Effect of seasonally-varying hydrology and circulation on
transport of trace elements through a moderately-sized
reservoir
Richard A. Wildman, Jr.1,2,3,* and Noelani A. Forde2,4
1 Harvard University Center for the Environment
2 Harvard University School of Public Health
3 present address: Quest University Canada, 3200 University
Boulevard, Squamish, BC V8B 0N8, Canada
4 present address: Department of Earth, Ocean and Atmospheric
Sciences, University of British Columbia, 2020-2207 Main Mall,
Vancouver, BC V6T 1Z4, Canada
* e-mail: [email protected]; Tel. 604 898 8047
Abbreviated title: Trace Element Transport through a
Reservoir
In preparation for Lake and Reservoir Management, last updated
12 June 2015
Abstract
We assessed the effect of Grand Lake, Oklahoma on the transport
of Fe, Mn, P, As, Zn, Pb, and Cd through the watershed of the Grand
River. We measured filtered and suspended sediment samples
collected upstream of, within, and downstream of this
moderately-sized reservoir and then used water flow to estimate
instantaneous, seasonal, elemental fluxes. In winter and spring,
when storms brought high flows to the reservoirs, Grand Lake
modified flood water minimally; trace element distributions were
determined by the passage of storm inflows through the reservoir.
In summer, Fe, Mn, P, and As were enriched in anoxic bottom water,
and they were exported out the dam, which draws water from below
the surface mixed layer. Fall overturn appeared to decrease
water-column concentrations of aqueous elements due to
precipitation and settling. Zinc did not track Fe and was instead
sequestered in Grand Lake during all seasons. Logistic regression
indicated that Zn predicts Cd concentrations, and so Grand Lake
probably sequesters Cd as well. Lead was less clear. This study
shows that watershed hydrology determines the transport of trace
elements through a reservoir during times of high flow but that
vertical circulation and biogeochemistry dominate during summertime
and autumn low flows. It thus can aid reservoir managers in
understanding when and how upstream pollution may be retained by or
passed through reservoirs to river systems downstream.
Key words: anoxia, flood, hydrology, reservoir, stratification,
trace metals, watershed
Introduction
[1] Reservoirs of moderate or large size influence chemical
transport in their watersheds. They are often located on large
rivers to maximize the ratio of watershed area to lake area, and so
they often receive large loads of dissolved and particulate
chemicals relative to lakes of similar size (Kalff 2002). As
reservoirs trap suspended sediment, they trap a particulate
chemical load (Horowitz et al. 2001, Lee et al. 2001, Castelle et
al. 2007, Wildman et al. 2011). Dissolved, inorganic, non-nutrient
chemicals (e.g., trace metals) have been characterized in rivers
(e.g., Horowitz et al. 2001, Müller et al. 2008), but less
attention has been devoted to the modification of the downstream
transport of such chemicals by reservoirs.
[2] The hydrology of sizeable reservoirs and their major
tributaries can affect chemical transport downstream in two
important ways. First, increased flow in tributaries brings
substantially increased chemical loads to a reservoir (Horowitz
2008), and so reservoirs might have the largest effect on chemical
transport in their watersheds during times of high flow. Second,
sizeable reservoirs contain lacustrine zones when tributaries are
at base flow (Kalff 2002). In temperate latitudes, these contain
stratification in summer and the potential for hypolimnetic anoxia
and subsequent reaeration during overturn or release out the dam.
This can lead to reductive dissolution of iron-oxide minerals,
which can leave the reservoir in dam releases (Ashby et al. 2004).
Elements that sorb to metal-oxides (e.g., arsenic and phosphorus)
can behave similarly to Fe in reservoirs (e.g., Kneebone and Hering
2000). The overlaid, seasonally-varying influences of watershed
hydrology and biogeochemistry suggest that reservoir managers
cannot predict with confidence the effect of a specific reservoir
on elemental transport in a watershed despite the importance of
trace-element cycling for recreational water quality and
ecology.
[3] The purpose of this study was to explore the effect of
seasonal variation in flow and vertical circulation on the
transport of a suite of trace elements through Grand Lake,
Oklahoma. We studied metals (Fe, Mn, Pb, Zn, Cd), a metalloid (As),
and a nutrient (P) that represent most of the primary abiotic
threats to water quality in this watershed. Grand Lake is
particularly interesting because it lies at the upstream end of a
chain of reservoirs and downstream of multiple sources of
contamination (see below). Thus, the transport of trace elements
through this reservoir is important for management of water quality
for some distance through its watershed.
Study Site
[4] Grand Lake (sometimes known as Grand Lake O’ the Cherokees)
is a moderately-sized (36 m maximum depth, >80 km long)
reservoir in northeast Oklahoma (Figure 1). Its watershed is 26,600
km2, and <30% falls within another flood control project well
upstream, so most of the flow into Grand Lake is unregulated. This
implies considerable variation in inflow rates during a year. The
median of recorded daily mean inflows in the 10 years preceding
this study was 94.1 m3/s (3,322 cfs), although, during our study
(December 2010 through November 2011, see below) drought conditions
induced median inflows of 34.7 m3/s (1,225 cfs; Wildman,
submitted). Small storms in late winter induced inflows from 85–485
m3/s (3,002–17,128 cfs), and two large storms in spring increased
inflows above 550 m3/s (19,423 cfs) for less than a week each
(Wildman, submitted). Despite these variations, the water level of
Grand Lake generally varies <1 m throughout the year because,
except when it retains floodwater, outflows match inflows to
satisfy a management objective of maintaining consistent water
levels for months at a time (D. Townsend, Grand River Dam
Authority, personal communication). Consequently, the reservoir
volume was near a neutral water balance in December, increasing by
40 megaliters/day (ML/day) in February during storm inflows,
decreasing by 16 ML/day in May as water was released following a
large storm, and decreasing slightly in August and November as part
of minor water-level drawdowns (Wildman, submitted).
[5] Inflows to Grand Lake come primarily from the Neosho River
in the northwest, Spring River in the north, and Elk River in the
east, which contributed 75% of the water released from the dam
during this study. The former two of these drain predominantly
agricultural land. The Elk River drains forested uplands and
receives permitted discharge from poultry plants. In the northwest,
Tar Creek drains the Tar Creek Superfund Site, a historic Pb and Zn
mining district with abundant metal-rich mine tailings (Schaider et
al. 2007, Andrews et al. 2009). In the southeast, several
industrial-scale chicken farms, which might release arsenic to the
environment (Hilleman 2007), operate in the Honey Creek
watershed.
[6] The confluence of the Neosho and Spring Rivers, which has
been submerged by Grand Lake, was the historical origination of the
Grand River; now, the Grand River originates from Pensacola Dam.
Penstocks of the dam, which are used for the entirety of dam
releases except during floods, are 4 m tall screened openings
centered ~16 m below the water surface and located at the
southwestern end of the dam.
[7] Grand Lake is a warm, monomictic reservoir. During our
study, it turned over in October, reached a vertically-mixed
minimum temperature of 5 C in winter, and exhibited stratification
by early May. In mid-summer, stratification was pronounced; the
surface mixed layer was 8–11 m thick (Wildman, submitted). Grand
Lake is eutrophic (OWRB 2009). Its metalimnion and hypolimnion are
anoxic in summer, and the entire water column readily becomes oxic
upon overturn in autumn (Wildman, submitted).
Methods
[8] Water samples were collected during excursions in December
2010, February/March, May, August, and November 2011 from 4
(December and November) or 5 (February-August) locations spaced
roughly evenly across the thalweg of Grand Lake, the tributaries
described above, and the Grand River <500 m below the dam. Based
on circulation parameters that were measured concurrently, 1–4
depths at each site were sampled. One sample was always collected
from the surface mixed layer (SML); additional samples were
collected from below the SML and from water ≤1 m from the
sediment-water interface. Samples were pumped through silicone
tubing, filtered through pre-weighed 0.45 μm polyethersulfone
membranes either within 24 h in a class-100 clean bench
(December-May) or immediately inline (August-November), and
acidified with ultrapure nitric acid within 12 h. Field blanks and
occasional field duplicates verified the low background
concentrations of analytes and reproducibility of field
protocols.
[9] Aqueous-phase elements were quantified by
inductively-coupled plasma (ICP) mass spectrometry (Mn, Pb, Zn, Cd,
As) and ICP optical emission spectrometry (Fe, P). Measurements
were calibrated with standard solutions made by diluting
commercially-available stock solutions. Detection limits were 0.1
μg/L for Mn, Pb, Zn, Cd, and As and 1 μg/L for Fe and P.
[10] Suspended-sediment-associated elements were quantified
after extraction from filter membranes, which were first oven-dried
(at <65 C until mass was constant) and weighed to determine
sediment mass. Membranes were subsequently microwave-digested in
concentrated ultrapure nitric acid. Digestates were diluted 1:10
with water and analyzed like the water samples described above.
Further 100-fold or 1000-fold dilutions were usually necessary; 5%
ultrapure nitric acid was used. Digestion efficiency was verified
by digesting NIST 2709 and 2711 certified reference materials
several times along with batches of samples; filter and liner
blanks verified trace-metal-clean techniques. Detection limits of
these measurements varied between samples because, although the ICP
detection limits were constant, the digestate volumes, sediment
masses on filters, and volumes of water filtered were not.
Detection limits varied between 0.0–71.9 mg/kg with a median of 0.5
mg/kg and an IQR of 0.2–1.7 mg/kg when expressed as mass of element
per mass of bulk solid.
[11] Elemental fluxes were calculated for each sampling
excursion by multiplying the average flow of the days when sampling
occurred by the concentrations of elements in samples collected
from tributaries and the dam tailrace and then subtracting the
latter from the sum of the former. When concentrations were below
detection limit (BDL), half the detection limit was used for this
calculation.
[12] Contour plots were created from measurements at discrete
depths and distances from the dam using Tecplot 360 (Tecplot, Inc.;
Bellevue, Wash.). It was necessary to interpolate vertically based
on circulation parameters before allowing Tecplot to interpolate
longitudinally between sampling locations. Relationships between
trace elements and qualitative predictor variables (e.g., depth,
location, season) were explored using principal component analysis
(PCA). This statistical technique expresses the variance of a
many-dimensional dataset not on axes that correspond to individual
variables but on new, orthogonal axes called principal components
(PCs) that are aligned with successively decreasing fractions of
the variance of the dataset (see Shine et al. 1995 for excellent
background and a previous implementation of this technique). This
allows visualizations of groupings of both variables and samples
based on the concentrations of the trace elements measured in this
study. In filtered samples, when some elements were too often BDL
to permit useful inclusion in PCA, relationships with other
elements were instead explored with multivariable logistic
regression. These analyses treated trace elements for which many
above-BDL concentrations were available as continuous predictor
variables for low-concentration elements that were evaluated as
bimodally-distributed (i.e., above or below the detection limit)
response variables.
Results
Concentrations of Trace Elements
[13] Iron in filtered samples (henceforth, “Fef” and similar)
ranged from BDL to 220 μg/L with a median of 15 μg/L and an
interquartile range (IQR) of BDL–33 μg/L (Figure 2). Across all
sampling excursions, concentrations in tributaries did not differ
meaningfully from those in the reservoir. Concentrations were
insignificantly lower below the dam. Elevated concentrations of Fef
occurred in anoxic summertime bottom water and in the large inflow
captured in the February/March sampling, indicating that, during
different seasons, aqueous-phase Fe enters the reservoir from both
internal loading and the watershed upstream. Otherwise, spatial
variation was minor (Figure S1, where “S” denotes the Supplement).
Oxic waters were not devoid of Fef; although concentrations in oxic
water were BDL in autumn and winter, they were 10–35 μg/L in warmer
months.
[14] We observed a general resemblance between Mnf, Pf, Asf, and
Fef. Anoxic bottom water contained 1–4 mg Mnf/L, which far exceeded
Fef concentrations in the same samples; the IQR of Mn for all
samples was 2.2–56.9 μg/L (Figure 2). Hypolimnetic anoxia in summer
led to concentrations 2–3 orders of magnitude higher than in
tributaries and downstream. Dam releases were 93 μg/L and 22 μg/L
in summer and autumn, respectively; the former value exceeds the
United States secondary drinking water standard (50 µg/L) (US EPA
2013), which provides a useful comparison for this concentration.
The IQR of Pf concentrations was 22–140 μg/L (Figure 2). It was
higher in the reservoir than in tributaries during much of the year
and especially in summertime bottom water. Concentrations of Asf
were <5 μg/L (Figure 2). Anoxic hypolimnetic waters reached 4.7
μg/L of Asf; concentrations were low (1–2 μg/L) and invariant
otherwise. Aside from variation with depth as described here,
spatial variation of these elements was minor (Figures S2–4).
[15] Elements derived from mines upstream behaved differently
than Fef, Mnf, Pf, and Asf. Anoic water did not show an enrichment
of Znf, unlike Fef, Mnf, Pf, and Asf were (Figure 2). Its
concentrations were elevated well above 1000 μg/L in Tar Creek and
>100 μg/L in the main tributaries of Grand Lake and in the upper
part of the reservoir. Concentrations of Znf were consistently
higher in the tributaries feeding the northern end of Grand Lake
than in our sampling location >30 km downstream (99 vs. 24 µg/L
in December, 157 vs. 51 µg/L in February, 42 vs. 8 µg/L in May, and
5 vs. 0.9 µg/L in August). Concentrations in the rest of the
reservoir were <15 μg/L with no clear spatial pattern (Figure
S5). The highest concentrations of Pbf and Cdf occurred in Tar
Creek in February; values were 1.9 µg/L and 1.7 µg/L, respectively.
Otherwise, these elements were usually BDL in filtered samples.
[16] The IQR of Fe in suspended sediment (henceforth, “Fess” and
similar) was 7,800–24,600 mg/kg with extrema an order of magnitude
lower and higher, respectively (Figure 3). Highest values occurred
in bottom water during autumn, and these high concentrations also
occurred downstream in autumn (Figure 3). Otherwise,
suspended-sediment-associated concentrations in the reservoir and
below the dam resembled those in the tributaries, which did not
vary appreciably throughout the year (Figure S6).
[17] The IQRs of Mnss and Pss were 350–3,320 mg/kg and
1,070–2,820 mg/kg, respectively (Figure 3). Spatiotemporal trends
of these two elements closely resembled those of Fess (Figures S7,
S8). Concentrations of Asss were low and invariant (generally
<50 mg/kg) except for elevated values (100–8,900 mg/kg) during
winter (Figure 3). Other than high values in February, Asss in
tributaries did not vary across tributaries or time (Figure S9).
Elevated Znss occurred in floodwater found in the middle of the
reservoir in February/March and in the inflow region in May (Figure
S10). Like other elements, Znss, Pbss, and Cdss increased in autumn
in deep water (Figures 3, S10, S11).
Data Analysis
[18] Flux of aqueous trace elements varied seasonally (Table 1).
Large loadings of all elements measured in this study were observed
in both phases in February/March when inflows were much larger than
dam releases. Loadings to the reservoir were much higher for Pbss,
Znss, Cdss, and Asss than for these elements in the aqueous phase.
At other times, flux of suspended-sediment-associated elements was
of the same order of magnitude as aqueous elements and sometimes
smaller. In both phases, fluxes of Pb, Cd, and As were generally
much smaller than those of Fe, Mn, Zn, and P. In May, when the dam
was releasing water from the downstream end of Grand Lake to
accommodate large inflows at the upstream end, we observed a net
export of Fef, Fess, Mnss, Pf, and Pss whereas Znf, Znss, and Mnf
were loaded to the reservoir. In August and November, most elements
were exported, though fluxes were small because water flux was
small. In August, flux calculations indicated a small loading of
Fef to the reservoir and a much larger export of Fess.
[19] Variance in the PCA performed on filtered samples
(henceforth, “PCAf”) was broadly distributed, with just 38%
described by the first PC (henceforth, “PC1f” and similar), 21% by
PC2f, and 16% on each of PC3f and PC4f. A biplot showing the
relationship of variables and samples to these axes (termed
“loadings” and “scores,” respectively, in statistical jargon; see
Shine et al. 1995 for details) is characterized by high values on
PC1f for all elements included in this analysis and a range of
values on PC2f with Fef and Asf together and opposite from Znf
(Figure 4). This indicates general covariance of Fef and Asf across
all the samples of the dataset as well as noncovariance between Znf
and these elements. Because of numerous concentrations BDL, Cdf and
Pbf were excluded from the PCAf.
[20] The plot of PCf scores showed that most samples collected
in either river inflows during high-flow months plotted with high
values on PC1f and PC2f, whereas many deep-water samples collected
in summer plotted with high values on PC1f and low values on PC2f
(Figure 4). These two groupings, which are the most pronounced of
the dataset, corroborate other observations in this study by
showing that, across five variables, the highest concentrations in
this dataset occur in voluminous inflows or in anoxic bottom water.
Of these, the river inflows are low in Fef and Asf and high in Znf,
whereas the opposite is true for anoxic bottom water.
[21] Multivariate logistic regression identified Znf as the only
significant predictor for Cdf (Figure 5). The relationship was
robust; McFadden’s pseudo R2 was 0.69. Conversely, Pbf was
predicted by Fef and Znf with a McFadden’s pseudo R2 of only
0.39.
[22] In particulate samples, PCs1–3 explained 42%, 25%, and 15%
of the variance of the dataset, respectively. No clear trends could
be discerned from the biplots resulting from this PCA.
Discussion
[23] Compared to previous measurements of streambed sediment
from the Neosho River, the Spring River, and Tar Creek (Andrews et
al. 2009), Mnss, Znss, Pbss, and Cdss measured in this study fell
in the same range. The same was true of Fess except in downstream
samples in February/March and bottom water samples in November,
which had much higher concentrations than measured by Andrews et
al. (2009; median Fe ≈ 12,000 mg/kg). Compared to a survey of
fluvial sediment samples collected from 296 river and stream sites
across the United States that drained either >50% agricultural
land or >50% rangeland (Horowitz and Stevens 2008), samples from
this study were somewhat higher in Mnss, Pss, and Asss except in
anoxic summertime bottom water, when they were much higher. Both
Znss and Cdss were consistently 10–100 times higher and Pbss was
approximately 10 times higher than data measured in fluvial
sediment in other rivers across the United States. These
comparisons indicate that the suspended sediment of Grand Lake is
enriched in metals derived from upstream mining and can also be
enriched in other elements under certain limnologic conditions
(described below).
[24] Compared to previous measurements of water samples
collected in the Neosho River, the Spring River, and Tar Creek
(Andrews et al. 2009), Fef, Mnf, Znf, Pbf, and Cdf data from this
study fall in the same ranges with two exceptions. First,
redox-active metals (i.e., Fef and Mnf) were higher in floodwater
and in the anoxic bottom water measured in this study, and, second,
Andrews et al. measured much higher values of Znf, Pbf, and Cdf in
Tar Creek than we measured in Grand Lake. We are aware of no
previous measurements of these elements on the Elk River or Honey
Creek or of aqueous-phase metal profiles in Grand Lake.
[25] In this study, flux of Fe occurred mostly in particulate
form whereas, in most seasons, >50% of the flux of P and As
occurred in filtered samples. In February and August, the flux of
Mnf was greater than that of Mnss and the reverse was true
otherwise; flux of Zn species were similarly inconsistent across
sampling excursions. Although the majority of mass flux can
frequently be attributed to the aqueous phase in some elements in
this study, this does not imply that the solid phase does not
dominate the yearly mass balance of all elements as has been
observed in some major rivers (Horowitz et al. 2001) because at
least 12 hydrologically-spaced measurements are needed to
accurately determine a yearly flux (Horowitz 2008). However, our
flux calculations do provide insight into the effect of hydrology
on the transport of elements through the reservoir because they
describe widely-varying inflow hydrology.
[26] Both the rising and the falling limbs of storm hydrographs
(observed in our February/March and May excursions, respectively)
loaded trace elements into Grand Lake. This can be explained by the
imbalance of element-rich inflows and lower-concentration outflows
from a long reservoir that is not longitudinally mixed. These
high-inflow periods were also characterized by little difference
between the trace element chemistry of the reservoir and its
tributaries. We attribute this to low water residence time, low
primary production, weak or nonexistent stratification, and an oxic
water column that made biogeochemical modification of trace
elements unlikely (Wildman, submitted). These observations resemble
those from another well-flushed reservoir basin in which water
chemistry matched that of the inflowing river (Bellanger et al.
2004). Our data from Grand Lake suggest that, during stormy
seasons, managers of Grand Lake and similar reservoirs can expect
minimal modification of floodwater by the reservoir and spatial
distributions of trace elements that are determined mostly by the
passage of floodwater through the system.
[27] When flows subsided, summer stratification led to
hypolimnetic anoxia (Wildman, submitted). Subsequent elevated
concentrations of redox-active elements in filtered bottom water
samples indicate reductive dissolution of metal oxides in sediment
and settling particles, and elevated concentrations of Pf and Asf
suggest desorption of these elements from metal oxides. Grand Lake
exported Fe, Mn, and P from its anoxic hypolimnion, with Mn and Fe
measured downstream predominantly in filtered and particulate
samples, respectively. However, Fe may be leaving the dam in the
aqueous phase and precipitating immediately upon entering oxic
tailwater due to the rapid oxidation kinetics of Fe2+ (Morgan
2005). We suspect that this explains the low concentrations of Fef
below the dam that lead to an apparent net loading of Fef to the
reservoir in August. This export of Fe, Mn, P, and As in August
despite the low volume of water exchange indicates that reductive
dissolution of particles in the anoxic bottom water represents a
notable source of dissolved trace elements to the river below the
dam. The shift of control of trace element cycling from inflow
hydrology to lacustrine biogeochemistry when inflow rates were low
has been observed elsewhere (Bellanger et al. 2004).
[28] Much lower water-column concentrations of Fef, Mnf, Pf, and
Asf shortly after autumn overturn indicate oxidative precipitation
of Fe and Mn and scavenging of Pf and Asf onto particles within the
reservoir. This is consistent with our observation of elevated
concentrations of Fess, Mnss, Pss, and Asss in bottom waters during
this season.
[29] The broad distribution of variance in both PCAs suggests
that elements in this study respond to a range of relatively
balanced influences. While these PCAs alone do not indicate clear
trends, the values of variables on PC2f combine with spatial
distributions and mass flux calculations to indicate that, while
Asf and Fef covary in this system, Fef has minimal influence over
Znf. This observation resembles those made in the anoxic
hypolimnion of a well-studied eutrophic lake (Achterberg et al.
1997). In Grand Lake, Znf was loaded to the reservoir in all
seasons and did not respond to biogeochemical depletion of DO. This
implies that reservoir managers in the Grand River Basin can rely
on Grand Lake to sequester Znf and thus limit the downstream
transport of mine waste across a range of hydrologic conditions.
The logistic regression model showing that Znf predicts the
presence of Cdf indicates that this Grand Lake probably also
sequesters Cdf. This is particularly advantageous for reservoir
managers because it implies that broad trends of Cdf can be
understood indirectly by measuring Znf, which requires less
sensitive instrumentation to characterize in this system because of
its higher concentrations. The behavior of Pbf is less clear in
part because Fef was also a significant predictor for this
element.
[30] Whereas sequestration of Znss probably is controlled by
straightforward particle settling in the reservoir, previous
research at the Tar Creek Superfund Site can provide insight into a
possible mechanism of Znf sequestration in the upper region of
Grand Lake. In waste piles of the Tar Creek mining district, Zn
exists as sphalerite (ZnS) and as a sorbate on amorphous
Fe-(hydr)oxides (O’Day et al. 1998), which out-compete carbonate,
sulfate, and silicate for Zn atoms (Carroll et al. 1998). In oxic
waters, sphalerite dissolves (Bostick et al. 2001). In Tar Creek,
much of the resulting Zn(aq) precipitates as Zn-carbonate or sorbs
to Fe-oxides (Bostick et al. 2001, Schaider et al. 2014), while the
remaining Zn(aq) serves as the probable source of the high
concentrations of Znf observed in this study. The lack of
association between Znf and Fef in filtered samples from Grand Lake
implies that sorption of excess Znf to Fe-oxide particles from the
Neosho and Spring Rivers is an unlikely mechanism for Znf
sequestration. However, attenuation of Znf by precipitation as
carbonate minerals is plausible because the Spring River is likely
rich in carbonate (suggested by abundant limestone in its watershed
and elevated pH relative to the Neosho River; Andrews, 2009) and
precipitation of Zn-carbonate particles is thermodynamically likely
in Tar Creek waters (Schaider et al. 2014). Further research would
be required to investigate this potential mechanism of Znf
sequestration, but doing so is relevant for reservoir management
because it will demonstrate how transferable observations at Grand
Lake are for other reservoirs downstream of mining areas.
Conclusions
[31] During our study period, flux of Fe, Mn, P, and As through
Grand Lake was controlled by hydrology when inflows were high
(i.e., winter and spring) and, during these times, spatial
distribution of trace elements in the reservoir was without
meaningful trends. When flows were minimal, these elements were
exported out the dam from an anoxic hypolimnion. These trends were
not observed for Zn, which was loaded to the reservoir in all
seasons. Concentrations of Pb and Cd were consistently low, but
Cd(aq) seemed to be predicted by Znf. This implies that the
frequency of large inflows and the volume of summertime
hypolimnetic water released from Pensacola Dam will be primary
drivers of the yearly flux of most metals through Grand Lake and
that Grand Lake sequesters Zn, preventing its transport
downstream.
Supporting Information
Figures S1–11 show contour plots of individual elements in Grand
Lake during each sampling excursion to allow visualization of
spatial trends.
Acknowledgments
This study was enabled by the generous logistical, laboratory,
and personnel support of the Grand River Dam Authority,
specifically Darrell Townsend, Sam Ziara, Jacklyn Jaggars, Scott
Cox, and Sean Allred. Steve Nikolai, Andy Dzialowski (Oklahoma
State University), and Lance Phillips (Oklahoma Water Resources
Board) shared important ancillary data. Mollie Thurman and Emily
Estes (Harvard) provided essential sampling support, and Nick
Lupoli, Chitra Amarasiriwardena, and Zhao Dong (Harvard) provided
analytical support. Rebecca Jim, Earl Hatley (L.E.A.D. Agency), and
Laurel Schaider (Harvard) provided essential background information
and helpful discussion. Jim Shine (Harvard) provided essential
support in all aspects of this project. This project was funded by
a French Environmental Fellowship granted to Wildman through the
Harvard University Center for the Environment and by a gift from
the Akatsuka Orchid Company, Ltd.
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Figure Captions
Figure 1. Map of Grand Lake showing its general location in
Oklahoma and the USA. Arrows indicate direction of water flow from
major tributaries the Neosho River (NR), the Spring River (SR), and
the Elk River (ER) and minor tributaries Tar Creek (TC), Buffalo
Creek (BC) and Honey Creek (HC) through the reservoir and into the
Grand River (GR). Circles indicate locations where water and
suspended sediment samples were collected, though samples were not
collected at all locations on all sampling excursions.
Figure 2. Distribution of trace element concentrations in
filtered water samples. Column abbreviations: “up” denotes samples
collected upstream of Grand Lake in tributaries, “<8 m” and “≥8
m” denote water-column samples collected less than 8 m deep (the
bottom of the summertime surface mixed layer) and greater than or
equal to 8 m deep, respectively, and “down” denotes samples
collected downstream of Pensacola Dam.
Figure 3. Distribution of trace element concentrations in
suspended sediment samples. Column abbreviations: “up” denotes
samples collected upstream of Grand Lake in tributaries, “<8 m”
and “≥8 m” denote water-column samples collected less than 8 m deep
(the bottom of the summertime surface mixed layer) and greater than
or equal to 8 m deep, respectively, and “down” denotes samples
collected downstream of Pensacola Dam. Since the detection limit
varied between samples, only the approximate maximum detection
limit is denoted where appropriate.
Figure 4. Biplot of Principal Component Analysis of elemental
concentrations in filtered samples. Loadings are depicted as open
double circles and correspond to upper and right-hand axes. Scores
are other symbols and correspond to lower and left-hand axes.
Scores are represented as: upstream samples from February/March and
May (i.e., high-flow months), filled blue diamonds; other upstream
samples, open blue diamonds; summertime surface mixed layer, filled
red squares; other lake samples from <8 m deep, open red
squares; summertime metalimnion and bottom water, filled green
triangles; other lake samples from ≥8 m deep, open green triangles;
and downstream samples, black circles.
Figure 5. Plot of logistic function. The probability of
concentration of Pb in filtered samples exceeding the detection
limit was regressed on the logarithm of the concentration of Zn in
filtered samples. Observed values are shown in open diamonds, and
the logistic function fitting the data is depicted with the dotted
line.
Tables
Table 1A: Mass flux of trace elements in filtered samples
(g/d)
excursion
Fe
Mn
Pb
Zn
Cd
As
P
loading via all tributariesa
December
64
67
0
280
0
3
310
Feb./Mar.
5,400
2,100
10
2,000
6
82
17,900
May
400
370
1
650
1
80
2,000
August
10
20
0
10
0
5
110
November
50
30
0
80
0
2
180
net flux through Grand Lakeb
December
63
45
0
280
1
−2
7
Feb./Mar.
5,200
2,000
9
1,900
5
51
13,100
May
−1000
160
−1
490
−1
40
−2,700
August
4
−1,200
−1
−10
−1
−6
−290
November
−120
−190
0
60
0
−8
170
Table 1B: Mass fluxb of trace elements in suspended sediment
samples (g/d)
excursion
Fe
Mn
Pb
Zn
Cd
As
P
loading via all tributariesa
December
1,500
190
2
300
0
1
250
Feb./Mar.
240,000
2,300
960
25,700
54
19,600
13,200
May
11,000
550
41
1,900
4
3
840
August
700
140
3
59
0
0
52
November
2,290
160
7
130
1
1
155
net flux through Grand Lakeb
December
1,100
180
−1
150
0
0
160
Feb./Mar.
220,000
1,400
940
25,100
54
18,900
12,600
May
−24,000
−740
−16
990
3
−8
−1,500
August
−1,300
−8
1
22
0
−1
−97
November
450
−510
5
100
1
−1
−24
a sum of loading via all tributaries
b loading via tributaries (i.e., the top portion of each section
of the table) minus export out the dam (not shown); positive values
imply net loading and negative values imply net export
Figures
Figure 1: Map
Figure 2: Distribution of trace element concentrations in
filtered water samples
Figure 3: Distribution of trace element concentrations in
suspended sediment samples
Figure 4: Biplot of Principal Component Analysis of elemental
concentrations in filtered samples
Figure 5: Plot of logistic function
Supporting Information: Figure Captions
Figure S1. Contour plots of Fe in filtered samples (expressed in
μg/L) collected in Grand Lake during our study. Numbers indicate
the measured concentrations on which the contours are based, and
the locations of those numbers on the figures indicate the
locations and depths at which those samples were collected. Values
in boxes connected to the reservoir by arrows indicate (from left
to right) concentrations in the Grand River leaving the dam, the
Elk River ~10 km upstream of the reservoir and 25 km upstream of
the reservoir main channel, and the flow-weighted average of the
Neosho and Spring Rivers (measured 35 and 30 km upstream of the
region depicted on the contour plot, respectively) to the main
channel of the reservoir. The color bar at bottom applies to all
panels; “BDL” means “below detection limit.”
Figure S2. Contour plots of Mn in filtered samples (expressed in
μg/L) collected in Grand Lake during our study. Numbers indicate
the measured concentrations on which the contours are based, and
the locations of those numbers on the figures indicate the
locations and depths at which those samples were collected. Values
in boxes connected to the reservoir by arrows indicate (from left
to right) concentrations in the Grand River leaving the dam, the
Elk River ~10 km upstream of the reservoir and 25 km upstream of
the reservoir main channel, and the flow-weighted average of the
Neosho and Spring Rivers (measured 35 and 30 km upstream of the
region depicted on the contour plot, respectively) to the main
channel of the reservoir. The color bar at bottom applies to all
panels; “BDL” means “below detection limit.”
Figure S3. Contour plots of P in filtered samples (expressed in
μg/L) collected in Grand Lake during our study. Numbers indicate
the measured concentrations on which the contours are based, and
the locations of those numbers on the figures indicate the
locations and depths at which those samples were collected. Values
in boxes connected to the reservoir by arrows indicate (from left
to right) concentrations in the Grand River leaving the dam, the
Elk River ~10 km upstream of the reservoir and 25 km upstream of
the reservoir main channel, and the flow-weighted average of the
Neosho and Spring Rivers (measured 35 and 30 km upstream of the
region depicted on the contour plot, respectively) to the main
channel of the reservoir. The color bar at bottom applies to all
panels; “BDL” means “below detection limit.”
Figure S4. Contour plots of As in filtered samples (expressed in
μg/L) collected in Grand Lake during our study. Numbers indicate
the measured concentrations on which the contours are based, and
the locations of those numbers on the figures indicate the
locations and depths at which those samples were collected. Values
in boxes connected to the reservoir by arrows indicate (from left
to right) concentrations in the Grand River leaving the dam, the
Elk River ~10 km upstream of the reservoir and 25 km upstream of
the reservoir main channel, and the flow-weighted average of the
Neosho and Spring Rivers (measured 35 and 30 km upstream of the
region depicted on the contour plot, respectively) to the main
channel of the reservoir. The color bar at bottom applies to all
panels; “BDL” means “below detection limit.”
Figure S5. Contour plots of Zn in filtered samples (expressed in
μg/L) collected in Grand Lake during our study. Numbers indicate
the measured concentrations on which the contours are based, and
the locations of those numbers on the figures indicate the
locations and depths at which those samples were collected. Values
in boxes connected to the reservoir by arrows indicate (from left
to right) concentrations in the Grand River leaving the dam, the
Elk River ~10 km upstream of the reservoir and 25 km upstream of
the reservoir main channel, and the flow-weighted average of the
Neosho and Spring Rivers (measured 35 and 30 km upstream of the
region depicted on the contour plot, respectively) to the main
channel of the reservoir. The color bar at bottom applies to all
panels; “BDL” means “below detection limit.”
Figure S6. Contour plots of Fe in suspended sediment samples
(expressed in mg/g) collected in Grand Lake during our study.
Numbers indicate the measured concentrations on which the contours
are based, and the locations of those numbers on the figures
indicate the locations and depths at which those samples were
collected. Values in boxes connected to the reservoir by arrows
indicate (from left to right) concentrations in the Grand River
leaving the dam, the Elk River ~10 km upstream of the reservoir and
25 km upstream of the reservoir main channel, and the flow-weighted
average of the Neosho and Spring Rivers (measured 35 and 30 km
upstream of the region depicted on the contour plot, respectively)
to the main channel of the reservoir. The color bar at bottom
applies to all panels; “BDL” means “below detection limit.” The
reservoir is left white in December because the small number of
samples precludes creation of an informative contour plot.
Figure S7. Contour plots of Mn in suspended sediment samples
(expressed in mg/g) collected in Grand Lake during our study.
Numbers indicate the measured concentrations on which the contours
are based, and the locations of those numbers on the figures
indicate the locations and depths at which those samples were
collected. Values in boxes connected to the reservoir by arrows
indicate (from left to right) concentrations in the Grand River
leaving the dam, the Elk River ~10 km upstream of the reservoir and
25 km upstream of the reservoir main channel, and the flow-weighted
average of the Neosho and Spring Rivers (measured 35 and 30 km
upstream of the region depicted on the contour plot, respectively)
to the main channel of the reservoir. The color bar at bottom
applies to all panels; “BDL” means “below detection limit.” The
reservoir is left white in December because the small number of
samples precludes creation of an informative contour plot.
Figure S8. Contour plots of P in suspended sediment samples
(expressed in mg/g) collected in Grand Lake during our study.
Numbers indicate the measured concentrations on which the contours
are based, and the locations of those numbers on the figures
indicate the locations and depths at which those samples were
collected. Values in boxes connected to the reservoir by arrows
indicate (from left to right) concentrations in the Grand River
leaving the dam, the Elk River ~10 km upstream of the reservoir and
25 km upstream of the reservoir main channel, and the flow-weighted
average of the Neosho and Spring Rivers (measured 35 and 30 km
upstream of the region depicted on the contour plot, respectively)
to the main channel of the reservoir. The color bar at bottom
applies to all panels; “BDL” means “below detection limit.” The
reservoir is left white in December because the small number of
samples precludes creation of an informative contour plot.
Figure S9. Contour plots of As in suspended sediment samples
(expressed in μg/g) collected in Grand Lake during our study.
Numbers indicate the measured concentrations on which the contours
are based, and the locations of those numbers on the figures
indicate the locations and depths at which those samples were
collected. Values in boxes connected to the reservoir by arrows
indicate (from left to right) concentrations in the Grand River
leaving the dam, the Elk River ~10 km upstream of the reservoir and
25 km upstream of the reservoir main channel, and the flow-weighted
average of the Neosho and Spring Rivers (measured 35 and 30 km
upstream of the region depicted on the contour plot, respectively)
to the main channel of the reservoir. The color bar at bottom
applies to all panels; “BDL” means “below detection limit.” The
reservoir is left white in December because the small number of
samples precludes creation of an informative contour plot.
Figure S10. Contour plots of Zn in suspended sediment samples
(expressed in mg/g) collected in Grand Lake during our study.
Numbers indicate the measured concentrations on which the contours
are based, and the locations of those numbers on the figures
indicate the locations and depths at which those samples were
collected. Values in boxes connected to the reservoir by arrows
indicate (from left to right) concentrations in the Grand River
leaving the dam, the Elk River ~10 km upstream of the reservoir and
25 km upstream of the reservoir main channel, and the flow-weighted
average of the Neosho and Spring Rivers (measured 35 and 30 km
upstream of the region depicted on the contour plot, respectively)
to the main channel of the reservoir. The color bar at bottom
applies to all panels; “BDL” means “below detection limit.” The
reservoir is left white in December because the small number of
samples precludes creation of an informative contour plot.
Figure S11. Contour plots of Pb in suspended sediment samples
(expressed in μg/g) collected in Grand Lake during our study.
Numbers indicate the measured concentrations on which the contours
are based, and the locations of those numbers on the figures
indicate the locations and depths at which those samples were
collected. Values in boxes connected to the reservoir by arrows
indicate (from left to right) concentrations in the Grand River
leaving the dam, the Elk River ~10 km upstream of the reservoir and
25 km upstream of the reservoir main channel, and the flow-weighted
average of the Neosho and Spring Rivers (measured 35 and 30 km
upstream of the region depicted on the contour plot, respectively)
to the main channel of the reservoir. The color bar at bottom
applies to all panels; “BDL” means “below detection limit.” The
reservoir is left white in December because the small number of
samples precludes creation of an informative contour plot.
Supporting Information: Figures
Figure S1: Fe in filtered samples
Figure S2: Mn in filtered samples
Figure S3: P in filtered samples
Figure S4: As in filtered samples
Figure S5: Zn in filtered samples
Figure S6: Fe in suspended sediment samples
Figure S7: Mn in suspended sediment samples
Figure S8: P in suspended sediment samples
Figure S9: As in suspended sediment samples
Figure S10: Zn in suspended sediment samples
Figure S11: Pb in suspended sediment samples
Wildman and Forde, page 1
-1000100200300400500
Fe (μg/L)
DL
P (μg/L)
00110100100010000
Mn (μg/L)
0.10.01
Zn (μg/L)
columnssame as Dec.Dec. Feb./Mar. May Aug. Nov.
012345
As (μg/L)
columns same as Dec.Dec. Feb./Mar. May Aug. Nov.
-1000100200300400500
Fe (μg/L)
DL
P (μg/L)
00110100100010000
Mn (μg/L)
0.10.01
Zn (μg/L)
columnssame as Dec.Dec. Feb./Mar. May Aug. Nov.
012345
As (μg/L)
columns same as Dec.Dec. Feb./Mar. May Aug. Nov.
101001,00010,000100,0001,000,000
Fe (mg/kg)
Mn (mg/kg)
1001,00010,000100,000
Zn (mg/kg)
P (mg/kg)
01101001,00010,000
Pb (mg/kg)
~max DL
As (mg/kg)
columns same as Dec.Dec. Feb./Mar. May Aug. Nov.
0110100
Cd (mg/kg)
columns same as Dec.
~max DL
Dec. Feb./Mar. May Aug. Nov.
101001,00010,000100,0001,000,000
Fe (mg/kg)
Mn (mg/kg)
1001,00010,000100,000
Zn (mg/kg)
P (mg/kg)
01101001,00010,000
Pb (mg/kg)
~max DL
As (mg/kg)
columns same as Dec.Dec. Feb./Mar. May Aug. Nov.
0110100
Cd (mg/kg)
columns same as Dec.
~max DL
Dec. Feb./Mar. May Aug. Nov.
-1.0-0.50.00.51.0-1.0-0.50.00.51.0
-505-505
PC2 (21% of variance)PC1 (38% of variance)ZnPMnFeAs
0.00.51.00123probability of Cd >DLlog [ Zn concentration
(µg/L) ]
B
D
L
B
D
L
B
D
L
1
9
5
1
2
1
1
3
5
2
1
0
d
e
p
t
h
(
m
)
0
1
0
2
0
3
0
4
0
1
2
2
2
1
2
3
1
2
2
4
D
e
c
.
2
0
1
0
0
.
2
1
9
9
