morel et al., 1998], (3) variables that

increase the availability of other Hg species (e.g., pH,

quality and size of organic matter pool, and iron availability), (4) physical factors such as watershed size, topography,

and land cover, which regulate the amount of Hg transport

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when watershed connectivity to the stream is high [Munthe

et al., 2007], (5) wetland Hg processing, including DOC

sinks and methylation potentials, (6) temperature-varied rate

constants, and (7) in-stream processing, including additional

particulate dynamics that influence HgT concentrations and

fluxes (such as wetland particulate transport, benthic sediment re-suspension, and stream bank erosion). Models of

HgT dynamics are also complicated by imperfections in

hydrological models. Therefore, improvements in hydrological mechanisms in the models are also needed, including: (1) external links to groundwater models, particularly

for Coastal Plain systems [Lu et al., 2006]; and (2) recent

advances in hydrologic modeling, which could potentially

decrease variability in flux estimates due to errors in runoff

simulation. Finally, temporal gaps in data may not fully

capture HgT concentration-flow dynamics, which informs

modeling efforts. For example, our current data set is based

on synoptic sampling (n = 41) during a variety of flow

conditions. While this level of sampling may be desirable

for statistical methods (i.e., due to serial correlation), highfrequency sampling is beneficial for interpreting complex

watershed processes. Our results therefore highlight the

benefit of additional monitoring of in-stream, out-of-channel,

and upland HgT processing in Coastal Plain systems and

inclusion of these processes in watershed Hg modeling.

5. Summary and Conclusions

[51] We present one of the first studies using multiple

modeling techniques and empirical data analysis concurrently to explore Hg cycling and Hg modeling in a Coastal

Plain watershed. This study expands beyond the dynamics

represented in a single model or data set alone by simultaneously analyzing two spatially explicit watershed Hg

models (GBMM, VELMA-Hg), an empirical model driven

by TOPMODEL hydrology (TOPLOAD), a seasonal load

estimator model (LOADEST), and observed data to explore

Hg dynamics in a diverse, mesoscale, Coastal Plain watershed. Using this complementary, value-added modeling

approach, we (1) explore various conceptualizations of

watershed Hg cycling in McTier Creek Watershed and

begin to elucidate the important factors — represented in

the combined multiple watershed Hg models and data —

influencing watershed Hg fluxes and concentrations at the

basin outlet and (2) highlight research needed to improve

modeling and characterization of watershed Hg dynamics.

[52] We found similar rainfall-runoff results for three models with different conceptualizations of upland watershed

hydrologic dynamics, highlighting the need for a “modified”

interpretation of peak flow hydrology in this Coastal Plain

system. Specifically, this modified interpretation considered

the additional importance of shallow groundwater flow paths

that respond similarly to high gradient transport in upland

systems during and following precipitation events. Consistent

with this conceptualization, results suggest that shallow subsurface flow and subsequent discharge, and to a limited extent

overland flow, are potentially important controls on the

mobilization of particulate HgT during periods of high uplandto-stream connectivity. Other processes, such as stream bank

erosion and benthic sediment re-suspension, may potentially

lead to increased particulate HgT in the water column.

VELMA-Hg simulations explained a portion of dissolved

aqueous HgT, particularly that HgT can be directly transported

from VSAs in the watersheds to surface waters during highrainfall events. Observed data suggested that flushing of DOCHgT complexes from surface soils may also be important

during this period, which aligns with the “modified” hydrological interpretation of watershed dynamics that focuses on

responsive VSA and shallow subsurface contributions of HgT.

DOC-bound HgT becomes much more dominant during base

flow conditions, when DOC is the primary mechanism transporting HgT to the stream. TOPLOAD results suggest that HgT

in the stream during base flow conditions is predominately

from saturated subsurface flows, or groundwater sources.

However, during flows peaking above base flow, contributions from infiltration excess overland flow and unsaturated

subsurface flows create peak HgT load conditions. Finally,

strong seasonal trends in the simulated and observed data were

not evident for the two-year simulation period; however, seasonal analysis confirmed that the dissolved fraction of HgT

remains most important in this watershed.

[53] Based on collected data and model simulations, we

also suggest that a spatially explicit watershed Hg model that

incorporates, at minimum, both erosion and DOC complexation is needed to reliably simulate HgT loadings to

Coastal Plain surface waters. However, improvements to

current watershed Hg models are needed including additional biogeochemical processes (e.g, sulfate), wetland

cycling, links to groundwater models, and advancements in

hydrological modeling.

[54] Our findings demonstrate advancement in watershed

Hg research and modeling in Coastal Plain watersheds. The

gaps in knowledge we identify provide a potential stepping

stone for important questions concerning Hg science and

modeling at the watershed scale.

[55] Acknowledgments. We thank Alex Abdelnour, Marc Steiglitz,

Bob McKane, and Fei Fei Pan for assistance in developing the VELMA-Hg

model. We appreciate helpful suggestions from Mark Gabriel and two anonymous reviewers. This paper has been reviewed in accordance with the U.S.

Environmental Protection Agency’s and the U.S. Geological Survey’s peer

and administrative review policies and approved for publication. Mention of

trade names or commercial products does not constitute endorsement or recommendation by the U.S. Government. Statements in this publication reflect

the authors’ professional views and opinions and should not be construed to

represent any determination or policy of the U.S. Environmental Protection

Agency but do represent the views of the U.S. Geological Survey.

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