GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 27, 52–64, doi:10.1002/gbc.20020, 2013
Temperate reservoirs are large carbon sinks and small CO2
sources: Results from high-resolution carbon budgets
Lesley B. Knoll,1,2 Michael J. Vanni,1 William H. Renwick,3 Elizabeth K. Dittman,4,5 and
Jessica A. Gephart4,6
Received 14 February 2012; revised 16 December 2012; accepted 22 December 2012; published 27 January 2013.
[1] Sediment organic carbon (C) burial and CO2 fluxes in inland waters are quantitatively
important in regional and global carbon budgets. Estimates of C fluxes from inland waters are
typically based on limited temporal resolution despite potential large variations with season
and weather events. Further, most freshwater C budget studies have focused on natural
soft-water lakes, while reservoirs and hard-water systems are globally numerous. Our study
quantifies C fluxes in two hard-water, human constructed reservoirs (Ohio, USA) of
contrasting watershed land use (agriculture vs. forest) using high-resolution mass balance
budgets. We show that during a dry summer, C retention and export via the dam were reduced
compared to a wet summer. Both reservoirs were net CO2 sources during a wet summer, but
CO2 sinks during a dry summer. Despite weather-related summer differences, annual C fluxes
within each reservoir were similar between years. Both reservoirs appear to be net autotrophic
despite often being CO2 sources based on budgets. This is likely because CO2 fluxes in our
hard-water reservoirs were more strongly associated with DIC than DOC. Using our C fluxes
and statewide watershed land use, we determined the regional importance of Ohio reservoirs
in OC burial and CO2 emissions. We estimate that Ohio reservoirs bury up to 4 times more
OC, but emit <25% of CO2, than predicted based on their area and recent global mean
estimates in lentic ecosystems. Our results provide evidence that moderately old (~50 years),
temperate hard-water reservoirs are important OC sinks but contribute little to CO2 emissions.
Citation: Knoll, L. B., M. J. Vanni, W. H. Renwick, E. K. Dittman, and J. A. Gephart (2013), Temperate reservoirs are
large carbon sinks and small CO2 sources: Results from high-resolution carbon budgets, Global Biogeochem. Cycles, 27,
52–64, doi:10.1002/gbc.20020.
1.
release carbon at high rates, rendering them relevant to
regional and global carbon budgets [Cole et al., 2007;
Tranvik et al., 2009]. For example, inland waters receive
large quantities of terrestrial organic carbon (generated by
terrestrial primary production), and some of this carbon is
permanently buried in their sediments and represents a
significant global C sink [Cole et al., 2007; Dean and
Gorham, 1998; Mulholland and Elwood, 1982]. Global
annual burial of organic carbon (OC) in the sediments of
lakes (~50 Tg yr-1) and reservoirs (~180 Tg yr-1) exceeds
that buried in ocean sediments (120 Tg yr-1) [Cole et al.,
2007; Dean and Gorham, 1998; Sarmiento and Sundquist,
1992; Tranvik et al., 2009].
[3] Many inland waters are also considered net heterotrophic, i.e., they are sources of CO2 to the atmosphere [Cole
et al., 1994]. CO2 emissions by inland waters and streams
are estimated to be 1.4 Pg C yr-1, which is a relevant value in
terms of global carbon cycling [Tranvik et al., 2009].
Carbon inputs to the majority of north temperate and boreal
soft-water lakes are dominated by dissolved organic carbon
(DOC) [Dillon and Molot, 1997; Rantakari and Kortelainen,
2008]. Terrestrial DOC inputs increase in-lake microbial
production and respiration above what is possible without this
addition and allows lakes to be supersaturated with CO2
[Duarte and Prairie, 2005]. Because terrestrial DOC inputs
Introduction
[2] A desire to balance the global carbon budget was the
main impetus for quantifying carbon (C) transport, storage,
and emissions in inland waters (lakes, reservoirs, and ponds)
and streams in the 1980s and 1990s [Kling et al., 1991;
Mulholland and Elwood, 1982; Schlesinger and Melack,
1981]. Historically, many believed that inland waters would
play an insignificant role in regional and global C cycling,
because they comprise only about 3% of the earth’s continental surface [Downing et al., 2006]. Despite their small
global extent, inland waters receive, process, sequester, and
1
Department of Zoology, Program in Ecology, Evolution, and
Environmental Biology, Miami University, Oxford, Ohio, USA.
2
Lacawac Sanctuary, Lake Ariel, Pennsylvania, USA.
3
Department of Geography, Program in Ecology, Evolution, and
Environmental Biology, Miami University, Oxford, Ohio, USA.
4
Department of Zoology, Miami University, Oxford, Ohio, USA.
5
Department of Biology, North Carolina State University, Raleigh,
North Carolina, USA.
6
Department of Environmental Sciences, University of Virginia,
Charlottesville, Virginia, USA.
Corresponding author: L. B. Knoll, Lacawac Sanctuary, 94 Sanctuary
Road, Lake Ariel, PA 18436, USA. ([email protected])
©2013. American Geophysical Union. All Rights Reserved.
0886-6236/13/10.1002/gbc.20020
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KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
fuel lake CO2 fluxes, it could be argued that aquatic CO2 emissions are part of the terrestrial carbon cycle, but traditional
measures of terrestrial net ecosystem exchange would not
quantify these emissions via inland waters [Buffam et al.,
2011]. Importantly, inclusion of aquatic CO2 fluxes to regional
carbon budgets can turn a region from being a sink of
atmospheric carbon to being equilibrated with the atmosphere
[Richey et al., 2002] and more adequately describe the overall
regional C budget [Buffam et al., 2011].
[4] Another consideration with CO2 fluxes in lakes, is that
most C flux studies focus on soft-water and boreal lakes, and
recent work suggests that they process carbon very differently than saline and hard-water lakes [Duarte et al., 2008;
Finlay et al., 2010; Lopez et al., 2011; Tranvik et al.,
2009]. Hard-water lakes are globally abundant [Wetzel,
2001]; and in these systems, terrestrial C inputs are often
dominated by inorganic C. The regulation of CO2 fluxes in
hard-water systems appears to be driven by hydrological
inputs of inorganic C rather than by in-lake metabolism
[Duarte et al., 2008; Finlay et al., 2009; Finlay et al.,
2010; Lopez et al., 2011; Stets et al., 2009]. For example,
hydrological inputs of inorganic carbon can lead to in-lake
efflux of CO2 through either direct atmospheric exchange
or as the evolution of CO2 from the formation of CaCO3
[Finlay et al., 2010; Kling et al., 1992; Stets et al., 2009;
Striegl and Michmerhuizen, 1998]. These losses are not
related to microbial activity and can exceed CO2 efflux
from biological metabolism [Finlay et al., 2010; Stets
et al., 2009].
[5] Given that different inland water ecosystems may have
contrasting dominance of OC and IC inputs and subsequently divergent in-lake C processing, it is not surprising
that inland waters from varying landscapes and latitudes
with disparate productivities, display a large range of CO2
fluxes, carbon burial, and downstream export of carbon
[Tranvik et al., 2009]. Thus the magnitude and direction of
CO2 flux and the magnitude of carbon burial will likely
depend on a suite of abiotic and biotic factors including:
productivity, geology, land use, lake and watershed morphometry, and water body type (e.g., natural lake, human
constructed reservoir, wetland). Reservoirs are globally numerous and continue to be built for irrigation, water supply,
hydroelectric power, and recreation [Downing et al., 2006;
Smith et al., 2002]. Reservoir carbon fluxes likely differ
from natural lakes, primarily because reservoirs often
receive elevated carbon and nutrient inputs via streams and
because they generally have large watershed area to reservoir area ratios. Both of these landscape attributes can result
in the atmospheric drawdown of CO2 by phytoplankton production, elevated burial of terrestrially derived particulate
and dissolved carbon, and elevated burial of carbon produced within the reservoir [Cole et al., 2007; Dean and
Gorham, 1998; Downing et al., 2008; Hanson et al., 2004;
Mulholland and Elwood, 1982; Stallard, 1998; Tranvik
et al., 2009]. Work has been conducted in reservoirs on
carbon burial in the sediments [Dean and Gorham, 1998;
Downing et al., 2008; Mulholland and Elwood, 1982;
Ritchie, 1989] and CO2 fluxes in reservoirs [Abril et al.,
2005; Demarty et al., 2009; St Louis et al., 2000], but few
have simultaneously quantified C inputs, outputs, CO2
fluxes, and burial [Finlay et al., 2010; Lopez et al., 2011].
Additionally, there is a lack of research in hypereutrophic
reservoirs, which are expected to have the highest rates of
C burial and CO2 influxes [Cole et al., 2007].
[6] Currently, accurate estimates of regional and global C
fluxes in reservoirs are compromised by a lack of wellconstrained C budgets for these systems. To accurately
determine whether reservoirs are a net C source or sink,
simultaneous quantification of burial and CO2 fluxes is
needed, but few studies have examined both. Moreover, it
is rare to have C flux measurements with enough resolution
to determine the importance of temporal variation at both
annual and weather-event scales. Past studies have primarily
used a multi-site “snapshot” approach, comparing numerous
water bodies with limited sampling frequency (often once
per water body). This approach allows for comparisons
among many systems over a large geographical range, but
it does not account for variation in C fluxes among years,
seasons or weather events. Given that climate change is
already altering precipitation patterns [Karl et al., 2008],
we need to understand how C fluxes and net C balance in
reservoirs vary over different time scales. Intensive studies
are needed to complement the multi-site approach. Infrequent sampling also limits confidence in extrapolated annual
fluxes, i.e., the error associated with an annual flux rate is
either not estimated or can be relatively large [Lehrter and
Cebrian, 2010].
[7] We constructed carbon budgets to: 1) examine
whether two hard-water reservoirs of contrasting watershed
land use (agricultural versus forested) are net C sources or
sinks, 2) evaluate whether organic or inorganic carbon
appear to drive CO2 fluxes in our hard-water reservoirs,
and 3) determine the significance of temporal/weather variability on C fluxes. We predicted that the agricultural reservoir would be a large CO2 sink and large net C sink, while
the forested reservoir would be a small CO2 source and
moderate net C sink. Fluxes of all carbon forms (DOC;
POC, particulate organic carbon; DIC, dissolved inorganic
carbon; PIC, particulate inorganic carbon) were measured
over two complete water years (WY) using a high-frequency,
flow-dependent sampling regime at stream inlets and dam
outlets (Figure 1). CO2 atmospheric exchange was measured
at a shallow and deep site within each reservoir (Figure 1).
High-resolution sampling allowed us to estimate uncertainty
in annual fluxes, facilitating comparisons between years and
lakes. Using data on the distribution and sizes of reservoirs
in the state of Ohio, we then were able to confidently extrapolate our estimates to the regional scale.
2.
Methods
2.1. Study Sites
[8] Acton Lake is a hypereutrophic reservoir, located in
southwestern Ohio, USA (39 340 N, 84 44.50 W), with watershed land use dominated by intensive row-crop agriculture
(Table 1; Figure 1; Knoll et al., 2003). Acton was built in
1957 and the quantity of agricultural land use in the watershed has not changed drastically over the lifetime of the reservoir. However, row-crops have increased from about 50%
of agricultural land in the 1950s to approximately 95% today, with a consequent decrease in pasture [Medley et al.,
1995; Renwick et al., 2008]. Row-crop agricultural practices
have changed considerably in the watershed with a pronounced increase in conservation tillage in the 1990s
53
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
mainly deriving from sandstone, shale, and siltstone. Carbon
fluxes were measured at the East Branch of Sunday Creek,
the main stream flowing into the reservoir, which represents
70% of the watershed drainage [Vanni et al., 2011].
2.2. Stream and Dam Carbon/Water Fluxes
[10] Streams flowing into the reservoirs (three on Acton
and one on Burr Oak) were sampled intensively during
two water years (1 October 2006 – 30 September 2007 and
1 October 2007 – 30 September 2008) for dissolved and particulate constituents of carbon (DOC, DIC, POC, PIC) and
suspended solids (SS). We employed a high-resolution,
flow-dependent sampling regime (i.e., more frequent sampling during storm events) utilizing ISCO automated water
samplers. During baseflow periods, carbon samples were
taken from streams three times per week, and suspended
solids (SS) samples were taken once per day using ISCO
samplers [Vanni et al., 2001]. During storm events, both
carbon and SS samples were taken every 8 hours throughout
the entire event. Sampling frequency during storms events
was intensified, because during storms the concentrations
of suspended sediments and nutrient forms increase significantly [Vanni et al., 2001]. ISCO samplers were also used
to collect water exiting the reservoirs via their dam outflow.
During low flow periods, outflow samples were taken three
times a week; and during high flow periods, samples were
taken daily. Samples were collected at a lower frequency at
the dam outflows, because we expected concentrations in
the outflow to change during storms but not as rapidly as
in the inflow streams. Samples from the streams and outflow
were collected from ISCO samplers every seven days, and
carbon concentrations were not affected by this length of
storage time. We processed many samples from each stream
inflow or dam outflow for each C form (mean from stream
inflow: DIC = 507, DOC = 469, PC = 162, SS = 1892; mean
from dam outflow: DIC = 299, DOC = 276, PC = 292).
[11] DIC was measured on samples passed through precombusted Gelman AE glass fiber filters (1.0 mm nominal
pore size) using a gas chromatograph (Shimadzu GC-8A)
following the syringe gas-stripping method [Stainton, 1973].
Samples for DOC were filtered through a pre-combusted
Gelman GFF glass fiber filter (0.7 mm nominal pore size)
and subsequently analyzed on a Total Organic Carbon
analyzer (Shimadzu TOC-V). Particulate carbon samples were
analyzed on a CHN elemental analyzer (Perkin-Elmer Series
2400 CHN Analyzer, Waltham, MA, USA). Prior to analysis,
particulate carbon samples were filtered onto pre-combusted
Gelman AE glass fiber filters (1.0 mm nominal pore size).
Two subsamples were taken in order to determine POC and
PIC. To measure PIC, one subsample was ashed at 550 C
for four hours. The other subsample, used to estimate total
particulate carbon, was not ashed. POC concentrations were
then determined as the difference between the non-ashed
and ashed samples. To minimize costs, particulate carbon
(PC, i.e., POC plus PIC) was analyzed only on a sub-set of
the samples, because previous work in Acton and Burr Oak
streams has shown a strong relationship between PC and suspended solids [Renwick et al., 2008; Vanni et al., 2001], which
is much less expensive to measure. Therefore, we used streamspecific SS-PC regressions to estimate PC concentrations on
samples for which PC was not measured. All regressions using
untransformed data between SS and PC had an r2 greater than
Acton
Reservoir
Burr Oak
Reservoir
1. Acton watershed
2. Burr Oak watershed
3. Little Four Mile stream inlet
4. Four Mile stream inlet
5. Marshall’s Branch stream inlet
6. Shallow lake site
7. Deep lake site
8. Dam outlet
9. East Branch stream inlet
10. Shallow lake site
11. Deep lake site
12. Dam outlet
Figure 1. Map displaying the study reservoirs and the location of sampling sites.
Table 1. General Characteristics of the Two Study Reservoirs. Concentrations and pH Represent Simple Means of All Dates Sampled in
the Two-Year Time Period (n = 61 for Acton, n = 29 for Burr Oak)
Percent agricultural
(% of watershed area)
Percent forested
(% of watershed area)
Surface area (km2)
Watershed area (km2)
Mean depth (m)
Residence time (yr)
Chl – a (mg L-1)
Total P (mg L-1)
DOC (mg L-1)
DIC (mg L-1)
POC (mg L-1)
pH
Acton
Burr Oak
(agricultural)
(forested)
80.80
6.20
12.40
83.20
2.50
257.00
3.90
0.80
59.00
121.00
3.70
31.30
3.70
8.50
2.70
86.00
4.50
0.18
21.00
44.00
3.30
12.10
1.80
8.30
[Renwick et al., 2008]. Soils in the Acton watershed are
high-lime, glacial till capped with very productive silt loess
[Medley et al., 1995]. We quantified carbon fluxes in the
three main streams draining into Acton (Figure 1). These
streams, Four Mile Creek, Little Four Mile Creek, and Marshall’s Branch collectively represent 86% of the watershed
drainage [Renwick et al., 2008; Vanni et al., 2001].
[9] Burr Oak Lake, in southeastern Ohio (39 31.70 N,
82 2.60 W), is moderately productive and has land use dominated by forests (Table 1; Figure 1; Knoll et al., 2003). Burr
Oak was built in 1950; and since this time, the percentage of
forest in the watershed has increased from approximately
40% to 81% due to the establishment of the Wayne National
Forest in 1934 and the subsequent regrowth of forest from
previously cleared land [Birch and Wharton, 1982; Vanni
et al., 2011]. Burr Oak’s watershed is unglaciated, with soils
54
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
residual from that regression [Vanni et al., 2001]. Once
hourly loadings were obtained, they were then summed to
obtain daily, monthly and annual loadings.
[14] Discharge via dams (QDAMh) was calculated differently for Acton and Burr Oak due to differences in data
availability. For Acton, hourly QDAMh was calculated by
taking into account water inputs and outputs using the
following equation:
0.8. Predicted PC concentrations were then split into PIC and
POC constituents based on regressions from the analyzed samples. Since PIC concentrations were often zero or negligible
and the percentage of PIC (relative to total PC) did not vary significantly with flow, we assumed PIC was a fixed percentage of
PC (Four Mile mean = 5.7%, n = 84; Little Four Mile mean =
5.1 %, n = 69, Marshall’s Branch mean = 3.7%, n =78; East
Branch mean = 6.1%, n =54). Samples for SS were filtered onto
pre-weighed, pre-combusted Gelman AE glass fiber filters
(1.0 mm nominal pore size). SS was then determined as the
difference between dry mass on pre-combusted AE filters before
and after filtering by weighing samples on a Mettler UMT ultramicrobalance (Mettler-Toledo, Columbus, OH, USA).
[12] For Acton streams, stage was recorded every ten
minutes using pressure transducers and dataloggers. Hourly
discharge (QSTREAMS) was then calculated using standard
rating-curve techniques [Renwick et al., 2008; Vanni et al.,
2001]. We were not able to directly record stage on the Burr
Oak stream. Hourly QSTREAMS for this stream was therefore
calculated using the following method:
QDAMh ¼ QSTREAMSh
þPrecipitation- PET-Change in lake volume;
where QSTREAMSh equals the sum of discharge from the three
gaged streams divided by 0.86 to scale up to entire watershed discharge. Hourly precipitation and temperature for
potential evapotranspiration (PET) were obtained from the
EPA CASTNET (Clean Air Status and Trends Network)
database at the OXF122 site (Oxford) located ~5 km from
Acton. Change in lake volume was calculated using hourly
lake level data (continuously recorded via a lake level gauge)
and lake bathymetry. For Burr Oak, direct values for QDAMh
were available, because these data are recorded hourly by the
US Army Corps of Engineers, Huntington District.
[15] Carbon exports via the dams were generally calculated the same as stream loadings. However, only simple
interpolation was used to interpolate missing hourly carbon
concentrations, since relationships between discharge and
C concentrations are not particularly strong at dam outflows.
Similarly, we did not find strong relationships between SS
and PC, so we measured PIC and POC on all samples using
methods described above for streams. Hourly export was
calculated as hourly concentration (Ch) multiplied by hourly
discharge (QDAMh). Hourly exports were then summed to get
daily, monthly or annual exports.
QSTREAMS ¼ QDAM -Precipitation þ PET þ Change in lake volume; (1)
where QDAM equals the hourly discharge from the dam
outflow. These data were obtained from the US Army Corps
of Engineers, Huntington District, which operates the Tom
Jenkins Dam at Burr Oak. Hourly precipitation data and
temperature data for potential evapotranspiration (PET) were
obtained for the DCP114 site (Deer Creek) from the EPA
CASTNET (Clean Air Status and Trends Network) database
(~ 90 km east of Burr Oak). Change in lake volume was calculated using hourly lake level data (also obtained from the
US Army Corps of Engineers) and lake bathymetry.
[13] Carbon and SS loading from the streams were calculated similarly to previous studies [Renwick et al., 2008; Vanni
et al., 2001]. These loading estimates follow a commonly used
method that has been used for decades for studies in which
samples are collected frequently [Porterfield, 1972]. Hourly
loading was calculated using the following equation:
Lh ¼ Ch x QSTREAMSh ;
2.3. Carbon Dioxide Fluxes
[16] In order to calculate atmospheric flux of CO2, atmospheric and lake partial pressure CO2 (pCO2, matm) were
determined. Lake pCO2 was quantified using two methods;
it was either directly quantified from reservoir water, or
using an established method, it was estimated from surface
water DIC and pH measurements, correcting for temperature
and ionic strength [Cole et al., 1994; Kling et al., 1992].
Samples for direct pCO2 measurements were collected at
two sites within the reservoirs two to four times per month,
when boat sampling was possible. One site was located near
stream inlets at a shallow lake site, and the other was near
dam outlets at a deep lake site (Figure 1). At each site, pH,
temperature, and conductivity were taken with calibrated
hand-held meters (YSI Model 60, YSI Model 30, respectively), and a sample for DIC was collected at 0.1m. Directly
measured pCO2 samples were collected in duplicate in gastight, glass syringes. Surface water pCO2 was quantified using the headspace equilibration method at a depth of 0.1m
[Cole et al., 1994; Raymond et al., 1997]. Atmospheric
pCO2 in the ambient air, 1m above the reservoir surface,
was collected at the same time as surface water pCO2. Surface
water and atmospheric pCO2 samples were measured in the
laboratory using a gas chromatograph fitted with a thermal
conductivity detector (Shimadzu GC-8A). To verify that calculated pCO2 estimates were comparable to those from the
headspace equilibrium method, we quantified pCO2 using
(2)
where, Lh is hourly loading, Ch is the C concentration for hour
h, and QSTREAMSh is the mean discharge for hour h. When there
was a sample taken during a particular hour, the concentration
from that sample was assumed for the entire hour. For hours
when no samples were taken, we used a discharge (Q) proportionate interpolation method for DIC, DOC, and SS [Vanni
et al., 2001]. Q-proportionate interpolation can be used when
a strong relationship between discharge and concentration is
found, because it adjusts for variations in concentration due
to variations in discharge and changes in discharge that occur
in between sample points. For this interpolation method, the
slope of logQSTREAMSh - logCh regression is used. Residuals
from the regression are linearly interpolated though time and
applied to the calculation of concentrations:
Rh ¼ Rprev þ
(5)
Ch ¼ 10ðRh þB0 þB1 logQhSTREAMS Þ ; and
(3)
Rnext Rprev x h hprev = hnext hprev ; (4)
where B0 and B1 are the intercept and slope of the logQSTREAMSh - logCh regression, and Rh is the interpolated
55
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
99% OC in Burr Oak [Vanni et al., 2011]) to the total C
retention. To estimate the mass of incoming IC converted
to OC or consumed as alkalinity, we subtracted IC inputs
(stream loading and influxes of CO2) from IC outputs
(dam export and effluxes of CO2) and the mass of IC
retained in the sediments. We are unable to directly identify
these processes or quantify their rates. However, we expect
them to be considerable, particularly alkalinity consumption
in Burr Oak where DIC stream input concentrations are high
(20 mg L-1), and the percentage of IC making up total carbon
content in lake sediments is low (1%). We did not include a
value depicting conversion of OC to IC, because we think
this conversion is not quantitatively important in our hardwater systems. While there is good evidence for photobleaching in Acton and Burr Oak [Overholt et al., 2008],
the conversion of OC to IC is not likely to be quantitatively
important in the overall C budget. For example, even if all of
the DOC loading from the streams was converted to DIC,
this would alter DIC by only 6% in Acton and 20% in Burr
Oak. Even in soft-water systems with low DIC, photobleaching serves primarily to convert high molecular weight
DOC to lower molecular weight compounds with little
change in DOC concentration [Osburn et al., 2001].
[21] As a check on our C budgets, we also calculated alkalinity balances. If alkalinity loss in our reservoirs is due to
CaCO3 precipitation, we would expect to see a balance
between the alkalinity brought in from stream inlets with
the sum of alkalinity lost via dam outlets and buried IC (this
value is derived from our C budgets). We would also expect
to see a balance between the difference of alkalinity stream
inputs and alkalinity dam outputs (as buried CaCO3) with
IC burial from our C budgets. Our data do not allow us to
do a complete alkalinity balance, because we lack pH and
temperature data in the streams and at the dam outlet station.
However, based on our data availability, we created alkalinity balances for Acton and Burr Oak during the summer
months only. We estimated stream and dam outlet alkalinity
by using within lake pH, lake temperature, and DIC concentration from the stream inlet and dam outlet. The within lake
data were obtained from our shallow site (near stream
inflows) and our deep site (near dam outflows). This is not
ideal as lake pH and lake temperature may not match inlets
and outlets. We then calculated alkalinity stream load and
dam export using similar methods as our C load and export
calculations. Alkalinity flux in eq day-1 was then converted
to Mg day-1 assuming 1 mol equals 1 equivalent. This seems
a reasonable assumption, because HCO-3 makes up 96% of
alkalinity in both Acton and Burr Oak (based on pH).
[22] Carbon budgets were constructed for the 2007 and
2008 WYs as well as the summers (i.e. May – October) of
2007 and 2008. We calculated summer budgets for two
reasons: to compare to other studies in which fluxes were
measured only in summer and to account for high variation
in precipitation between summers. This allowed us to examine
how weather mediates C budgets.
both methods from Burr Oak and Acton for numerous dates
and found a strong relationship (both lakes: n = 120, r2 = 0.72).
[17] Chemically enhanced CO2 flux calculations
(mmol m-2 day-1) for each sampling date were calculated
using the following equation:
FCO2 ¼ kenh x½CO2 ðH2 OÞ CO2 ðeqÞ;
(6)
where FCO2 is atmospheric CO2 flux, CO2(H2O) is the measured surface-water CO2 concentration, and CO2(eq) is the
equilibrium CO2 concentration adjusting for Henry’s
constant for CO2 [Plummer and Busenberg, 1982]. Piston
velocity (cm hr-1), k, can be calculated for a lake with an
established equation [Cole and Caraco, 1998]; however, k
values must be adjusted if pH is > ~8.0 for substantial periods of time, because chemical reactions at elevated pH can
affect the diffusion rate [Bade and Cole, 2006; Wanninkhof
and Knox, 1996]. Acton and Burr Oak reservoirs often have
pH above 8.0; thus, piston velocity must be corrected for
chemically enhanced diffusion. The chemical enhancement
factor, a, was quantified using established equations based
on gas piston velocity, temperature, and pH [Bade and Cole,
2006; Stets et al., 2009; Wanninkhof and Knox, 1996].
Piston velocity, corrected for chemically enhanced diffusion,
kenh, was calculated as kenh = ak.
[18] Because it was not feasible to sample the reservoirs
during winter months, we were unable to measure CO2
fluxes year-round. For Acton, we could sample CO2 fluxes
during nine months of the year. For two of the remaining
months, the reservoir was generally covered by ice so there
would be no CO2 movement between the reservoir and atmosphere. For the remaining month (when ice cover was intermittent and unstable), an average of all the measured
fluxes was used. CO2 fluxes were directly measured from
Burr Oak seven to eight months of the year. For the remaining ice-free months, we estimated pCO2 using temperature
and pH data available from the Burr Oak Regional Water
District and using DIC concentrations collected from the
dam outflow using automated water samplers (see above).
2.4. Carbon Mass Balance Budgets
[19] To create total carbon budgets, a mass-balance
approach was applied using the following equation:
CRETENTION ¼ CSTREAMS þ CO2FLUX CDAM ;
(7)
where CRETENTION is the net retention of carbon in each reservoir, CSTREAMS is the loading of carbon into the reservoirs
via stream inlets, CO2FLUX is positive if there is an efflux and
negative if there is an influx, and CDAM is the export of
carbon from the reservoir via dam outlets.
[20] Carbon retention in our budgets was calculated by
difference of C inputs and C outputs, because sediment resuspension and the age of the reservoirs (< 60 years old)
make it difficult to obtain accurate C burial rates on a yearly
time-scale. We feel this approach is appropriate because our
rates are similar to long-term burial rates [Vanni et al.,
2011], and the annual change in C mass within the water
columns in either water year represented only 0.2-3% of C
stream inputs minus C dam outputs. To determine the relative contribution OC and IC to retention, we applied the percentage of OC or IC found in the sediments from long-term
sediment coring data in these reservoirs (55% OC in Acton,
2.5. Bootstrapping Analyses
[23] Bootstrapping was used to provide error estimates on
total C retention (Mg year-1) using total C inputs via streams
and total C outputs via dams (stream inputs – dam outputs).
All CO2 fluxes were included as retention in these estimates
because at annual scales, there was net CO2 efflux from both
56
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
100
% total carbon load
lakes in both years. This approach is similar to a recent nitrogen (N) retention study in which N removal from lakes and
reservoirs was equal to N inputs minus N outputs via surface
water outlets and denitrification losses [Harrison et al.,
2009]. Separate error estimates on CO2 fluxes were not
made, because our CO2 measurements were collected at a
coarser temporal resolution. Bootstrapping was conducted
by breaking each water year into wet and dry time periods,
which resulted in two time periods for WY 2007 and three
for WY 2008. The data were split because of large fluctuations based on seasonal variability. These splits helped to
ensure that bootstrapping subsamples would come from a
similar sampling distribution. Each bootstrap simulation randomly chose a retention value (stream inputs – dam outputs)
from the appropriate time period. To obtain each annual
estimate, a stratified bootstrap was run over the two 2007
time periods and over the three 2008 time periods, i.e., the
proportion of samples drawn from each period was equal
to the proportion of the annual dates that period represented.
Bootstrap simulations were performed 99,999 times and
were run for mean and 95% confidence intervals. All bootstrap simulations were run using the boot package in R
[Canty and Ripley, 2010].
a)
80
DIC Inlet
DOC Inlet
POC Inlet
PIC Inlet
60
40
20
0
% of total carbon export
100
b)
80
DIC Outlet
DOC Outlet
POC Outlet
PIC Outlet
60
40
20
Burr Oak 2008
3.1. C Stream Inputs and Dam Outputs
[24] Over the two water years, DIC was the dominant carbon
form contributing to total carbon load for both reservoirs, while
PIC was the least dominant form (Figure 2). Of the remaining
carbon constituents, POC represented a greater fraction of
TOC loading than DOC for Acton’s inlets (DOC/TOC = 37%
in 2007, 34% in 2008), while DOC was greater than POC in
Burr Oak’s inlet (DOC/TOC = 68% in 2007, 69% in 2008;
Figure 2). The percentage of total export from the dam outlets
by C form followed the same pattern for both reservoirs in that
DIC > DOC > POC > PIC (Figure 2) with DOC comprising a
larger percentage of TOC export than POC in both Acton
(DOC/TOC = 61% in 2007, 58% in 2008) and Burr Oak
(DOC/TOC = 83% in 2007, 86% in 2008).
[25] Acton had greater monthly carbon fluxes (Mg C month-1)
than Burr Oak for all C forms via the inlets or outlets, most
notably for DIC and POC (Figure 3). Monthly retention efficiency (i.e., retention/stream inputs) for each carbon form
for each month varied between the reservoirs, with Burr
Oak often retaining a larger percentage than Acton
(Figure 3). DIC retention efficiency in Acton was moderate,
except for a high retention period during the late summer of
the 2007 water year and 2008 water year (Figure 3), potentially owing to low DIC inputs and high algal production
(i.e., DIC uptake) during this period. Furthermore, during
the late summer of 2007, we also found DIC concentration
to be higher in the inlets than outlets for Acton (45, 30 mg
L-1, respectively) and Burr Oak (25, 12 mg L-1, respectively) indicating retention of DIC within the lakes. It
should be noted that DIC retention efficiency calculations
do not incorporate CO2 fluxes, and thus we cannot distinguish retention as those retained in the sediments and
those that are lost to the atmosphere as CO2. DOC retention
efficiency in Acton was often negative, indicating net
DOC export (outputs exceed inputs), except during periods
of low-flow (Figure 3). DOC retention efficiency in Burr
Burr Oak 2007
Results
Acton 2008
Acton 2007
0
3.
Figure 2. The percentage of carbon load (a) and export
(b) by each carbon form for each water year. As depicted
in the graphs, PIC contribution was extremely small.
Oak was always positive, and during most of the 2007
summer, DOC retention was nearly 100% (Figure 3). In agreement with these results, we found DOC concentrations to be
higher at the inlet than the outlet (3.8, 3.1 mg L-1, respectively). Additionally, flow from the outlet was low during
these periods. Particulate carbon tended to be retained in
Acton in moderate quantities, but during time periods with
low-flow, export via the outlet far exceeded inlet loading,
probably due to export of phytoplankton-derived C. This
resulted in exceptional net POC export during a few months
(Figure 3). Like other C forms, POC retention efficiency in
Burr Oak was always positive and was less variable than
Acton, and as in Acton, retention efficiency was high during
low-flow and moderate the rest of the year (Figure 3).
3.2. CO2 Fluxes
[26] CO2 fluxes (Mg C month-1) with the atmosphere
were often near zero in Acton, with an influx noted in
the summer of 2007 and a moderate efflux found at the
end of the 2007 water year and beginning of 2008 water year
(Figure 4). CO2 fluxes in Burr Oak were more temporally variable, displaying effluxes during the fall and winter seasons, an
influx in the summer of 2007, and fluxes near zero during the
2008 summer (Figure 4). In both lakes, there was a trend
toward lower effluxes and sometimes influxes in midsummer
time periods.
[27] In Acton and Burr Oak, pCO2 (matm) was correlated
with temperature, pH, and DIC concentration, while pCO2
57
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
Acton (agricultural watershed)
100
50
0
-50
-100
150
1000
100
500
50
0
c)
DOC Flux (Mg month-1)
100
50
0
-50
-100
200
1500
0
100
50
0
-50
-100
d)
100
50
0
-50
-100
75
125
100
75
50
25
0
50
25
0
e)
POC Flux (Mg month-1)
Burr Oak (forested watershed)
b)
Retention efficiency
DIC Flux (Mg month-1)
a)
200
0
-200
-400
-600
-800
-1000
500
400
300
200
100
0
f)
40
Inlet
Outlet
% retention
100
50
0
-50
-100
20
0
Oct-06 Feb-07 Jun-07 Oct-07 Feb-08 Jun-08 Oct-08
Oct-06 Feb-07 Jun-07 Oct-07 Feb-08 Jun-08 Oct-08
CO2 Flux (Mg C month-1)
Figure 3. Carbon fluxes and retention efficiencies in the reservoirs (a-f). Fluxes (Mg month-1) of DIC,
DOC, and POC to reservoirs from stream inlets (open circles) and from reservoir dam outlets (closed circles)
are represented on each lower panel. C retention efficiency (on a monthly basis) is located on the upper
panel; positive values indicate net retention, while negative values indicate net export of that carbon form
(i.e., outputs exceed inputs). The horizontal dotted line is placed at 0, which denotes the boundary between
net C retention in the reservoirs vs. net C export from the reservoirs.
60
pH remains the best predictor variable of pCO2 in the study
reservoirs (Table 2).
40
20
0
3.3. Growing Season Carbon Budgets
[28] Acton inlet TC loading by mass during the growing
season (Mg summer-1) was much higher in 2008 than 2007
for the May – October budget corresponding to higher stream
discharge in 2008 during these months (Figures 5 and 6).
Similarly, TC outputs via the outlet were also higher in
2008 (Figure 5). CO2 fluxes were in opposite directions
between years (i.e. net influx in 2007, net efflux in 2008),
but in both years the fluxes were relatively low in magnitude
and comprised a small percentage of TC fluxes (Figure 5).
The majority of IC was lost from the reservoir water column
via outlet fluxes in 2007 and 2008, while OC losses via the
outlet and retention were similar (Figure 5).
[29] Inlet loading of TC by mass into Burr Oak was quite
different between the May – October 2007 and 2008 budgets
with higher loading in 2008 (Figure 5). As for Acton, precipitation and discharge were higher in these months in 2008 as
were loading of all C forms during this time period (Figures 5
and 6). During the growing season, TC fluxes via the outlet
were more than 20X greater in 2008 than 2007, while retention was similar between the years with OC dominating the
-20
Acton
Burr Oak
-40
-60
Oct
2006
Jan
Apr
Jul
2007
Oct
Jan
Apr
Jul
Oct
2008
Figure 4. Average lake-wide CO2 fluxes in Acton and
Burr Oak. Positive values indicate the lake was releasing
CO2 and negative values indicate CO2 in-gassing. The vertical dotted line separates water years.
was correlated with primary production, TP, and chlorophyll-a
only in Acton (Table 2). pCO2 was unrelated to DOC in either
reservoir (Table 2). In both reservoirs, pH was considerably
better at explaining pCO2 variation than any of the other predictor variables. Since some of the pCO2 values were estimated using pH, pCO2 – pH regressions were also generated
using dates only in which pCO2 was directly measured in the
field. Comparisons between the pH regressions with either direct or estimated pCO2 revealed little difference and show that
58
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
Table 2. Regression Relationships Between pCO2 (the Dependent Variable) and Physical, Chemical, and Biological Parameters. The pH
Was Used to Estimate pCO2 When Direct Measurements Were Not Available. The pH Regressions Were Generated Using All pCO2 Data
and With Dates Where Only Direct Measurements Were Taken
Acton (agricultural)
Independent Variable
Temperature C
DIC (mg L-1)
PPr (mg C m-2 day-1)
DOC (mg L-1)
TP (mg L-1)
Chl – a (mg L-1)
pH all (n = 159, 72)
pH direct only (n = 89, 42)
Burr Oak (forested)
Regression equation
r
2
p-value
Regression equation
1128 – 26 * temp
51+ 15*DIC
861 – 0.135*PPr
0.08
0.03
0.16
0.02
0.04
0.04
0.59
0.52
<0.001
0.039
<0.001
0.104
0.013
0.027
<0.001
<0.001
1553 – 43 * temp
416 + 72*DIC
308+ 2.1*pH
766 – 3.35*chl
7453 – 811*pH
7054 – 767*pH
5744 – 629*pH
5737 – 628*pH
r2
p-value
0.22
0.11
0.01
0.06
0.01
0.01
0.55
0.49
<0.001
0.006
0.876
0.178
0.823
0.776
<0.001
<0.001
C pool (Figure 5). Growing season CO2 fluxes were a moderately high influx in 2007 and an efflux in 2008 (Figure 5).
3.4. Annual Carbon Budgets
[30] Annual Acton and Burr Oak budgets revealed that TC
loading was strikingly similar in 2007 and 2008 despite
large differences in their growing season budgets (Figure 5).
On an annual basis, 2008 was slightly wetter (discharge was
1.1-1.4X higher) than 2007 (Figure 6); in addition, based
on precipitation, 2008 was wetter and 2007 was drier than
an average year [Vanni et al., 2001]. Seasonal patterns of
precipitation and stream discharge showed that both were
higher during late fall and winter in WY 2007 than 2008
(even though 2007 was drier on an annual basis), particularly for the agricultural reservoir (Figure 6). High discharge
during these months in 2007 corresponded with high DIC
and POC loading, likely compensating for low TC loads
during summer 2007 (Figures 5 and 6).
[31] Both reservoirs were net C sinks but of different magnitudes. Annually, total C retention rate was 3-4X greater in
Acton, while CO2 flux to the atmosphere was 2-39X greater
in Burr Oak (Figure 5). Annually, both reservoirs were a
small source of CO2 to the atmosphere with the mesotrophic
Burr Oak having higher CO2 evasion than the hypereutrophic
Acton (Figure 5). Carbon retention in Burr Oak was dominated by OC, while OC and IC were approximately equivalent contributors to Acton C retention (Figure 5).
[32] We generated bootstrapped estimates of confidence
intervals on annual retention rates (Figure 5). Generally,
confidence intervals were <30% for total C ( the bootstrapped mean), imparting a relatively high level of confidence in our estimates.
Figure 5. Carbon budgets for 2007 and 2008 summers
(a-d) and water years (e-h). Solid arrows indicate OC
fluxes and open arrows indicate IC fluxes. The values in
boxes represent the mass of IC lost via conversion to
OC or consumed as alkalinity. Flux units are Mg
summer-1 for the May – October six month time period
(a-d) and Mg year-1 for the twelve month water year
(e-h). Boxes below the water year budgets represent estimated mean retention (Mg year-1) from bootstrapping
analysis for the 2007 and 2008 water years. Values in
parentheses represent bootstrapped 95% confidence intervals. The bootstrapped retention estimates are based on
total C inputs (via streams) minus total C outputs (via
dam) with CO2 fluxes included as retention.
4.
Discussion
[33] Reservoir carbon budgets revealed that, on an annual
basis, both were a small source of CO2 to the atmosphere
with the forested, mesotrophic Burr Oak having higher
CO2 evasion (78 – 102 Mg yr-1) than the agricultural, hypereutrophic Acton (2 – 56 Mg yr -1). These results are surprising, since many researchers have suggested that inland
waters with high primary production rates are likely to be
large sinks of atmospheric CO2 [Cole et al., 2007; Downing
et al., 2008; Hanson et al., 2004; Tranvik et al., 2009].
However, parameters associated with lake metabolism were
59
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
Acton (agricultural watershed)
Precipitation (mm month-1)
250
a)
Burr Oak (forested watershed)
b)
2007
2008
200
150
100
50
0
15
c)
d)
Discharge (m3 sec-1)
12
9
6
3
Sept
Jul
Aug
Jun
Apr
May
Mar
Jan
Feb
Dec
Oct
Nov
Sept
Jul
Aug
Jun
Apr
May
Mar
Jan
Feb
Dec
Oct
Nov
0
Figure 6. Monthly precipitation (a,b) and mean monthly discharge (c,d) from stream inlets to the reservoirs
during the two water years.
annual variation [Finlay et al., 2010]. CO2 flux rates were
somewhat lower in Acton (average = +3.5, range = 15.0
to +21.1 mmol C m-2 day-1) and Burr Oak (average = +11.6,
range = 28.0 to +46.4 mmol C m-2 day-1) than in the six
mesotrophic – eutrophic, Canadian lakes (range = 100 to
+200 mmol C m-2 day-1[Finlay et al., 2010] but were more
comparable to two low-productivity, hard-water lakes in
Minnesota [Stets et al., 2009], large (100 – 3000 km2) eutrophic reservoirs in Canada that were > 30 years old [Demarty
et al., 2009], as well as boreal and temperate lakes in general
[Cole and Caraco, 1998; Del Giorgio et al., 1999; Rantakari
and Kortelainen, 2005]. In addition, both reservoirs often had
pH above 8, particularly in late summer, which resulted in
considerable chemical enhancement factors (Acton mean =
3.0, range 1.0 – 8.4; Burr Oak mean = 2.1, range 1.0 – 10.5).
Chemical enhancement factors in Acton and Burr Oak are
comparable to other hard-water lakes where CO2 fluxes were
also found to contribute relatively small fluxes total C fluxes
[Finlay et al., 2009; Stets et al., 2009].
[35] OC retention per unit reservoir area was 274–340 g
m-2 yr-1 in Acton and 126–133 g m-2 yr-1 in Burr Oak. These
rates are higher than those found in most natural lakes,
6–94 g m-2 yr-1 [Mulholland and Elwood, 1982] but generally
lower than the recently reported median rate in small,
eutrophic agricultural reservoirs (2122 g m-2 yr-1) in Iowa
either unrelated or weakly related to pCO2 in Acton and Burr
Oak. This deviates from studies in low-productivity, low-pH
lakes where DOC is often positively correlated with pCO2
[Jonsson et al., 2003; Sobek et al., 2003] but is consistent
with results from other hard-water lakes [Finlay et al.,
2009; Finlay et al., 2010; Lopez et al., 2011; Tranvik
et al., 2009]. Our reservoirs are also generally considered
net autotrophic based on whole lake ecosystem metabolism
measurements using diel dissolved oxygen dynamics; a
recent study using metabolism measurements showed that
Acton Lake is net autotrophic in the summer (Solomon
et al., 2013). Thus, it appears that our study reservoirs
are net CO2 sources, based on C budgets, but that these
emissions are not as strongly linked to lake metabolism as
they are in soft-water lakes. Further, our reservoirs are generally considered net autotrophic when using whole lake
ecosystem metabolism estimates. These are important distinctions and given that productive, hard-water lakes and
reservoirs are globally abundant in area and volume [Wetzel,
2001], estimation of the role of lentic ecosystems in global C
budgets will require more thorough knowledge of these
systems.
[34] Whole-lake mass balance budgets for six hard-water
lakes in Canada revealed that CO2 fluxes accounted for
~2% of total C fluxes, and there was large CO2 inter60
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
better estimates of these fluxes could significantly alter inferences drawn from lake budgets [Finlay et al., 2010; Sobek et
al., 2006; Stets et al., 2009], particularly if C loading is underestimated during storm events. In general, variation in nonsummer precipitation will likely generate large differences in
C runoff and loads in temperate areas, compared to variation
in summer when terrestrial evapotranspiration is greater. In addition, CO2 emissions from lakes and reservoirs are relatively
well described, but less is known about CO2 sources because
until very recently, complete carbon budgets were rare.
[38] Mass-balance carbon budgets are an ideal way to elucidate carbon fluxes and dynamics in inland waters [Andersson
and Sobek, 2006]; however, they are often constrained by the
logistics and expense of accurately measuring all possible
fluxes. While the current study was an improvement on many
prior studies due to our high resolution estimates of DIC,
DOC, POC, and PIC, we were unable to estimate some fluxes
in detail. Ice-out release of CO2 can be a significant flux,
particularly in systems with high DOC loads such as boreal
lakes. Limited work indicates that ice-out CO2 flux in some
hard-water lakes may represent a small contribution to total
annual CO2 flux [Finlay et al., 2010]. Our estimates of CO2
fluxes for Acton during the winter and ice-out were
constrained by our ability to safely sample the lake. Thus,
our winter CO2 efflux estimates for Acton may be underestimated and represent a source of uncertainty in our budget.
We had year-round pH, DIC, and temperature data for Burr
Oak, so were able to estimate CO2 fluxes during these periods.
Estimating k, piston velocity, from wind speed may also be an
additional source of uncertainty in our CO2 flux calculations.
However, empirical relationships between wind speed and k
are more problematic in small (< 0.5 km2), wind sheltered
lakes [Cole et al., 2010] than in reservoirs like Acton and Burr
Oak. We also did not directly measure CaCO3 precipitation
rates in our reservoirs. This process is important because it
removes alkalinity from the water column and also increases
CO2 evolution. Furthermore, if alkalinity is conserved in the
reservoirs, we should be able to take the difference between
alkalinity inputs and outputs to/from the water column and
attribute this loss to CaCO3 precipitation and thus IC burial.
Using limited data, we found agreement with our budgets
and estimated alkalinity balances in Acton (Table 3). For Burr
Oak, our alkalinity balance overestimated IC burial, and we
attribute this to alkalinity consumption and CaCO3 dissolution
(Table 3). Burr Oak may have increased alkalinity consumption via nitrification during fall turnover, because this reservoir
has high ammonium concentrations in the hypolimnion
(M.J. Vanni, unpublished data) and a larger volume of anoxic
waters than Acton (1.5 x 106, 8.2 x 105 m3, respectively). In a
reservoir with similar chemical conditions as Burr Oak,
permanent IC burial was 88% lower than deposited carbonate
due to dissolution [Wang et al., 2012]. Uncertainty in the loss
of IC from Burr Oak is a source of potential error in our
budgets.
[39] We did not measure direct atmospheric deposition of
DOC, DIC, or POC onto reservoir surfaces. In seven unproductive lakes in Ontario, atmospheric inputs of DIC comprised 1 to 8% of total DIC inputs while atmospheric DOC
inputs ranged from 2 to 13% of total DOC inputs [Dillon
and Molot, 1997]. Stets et al. [2009] showed that organic
carbon inputs via precipitation were only important in a
closed-basin lake while they represented a minor influx in
[Downing et al., 2008]. Differences between Iowa rates and
those in Acton, a hypereutrophic reservoir dominated by
row-crop agricultural land use, may be due to improved land
management in Acton’s watershed. Since the 1990s, conservation tillage has become the dominant land management practice in Acton’s watershed resulting in reduced nutrient and
sediment loads into Acton via streams [Renwick et al.,
2008]. We also note that our OC retention rates are similar
with those based on sediment cores in both Acton and Burr
Oak [Vanni et al., 2011]. IC retention rates in Acton were
224–279 g m-2 yr-1 while only 1.27-1.34 g m-2 yr-1 in Burr
Oak. Few studies have reported IC burial rates in lentic waters,
but rates in Burr Oak are lower than previous reports in hardwater lakes, while Acton rates are up to 5X greater [Finlay
et al., 2010; Stets et al., 2009], perhaps because Acton water
is supersaturated with CaCO3 [Green et al., 1985].
[36] Our results also show the temporal scale dependence
of carbon budgets. Within each reservoir, variability for
any given C flux was much greater between summers than
between water years. Within-reservoir differences were especially pronounced in early summer (May-July), probably
because of variable precipitation and stream discharge.
Summer 2008 discharge was 4.8-6.4X higher than summer
2007, resulting in greater stream C inputs than in 2007.
Within each reservoir, C retention efficiency (retention/
stream inputs) was higher in the dry summer, probably
because of increased water residence time given that water
flow from the outlet during late summer was either nonexistent or negligible. We also found that, in 2007 and 2008,
water residence time was greater in Burr Oak than Acton
(Table 1), which likely played a role in Burr Oak retaining a
higher percentage of C. Also, both reservoirs were CO2 sinks
in the dry summer but CO2 sources during the wet summer.
Previous work suggests that precipitation may be positively
related to the magnitude of CO2 evasion in boreal lakes
[Einola et al., 2011], where CO2 fluxes are associated with
terrestrial DOC inputs [Sobek et al., 2003]. We observed a
similar relationship with precipitation in our hard-water reservoirs even though CO2 fluxes did not appear to be tightly
coupled with DOC. DIC inputs would also be expected to increase during precipitation events [Raymond and Oh, 2007].
Nonetheless, given the similar trends in these disparate water
bodies, we may be able to assume that dry summers will
generally have elevated C retention efficiency, coupled with
either reduced CO2 evasion or a CO2 influx, in contrast to
wet summers. Identifying patterns such as these is central to
understanding the consequences of climate change and altered
hydrological regimes on C budgets.
[37] We also highlight the role of variable stream inputs,
because the largest inputs often occurred during time periods
not included in typical summer studies. Thus, 67-79% of
annual POC loads via streams occurred collectively during
only 10% of days (i.e., dates on which daily discharge was >
in the 90th percentile), and 62-89% of these dates occurred
outside of summer [May-October; Knoll, 2011]. Our high
resolution stream data also allow us to differentiate between
the relative contribution of POC and DOC to OC loading. In
Acton, POC can represent 17-78% of the OC load (mean =
44%) and 12-50% in Burr Oak (mean = 25%), reflecting
the large variation that would not be captured without storm
based sampling regimes. Further, C inputs via streams are
only rarely measured directly in carbon budgets. Therefore,
61
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
Table 3. Comparison of Alkalinity Balances With C Budgets. Note All Time Periods Are From May – October Except for Acton
2008 (May – September).
Year
Lake
Stream
alkalinity load
(Mg summer-1)
2007
2008
2007
2008
Acton
Acton
Burr Oak
Burr Oak
384.6
2238.0
97.3
289.4
Dam alkalinity
export
(Mg summer-1)
IC buried from
C budgets
(Mg summer-1)
Alkalinity
export + IC buried
(Mg summer-1)
Alkalinity load – alkalinity
export as buried CaCO3
(Mg summer-1)
260.3
1743.3
6.4
111.2
49.0
215.7
1.5
1.9
309.3
1959.0
7.9
113.1
62.2
247.4
45.4
89.1
[41] Using recently published estimates of global OC
burial and CO2 emission rates of lentic ecosystems (lakes
and reservoirs [Cole et al., 2007; Tranvik et al., 2009]), we
calculated the percentage of global lentic fluxes attributable
to Ohio reservoirs. We also examined whether these fluxes
are proportional to the lentic area they occupy using
published global estimates of lake and reservoir area
[Downing et al., 2006; Meybeck, 1995] and C fluxes [Cole
et al., 2007; Tranvik et al., 2009]. We used high [Downing
et al., 2006; Tranvik et al., 2009] and low [Cole et al.,
2007; Meybeck, 1995] estimates of global C fluxes and
global lentic area to account for uncertainty. We find that
Ohio reservoirs account for 0.02-0.05% of total global OC
burial but only 0.001-0.002% of global CO2 emissions in
lentic ecosystems (Table 4). Ohio reservoirs bury between
0.8-4.4X of the C mass that would be predicted based
only on water body area, i.e., (Ohio lentic burial/global
lentic burial) / (Ohio lentic area/global lentic area). On the
other hand, CO2 emissions by Ohio reservoirs represent only
5-21% of emissions expected based on their area. We
initially expected that productive Ohio reservoirs would be
CO2 sinks, but our results indicate that they are generally
small sources. Even when considering additional eutrophic
Ohio reservoirs dominated by agricultural land, we find that
these systems are sources of CO2 (Figure 7). These additional reservoirs may behave as Acton in that they receive
large quantities of inorganic carbon from their watersheds,
emit a portion of inorganic carbon as CO2, and are net
CO2 sources despite being autotrophic. Thus, Ohio reservoirs appear to retain proportionally more C, but emit proportionally less CO2, than the global average. This is likely
a consequence of their relatively large watershed areas and
carbonate bedrock, which results in large quantities of C,
particularly POC and DIC, being delivered to these reservoirs (Figure 3).
a lake with high surface water input. Given that Acton and
Burr Oak have large watersheds, and hence water inputs
via surface runoff, we suspect that atmospheric inputs of C
would represent a small and insignificant influx into these
reservoirs. In addition, based on nutrient budgets for Acton
Lake, atmospheric inputs of C are likely to be small compared to stream inputs [Vanni et al., 2011]. Groundwater
inputs into some lakes are extremely rich in DIC and CO2
and can thus contribute greatly to these inputs [Stets et al.,
2009; Striegl and Michmerhuizen, 1998]. We did not estimate groundwater inputs of C, but for both reservoirs,
groundwater contributes a small amount of the hydrological
inputs (W.H. Renwick, unpublished data).
[40] Because we used a high resolution sampling regime,
and recognizing the uncertainties discussed above, we felt
confident in our retention and CO2 emission rates. Thus,
we scaled up C fluxes regionally, specifically for the state
of Ohio, by estimating OC burial and atmospheric CO2 exchange rates in Ohio tributary reservoirs >0.5 km2 (n = 105
reservoirs statewide). We used watershed land use data
[Hagenbuch, 2010] to classify reservoirs as either dominated
by agriculture, forest, or of mixed land use (i.e., agriculture
and forest). We then applied mean CO2 fluxes and OC burial
from our budgets to three reservoir classification types
(agricultural, forested, or mixed), using the average of agricultural and forested for mixed land use (Table 4). We
assessed only OC burial to facilitate comparison with past
global studies [Cole et al., 2007; Tranvik et al., 2009]. We
estimate that Ohio reservoirs bury 105 Gg OC yr-1 and emit
8.5 Gg of C as CO2 yr-1 (Table 4). Reservoirs in agricultural
landscapes buried 53% of statewide OC, while forested
reservoirs buried only 8% (39% was buried by mixed land
use). Agricultural and forested reservoirs each emitted
~25% of the CO2 emissions by Ohio reservoirs (those in
mixed land use emitted 50%).
Table 4. CO2 Emissions And OC Burial In Ohio Reservoirs and Global Lentic Water Bodies.
System
Total surface
area of
reservoirs,
statewide or
global (km2)
% of global
Mean
area of lentic reservoir
ecosystems
CO2
emissions
(lakes plus
reservoirs) (g m-2 yr-1)
Ohio reservoirs
433
0.01 – 0.02a
Agricultural
180
Forested
62
Mixed
191
Global lentic
2000–4200 x103 a
water bodies
11.47
33.56
22.51
Statewide
or global
CO2
emissions
(Gg yr-1)
% of global CO2
emissions
occurring
from reservoirs,
statewide
8.50
2.07
2.09
4.30
390 – 770 x103 b
0.001 – 0.002b
Mean
reservoir
OC burial
(g m-2 yr-1)
307.11
129.37
218.24
% of global
OC burial
occurring
from reservoirs,
statewide
Statewide or
global OC
burial
(Gg yr-1)
105.53
55.42
8.07
41.66
230 – 600 x103
0.02 – 0.05b
b
a
based on low and high global lake area estimates from references Meybeck [1995] and Downing et al. [2006], bbased on low and high estimated global C
fluxes from Cole et al. [2007] and Tranvik et al. [2009].
62
KNOLL ET AL.: HIGH-RESOLUTION RESERVOIR CARBON BUDGETS
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pCO2 ( atm)
3000
2000
1000
0
0
20
40
60
80
100
% agricultural land use in watershed
Figure 7. pCO2 in 18 Ohio reservoirs (excluding Acton
and Burr Oak). Reservoirs were sampled either once or on
multiple dates at a deep site near the dam outflow during the
growing season [Knoll, 2011]. Thus, each point represents a
sampling event for a given lake. The dotted line indicates
390 pCO2 matm, the average atmospheric concentration.
Concentrations above the line indicate the reservoir is supersaturated with CO2.
[42] Our budget results suggest that moderately to highly
productive waters are not necessarily large CO2 sinks as previously expected [Cole et al., 2007; Hanson et al., 2004],
and that watershed land use and hydrology (specifically, precipitation variability) modulate C fluxes. Regional estimates
also suggest that Midwestern US reservoirs are burying significant amounts of OC but that the magnitude of CO2 flux is
unexpectedly low and in the opposite direction as predicted
for productive systems. The role of inland waters in regulating carbon will vary with changing climate, and the nature of
these shifts will depend upon watershed and lake characteristics. To gain insights into how climate change may modify
these fluxes in diverse systems, we can use high resolution
baseline data and compare it to periods of extreme climatic
events such as droughts or extreme storm events.
[43] Acknowledgments. We thank Robert Moeller, Stephen Glaholt,
Burr Oak Regional Water District, and Beth Mette for providing assistance
in the field and laboratory. We also thank Lyz Hagenbuch and the Ohio Division of Wildlife for land use and reservoir area data. Jon Cole and Edward
Stets provided valuable assistance with CO2 flux calculations. This research
was supported by a Miami University Research Enrichment Grant to M.J.V.
and W.H.R., Miami University Field Workshop funds to L.B.K., an NSF
REU Site grant (DBI 0353915), and two NSF LTREB grants (DEB
0235755 and 0743192) to M.J.V. and W.H.R. The research was also funded
in part by the United States Environmental Protection Agency (EPA) under
the Science to Achieve Results (STAR) Graduate Fellowship Program to L.
B.K. EPA has not officially endorsed this publication and the views
expressed herein may not reflect the views of the EPA.
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