River sediments falling dry at low water levels are
sources of CO2 to the atmosphere. While the general relevance of
CO2 emissions from dry sediments has been acknowledged and some
regulatory mechanisms have been identified, knowledge on mechanisms and temporal
dynamics is still sparse. Using a combination of high-frequency measurements
and two field campaigns we thus aimed to identify processes responsible for
CO2 emissions and to assess temporal dynamics of CO2 emissions
from dry sediments at a large German river.
CO2 emissions were largely driven by microbial respiration in the
sediment. Observed CO2 fluxes could be explained by patterns and
responses of sediment respiration rates measured in laboratory incubations.
We exclude groundwater as a significant source of CO2 because the
CO2 concentration in the groundwater was too low to explain CO2
fluxes. Furthermore, CO2 fluxes were not related to radon fluxes, which
we used to trace groundwater-derived degassing of CO2.
CO2 emissions were strongly regulated by temperature resulting in large
diurnal fluctuations of CO2 emissions with emissions peaking during the
day. The diurnal temperature–CO2 flux relation exhibited a
hysteresis which highlights the effect of transport processes in the
sediment and makes it difficult to identify temperature dependence from
simple linear regressions. The temperature response of CO2 flux and
sediment respiration rates in laboratory incubations was identical. Also
deeper sediment layers apparently contributed to CO2 emissions because
the CO2 flux was correlated with the thickness of the unsaturated zone,
resulting in CO2 fluxes increasing with distance to the local
groundwater level and with distance to the river. Rain events lowered
CO2 emissions from dry river sediments probably by blocking CO2
transport from deeper sediment layers to the atmosphere. Terrestrial
vegetation growing on exposed sediments greatly increased respiratory
sediment CO2 emissions. We conclude that the regulation of CO2
emissions from dry river sediments is complex. Diurnal measurements are
mandatory and even CO2 uptake in the dark by phototrophic
micro-organisms has to be considered when assessing the impact of dry
sediments on CO2 emissions from rivers.
IntroductionCO2 emissions from dry river sediments – significance
Streams and rivers are known to play an important role in the global
carbon cycle. The transport of continental carbon to the ocean is mainly
regulated by rivers (Schlesinger and Melack, 1981). Moreover,
carbon in rivers undergoes transformation processes and can be temporarily
stored by means of sedimentation and photosynthesis or released due to
biological respiration (Battin et al., 2009). One distinctive
feature of rivers is their frequently changing water level. Climate change
is expected to further increase the seasonal and the inter-annual
variability of rivers and hydrological regimes (Bolpagni et al.,
2019; Coppola et al., 2014). In Europe, more frequent and longer-lasting
droughts are expected during summers, which lead to low water levels in
streams and rivers (Spinoni et al., 2018). Consequently,
previously submerged river sediment will be exposed to the atmosphere and
influenced by drying (Steward et al., 2012). It has been shown that these
exposed sediments can emit high amounts of CO2 (von Schiller et
al., 2014) and may represent a globally relevant carbon source to the
atmosphere (Marcé et al., 2019).
Regulation of CO2 emissions from dry sediments
While the relevance of CO2 emissions from dry river sediments has been
acknowledged, only little is known about underlying mechanisms and temporal
patterns. A recent study identified organic matter content and moisture as
common drivers of CO2 emissions from dry aquatic sediments (Keller
et al., 2020). However, high variability prevents the prediction of CO2
fluxes for particular sites. Case studies showed that CO2 emissions are
affected by temperature (Doering et al., 2011), emergent vegetation
(Bolpagni et al., 2017), organic matter (Palmia et al., 2021),
water content (Martinsen et al., 2019), or the frequency of dry–wet
cycles (Machado dos Santos Pinto et al., 2020). Although it is
known that CO2 emission from dry sediment may change with time,
existing studies are based on single or few measurements. Few studies
addressed temporal variability of CO2 emissions, but nothing is yet
known about short-term dynamics of greenhouse gas (GHG) emissions from dry aquatic sediments.
Investigating temporal variability of CO2 fluxes should provide
information about the potential sources of emitted CO2. Knowing sources
of emitted CO2 from dry sediments is crucial to be able to model or
scale up GHG emissions from these systems.
Possible sources of CO2
Carbon emissions from desiccated sediments derive from a number of possible
biotic and abiotic sources (Marcé et al., 2019). Microbial
respiration is known to contribute to CO2 emissions
(Weise et al., 2016), similar to soil respiration. Organic
matter originating from organic particle sedimentation may be mineralized to
CO2 or CH4. It is typically observed that CH4 emissions from
dry sediments are low, indicating that anaerobic mineralization plays a minor
role (Marcé et al., 2019).
In contrast to respiration, abiotic processes are rarely taken into account
as sources of CO2 (Rey, 2015). Yet, recent findings revealed a
spatial variability of CO2 fluxes from dry river sediments with highest
fluxes near to the river (Mallast et al., 2020). As a possible
explanation, the authors hypothesized that at decreasing river water level a
groundwater flow gradient towards the river would transport groundwater to
the river (Peters et al., 2006). Groundwater is usually
10- to 100-fold super-saturated with CO2 (Macpherson, 2009).
Near to the river the thickness of the unsaturated layer approaches zero and
CO2 rich groundwater reaches the surface sediment where CO2 would
eventually degas.
Aim of study
Given the uncertainty of the origin of CO2 emitted from dry river
sediments, in this study we aimed to test the hypothesis of Mallast et
al. (2020) that CO2 emissions from dry sediments of larger rivers are
driven by groundwater degassing. If groundwater was a significant source of
CO2, we hypothesize a only weak temperature dependence of CO2 emissions.
We applied a combination of automatic high-frequency measurements and
detailed studies using a variety of methods to identify the source of
CO2 emissions from dry sediments at a large German river and to
understand their temporal dynamics and drivers.
Material and methodsStudy site
The study was conducted at the lowland part of the river Elbe, one of the
largest rivers in central Europe with a discharge average of about 559 m3 s-1 at the city of Magdeburg (Weigold and
Baborowski, 2009). Near Magdeburg, the middle Elbe can be characterized as a
free-flowing, lowland river with comparable large floodplains, only regulated
by groyne fields. Such groyne fields are the dominant shore type along the
German part of the river (Bussmann et al., 2022).
Hence, seasonal water level fluctuations are shaping the different habitats
alongside the river, ranging from alluvial forests and pastures to sandy
beaches (Scholten et al., 2005). The study site is
located near the farm “Apfelwerder” at river km 314 in between two groins
and is characterized by a slight slope from the river to the adjoining
pasture (52.038398∘ N, 11.715495∘ E). Groynes extended about 50 m into the
river, and distance between groynes was 130±37 m. A sandy beach of
about 2 to 5 m with sparse vegetation (Persicaria lapathifolia, Rorippa amphibia, Polygonum aviculare) could be found directly at the
river, while the vegetation became denser with distance to the river (Fig. S1 in the Supplement).
High-frequency measurementsAutomatic flux chambers, water table levels, and environmental data
To cover the temporal dynamics of CO2 fluxes three opaque automatic
chambers (CFLUX-1 Automated Soil CO2 Flux System, PP systems, Amesbury,
Massachusetts, USA), were installed (Fig. S1). The chambers measured
CO2 fluxes once every hour. Each flux measurement lasted 5 min and
between flux measurements the chambers were open for 55 min. CO2 fluxes
were calculated from the linear increase of CO2 during a closure time
of 5 min. Each chamber was equipped with a soil moisture and temperature
probe (Stevens HydraProbe, Stevens Water Monitoring Systems, Portland,
Oregon, USA). Due to fluctuating water levels over the summer of 2020
(Fig. 1), it was not possible to measure CO2
fluxes from the sediment continuously over the whole measurement period. The
chambers were set up in the periods from 1 May to 10 June, from 3 to 6 August, and from 17 to 26 September;
moreover, during deployment they needed to be moved occasionally.
Automatic flux chamber data were discarded when the collar was flooded or
the sand was washed away by waves, which resulted in CO2 concentrations
fluctuating around ambient concentration. The final dataset contained 3128
flux measurements.
Because we did not know the exact elevation of our research site, we
installed the chambers at defined heights relative to the gauge “Magdeburg
Strombrücke” (located 13 km downstream of the study side, zero point of
gauge = 39.885 m above mean sea level; ELWIS, 2020).
Therefore, the distance to the river and the height over water level were
determined once, along a transect. Out of these parameters, a slope was
calculated and afterward used to position the automatic chambers in the
field. Positions, where the automatic chambers were placed were related to
gauge levels 75, 85, and 95 cm. In other words: when the gauge recorded a
water level of, e.g., 75, a chamber at the “75 cm position” was located
directly above the water line at our research site. The thickness of the
unsaturated sediment was calculated as the difference between the height
above zero gauge for each chamber and the actual river level. Weather data
from the German Weather Service were obtained for the monitoring station
Magdeburg 15 km from Apfelwerder (DWD, 2020).
Water level of the Elbe River at gauge Magdeburg
Strombrücke (13 km downstream) in summer 2020. Colored lines indicate
positioning of automatic flux chambers. For example a horizontal line at 95 cm means that a particular chamber was located at the water line when the
gauge recorded a water level of 95 cm. Vertical dotted lines indicate
intensive sampling campaigns.
To investigate spatial variability, between May and September, transects of
sediment respiration were measured with a portable soil respiration system
(EGM-5 Portable CO2 Gas Analyzer + soil respiration chamber, PP Systems, Amesbury,
Massachusetts, USA) equipped with the same soil moisture and temperature
probe as the automatic chambers. On each occasion 12 flux measurements along
a 15 m long transect from the water upslope were recorded. The opaque chamber
was placed on vegetation-free spots to make sure that sediment respiration
was measured. At each measuring spot we took note whether plants were
growing nearby.
Detailed sampling campaigns
To more closely investigate the mechanisms behind the CO2 flux, two intensive
measurement campaigns were carried out on 4 August 2020 and 23 September 2020.
Manual chamber measurements
To quantify CO2 fluxes at different distances to the river and also
check for CH4 emissions, manual chamber measurements were done in 1 m
steps away from the flowing water, along a transect which was characterized
by an uphill slope of ∼ 11.5 %. Collars (39 cm diameter)
were installed at four sites along the transect a day in advance to minimize
disturbance during measurements (Fig. S1b). For flux measurements, an
opaque chamber (V=0.0239 m3, A=0.1195 m2) equipped with a
pressure vent tube was placed on a collar. The concentrations in the chamber
were measured every 30 s for ∼ 5 min with a multicomponent Fourier-transform infrared
(FTIR) gas analyzer (DX4000, Gasmet Technologies GmbH, Helsinki, Finland). The
FTIR gas analyzer continuously measures CO2, CH4, and nitrous
oxide (N2O) with an accuracy of ±4 ppm CO2 and ±0.1 ppm CH4 and N2O (Gasmet Technologies GmbH 2018). Hence, the
detection limit of the CO2 flux was ∼ 2 mmol m-2 d-1, while the CH4 flux was detectable if above 0.12 mmol m-2 d-1 and for N2O if above 0.2 mmol m-2 d-1. Fluxes were
calculated from the linear increase of the respective gas mixing ratio
(Gómez-Gener et al., 2015) with time using the R package glimmr
(Keller, 2020).
Rn sediment efflux measurements
To assess groundwater degassing, 222Rn measurements were performed. The
geogenic gas 222Rn is a commonly used natural tracer for groundwater
influence in aquatic systems and is additionally known as a useful tool to
trace the origins of CO2 (Cook and Herczeg, 2000). Therefore,
222Rn concentrations and fluxes were measured with a portable radon
detector (RAD7 Radon Detector, DURRIDGE, Billerica, Massachusetts, USA) to
determine the groundwater influence on CO2 fluxes from dry river
sediments. The measurements of the RAD7 are based on electrostatic
collection of alpha emitters with spectral analysis. Measuring with the
“Normal” mode counts decays of both polonium decay products of 222Rn
(218Po, 214Po). The counts were measured over 1 h and averaged, with a
1σ standard deviation and expresses as decays per second [Bq]. The
measurement range lies between 4–750 000 Bq m-3 with an accuracy of
±5 %.
The 222Rn concentration in 300 mL samples from groundwater (2.3.3) and
the river was measured with the Wat250 mode. In addition, soil 222Rn
emissions were estimated with closed chamber measurements with the RAD7 over
3 h (one Rn measurement per hour). Assuming that groundwater is the main
source of CO2 and that 222Rn moves at the same mass flow as
CO2 (Megonigal et al., 2020), the same spatial dependence of CO2
and 222Rn fluxes would be expected in the case of groundwater being the
major source of CO2. For this reason, 222Rn chamber measurements
were performed simultaneously at two different positions: one with low and
one with high CO2 flux. We used two chambers of different sizes and
corrected 222Rn flux measurements [Bq m-3 d-1] for different
chamber geometry by multiplying with the volume [m3] and dividing by
the area [m2] of the chamber to get the 222Rn flux [Bq m-2 d-1].
Water + sediment sampling
For groundwater, sampling piezometers with a diameter of 2.7 cm and a length
of 100 cm were installed next to each collar (Fig. S1b) a day before the
sampling campaign.
To determine the thickness of the unsaturated zone, the water level in the
piezometers was measured with an electric contact gauge. In situ parameters pH,
conductivity, temperature, and O2 saturation were measured in the
piezometers and the river with a multiparameter probe (WTW®
MultiLine® Multi 3630 IDS, Xylem, Rye Brook, New York, USA).
To analyze dissolved CO2 and CH4 concentrations, water samples
were taken from the piezometers and the river using a syringe. Atmospheric
air was added, with a headspace ratio of 1 : 1. After shaking for 2 min
the headspace was transferred to 12 mL evacuated Exetainers (Labco
Exetainers®, Labco Limited, Lampeter, UK) and stored till
further analysis in the laboratory. Air samples were taken for headspace
correction. Water samples for chemical analysis were collected in crimp
vials without a headspace, stored at 4 ∘C, and later analyzed in
the laboratory.
Soil samples from the 0–5 cm layer were taken around each collar for
incubation experiments. Samples were filled into plastic bags, were stored at
4 ∘C, and analyzed in the laboratory within a week.
Potential CO2 production in laboratory incubations of sediment
Incubation experiments were set up to analyze the potential microbial
respiration in dry river sediments under controlled conditions. For this
purpose, fresh soil samples (25 g wet weight) taken along the transect were
incubated in ∼ 130 mL vials in replicates of four at 19.5 ∘C. To determine the temperature dependence of microbial
respiration, four replicate samples of 25 g were incubated at 4, 12, 19.5, 28,
and 35 ∘C. From each vial, four to five gas samples were taken over an
incubation period of 2 to 3 d by a Pressure-Lok® syringe
(Pressure-Lok® glass syringe, Valco Instruments, Houston, Texas, USA) and analyzed by gas chromatography for CO2. Respiration
rates were calculated from the linear increase of the CO2 content in
the incubation vials divided by dry sediment weight.
To evaluate the temperature response of the microbial respiration in the
sediment the Q10 temperature coefficient and the activation energy (Ea) was
calculated (Dell et al., 2011). The activation energy was calculated as
the slope of Arrhenius plots as described in Gillooly et al. (2001).
To compare respiration data from lab incubations to CO2 fluxes measured
in the field rates, rates of respiration per gram dry weight [µmol g-dw d-1] were converted to fluxes by multiplying with sediment bulk
density [g-dw cm-3] and the thickness of the reactive sediment layer
which we assume to be equal to the thickness of the unsaturated zone [cm].
Analytics
CO2 and CH4 concentrations in gas samples were measured with a gas
chromatograph (GC) (SRI 8610C, SRI Instruments Europe, Bad Honnef, Germany).
The GC was equipped with a flame ionization detector and a methanizer which
allowed for simultaneous measurement of CO2 and CH4 with an accuracy
of <5 %. Dissolved gas concentrations were calculated using
temperature-dependent Henry coefficients (UNESCO/IHA, 2010). Because
the carbonate system in the headspace vial may change during headspace
equilibration, CO2 concentrations were corrected for alkalinity as
described in Koschorreck et al. (2021).
To analyze dissolved inorganic carbon (DIC), and dissolved organic carbon
(DOC) water samples were filtered with a glass microfiber filter (Whatman
GF/F). DIC and DOC concentrations were analyzed based on high-temperature
oxidation and nondispersive infrared (NDIR) detection (DIMATOC® 2000, DIMATEC
Analysentechnik, Essen, Germany). The alkalinity of the water samples was
determined by titration with HCl to pH of 4.3. To determine the
concentration of the cations K+, Na+, Ca2+, and Mg2+, the
water samples were filtered with a 0.45 µm syringe filter, acidified
with HNO3, and analyzed with an inductively coupled plasma optical emission spectrometer (ICP OES) (Optima 7300 DV, PerkinElmer,
USA). The anion concentrations of SO42- and Cl- were measured
with ion chromatography (Dionex ICS 6000, Thermo Fisher Scientific, Waltham,
Massachusetts, USA).
Soil samples were analyzed to determine soil moisture content, bulk density,
and organic matter from weight loss after drying for at least 2 d to
constant weight at 105 ∘C and loss on ignition (LOI) at
550 ∘C, respectively. Sediment texture was determined by the FAO
method (FAO, 2020).
Statistics
CO2 flux datasets from manual and automatic measurements were visually
checked for normal distribution with Q–Q plots. Data were summarized by
distance to the river and tested with a one-sample t test to determine if
measured fluxes differed significantly from zero.
Spearman rank correlation was used to identify relationships between
environmental variables and the observed CO2 flux and to identify the
strength and direction of these relations (Leyer and Wesche, 2007).
Additionally, representative periods and single days were selected from
automatic measurements to analyze patterns hidden by the temporal
variability of the data. The measured environmental variables of sediment
temperature, sediment moisture, thickness of the unsaturated zone, organic
matter content, and precipitation were used for correlation analysis. Water
level and climate data were averaged over 1 h. Linear mixed-effects
models (LMEs) were applied to predict the influence of the environmental
variables on the CO2 flux at the study site for variables for which a
linear relationship with the CO2 flux was presumed. Model selection was
done by removing predictors and comparing conditional R2 values of
different models. To apply simple linear regression models and LMEs,
assumptions of normality and homoscedasticity were visually checked with
diagnostic plots, including residuals vs. fitted and Q–Q plot. Flux data
were log-transformed for LME analysis. Because of occasional small negative
fluxes, we shifted all fluxes to positive values by adding 121 mmol m-2 d-1 prior to transformation (120 was the value of the largest
negative flux). Statistical analysis was performed using R
(R-Core-Team, 2016).
ResultsLong-term data
The river showed a typical summer discharge situation with a water level
mostly below 1 m, interrupted by a high-discharge event at the end of June
(Fig. 1). Considerable areas of dry sediments only
emerged during 6 weeks in early summer and short periods in the first week
of August and in September. CO2 fluxes measured during these periods
showed high diurnal and seasonal fluctuations (Fig. 2). Fluxes fluctuated over 3 orders of magnitude between -120 and 1135 mmol m-2 d-1 with a median of 98 and a mean ± SD of 149±155 mmol m-2 d-1. Fluxes fluctuated in a narrow range below 200 mmol m-2 d-1 during the first phase of the investigations in May. Due
to rising water level, on 17 May we moved the chambers higher up where
we measured both higher fluxes and larger diurnal amplitudes. When the water
level decreased after 20 May, we moved the chamber down to freshly
emerged sediment. There, CO2 fluxes were similar to the fluxes measured
10 cm higher during the first half of May and tended to increase with
increasing time since drying. Negative fluxes were observed in 193 out of
3128 flux measurements (= 6 % of all fluxes). Negative fluxes were
observed especially during the beginning of the measurement period and at
sites near to the water. Interestingly, negative fluxes nearly exclusively
occurred during the day between 10:00 and 18:00, peaking in the afternoon
(Fig. S2). Chambers installed closer to the water measured lower and less
variable fluxes than chambers installed higher upslope.
Fluxes showed considerable short-term variability. Variability was not
constant during the investigated period but especially high after June.
Clear diurnal patterns were observed during the entire study but most
pronounced in September.
CO2 fluxes in millimole per square meter per day measured with automatic
chambers (a) and corresponding water level of the river measured 13 km
downstream at the gauge Magdeburg Strombrücke (b). Colors indicate
the elevation of the chambers. For example the 75 cm position means that
the chamber was directly above the water line when the gauge reading was 75.
Lines indicate smoothed data ± SD using locally estimated scatterplot smoothing (LOESS) smoother with a span 0.1.
The grey areas indicate confidence intervals.
Regulatory factors: sediment moisture, temperature, water level, climate
The observed diurnal pattern with higher CO2 fluxes during the day
suggested a temperature regulation of the flux. The CO2 flux was indeed
weakly (Spearman p<0.05) correlated with the thickness of the
unsaturated zone (R2=0.31), sediment temperature (R2=0.19,
Fig. 3a), and moisture (R2=-0.19), as well
as precipitation (R2=-0.12). A linear mixed-effects model with site
where the chamber was placed as random factor and temperature and thickness
of the unsaturated zone as fixed factors explained 0.61 % of the
variability. Adding moisture did not further improve the LME (Table S1 in the Supplement).
The temperature response of the CO2 flux was not very clear, however,
if all data were plotted together (Fig. 3a), but if
data from single days were plotted, a clear pattern emerged
(Figs. 3b and S3). The temperature response
of the flux was affected by the time of day resulting in typical hysteresis
curves. Warming during the day resulted in exponentially increasing fluxes.
However, fluxes stayed high despite cooling which started in the afternoon – the
temperature response of the flux was clearly delayed. From the CO2
flux–temperature relation (Fig. 3a) an activation
energy of 0.56 eV (37 kJ mol-1) could be calculated which corresponds
to a Q10 of 1.7 between 10 and 20 ∘C.
CO2 flux in millimole per square meter per day (obtained from three automatic
chambers) depending on sediment temperature of (a) all data and (b) only data from
2 June as an example for hysteretic response to temperature. Color
indicates hour of measurement.
A closer look at data from a single week in September revealed how temperature,
thickness of the unsaturated zone, and precipitation interacted in
regulating the flux (Fig. 4). Temperature drove
the very clear diurnal amplitude, but the absolute level of the flux was
higher with increasing thickness of the unsaturated zone (which was
accompanied by sediment drying). A single precipitation event on 25 September
resulted in a sudden increase in sediment moisture which was
accompanied by a clear drop of the CO2 flux. If only data for the
period shown in Fig. 4 were considered, a linear
model containing sediment temperature and moisture and the interaction
between temperature and moisture explained 46 % of the variance.
Example high-frequency dataset showing (a) CO2 flux (F)
measured by an automatic chambers, (b) sediment temperature (Tsoil; 0–5 cm
depth), (c) sediment moisture (Msoil; 0–5 cm depth) (d) precipitation
(Precip) recorded in hourly resolution, and (e) thickness of the unsaturated
zone (UZ; distance between water table level and ground surface).
Spatial gradient of CO2 flux
Manual chamber measurements at different distances to the water revealed a
spatial gradient of the CO2 flux. CO2 fluxes were lowest near to
the water line where sediment moisture was highest
(Fig. 5) and fluxes increased with distance to the
water. This was also visible in the automatic chamber data when chambers
were placed at different distances to the water (compare
Fig. 2). The chamber which was placed nearer to
the water recorded consistently lower fluxes. This is also consistent with
the observed positive correlation between CO2 flux and the thickness of
the unsaturated zone.
CO2 flux (mmol m-2 d-1) as measured with a manual
chamber (a) and sediment moisture (vol %) measured with a probe in 0–5 cm
depth (b) depending on the distance to the water. The white lines indicates
the plant line. The area below this line was free of vegetation. There were never
plants inside chambers.
We also observed higher CO2 fluxes in the vicinity of plants. Plants
were consistently found from about 3 m from the water uphill. Fluxes above
this “plant line” (indicated by the white line in
Fig. 5) tended to be higher than fluxes from the
vegetation-free area nearer to the water.
In sum, our field-based measurements provide strong evidence that
respiration in the sediment was the major driver of the observed CO2
flux. To further support this conclusion detailed investigations were
carried out.
Detailed investigations
The sediment pore water was quite similar to river water with respect to
electric conductivity and dissolved solutes including DIC
(Table 1). The water level difference between the
wells and the river was below the detection limit – the hydraulic gradient
was virtually zero during our sampling campaigns. The shallow hydraulic
gradient and the similar chemistry suggest a large influence of river water
on the sediment pore water. In contrast, concentrations of dissolved gases
were quite different with high concentrations of CO2 and CH4 and
low concentrations of O2 in the pore water. Pore water concentrations
of CO2 increased with distance to the river, while CH4
concentrations tended to be highest near to the river. In August, the river
water was slightly undersaturated with respect to CO2. The sediment
was poor in organic matter (LOI < 1 %) and texture was loamy sand.
GHG emissions were dominated by CO2, while CH4 fluxes were low and
N2O fluxes were always below the detection limit (Table 1).
Sediment, groundwater, and river water properties at the two sampling
campaigns.
ParameterUnit4 August 2020 23 September 2020 Distance to rivermriver1356river1234CO2 fluxmmol m-2 d-1-333871531533610349142126CH4 fluxmmol m-2 d-10.73.4000.660.500-0.6Unsaturated zonecm–10316278–9193236.5Moisture[vol %]–30132512–3025–9Organic matter in sediment[% LOI]–0.780.391.110.94–0.850.97–0.52CH4µmol L-10.318111162.518918621270CO2µmol L-113.36108831960368132119389911181024DICmg L-142234849502470646455Alkalinitymg L-11.93.53.53.63.11.94.54.85.34.7DOCmg L-113.16.99.31213.56.319.49.911.511SO42-mg L-17944716774797.3203192pH8.37.26.86.66.687.27.37.27ConductivityµS cm-16406106586401563601696655647640O2mg L-19.10.81.11.929.33.42.544
Diffusive fluxes from the river were calculated from concentrations using
the gas transfer coefficient from Matoušů et al. (2019).
CO2 fluxes versus Rn fluxes
Groundwater contained more than 1 order of magnitude higher Rn
concentrations than the river water (Table 2). As an indicator of
groundwater degassing and possible evasion of CO2, we measured the flux
of radon out of the sediment, assuming groundwater as a major source. Rn
fluxes were higher in September than in August although the Rn concentration
in the groundwater was similar in both months (Table 2). The flux of radon out of the sediment was, however, not much different
at two different distances to the river, while the CO2 flux differed by
about 1 order of magnitude between the same sites. If groundwater was the
source of CO2, we would expect Rn fluxes to be related to CO2
evasion from groundwater; thus our data indicate that higher CO2
fluxes were not originating from groundwater.
Flux of radon measured as 222Rn increase in static chambers
compared to CO2 flux measured in the same chambers and radon
concentration determined as detected activity [Bq m-3] in the
groundwater sampled in wells directly beside the chambers as well as in the
river water (0 m distance).
DateDistance to river222Rn fluxCO2 flux222Rn in water[m][Bq m-2 d-1][mmol m-2 d-1][Bq m-3]5 August0327±10916518±206090±418363110±3123 September0532±13511747±416650±4364205169±36Sediment respiration rates
To check whether the observed CO2 fluxes could be explained by
microbial respiration in the sediment, laboratory incubations were carried
out. Sediment respiration rates as measured in laboratory incubations were
0.9±0.45µmol g-1 d-1 in August and 0.64 µmol g-1 d-1 in September with rates increasing with distance to the
river. Potential CO2 fluxes calculated from these rates were similar or
higher than CO2 fluxes measured in situ (Fig. 6).
Thus, sediment respiration was high enough to explain the observed CO2
emissions.
Potential CO2 flux determined from laboratory incubations of
sediment compared to in situ CO2 fluxes depending on distance to the river.
Potential fluxes per unit area were calculated from sediment respiration
rates [mmol g-dw-1 d-1], the thickness of the unsaturated zone
[cm], and the bulk density of the sediment [g-dw cm-3].
Temperature dependence of sediment respiration
Sediment respiration increased exponentially with temperature
(Fig. 7) resulting in a Q10 of 2.5. The calculated
activation energy of 0.7 eV was similar to the activation energy calculated
from the automatic chamber data. The comparison with the temperature
response of the CO2 flux measured by the automatic chambers (line in
Fig. 7) visualizes the similar temperature
response of sediment respiration and in situ fluxes.
Temperature dependence of sediment CO2 production
(sediment respiration) in laboratory incubations depending on temperature
(dots show mean ± SD of four replicates). For comparison, the line shows
the average temperature response of the CO2 flux measured by automatic
chambers, calculated by fitting the data from Fig. 3a to the Arrhenius equation.
DiscussionSource of the CO2
Both our continuous data and detailed measurements show that the CO2
emitted from dry Elbe sediments originated from respiration in the sediment
rather than from groundwater. This conclusion is consistently supported by
numerous pieces of evidence:
The observed CO2 fluxes could be fully explained by sediment
respiration measured in laboratory incubations. From soil respiration
measurements, it is known that basal respiration as measured in
laboratory incubations cannot be equivalent to soil CO2 emissions
(Reichstein et al., 2000). A major difference between both
methods is the exclusion of root respiration in bottle incubations which
would lead to an underestimation of total soil respiration in root-free
assays such as bottle incubations (Hanson et al., 2000). Thus, our
sediment respiration rates measured in the laboratory are probably
conservative estimates which even strengthens our argumentation.
The temperature response of the CO2 flux was very similar to the
measured temperature response of sediment respiration and showed Q10 values
typical for biological processes (Yvon-Durocher et al., 2012) and soil
respiration (Hamdi et al., 2013). Potential evaporation on the other hand
depends on radiation, vapor pressure, and wind speed (Penman, 1948)
and only indirectly on surface temperature (Kidron and
Kronenfeld, 2016). The temperature dependence of evaporation of soils
depends on a complex interaction of texture and soil moisture and is not
easy to predict (e.g., Federer, 2002). The observed temperature
dependence provides strong evidence for respiration being the primary driver
of the CO2 flux.
CO2 emissions increased with distance to the river. If groundwater was
a major source of CO2 emissions, we would expect higher emissions at
lower sediment elevation where groundwater potentially exfiltrated into the
sediment. If there was a hydraulic groundwater gradient towards the river,
this gradient should be steepest near to the river resulting in highest
groundwater flux and potential outgassing near the river.
The CO2 flux was proportional to the volume of the unsaturated
sediment. If CO2 originated from groundwater emissions, we would expect
even a negative correlation because the transport of CO2 from the
groundwater surface to the sediment surface should be inhibited by a larger
unsaturated zone.
Higher CO2 emissions were not accompanied by higher Rn emissions.
Groundwater typically contains high Rn concentrations, and Rn is a proven
tracer to investigate groundwater input into surface waters (Perkins et
al., 2015; Cook and Herczeg, 2000). We observed emission of Rn from the
sediments indicating some influence of groundwater on the sediments. Rn
emission at different distances from the river was identical. Thus, the
thickness of the unsaturated sediment did not affect Rn emissions, showing
that the anoxic zone itself was probably not a source of Rn. Soil Rn
concentrations are known to be affected by meteorological and soil physical
conditions (Asher-Bolinder et al., 1971). Similar Rn
emissions, as observed in our study, are therefore an indication for similar
sediment physical conditions. However, the magnitude of Rn emissions did not
correspond to the magnitude of the CO2 emissions, indicating that the
CO2 flux was independent from groundwater outgassing.
As we did not see hydraulic gradients indicative of larger groundwater
inflow at our location of study, CO2 concentrations in the groundwater
were too low to explain the observed CO2 flux. Groundwater degassing is
relevant in situations when groundwater is pumped to the surface
(Wood and Hyndman, 2017) or seeps into surface waters
(Duvert et al., 2018). In rivers it might be relevant at seep sites
which probably especially occur after fast water level drops and at
extremely low water level.
Taken together our data consistently show that the observed CO2
emissions originated from respiratory CO2 production in the sediment.
After having identified the primary source of CO2 we now look on the
regulators of the magnitude of the CO2 emissions.
Regulation of CO2 emissions
Temperature is a master variable regulating several biogeochemical
processes. Our temperature dependence (Q10= 2.5, Ea = 0.7 eV) is in line
with the temperature response of numerous ecological processes. A meta-analysis of 63 studies of temperature dependence of soil respiration
revealed a mean Q10 of 2.6 (Hamdi et al., 2013). Diverse types of
ecosystems have an activation energy of respiration of 0.65 eV
(Yvon-Durocher et al., 2012) which is very similar to our study.
Temperature was an important regulator not only because of the temperature
dependence of sediment respiration but also because the diurnal temperature
amplitude was quite large. Sediment temperature not only ranged between 2.8
and 32 ∘C during the study period, but the complete temperature
amplitude of about 20 ∘C could be observed during single days
(Fig. 4). The large diurnal amplitude at these
sites is favored by a lack of shadow and the fast heating of the sand which
can lead to temperatures easily exceeding 40 ∘C (Mallast et
al., 2020).
Although the temperature dependence of the CO2 flux is evident, it was
not easily visible in flux versus temperature plots which show a large
scatter (Fig. 3a). Only when looking at single
days, a typical hysteresis pattern (Fig. 3b) became
apparent. Such hysteresis curves have frequently been observed in high-frequency datasets of soil respiration (e.g., Riveros-Iregui et al.,
2007). They originate from a phase lag between temperature and CO2
flux and can be explained by different transport of heat and CO2 in
soils (Phillips et al., 2011) or by variable C supply from plants
(Oikawa et al., 2014). The rotation direction as well
as the shape of the ellipsoid depends on the vertical profile of temperature
and activity in the soils as well as on the depth were soil temperature was
measured. We measured temperature in 5 cm depth and obtained counterclockwise
hysteresis which means that CO2 emissions were delayed relative to
temperature measurements. A plausible explanation is that a large part of
the CO2 was produced in deeper sediment layers where the daily
temperature maximum was reached later. This is consistent with the observed
positive correlation between CO2 flux and the thickness of the
unsaturated zone. Theoretically the effect could also be caused by delayed
outgassing of CO2 from deeper sediment layers due to CO2 transport
limitation. However model calculations had shown that this mechanism was
less relevant for shaping diurnal hysteresis in soils (Phillips et al.,
2011). We quantified the delay by shifting flux and sediment–temperature
data against each other (Fig. 8). By correlating
the flux with the temperature 3 h before, we obtained the best linear
correlation (R2=0.97) for the data in Fig. 3b. However, the time shift which produced the best linear fit differed
between days (min = 0, max = 10, mean ± SD =4.8±3.7 h) with a
median of 4 h and no apparent differences between sites. Also the
R2 of the best fit differed between 0.2 and 0.97. Thus, the hysteresis
pattern obviously depended on the day of measurement and it is not possible
to derive a general relation which then could be used to analyze
temperature–flux relations of time-shift-corrected data.
Hysteresis loop for 2 June (same data as in Fig. 3b) with
flux data shifted for various hours; 3 h shifted means that the flux at
10:00 local time (CEST) was correlated with the temperature at 07:00.
Wetting of dry soils typically triggers a pulse of CO2 production
(Birch, 1958). However, in our case wetting events caused by
rainfall reduced the CO2 flux as exemplified in
Fig. 4. This shows that CO2 production in the
sediment was not water limited and/or that the CO2 flux was rather
transport limited when rainwater blocked gas-filled pores
(Asher-Bolinder et al., 1971). At sediment moisture around
30 % in sandy sediments as measured in our study microbial activity in the
sediment is probably not water stressed and consequently not stimulated by
wetting. Thus, it is probable that the reduced CO2 flux after rain
events was caused by physical blocking of soil pores. This is consistent
with the observed long-term increase of the CO2 flux with decreasing
moisture. Direct mechanistic dependence, however, is difficult to show
because moisture also correlates with the thickness of the unsaturated zone
(water level of the river relative to the sediment surface). This is why
adding moisture to our mixed model only marginally increased the predictive
power of the statistical model.
The thickness of the unsaturated zone was a strong predictor of the CO2
flux. The entire unsaturated zone obviously contributed to the CO2
flux. This is plausible because the intermediate sediment moisture both
favored microbial processes and enabled gas exchange through gas-filled
pores. This may also explain high CO2 fluxes in situations with
extremely high sediment surface temperature (Mallast et al., 2020). Even
if under such conditions CO2 production is inhibited at the surface,
respiration in deeper layers may maintain high CO2 emissions.
The occurrence of vegetation, although excluded from our chamber
measurements and restricted to the vicinity of the chambers, obviously is a
game changer, largely stimulating sediment CO2 emissions. From our data
we cannot fully distinguish whether higher fluxes near plants were caused by
the plants or only by distance to the water (which is equivalent to the
thickness of the unsaturated zone). However, the thickness of the
unsaturated zone increased continuously, while the plant line represents
a sudden change of conditions. Our data show a consistent high CO2 flux
above the plant line. It is known that root respiration may
contribute about 50 % to soil respiration (Hanson et al., 2000) and
soil respiration is typically correlated with root biomass
(Tufekcioglu et al., 2001). Thus, as we did not use trenched
collars to exclude roots from chamber fluxes, it is highly probable that
plants contributed to the elevated CO2 emissions through root
respiration or provision of root exudates above the plant line. Higher
sediment CO2 emissions, however, do not mean net CO2 emissions
from the ecosystem since the vegetation growing on the dry sediments also
fixes carbon and can even turn exposed sediments into a carbon sink
(Bolpagni et al., 2017). To assess the effect of emerging vegetation on
the overall carbon cycle of dry sediments, other methods like plant biomass
determination or flux measurements including photosynthesis in transparent
chambers are necessary.
CO2 uptake by the sediment
We frequently observed CO2 uptake by the sediment, although there were
no plants and no light in our chamber. This is known from other studies and
has been attributed to inorganic processes (Ma et al., 2013; Marcé et
al., 2019). In our case the observed CO2 uptake could also be explained
by the interaction of the sediment with river water. During May and June the
river was undersaturated with CO2 (Fig. S4). The groundwater
chemistry data show a gradient of concentrations increasing with distance to
the river. This shows that the sediment pore water near to the river was
affected by river water. Interestingly, negative fluxes were nearly
exclusively observed during the daylight hours. A plausible explanation
would be that ship-induced wave action might have triggered occasional river
water intrusion and CO2 uptake by the sediment (Hofmann
et al., 2010). This mechanism, however, cannot explain negative fluxes in
September when the river was oversaturated with CO2 (Fig. S4).
Dark CO2 uptake could theoretically be caused by chemoautotrophic
micro-organisms like nitrifiers. However, chemoautotrophic CO2 uptake
should not be stimulated by light and is thus not consistent with our
observation of nearly exclusive CO2 uptake during the day.
A straightforward explanation for negative CO2 fluxes during the day is
CO2 uptake by phototrophic organisms. Algae and cyanobacteria are known to have active carbon concentrating mechanisms (CCMs) which allow
CO2 uptake also in the dark (Giordano et al., 2005). Phototrophs
living at the surface of dry sediments are facing a harsh environment with
high salinity in thin water films covering particles and high irradiation
and temperature – all factors favoring the activation of CCMs
(Beardall and Giordano, 2002). Dark CO2 uptake is a common
observation in 14CO2 uptake measurements and known to depend on
pre-darkness light conditions (Legendre et al., 1983). In
pure cultures, it has been shown that CO2 uptake by algae may proceed
for more than an hour in darkness (Goldman and Dennett, 1986; Ohmori et
al., 1984). Thus, it is highly plausible that the observed CO2 uptake
by dry sediments was caused by photosynthetic algae and/or cyanobacteria.
Future studies including chlorophyll analysis of sediments or the
application of specific inhibitors may clarify the mechanism behind CO2
uptake in exposed river sediments.
Implications
Photosynthetic uptake of CO2 in the dark would have consequences for
the interpretation of dark chamber measurements. If a chamber is placed on
the sediment, photosynthetic CO2 uptake may proceed for an unknown
period of time. The fact that no net uptake was observed in the night shows
that the capability of dark CO2 uptake could not be sustained for
periods longer than 1 h, which is consistent with pure culture
observations (Goldman and Dennett, 1986). However, flux
measurements are usually performed within a few minutes making it highly
probable that they include eventual photosynthetic CO2 uptake.
Comparison of transparent and opaque chamber measurements are sometimes used
to detect photosynthesis of algae. Our results imply that such
interpretation have to be treated with care because photosynthetic CO2
uptake may proceed during dark flux measurements.
Our median CO2 flux of 98 mmol m-2 d-1 would result in annual
emissions of 429 g C m-2 yr-1 which is in the range of fluxes
typical for temperate ecosystems (Doering et al., 2011) and similar to
fluxes reported for dry Elbe sediments (Mallast et al., 2020) but high
compared to the gravel bed of an alpine river (38 mmol m-2 d-1,
Doering et al., 2011), and low compared to exposed sediments of
Mediterranean streams (781 mmol m-2 d-1, Gómez-Gener
et al., 2016). Although our observations thus fit the reported range, these
differences as well as the large variation of fluxes observed in our high-frequency measurements (-120 to 1135 mmol m-2 d-1 – this range is
larger than the range of typical fluxes for all kinds of terrestrial
ecosystems as compiled by Doering et al., 2011) imply that care must
be taken when upscaling fluxes not only for certain ecosystems but also for larger
scales.
The observed hysteresis obscures flux–temperature relations if measurements
were only performed at one time during the day. Thus, temperature regulation
of dry sediment CO2 emissions might be more relevant and more complex
than identified in a recent study (Keller et al., 2020).
Our high-frequency measurements show that standard measuring protocols are
probably underestimating CO2 emissions from dry sediments because high
fluxes in the night resulting from a delayed temperature response are not
considered. The median flux measured between normal working hours (08:00–18:00) was 87 mmol m-2 d-1 compared to 98 mmol m2 d-1 if
all data were considered. Thus, only measuring during daytime would lead to
a flux underestimation of 11 %. We therefore recommend to assess temporal
shifts in flux–temperature responses in order to obtain better estimates for
upscaling based on a representative choice of flux data.
Our results are partly contradicting results from Mallast et al. (2020)
who observed highest CO2 emissions near to the waterline. The two
studies, however, are not directly comparable because the previous study by
Mallast et al. (2020) was carried out under extreme drought conditions.
Under such conditions, deeper lying sediments, which tend to be higher in
organic matter and less sandy, were exposed to the atmosphere. Such
conditions should favor CO2 emissions (Keller et al., 2020).
Furthermore the very dry conditions (<10 % sediment moisture)
under the extreme drought might have inhibited microbial processes in the
sandy sediment. While the drivers of CO2 emissions from dry sediments
are known, their complex interaction makes it difficult to predict CO2
emissions under a given situation.
The observed relation between CO2 flux and distance to the river,
however, might facilitate upscaling of CO2 emissions from dry river
sediments. The width of the dry sediment zone can be extracted from
satellite images or aerial photographs. The observed consistent spatial
pattern also implies that the CO2 flux was probably not much affected
by time after exposure. Thus, combining few diurnal datasets of CO2
flux and lateral transects with seasonal data of the width of the dry
sediments zone along a river is a promising approach to quantify total
CO2 emissions from such systems.
Conclusions
We could clearly show that CO2 emissions from dry river sediments under
the given conditions here were primarily driven by respiration in the
sediment. Thus, existing knowledge about soil respiration might also apply
to dry river sediments.
Scheme of processes and drivers of CO2 fluxes from dry river
sediments. Green arrows indicate positive effects; red arrows indicate negative effects.
We could further show that CO2 emissions were regulated by temperature
and the thickness of the unsaturated zone (Fig. 8). The observed
hysteresis effect clearly shows that simple correlations between
environmental parameters and CO2 emissions from sediments may be too
simplistic to study regulatory mechanisms. Positively spoken the analysis of
such hysteresis relations may allow conclusions about underlying mechanisms
(Musolff et al., 2021).
Our data show that the occurrence of terrestrial vegetation has a large and
not yet assessed impact on the carbon cycle of dry sediments. To assess the
effect of vegetation, not only ecosystem production but also the fate of plan biomass upon re-flooding has to be quantified. While it is clear that
CO2 emissions from dry river sediments are relevant, the exact
quantification of the effect of low river levels on the river carbon cycle
remains challenging. Short-term temporal variation is very high and probably
equally relevant as seasonal variability. Any attempt to quantify annual GHG
emissions or the relevance of dry river sediments for carbon cycling needs
to address temporal dynamics of CO2 emissions from dry river sediments.
Data availability
The high-frequency dataset is supplied as a Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-5221-2022-supplement.
Author contributions
MK initiated the study and prepared the manuscript with contributions from
all co-authors. LT and MK performed measurements. All authors planned
measurements and discussed the results.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Thanks to Martin Wieprecht for his excellent help during fieldwork and to
Ulrike Berning-Mader and Corinna Völkner for their instructions and help
in the laboratory of the University of Münster and UFZ.
Thanks to Christian Wilhelm for advice regarding dark CO2
fixation, Bertram Boehrer for discussion and Peifang Leng for help using
R.
Financial support
This work was supported by funding from the Helmholtz Association in the
framework of Modular Observation Solutions for Earth Systems (MOSES).The article processing charges for this open-access publication were covered by the Helmholtz Centre for Environmental Research – UFZ.
Review statement
This paper was edited by Gabriel Singer and reviewed by Kenneth Thorø Martinsen and one anonymous referee.
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