Arctic rivers will be increasingly affected by the
hydrological and biogeochemical consequences of thawing permafrost. During
transport, permafrost-derived organic carbon (OC) can either accumulate in
floodplain and shelf sediments or be degraded into greenhouse gases prior to
final burial. Thus, the net impact of permafrost OC on climate will
ultimately depend on the interplay of complex processes that occur along the
source-to-sink system. Here, we focus on the Kolyma River, the largest
watershed completely underlain by continuous permafrost, and marine
sediments of the East Siberian Sea, as a transect to investigate the fate of
permafrost OC along the land–ocean continuum. Three pools of riverine OC
were investigated for the Kolyma main stem and five of its tributaries:
dissolved OC (DOC), suspended particulate OC (POC), and riverbed sediment OC
(SOC). They were compared with earlier findings in marine sediments. Carbon isotopes
(δ13C, Δ14C), lignin phenol, and lipid biomarker
proxies show a contrasting composition and degradation state of these
different carbon pools. Dual C isotope source apportionment calculations
imply that old permafrost-OC is mostly associated with sediments (SOC;
contribution of 68±10 %), and less dominant in POC (38±8 %), whereas autochthonous primary production contributes around 44±10 % to POC in the main stem and up to 79±11 % in tributaries.
Biomarker degradation indices suggest that Kolyma DOC might be relatively
degraded, regardless of its generally young age shown by previous studies.
In contrast, SOC shows the lowest Δ14C value (oldest OC), yet
relatively fresh compositional signatures. Furthermore, decreasing mineral
surface area-normalised OC- and biomarker loadings suggest that SOC might be
reactive along the land–ocean continuum and almost all parameters were
subjected to rapid change when moving from freshwater to the marine
environment. This suggests that sedimentary dynamics play a crucial role
when targeting permafrost-derived OC in aquatic systems and support earlier
studies highlighting the fact that the land–ocean transition zone is an efficient
reactor and a dynamic environment. The prevailing inconsistencies between
freshwater and marine research (i.e. targeting predominantly DOC and SOC
respectively) need to be better aligned in order to determine to what degree
thawed permafrost OC may be destined for long-term burial, thereby
attenuating further global warming.
Introduction
Permafrost regions store approximately half of the global soil organic
carbon (OC)
(Hugelius
et al., 2014; Zimov et al., 2006a). Amplified warming of the Arctic,
currently three times as fast as the global average
(IPCC,
2021), warms permafrost on a global scale (Biskaborn
et al., 2019). Permafrost thaw and associated shifts in hydrology
(Walvoord and Kurylyk, 2016), impact regional
carbon cycling through the release of organic matter from this previously
frozen pool to the fluvial network. In addition, the release of nutrients
and sediment leads to a multitude of effects on the biogeochemical
properties of inland and coastal waters
(Terhaar
et al., 2021; Vonk et al., 2015). Furthermore, decomposition of OC from
thawing permafrost soils releases greenhouse gases (CO2, CH4) into
the atmosphere, causing further climate warming
(Schuur et al., 2015).
Arctic rivers, like rivers in general, serve as integrators of their
catchments, tracking changes in terrestrial signatures of the transported
organic matter at the river mouth, and can therefore be used as indicators
for watershed-wide processes such as permafrost thaw or soil remobilization
(van Dongen et al.,
2008; Wild et al., 2019; Feng et al., 2013). Based on river mouth
monitoring, the six largest Arctic rivers are estimated to transport 40 Tg
of fluvial OC, of which 34 Tg DOC and 6 Tg POC, into the Arctic Ocean
(Holmes
et al., 2012; McClelland et al., 2016). These estimates serve as important
baseline data for terrestrial carbon export to the Arctic Ocean. However,
fluvial OC cycling already occurs in headwater streams, and extends beyond
the river mouth to the shelf seas. Inland waterways are known not just to
conservatively channel fluvial OC towards the ocean but also on the one hand to
actively degrade OC into greenhouse gases and on the other hand to sequester OC
on short and long timescales (days to millennia)
(Cole
et al., 2007; Drake et al., 2018). Similarly, the breakdown of terrestrial OC in
the marine environment
(e.g., Alling
et al., 2010; Bröder et al., 2018), subsequent ocean acidification
(Semiletov et al., 2016) and increase in marine primary
production (Terhaar et al., 2021) have been the focus
of recent studies. To better assess the processing and fate of terrestrial
organic matter in aquatic systems, we should regard these environments as being
linked in a land–ocean continuum or as a carbon cycle “without boundaries”
(Battin et al., 2009).
For a complete assessment of fluvial OC, one needs to look at three
different compartments: dissolved organic carbon (DOC; operationally defined
as <0.7µm), suspended particulate organic carbon (POC;
>0.7µm), and sedimentary organic carbon (SOC). In the six
largest Arctic rivers, DOC concentrations are generally higher than POC concentrations
(Holmes
et al., 2012; McClelland et al., 2016); however, DOC consists predominantly
of recent terrestrial material, whereas POC is predominantly sourced from
deeper soils and permafrost (Wild et al.,
2019). The fraction of DOC that is derived from Yedoma permafrost,
Pleistocene-aged permafrost deposits rich in OC, along the Kolyma River, is
rapidly degraded upon thawing
(Mann
et al., 2015; Rogers et al., 2021; Vonk et al., 2013). In contrast, POC
derived from thermal erosion of river banks and coastlines, thermokarst, and
other abrupt permafrost thaw features may be less prone to rapid
degradation, and transported over longer distances
(Keskitalo
et al., 2022; Salvadó et al., 2016). Concentrations, fluxes, and isotopic
signatures of POC in Arctic rivers have been studied in the past decade
(McClelland
et al., 2016; Wild et al., 2019), including more recent studies on the
molecular structure and degradation
(e.g.,
Kolyma river; Bröder et al., 2020; Keskitalo et al., 2022). However, the
cycling and degradation of POC during lateral aquatic transport, and
especially its interplay with DOC and SOC, remain elusive.
To better understand the interaction and exchange of POC with river- and
marine sediments, as well as DOC, all these OC pools need to be considered.
Yet to date, studies on riverine SOC transport and degradation are limited
and contradictory. In the Danube River, SOC concentrations and
mineral-specific surface area-normalised biomarker loadings decrease
downstream, suggesting significant SOC degradation during fluvial transport
(Freymond et al., 2018). On the
contrary, Scheingross et al. (2019) found in an experimental setting that particle abrasion and turbulent
mixing of POC in the water column has only a limited effect on degradation,
and suggests that degradation takes place mostly during floodplain storage
of sediment. Repasch et al. (2021) (Rio
Bermejo, Argentina) and Hilton et al. (2015) (Mackenzie River, Canada) show that eroded POC is efficiently
transported by rivers, redeposited in floodplains or basins offshore,
and suggest that sediment transport time and mineral protection of OC
might regulate the magnitude and rate of POC degradation. Additionally, processes
such as leaching of POC and SOC, and, vice versa, adsorption of DOC to soil
or mineral particles, influence both the composition and degradability of
OC (mineral binding ballasts), and slow down the degradation of OC
(Hemingway
et al., 2019; Keil et al., 1994; Keskitalo et al., 2022; Kleber et al.,
2021; Vonk et al., 2010b), whereas leaching of OC to the dissolved phase
increases its potential for degradation
(Abbott
et al., 2014; Mann et al., 2015; Rogers et al., 2021; Vonk et al., 2013). No
previous studies, to our knowledge, have addressed transport and
degradation of SOC in the Kolyma River using riverbed samples upstream from
the Kolyma River mouth.
Here, for the first time in this river system, we combine the investigation
of three fractions of fluvial OC (dissolved, particulate, and sedimentary),
along a 250 km-long river transect in the lower reaches of the Kolyma River,
including five of its tributaries. Furthermore, we connect our fluvial data
with published records from a 1000 km-long transect across the East Siberian
Sea (ESS)
(Tesi
et al., 2014; Vonk et al., 2010a,
2012;
Bröder et al., 2019; Salvadó et al., 2016). We applied a variety of
bulk analyses (OC %, δ13C, Δ14C,
mineral-specific surface area), and used molecular geochemical tracers
(long-chain n-alkanoic acids, and lignin and cutin-derived products) to
untangle, for each fraction of OC, its sources and the effect of
fractionation and degradation during its transport via different aquatic
(freshwater, marine) and sedimentary (i.e. river, delta, estuary, shelf)
environments of the land–ocean continuum.
MethodsStudy area and sample locations
The Kolyma River in Northeast Siberia is the world's largest watershed
(653 000 km2) entirely underlain by continuous permafrost
(Holmes et al., 2012). Its discharge
follows a distinct seasonal pattern typical of Arctic rivers, with a strong
peak during the spring freshet, and lower baseflow in winter. Annual water
discharge is 109±7 km3
(Holmes et al., 2012), and the average
annual DOC and POC flux from the Kolyma River to the East Siberian Sea is
818 Gg (109 g) and 123 Gg per year respectively
(Holmes
et al., 2012; McClelland et al., 2016). In its lower reaches, the river
flows roughly northward through lowlands that consist of icy loess-like
Yedoma deposits, or ice complex permafrost deposits (ICD), of the Pleistocene
age. This Yedoma permafrost has a high OC content (2 %–5 %;
Zimov et al., 2006b). Most of the Kolyma watershed is
covered by boreal forests (taiga) dominated by the Cajanderi larch (Larix cajanderi Mayr), and
the Kolyma Delta further north is in the tundra biome.
Sampling of the Kolyma River took place from the Northeast Science Station
in Cherskiy during summer 2018, from 23 July to 3 August, directly after
spring freshet (Fig. A1). We covered a 250 km-long transect of the Kolyma
River starting at 68.63890∘ N 159.12080∘ E, where the
river passes a ca. 10 km-long Yedoma-deposit riverbank exposure (Duvanny
Yar, DY) to the delta outflow into the East Siberian Sea, including sampling
the less-studied western delta branch of the Kolyma River (K1–K6, Fig. 1b,
Table 1). In addition to the samples from the Kolyma River main stem,
samples were taken from several tributaries with varying catchment sizes.
Two of the larger tributaries of the Kolyma were sampled, the Maly Anyuy
(MA), and the Bolshoy Anyuy (BA), with a catchment size of 49 800 and 57 300 km2 respectively, and a smaller tributary, the Panteleikha (PAN; 1630 km2), where an algal bloom was observed at the time of sampling (30 July 2018). In addition, two small streams with contrasting characteristics
were sampled: (i) Y3 (∼17 km2), characterised by a
relatively high DOC load and low POC load, representing soil leaching and
active layer drainage (Bröder et al.,
2020), and (ii) a thaw stream at Duvanny Yar (DY TS; <0.1 km2),
characterised by an extremely high POC load, and a relatively low DOC load,
representing eroding Yedoma permafrost (Vonk
et al., 2013). At Duvanny Yar, additional samples were taken from a thawing
permafrost headwall, and from the outflow of a thaw stream into the Kolyma
River (DY KOL) to characterise the Yedoma permafrost endmember and mixing of
the thaw streams with Kolyma waters.
We compare our samples with the data reported in Bröder et al. (2020),
including POC samples from the Kolyma River (sampled at Cherskiy) and the
tributary stream Y3, covering the open-water seasons (late May until late
September or early October) of 2013 and 2015. These samples were included in
the present study to give an insight into temporal variations at these
locations, in addition to spatial variations along the transect.
Furthermore, this new dataset is compared with published data on surface water
DOC and POC, and surface sediments from the East Siberian Sea. The East
Siberian Sea is situated between the Laptev Sea and the New Siberian Islands
to the west and the Chukchi Sea and Wrangel Island to the east (Fig. 1a). It
covers an area of approximately one million square kilometres, and has an
average depth of 58 metres. Previous publications
(Tesi
et al., 2014; Vonk et al., 2010a, 2012) have characterised surface water DOC
and POC in the ESS, along with underlying surface sediments, following the
paleoriver valley of the Kolyma up to 600 km offshore (Fig. 1c). The
samples along this transect were collected on 3–5 September 2008, and
started ca. 12 km farther offshore than our farthest river transect point
(K6). An increase in salinity was measured in surface water moving from K 5
(0.15) to K 6 (2.6) to the first point of the marine transect (YS-34B, 17.8;
Vonk et al., 2010a). Data from a more recent cruise (between 31 July and 4 August 2014) are used to extend this transect up to 1000 km offshore
(Bröder
et al., 2019; Salvadó et al., 2016). The ESS around the extended
transect is influenced by the Pacific inflow and the Transpolar Drift
farther offshore, and the West to East flowing Siberian Coastal Current
closer to shore (Stein and Macdonald, 2004; Dudarev et al., 2022). The list
of ESS station locations and data used in this study can be found in Table A1.
Sample locations, description, sampling data, short ID, and the
distance of each location to the mouth of the Kolyma River.
Short IDLocation descriptionDate sampledLatitude (∘ N)Longitude (∘ E)Distance to ocean (km)Kolyma K 1Before Duvanny Yar23 Jul 201868.63890159.12080240K 2At Cherskiy31 Jul 201868.77598161.26494110K 3Eastern branch28 Jul 201869.20045161.4404460K 4Western branch31 Jul 201869.09501161.0170060K 5Main delta channel28 Jul 201869.53432161.9355510K 6Outflow to ESS28 Jul 201869.67805162.386320Duvanny Yar DY TSYedoma thaw stream2 Aug 201868.62987159.14470230DY KOLKolyma directly at thaw stream outflow23 Jul 201868.63060159.15478230Tributaries BABolshoy Anyui1 Aug 201868.46015160.78267160MAMaly Anyui1 Aug 201868.45193160.81279160Y3Y326 Jul 201868.75919161.44769120PANPanteleikha30 Jul 201868.70301161.51472120Sampling and sample processingParticulate and dissolved organic matter, and solid phase extractions
About 20 L of surface water was collected in LDPE bags (Vitop, Rink GmbH) in
the centre of the river at each location, except for sample DY KOL, which
was sampled at the shore of the Kolyma in the outflow of a thaw stream
(Table 1). Within 12 h of sampling, the collected surface water was
filtered through pre-combusted (400 ∘C, 12 h including
temperature ramping) and pre-weighed glass fibre filters (pore size 0.7 µm, Whatman GF/F). Small GF/F filters (diameter 47 mm; glass
filtration tower, Wheaton) were used for total suspended particulate matter
(SPM), POC concentration, and carbon isotope analyses, whereas large GF/F
filters (diameter 90 mm, pore size 0.7 µm, Whatman; custom made,
stainless steel filtration tower) were used to collect larger quantities of
suspended material for biomarker analysis. Filters were stored and
transported frozen (-20∘C), and freeze-dried before further
analyses.
The filtrate (DOC) was stored in pre-combusted 40 mL amber glass vials,
acidified to pH 2 with concentrated HCl, and transported refrigerated (+5∘C) and dark. After subsampling, the remaining filtrate (0.8 to
12.8 L, depending on DOC concentration) was used for the solid phase
extraction (SPE) of DOC, following the method of
Louchouarn et al. (2000) and
Spencer et al. (2010).
For this purpose, the filtrate was acidified to pH 2 using concentrated HCl
(37 %) and 2 % methanol was added to aid extraction efficiency
(Spencer et al., 2010). The
acidified filtrate was pumped through a pre-rinsed SPE cartridge (60 mL Mega
Bond-Elut C18; Agilent) using a peristaltic pump with flexible silicone
tubing (Cole-Parmer instrument company). The loaded SPE cartridges were
stored and transported refrigerated (+5∘C) and dark. Back at
the Vrije Universiteit Amsterdam, the SPE cartridges were extracted by
eluting twice with 40 mL of methanol into pre-combusted glass vials, which
were subsequently dried on a hot plate at 40–50 ∘C under a
stream of N2. The recovery of the SPE procedure was 63±7 % (n=12).
Riverbed sediment organic matter
Riverbed sediments of the Kolyma main stem were sampled using a Van Veen
grab-sampler, sampling surface sediment up to 1–5 cm, and stored in
sterile Whirl-Pak® bags. These samples represent recently
deposited sediment (i.e. with a large fraction of silt and clay) in more
quietly flowing locations of the river and delta. Within 12 h of
collection, sediments were frozen (-20∘C) and remained so during
transport. At the laboratory at the Vrije Universiteit Amsterdam, the
samples were freeze-dried, and sieved through a 200 µm and a 63 µm mesh, resulting in three size fractions of sediment: coarse sand
(>200µm), fine sand (63–200 µm) and a combination
of silt and clay (<63µm).
Particles coarser than silt (>63µm) are quickly
deposited during sediment transport, and carry little mineral-associated OC.
In contrast, the fine sediment fraction (<63µm) carries the bulk
of the mineral-associated OC
(Coppola
et al., 2007; Keil et al., 1994; Tesi et al., 2016) and is considered to
represent an integrated signal of suspended matter transported by the river
(Freymond et al., 2018).
Therefore, in this study, we focus only on the fine, easily transportable
fraction of the sediment. The term “SOC” here therefore refers to the OC
content of the <63µm sediment fraction. This fractionation
step allows us to cross-compare the same fraction of sediment and OC at
different locations along the river transect and beyond, on the shelf,
despite the heterogeneity of bulk sediments.
Mineral-specific surface area analysis
For mineral surface area (SA) measurements, subsamples of about 1.5 g
freeze-dried sediment were combusted at 450 ∘C for 12 h to remove
OC, rinsed twice with MilliQ to remove salt and ashes, and freeze dried
again. Directly prior to analysis, the samples were degassed for a minimum
of 2 h at 300 ∘C under vacuum. The analyses were performed at
the Vrije Universiteit Amsterdam on a Quantachrome Nova 4200e, using the
six-point Brunauer–Emmett–Teller method (Brunauer et
al., 1938). The SA measurements were regularly checked against two certified
reference materials (5.41 and 27.46 m2 g-1).
Bulk elemental analysesCarbon concentrations and stable carbon isotope analyses
Concentration of DOC and DOC-δ13C were analysed with an
Aurora1030 TOC analyser coupled to a Delta V Advantage isotope ratio mass
spectrometer (IRMS) at KU Leuven (Belgium), following the method described by
Deirmendjian et al. (2020).
The POC concentrations, and POC-δ13C were measured on a
combined elemental analyser – isotope ratio mass spectrometer (EA-IRMS) at
the National Research Council Institute of Polar Sciences (Bologna, Italy).
Before subsampling, the concentration of SPM was determined by weighing the
sediment-loaded filters after freeze-drying and dividing by the volume of
water filtered. A subsample was punched out of each 47 mm GF/F filter,
placed in a pre-combusted silver capsule, and weighed. Inorganic C was
removed by adding 50 µL of 1 M HCl twice to the silver capsules.
After oven drying (over NaOH pellets to neutralise acid, at 60 ∘C), the silver capsules were wrapped in tin capsules to aid combustion
during analysis.
Sediment (<63µm fraction) was crushed and homogenized in an
agate mortar, and two subsamples of each sample were weighed into
pre-combusted silver capsules for total OC and δ13C analyses.
The sediment was acidified as described above for the filters to remove
inorganic C, wrapped in tin capsules after acidification, and measured for OC
at the Sediment Laboratory and for δ13C at the Stable Isotope
Laboratory of the Vrije Universiteit Amsterdam (The Netherlands). All
δ13C values are reported in ‰ relative to
the international standard Vienna Pee Dee Belemnite (VPDB).
Radiocarbon analyses
Radiocarbon (14C) analyses were carried out using an EA coupled to a
MICADAS accelerator mass spectrometer at the Laboratory of Ion Beam
Physics of the Swiss Federal Institute of Technology (ETH, Zürich,
Switzerland), following the method described in
McIntyre et al. (2017). A second subsample of
the GF/F filters (POC) was punched out and a subsample of sediment (SOC) was
taken and weighed in pre-combusted silver capsules for 14C analyses.
For these samples, inorganic carbon was removed by fumigation in a
desiccator with 37 % HCl at 60 ∘C for 72 h
(Komada et al., 2008).
After fumigation, samples were dried over NaOH pellets at 60 ∘C
for 72 h to neutralise the acid, and wrapped in tin capsules. The final
14C results are corrected for constant background contamination using
the method described in Haghipour et al. (2018). All
radiocarbon data are presented either as Δ14C
(‰) or as conventional, uncalibrated radiocarbon age
(years) (Stuiver and Polach, 1977).
Molecular and biomarker analysesCuO oxidation products
Microwave-assisted alkaline CuO oxidation was carried out at the laboratory
of the Vrije Universiteit Amsterdam to extract lignin and cutin products
from SPE-DOC and SOC samples, following the method of
Goñi and Montgomery (2000). In summary, Teflon
extraction vessels were loaded with ∼2–4 mg OC, 500 mg CuO
and 50 mg ferrous ammonium sulfate. For SPE-DOC samples, 10 mg of glucose
was added to prevent superoxidation of lignin polymers. Then, 10 mL of
degassed 2 N NaOH solution was added under oxygen-free conditions. The
oxidation was performed using a MARS 6 microwave (CEM Cooperation) at 150 ∘C (1600 W, 8 min ramp, with continued heating for 90 min). The
resulting extract was centrifuged, transferred to a pre-combusted glass
vial, and an internal recovery standard (Ethyl vanillin; Sigma-Aldrich) was
added. The samples were acidified to pH 1 by adding concentrated HCl, and
then extracted twice with ethyl acetate. The samples were dehydrated with
anhydrous Na2SO4, transferred to combusted amber glass vials, and
dried under a flow of N2. Prior to analyses on an Agilent gas
chromatograph-mass spectrometer (GC-MS) at the National Research Council
Institute of Polar Sciences (Bologna, Italy), samples were re-dissolved in
pyridine and methylated with N, O-Bistrifluoroacetamide. The individual lignin phenols, benzoic
acids, and p-hydroxybenzenes were quantified by comparison with commercially
available standards, and quantification of cutin-derived products was carried out
using the response of trans-cinnamic acid.
Lipid biomarker analyses
For the extraction of lipid biomarkers from POC, freeze-dried 90 mm GF/F
filters were selected and placed in pre-extracted Teflon extraction vessels.
For some locations, multiple filters (up to three) had to be extracted to
obtain enough material (∼6 to 26 mg OC). For riverbed SOC,
∼2 g of sediment was weighed in per extraction vessel,
containing ∼12–17 mg OC. Samples were solvent-extracted
twice with 15 mL DCM : MeOH (9:1v/v) at 100 ∘C (1600 W, 5 min
ramp, continued heating for 15 min), using a MARS 6 microwave (CEM
Cooperation). The resulting extract was saponified with 10–15 mL of KOH
in methanol (0.5 M) at 70 ∘C for 2 h. Subsequently, 5–10 mL of
MilliQ water with 2 % NaCl was added. The neutral fraction (containing
n-alkanes) was extracted with hexane (3×10 mL), after which the samples
were acidified to pH 2 with concentrated HCl. The acid fraction was then
extracted with hexane : DCM (4:1v/v), methylated with BF3-MeOH (80 ∘C, 30 min), and extracted with DCM after addition of MilliQ
water. The acid fraction was further cleaned of impurities by column
chromatography (SiO2, water-deactivated), by eluting first with hexane,
then DCM : hexane (4:1) and DCM. The cleaned methylated n-alkanoic acids,
concentrated in the DCM : hexane fraction, were then analysed on a GC-MS at
the National Research Council Institute of Polar Sciences (Bologna, Italy).
Quantification of high molecular weight (HMW; carbon chain length 24–30)
n-alkanoic acids was done by comparison with commercially available standards
(alkanoic acid C22, C24, C26, C28 and C30; Sigma-Aldrich). The carbon
preference index (CPI) of the HMW n-alkanoic acids is calculated as the ratio
between even and odd carbon chain lengths (Eq. 1):
CPI=12⋅[C23+C25+C27+C29]+12⋅[C25+C27+C29+C31][C24+C26+C28+C30].
Endmember analyses
Source apportionment models are commonly used to distinguish different
source contributions to the total OC pool based on their isotopic signature.
Dual-carbon isotope endmember mixing models have proven to be useful tools
for disentangling the various sources of organic matter in different
environments, as these Markov chain Monte Carlo (MCMC) techniques account
for uncertainties in both the endmember values and the uncertainties
in sample measurements and thus provide better constraints on the relative
contributions of different sources to bulk OC
(Andersson
et al., 2015; Bosch et al., 2015; Vonk et al., 2012; Wild et al., 2019). For
this sample set, we identified three different OC sources that contributed
to the POC and SOC, and calculated their relative fractions using a
dual-isotope δ13C and Δ14C endmember mixing model.
Our approach combines an isotopic mass-balance source apportionment model,
Bayesian MCMC, which uses dual-isotope signatures (endmembers) from bulk OC
to differentiate between the following three sources: (i) permafrost OC; (ii) modern vegetation and surface soil OC; (iii) riverine primary production OC
(for the Kolyma samples) or marine primary production OC (for the ESS
samples). We defined the endmember for permafrost OC as a mixture of
Pleistocene ice complex deposits (ICD) and Holocene permafrost (including
Holocene peat), with a δ13C value of -26.3±0.7 ‰
(Vonk et al., 2012) and a
Δ14C value of -761.2±120 ‰. This
Δ14C value was derived as the mean of the ICD endmember (-954±65.8 ‰; n=329;
Wild et al., 2019) and the Holocene/peat
permafrost endmember (-567±157 ‰; n=138;
Wild et al., 2019) assuming approximately
equal carbon stock input of these two pools in this region
(Zimov et al., 2006a).
Different weighing of these two permafrost OC pools (e.g., spatial
area-weighing of ICD coverage giving a Δ14C value of -683.7±136 ‰) did not significantly change the result
of the model. The endmember for the second source, modern vegetation and
surface soil OC (including the active layer, soil OC, and recent vegetation;
hereafter “vegetation/soil OC”), was adapted from
Wild et al. (2019) with a δ13C value of -27.2±1.1 ‰ (n=150) and a
Δ14C value of -52.7±137.3 ‰ (n=118). Wild et al. (2019) presented
endmembers of these sources separately, but, owing to the inclusion of a
primary production source as a third source, we combined them into one
contemporary terrestrial endmember. Therefore, their values were averaged,
equally weighted to one endmember. The third source, primary production OC
(fluvial or marine), has an endmember δ13C value of -32.1±3.0 ‰ and a Δ14C value of +11.0±37 for riverine samples (henceforth named “Riverine PP OC”), whereas
the endmember for marine samples (“Marine PP OC”) is δ13C=-24.0±3 ‰, Δ14C=+60±60 ‰ (Vonk et al., 2012). The riverine PP OC endmember is
based on a compilation of samples and using the endmember values of previous
studies: δ13C=-30.5±2.5 ‰,
Δ14C=+41.9±4.2 ‰
(Winterfeld
et al., 2015a), δ13C=-30.6±3.3 ‰, Δ14C=+48±11 ‰
(Wild et al., 2019), and the sample of the
Panteleikha River from this study (δ13C=-33.5 ‰, Δ14C=-26 ‰), where an algal bloom was observed during the study period. For the marine
δ13C endmember (-24.0±3 ‰), we also
tested a value of -21±1 ‰, used in
Bröder et al. (2016) for ESS
sediments. The modelling results showed a minimal, non-significant change
for SOC in endmember contributions (Fig. A2). However, for POC, there was a
large shift on the first part of the marine transect from POC being marine
PP dominated to being vegetation/soil OC dominated using the -21±1 ‰ endmember (Fig. A2). This is probably related to the
sharp transition from a riverine PP to a marine PP endmember, whereas in
reality the transition is not as sharp and likely a mixture of these two
sources within the estuary. The contribution of the permafrost endmember to
the POC pool was not significantly affected by this shift. Increasing or
decreasing the standard deviation of either of the marine PP endmembers (-24 ‰
and -21 ‰) from ±1 ‰ to
±3 ‰ did not make a difference.
The dual-isotope/three-sources version of the MCMC source apportionment
model was adapted from
Bosch et al. (2015). We
used MATLAB (version 2021a) to model contributions of the three different
sources, with the following model parameters: 1 000 000 iterations, a
burn-in (initial search phase) of 10 000, and a data thinning of 10. For
further details on the method see
(Andersson
et al., 2015; Andersson, 2011; Bosch et al., 2015).
Results and Discussion
The Kolyma River transports fluvial organic matter towards the East Siberian
Sea in three different compartments: the dissolved, particulate, and
sedimentary OC pools. Our study targets all these compartments and adds a
spatial dimension by not only sampling along a 250 km main stem transect,
but also including a range of tributaries, and extending the riverine
transect ∼1000 km across the ESS using existing data (Table A1;
Bröder
et al., 2019; Salvadó et al., 2016; Tesi et al., 2014; Vonk et al.,
2010a). In contrast to previous studies (e.g., Bröder et al., 2020;
McClelland et al., 2016), we do not focus on the seasonal OC variability
within fluvial systems (i.e. comparing different stages of the hydrograph),
but aim to convey a consolidated picture of riverine dissolved, particulate,
and sedimentary OC delivered to the East Siberian Sea, and to give an insight
into the processes that affect these OC pools along the land–ocean continuum.
Three contrasting OC pools: concentrations of DOC, POC, and SOC
In Arctic rivers, DOC and POC concentrations vary significantly during
seasons. Concentrations found in this study (Table 2) match the typical
range of DOC and POC values of the Kolyma River in the late summer season
(Bröder
et al., 2020; Holmes et al., 2012; McClelland et al., 2016). The DOC
concentrations along the Kolyma River transect range from 2.76 to 4.97 mg L-1, which is a little higher than DOC in ESS surface waters
(∼0.6–1.8 mg L-1; Salvadó et al., 2016; Alling et
al., 2010). The POC concentrations during this period range from 1.49 to
2.73 mg L-1 and show a rapid decrease once offshore in the ESS, from
2.7 mg L-1 at location K6 to 0.2 mg L-1 approximately 50 km
farther at location YS34B (at a water depth of 10 m;
Vonk et al.,
2010a, Fig. 1). The Kolyma tributaries PAN and Y3 show notably higher DOC
and POC concentrations of 21.5 and 9.71 mg L-1 DOC, and 4.50 and 2.38 mg L-1 POC, respectively, than the Kolyma. The sample DY TS
shows extremely high concentrations of DOC (103 mg L-1) and POC
(>7300 mg L-1), which are in the same range as other thaw
streams at this location (Vonk et al.,
2013). Sample DY KOL, located right at the outflow of a thaw stream into the
Kolyma River, shows that the extremely high concentrations of DOC and POC
coming from DY thaw streams are quickly diluted by river water and/or
settle rapidly to the riverbed. The DOC concentration in this sample is in
the same range as the Kolyma main stem (2.75 mg L-1), whereas the POC
concentration remains elevated at 103 mg L-1. The SOC concentrations in
the <63µm fraction of riverine sediment show values ranging
from 0.45 % to 1.0 % (0.76±0.19 %, mean ± standard
deviation, n=5) along the Kolyma River transect, which are slightly
lower than ESS SOC (which was not sieved), with concentrations between 0.80 %
and 1.76 % (mean of 1.15±2.94 %, n=18;
Salvadó
et al., 2016; Vonk et al., 2010a). The fraction of OC in particulate matter
(OC concentrations normalised to TSS) is much higher than in SOC, ranging
from 6.7 % to 12.8 % within the Kolyma and with values up to 47 % for the
Pantaleikha, pointing towards a significant contribution of primary
production (i.e. pure organic matter without minerals) to the particulate
load.
Bulk data for sediment organic carbon (SOC), dissolved organic
carbon (DOC), and particulate organic carbon (POC). Including concentrations,
surface area (SA), organic carbon loading and isotopic data δ13C, Δ14C, conventional, uncalibrated radiocarbon age
(years), and fraction modern (Fm) with the measurement error.
Short IDSOCSAOC loadingGrain sizeδ13CΔ14C14C ageFmFm errorSediment <63µmwt. %m2 g-1mg OC m-2median µm‰‰years±Kolyma K 20.829.60.8539.5-27.1-52158500.4830.003K 30.668.70.7539.4-26.9-58670200.4180.004K 41.0011.90.8437.0-27.1-53060000.4740.003K 50.8811.90.7431.5-27.4-53761200.4670.005K 60.458.40.5446.8-27.2-57968900.4240.004Duvanny Yar DY PF0.6313.40.4734.5-26.1-96526 8000.0360.001DOC and POCDOCDOC δ13CPOCPOCSPMPOC δ13CPOC Δ14CPOC 14C ageFmFm errormg L-1‰mg L-1wt. %mg L-1‰‰years±Kolyma K 12.76-28.31.4212.811.2-31.6-22119400.7850.010K 22.96-30.01.716.6825.6-29.0-37937600.6270.007K 33.60-30.01.717.2823.5-29.8-30628700.7000.008K 43.49-29.41.498.1218.4-30.0-36235400.6430.007K 53.23-28.81.679.1018.4-30.7-29627500.7100.010K 64.97-27.32.738.1233.7-29.9-30128100.7050.008Duvanny Yar DY TS103-29.873251.226.00×105-25.5-86015 7300.1410.002DY KOL2.75-28.715.74.50348-26.3-85915 6700.1420.003Tributaries BA4.43-29.51.707.8521.7-31.3-17514800.8320.009MA3.16-28.91.2915.98.11-33.3-34833700.6580.006Y321.5-29.42.3818.313.0-32.5-16015000.8300.010PAN9.71-31.24.5046.69.67-33.5-261450.9840.010Three contrasting OC pools: isotopes of DOC, POC, and SOC
Each organic carbon pool (DOC, POC, and SOC) shows distinctly different
stable carbon isotope (δ13C) and radiocarbon isotope (Δ14C) ratios, which are important tools in characterising OC and
tracing OC from different sources. The DOC-δ13C along the
Kolyma River transect ranges from -27.3 ‰ to -30.0 ‰ (Table 2), which is comparable with previously published data
(Feng
et al., 2013; Mann et al., 2015; Wild et al., 2019). Although we have not
measured DOC-Δ14C in this work, earlier studies show that
the Kolyma River and its tributary DOC is relatively young (Δ14C within the
range +150 ‰ to -100 ‰;
Neff et
al., 2006; Wild et al., 2019). The DOC-δ13C of the tributaries
and Duvanny Yar are within the same range as the Kolyma, except for sample PAN,
which shows a lower δ13C value of -31.2 ‰.
An earlier study on a Duvanny Yar thaw stream found DOC-Δ14C
values between -974 ‰ and -911 ‰ (up to 30 000 years old)
(Vonk et al., 2013). However, such old DOC
has not been found in the main Kolyma River, likely because of the rapid turnover
times of permafrost DOC in Arctic waters
(Rogers et al., 2021).
Δ14C versus δ13C values of the Duvanny
Yar (DY), Kolyma, tributary, and East Siberian Sea (ESS) samples. Triangles
are POC samples, diamonds are SOC samples. The Kolyma main stem is in dark blue,
ESS in yellow, tributaries in red, and the DY in green. The boxes represent the
endmembers as defined in Sect. 2.6. The triangles with standard deviation
show the mean ± standard deviation of Kolyma particulate organic
carbon (POC, faded blue) and Y3, one of the tributaries, POC (faded red)
samples of Bröder et al. (2020) for reference.
The δ13C of Kolyma POC ranges from -29.0 ‰ to
-31.6 ‰ , and the Δ14C ranges from -221 ‰ to
-379 ‰, corresponding to 1940 to 3760 years (Fig. 2). The
δ13C values of the Kolyma transect correlate with POC % (the
OC weight % of dried particulate matter; R2=0.91, p<0.01) and Δ14C (R2=0.79, p<0.01), in other
words, samples with a high POC % have a more depleted δ13C
value and a less negative (younger) Δ14C value, both supporting
a significant contribution from riverine production. The tributary and
Duvanny Yar samples are clearly different from those of the Kolyma in their isotopic
signature of POC: the two Duvanny Yar POC samples are higher in δ13C (-25.5 ‰ and -26.3 ‰), and have a substantially
lower Δ14C (-859 ‰ and -860 ‰; 15 700 years)
than Kolyma POC, whereas the other tributaries (PAN, MA, BA, and Y3) show
generally lower δ13C values (-31.3 ‰ to
-33.5 ‰) and higher (i.e. younger) Δ14C
values (-26 ‰ to -348 ‰; 145 to 3370 years) than Kolyma POC.
The Δ14C-POC values in the Kolyma (K1–K6) are within the same range
as Kolyma summer POC of 2013 and 2015 (-314±83 ‰, n=38; Bröder et al., 2020; Fig. 2), and slightly younger than the mean Kolyma summer POC between 2003 and
2011 (-463±15 ‰, n=32;
McClelland et al., 2016).
These trends in δ13C and Δ14C in POC point towards
the influence of a younger, more 13C-depleted source of OC in the
Kolyma River and especially in the tributaries. A similar trend was found in
Bröder et al. (2020), suggesting the
influence of riverine primary production. In situ production of OC by
fluvial organisms in Arctic rivers and streams has not received much
attention, but frequently displays very low δ13C values (e.g.,
-30.5±2.5 ‰ in
Winterfeld
et al. (2015a) Lena River; -30.6±3.3 ‰ Ob and
Yenisey rivers, Galimov et al. (2006); -33.4±4.2 ‰ in
Shakil et al. (2020), streams on the Peel
Plateau). The lower δ13C values of heterotrophic OC are due to
contributions of recycled CO2 that sources from terrestrial organic
matter breakdown, which is already relatively low in δ13C
(Meyers, 1994).
Winterfeld
et al. (2015a) applied a source-apportionment approach to Lena River POC in
summer and found that primary production accounted for up to 80 % of the
fluvial POC. This “recycled carbon” (Wild
et al., 2019) appears to be an important component of summer POC transport,
which is reflected in the overall 13C-depleted values in the Kolyma
River and tributaries' POC pool (Fig. 2, and Sect. 3.3).
Carbon isotopes over the transect distance, with the riverine part on
the left side of each figure, and the marine part on the right. (a)δ13C ratio (in ‰ relative to VPDB) of Duvanny Yar
(DY; green), Kolyma (blue), and East Siberian Sea (ESS; yellow) sediment
organic carbon (SOC). (b)δ13C ratio of DY (green), Kolyma
(blue), tributaries' (red), and ESS (yellow) particulate organic carbon
(POC). (c)Δ14C ratio of DY, Kolyma, and ESS SOC. Lower Δ14C ratios indicate older OC. (d)Δ14C ratio of DY,
Kolyma, tributaries', and ESS POC.
Comparing the carbon isotope data of fluvial POC collected in this study
with surface water POC collected along the extended Kolyma River transect in
the ESS shows a large difference between the terrestrial and marine samples
(Figs. 2, 3b and d). The ESS POC is distinctly younger than the Kolyma POC:
Δ14C values between -28 ‰ and -75 ‰ for the
inner ESS and between -69 ‰ and -240 ‰ for the outer ESS,
and similarly less negative δ13C: ranging from -23.3 ‰ to
-29.1 ‰, with a trend towards higher values moving from
the river mouth further offshore
(Salvadó
et al., 2016; Vonk et al., 2010a). This abrupt transition between fresh
water and saline water POC composition is likely tied to the different
phytoplankton communities present in these respective environments, as seen
in other Arctic river deltas (e.g., Kraberg et al., 2013; Lena River delta).
We find that the Kolyma SOC is distinctly older (Δ14C of -521 ‰ to
-586 ‰; 5850 to 7020 years) and shows less negative
δ13C than POC, displaying a narrow range in δ13C
values (-26.9 ‰ to -27.4 ‰) (Figs. 2, 3a and c). On the
other hand, Kolyma River SOC is distinctly younger than the Yedoma
permafrost material from Duvanny Yar (Fig. 2). The Yedoma permafrost sample
DY PF shows an extremely low Δ14C value of
-965 ‰ (26 800 years), and a slightly higher δ13C ratio than Kolyma POC-. The ESS SOC close to shore shows a similar
age to the Kolyma SOC in this study, with a trend towards less negative
Δ14C values further offshore (-624 ‰ to -332 ‰, Δ14C). In ESS SOC-δ13C, a trend can be seen
moving from -27.1 ‰, close to the Kolyma SOC, towards
higher δ13C values of -22.4 ‰ further
offshore (Fig. 3a). This increase in δ13C and Δ14C
values of POC and SOC moving from river to shelf is likely due to the
increased contribution of marine PP OC to the SOC and POC pools further
offshore, together with sorting and settling of terrestrial and
permafrost-derived OC
(Bröder
et al., 2018; Tesi et al., 2014; Vonk et al., 2012), processes that will be
discussed in more detail in the next sections.
Quantifying the sources of OC: End member mixing analyses
The Δ14C and δ13C signatures of POC and SOC can be
used to quantify the relative contributions of different organic carbon
sources (i.e., permafrost OC; vegetation/soil OC; riverine PP OC for the
Kolyma and marine PP OC for the ESS) to these two carbon pools, following
the method described in Sect. 2.6. We revisit the endmember mixing results
from Vonk et al. (2012)
for the ESS and from Wild et al. (2019)
for the Kolyma River to connect river and shelf environments with the newly
defined endmembers (see Sect. 2.6 for endmember definitions). Relative
contributions of the different sources varied considerably between POC and
SOC, and Kolyma and ESS (Fig. 4).
Mean relative contribution (± standard deviation from Monte
Carlo simulations) of three endmembers over transect distance for (a) surface water particulate organic carbon (POC) and (b) sediment organic
carbon (SOC), based on dual carbon isotope (Δ14C and δ13C) endmember analyses. For the riverine part of the transect
(Kolyma; left side of the figure), the endmembers are: Permafrost organic
carbon (OC) in grey, Vegetation/soil OC in green, and Riverine primary
production (PP) OC in cyan. For the marine part of the transect (East
Siberian Sea; right side of the figure), the endmembers are: Permafrost OC
in grey, Vegetation/soil OC in green, and Marine primary production OC in
dark blue. Definition of the endmembers are described in Sect. 2.6 and can
be seen Fig. 2.
Along the river transect, Kolyma River main stem POC consists largely of
riverine PP OC (44±10 %), while the tributary POC shows even
higher riverine PP OC contributions of 64±10 % (mean ± standard deviation). The contribution of vegetation/soil OC is roughly equal
for POC and SOC, ranging from 18±14 % in Kolyma main stem POC, 15±11 % in tributary POC and 19±12 % in Kolyma SOC.
Permafrost OC is the dominant source in Kolyma SOC (68±10 %), and
the second largest contributor to the Kolyma main stem POC (38±8 %). As expected, the contribution of permafrost OC is highest in Duvanny
Yar POC and SOC samples (93±4 %).
Source apportionment modelling on the Kolyma POC data from Bröder et al. (2020) shows that the mean contribution of permafrost OC to Kolyma POC is in
the same range (38±9 %) as in this study over their whole sampling
period (ranging from spring 2012 to fall 2015), while the contribution of
vegetation/soil OC is slightly (26±16 %) higher, and the
contribution of riverine PP OC is slightly (37±12 %) lower than in
the samples of this study. This could be due to the timing of the sampling;
Bröder et al. (2020) also include the early and late summer when
riverine PP may not be high, while our study likely includes the peak of the
riverine PP production. At tributary Y3, including this dataset, the
contribution of permafrost OC is only 11±6 %. The bulk of the POC
in Y3 comes from the other two sources: 51±19 % from riverine and
38±20 % from vegetation/soil, which is in line with the conclusion
of Bröder et al. (2020), that the Y3 tributary does not have the
erosional force to mobilize permafrost.
For the marine transect, the riverine primary production endmember was
“replaced” with the marine primary production endmember, since marine
primary production is absent in the river, and riverine primary production
OC is thought to be rapidly recycled in a marine setting, which is supported
by the rapid shift towards a higher δ13C ratio of POC in the
first part of the offshore ESS transect. Marine primary production appears
to be the dominant source of POC in the ESS, supplying roughly half of the
OC along the entire ESS transect (47±12 %). Furthermore, we find
similar results as
Vonk
et al. (2012, 2010a) for ESS SOC: an increase in the contribution of marine
PP OC (from 10 % to ∼50 %; Fig. 4b), and a steady
decrease of the two terrestrial endmembers farther offshore. Notably, the
permafrost OC endmember remains the dominant source of OC up to 500 km
offshore, decreasing from 70 % to ∼40 %, before marine PP
OC becomes dominant. Note that we have not incorporated lateral transport
times of sediment OC (estimated up to 3600 years. across the Laptev
shelf; Bröder et al., 2018) that affect
all terrestrial OC during sedimentary transport. In contrast, for POC we
find an initial sharp decrease in the contribution of permafrost OC, from
∼40 % in the Kolyma River to ∼10 % at the
first transect point offshore (Fig. 4a), remaining around 10 % for the
entire length of the transect. Likely, the permafrost OC consists mostly of
mineral-bound OC, material that has been shown to rapidly settle in the
near-shore region
(Jong
et al., 2020; Karlsson et al., 2011; Vonk et al., 2010b), which may explain
the high permafrost contribution to the SOC pool and rapid decrease of
permafrost OC in the POC pool. On the contrary, primary production biomass
and organic debris is not mineral-bound and can be transported over long
distances (Karlsson et al., 2011; Vonk et al., 2010b; Tesi et al., 2016),
which is reflected in the relatively high contributions of these pools in
the POC of the ESS.
Sources of OC: lignin biomarker concentrations and proxies
Terrigenous biomarkers such as lignin derived phenols, cutin-derived hydroxy
fatty acids and HMW n-alkanoic acids can be used to further trace the source,
pathway, and fate of OC in rivers and in the marine environment
(e.g.,
Freymond et al., 2018; Tesi et al., 2014). The lignin content, either
normalised to OC content (as mg g-1 OC) or to mineral surface area (as
µgm-2), refers to the sum of vanillyl (V), syringyl (S) and
cinnamyl (C) phenols, and is an indicator for the contribution of higher
vascular plant material to the total organic matter pool
(Goñi and Hedges, 1992). The ratios between lignin phenol
groups S / V and C / V can be used for tracing the various types of plants
generating these phenols (Hedges and
Mann, 1979). These lignin source proxies have been extensively used to
characterize and trace different pools of OC on land, in rivers and in the
marine environment
(e.g.,
Amon et al., 2012; Goñi et al., 2000).
The lignin concentrations in DOC of the Kolyma transect range from 1.70 to
5.11 mg g-1 OC (Table 3), and the DOC lignin concentration in
the four tributaries (MA, PAN, Y3 and BA) are in the same range as the
Kolyma transect (3.61 to 5.62 mg g-1 OC). These are in the same range
as earlier results in the Kolyma River (4.7 mg g-1 OC;
Feng et al., 2017; 6.5 mg g-1 OC
Amon et al., 2012).
Sample DY TS is a notable exception, with a higher lignin concentration of
11.79 mg g-1 OC. For the ESS, only a few dissolved lignin
concentrations are published, and they are an order of magnitude lower than
in the Kolyma River, at roughly 0.2 mg g-1 OC at four points in
the outer ESS (Salvadó et al., 2016). Lignin concentrations in Kolyma
SOC (<63µm) range from 6.46 to 14.87 mg g-1 OC, which is
clearly higher than in DOC, but lower than in the first two sampling points
in the ESS (not sieved; 28.40 and 16.00 mg g-1 OC;
Salvadó
et al., 2016). Farther offshore, the lignin concentrations in SOC gradually
decrease to 0.10 mg g-1 OC, indicating a decreasing influence of
terrestrial biomass on the total OC pool, which is supported by the bulk C
isotopes
(Salvadó
et al., 2016).
A similar pattern is evident in cutin concentrations. For the Kolyma
transect, they range from 0.40 to 1.35 mg g-1 OC for DOC and 2.18 to
5.67 mg g-1 OC for SOC. The cutin concentrations of the four
tributaries are in the same range (0.52 to 1.23 mg g-1 OC) as for
Kolyma DOC, while in the Duvanny Yar thaw stream (DY TS) the concentrations
are much higher at 4.79 mg g-1 OC. The cutin to lignin ratio is higher
for SOC than for DOC in the main Kolyma samples (0.40±0.12 versus
0.21±0.11, respectively; Fig. A3), which could be due to a
methodological bias: the SOC cutin to lignin ratio could be artificially
raised by the sieving step while processing the sediments, thus the
lignin-rich organic debris could remain in the coarse fraction (Tesi et al.,
2016).
The ratios between syringyl / vanillyl (S / V) and cinnamyl / vanillyl
(C / V) can be used as biomarker source proxies for sediment organic carbon
(SOC; diamonds), dissolved organic carbon (DOC; circles) from the Kolyma
River (blue), Duvanny Yar (green), East Siberian Sea (ESS; yellow) and a few
smaller tributaries of the Kolyma (red). Ranges for vegetation and tissue
types (boxes) are based on
Goñi et al. (2000).
To further pinpoint the source of higher plant-derived OC, ratios between
lignin phenol groups (C / V and S / V) can be used to distinguish different
vegetation sources: woody versus non-woody material and gymnosperm versus
angiosperm material
(Hedges and Mann, 1979;
Goñi and Hedges, 1992; Goñi and Montgomery, 2000). The S / V and C / V
ratios show fairly consistent values for the Kolyma transect DOC, with an
S / V ratio of 0.41 to 0.48 and a C / V ratio of 0.12 to 0.18 (Fig. 5). For the
Kolyma SOC, the S / V and C / V ratios are slightly higher than those for DOC
(0.51 to 0.54 and 0.21 to 0.48). These ratios indicate a roughly equal mix
of gymnosperm and angiosperm material in both DOC and SOC within the Kolyma
main stem (Fig. 5), and an equal mix of woody and non-woody material in DOC.
This is in accordance with earlier studies on the sources of DOC in the
Kolyma River
(Amon et al.,
2012), and indicative of the mixed vegetation of the Kolyma watershed
(taiga- and tundra vegetation). The DOC sample DY TS appears to consist
completely of non-woody angiosperm organic matter, with very high S / V and
C / V ratios of 0.97 and 0.31, respectively. However, the DOC sample at DY KOL
and the permafrost SOC sample DY PF show to be more of a mix of angiosperm
and gymnosperm soft tissue material (S / V 0.55, C / V 0.33), in the same range
as Kolyma SOC and DOC, and in line with other studies on Yedoma deposits
(Winterfeld
et al., 2015b; with 0.51–1.24 for S / V and 0.27–1.07 for C / V, Lena
Delta Pleistocene and Holocene deposits).
Sorting and degradation of OC along the land-ocean continuum: OC and
biomarker loading
Changes in surface area normalized concentrations (i.e., loadings) of
terrestrial OC at bulk and molecular level, measured across coastal shelves
(e.g., Tesi et al., 2016;
Bröder et al., 2018) and along riverine transects (e.g.,
Freymond et al., 2018),
provide means to quantify loss of OC due to degradation
(Aller
and Blair, 2006; Keil et al., 1997). Due to the large variability in
hydrodynamic conditions and heterogeneous sediments found within a river,
and even more so along a river to shelf transect, it is necessary to trace a
specific fraction (e.g., through a consistent method of sediment
fractionation) to be able to directly compare sediments across dynamic
land-ocean transects. Mineral SA normalization is useful for the fraction
high in mineral-bound OC (generally the <63µm fraction:
silt and finer), but works less well on sediment with either large fractions
of non-mineral bound carbon (e.g., loose organic debris) or material low in
mineral-bound OC (e.g., coarse sand). In addition, the <63µm fraction is the most easily transported fraction of sediment in rivers,
even at lower flow velocities, and is thus the fraction that is transported
farthest offshore, as coarse organic debris and sand quickly settles near
the coasts
(Tesi
et al., 2016; Wakeham et al., 2009).
The SA-normalised OC-loadings of the <63µm river sediment
range from 0.54 to 0.85 mg OC m-2, with no apparent trend along the
main stem transect. These OC loadings are within the range of typical
river-influenced sediments (0.4–1.0 mg OC m-2;
(Keil et al., 1994; Mayer, 1994;
Blair and Aller, 2012), and similar
to OC loadings found in other river systems
(Freymond et al., 2018;
Danube River; similar sediment sampling protocol). The loading of OC on
Kolyma sediment is on average higher than for the surface sediments of the
ESS (Bröder et al., 2019;
0.19 to 0.46 mg OC m-2), and a decreasing trend in OC loadings can be
seen with increasing distance from land/water depth, suggesting that loss of
mineral-bound OC occurs during offshore transport
(Keil et al., 1997).
Organic carbon (OC) and biomarker concentrations normalised to
mineral surface area over transect distance for the Kolyma (blue), Duvanny
Yar (green), and East Siberian Sea (ESS; yellow) sediment organic carbon
(SOC) samples. (a) OC concentrations (mg m-2), (b) Lignin
concentrations (µgm-2), (c) Cutin concentrations (µgm-2), and (d) HMW n-alkanoic acids (C24–C30) concentrations (µgm-2).
For biomarkers, the SA-normalized lignin concentrations of Kolyma River
sediment vary between 5.13 and 12.67 µgm-2 (Fig. 6b), while
the SA-normalized cutin concentrations are lower, ranging between 1.17 and
4.18 µgm-2 (Fig. 6c). The SA-normalised HMW acid concentrations
are between 2.26 and 2.92 µgm-2 (Fig. 6d). For Duvanny Yar, the
OC and biomarker loadings are lower than for Kolyma sediments, due to the
combination of high SA and lower OC and biomarker concentration.
Freymond et al. (2018) found
HMW n-alkanoic acid loadings of 0.4–1.5 µgm-2, and lignin
loadings between 0.6 and 26.4 µgm-2 in sediments from the
Danube River and its tributaries, using the same sampling and similar
extraction methods, which is on the same order of magnitude as for the
Kolyma River.
Along the ESS transect sediments, a sharply decreasing trend in lignin
loadings was found in earlier studies
(Karlsson
et al., 2011; Tesi et al., 2014; Vonk et al., 2010a), from 28.4 µgm-2 near the coast down to 0.1 µgm-2 on the outer
shelf (Fig. 6b), and a similar trend for cutin, from 2.4 to 0.1 µgm-2 (Fig. 6d), and HMW acids from 3.4 to 0.8 µgm-2 (Fig. 6c). When comparing the marine end of the riverine transect with the
shallowest sample of the marine transect, the lignin and cutin
concentrations seem disconnected at this riverine-marine interface: the
lignin loadings of the riverine sediment samples appear lower than expected
while the cutin concentrations appear higher than expected, which is also
reflected in the cutin/lignin ratios (Fig. A3). Only the riverine HMW
n-alkanoic acid concentrations align well with the beginning of the marine
transect. This discrepancy in behaviour of different biomarkers could be due
to their different affinity towards mineral particles. We recognise that we
are comparing sieved (<63µm) riverbed samples with
non-sieved (bulk) marine samples, but as Tesi et al. (2016) showed that 88 %–95 % of the marine sediments in the eastern part of the ESS consist of
the <63µm fraction, we do not expect this size difference
to be the main contributor to the observed discrepancy. Instead, earlier
sediment partitioning studies that fractionated sediments by both density
and size, found that lignin is mostly present in the low-density fraction
(<1.8 g mL-1) as coarse organic debris
(Wakeham
et al., 2009; Tesi et al., 2016). It is true that low-density material is
often relatively large in size, which in our case (sieving river sediments
through 63 µm) places low density material in the coarse fraction.
This may explain the lower concentrations of lignin for the riverine
transect. In contrast, cutin-derived acids are more closely associated to
fine mineral particles, and HWM n-alkanoic acids are more evenly distributed
among sediment fractions
(Tesi
et al., 2016), explaining the better match of these two biomarker groups in
comparing Kolyma <63µm sediment to the ESS transect.
Freymond et al. (2018)
propose normalizing OC and biomarkers to SA as the benchmark for comparing
river and marine sediments. However, our results point out that this
approach seems to work only for certain biomarker groups, since the method
we apply based on Freymond et al. (2018), appears to underestimate lignin
and overestimate cutin concentrations. Therefore, we propose to use sediment
fractionation methods not purely on size but also on density, and to apply
these techniques consistently for all samples, ideally along transects that
stretch across the entire river to shelf continuum. While multiple
fractionation steps are often time and labour intensive, our results suggest
that fractionating only by size (i.e., sieving over 63 µm) is not
enough to completely resolve sorting and degradation dynamics of terrestrial
OC across the dynamic land-ocean interface, since certain biomarker groups
have affinity for different (density) fractions of the sediment.
Degradation state of OC along the land-ocean continuum: biomarker
proxies
The relative abundances of specific lignin phenol compound classes can be
used as proxies for the overall degradation status of organic carbon, for
instance, the acid / aldehyde ratios of vanillyl (Vd / Vl) and syringyl (Sd / Sl)
phenols are often used as an indicator for degradation of plant organic
matter
(Hedges
et al., 1988; Opsahl and Benner, 1995). More degraded lignin yields more
acids relative to aldehydes in the CuO extraction process, which is
reflected in a higher Vd / Vl and Sd / Sl ratio. However, these ratios are also
influenced by leaching and adsorption processes
(Hernes et al., 2007). Another CuO-oxidation
product that is frequently used as a degradation indicator is
3,5-dihydroxybenzoic acid (3,5Bd), due to the recalcitrant nature of 3,5Bd,
the ratio 3,5Bd / V increases with OC degradation in soils and sediments
(Houel et al., 2006).
In addition to CuO-oxidation products, the ratio between odd and even HMW
n-alkanoic acids, the carbon preference index (CPI), can be used as a
degradation proxy. The CPI is indicative of organic matter maturity, since
fresh plant material has a strong even-over-odd preference for n-alkanoic
acids (Freeman and Pancost, 2014; Eglinton and
Hamilton, 1967), which is lost with ongoing degradation. Thus, organic
matter with lower CPI values is considered to be more degraded.
Biomarker degradation proxies of sediment organic carbon (SOC;
diamonds), dissolved organic carbon (DOC; circles) and particulate organic
carbon (POC; triangles) from the Kolyma River (blue), Duvanny Yar (green),
East Siberian Sea (ESS; yellow) and a couple smaller tributaries of the
Kolyma (red) over transect distance. For visual aid the arrows in the middle
point towards more degraded values. (a) Acid / aldehyde ratio of Vanillyl
phenols (Vd / Vl) of SOC, and (b) of DOC. (c) Acid to aldehyde ratio of
Syringyl phenols (Sd / Sl) of SOC, and (d) of DOC. (e) 3,5Bd / V ratio of SOC,
and (f) DOC. (g) the carbon preference index of HMW n-alkanoic acids (C24–C30) of SOC, and (h) of POC.
SOC and DOC in the Kolyma River display distinctly different Vd / Vl and Sd / Sl
ratios. The SOC shows low values of Vd / Vl and Sd / Sl, ranging from 0.21 to
0.48 and 0.39 to 0.47, respectively (Fig. 7a and c), indicating that SOC is
relatively fresh and not degraded. In contrast, the DOC shows higher values,
indicating more degradation, with Vd / Vl ranging from 1.77 to 2.35, and Sd / Sl
ranging from 1.06 to 1.39 (Fig. 7b and d). Similarly, the 3.5Bd / V ratios are
lower for Kolyma SOC (0.07–0.14) than for DOC (0.20 to 0.32) (Fig. 7e and
f). In the tributaries, a wider range of Sd / Sl and Vd / Vl ratios was found in
DOC, ranging from 0.67 to 3.83 and from 1.22 to 4.89, respectively. Notably,
the Yedoma thaw stream (DY TS) shows the highest Vd / Vl and Sd / Sl ratio, and,
in contrast, the lowest 3,5Bd / V ratio among all DOC samples, meaning it is
fresh in terms of 3,5Bd / V, but degraded according to Vd / Vl and Sd / Sl. The CPI of HMW n-alkanoic acids,
measured in SOC and POC, is slightly lower (i.e., more degraded) in SOC
(5.20 to 6.45) than in POC (6.32 to 7.63) in the Kolyma River, while sample
DY PF shows a higher (i.e., fresher) CPI of 7.65 (Fig. 7g and h). In an earlier
study on Yedoma permafrost (or “ice complex deposits”) in the Lena Delta
(Sánchez-García et
al., 2014), a wide range of CPI values was found, between 3.0 and 12.0. The
CPI of HMW n-alkanoic acids of
Sánchez-García et
al. (2014) (mean of 6.6±2.7, n=17) is however very close to our
sample DY PF. For two POC samples (K1 and K4), odd HMW n-alkanoic acid
concentrations were below the detection limit, so the CPI of these two
samples could not be calculated.
Molecular biomarker data for sediment organic carbon (SOC),
dissolved organic carbon (DOC), and particulate organic carbon (POC).
Short IDLignin ΛCutin Cutin / LigninS / VC / VSd / SlVd / Vl3,5-Bd / VHMW acidsaCPISediment <63µmmg g-1 OCµgm-2mg g-1 OCµgm-2µgg-1 OCµgm-2Kolyma K 214.8712.674.623.940.310.530.270.230.410.1134302.925.94K 312.469.395.263.960.420.520.310.250.450.0930022.266.27K 413.4311.343.953.340.290.510.330.220.450.0731142.636.45K 59.196.775.674.180.620.540.480.290.470.0838362.835.60K 66.463.462.181.170.340.540.210.220.490.1443092.315.20Duvanny Yar DY PF6.753.151.360.640.200.550.330.270.390.0223441.097.65DOC POC Kolyma K 13.111.350.430.440.161.021.770.2527bK 24.240.960.230.410.131.312.270.277987.56K 34.240.660.160.460.131.392.350.322717.63K 45.110.590.120.450.151.272.10.299bK 51.70.400.240.450.121.241.80.32547.25K 65.080.440.090.480.181.061.780.253686.32Duvanny Yar DY TS11.794.790.410.970.313.834.890.05DY KOL5.311.270.240.550.191.452.060.298987.3Tributaries BA5.620.520.090.520.181.031.740.3MA3.611.230.340.610.221.431.850.38Y34.60.540.120.540.190.671.220.35PAN4.990.610.120.540.241.372.610.27
a HMW n-alkanoic acids with chain lengths C24–C30. bNot enough to calculate CPI
When connecting the Kolyma transect to the ESS, both the Vd / Vl, Sd / Sl and
3,5Bd / V ratios are consistently higher in ESS sediments than in Kolyma
sediments (Fig. 7a, c, e). While Vd / Vl and Sd / Sl show no clear trend across
the entire transect, the trend in 3,5Bd / V appears to connect well with the
riverine transect, with increasing ratios (i.e., more degraded OC) farther
offshore. For DOC, data from the outer ESS show similar Vd / Vl ratios, and
slightly higher Sd / Sl and 3,5Bd / V ratios than riverine DOC (Fig. 7b, d, f).
The CPI of ESS SOC clusters around ∼4 (Fig. 7g), which is
considerably lower (i.e., more degraded) than Kolyma SOC. For POC, the CPI
does not show a clear trend across the river-shelf transect (Fig. 7h),
however, it remains higher (i.e., fresher) than SOC, which is in line with
the results of Salvadó et al. (2016).
Our results for all four degradation proxies (Vd / Vl, Sd / Sl, 3.5Bd / V, and HMW
n-alkanoic acid CPI) in the sedimentary OC pool suggest that riverine SOC is
less degraded than its marine counterpart, likely due to the relatively
short residence time of SOC in rivers
(years to decades;
Repasch et al., 2021; Hilton et al., 2015), as compared to SOC in shelf
sediments (centuries to millenia; Bröder et
al., 2018). Additionally, we found that within the river there is relatively
little change with distance downstream, and, similarly, the changes over
distance on the marine side are limited or very gradual. However, on the
relatively short (ca. 30–50 km) transition zone between the freshwater
system and the marine system there is a large change in most measured
dissolved, particulate and sedimentary parameters.
When one looks at patterns across different pools such as DOC versus SOC or
POC versus DOC, the patterns are more ambiguous. This is likely caused by
(i) a variety of processes such as leaching, sorption or fractionation that
are at play between these pools, in addition to (ii) the temporal aspect
that is different for DOC and POC (daily to seasonal snapshots) than SOC
(integrating several years to decades). Generally, however, we can say that
SOC and POC appear relatively fresh (despite having a high radiocarbon age)
and DOC appears more degraded (yet with a lower radiocarbon age), as is also
found in previous studies
(Feng
et al., 2017; Goñi et al., 2000; Salvadó et al., 2016; Tesi et al.,
2014; Vonk et al., 2013, 2010a). The SOC pool is the main (temporary) storage
place for permafrost thaw-derived OC, and we propose to devote more
scientific attention to the physical and chemical processes affecting the
transport and degradation of this fraction, as eventually this will
determine the fraction of permafrost OC that can be captured for long
term-burial.
Conclusions and outlook
The aim of this study was to use an integrated approach to investigate the
changes of different phases of OC (dissolved, particulate and sediment OC),
and the effect of fractionation and degradation on permafrost-derived OC
during transport over large distances along the land-ocean continuum. We
conclude that permafrost-derived OC makes up the bulk of the total SOC along
the source-to-sink system, and accounts for a significant part of the POC in
the Kolyma River. In contrast, the contribution of permafrost-derived OC is
marginal to POC in the marine realm and to DOC across the entire transect,
despite the presence of 14C depleted sources within the watershed.
Overall, this highlights the importance of accounting for all carbon pools
in order to allow for comparisons between fluvial and marine systems across
different temporal scales.
We found a decrease in OC and terrigenous biomarkers normalised to sediment
mineral surface area across the transect, indicating loss through
degradation of terrestrial OC over transport distance, and especially
pronounced changes in the transition zone between the freshwater and marine
realm. Molecular biomarker proxies indicating OC degradation show a
remarkably “fresh” biomarker signature for SOC, despite its generally
lower Δ14C values (i.e. older) than DOC and POC. Biomarker
degradation proxies along the land-ocean continuum generally compare well
between river samples and marine samples, yet show diverse degradation
patterns when comparing between different OC pools (e.g., DOC versus SOC).
Processes such as leaching and sorption, causing transfer of OC between DOC
and POC pools, may explain some of the patterns we observed, in addition to
the contrasting timescales that these pools represent (from days to years).
We therefore want to emphasize that an integrated approach is necessary to
obtain a complete picture of OC transport along the river-ocean continuum,
and recommend to (a) minimally compare one pool (e.g., SOC) across
land-ocean transects, and ideally (b) compare all pools (SOC, POC, DOC)
consistently across land-ocean transects. Furthermore, as we here have shown
that permafrost-derived OC is mostly transported within the SOC fraction, we
recommend increasing scientific focus on the sedimentary fraction when
studying the fluvial and marine fate of permafrost OC.
Finally, we want to acknowledge that large discrepancies remain between the
freshwater and marine research fields when studying OC dynamics as marine
studies seem to focus mostly on SOC, while river studies mostly target DOC.
It is necessary to connect these two environments and make sure to (i) apply
common consistent methodology, and (ii) increase emphasis on the dynamic
terrestrial-marine transition zone in order to completely resolve the fate
of terrestrial OC along river-shelf systems.
Discharge of the Kolyma River at Kolymskoye, about 40 km upriver
of sampling point K 1, in 2018 (Shiklomanov et al., 2021, Artic GRO
dataset). Sampling period of this study highlighted in blue, directly after
spring freshet. In grey dotted lines the discharge of the Kolyma River
during the 2008 and 2014 East Siberian Sea sampling campaigns for
comparison.
East Siberian Sea (ESS) sample locations, names, and distance from
the mouth of the Kolyma River for sediment samples, surface water dissolved
organic carbon samples (DOC) and surface water particulate organic carbon
samples (POC). Data used in this study was gathered from four earlier
publications: Bröder et al. (2019), Salvadó et al. (2016), Tesi et
al. (2014), Vonk et al. (2010a). Specifically,
Tesi
et al. (2014) and Vonk et al. (2010a) have characterized surface water DOC
and POC in the ESS, along with underlying surface sediments, following the
paleo river valley of the Kolyma up to 600 km offshore, collected on 3–5 September 2008, and data from a more recent cruise (between 31 July and 4 August 2014) are used to extend this transect up to 1000 km offshore
(Bröder
et al., 2019; Salvadó et al., 2016).
Sample name/siteLatitudeLongitudeDistance fromWater depthReferences for data(∘)(∘)river mouth (km)(m)Sediment YS034B69.71162.69-5010Tesi et al. (2014), Vonk et al. (2010a)YS03569.82164.06-10031Tesi et al. (2014), Vonk et al. (2010a)YS03669.82166.00-17032Tesi et al. (2014), Vonk et al. (2010a)YS03770.13168.01-26042Tesi et al. (2014), Vonk et al. (2010a)YS03870.70169.13-33036Tesi et al. (2014), Vonk et al. (2010a)YS03971.22169.37-39044Tesi et al. (2014), Vonk et al. (2010a)YS04071.48170.55-44049Tesi et al. (2014), Vonk et al. (2010a)YS04171.97171.79-51043Tesi et al. (2014), Vonk et al. (2010a)YS08675.30174.40-760200Tesi et al. (2014), Vonk et al. (2010a)YS08875.10172.19-700142Tesi et al. (2014), Vonk et al. (2010a)YS09074.67172.39-66063Tesi et al. (2014), Vonk et al. (2010a)YS09174.43170.85-62056Tesi et al. (2014), Vonk et al. (2010a)SWE-6073.52169.46-51043Bröder et al. (2019), Salvadó et al. (2016)SWE-6174.11170.90-58051Bröder et al. (2019), Salvadó et al. (2016)SWE-6374.68172.37-66064Bröder et al. (2019), Salvadó et al. (2016)SWE-6474.94172.69-700120Bröder et al. (2019)SWE-6575.16173.19-720170Bröder et al. (2019)SWE-6675.90174.30-810239Bröder et al. (2019), Salvadó et al. (2016)SWE-6776.32175.61-860468Bröder et al. (2019), Salvadó et al. (2016)DOC SWE-6073.52169.46-510Salvadó et al. (2016)SWE-6174.11170.90-580Salvadó et al. (2016)SWE-6374.68172.37-660Salvadó et al. (2016)SWE-6675.90174.30-810Salvadó et al. (2016)Sample name/siteLatitudeLongitudeDistance fromReferences for data(∘)(∘)river mouth (km)POC YS-34B69.71162.69-50Tesi et al. (2014), Vonk et al. (2010a)YS-3569.82164.06-100Tesi et al. (2014), Vonk et al. (2010a)YS-3669.82166.00-170Tesi et al. (2014), Vonk et al. (2010a)YS-3770.13168.01-260Tesi et al. (2014), Vonk et al. (2010a)YS-3870.70169.13-330Tesi et al. (2014), Vonk et al. (2010a)YS-3971.22169.37-390Tesi et al. (2014), Vonk et al. (2010a)YS-4071.48170.55-440Tesi et al. (2014), Vonk et al. (2010a)YS-4171.97171.79-510Tesi et al. (2014), Vonk et al. (2010a)SWE-6073.52169.46-510Salvadó et al. (2016)SWE-6174.11170.90-580Salvadó et al. (2016)SWE-6374.68172.37-660Salvadó et al. (2016)SWE-6675.90174.30-810Salvado et al. (2016)
Mean relative contribution (plus and minus one standard deviation
from Monte Carlo simulations) of three endmembers over transect distance for
(a) surface water particulate organic carbon (POC) and (b) sediment organic
carbon (SOC), based on dual carbon isotope (Δ14C and δ13C) endmember analyses (EMMA). For the riverine part of the transect
(Kolyma; left side of the figure), the endmembers are: Permafrost organic
carbon (OC) in grey, Vegetation/soil OC in green, and Riverine primary
production (PP) OC in cyan. For the marine part of the transect (East
Siberian Sea; right side of the figure), the endmembers are: Permafrost OC
in grey, Vegetation/soil OC in green, and Marine primary production OC in
dark blue. This figure was made using the alternative Marine PP endmember
(δ13C=-21±1 ‰), for comparison
to Fig. 4 of the main text, which uses the Marine PP endmember of -24±3 ‰.
Cutin to Lignin ratios of Kolyma (blue), Duvanny Yar (green),
tributaries (red), and East Siberian Sea (ESS; yellow) sediment organic
carbon (SOC; diamonds) and dissolved organic carbon (DOC; circles).
Data availability
All data that support the findings of this study are included within the
article and/or are available for download in Bröder et al. (2019), Salvadó et al. (2016), Tesi et al. (2014), Vonk et al. (2010a). The East Siberian Shelf sediment data has recently been compiled in the CASCADE database (10.17043/cascade-2; Martens et al., 2021).
Author contributions
The conceptualisation of the study was done by JEV and DJ, with funding acquired by JEV. The field study and sample collection was done by DJ, LB, KHK, AD and NZ, the lab analyses by DJ, LB, TT and NH, and the data analyses by DJ, TT and PP. Lab- and field resources were used from TT, NZ, AD, NH, TIE and JEV. Writing of the original draft was done by DJ with valuable input from all authors.
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
We want to thank the owners and staff of
the Northeast Science Station (Cherskiy, Russia) for their logistical
support during the field campaign. We want to thank Suzanne Verdegaal-Warmerdam, Oscar Kloostra, Martine Hagen and Roel van Elsas of the
VU Amsterdam Sediment Lab and Stable Isotope Lab, and the staff of the ETH
Laboratory of Ion Beam Physics for their help with the laboratory analyses.
Financial support
This research has been supported by the European Commission, H2020 Research Infrastructures (THAWSOME (grant no. 676982)).
Review statement
This paper was edited by Yuan Shen and reviewed by two anonymous referees.
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