Waters impounded behind dams (i.e., reservoirs) are
important sources of greenhouses gases (GHGs), especially methane (CH4), but
emission estimates are not well constrained due to high spatial and temporal
variability, limitations in monitoring methods to characterize hot spot and
hot moment emissions, and the limited number of studies that investigate
diurnal, seasonal, and interannual patterns in emissions. In this study, we
investigate the temporal patterns and biophysical drivers of CH4
emissions from Acton Lake, a small eutrophic reservoir, using a combination
of methods: eddy covariance monitoring, continuous warm-season ebullition
measurements, spatial emission surveys, and measurements of key drivers of
CH4 production and emission. We used an artificial neural network to
gap fill the eddy covariance time series and to explore the relative
importance of biophysical drivers on the interannual timescale. We combined
spatial and temporal monitoring information to estimate annual
whole-reservoir emissions. Acton Lake had cumulative areal emission rates of
45.6 ± 8.3 and 51.4 ± 4.3 g CH4 m-2 in 2017 and 2018,
respectively, or 109 ± 14 and 123 ± 10 Mg CH4 in 2017 and
2018 across the whole 2.4 km2 area of the lake. The main difference
between years was a period of elevated emissions lasting less than 2 weeks
in the spring of 2018, which contributed 17 % of the annual emissions in
the shallow region of the reservoir. The spring burst coincided with a
phytoplankton bloom, which was likely driven by favorable precipitation and
temperature conditions in 2018 compared to 2017. Combining spatially
extensive measurements with temporally continuous monitoring enabled us to
quantify aspects of the spatial and temporal variability in CH4
emission. We found that the relationships between CH4 emissions and
sediment temperature depended on location within the reservoir, and we observed a clear
spatiotemporal offset in maximum CH4 emissions as a function of
reservoir depth. These findings suggest a strong spatial pattern in CH4
biogeochemistry within this relatively small (2.4 km2) reservoir. In
addressing the need for a better understanding of GHG emissions from
reservoirs, there is a trade-off in intensive measurements of one water body
vs. short-term and/or spatially limited measurements in many water
bodies. The insights from multi-year, continuous, spatially extensive
studies like this one can be used to inform both the study design and
emission upscaling from spatially or temporally limited results,
specifically the importance of trophic status and intra-reservoir
variability in assumptions about upscaling CH4 emissions.
Introduction
Reservoirs are a globally important source of methane (CH4) and other
greenhouse gases (GHGs) to the atmosphere, with recent estimates attributing
773 Tg carbon dioxide equivalent (CO2 e) per year to reservoir surface
emissions, nearly 80 % (607 Tg CO2 e yr-1) as CH4 (Deemer et al., 2016). This is roughly half the global CH4 burden from rice cultivation, estimated as 1100–1360 Tg CO2 e yr-1 (Ciais et al., 2013). The dominance of CH4 in reservoir GHG budgets is due to the combination of gross CH4 emissions and methane's large warming potential relative to
CO2. Inland
waters (lakes, rivers, and reservoirs) can be hot spots for the decomposition of
organic matter, and respiration from these waters globally may offset the
terrestrial carbon sink by up to 60 % (Cole et al., 2007; Ciais et al.,
2013). The carbon dynamics of reservoirs are of special interest for several
reasons. Reservoirs generally receive more sediment input (hence organic C)
from their watershed than comparable lakes as they tend to be located lower
in the landscape and have a larger ratio of catchment area to surface area
(Hayes et al., 2017). Reservoirs also tend to drain watersheds with more
agricultural or urban land use than the natural lake watersheds (Thornton et
al., 1990). The distribution of lakes and reservoirs across the United
States is such that in many parts of the country total lentic surface area
is dominated by reservoirs. Furthermore, emissions from reservoirs are
considered anthropogenic and thus should be included in national GHG emission inventories reported to the United Nations (Lovelock et
al., 2019).
Emissions of GHGs from reservoirs are highly variable in space and time,
making reservoir GHG budgets difficult to constrain. This is especially true
for CH4, the production and emission pathways of which are highly
dynamic. One key production pathway of CH4 in water bodies is
methanogenesis in anoxic sediment. Some of this CH4 dissolves into the
water column where it may be oxidized into CO2 by methanotrophs or may
diffuse to the atmosphere. Methane may also accumulate as bubbles in the
sediment until the buoyant force of the gas bubble overcomes the overlying
static pressure. The rate of this CH4 bubbling, or ebullition, is
affected by several biological and physical factors including carbon
substrate availability, sediment temperature, oxygen availability,
turbulence, and overlying pressure (Tuser et al., 2017). Thus, ebullition is
highly variable in space and time (Wik et al., 2016). Another potentially
important source of CH4 is production in oxic surface water, considered
a “paradox” until recently (Schmidt and Conrad 1993; Grossart et al.,
2011; Tang et al., 2014, 2016; DelSontro et al., 2018b). The rate of
diffusive flux from surface waters can be highly dynamic as it depends on
the balance between production and emission (Hartmann et al., 2020).
Although the body of knowledge on CH4 emissions from inland waters has
grown considerably over the past decades, the high degree of spatial and
temporal variability in emissions, coupled with limitations in monitoring
methods, mean that many questions about reservoir emission behavior remain.
Recent studies have highlighted the importance of interannual patterns (Room
et al., 2014), seasonal patterns (Yvon-Durocher et al., 2014), diurnal
patterns (Podgrajsek et al., 2014; Deshmukh et al., 2014), sub-daily pulse
events (Zhang et al., 2021), lake-zone spatial patterns (Juutienen et al.,
2009; DelSontro et al., 2011; Maeck et al., 2013; McClure et al., 2020), and
the relative contributions of hot spots (Wik et al., 2016; Beaulieu et al., 2016), hot moments (Bastien et al., 2011; Demarty et al., 2011; Jammet et
al., 2015; Beaulieu et al., 2018; Harrison et al., 2018), and food web
dynamics (Bartosiewicz et al., 2021; Grasset et al., 2018) in accurately
characterizing lake and reservoir CH4 emissions. Under-sampling in
irregular systems leads to underestimation (Wik et al., 2016). The synthesis
by Deemer et al. (2016) showed that reservoir GHG emission studies using
spatially integrated methods reported higher FCH4 than studies using
survey methods. Despite the need to better capture the spatiotemporal
dynamics of reservoir CH4 fluxes (FCH4) and its drivers, most
monitoring studies to date have used survey methods that are often
short-term, intermittent, and/or spatially limited.
Use of micrometeorological methods such as eddy covariance (EC) to monitor
reservoir FCH4 can address many of the monitoring challenges by
providing pseudo-continuous, long-term, spatially integrated flux
measurements. A low-power open-path CH4 sensor capable of making
measurements for EC has only been available since circa 2011 (McDermitt et
al., 2011), and using micrometeorological techniques to measure fluxes over
open water (vs. land) can be difficult due to siting, footprint, and
boundary layer turbulence considerations (Kenny et al., 2017; Higgins et
al., 2013; Sahlee et al., 2014). Thus, relatively few studies have used EC
to characterize FCH4 over inland waters (Jammet et al., 2015, 2017; Deshmukh et al., 2014; Eugster et al., 2011; Schubert at al.,
2012; Podgrajsek et al., 2014a, b; Beaulieu et al.,
2018). Further highlighting the scarcity of studies using this technique,
the recent FLUXNET-CH4 synthesis (Knox et al., 2019) of long-term
(> 1 year) EC monitoring of FCH4 had only two open-water
sites among the 60 included. To our knowledge, this study is only the second
to report pseudo-continuous, multi-year FCH4 results over open water,
and the first to report long-term FCH4 over open water in a temperate
region, for a eutrophic system, and for a reservoir.
This study reports the results of 2 years of pseudo-continuous (via EC and
active funnel traps for ebullition), spatially extensive (via
spatially balanced CH4 emission surveys) measurements of FCH4 and
key drivers of CH4 production and emission. We organize our findings
around two questions that can inform both the design of future monitoring
studies and emission upscaling from limited results: (1) How important can
interannual and intra-lake variability be in a single reservoir, and what
causes it? (2) What does this tell us about how limited monitoring resources
can best be used to constrain reservoir methane emissions?
Map of Acton Lake (a), showing the location of multiple monitoring
methods: eddy covariance flux tower sites (red circles), active funnel traps
and biweekly chamber measurements (dark blue squares), spatially
extensive survey sites (light blue circles), and the weather station and
thermistors operated by Miami University (purple triangles). The lake
contour lines represent ∼ 1 m depth increments. Inset image
shows the location of Acton Lake in southwest Ohio. The Google Earth image
(b) shows the 80 % cumulative footprint probability distribution at each
eddy covariance flux tower site at 10 % intervals.
MethodsSite description
Acton Lake is a small hypereutrophic reservoir located in southwest Ohio
(39.57∘ N, 84.74∘ W; 262 m a.s.l.; Fig. 1a). The dam was constructed in 1956, and
the reservoir and surrounding state park have been managed by the Ohio
Department of Natural Resources since 1957. The reservoir's surface area is
2.4 km2, it has a maximum depth of ∼ 8 m, and the area
near the dam undergoes thermal stratification in the summer. Although Acton
Lake is immediately surrounded by a forested state park, land use in its
watershed is > 80 % agricultural, with the majority used for
intensive row cropping (Renwick et al., 2018). We used four main methods to
monitor CH4 fluxes (FCH4) from Acton Lake during 2017 and 2018:
(1) the EC technique, (2) continuous ebullition monitoring with active
funnel traps, (3) biweekly chamber measurements of diffusive emissions, and
(4) spatially extensive surveys. The locations of the EC tower sites, active
funnel trap and biweekly chamber measurement sites, and spatially extensive
survey sites are depicted in Fig. 1a; the cumulative footprint probability
distribution of the two flux tower sites is shown in Fig. 1b. The EC
instrumentation was sited in the shallow region of Acton Lake due to
logistical constraints related to both tower installation and boat traffic
in the reservoir. How the methods were used in this study is summarized in
Table S1. We used auxiliary meteorological and limnological measurements
from stream gauging stations, a weather station, and thermistor string
maintained by the Miami University (Renwick et al., 2018; Andersen et al., 2020), the locations of which are also shown in Fig. 1a.
Eddy covariance flux measurements
This site is registered as AmeriFlux site US-Act; information about the site
and the flux data presented in this study are available online
(https://ameriflux.lbl.gov/sites/siteinfo/US-Act, last access: 28 August 2021). The EC
instrumentation consisted of an ultrasonic anemometer to measure three-dimensional wind
speed and direction (Model 81000, R.M. Young Company, Traverse City, MI,
USA) and open path infrared gas analyzers (IRGAs) for measuring the number
density of CH4 (LI-7700), as well as CO2 and water vapor (LI-7500A, LI-COR
Biosciences, Lincoln, NE, USA). The EC data streams were recorded at 10 Hz
by a data logger (LI-7550, LI-COR Biosciences, Lincoln, NE, USA), which was
also equipped with a temperature sensor and a pressure transducer. The EC
system was deployed from a dock piling 20 m from the northwestern shore of
Acton Lake from 1 February 2017 through 14 April 2018 (“EC S-1” in Fig. 1).
The instruments were brought to the lab for calibration and maintenance on
15 April 2018, then redeployed on a tower installed into the reservoir
sediment in the northeast corner of the reservoir on 5 May 2018 (“EC S-2”
in Fig. 1). The system was shut down on 1 December 2018. Images of the EC
system at each deployment location are included in the Supplement (Fig. S1). In
addition to the EC setup, the flux tower was equipped with a net radiometer
(NRLite2, Kipp and Zonen, Delft, The Netherlands), a cellular modem for
remote communication (AirLink, Campbell Scientific, Logan, UT, USA), and a
time-lapse camera (WCT-00125 TimelapseCam, Wingscapes, Calera, AL, USA). The
time-lapse camera was used to determine periods of ice cover. The system was
powered by solar panels and a battery bank regulated via a solar charge
controller (SunSaver, Morningstar Corporation, Newtown, PA, USA). All
components of the EC system were run on a 12 V system until relocation to the
aquatic tower, when the EC setup (LI-7700, LI-7500A, and LI-7500 infrared gas analyzers; Model 81000 sonic anemometer) was retrofitted to run on 24 V.
The raw 10 Hz EC data were processed into 30 min fluxes using the software
EddyPro v. 6.2 (LI-COR Biosciences, Lincoln, NE, USA). We used measurements
of water depth from the Miami University weather station to determine
instrument height above water surface on an hourly time step, integrated into
the flux processing as a dynamic metadata file. Additional processing steps
followed community standards and included filtering the 10 Hz CO2
measurements when CO2 signal strength was < 70, double
coordinate rotation, block averaging, time lag compensation using covariance
maximization, WPL density correction (Webb et al., 1980), and correction for
high-pass and low-pass filtering effects (Moncrieff et al., 2004, 1997). The area contributing to the measured flux was characterized
for both sites using the online two-dimensional flux-footprint prediction
tool (Kljun et al., 2015). We used R for postprocessing, and the code is
available on GitHub (https://github.com/USEPA/actonEC, last access: 28 August 2021). The 30 min fluxes
were rejected when the period did not pass the tests for stationarity and
developed turbulent conditions (quality control, QC, level 2 per the integrated scale of Foken
et al., 2004). EC S-1 fluxes were further filtered for periods when winds
were from the shore (between 195∘ and 330∘); at EC S-2
we filtered for periods of low turbulence using a friction velocity
(ustar) threshold of 0.07 m s-1, based on the site-specific
relationship between ustar and fluxes of CH4 and CO2 (Aubinet
et al., 2012). We did not use ustar filtering at EC-S1 because the
temporal coverage was insufficient to determine a ustar threshold. We
define “acceptable” data or “acceptance rate” as those data meeting the
EC QA/QC (quality assurance/quality control) requirements, while “data coverage” includes non-operability due
to power or instrument failures.
The overall EC FCH4 data acceptance rate for the 2-year monitoring
period (26 January 2017–13 November 2018) was 31.3 % (Fig. S2). In
2017, the data acceptance rate was lower, 23.4 %, due to power issues and
the need to filter for wind direction at the near-shore EC S-1 site where
the instrumentation was located for the whole year vs. 39.8 % in 2018 when
the instrumentation was relocated in the spring to the mid-reservoir EC S-2
site. The data coverage for the period of monitoring from EC S-2 (May through
November) was 52.8 %. Re-siting removed the need to filter periods based
on wind direction and coincided with an improvement to the battery system
that reduced incidences of power failure. At EC S-1, non-operability of the
LI7700 due to power loss or other issues caused the majority of data
rejection (40.4 % of total monitoring periods), followed by filtering for
wind direction (28.1 %), and quality control filtering (7.8 %). At EC
S-2, power loss caused the majority of gaps (36.3 %), followed by quality
control filtering (16.6 %).
Active funnel trap ebullition measurements
The active funnel traps (AFTs) were based on the design of Varadharajan et al. (2010) and have been previously described by Beaulieu et al. (2018).
Briefly, they consisted of a 0.3 m2 funnel attached to a rigid tubing
gas collection chamber equipped with a differential pressure sensor to
monitor accumulated gas volume on a 5 min time step. We modified the
Varadharajan et al. (2010) design by incorporating siphons that auto-purge the collected
bubble gas and refill the tubing volume with water. This modification keeps
the AFTs from becoming filled with gas, allowing them to make useful
measurements for longer periods of time. Trap gas samples were collected
biweekly and analyzed via a gas chromatograph equipped with a flame
ionization detector (Bruker 450 GC, USA) to determine the composition of the
bubble gas. The active trap data reduction followed the method described in
Varadharajan et al. (2010) and Varadharajan and Hemond (2012). Circuit
calibration to determine the relationship between voltage and height was
performed pre- and post-trap deployment in the 2017 field season and
post-deployment in the 2018 field season. The volume of gas in the trap is
calculated as follows:
AFTvol=(Circvolt×m+b)×πAFTd22,
where AFTvol is the volume of gas in the funnel trap, Circvolt is
the voltage output from the differential pressure sensor, m and b are the
sensor-specific laboratory calibration multiplier and offset coefficients,
and AFTd is the diameter of the funnel tubing. We used a 12-point
moving average (60 min) to smooth the gas volumes and minimize noise.
Periods with known issues were filtered out of the dataset (e.g., power
issues, trap drift from target location, etc.), as were large negative
fluxes that reflected siphon purges. Following Varadharajan and Hemond
(2012), we calculated fluxes on multiple time-bin widths (30 min, 1, 2, 6,
12, 24, 48 h) but used the 2 h rolling time step for calculating the flux
used in our final analysis:
FCH4eb=AFTvol[CH4]Tf-TiAF,
where AFTvol is the volume of gas in the trap (m3), [CH4] is the CH4 concentration in the bubble gas (mg CH4 m-3), Tf–Ti is the elapsed time (s), and AF is the cross-sectional area of
the funnel (m2). The AFT data reduction was performed in R, and the
scripts are available online (https://github.com/USEPA/actonEC, last access: 7 September 2021).
The AFTs were deployed in late spring and retrieved in the fall each year.
The shallow AFT (U-14) monitored ebullition from 9 May to 3 October in 2017
and from 6 June to 11 December in 2018. The deep AFT (U-12) monitored
ebullition from 10 May to 30 October in 2017 and from 24 May to 9 November 2018.
Chamber diffusion measurements
Diffusive FCH4 was measured with a floating chamber biweekly at two
sites during the field season. We used a rectangular, round-ended aluminum
chamber with external polyvinyl chloride floats and a headspace fan, based
on the CSIRO chamber described in Zhao et al. (2015). An ultra-portable
greenhouse gas analyzer (UGGA; PN: 915-0011, ABB, Los Gatos, CA) monitored
the change in CH4 mixing ratio in the chamber headspace over the
duration of the chamber deployment (> 1–5 min), measuring at
1Hz and recording an averaged measurement every 5 s. We monitored the
real-time UGGA time series to prevent ebullitive emissions from overwhelming
the diffusive emission measurements. If a spike in CH4 concentration
was detected, we re-set the chamber. The floating chamber data reduction
method has been described in detail in Beaulieu et al. (2016). Briefly, we
used the following equation to calculate diffusive fluxes (moles m-2
s-1):
FgasD=dχgasdtVAPRT,
where dχgas/dt is the rate of change in the mixing ratio of CH4
in the chamber headspace (ppm s-1), V is the chamber volume (m3),
A is the chamber surface area (m2), P is the pressure in the chamber
headspace, R is the universal gas constant, and T is the temperature in the
chamber headspace. The rate of change dχgas/dt for each chamber
deployment was determined via fitting linear and nonlinear models to the
dataset and using Akaike information criterion (AIC) to choose the more
appropriate model. Only models with an r2>0.9 were
retained. Data analysis and reduction was performed using R, and the scripts
are available online (https://github.com/USEPA/actonEC, last access: 7 September 2021).
Biweekly chamber monitoring was conducted from 10 May to 11 December in
2017, and from 18 May to October to 13 December in 2018. Note that the
chamber monitoring began earlier and ended later than the AFT monitoring
each year due to technical issues with the AFTs.
Water measurements
Water temperature depth profiles were recorded continuously at two sites
close to U-14 and U-12 (Fig. 1) using thermistors. At the shallow site
(U-14) a string of seven thermistors (RBRsoloT, RBR Ltd., Ottawa, ON,
Canada) were deployed at 0.1, 0.25, 0.5, 0.75, 1, and 1.5 m below the air–water
interface and at the sediment–water interface. We used this temperature
profile to characterize water column stability in the footprint of the EC
flux measurements based on the Brunt–Väisälä buoyancy frequency using the R
package rLakeAnalyzer (Winslow et al., 2019). The Brunt–Väisälä buoyancy
frequency was used to indicate water column stability. It represents the
frequency at which a parcel of fluid will oscillate when displaced
vertically, a measure of resistance to mixing. A high oscillation frequency
indicates strong resistance to mixing, whereas a low frequency indicates
little resistance to mixing. At the deep site (U-12), sondes measuring
temperature (ProODO, YSI Incorporated, Yellow Springs, OH, USA) were
deployed at 0.1, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, and 8 m below the air–water
interface. Water temperature, specific conductivity, dissolved oxygen, pH,
and chlorophyll a (chl a) were measured biweekly with a YSI multiparameter sonde at
0.1 and 1.5 m below surface at the shallow site (U-14) and 0.1, 1, 2, 3, 4,
5, 6, 7, and 8 m below surface at the deep site (U-12). Water samples for
chlorophyll analysis were collected by Miami University near the reservoir
inlet. Water samples were collected with an integrated tube sampler from the
water surface to the euphotic zone depth. Chlorophyll samples were collected
on 1.0 µm glass fiber filters and frozen at -20 ∘C in opaque
containers until processed. They were extracted in 95 % ethanol for 24 h
and analyzed with a TD-700 (Turner Designs, San Jose, CA, USA).
Dissolved gas surface and profile samples were collected biweekly from both
U-12 and U-14 using the headspace equilibration method. We collected water
samples at depths of 0.1, 2, 4, 6, and 7 m at U-12 and at 0.1, 0.75, and 1.3 m at U-14. Using a 140 mL plastic syringe with a two-way stopcock, we added 25 mL of ultra-high-purity helium to a syringe, then added 115 mL of sample
water, and agitated all samples for 5 min. We then transferred the
headspace gas to pre-evacuated 12 mL glass vials topped with a
silicone-coated Teflon septum stacked on top of a chlorobutyl septum (Labco
Ltd., UK). The headspace gas samples were analyzed using gas chromatography
(see Sect. 2.3) to determine the CH4 composition, and the dissolved
CH4 concentrations were calculated using measured headspace composition
and the temperature-specific Bunsen solubility coefficients (Yamamoto et
al., 1976). Full documentation of the calculations is available at the
National Ecological Observatory Network's GitHub repository (https://github.com/NEONScience/NEON-dissolved-gas, last access: 7 September 2021).
Whole-reservoir surveys
We conducted six surveys of Acton Lake over the summers of 2017 and 2018 to
estimate whole-reservoir FCH4. The 15 sample collection sites
(Fig. 1, light blue circles), were determined using a generalized random
tessellation survey (GRTS) design (Stevens and Olsen 2004; Olsen et al., 2012), a probability design that has been shown to reduce uncertainty
relative to other designs (Beaulieu et al., 2016). At each site, we measured
CH4 diffusion, CH4 ebullition, and surface water quality
parameters. Survey measurements of diffusive FCH4 were conducted with
floating chambers in the same manner as described in Sect. 2.4. Survey
measurements of ebullitive FCH4 were conducted with passive funnel
traps (PFTs) deployed overnight (> 15 h). The PFTs are a
simplified version of the AFTs described in Sect. 2.3: they consist of a
0.3 m2 funnel attached to a section of tubing for gas collection but
do not have a pressure sensor or siphon. Upon retrieval, the total time of
deployment and total volume of gas in the tubing were recorded, and three 25 mL samples of the gas were collected for gas composition analysis via a gas chromatograph (see Sect. 2.3). Ebullitive FCH4 from the PFTs was also
calculated using Eq. (2) (Sect. 2.3), but the trap volume was
determined by direct measurement of the collected gas, and Tf–Ti is defined as the deployment period. Dissolved gas sample collection
and depth profiles of water quality parameters were taken at one deep site
(U-12) and one shallow site (U-14) during each whole-reservoir survey. The
surveys were initiated on 10 July, 31 August, and 4 October 2017 and 10
July, 14 August, and 20 September 2018 and concluded the
following day.
Gap filling and upscaling
We use the term “gap filling” to refer to our method to determine values
for missing observations in our measurement time series, while “upscaling”
refers to the best estimate of whole-reservoir FCH4. For this analysis,
we separated the year into different seasons, categorizing November through
March as “winter”, or the cold season, and May through September as
“summer”, or the warm season. We refer to April and October as the
“shoulder” season. The spring burst period is defined as 24 May through 4
June. For the EC time series, we developed an artificial neural network (ANN)
to gap fill 30 min FCH4 using predictor variables with biophysical
links to CH4 production and emission: sediment temperature (sedT), air
temperature, latent heat flux (LE), sensible heat (H), wind speed,
ustar (friction velocity, a measure of turbulence), photosynthetically
active radiation, overlying static pressure, and change in static pressure,
in which static pressure is the sum of overlying atmospheric and hydrostatic
pressure. We also included indicators for the tower location, hour of day,
and day of year as drivers. Gaps in the sedT, air temperature, wind speed,
wind direction, and static pressure time series were filled using
observations from a nearby weather station. Gaps in LE, H, and ustar
were gap filled using the mean diurnal course function from the R package
REddyProc (Wutzler et al., 2019) on the 30 min time step. We used k-means
clustering to assign 10 clusters before selecting the training, testing,
and validation datasets. The cluster assignments allowed us to select
subsets with probabilities proportional to the clusters, ensuring that the
clusters were not over- or underrepresented as a result of the splits. We
employed a selective ensemble approach to optimize the ANN model
performance using the R package nnet (Venables and Ripley, 2020). Each ANN
ensemble included models with 5–20 layers and 50 different starting weights,
for a total of 800 model results. The top 100 models were selected based on
the testing R2 results, and then the median CH4 value from the best
100 models was used as the predicted flux. To characterize both sampling and
model uncertainty, we replicated this procedure with 20 resamplings of the
data. For each half hourly FCH4 we calculated the median predicted
value of the best 100 models in each of the 20 ensembles of 800 models (cf. Knox et al., 2016). Missing half hourly FCH4 values were gap filled
using the median of the medians from the 20 ensembles. ANN modeling and
gap filling was performed in R, and the scripts are available online (Barnett
et al., 2021).
We gap filled short gaps in the AFT continuous datasets using linear
interpolation and calculated annual emissions via summing the daily
observations. We gap filled the biweekly chamber measurements of diffusive
FCH4 via linear interpolation. For periods at the start and end of the
monitoring seasons with chamber measurements but no AFT measurements, we
used the typical ratio between diffusive and ebullitive FCH4 to
estimate total FCH4 for the site. We gap filled the spatial survey
measurements by interpolating between each of the three annual surveys. To
estimate annual emission, we applied the FCH4 value determined by the
first survey of the year to every day between 1 May and the first survey
and the FCH4 value determined by the last survey of the year
through 15 October. We assumed an FCH4 of zero between 15 October and 1
May for both the spatial survey dataset and the AFT plus chamber datasets.
To upscale to whole-reservoir FCH4, we used a hybrid approach,
combining results from EC, the deep site (U-12) AFT, and the spatial
surveys. We stratified Acton Lake into shallow (< 3 m) and deep
(≥ 3 m) areas and used reservoir bathymetry to determine the surface
area for the shallow and deep portions: 0.8 and 1.6 km2,
respectively. The depth cut-off of 3 m roughly corresponds to the greatest
depth of the EC footprint. We then used FCH4 measured by EC to
characterize the shallow portion of the reservoir. For the deep portion, we
calculated the ratio (reservoir ratio, or RR) between the measured FCH4
(ebullitive + diffusive) at the U-12 AFT (hereafter, deep AFT FCH4)
and the mean of FCH4 measured at the other deep sites (U-01, U-04,
U-05, U-08, U-11, U-12, U-13, U-15, U-16, U-17, and U-18; see Fig. 1). We
calculated this RR for each of the six spatial survey dates. To characterize
FCH4 in the deep portion of the reservoir, we applied the RR from the
first survey to the deep AFT FCH4 continuous time series data collected
before 10 July 2017 and likewise applied the RR from the last survey to the
time series data collected after 20 September 2018. For the periods
in between, we used linear interpolation to produce a daily RR and applied
that to the deep AFT FCH4 continuous time series. We weighted the
cumulative shallow and deep CH4 areal emissions by the shallow and deep
fraction of the reservoir to determine the whole-reservoir CH4
emissions. We refer to this estimate of whole-reservoir emissions as the
“hybrid” upscaled estimate.
Uncertainty analysis
We parameterized the uncertainty in the EC time series of FCH4 using
three different measures: the random measurement error, the bias error of
the gap-filled dataset, and the 95 % confidence intervals of the
gap-filled dataset. The random measurement error is calculated from the
variance of the covariance (Finkelstein and Sims, 2001) and reflects
instrument noise, variation in footprint over a given 30 min flux
integration period, and the stochastic nature of turbulence. As described in
Jammet et al. (2017), the random error decreases with increasing dataset
size and is negligible at the resolution of cumulative annual fluxes but can
be substantial for individual flux measurements (Richardson et al., 2006;
Moncrieff et al., 1996). The random error was calculated as part of the
EddyPro processing, and we report the summary statistics in Sect. 3.2.
Unlike random errors, systematic biases can accumulate to affect the
cumulative seasonal or annual flux. Although the measurement bias cannot be
quantified, we calculated the systematic bias in the annual fluxes due to
gap filling following Moffat et al. (2007) and Jammet et al. (2017):
BE=1N∑(pi-oi),
where N is the number of values in the validation time series, p is the
values predicted by the ANN, and o is the observed values in the validation
time series. The bias error was multiplied by the total number of gap-filled
values to obtain the total annual bias. We calculated the 95 % confidence
interval of the gap-filled dataset using the distribution of the 20 ANN
medians extracted from the 20 resamplings, which consider both sample and
model uncertainty (Knox et al., 2016).
We used root-sum-squared error propagation of the error in AFTvol and [CH4] to characterize the uncertainty in ebullitive FCH4 measured
by the AFTs. Compared to error in AFTvol, the error contribution from
other terms in Eq. (2) was negligible. As described in Varadharajan et al. (2010), we propagated the error in m, offset, and electronic noise through Eq. (1), adding a 2 mL dead volume error each time the AFTs flushed to account for
gas that could be trapped in the fittings at the top of the collection
chamber. Our mean slope and slope error were similar to those reported in the methods of the Varadharajan et al. (2010) paper (31 and 0.31, respectively, compared to 28
and 0.5); the mean (Vzero) and standard deviation (ΔVzero)
of the offset terms we used were slightly larger: 0.51 and 0.071 V for the
shallow site and 0.41 and 0.045 V for the deep site (compared to 0.15 and
0.015); our calculated electronic noise (ΔVout) was smaller
(0.4 mV vs. 3 mV in Varadharajan et al., 2010), so we defaulted to their value. The
standard deviation between the multiple trap gas samples was used as the
uncertainty in [CH4]. This term was generally small compared to the
uncertainty due to AFTvol error. The cumulative errors were propagated by
summing in quadrature.
The whole-reservoir surveys provide an estimate of FCH4 integrated
across the entire reservoir surface area and a 95 % confidence interval
range (Beaulieu et al., 2016). Variance estimates calculated from GRTS
incorporate spatial autocorrelation, if present, resulting in smaller
uncertainty ranges than survey approaches that ignore spatial
autocorrelation (Stevens and Olsen, 2003). The GRTS design and data
reduction were executed in R using the spsurvey package (Kincaid et al.,
2019). We propagated the cumulative uncertainties across 2017 and 2018 by
taking the 95 % confidence interval of each survey and summing them in
quadrature.
The uncertainty in the hybrid approach to the upscaled cumulative
whole-reservoir emissions was also determined by error propagation,
combining the uncertainty in the deep AFT measurements, the spatial surveys,
and the EC measurements.
Statistical and quantitative analysis
For these analyses, we used the non-gap-filled measurement time series. We
quantified the relationship between sediment temperature (sedT) and
FCH4 using Q10 and breakpoint analyses. The concept of an “ecological
Q10” (DelSontro et al., 2016) follows from the physiological exponential
relationship between metabolic processes and temperature. In contrast to
physiological Q10 values, ecological Q10, hereafter “ecoQ10”, values are
muddied by time lags and competing rate enhancers and inhibitors (e.g., that
temperature affects both methanogens and methanotrophs; Segers, 1998; Duc et
al., 2010; Lofton et al., 2014). While the physiological Q10 value for
methanogenesis converges around 4 (Yvon-Durocher et al., 2014), ecoQ10
values for methane fluxes have been reported to range from 1 to 35 (e.g., DelSontro et al., 2016; Wik et al., 2014; Duc et al., 2010). We calculated
the ecological Q10 (DelSontro et al., 2016) using the following equation:
ecoQ10=1010b,
where b is the slope of the regression between temperature and FCH4.
We also used a two-dimensional Kolmogorov–Smirnov test (2DKS; Garvey et al,
1998) to quantify the temperature breakpoint distinguishing winter
conditions when FCH4 is near zero and unrelated to temperature from
warm weather conditions when FCH4 is elevated and positively
correlated with temperature. The 2DKS test is a non-parametric statistic
that uses measures of disagreement to define the largest difference between
cumulative distribution functions, that is, a threshold or breakpoint (Lopes
et al., 2008). We applied the 2DKS test to each of the continuous FCH4
monitoring datasets: EC, shallow AFT, and deep AFT, each for 2017 and 2018
for a total of six 2DKS tests.
We looked at diurnal patterns on monthly and daily timescales. For the
monthly timescales we binned 30 min periods and took the median. For
daily timescales we adapted the methods used by Podgrajsek et al. (2014) to
quantify “strong” diurnal patterns. For 24 h periods with at least
eight nighttime and eight daytime non-gap-filled 30 min flux
measurements, we compared the median of daytime FCH4 to nighttime
FCH4. The period was defined as having a strong diurnal pattern both if the difference between daytime vs. nighttime FCH4 median was
> 50 % and if the contiguous points in the 30 min time
series were smooth, i.e., more similar than points separated in time. We
determined smoothness using visual inspection.
We compared the cumulative FCH4 measured from Acton Lake during each
year of this study to output from the size-productivity model (DelSontro
et al., 2018a). This model relates total CH4 emissions to chl a levels per the following equation:
log10totalCH4+1=C1×log10chla+C2,
where the coefficients C1 and C2 are equal to 0.778 ± 0.118 and 0.940 ± 0.122, respectively. Although the equation is unitless, it relates total CH4 in units of milligrams C per square meter per day (mg C m-2 d-1) to chl a in units of micrograms per liter (µg L-1).
Time series of FCH4 monitored via multiple methods: eddy
covariance (violet), the sum of the shallow AFT and interpolated chamber
measurements (blue, site U-14), the sum of the deep AFT and interpolated
chamber measurements (green, site U-12), and via the spatially integrated
lake-wide surveys (yellow). The error bars for the lake surveys indicate the
95 % confidence interval of the mean. Error margins for the other
measurements are omitted for figure legibility. The spring burst period was
24 May–4 June 2018.
Seasonal methane fluxes reported as mean fluxes and cumulative
areal emissions from Acton Lake characterized by different measurement
techniques. The eddy covariance method measures total (diffusive + ebullitive + other) fluxes.
ResultsTemporal patterns in <inline-formula><mml:math id="M245" display="inline"><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">CH</mml:mi><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>
We observed a consistent pattern of elevated FCH4 during the warm
season across all measurement methods (Fig. 2). In both monitoring years,
the majority of cumulative total CH4 emissions (> 85 %)
occurred in the 5 months between 1 May and 30 September, when air and
sediment temperatures were warmer (Fig. 4a), and latent heat fluxes were
elevated (Fig. 4b). We observed larger-magnitude CH4emissions in
2018 relative to 2017 at Acton Lake across each observation type except for
the deep site (Table 1). The EC and spatial survey results indicated similar
warm-season mean fluxes in 2017: 9.73 ± 0.67 and 9.98 ± 6.2 Mg CH4 m-2 h-1. Results from both methods indicated
larger-magnitude mean FCH4 in 2018: 17.5 ± 0.38 Mg CH4 m-2 h-1 per the EC system and 13.0 ± 6.6 Mg CH4 m-2 h-1 per the spatial surveys (Table 1). Both the shallow site
results also indicated elevated FCH4 in 2018 relative to 2017, while
the deep site results were effectively the same (Table 1). The
lower-magnitude mean FCH4 measured at the shallow site compared to the
mean FCH4 measured by EC is likely due to the under-representation of
hot spots (Wik et al., 2016). The wintertime FCH4 measured by EC
indicates that during the winter months FCH4 dropped by more than an
order of magnitude to a baseline close to zero: between 1 November and 1 April
FCH4 was 0.60 ± 0.69 Mg CH4 m-2 h-1. The surface
of Acton Lake was frozen for several periods during the 2017–2018 winter: 27
December 2017–10 January; 13–21 January; and 5–15 February 2018, during which
FCH4 was 0.08 ± 0.46 Mg CH4 m-2 h-1.
Median variable importance ranking for the drivers of the
artificial neural network gap-filling model in terms of percent importance
to the predictive power of the model. This ranking is based both on
intra-model variability (i.e., the effect of model architecture and random
seed selection) and on intermodel variability (i.e., the effect of data
selection for the training, testing, and validation datasets). DOY = day
of year, Delta Static P is change in overlying static pressure, sedT is
sediment temperature, LE is latent heat flux, Static P is static pressure,
Wind Dir is wind direction, H is sensible heat flux, uStar is friction
velocity, PAR is photosynthetically active radiation, and HOD is hour of day.
The non-gap-filled, quality-filtered 30 min FCH4 measurements had a
mean random error (±SD) of 1.3 ± 1.9 and 1.8 ± 1.7 Mg CH4 m-2 h-1 in 2017 and 2018, respectively, or 15.5 % and
13.7 % of the mean annual fluxes. The fractional errors were larger in the
winter months when FCH4 was small (mean winter random error: 23 %)
and smaller during the warmer months when FCH4 was larger (mean summer
random error: 15 %). Both the magnitudes and patterns in the random errors
are similar to those observed by Jammet et al. (2017) in a subarctic aquatic
ecosystem. Similarly, we found gap filling our FCH4 time series with
ANN worked well with a few exceptions. The median R2 value for the 20
extractions was 0.79, and the cumulative bias error was minimal: the 20 ANN
extractions yielded a median bias of 0.25 (range of -3.7 to 3.5) g CH4 m-2 or up to 3.3 % of cumulative emissions over the 2-year
monitoring period. The ANN establishes nonlinear predictive power to each
of the driver inputs, defined as a “variable importance factor” (VIF) in
terms of a percent importance to the predictive power of the model. The
median VIFs from the 20 ANN extractions are plotted in Fig. 3; a consistently
high ranking across runs indicates a strong relationship with FCH4. The
biophysical drivers with the highest variable importance were static
pressure (the sum of water pressure and air pressure), change in static
pressure, and sediment temperature.
The most substantial difference between the two monitoring years is the
period of elevated emissions in late May to early June observed by the EC
monitoring in 2018 but not 2017 (hereafter “spring burst”). We define the
spring burst as the period from 24 May through 4 June, in which the daily average
FCH4 observed by EC was ≥ 25 Mg CH4 m-2 h-1.
Maximum FCH4 of 62.0 Mg CH4 m-2 h-1 occurred on 29 May
2018. While the 2017 EC monitoring does indicate a small burst in FCH4
of 20.4 Mg CH4 m-2 h-1 on 5 June, overall FCH4 was much
smaller: mean FCH4 for 24 May–4 June 2017 was 3.6 ± 1.8 Mg CH4 m-2 h-1. Although the AFT at the shallow site was not
operational during the spring burst, diffusive FCH4 measurements
indicate that FCH4 was elevated at that site compared to the deep site.
Although none of the spatial surveys coincided with the spring burst period,
the deep site monitoring indicates that the spring burst did not extend to
the deeper parts of the reservoir. The cumulative CH4 emission over the
2018 12 d spring burst period was 10.8 g CH4 m-2 which is
15 % of the cumulative annual emissions measured by EC in 2018 (Table 1)
and which accounts for 59 % of the difference in the EC cumulative annual
emissions between 2017 and 2018.
Meteorological and limnological conditions over the study period:
(a) daily mean of air (red) and sediment (black) temperature; (b) daily mean
latent and sensible heat fluxes (LE: black; H: red); (c) daily cumulative precipitation (mm); (d) stream inflow (m3 s-1);
(e) water depth in the footprint of the flux tower (m); (f) Brunt–Väisälä frequency, a measure of water column mixing potential
(s-1); and (g) the water temperature profile at the deep site (U-12).
Grey bars indicate the time frame of the 2018 spring burst of CH4
emissions.
The differences between the 2017 and 2018 monitoring years continue past
the early summer (Figs. 2, 4). During 2017, FCH4 increased to a
maximum in late summer, and then declined back to the winter baseline. Maximum
emissions at the deep site in 2017 lagged and were dampened compared to the
shallow site. In contrast, the 2018 summer and fall in the shallow portion
of the reservoir (EC and shallow site) were characterized by episodic
emission pulses and declines before tapering down to the winter baseline.
The deep site emissions were in phase with the shallow site but did not have
the same pulses. There was a late season pulse at the deep site in 2018 that
coincided with reservoir turnover (Fig. 4g) and a drop in dissolved
CH4 below the thermocline at the deep site (Fig. S3).
We used the EC measurements of FCH4 to look for diurnal patterns in
emissions. We found that Acton Lake did not have a clear overarching
diurnal pattern when aggregated over monthly timescales, (Fig. S4), but out
of the 168 d with adequate data coverage for diurnal analysis, 18.5 %
(31 d) displayed strong diurnal patterns: 16 with elevated daytime
emissions and 15 with elevated nocturnal emissions. Very few of these
strong diurnal pattern days were contiguous: there were only four instances
of strong diurnal patterns persisting for 2 or more consecutive days. The
periods with strong diurnal patters when FCH4 peaked during the day
were correlated with latent heat flux (Figs. S5, S6), while periods when
FCH4 peaked at night were correlated with air pressure (Figs. S5, S6). While we looked for evidence of synoptic patterns in FCH4 due to
changes in overlying pressure from frontal systems (cf. Liu et al., 2016)
and due to underwater turbulence (Fig. S7), we did not see evidence of
impact on FCH4 from these drivers during the study period.
Cumulative areal emissions in 2017 and 2018 from EC, sum of AFT
and chamber, spatial survey monitoring, and hybrid upscaling results (g CH4 m-2). Vertical lines intersecting the lake survey trace
represent the 95 % confidence interval of the lake-wide FCH4
estimate.
There are notable differences in the cumulative annual areal emissions
across methods and years (Table 1, Fig. 5). The impact of the spring burst
is evident in the interannual difference between the EC cumulative
emissions, which were 40.7 ± 5.88 and 71.4 ± 4.2 g CH4 m-2 in 2017 and 2018, respectively. The cumulative areal emission
measured by EC from 1 October 2017 through 1 May 2018 was 6.66 ± 3.1 g CH4 m-2, on the same order as the uncertainty range in the annual
values. As follows from the patterns in the mean fluxes discussed above, the
results from the spatial surveys and the shallow trap also indicate elevated
cumulative annual emissions in 2018 compared to 2017, while the results from
the deep site indicate similar emissions over both years. The implications
of the spring burst for whole-reservoir upscaled total annual CH4
emissions is discussed below, but the best estimate of reservoir-wide
cumulative annual areal emissions from the hybrid approach yields 45.6 ± 8.3 and 51.4 ± 4.3 g CH4 m-2 for 2017 and 2018,
respectively (Fig. 5). Scaling up to the 2.4 km2 area of Acton Lake,
the hybrid approach indicates that this reservoir was a source of 109 ± 14 and 122 ± 10 Mg CH4 to the atmosphere in 2017 and
2018, respectively.
Spatial patterns in <inline-formula><mml:math id="M355" display="inline"><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">CH</mml:mi><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>
The results from the six spatial surveys indicate an inconsistent spatial
pattern in FCH4 that differs from previous findings on CH4
emissions from temperate, eutrophic reservoirs which have shown that the
river–reservoir transition zone near the tributary inlets tends to be a
hot spot for emissions compared to the lacustrine zone (Beaulieu et al.,
2014, 2016; DelSontro et al., 2011; Tuser et al., 2017).
The survey results from Acton Lake indicate relatively similar rates of
FCH4 across most of the reservoir surface area (Fig. 6) and a weak but
significant (n=90, R2=0.1, p<0.005) positive
relationship between ebullition and reservoir depth (Fig. S8).
At the whole-reservoir scale, ebullition was a dominant emission pathway
for CH4 relative to diffusion, accounting for 82 %–94 % of total
FCH4. However, at certain sites diffusive FCH4 contributed a
larger proportion of the total flux (Fig. S9). The four sites with mean
ebullitive to total FCH4 ratios less than 0.8 are also the four
shallowest sites (see Fig. 1): U-09, U-14, U-07, and U-06, with mean
observed depths of 1, 1.3, 1.5, and 2 m respectively. This pattern from the
spatial surveys is also reflected in the results from the more frequent
measurements made at the shallow and deep site: ebullition accounted for
58 % of the total FCH4 at the shallow site in both 2017 and 2018,
while ebullition accounted for 86 % and 88 % of total FCH4 at the
deep site in 2017 and 2018, respectively. Emission behavior at sites U-09
and U-06 was substantially different than at other sites: these two sites
had consistently low FCH4 and tended to have higher rates of CH4
diffusion than ebullition. Much of this behavior is likely explained by the
proximity of these sites to Acton Lake's swimming beach, which has a sandy
substrate that likely inhibits methanogenesis at these sites. These sites
were included as part of the random GRTS sampling design.
DiscussionComparison with other systems and methods
The hybrid upscaling approach we used in this study leverages the best
available information from our measurements to characterize both the spatial
and temporal variability of Acton Lake: EC monitoring for the shallow
portion of the reservoir and the continuous deep site monitoring scaled by
the spatial survey site measurements for the deep portion of the reservoir.
If we used the EC monitoring results alone to upscale to whole-reservoir
emissions, that would assume the spring burst pattern affected the whole
reservoir (Fig. 5). However, we know the spring burst did not affect the
deep site (Fig. 2). Thus, a key uncertainty around this upscaling method is
estimating what portion of the reservoir was affected by the spring burst of
emissions in 2018. The cumulative FCH4 measured by EC was 77 %
greater in 2018 than 2017, compared to a difference of only 11 % per the
hybrid approach. Adding one or more AFT sites along the depth gradient of
the reservoir would be one way to decrease uncertainty in the extent of the
spring burst and improve confidence in upscaled FCH4 estimates.
Comparing cumulative annual areal emissions from the hybrid upscaling
approach (45.6 ± 8.3 and 51.4 ± 4.3 g CH4 m-2 for 2017
and 2018, respectively) to other reservoir CH4 emission rates reported
in the literature is not straightforward due to differences in monitoring
methods and temporal coverage. One important reason earlier studies of
reservoir FCH4 may be biased low is that they only measured CH4
diffusion: Deemer at al. (2016) found that the mean FCH4 reported in
studies measuring ebullition and diffusion was over double that of diffusion-only
FCH4 studies. Another potentially important source of bias is temporal
coverage. Most studies that report FCH4 from inland waters monitor
during the warm season, with less than 6 months of measurements (cf. Deemer et al., 2016; DelSontro et al., 2018a; Bastviken et al., 2011), and the
mean FCH4 value is then extrapolated to annual total emissions.
However, we measured very low (on the same order as the warm-season
uncertainty) wintertime FCH4 in this study. On the other hand, the
spring burst phenomenon we observed demonstrates the importance of
continuous monitoring of midlatitude eutrophic reservoirs during the full
warm season to capture hot moments of FCH4. A related consideration is
a method's ability to capture spatial and temporal variability in FCH4 during the study period. Deemer et al. (2016) noted that studies using the
eddy covariance method reported substantially higher values of FCH:
∼ 92.5 g CH4 m-2 yr-1 (Deshmukh et al., 2014)
and ∼ 160 g CH4 m-2 yr-1 (Eugster et al.,
2011), which are on the same order as the Acton Lake cumulative annual
emissions (Table 1). The two open-water sites included in the CH4 EC
meta-analysis by Knox et al. (2019) were natural lakes in temperate regions
with cumulative annual emissions of ∼ 15 g CH4 m-2 yr-1. This difference in FCH4 speaks to the need for building a
representative dataset across both methods and ecoregions.
Total (ebullitive + diffusive) FCH4 measured during
mid-summer, late-summer, and fall spatial surveys at Acton Lake during 2017
(a, b, c) and 2018 (d, e, f). Dots indicate magnitude of FCH4 per
the z-axis scale, and vertical black lines connect red dots to their
corresponding sampling location. Dot color indicates whether a sampling site
is in the shallow (< 3 m, lavender) or deep (> 3 m, royal
purple) area of the reservoir.
Nevertheless, Acton Lake's annual FCH4 is relatively high compared to
other reservoirs. It falls in the fourth quintile (> 60 %) of
the reservoir emission rates that included ebullition reported in Deemer et
al. (2016); the warm season FCH4 falls in the upper quintile
(> 80 %) of those reservoirs. The warm season FCH4 also
falls into the upper quartile (> 75 %) of the 32 temperate
reservoirs surveyed by Beaulieu et al. (2020). This result strengthens the
finding that midlatitude, eutrophic reservoirs in the midwestern USA can support
high CH4 emission rates (cf. Beaulieu at al., 2014, 2016) than would be
predicted by age and latitude alone (Barros et al., 2012). The high annual
FCH4 also supports the emerging body of knowledge around the importance
of reservoir productivity as a key indicator for FCH4 (cf. Deemer et
al., 2016; West et al., 2012; DelSontro et al., 2018b).
Implications for upscaling
The key question in upscaling any set of measurements to characterize an
ecosystem is “what is representative of reality?”. This study leveraged a
combination of continuous and spatially extensive monitoring methods to
investigate the spatial and temporal variability in a reservoir. The results
from the six spatial surveys indicate an inconsistent spatial pattern in
FCH4 that differs from previous findings on CH4 emissions from
temperate, eutrophic reservoirs which have shown that the river–reservoir
transition zone near the tributary inlets tends to be a hot spot for
emissions compared to the lacustrine zone (Beaulieu et al., 2014, 2016; DelSontro et al., 2011; Tuser et al., 2017). The spring burst
of elevated emissions that we observed in 2018 but not 2017, and in the
shallow portion of the reservoir but not at the deep site, is the largest
contributor to the spatial and temporal variability in this study. In this
section we will analyze the spring burst and factors that could have
contributed to it. Other patterns in intra-reservoir spatial and temporal
variability linked to sediment temperature and other biophysical drivers are also
discussed.
Daily air and sediment temperature (a, left) and chlorophyll a (an
indicator for algal biomass, b, right) in 2017 and 2018. The grey bar
indicates the spring burst period of elevated FCH4 in 2018, likely
supported by elevated sediment temperature and algal biomass levels that
year.
Spring burst
Differences in phytoplankton populations and sediment temperature, partially
driven by precipitation differences, provide insight into why the spring
burst of emissions occurred (1) in 2018 but not 2017 and (2) in the littoral
area of the reservoir but not the deeper areas. Chlorophyll a (chl a) levels
measured a few days before the spring burst period show elevated levels in
the shallow portion of the reservoir in 2018 compared to 2017, while levels
near the outflow were similar between the two years (Fig. 7a). This
increase in chl a levels coincided with an increase in shallow sedT to
27 ∘C, (Fig. 7b). These differences in chl a and sedT near the
inflow can be tied to differences in precipitation between the two years:
spring of 2017 was relatively wet, with 31.0 cm of rainfall and 20.9×106 m3 of stream inflow in May (Fig. 4c, d) which
drove substantial fluctuations in reservoir water levels (Fig. 4e). These
rain events also led to a decrease in sedT from 22.5 to 18 ∘C
prior to the onset of the spring burst timeframe (Fig. 7b) due to the
inflow of cooler stream water and the cooling of ambient air temperature. In
contrast, May of 2018 was relatively dry, with 12.3 cm of rain, 9.45×106 m3 of stream inflow (Fig. 4c, d), and stable
reservoir water levels (Fig. 4e). The phytoplankton bloom in the shallow
portion of the reservoir leading up to the spring burst period was likely
catalyzed by the conducive water temperature, turbidity, and water level
stability. Elevated levels of dissolved ammonium (NH4), total
phosphorous (TP), soluble reactive phosphorus (SRP), and particulate organic
carbon (POC) near the inflow during the 2018 spring burst support our understanding that the
conditions in the littoral area in 2018 were different than those in 2017
and that this interannual difference did not occur in the deep portion of
the reservoir (Table 2).
Dissolved nutrient and carbon data for the inflow and outflow
during the study period, reported as the mean of weekly samples taken
between April and October and as the value measured for the week of the
2018 spring burst (24 May–4 June). Dissolved nutrient data include total
nitrogen (TN), ammonium (NH4), nitrate (NO3), total phosphorus
(TP), and soluble reactive phosphorus (SRP). Dissolved carbon was measured
as particulate organic carbon (POC).
2017 2018 Mean Spring burst Mean Spring burst Analyte (units)InflowOutflowInflowOutflowInflowOutflowInflowOutflowTN (mg N L-1)5.695.308.278.122.051.783.393.03NH4 (mg N L-1)0.050.070.020.020.050.050.170.07NO3 (mg N L-1)0.970.891.691.620.250.220.470.43TP (µg P L-1)11599.998.676.614180.4254110SRP (µg L-1)20.224.42.665.3511.59.6915.72.81POC (mg L-1)3.532.693.422.964.092.744.483.06
There are at least two established mechanistic connections between
phytoplankton blooms and enhanced CH4 production and emission, and
either or both could have driven the spring burst observed in this study.
One mechanistic connection between autochthonous organic carbon (autoOC,
i.e., phytoplankton-derived) and FCH4 is the stimulation of
methanogenesis from the input of this labile C source as the phytoplankton
die and settle on the sediment. Several lab studies have demonstrated that
the addition of autoOC can lead to enhanced CH4 production rates
(Schwartz et al., 2008; West et al., 2012, 2015; Grasset et al., 2018). A
recent study using in situ measurements found that heat-wave-induced
cyanobacterial blooms and subsequent input of autoOC to the sediment could
lead to pulses of CH4 emissions up to an order of magnitude larger than
baseline levels (Bartosiewicz et al., 2021). The 2018 crash in phytoplankton
that coincided with the spring burst (as indicated by chl a measurements;
Fig. 7a) evidences a large input of autoOC to the sediment during the
spring burst. A second possible mechanistic connection is production of
CH4 by phytoplankton in the oxic surface water. A recent study by
Hartmann et al. (2020) combined in situ measurements of phytoplankton
communities, CH4, and CH4 isotopes with lab incubations and
demonstrated that all major phytoplankton classes could produce CH4
under oxic conditions. Phytoplankton CH4 production in the surface
mixed layer supersaturates the upper water column with CH4 and leads
to enhanced diffusive emissions, and phytoplankton biomass has been found to
be the primary driver of diffusive FCH4 in some reservoir systems
(McClure et al., 2020). Strong diurnal patterns in FCH4 surrounding the
spring burst correlated with latent heat flux (LE), an indicator of warm,
windy, convective conditions of enhanced air–water gas exchange (Figs. S5,
S6). This suggests that during the spring burst the surface waters were
supersaturated with CH4 and diffusive emissions were the dominant
pathway during that time. Including measures of phytoplankton CH4
production in the surface mixed layer in future studies would be helpful in
differentiating which production pathway led to elevated dissolved CH4.
Time series of sedT and ebullition in 2017 at the shallow (solid green line) and deep (dashed blue line) sites. The light grey bar highlights
the period of maximum ebullition and sedT at the shallow site; the dark grey
bar highlights the corresponding period at the deep site.
The difference in hydrologic regimes and subsequent availability of autoOC
vs. allochthonous OC (alloOC, i.e., particulate or dissolved C derived
from terrestrial plant tissue) may also shed light on interannual
differences beyond the spring burst. The lab study by Grasset et al. (2018)
found that while additions of autoOC led to pulses of FCH4, alloOC took
longer to decompose, and additions led to more gradual but sustained
FCH4. Thus, the wet spring of 2017 may have loaded the reservoir with
slow-burning alloOC, which could partially explain the smaller magnitude of
FCH4 pulses in 2017 compared to 2018 (Fig. 2).
The impact, or lack thereof, of the spring burst on reservoir-wide
cumulative FCH4 has implications for the value of higher-resolution
measurements. This is analogous to the question of whether the increased
complexity of process-based models improves prediction over empirical models
(cf. Adams et al., 2013). While the EC monitoring results almost doubled
from 2017 to 2018, the hybrid upscaled estimate had only an 11 %
difference (Table 1, Fig. 5). Furthermore, the cumulative FCH4
determined via the lake-wide surveys was closer to the hybrid upscaled
estimate than the EC results in 2018 (Fig. 5). Using the recent
size-productivity model (DelSontro et al., 2018a) to predict FCH4 at
Acton Lake based on mean annual chl a levels (Eq. 7, Fig. 7) yields estimates
of 11.1 and 10.3 Mg CH4 m-2 h-1 for 2017 and 2018,
respectively. These values are in the same range as the warm season mean
fluxes determined via the hybrid approach for Acton Lake (Table 1). However,
the model results contrast with measured results in terms of which year had
higher FCH4. Furthermore, the model results would overestimate
cumulative annual FCH4 for Acton Lake as they do not take low
wintertime emissions into account.
Sub-annual climatic patterns and productivity dynamics may become more
important in understanding and predicting reservoir FCH4. Recent
research demonstrates how warmer springs have increased the frequency and
intensity of cyanobacterial blooms in midwestern US reservoirs over the past
two decades (Smucker et al., 2021), and continued warming will likely
intensify this phenomenon. There is also a burgeoning body of knowledge that
points to the importance of phytoplankton ecology on lake and reservoir
CH4 production in terms of both the amount (Hartman et al., 2020;
McClure et al., 2020; Zhang et al., 2021) and type (Bartosiewicz et al.,
2021). Furthermore, the underlying factors that led to the 2018 spring
burst at Acton Lake may be more common in the future and have a greater
effect on the reservoir CH4 budget.
Additional intra-lake variability
Beyond the spring burst, we observed additional patterns of intra-lake
spatiotemporal variability in FCH4 related to sediment temperature
(sedT). Temperature is an important control on metabolic processes such as
methanogenesis, but other signals can complicate the relationship between
temperature and FCH4 at the scale of ecosystem fluxes. Nevertheless,
sedT emerged as a key predictor of FCH4 in this study. The ANN model
used to gap fill the EC monitoring ranked sedT as one of the most important
biophysical predictors of FCH4 along with absolute static pressure,
change in static pressure, and latent heat flux (Fig. 3). A strong
indication of the intra-lake patterns in drivers and emissions is that
maximum ebullitive FCH4 observed by the AFTs coincided with maximum
sedT at both the shallow (U-14) and deep (U-12) monitoring sites in 2017
(Fig. 8). This maximum occurs in early August at U-14 vs. mid-September
at U-12, a phase shift that reflects the time delay in heat transfer to the
deeper sediment. This phase shift could also (speculatively) have been
affected by the time delay in nutrient and OC transfer from the inlets. This
pattern was not as pronounced in 2018 (Fig. S10) perhaps due to differences
in the precipitation regime that affected reservoir metabolism.
Summary statistics describing the relationship between FCH4
and sediment temperature per the ecoQ10 analysis and the two-dimensional
Kolmogorov–Smirnov test (2DKS) threshold analysis.
Eddy covarianceAFT shallowAFT deepecoQ102017 value6.9635.130.42017 R20.850.480.602018 value5.6435.830.72018 R20.830.850.38Threshold (2DKS)2017 sedT threshold14.122.217.92017 test statistic0.2260.1660.2042018 sedT threshold17.423.013.32018 test statistic0.2340.1900.138
We used ecoQ10 and 2DKS threshold analysis to further investigate the role
of sediment temperature on regulating FCH4 in both the deep and shallow
portions of Acton Lake. Both of these quantitative analyses of the
relationship between FCH4 and sedT yielded statistically significant
results (Table 3), and each monitoring method had consistent ecoQ10 values
and 2DKS threshold temperatures across the two study years (Table 3, Fig. S11). The EC method had a much lower ecoQ10 value than the AFT sites, the
latter of which were comparable to maximum ecoQ10 values reported in other
studies (DelSontro et al., 2016). The relatively low ecoQ10 value for the EC
method may be due to the different temperature response of ebullitive vs. diffusive
emission pathways or to a spatial mismatch between the measured sedT and
the EC flux footprint. For these reasons, we focus on the AFT sites in
interpreting the ecoQ10 and threshold temperature results in terms of
intra-lake spatial variability. The ecoQ10 values indicate a stronger
relationship between sedT and ebullitive FCH4 at the shallow site than
the deep site. Despite a greater ecoQ10 value, ebullitive FCH4 at the
shallow site did not respond to warming in the spring until water
temperatures reached a threshold of ∼ 22.5 ∘C, whereas
ebullitive FCH4 at the deep site responded to warming at a much lower
temperature threshold (13–18 ∘C; Table 3). Furthermore, mean
ebullitive FCH4 was very similar between the two sites (Table 1)
despite a 6∘C difference in maximum sediment temperature. These
patterns suggest that methanogens at the deep site may be better adapted to
the consistently cooler conditions found in the hypolimnion of Acton Lake,
which has important implications for predictive models employing ecoQ10 or
threshold values to parameterize FCH4 as a function of sedT.
Alternatively, the differences in temperature sensitivity between the deep
and shallow site may reflect differences in substrate quality and/or
quantity related to spatial patterns in sedimentation and productivity
(Berberich et al., 2019). Regardless of the underlying mechanism, these
patterns illustrate strong spatial patterning in CH4 biogeochemistry
within this 2.4 km2 reservoir.
Conclusions
In this study we investigated temporal patterns and biophysical drivers of
CH4 fluxes from a eutrophic temperate reservoir using multiple methods
including eddy covariance. Sediment temperature and the overlying static
pressure were the most important biophysical drivers of FCH4 per the
ANN model results. Water chemistry and chl a measurements indicate that the
spring burst of elevated FCH4 coincided with a phytoplankton bloom.
Comparing the two observation years indicated that the climatic conditions
of precipitation and temperature were more conducive to a phytoplankton
bloom in 2018 than 2017. In contrast to previous studies, we saw a weak
positive correlation between FCH4 and reservoir depth, we did not find
a strong relationship between FCH4 and underwater turbulence, nor did
we observe consistent diurnal patterns in FCH4.
We found that Acton Lake had cumulative annual CH4 areal emissions of
45.6 ± 8.3 and 51.4 ± 4.3 g CH4 m-2 in 2017 and 2018,
respectively. These levels of emissions place Acton Lake in the upper
quartile of emission rates reported from reservoirs (Deemer et al., 2016),
further supporting the concept that highly productive midlatitude
reservoirs can have higher-magnitude CH4 emission rates than would be
predicted by age and latitude alone (DelSontro et al., 2018a). A spring burst
of FCH4 observed over a 2-week period in 2018 but not 2017 accounted
for 59 % of the difference in cumulative emissions between years. This
difference between consecutive years highlights the importance of multi-year
studies (cf. Room et al., 2014) and the importance of characterizing
temporal variability in open-water systems, which Williamson et al. (2020)
illustrated exceeded spatial variability for several physical, chemical, and
biological metrics.
The EC technique holds much promise for improving our understanding of the
biophysical drivers of gaseous fluxes, with a few caveats. In addition to
the pseudo-continuous temporal coverage, the EC measurement footprint
encompasses a much larger area than traditional gas flux measurement
techniques (e.g., dissolved gas sampling, chambers, inverted funnel traps),
increasing the likelihood of integrating fluxes over a distribution of hot
spots. However, care must be taken in the siting, quality control, and
interpretation of results. The authors reemphasize the recommendation given
by Vesala et al. (2012): for best results, close collaboration is needed
between biometeorologists and limnologists to understand what is going on
both above and below the water. For future studies of reservoir FCH4
using EC, we recommend siting the monitoring tower in the area of the
reservoir with the highest variability in CH4 emissions, likely near
the inlet, and setting up multiple AFTs across the reach of the reservoir to
constrain spatial patterns. Future studies that incorporate more direct
measurements of phytoplankton dynamics would also be useful to improve our
understanding of drivers of CH4 production and emission that may be
more common with future warmer springs and extremes in precipitation
patterns.
The EC results in this study further our understanding of the interaction
between precipitation, sediment temperature, algal productivity levels, and
FCH4. This study adds to our understanding of open-water flux processes
at appropriate spatial and temporal scales while highlighting a way to
present and compare EC and whole-reservoir survey data in appropriate
contexts.
Code and data availability
The datasets and R code used for the analysis in this study are available on
Zenodo. The raw data and R code are available in “R Code for: Temporal trends in methane emissions from a small eutrophic reservoir: the key role of a spring burst” at 10.5281/zenodo.4540271 (Barnett et al., 2021a), and supplemental ANN resampling data are
available in “Artificial Neural Network (ANN) Resampling Results for Gap
Filling Eddy Covariance Data” at 10.5281/zenodo.3995098 (Barnett et al., 2021b).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-18-5291-2021-supplement.
Author contributions
SW, JJB, and JTW developed the research concept and experimental design. MJV and TW supported establishing the monitoring site and provided auxiliary monitoring data. DAB, SW, and JJB deployed instrumentation and conducted sampling with assistance from TW. DAB managed field sampling equipment and logistics. SW conducted the data reduction, analysis, and visualization with assistance from JJB, JTW, and WB. WB developed the artificial neural network gap-filling routine with input from JJB and SW. SW, JJB, and JTW interpreted the data. SW drafted the manuscript, and all coauthors provided input on edits and revision.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
The views expressed in this article are those of the authors and do not
necessarily reflect the views and policies of the US Environmental
Protection Agency (EPA). Any mention of trade names, manufacturers or products
does not imply an endorsement by the US government or the US
EPA. The EPA and its employees do not endorse any
commercial products, services, or enterprises.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank David Wesler and other personnel at Hueston Wood State Park for all
of their support in our monitoring efforts at Acton Lake. We are very
grateful to the members of the EPA Scientific Dive Unit for their assistance
in installing the mid-lake tower: Steve Donahue, Brad White, Frank Borsuk,
David Light, Nathan Doyle, and Leah Ettema. We also thank Gil Bohrer and
Jorge Villa for their guidance and assistance with the mid-lake tower. We
thank Ryan Daly, Bill Mitchell, and Garrett Wiley for assistance with design
and fabrication of tower hardware and power systems. We are grateful for the
additional laboratory and field support provided by Karen White, Paul Trygstad, Eleanor Silver, Megan Berberich, Keith Bisbe, Aiden Pemberton,
Page Jordan, and Tom Radford. We would also like to thank the three
anonymous referees who provided valuable constructive feedback that improved
the quality of this paper. We acknowledge that Acton Lake is located within
the traditional homelands of the Myaamia and Shawnee people, who along with
other indigenous groups ceded these lands to the United States in the first
Treaty of Greenville in 1795.
Financial support
This research was not funded via grants. It was supported by the United States Environmental Protection Agency Office of Research and Development.
Review statement
This paper was edited by Ji-Hyung Park and reviewed by Cynthia Soued and two anonymous referees.
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