High biogenic silica (BSi) concentrations occur sporadically in lake sediments
throughout the world; however, the processes leading to high BSi concentrations
vary.
We explored the factors responsible for the high BSi concentration in sediments
of a small, high-latitude subarctic lake (Lake 850). The Si budget of this
lake had not been fully characterized before to establish the drivers
of BSi accumulation in this environment.
To do this, we combined
measurements of variations in stream discharge, dissolved silica (DSi) concentrations, and stable
Si isotopes in both lake and stream water with measurements of BSi content in
lake sediments. Water, radon, and Si mass balances revealed the importance
of groundwater discharge as a main source of DSi to the lake, with
groundwater-derived DSi inputs 3 times higher than those from ephemeral
stream inlets. After including all external DSi sources (i.e., inlets and groundwater
discharge) and estimating the total BSi accumulation in the sediment,
we show that diatom production consumes up to 79 % of total DSi input.
Additionally, low sediment accumulation rates were observed based on the
dated gravity core. Our findings thus demonstrate that groundwater discharge
and low mass accumulation rate can account for the high BSi accumulation
during the last 150 cal yr BP. Globally, lakes have been estimated to
retain one-fifth of the annual DSi terrestrial weathering flux that
would otherwise be delivered to the ocean. Well-constrained
lake mass balances, such as presented here, bring clarity to those estimates
of the terrestrial Si cycle sinks.
Introduction
Diatoms – unicellular golden-brown algae – are found worldwide in all
aquatic environments, wetlands, and soils .
Diatoms take up dissolved silicic acid, H4SiO4, expressed here as dissolved
silica (DSi), and build their shells in the form of amorphous silica, also
known as biogenic silica (BSi). Diatom production is thus a crucial component
in the global Si cycle . Massive accumulations of fossil
diatom frustules in sediments have been observed in multiple lakes situated
in silicon-rich environments, especially on volcanic bedrock, such as Lake Challa,
Tanzania–Kenya , or in hydrothermally active areas, such
as Yellowstone Lake, US , or Lake Mývatn, Iceland
. However, lakes without volcanism can also accumulate
high concentrations of BSi in the sediment . One example
is high-elevation and high-latitude lakes, where BSi concentrations as
high as 60 weight percent (wt %) of SiO2 have been found .
In addition, high BSi concentrations in sediment have been observed in
Lough Neagh, Northern Ireland ; Lake Baikal ;
Lake Edward ; and Lake Malawi .
The processes responsible for the diatom-rich sediment formation in
these non-volcanic settings, however, are poorly understood.
High BSi accumulation in sediment has been hypothesized to require
sufficient DSi in the water column for diatoms to
grow and low detrital input to minimize dilution of autochthonous
BSi . DSi ultimately originates from weathering
of bedrock, and it is transported by rivers through the environment
where it can be removed by biological or chemical processes,
such as secondary clay mineral formation or
amorphous silica precipitation .
DSi concentrations in the environment are influenced by factors
such as vegetation type and
bedrock type and indirectly by
climate forcing or watershed geomorphology
. In particular, attention has been paid to the
relative importance of groundwater discharge as a main source of
DSi for a few lakes, such as Lake O'Hara, British Columbia ;
Lake Mývatn, Iceland ; Crystal Lake, Wisconsin
; at the mouth of the Changjiang river system,
China ; and in Canadian and Siberian rivers .
However, the significance of groundwater discharge is still often
overlooked in studies about Si dynamics in lakes.
The contribution of groundwater to the lake Si cycle can be evaluated using
Si isotopes. Stable Si isotopes are used to trace variation in DSi sources
or diatom production and discern processes affecting BSi accumulation
in lake sediments. Among the three stable isotopes (28Si, 29Si and 30Si)
diatoms preferentially take up the lighter 28Si. Diatoms
tend to fractionate the Si isotopes with a fractionation factor of -1.1 ‰ , which means that the diatom BSi will have an isotopically
lighter ratio compared to the source DSi. Riverine DSi usually shows
isotopically heavier ratios compared to groundwater, as there are more
processes that fractionate Si isotopes during river transport .
Therefore, stable Si isotopes are an ideal tracer for the contribution of groundwater.
Here, we investigate the diatom-rich sediment formation in Lake 850
through water and silicon mass balances. Lake 850,
northernmost Sweden, is an ideal case study with a high content of BSi
in the sediment, ca. 40 wt % . Oxygen
isotopes from diatoms suggested that the lake's isotopic ratio is
mostly influenced by summer precipitation and variations in the
ephemeral inlet streams . Unlike previous studies
in this lake, we hypothesize that groundwater discharge is an important
mechanism controlling lake DSi concentrations. To test this hypothesis,
we estimate groundwater flows discharging into the lake using a water
and a radon (222Rn) mass balance. DSi concentration and stable
Si isotope mass balances were used to determine Si sources for the
diatom-rich sediment deposited in recent decades.
Study area
Lake 850 (68∘15′ N, 19∘07′ E) is located 14 km southeast from the Abisko
Research Station (388 m a.s.l.), northern Sweden. From 1913 to 2019, the mean
annual surface atmospheric temperature was -0.4 ∘C, whereas during the study years
(2018–2019) the mean annual temperature was 0.03 ∘C. Further,
the mean surface atmospheric temperature during the aquatic growing season
in 2018–2019 (June to August)
was 10.1 ∘C (1 SD = 2.8 ∘C), and the long-term
(1913–2019) mean summer temperature was 9.8 ∘C (1 SD = 3.6 ∘C)
. During the ice-free period direct surface precipitation contribution from the watershed
was estimated from the mean precipitation of 48 mm month-1.
Lake 850 lies above the tree limit (600 m a.s.l) at 850 m a.s.l.
The lake surface area is 0.02 km2, with a maximum depth of 8 m and a catchment
area of 0.35 km2. The lake's deep basin represents
48 % of the lake surface area.
The underlying bedrock is composed
of granites and syenites and is overlain by a thin layer of till. The catchment
vegetation is comprised of Arctic species of mosses, grasses, and shrubs .
There are two ephemeral inlets (max 6 cm deep) in the eastern part of the
lake and one outlet (10 cm deep) in the western part (Fig. , Table S1).
In addition to streams, additional sources of water to rivers and lakes can be snow
patches or inputs of groundwater . From mid-October until
late May–early June, the lake is ice-covered. The catchment is snow-covered
from mid-September to mid-June. In August, the lake is well-mixed, with
no thermal stratification. The lake is classified as oligotrophic and
has a pH of 6.8 and a dissolved organic carbon concentration of
2.3 mg L-1.
Sampling sites of Lake 850 (northern Sweden). Inlets and outlet
streams are signified by white arrows. Plotted in R using the package
ggmaps and modified.
Numerical analyses – mass balance modelsRadon mass balance
Radon (222Rn, hereafter Rn) is produced from the radioactive decay
of 226Ra (Ra hereafter) present in rocks, soils, and sediments. Radon
emanates from Ra-bearing minerals, enters the groundwater, and is transported
through the aquifer. Groundwaters usually contain Rn concentrations orders
of magnitude higher than surface waters, and groundwater discharging into
surface waters can thus be easily detected by a Rn enrichment with respect
to surface waters .
Groundwater discharge into Lake 850 was quantified using a Rn mass balance
approach and assuming steady state .
For steady-state conditions, the groundwater flow discharging into the lake can
be determined by the imbalance between all Rn sources and loss terms (Eq. ).
The sources of Rn are the main inlet streams (n=2),
Rn production by dissolved Ra in the water column, Rn diffusion from underlying
sediments, and groundwater discharge. Radon losses include radioactive decay,
atmospheric evasion, and losses through outlet streams (n=1). Losses by
recharge into underlying aquifers are considered minor, because the concentration
of Rn seeping into sediments is usually much lower than that seeping into the lake
. By evaluating all Rn source and loss terms, the groundwater
flow discharging into the lake can be determined using the following equation:
QgwRngw+FsedA+λRalakeV+QinRnin=FatmA+λRnlakeV+QoutRnout,
where Qgw is the unknown groundwater discharge [m3 d-1];
Qin and Qout are the discharge from inlet and outlet streams
[m3 d-1], respectively; Rnlake and Rngw are the concentrations
of Rn [Bq m-3] in lake water and groundwater, respectively; Rnin and
Rnout are the concentrations of Rn [Bq m-3] in the main inlet and
outlet streams, respectively; Ralake is the concentration of Ra in the lake water
column [Bq m-3]; Fsed is the net diffusive flux of Rn per unit area
from lake sediments [Bq m-2 d-1]; Fatm is the loss of Rn to the
atmosphere [Bq m-2 d-1]; λ is the radioactive decay constant
of Rn [d-1]; and A [m2] and V [m3] are the area and volume
of the lake, respectively.
The calculation of Rn loss to the atmosphere was based on the empirical equation
by :
Fatm=kRnlake-αRnair,
where k is the gas transfer coefficient [m d-1] based on an empirical
relationship that relates k with wind speed and lake area ,
and α is the air–water partitioning of Rn corrected for salinity and
temperature .
The steady-state assumption implies that Rn sources and Rn sinks are balanced and
constant with respect to the timescale of Rn within the system. The residence time
of Rn in the lake can be calculated by dividing the Rn inventory in the lake
water by the sum of all tracer losses using the equation
tRn≈1λ+QoutV+kh,
where λ is the radioactive decay constant of Rn [d-1],
Qout is the discharge from the outlet stream, V is the lake
volume, k is the gas transfer coefficient [m d-1] based on an empirical
relationship that relates k with wind speed and lake area ,
and h is the lake depth [m].
Groundwater discharges (Qgw) were estimated for August and September 2019.
For the remaining months, we interpolated the estimated values by assuming two
different scenarios of (i) constant or (ii) variable groundwater inflows over the
year (see Appendix ) for variable groundwater
inflows scenario).
Water balance
The lake water balance was calculated from the volumetric water
balance equation:
ΔV=Qin+P+Qgw-Qout-E,
where ΔV is the change in lake water volume; Qin and Qout are the
stream inflow and outflow, respectively; Qgw is the groundwater inflow; P
is precipitation; and E is evaporation. Monthly summer precipitation of 48 mm
has been considered to be included in the stream inflow term. Evaporation
and precipitation have been shown to only have a small contribution to the lake
water balance, and thus they are considered negligible here .
Silicon mass balance
The DSi flux into and from the lake is calculated as ϕ=Q⋅c,
where Q is discharge [L s-1] and c is DSi concentration
[mg SiO2 L-1]. The DSi balance is then calculated as
ΔDSi=ϕin+ϕgw-ϕout-ϕBSi,
where ΔDSi is the change of lake DSi [mg SiO2 yr-1],
and ϕin, ϕout, and ϕgw are the
DSi fluxes of the inlet, outlet, and groundwater
discharge [mg SiO2 yr-1], respectively. Finally, ϕBSi
represents the flux of BSi into the sediment
[mg SiO2 yr-1], and it was calculated as
ϕBSi=(SAR⋅ρdry⋅BSiwt%⋅Ased)⋅1000,
where SAR is sediment accumulation rate [cm yr-1] calculated from the age–depth
model (see Methods Sect. ), ρdry is dry bulk sediment
density [g cm-3], BSi wt % is the mean of BSi content
in sediments, Ased
is the area of sedimentary basin of the lake in square centimeters, and 1000
is the unit conversion from grams to milligrams.
Assuming that the lake is in steady state, which means that the sum of input DSi fluxes equals the sum of output Si fluxes, thus ΔDSi=0, DSi concentration in groundwater was
then calculated by dividing ϕgw from Eq. () by Qgw.
The groundwater DSi flux in the ice-free period is dependent on inlet (ϕin),
outlet DSi flux (ϕout), and BSi flux to sediment (ϕBSi). However, during the
ice-covered period, the ϕgw is dependent only on ϕBSi, if there is some
(scenario 1, Appendix ) and on differences of
lake volume and DSi concentration. Thus, in order to solve Eq. (),
ϕBSi and lake DSi concentration changes in the ice-covered period are required.
The ϕgw during the ice-covered period is calculated by a mixing model
(see Appendix ).
To constrain DSi concentrations in groundwater, we have examined
three different scenarios considering different BSi fluxes (ϕBSi) to the sediment
driven by the length of diatom production. Two scenarios with maximal and minimal
monthly BSi flux appear in the Appendix aiming
to describe maximal and minimal diatom production period and
thus groundwater DSi concentrations. The scenario better
describing recent diatom production considers that the diatom growing season and
thus the BSi flux occur in 4 months, from June until August, in a year ,
and that scenario is presented here.
Silicon isotope mass balance
The variability of the isotopic Si ratio of the lake water is likely to
be biologically driven and, therefore, was described using a Si isotopic
fractionation model. We hypothesize that the lake has sufficient inlet and
groundwater supply to allow for DSi concentrations to remain high and that
DSi is unlimited for diatom growth; thus, an open-system model was used.
The open-system model describes the expected diatom
δ30SiBSi, as well as the post-uptake ratio of the
lake water δ30Sipostuptake.
7δ30SiBSi=δ30Siinitial+ε⋅f,8δ30Sipostuptake=δ30Siinitial-ε⋅(1-f),
where δ30Siinitial is the isotopic ratio of the initial
DSi source, ε is the fractionation factor of freshwater diatoms
-1.1 ± 0.41 ‰ , and f is the fraction
of remaining DSi calculated as f=coutcinitial, where
cinitial and cout are DSi concentrations before and after
diatom production uptake. Thus, (1-f) is the DSi utilization by diatom
production. The initial DSi concentration is calculated through a
mixing model with knowledge of the discharges (Qin and Qgw)
and DSi concentrations (cin and cgw) of the end-members.
The initial isotopic ratio of lake DSi before
diatom uptake is back-calculated from δ30Sipostuptake
(Appendix ). The known variables are the (1-f) and
the δ30Sipostuptake represented either in the lake isotopic composition
or in the lake outlet δ30Siout, if δ30Silake=δ30Siout.
Further, the groundwater isotopic composition can be calculated from the initial
isotopic Si mixture before diatom uptake and fractionation through an isotope
mixing model (see Appendix ).
Similar to the Si mass balance, the isotope Si mass balance was examined through
three scenarios that differ in BSi flux to the sediment representing different lengths
of diatom production (Appendix ). As differences
in BSi fluxes alter groundwater DSi concentrations, the isotopic composition also changes. However, the scenario describing the recent lake functioning
is used for the model presented here. Results of this model were compared with
measured data of δ30SiBSi and δ30Sipostuptake
(which equals δ30Silake). For validation, the groundwater
δ30Sigw for monthly steady state was calculated and compared
with data in the literature.
Materials and methodsSample collection, chemical analyses, and chronologyWater sampling
For DSi analyses, water samples from the ephemeral inlets and outlet streams and
lake waters were collected monthly from June to September 2019 (Fig. ,
Table S1). Additionally, samples of two profiles of lake water from the deepest part and
a shallower part of the lake were collected in August and September 2019.
All water samples were filtered directly in the field through a 0.45 µm
cellulose Sterivex™-HV Durapore filter and acidified with HCl to pH 2 in the laboratory.
DSi concentrations were analyzed by the automated molybdate-blue method
with a Smartchem 200, AMS™ discrete analyzer at Lund University
with an instrumental error of ±3.7 %. All values for DSi concentrations
are reported as milligrams of SiO2.
For Rn analyses, surface water samples (maximum of 1.5 m depth from the surface
or 0.5 m depth at the shallow depths) were collected from five different stations
(Fig. , Table S1). A deeper water sample (4 m depth) was
collected from the deeper central point of the lake to evaluate the potential stratification
of Rn concentrations. Samples of water from the main inlet and the outlet stream were also
collected. Water samples were collected in 1.5 L polyethylene terephthalate (PET)
bottles with no headspace using a peristaltic pump. Water was pumped directly into the
bottle and left overflowing to replenish the volume at least three times to ensure minimal
contact with air. Shortly after collection, Rn concentrations were determined using a
Rn-in-air alpha spectrometer RAD7 (Durridge Inc.) coupled to the Big Bottle RAD H2O
accessory (Durridge Inc.). All Rn concentrations were decay-corrected for the time
of collection.
Discharges from the inlet and outlet streams were determined by measuring the water
velocity at 60 % of the sampling point depth using the six-tenths-depth method
and creating a cross section through the tributary.
Sediment sampling
Two short (∼ 15 cm) sediment gravity cores were sampled with a HTH gravity
corer in March and August 2019 (Table S1). Both cores showed an
undisturbed water–sediment interface. One of the cores was sliced directly
in the field in 1 cm sections. Each section was weighed before and
after freeze drying to determine water content, porosity, and wet and dry
bulk densities. Total organic carbon (TOC) and total nitrogen (TN) analyses
were carried out on all freeze-dried samples, after packing 5 to 10 mg
of dry sediment into tin capsules. Five samples throughout the core were tested
for carbonate content by acidifying with HCl and heating to 60 ∘C
before the TOC measurements . The measurements were done
on a COSTECH ECS4010 elemental analyzer at the Department of Geology,
Lund University, with the mean analytical uncertainty for TOC of 0.3 wt %
based on duplicate analysis (n=14). The carbonate content calculated
as a difference in TOC between de-calcified and bulk sample was below 0.5 wt % and
thus considered negligible.
Biogenic SiO2 content in the sediment was analyzed by sequential
alkaline extraction . Freeze-dried and homogenized
samples were digested in 0.1 M Na2CO3 (sample reagent ratio 0.03/40 [g mL-1])
in a shaking bath at 85 ∘C for 5 h. Subsamples
of 100 µL were taken at 3, 4, and 5 h and neutralized
in 9.9 mL of HCl to examine for the dissolution of minerals.
The extracted DSi was measured using the automated molybdate-blue method
with a Smartchem 200, AMS System™ discrete analyzer at Lund University
with an instrumental error of ± 3.7 %.
As there were no changes in the amount of total Si extracted during the time course
of dissolution (n=3, slope ≈0), the mean BSi concentration
from all the values was
used to estimate BSi concentration with no correction applied for Si-containing minerals
. All values for BSi concentrations are reported as wt % of
SiO2.
All sediment samples were analyzed for radionuclide concentrations
(210Pb, 226Ra, and 137Cs) at Lund University.
210Pb, 226Ra, and 137Cs were determined by
direct γ-counting using a high-purity germanium detector ORTEC
(model GEM FX8530P4-RB). Freeze-dried and ground samples were sealed
for at least 3 weeks before counting to ensure secular equilibrium
of 226Ra daughters. 210Pb was determined through the
46 keV γ-emission and 226Ra through the 351 and
609 keV γ-emission of its daughter nuclides 214Pb
and 214Bi, respectively. 137Cs was measured by its
emission at 662 keV. Self-absorption was measured directly, and
the detector efficiency was determined by counting a National Institute
of Standards and Technology sediment standard.
Sediment core chronologies
were obtained by applying the Bayesian statistics approach with the software
package Plum . The Plum package was applied using
the default settings for the thickness of Bacon sections (1 cm).
Plum used the individual 226Ra measurements as an estimate of
the supported 210Pb concentration. The unsupported 210Pb
was found in the uppermost 7 cm, and the software package Plum
extrapolated the ages for the remaining 7 cm based on measured data.
To constrain the Rn mass balance, the second sediment gravity core was used for
equilibration experiments in order to determine Rn diffusion from
underlying sediments and the Rn concentration representative of the
groundwater discharging into the lake. Briefly, diffusive flux experiments
were carried out in the laboratory by incubating ∼ 200 g of
dry sediment placed into 500 mL PET bottles with Milli-Q® water,
as described in . Using the RAD7 coupled to the Big
Bottle RAD H2O accessory (Durridge Inc.), Rn concentrations were
monitored for 14 h. The rate of Rn diffusion from the sediment
(Fdiff) was derived from the exponential ingrowth of Rn concentrations
with time. The bottles containing grab sediments were then stored for
more than a month and periodically shaken. After this time, the Rn
concentration in water was measured using the RAD7 and converted into
groundwater end-member activities using porosity and bulk density as
described in .
Stable Si isotope analyses
Stable Si isotope analyses were performed on diatoms recovered from sediment,
lake, and stream water samples. Cleaned diatom material (n=3) from a published core
taken in 1999 was used to determine the stable silicon
isotope ratio in sedimentary diatoms and then used in mass balance models.
Briefly, pure diatom
samples (∼ 0.8 mg) were digested with 0.5 to 1 mL of
0.4 M NaOH (analytical purity) at 50 ∘C for at least 48 h. When all diatoms were dissolved, samples were diluted with Milli-Q®
water to prevent precipitation and fractionation of amorphous silica and
then neutralized by 0.5 to 1 mL of 0.4 M Suprapur® HCl.
The solutions were measured for their DSi concentration to obtain the Si recovery,
which was between 90 % and 100 %. Sample solutions were purified for Si isotope
analysis by cation-chromatographic separation using 1.5 mL cation-exchange Dowex®
50W-X8 (200–400 mesh) resin following the method of .
Silicon from filtered water samples was purified using the same cation-exchange
method . The international Si standard NIST reference material
RM-8546 (former NBS-28) and laboratory standard Diatomite were prepared by alkaline
NaOH fusion and purified following protocol by .
The reference material RM-8546 (former NBS-28) and laboratory standards
IRMM-018, Big-Batch, and Diatomite used in the Vegacenter, Swedish Museum of
Natural History, Stockholm, were prepared
by another type of fusion with LiBO2. Thus, our alkaline
NaOH-fused NBS-28 and Diatomite standards , purified identically to the samples, were matrix-matched to contain
3 mg L-1 Li IPC-MS standard. Similarly, all purified samples
were diluted to a concentration of 3 mg L-1 of Si in 0.12 M
SeaStar™ HCl matrix and doped with Li to contain 3 mg L-1 Li to
match the standard matrix.
The stable isotope measurements were carried out on a NuPlasma (II) HR multi-collector
inductively coupled plasma mass spectrometer (MC-ICP-MS, Nu Instruments™) with an
Apex HF desolvation nebulizer at the Vegacenter. The 28Si signal intensity
of full procedural blanks was determined to be less than 0.35 % of the total signal intensity;
thus no sample contamination was observed.
Silicon isotope data are reported as deviations of 30Si28Si and
29Si28Si from the NBS-28 reference solution in ‰, denoted
δ30Si and δ29Si as follows:
δ30Si=30Si28Sisample30Si28SiNBS28-1⋅1000.
Each sample was measured three times, bracketed by NBS-28 in between, and full chemical
replicates for diatom (n=3) and water samples (n=23) were measured for 65 % of all samples (total
measurements n=137).
Secondary reference materials Diatomite, Big-Batch, and IRMM-018 were measured
throughout all measuring sessions in a period of 3 years, with means of
δ30Si=1.26±0.19 ‰ (2SDrepeated, n=219)
for Diatomite,
δ30Si=-10.64±0.18 ‰ (2SDrepeated, n=77)
for Big-Batch,
and δ30Si=-1.77±0.18 ‰ (2SDrepeated, n=100)
for IRMM-018 for quality control purposes. All secondary reference material values
were in good agreement with values from a previous interlaboratory comparison .
The reproducibility of all samples was <0.2 %. At the Vegacenter,
the long-term precision for δ30Si is 0.15 ‰ (2 SD).
Summary of discharge from the inlets (Qin), the outlet (Qout) stream, and
groundwater discharge (Qgw); dissolved Si concentration as
mg SiO2 L-1
in the inlets (cin), the outlet (cout), and the lake water (clake); and
stable Si isotopic signal of the inlet (δ30Siin),
outlet (δ30Siout),
and lake (δ30Silake).
Lake 850 is a subarctic lake in a region with strong seasonality. The discharge from
inlets and the outlet streams show a decreasing trend throughout the ice-free period
from June through September (Table ). The highest water flow rates are
observed during the snowmelt period (June and July). Inflow from the stream inlets
to the lake in August is low, and both inlets are dry in September.
During the ice-free period direct surface precipitation is
0.65 L s-1, which represents only 1.4 % of the lake volume, and similar or
higher discharges are observed in the stream inlets from July to August. Therefore,
the influence of precipitation on the water mass balance is limited. The calculated
lake water residence time during the high-flow regime in June, defined as lake volume
(1.2×105 m3) divided by the lake outlet discharge (Table ),
is 65 d. During the rest of the year, the lake water residence time is between
5 months and 2.4 years.
Lake DSi concentration varies seasonally (Table ), with the highest values during
the ice-covered period in March, reaching 2.51±0.35 mg SiO2 L-1.
With snowmelt, the lake DSi decreases to 1.24±0.02 mg SiO2 L-1
in June and to its minimum value
of 0.96±0.06 mg SiO2 L-1 in August.
With the first snow in September, lake DSi
concentration rebounds, having values of 1.37±0.04 mg SiO2 L-1.
Data of DSi for
the inlets and the outlet streams show two different patterns during the year (Table ).
A lower inlet DSi concentration of 2.34±0.05 mg SiO2 L-1
is observed during snowmelt in June compared to July and August, when the inlet DSi concentrations increase
to 4.79±0.05 mg SiO2 L-1 and
5.05±0.12 mg SiO2 L-1, respectively.
The lake outlet DSi concentration shows little variability, with the lowest concentration
of 0.94±0.01 mg SiO2 L-1 in July and only a small
increase up to 1.12±0.03 mg SiO2 L-1
towards the end of the summer season in August. In September, when the inlet streams
are snow-covered, the DSi concentration in the outlet stream is the same as the lake
water concentration at 1.37±0.01 mg SiO2 L-1.
The stable Si isotope ratios of the lake, inlet, and outlet streams vary during
the year. The heaviest lake δ30Silake ratio, 1.27±0.15 ‰,
is observed during the ice-cover period, and the lightest ratio, 0.73±0.10 ‰,
occurs during the snowmelt in June (Table ). In June, the inlet has
a lighter δ30Siin of 0.02±0.10 ‰, whereas in August
the inlet isotopic ratio 0.78±0.15 ‰ has similar
values as the lake. The δ30Siout of the outlet in June is slightly heavier
(0.89±0.10 ‰) compared to the lake δ30Silake.
In July the outlet δ30Siout is lighter than the inlet one (Table ).
During the remainder of the year, the outlet δ30Siout is closely similar to the lake
and inlet δ30Silake.
Groundwater discharge
Surface lake Rn concentrations range between 94 and 136 Bq m-3 in August
and from 96–126 Bq m-3 in September. Dissolved Ra in lake waters is
assumed to be similar to dissolved Ra found in other lakes in the region
(1.4±0.6 Bq m-3).
However, the measured Rn inputs (the stream inlets) due to Ra decay were below 0.5 %,
compared to the net excess of Rn delivered by groundwater discharge. Thus, the inlet
Rn flux was neglected in the total Rn balance.
There was no significant vertical stratification of Rn concentration with Rn
concentrations in deep waters (105±26 and 79±24 Bq m-3) in
August and September, respectively. Equation () was solved analytically
to obtain the amount of groundwater discharging into the lake (Qgw) in August
and September 2019. Uncertainties of individual terms were included in the estimation
of the associated uncertainty .
Using the mean wind speed for the 48 h period prior to sampling (3.1±1.2 and
5.0±1.8 m s-1 in August and September, respectively)
resulted in kRn
estimates of 1.1±0.2 and 1.2±0.4 m s-1. Uncertainties include the
variation in wind speed and uncertainties associated with the empirical equation to
estimate kRn. Using the Rn concentration in lake waters, total losses of Rn to
the atmosphere are 123±32 and 138±32 Bq m-2 d-1 in August and
September, respectively. Radon losses due to decay were 125±22 and
123±15 Bq m-2 d-1,
respectively, where uncertainties are obtained from the analytical uncertainties
for Rn concentrations in lake waters. Losses of Rn through the outlet stream were
7±4 and 9±4 Bq m-2 d-1. Among all Rn losses, atmospheric
evasion (50 %) and decay (47 %) were the terms that have the largest contribution
to the Rn mass balance. Radon losses through the outlet stream are almost negligible (3 %).
Additionally, using the average kRn and Qout (Eq. ),
the average 222Rn residence time in Lake 850 is ∼2 d.
The modeled groundwater radon flux of Lake 850 (red circles)
based on Rn fluxes in Lake Almberga in Abisko (Olid et al., unpublished data),
the measured Rn fluxes in August and September (black cross),
and the calculated groundwater discharge in L s-1 throughout
the year (blue squares). Uncertainties are shown as error bars and with shading.
Diffusive Rn flux from underlying sediments (Fdiff) obtained from diffusion
experiments in the lab is 89±17 Bq m-2 d-1, and it is one of the
main sources of Rn into the system. Fluxes of Rn from the sediment compensate for up
to 57 % of total Rn losses. Uncertainties associated with this flux are from analytical
uncertainties in the slope for the regression analyses of the increase in Rn concentration
through time in the sediment diffusion experiment. Due to the low concentrations of Ra
in lakes from the same area (1.4±0.6 Bq m-3, C. Olid, unpublished data),
Rn inputs due to Ra decay were considered negligible in the Rn mass balance.
Rn inputs from groundwater are required to balance the Rn losses from the lake.
The Rn flux into the lake through groundwater discharge is calculated to be
164±51 and
178±39 Bq m-2 d-1
in August and September, respectively. Considering the lake area of 20 000 m2
and the Rn concentration in groundwater obtained from incubation experiments
(10 626±1720 Bq m-3), groundwater fluxes are
3.58±1.11
and 3.88±0.86 L s-1 for August and September, respectively. Note that
this is a conservative estimate for groundwater fluxes, because we use the highest
measured Rn concentration as the end-member.
Age–depth model of the gravity core. Red line is the median probability age from
all age–depth iterations. Grey shading represents age model probability and
contains 95 % confidence interval (dashed lines). The blue rectangles are
the unsupported 210Pb concentration in Bq kg-1.
Due to the lack of Rn measurements for the entire year, we estimated groundwater
inputs for the months where no sampling was carried out using two scenarios:
(i) constant groundwater inflow of 3.73±1.40 L s-1, calculated
as the mean of the August and September data, and (ii) modeled groundwater inflow
based on groundwater fluxes obtained from a lake survey in the Abisko region
in 2018–2019 (C. Olid, unpublished data), which ranged
from 2.28±0.50 L s-1
to 7.69±1.70 L s-1 (Fig. ). The annual Rn fluxes
follow a pattern of a distinct peak in discharge in June and a gradual decrease
towards July–October, reaching the base-flow level in November (Fig. ).
The ratio between the groundwater Rn flux in September in Lake 850 and the
groundwater Rn fluxes from the lake survey was used to model the missing
groundwater Rn fluxes in Lake 850 (Fig. , Appendix ).
Age–depth model, lithology, and mass accumulation rates
The age–depth model for the sediment core is shown in Fig. (or Fig. S2). The mean sediment
accumulation rate (SAR) was estimated to be 0.083±0.041 cm yr-1,
which equals a sediment accumulation rate of 12±6 yr cm-1 and a
mass accumulation rate (MAR) of 16.0±9.3 mg cm-2 yr-1. The
presence of mosses in the sediment was observed during the core processing and
also was described in the sediment lithology by . Changes in
the sediment content of aquatic or terrestrial mosses were also supported by
the C/N wt % ratio (Fig. ), suggesting this is the cause of changes in MAR.
Lake 850 sediment is composed of carbonate-free clay gyttja with a mean
TOC content of 11.4 wt %, mean TN of 1.1 wt %, and a resultant C/N wt % ratio
of 10.2 (Fig. ). Sediment porosity as high as 89.5 % is found
in the surface sediment, where sediment dry bulk density mean is 0.19±0.06 g cm-3.
The BSi concentration along the sediment varies from 13.2±0.28 wt % to
22.8±0.24 wt %, with the highest BSi concentration in the surface of the core.
The BSi concentrations reported here are lower than previous measurements.
For example, BSi concentration in the surface sediment of Lake 850 was
previously reported to be 40.3 wt % , which is twice the
value found here, demonstrating a high variability of BSi in the sediments.
From MAR and BSi wt % we estimated the BSi accumulation rate (ϕBSi).
BSi accumulation rates show stable values around 1.8 mg SiO2 cm-2 yr-1
in the upper 7 cm of the core, whereas an increase in BSi accumulation is observed towards
the bottom 7 cm of the core (Fig. ) likely connected to the higher MAR.
The mean BSi accumulation rate for the entire gravity core of
2.9±1.5 mg SiO2 cm-2 yr-1
was used as the BSi flux to sediment in the mass balance models.
The mean diatom isotopic ratio (δ30SiBSi), measured on cleaned diatoms
from the uppermost 8 cm of a sediment piston core from 1999 , is
0.07±0.43 ‰ (n=3).
Mass balance modelsWater balance
Two water balance scenarios were considered where changes in the lake level
were evaluated: (i) constant groundwater inflow over the annual cycle as an
additional water source and (ii) modeled groundwater discharges varying
throughout an annual cycle (Figs. and ). In both
scenarios, lake-level increases during the ice-covered period (Fig. , blue and green lines)
are a result of a potential groundwater inflow. This accumulated water
is released through the outlet when the lake ice starts to melt in May–June,
and the outlet discharge is thus high (Table ). After this period,
lake level is stabilized, and groundwater replenishes the original lake volume
during short periods over the summer.
When groundwater discharge is assumed to be constant (scenario i, 3.73±1.4 L s-1)
based on our data from August and September, the lake shows annual lake-level
changes up to 1.9 m (Figs. and , blue line).
From July to December, the lake volume is restored by the groundwater inflow,
and, on the annual timescale, the lake level would increase around 2 m
every year (Fig. , blue line).
Using the modeled annual groundwater inflow (scenario ii, Fig. ),
limited lake-level changes were observed. The maximum lake-level decrease is 1.4 m
during summer (Figs. and , green line), but groundwater discharge
restores lake level during upcoming months. Taking into account the uncertainties,
lake-level variation can be as great as 2.8 m or none
(Figs. and , green shading).
This scenario with the smallest lake-level changes is in agreement with
previous results of oxygen isotope mass balance . Therefore, we
used this water balance model further for the Si balances.
Silicon and silicon isotope mass balance
Based on the steady-state assumption, BSi accumulation occurs in conditions
when the total DSi influx is higher than
the stream DSi outflux. Therefore, we construct a Si mass balance based
on stream inlets and the outlet. The DSi influx through the inlet stream is not
sufficient to maintain lake DSi concentration at steady state in June
(red and blue triangles, Fig. a). In contrast, in July and August
sufficient DSi enters the lake to supply the outlet DSi flux. The monthly inlet
DSi flux is between 0.22±0.11 and 0.62±0.31 kg SiO2 d-1,
while the outlet DSi flux ranges from 0.19±0.10 to
2.21±1.11 kg SiO2 d-1.
However, diatom production is an additional sink of Si by creating
a BSi flux into the sediment. The DSi influx is, thus, not sufficient
to account for both the DSi outflux and the BSi flux into the sediment (Fig. a).
Therefore, an external source (i.e., groundwater discharge) must
supply additional DSi to compensate for the mean BSi flux
(2.9 mg SiO2 cm-2 yr-1)
into the sediment.
Groundwater discharges from scenario ii (Fig. ) were used to
build a Si mass balance and a Si isotope mass balance. Here, we assume that the
recent BSi flux into the sediment occurs only during the diatom growing season
(from June until September) Fig. a;. The
missing DSi flux resulting from the mass balance was considered to originate
from the groundwater flux, and thus we use this flux to calculate back the
groundwater DSi concentration and isotopic ratio.
The gravity core sediment properties (porosity and dry bulk density), mass accumulation
rate (MAR), and sediment density. Total organic carbon (TOC) and C/N showing
changes in lake carbon content and sources. Biogenic silica (BSi) and BSi flux calculated
from MAR and BSi concentrations. Shading shows 1 standard deviation.
During the diatom growing season, the modeled BSi flux into the sediment increases up
to 1.76±0.87 kg SiO2 d-1 (magenta line, Fig. a),
which produces DSi deficiency in the lake. To balance this deficiency, groundwater
discharge must supply between 1.62±1.21 and 3.39±1.81 kg SiO2 d-1
during the diatom growing season (cyan line, Fig. a). Considering the modeled
groundwater discharges derived from Rn mass balance, the DSi concentration in the
groundwater is estimated to range from 3.96±2.14 mg SiO2 L-1
to 5.85±2.99 mg SiO2 L-1
during diatom growth (cyan line, Fig. b). During the ice-covered
period the BSi flux into the sediment is considered to be negligible, while groundwater
is still flowing into the lake. The winter groundwater concentration is calculated
from the difference in the lake concentration from September
(1.02±0.91 mg SiO2 L-1)
to March (2.51±0.35 mg SiO2 L-1) (Appendix ).
Therefore, the groundwater discharging into the lake from late October until
mid-June is the only water inflow with a DSi concentration of
5.50±1.22 mg SiO2 L-1.
The Si isotope mass balance using the open fractionation model
shows that the higher demand of DSi in the productive months (Fig. a, b)
needs to have a lighter isotopic composition in order to produce the
δ30SiBSi of 0.07±0.43 ‰ measured on diatoms
preserved in the sediment.
The isotopically lighter source is assumed to be groundwater discharge, with
calculated ranges from -0.55±0.55 ‰ in July to
0.25±0.58 ‰ in September (Fig. c).
Using the modeled groundwater δ30Si, the expected δ30SiBSi
in all productive months varies from -0.49±0.49 ‰ to
-0.01±0.56 ‰ (not shown), values that are in agreement
with the sediment BSi of δ30SiBSi=0.07±0.43 ‰.
The production consumes 73 % of the initial DSi in June, 77 % in July and September, and 79 %
in August. During the ice-covered period from late October until mid-June, the
groundwater base flow is considered to be constant, calculated from the
difference of the lake isotopic ratios from September until March
(Appendix ), and thus the
δ30Sigw=1.43±0.82 ‰ (Fig. c).
Discussion
Lake 850 is unusual in terms of both the DSi and BSi concentration
in water and sediment, respectively. The maximum DSi concentration of
2.51 mg SiO2 L-1 in March is among the top 10 % of
lakes in northern Sweden . The mean BSi content
in the lake sediment of 40 wt % places Lake 850
in the upper 6 % of lake sediments studied worldwide .
Based on the positive correlation between water residence time and the relative retention
of DSi in lakes , Lake 850 with its DSi retention of 35±17 % of
the total DSi inlet input and a residence time ranging from 0.18 to 2.4 years
accumulates more DSi as BSi than expected.
Although several factors, including the morphology of the watershed ,
diatom production and low detrital input , vegetation ,
and preservation potential , are known to affect
sedimentation regimes and BSi accumulation resulting in a diatom-rich
sediment, we show here that groundwater input is an important factor
leading to the high BSi accumulation in Lake 850.
The combined results from the water, Rn, and Si mass balances indicated
the importance of an external source of DSi through groundwater discharge.
Groundwater inflow was the primary water and DSi supply to the lake, with
a contribution about 3 times higher than the stream inlets (Fig. a).
The Si and Si isotope mass balance models showed that groundwater DSi concentration
and isotopic composition varied during the ice-free period, compared to the
ice-covered period, when they were stable (Fig. b, c).
Long-term lake-level change calculated based on lake volume changes
and water balance. The purple line indicates the lake-level starting point.
The blue line with shading is the lake-level change with constant groundwater
flow (scenario i), and the green line with shading is the lake-level change
based on water balance with modeled groundwater discharges (scenario ii).
The significance of groundwater-sourced DSi to the lake's Si cycle is also evidenced by
the relatively lighter stable Si isotope ratio of diatoms from sediment,
which suggests that groundwater is the primary DSi source for diatoms.
Stream inputs could also be a source of DSi for diatoms, especially in early
spring, when snowmelt can deliver isotopically lighter DSi by displacement
of shallow groundwater into the stream inlet . However,
spring snowmelt water and groundwater in June are likely to have the same
isotopic composition (Fig. c) because the same factors, e.g., short residence
time in the watershed, are present in both types of water. Thus, only
by using mass balance is the quantification of each DSi source apparent,
providing evidence that groundwater supplies almost 4 times more DSi
compared to streamflow in June. Our results suggest that the groundwater
supply plays a crucial role in providing DSi for the production of
diatoms and accumulation of BSi in Lake 850.
Environmental controls on BSi accumulation
The results from our study can be applied more broadly to other lakes to
evaluate factors governing the accumulation of diatom-rich sediment. BSi-rich
sediments are likely to be found in lakes situated on silica-rich bedrock,
such as in Lake Challa, Tanzania–Kenya , or, as shown here,
in lakes with sufficient DSi inputs from groundwater that supply sufficient DSi
during the diatom growing season to alleviate potential DSi limitation of diatom
growth. In addition, lakes with high autochthonous carbon production and
deposition combined with very low mean sedimentation rates, which commonly
characterize Arctic lake sediments , as well as lakes with low-relief
watershed morphology and with low stream input, which combined yield low quantities
of fine-grained clastic input, are potential systems for high BSi accumulation
.
The role of groundwater in the water balance
The water balance coupled with the Rn mass balance indicated that groundwater
discharge is an essential water source for the lake. Both models of groundwater
inflow (constant and varying groundwater inputs) demonstrated changes in
lake volume as a result of high water discharge at the outlet of the lake
during spring snowmelt. More pronounced changes in lake volume were observed
in scenario i, where constant groundwater inflow was assumed (Fig. , blue line).
However, because the oxygen isotope data showed negligible evaporation and precipitation effect
on lake volume change , this model is not considered to be
the most realistic. Scenario ii, which considered a variable groundwater flow
(Fig. , green line) seems to be more realistic. The modeled
groundwater hydrograph (Fig. ) is comparable with the hydrograph
of the neighboring river Miellejohka (Fig. S1) and resembles the
hydrographs of groundwater discharge in studies of high-altitude lakes from
other regions . The results
from this model show that groundwater discharge is up to
5 times higher than the lake water outflow through the outlet.
Similarly, groundwater discharge corresponds to 3 % to 17 % of the lake
volume depending on the month.
Si and Si isotope mass balance model of Lake 850 throughout the year.
(a) Mass balance showing the stream DSi influx (blue triangles), the lake outlet
DSi outflux as negative flux (red triangles), and the diatom BSi flux based on a
diatom bloom season lasting 4 months (magenta dotted line), also as a negative flux.
The calculated groundwater DSi flux is shown as a positive flux (cyan line).
(b) The monthly changes in the DSi concentration of the inlet (blue triangles),
outlet (red triangles), lake (black crosses), diatom DSi uptake (magenta circles),
and groundwater (cyan line). (c) The stable Si mass balance showing
monthly variation in the isotopic composition of all DSi sources and sinks.
Shading and error bars represent uncertainties.
The role of groundwater in Si concentration mass balance and Si isotope mass balance
The lake Si mass balance (Fig. a) shows that modeled groundwater
concentration and flux of BSi vary through the year, which is similar to
observations from Crystal Lake in Wisconsin . Seasonal
variations in groundwater DSi concentration related to discharges were
also observed in Canadian rivers with groundwater inputs .
Moreover, the calculated BSi flux into the sediment is comparable (or higher)
with BSi fluxes observed in some of the North American Great Lakes
and lakes with diatomaceous sediment in the Arctic .
The model of stable Si isotopes shows little variation during the
ice-covered period, as no diatom production is expected. The modeled δ30Si
of groundwater for the ice-covered period (Fig. c)
falls into the range of measured groundwater isotopic composition
worldwide, which ranges from -1.5 ‰ to 2 ‰ .
However, the modeled groundwater ratio δ30Sigw is heavier
than found in other groundwater studies ,
which may reflect lower dissolution of primary minerals, longer groundwater
residence time, and possibly some clay mineral formation in the groundwater
pathway during the ice-covered period.
Further, no diatom production, and thus no associated Si isotope fractionation,
is expected in winter. Therefore, the δ30Silake is influenced by
the input of δ30Sigw
only and not by diatom production. The δ30Silake measured in March
is slightly lighter than all modeled δ30Sigw for the ice-covered
period, which can be explained by diatom dissolution in the uppermost sediment layers.
However, if the uncertainties of the modeled groundwater isotopic composition are
taken into account, the lake ratio is within the same range as the groundwater
ratio. Therefore, no additional processes must be present during the ice-covered
period, and the groundwater isotopic ratio is reflected in the lake isotopic
signal. With snowmelt, the decrease in the modeled δ30Sigw reflects the
increase in weathering of primary minerals and decrease in the groundwater
residence time due to higher discharges, as also observed in Arctic rivers .
The greatest variation in the isotopic ratio of groundwater occurs in August,
when the modeled groundwater isotopic composition is fully dependent on the changes
in BSi flux into the sediment. As the yearly BSi accumulation occurs
during the diatom growing season which is only 4 months,
the modeled groundwater must bring additional DSi to supply diatom production. Hence,
the isotopic model calculating the groundwater isotopic composition shows δ30Sigw
comparable with values for groundwater reported in a small number of other
studies . Further, the calculated δ30SiBSi
based on the initial mixture of the modeled groundwater and stream inlet
ratio gives results within the range of the measured δ30SiBSi.
Model uncertainties
The largest sources of uncertainty in the water and silicon balance models
(Figs. , , and ) are the discharge uncertainties
of the inlet and outlet and the winter groundwater discharges. The spring
snowmelt dynamically changes the inlet and outlet discharges, as has been
observed on rivers in the area, such as Miellejohka (Fig. S1).
With only a single sample every month, there is no information on variation in
the stream on a finer temporal scale. Thus, monthly stream flow and the modeled
groundwater discharges might be over- or underestimated. Further, uncertainties
in the isotopic model and the isotopic composition of the groundwater were
propagated from the mass balance model and from the stable isotopic measurements,
especially in the outlet water in August.
The water balance based on modeled groundwater inflow suggests that lake-level
changes throughout the year are within a range of 0 to 1.4 m (Fig. , green line),
and, thus, lake area and mean depth also vary throughout the year. Therefore,
the underlying assumptions of constant depth and area likely overestimate
lake-level change. For a more precise model of lake level, lake volume variations
and a detailed bathymetry of Lake 850 are needed. However, the importance of the
groundwater contribution to Lake 850 supports the evidence that groundwater
should be considered as an important water and DSi source for high-altitude
and high-latitude lakes, with support of data on groundwater DSi in
Lake O'Hara , Lake Mývatn , and
Crystal Lake .
Another source of uncertainties in the Si and Si isotope mass balance models
originates from the uncertainties on the age–depth model. The uncertainties
on MAR, which are calculated from the SAR and the densities, are as high as 50 %.
The high uncertainties in the sediment density and SAR are likely due to changes in the sediment composition and increased content
of mosses. Therefore, the BSi flux to the sediment carries similar or higher
uncertainty. As a result of those uncertainties, the modeled groundwater
concentrations and isotopic composition range greatly.
Additionally, the diatom preservation efficiency, which is globally around 3 %
in the oceans
and around 1 %–2 % in deep lakes of the total diatom
production, suggests that 97 %–99 % of diatom BSi is redissolved in the
water column in those environments. However, no estimates of sediment
preservation efficiency are available for small, cold lakes such as Lake 850.
Therefore, the mass balance can be slightly underestimated, in the case that
the BSi flux into the sediment, which was calculated from the sediment
record, represents only a fraction of the total production. To eliminate
this source of uncertainty, annual monitoring of diatom production and accumulation
would be needed.
Uncertainty also results from the variability among sediment cores in their BSi content.
BSi concentrations in the sediment vary from 13 wt % to 40 wt % in different cores
this study;. We have tested the combination of the MAR
(16.0 mg cm-2 yr-1) reported from this study with the highest BSi
of 40.3 wt % from a companion core from Lake 850 to evaluate
the impact of BSi flux on the groundwater concentrations. The yearly BSi flux
would need to increase by a factor of 2.2, which would result in 1.3 to 2.6 times higher
groundwater DSi concentration to support the BSi flux and keep Lake 850 at
steady state. However, the BSi content is variable within the sedimentary basin,
and thus the sedimentation rate is a crucial factor for the estimate of BSi
accumulation. For future model improvement, monitoring of all inlets, groundwater,
pore water, and the outlet together with sediment traps to constrain the
production, BSi flux, and dissolution would be needed.
Conclusions
The diatom-rich sediment in Lake 850 is formed because of high DSi supply by
groundwater during the diatom growing season coupled with low sedimentation
rates, which fosters a high accumulation of diatoms in the form of BSi.
Water and Si mass balance demonstrated the importance of groundwater as
a source of water and DSi, with fluxes that are 3 times greater than stream
input. Groundwater supplies lighter δ30Si, which is reflected
in the lighter diatom δ30Si ratio. By quantifying the groundwater
inputs, the Si and Si isotopic mass balances allowed for the
estimate of the stable Si isotope ratios of groundwater throughout
the year. The modeled isotopic ratio of groundwater falls into the same
range as the world groundwater δ30Si ratio .
Lakes on silica-rich bedrock, with low allochthonous input,
low sedimentation rates, low-relief watershed geomorphology, and
high groundwater input have high potential to accumulate BSi.
These water bodies with high BSi accumulation act as
important sinks of Si in the global Si cycle. Our results support the
importance of groundwater in the lake silicon budget and suggest that
this process should not be overlooked in future investigations on BSi
in lakes and global estimates of the terrestrial lake BSi sink.
MethodsModeling groundwater Rn fluxes
Radon fluxes from Lake Almberga from the Abisko region were estimated using
the same approach in summer and autumn 2018 and 2019 (C. Olid, unpublished data).
The derived Rn fluxes obtained from the lake survey were used here to model
groundwater fluxes through the year in Lake 850. To do this, we divided the
estimated groundwater Rn flux from the Rn mass balance in Lake 850 in September
(178±39 Bq m-2 d-1) by the mean groundwater Rn flux
obtained from the lake survey in September
(104±10 Bq m-2 d-1, C. Olid, unpublished data).
The derived ratio (1.71) was then multiplied by the mean groundwater
Rn fluxes from the lake survey to model the groundwater Rn fluxes in
Lake 850 for those months when Rn measurements were not available
(June, July, and September). Rn fluxes through groundwater during
the ice-covered period were assumed to be 59 % of September fluxes
(C. Olid, unpublished data; Fig. ; Table ).
The groundwater Rn flux from November to April was assumed to be constant
and equal to April estimations.
Estimated Rn fluxes in August and September with the derived
water discharges through groundwater based on the Rn mass balance.
Estimated lake-level variation between neighboring months throughout
the year with uncertainties as shading. No lake-level change is depicted by
the solid purple line. The blue line presents lake-level increase or decrease
from one month to another with constant groundwater discharge (scenario i),
and the green line is the rate of lake-level variation with modeled
groundwater discharges (scenario ii).
Groundwater DSi and δ30Si calculations during the ice-covered period
The groundwater concentration (cgw) during the ice-covered period was
calculated from the groundwater discharge, the lake volume from the water
balance, and the lake DSi differences between September and March though a
mixing model:
cgw=(cMar⋅(VSept+Vgw))-(cSept⋅VSept)Vgw,
where cMar is the lake concentration in March, cSept is the lake concentration
in September, VSept is the lake volume in September, and Vgw is the total
volume of water brought by groundwater in 8 months. The total water volume brought
by groundwater in 8 months was calculated from the modeled groundwater winter
discharges (Fig. ). The lake volume in September is taken from the water
balance model, where the modeled groundwater discharges were used
(Figs. and , green line).
Similarly, the cgw during the ice-covered period in the scenario
with continuous BSi flux to the sediment for a period of 8 months was calculated
by adding flux into the sediment in the mixing model:
cgw=(cMar⋅(VSept+Vgw))-(cSept⋅VSept)+ϕBSiVgw,
where the ϕBSi is the total flux of BSi to sediment in 8 months. The BSi flux into
the sediment for 8 months was calculated as a sum of the continuous monthly BSi
flux from September until March.
The isotopic composition of the groundwater during the ice-covered period,
based on measured data, was calculated as
δ30Sigw=δ30SiMar⋅((cSept⋅VSept)+(cgw⋅Vgw))-δ30SiSept⋅(cSept⋅VSept)cgw⋅Vgw,
where δ30SiMar is the lake isotopic composition in March, δ30SiSept
is the lake isotopic composition in September, cSept is the lake concentration in September,
cgw is the concentration of groundwater during the ice-covered period
(Eqs. or , depending on model), Vgw is the total volume of
water brought by groundwater in 8 months, and VSept is the lake volume in September.
Silicon isotope mass balance – δ30Sigw calculation
Due to the high groundwater input in Lake 850 proven by the Rn mass balance (see Sect. Groundwater discharge), the inlet δ30Si
does not represent the initial δ30Si used by diatoms. Therefore, the initial
δ30Si of DSi is a mixture of groundwater δ30Si
and inlet δ30Si
flux weighted. The δ30Siinitial was calculated from
δ30Sipostuptake, which equals δ30Silake, as
δ30Siinitial=δ30Sipostuptake+30ε⋅1-coutcinitial.
Further, the groundwater δ30Sigw which fits the measured data
and keeps the steady state, was calculated as
δ30Sigw==δ30Siinitial⋅((cin⋅Qin)+(cgw⋅Qgw))-δ30Siin⋅(cin⋅Qin)cgw⋅Qgw.
Mass balance models: extreme Si and Si isotope mass balances
The Si and Si isotopic mass balance models were tested for two extreme scenarios
to model the highest and the lowest possible concentration of groundwater brought
into the lake. Further, a scenario based on recent diatom growth season is modeled
(Table ). The DSi concentration and isotopic composition from the inlet
and outlet streams are similar in all three scenarios. The groundwater DSi
concentrations and isotopic composition are calculated from the groundwater
fluxes influenced by the three potential BSi fluxes into the sediment, representing
three possible lengths of diatom production. All scenarios use the open-system isotopic model to describe the effect of diatom production
on the lake water δ30Si ratio. The difference between the first and second
scenarios is the BSi flux into the sediment: scenario (1) considers BSi flux into the sediment
throughout the whole year representing lack of an ice-covered period, and scenario (2) considers BSi flux into
sediment only present from June until September . Scenario (3) utilizes the open-system isotopic model only for June, with no diatom production
the rest of the year, and thus no fractionation in the lake, which describes lake
behavior with only a short ice-free period. Here we describe only scenarios 1 and 3,
whereas in the main text scenario 2 is presented and discussed.
A summary of all three scenarios, which were examined through Si
and Si isotope mass balance models.
ScenarioBSi fluxDaily BSi fluxRange cgwRange δ30SigwDSi % consumedRange δ30SiBSitimeby production112 months0.58 kg SiO2 d-11.40 to 8.46 mg SiO2 L-1-0.64 ‰ to 1.37 ‰39 %–61 %-0.49 ‰ to -0.01 ‰24 months1.77 kg SiO2 d-13.96 to 5.85 mg SiO2 L-1-0.55 ‰ to 1.43 ‰73 %–79 %-0.49 ‰ to -0.01 ‰31 month7.08 kg SiO2 d-10.37 to 13.10 mg SiO2 L-1-0.09 ‰ to 1.43 ‰0 %–88 %-0.21 ‰Scenario 1: 12 months of BSi flux into sediment
A scenario assuming a constant BSi flux to the sediment throughout the year
(magenta line, Fig. a) simulates a situation when climate is
warming, and the diatom growth season is prolonged to maximum. Additionally,
this scenario was characterized by the minimal groundwater fluxes and DSi
concentrations. The DSi removal by diatoms is of 1.21±0.62 mg SiO2 L-1
monthly (magenta points, Fig. b). Therefore, with an added BSi flux
of 0.58±0.29 kg SiO2 d-1, the lake inlet does not supply
sufficient DSi for diatoms to grow. The groundwater DSi concentration is
calculated as the DSi flux needed to keep the lake balanced and sustain the
diatom production. The groundwater flux of DSi varies from 0.43±0.51
to 2.20±1.37 kg SiO2 d-1, depending on the season
(cyan line, Fig. a). The highest groundwater DSi flux occurs
in June, followed by a decreasing trend towards August, when it reaches
the minimum. From August until November, the groundwater DSi flux increases
and is stabilized after November, and it is constant until May. From the
calculated groundwater flux, the groundwater concentration is between
1.40±1.59 and
3.32±1.93 mg SiO2 L-1 during
the ice-free period, and, when combined with the lake DSi deficiency
at the end of the season, it is 8.46±0.40 mg SiO2 L-1
(cyan line, Fig. b) in the ice-covered period.
In scenario 1, with constant BSi flux into the sediment during the whole
year of 0.58±0.29 kg SiO2 d-1, high superficial and
groundwater discharges occur in June, with DSi concentrations of 2.34
and 3.32 mg SiO2 L-1, respectively (Fig. b). The stream inlet
has a light isotopic ratio of δ30Siin=0.02±0.10 ‰.
The initial DSi available for diatoms is a mixture of the groundwater and
the stream inlet, with δ30Siinital=0.22±0.36 ‰.
The groundwater was calculated from the δ30Siinital to have an isotopic
ratio of δ30Sigw=0.27±0.51 ‰ in June. Thus,
the expected BSi isotopic ratio was calculated to be -0.21±0.42 ‰,
which is within the range of mean measured δ30SiBSi=0.07±0.43 ‰
in the top sediment layers. The diatom production consumes approximately 61 %
of the DSi influx in June.
Although groundwater discharge culminates in July, compared with the decreasing
trend in the stream inlet, the isotopic composition of the lake in July is
influenced by both the groundwater and the stream. The DSi concentration of
the inlet is 4.79±0.03 mg SiO2 L-1, but with 4 times lower discharge
than groundwater. The calculated groundwater DSi concentration from the steady-state
model is only 1.56±0.95 mg SiO2 L-1 (Fig. b). Further, the
initial isotopic mixture for diatom growth δ30Siinitial=-0.03±0.10 ‰
is composed of the stream δ30Siin=0.72±0.10 ‰
and the groundwater δ30Sigw=-0.64±0.46 ‰ (Fig. c).
The expected BSi isotopic ratio is δ30SiBSi=-0.49±0.21 ‰,
which still falls within the mean measured δ30SiBSi=0.07±0.43 ‰
in the sediment. The
diatom production in July consumes 58 % of the DSi.
In August, the isotopic compositions of the stream inlet, lake, and outlet are similar.
The DSi concentration of the inlet is at its maximum, with an isotopic composition
of 0.78±0.15 ‰, but due to a very low inlet discharge it does not
affect the lake. The concentration and isotopic ratio of the outlet and the
lake are almost identical; thus, the groundwater input is
1.40±1.59 mg SiO2 L-1
of DSi (Fig. b), with an isotopic ratio of
δ30Sigw=0.14±0.73 ‰
(Fig. c). The expected BSi isotopic ratio is
δ30SiBSi=-0.31±0.67 ‰,
which is in agreement with the mean measured δ30SiBSi in the diatoms from sediment.
The diatom production in August consumes 39 % of the lake DSi.
September is the last month before the lake is ice-covered. There is no stream
inlet, as the watershed is snow-covered. The groundwater input, with a concentration
of 2.31±1.03 mg SiO2 L-1, is 4 times higher than the removal by
the lake outlet. This suggests that the lake level changes throughout
seasons, which is not considered in any of the Si mass balance and isotopic
models examined. The lake DSi of 1.37±0.04 mg SiO2 L-1 is fully influenced
by groundwater and diatom production. The groundwater isotopic ratio
is 0.65±0.55 ‰, and the diatom production uses 41% of the lake DSi.
The expected BSi isotopic ratio is δ30SiBSi=-0.01±0.47 ‰,
which is in agreement with the mean measured δ30SiBSi in the diatoms from sediment.
This scenario assumes that the groundwater concentration during the ice-covered
lake is recharging the lake DSi, while the BSi flux into the sediment is still
present (Fig. b). Applying the mixing model
(Eqs. and ),
groundwater DSi concentration (8.46±0.40 mg SiO2 L-1),
groundwater discharge, lake volume change during the ice-covered period,
and the difference of the isotopic composition of the lake water between
September (1.02±0.24 ‰) and March (1.27±0.10 ‰),
the isotopic ratio of the groundwater is calculated to be 1.37±0.55 ‰
(Fig. c).
Inlet (blue), outlet (red), and groundwater (cyan), and the BSi (magenta) fluxes,
concentrations, and isotopic composition. Scenario 1: the constant BSi flux into sediment
(a) influences the groundwater DSi concentration (b) and the silicon isotopic composition (c). Scenario 2: the effect of BSi flux adjusted to the diatom bloom season of
4 months (d) on the groundwater concentration (e) and isotopic composition (f). Scenario 3:
the diatom bloom represented by the BSi flux into the sediment is restricted to
1 month only. During June all BSi accumulated for 1 year is produced, and the groundwater
concentration (h) and the isotopic composition (i) are affected.
Scenario 3: only 1 month of BSi flux into the sediment
The third scenario is based on the inlet and outlet DSi fluxes but
assumes that diatom production occurs only in June. This scenario
could occur if the climate were to experience cooling and the diatom growth
period were extremely shortened. Additionally, this scenario demonstrated
the highest groundwater concentrations during the diatom growing season. The rest of
the year diatom production, and so the BSi flux into the sediment, is negligible or zero.
Therefore, the yearly accumulated BSi settles into the sediment within 1 month,
which yields a BSi flux of 7.08±3.62 kg SiO2 d-1
(magenta line, Fig. g). In this scenario, groundwater input
must be from 0.15±0.37 to 8.70±4.59 kg SiO2 d-1, and the DSi concentration ranges between 0.38±0.66
and 13.10±6.41 mg SiO2 L-1 during the ice-free period
(cyan line, Fig. h).
Similar to the second scenario (in the main text), to restore the lake DSi concentration
during the ice-covered period from late October to mid-June, groundwater DSi
concentration is around 5.50±1.22 mg SiO2 L-1.
Scenario 3 assumes the BSi flux into the sediment occurs only in June,
and the rest of the year there are no processes causing stable Si isotope
fractionation. This scenario originates from data in August and September,
when the δ30Si of inlet, outlet, and lake are very similar.
Only in June is there fractionation between the lake stream inlets and the lake,
which is described by the open-system-fractionation model. Therefore, the
groundwater concentration in June increases to 13.10±6.41 mg SiO2 L-1
(Fig. h),
with an isotopic ratio of -0.09±0.56 ‰ (Fig. i)
to sustain the diatom production represented by BSi flux into the sediment.
The production consumes 88 % of the available DSi.
In July, August, and September the groundwater DSi concentration is low,
as the lake does not have any production and thus no demand on the DSi.
The isotopic composition of the groundwater is 0.38±0.66 ‰,
0.48±1.19 ‰, and 0.57±0.14 ‰, respectively
(Fig. i). High uncertainties in the isotopic composition of
the groundwater reflect the uncertainties in the stream and groundwater
discharges and fluxes.
Discussion: scenario evaluation
Scenarios 1 and 3 of Si mass balance (Table , Fig. a and g) demonstrate how the groundwater concentration would change
with changes of length of diatom production. It is likely that the
diatom growth season would be driven by the changes in climate and thus
the ice-free period length. Our models aimed to estimate the changes
in the lake DSi and Si balance in those extreme changes of growing
season driven by changes in climate. However, the groundwater
concentrations are commonly higher than the superficial streams
, which is not the case in
scenarios 1 and 3. The groundwater DSi concentrations are lower than
in the stream inlet during the ice-free period in those two scenarios
(Fig. b and h), which suggests that those
scenarios have either missing or surplus data of the inlet and outlet
DSi concentration and discharges. A more complex model with variable
discharges of groundwater and stream inlets and outlet depending on
precipitation and evaporation changes would be needed. Therefore,
those two scenarios provide only a rough estimate hinting at the changes
in DSi and Si isotopic mass balances connected to changes in climate.
Data availability
All data, if not directly available in tables and appendices, are
available in the PANGAEA database (doi:10.1594/PANGAEA.929941). Alternatively all data are available upon request
to the authors.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-18-2325-2021-supplement.
Author contributions
PZ and DJC designed the research. PZ and CO carried out the fieldwork.
CO performed radon measurements and radon mass balance. PZ performed
the TOC, TN, BSi, DSi, and stable Si isotope analyses and performed the
data processing. All authors contributed to discussion and data interpretation.
PZ and CO wrote the paper with contributions and comments provided by JS, SCF, SO, and DJC.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We also thank Aldo Shemesh for providing diatom samples and Christian Bigler,
Reiner Gielser, and Carl-Magnus Mörth for advice and help with fieldwork design.
Further, we thank the organizations and the individuals who helped with the fieldwork
and provided us with equipment and advice: Thomas Westin, Keith W. Larson, Erik
Lundin, Svante Zachrisson, CIRC, and field assistants Albin Bjärhall, Mathilde Schnuriger, Lukas Guth,
Rosine Cartier, Geert Hensgens, and Jan Foniok. We acknowledge
Hans Schöberg and Melanie Kielman for assistance during sample
preparation and isotope data acquisition. This is Vegacenter contribution
number no. 036.
Financial support
This research has been supported by the Kungliga Fysiografiska Sällskapet i Lund (grant nos. 38469, 39787, and 40283) and by the Center for Geospehere Dynamics (UNCE/SCI/006) for Petra Zahajská. Further, this research was funded by the Vetenskapsrådet (grant awarded before 2014, no award number) attributed to Daniel J. Conley, the Svenska Forskningsrådet Formas (grant no. 2018-01217) allocated to Carolina Olid, the NSF EAR-1514814 assigned to Sherilyn C. Fritz, and the Fonds National de la Recherche Scientifique (FNRS, Belgium, FC69480) ascribed to Sophie Opfergelt.
Review statement
This paper was edited by Jack Middelburg and reviewed by three anonymous referees.
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