BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-18-3015-2021Spatial–temporal variations in riverine carbon strongly influenced by local
hydrological events in an alpine catchmentSpatial–temporal variations in riverine carbonWangXinLiuTingWangLiangLiuZongguangZhuErxiongWangSiminCaiYueZhuShanshanFengXiaojuanxfeng@ibcas.ac.cnState Key Laboratory of Vegetation and Environmental Change,
Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, ChinaUniversity of Chinese Academy of Sciences, Beijing, 100049, China
Headwater streams drain >70 % of global land areas
but are poorly monitored compared with large rivers. The small size and low
water buffering capacity of headwater streams may result in a high
sensitivity to local hydrological alterations and different carbon transport
patterns from large rivers. Furthermore, alpine headwater streams on the
“Asian water tower”, i.e., Qinghai–Tibetan Plateau, are heavily affected
by thawing of frozen soils in spring as well as monsoonal precipitation in
summer, which may present contrasting spatial–temporal variations in carbon
transport compared to tropical and temperate streams and strongly influence
the export of carbon locked in seasonally frozen soils. To illustrate the
unique hydro-biogeochemistry of riverine carbon in Qinghai–Tibetan headwater
streams, here we carry out a benchmark investigation on the riverine carbon
transport in the Shaliu River (a small alpine river integrating headwater
streams) based on annual flux monitoring, sampling at a high spatial
resolution in two different seasons and hydrological event monitoring. We
show that riverine carbon fluxes in the Shaliu River were dominated by
dissolved inorganic carbon, peaking in the summer due to high discharge
brought by the monsoon. Combining seasonal sampling along the river and
monitoring of soil–river carbon transfer during spring thaw, we also show
that both dissolved and particulate forms of riverine carbon increased
downstream in the pre-monsoon season due to increasing contribution of
organic matter derived from thawed soils along the river. By comparison,
riverine carbon fluctuated in the summer, likely associated with sporadic
inputs of organic matter supplied by local precipitation events during the
monsoon season. Furthermore, using lignin phenol analysis for both riverine
organic matter and soils in the basin, we show that the higher
acid-to-aldehyde (Ad/Al) ratios of riverine lignin in the monsoon season
reflect a larger contribution of topsoil likely via increased surface runoff
compared with the pre-monsoon season when soil leachate lignin Ad/Al ratios
were closer to those in the subsoil than topsoil solutions. Overall, these
findings highlight the unique patterns and strong links of carbon transport
in alpine headwater catchments with local hydrological events. Given the
projected climate warming on the Qinghai–Tibetan Plateau, thawing of frozen
soils and alterations of precipitation regimes may significantly influence
the alpine headwater carbon transport, with critical effects on the
biogeochemical cycles of the downstream rivers. The alpine headwater
catchments may also be utilized as sentinels for climate-induced changes in
the hydrological pathways and/or biogeochemistry of the small basin.
Introduction
Headwater streams, as the origins of the stream network, comprise nearly 90 %
of the total length of global river networks (Downing et al., 2012) and
drain >70 % of global land areas (Gomi et al., 2002). They are
hence important sources of water, sediment, organic matter and nutrients for
downstream regions (Gomi et al., 2002) and critical for maintaining
ecological functions of the entire fluvial system including biogeochemical
cycling (Biggs et al., 2017). However, headwater streams remain
under-investigated in terms of biogeochemical processes and carbon transport
patterns compared with large rivers so far, constraining an accurate
understanding of the “boundless carbon cycle” (Battin et al., 2008).
Headwater streams differ from large rivers in several aspects that may
affect their carbon transport. First, headwater streams have narrower width
and shallower depth than large rivers and are closely connected with
adjacent terrestrial ecosystems via water and solute exchange (Battin et
al., 2008; Öquist et al., 2014). Second, headwater
streams have smaller drainage areas and buffering capacity for water flow
than large rivers (Rivenbark and Jackson, 2004; Svec et al., 2005). In
addition, water in headwater streams has a shorter residence time (Caillon
and Schelker, 2020). The above characteristics collectively result in high
sensitivity of headwater streams to local hydrological alterations or
sporadic events such as precipitation (Benda et al., 2005; Richardson et
al., 2005). Thus, riverine carbon transport in headwater streams may show a
fast and strong response to local environmental variations and are receiving
increasing attention (Gomi et al., 2002; Mann et al., 2015; Biggs et al.,
2017) to improve our understanding of riverine carbon cycling (Flury and
Ulseth, 2019; French et al., 2020; Battin et al., 2008).
Studies on headwater carbon transport have largely examined tropical and
temperate streams in association with rainstorm events (Pereira et al.,
2014; Argerich et al., 2016; Johnson et al., 2006). By comparison, headwater
streams residing in alpine regions that are heavily affected by freeze–thaw
and snow melting events are less well studied (Mann et al., 2015; Bröder
et al., 2020; Chiasson-Poirier et al., 2020). The Qinghai–Tibetan Plateau,
known as the “Asian water tower”, harbors numerous headwater streams for
seven major Asian rivers (Immerzeel et al., 2010; Qiu, 2008). This region is
covered by large areas of glaciers, permafrost and seasonally frozen soils
(not underlain by permafrost layers), which are strongly affected by thawing
with increasing temperatures in the pre-monsoon spring. Thawing of frozen
soils and deepening of active layers can strongly affect catchment
hydrology, including creating vertical and lateral flows, increasing soil
filtration, and enhancing groundwater-surface water exchange and baseflow (Song
et al., 2019; Walvoord and Kurylyk, 2016). In addition, this region has a
continental monsoon climate and is hence significantly affected by intense
precipitation in the summer monsoon (Zou et al., 2017). Hydrological
alterations induced by both spring thawing and summer monsoon likely result
in unique seasonal patterns in riverine carbon transport in the
Qinghai–Tibetan streams compared to headwater streams in other regions
including the Arctic or tropical rivers (Zhang et al., 2013). The exported
carbon could originate from both recently fixed modern carbon from
terrestrial plants and aged carbon preserved in frozen soils within this
region (Song et al., 2020; Qu et al., 2017), which show different
decomposition characteristics (Mann et al., 2015) and thus may influence
regional carbon cycling. However, the seasonal transport dynamics of these
carbon pools along the Qinghai–Tibetan river continuum are relatively poorly
investigated. In particular, as one of the most climate-sensitive regions,
warming-induced alterations of permafrost zone and precipitation patterns
may change the hydrogeological characteristics of headwater streams (Chang
et al., 2018) and thus affect riverine carbon transport. Yet, the variations
in riverine carbon and its response to thawing and precipitation events in
headwater streams on the Qinghai–Tibetan Plateau are poorly resolved.
To illustrate the unique hydro-biogeochemistry of riverine carbon in alpine
headwater streams on the Qinghai–Tibetan Plateau, riverine carbon transports
were investigated in a small river (Shaliu River) feeding into the Qinghai
Lake at three different levels. First, we provide a year-long biweekly
monitoring record on the water discharge and concentrations of dissolved
organic carbon (DOC) and dissolved inorganic carbon (DIC) to estimate
monthly fluvial carbon fluxes for 1 year. Second, we use bulk
concentration and biomarker (lignin phenols) analyses to examine riverine
carbon composition and its variations along the Shaliu River by sampling at
a high spatial resolution in both pre-monsoon (spring thawing) and monsoon
seasons. Third, to further identify the influence of hydrological events on
the spatial–temporal variations in riverine carbon, we examine carbon
transport from the adjacent soils to the river during a 79 d thawing
period and a short-term monsoon precipitation event. The characterization of
lignin phenols, which are unique tracers of terrestrial-plant-derived
organic matter (OM; Hedges and Mann, 1979) and provide useful information on
the oxidation stage of terrestrial OM (indicated by lignin phenol
acid-to-aldehyde ratios; Bianchi and Canuel, 2011; Hedges et al., 1988),
allows us to investigate the sources and transport of terrestrial OM in
rivers. Based on these investigations, we hypothesize that the release of
carbon from frozen soils during thawing events leads to a downstream
increase in riverine carbon in the pre-monsoon season while carbon inputs
through surface runoff in short-term precipitation events may result in a
fluctuation of riverine carbon. Collectively, these investigations provide a
benchmark illustration of riverine carbon transport in headwater streams on
the Qinghai–Tibetan Plateau.
Materials and methodsStudy area
The Shaliu River, with a length of 110 km and a catchment area of 1442 km2 (Zhang et al., 2013) and a mean annual daily discharge of 7.5 m3 s-1 from 2015 to 2016 (from Gangcha hydrological station), is
the second largest river flowing into the Qinghai Lake on the northeastern
edge of the Qinghai–Tibetan Plateau at an altitude of 3200–3800 m above sea
level (Fig. 1a). The Shaliu River is a small river integrating headwater
streams of orders of 1–3 on the Qinghai–Tibetan Plateau. It flows through
a semiarid alpine region widely covered by seasonally frozen soils (Wang et
al., 2018) and is relatively well protected from human activities (Zhang et
al., 2013; Cheng et al., 2018). The Shaliu River basin is covered by
grassland (∼ 71 %) dominated by Potentilla anserina Rosaceae, Elymus nutans Griseb and Deyeuxia arundinacea (Liu et al., 2018);
bare land (16 %); and wetland (10 %; Cheng et al., 2018). The soils in
the basin are mainly Gelic Cambisol (IUSS working group WRB, 2015) underlain
by Triassic sandstone, late Cambrian metamorphic rocks (schist and gneiss),
granites (Zhang et al., 2013) and a widespread distribution of late
Paleozoic marine sedimentary rocks (Jin et al., 2009; Xiao et al., 2013),
and the latter rocks contain abundant limestone rich in carbonates (Bissell
and Chilingar, 1967). The Shaliu River basin is under a continental monsoon
climate characterized by warm, humid summers and cold, dry winters (Wang et
al., 2018). The mean annual temperature is -0.5∘C within the
basin (Li et al., 2013) and mean annual precipitation is 370 mm,
∼ 90 % of which occurs in the monsoon season (June to
September; Fig. S1; Wu et al., 2016). The seasonally frozen soils in the
watershed start to thaw in late April to early May (i.e., the onset of
pre-monsoon season) and refreeze in late October. The river is partly or
fully frozen from November to March.
Sampling sites along the Shaliu River (a) and soil temperature (T)
at the depth of 10 cm (referred to as topsoil) and 40 cm (subsoil) at SLH-1
and SLH-3 stations (b). The map in panel (a) is processed with ArcGIS 10.0.
The blue and red arrows in panel (b) indicate the sampling time for soil
solution at SLH-1 and SLH-3 during thawing events, respectively.
Sample collection
For annual flux assessment, the daily discharge of the Shaliu River was
obtained from Gangcha hydrological station (SLH-4) where river water was
collected biweekly from the middle of the stream using pre-washed high-density
polyethylene containers from May 2015 to April 2016. The high-density
polyethylene containers were cleaned with soapy water, soaked in 10 % HCl
solution for 24 h, rinsed three times using Milli-Q water before drying, and
re-rinsed three times with field river water prior to sampling. An aliquot
of the water was immediately filtered through 0.45 µm acetate syringe
filters and preserved with 0.02 % saturated mercury chloride (HgCl2)
in vials without headspace in the dark for DIC analysis. The remaining water
was filtered through pre-combusted (550 ∘C, 4 h) and pre-weighted
0.7 µm GF/F filters, preserved with a few drops of saturated
HgCl2, and kept frozen in acid-washed glass vials until DOC analysis.
For seasonal water sampling, river water was collected using the same method
as above at five evenly distributed stations along the Shaliu River in May
(pre-monsoon season) and August (monsoon season), 2015 (Fig. 1a; SLH-5 was
only sampled in the pre-monsoon season). Water temperature, pH, dissolved
oxygen (DO) and conductivity were measured in situ using a multi-parameter
device (ProfiLine Multi 3320, WTW, Germany). Water was filtered and
preserved as above for DIC and DOC analyses. Moreover, filtered water (0.7 µm) was kept frozen before measuring nitrogen (N) species and cations.
The filters were freeze-dried for total suspended solids (TSSs), particulate
organic carbon (POC), particulate inorganic carbon (PIC), particulate N and
particulate lignin phenol measurements. The remaining filtrates were
acidified to pH 2 and extracted by solid phase extraction (SPE) for
dissolved lignin phenol measurements.
To assess the influence of local hydrological events on riverine carbon, we
further monitored spring thawing and a monsoonal precipitation.
Specifically, soil temperature was monitored at 10 and 40 cm beneath the
surface at SLH-1 and SLH-3 stations (∼ 150 m away from the
river bank) at 2 h intervals from October 2017 to June 2018 using iButton
temperature loggers (DS1922L, Wdsen, China). Spring thawing occurred in late
April when soil temperature was >0∘C (Fig. 1b).
Soil solutions were collected two or three times from topsoil (0–10 cm) and
subsoil (30–40 cm) in April–June 2018 using a pre-arranged ceramic head
(0.2 µm), which is a porewater sampling device composed of a porous
ceramic head connected with a tube for pulling vacuum and retrieving sample.
Soil leachates were also directly collected by putting acid-washed carboys
under soil layers in exposed soil profiles in June 2018 (Wang et al., 2018).
Soil solutions and leachates were filtered (0.22 µm) immediately upon
collection and preserved as previously described for DOC and dissolved
lignin phenol analyses. For the monsoonal precipitation, river water was
sampled at 15 min intervals (four times) at SLH-4 station during a
precipitation event (lasting for 1 h) in August 2015 when a rainfall of
1.0 mm was recorded on the same day at the Gonghe weather station near
Shaliu River basin and filtered for the measurements of DOC, DIC, POC, PIC
and TSS.
To investigate connections between riverine and soil organic matter within
the basin, topsoil (0–10 cm) and subsoil (40–60 cm) samples were collected
from five locations 150–500 m away from the corresponding river sampling
station in August 2015. At each location, three soil cores were collected
from each of three random quadrats (1 × 1 m; intervals >500 m) using a stainless-steel gravity corer (diameter of 5 cm). Soils from
the same depth and same quadrat were homogenized and shipped back to the
laboratory. Freeze-dried soils were passed through a 2 mm sieve for soil
organic carbon (SOC), soil N and soil lignin phenol measurements after
removal of visible roots.
Bulk chemical analysis
The contents of riverine POC, particulate N, SOC and soil N were determined
by elemental analyzer (Vario EL III, Germany) after fumigation with
concentrated hydrochloric acid (HCl; Dai et al., 2019), and the analytical
precision (standard deviation for repeated measurements of standards) was
±0.1 %. POC concentration (mg L-1) was calculated based on POC
content (%OC) and TSS concentration. PIC was calculated by subtracting
POC from total particulate carbon quantified by an elemental analyzer without
fumigation.
The DOC, dissolved N and DIC concentrations were measured using a Multi N/C
3100 analyzer fitted with an autosampler (Analytik Jena, Germany).
Specifically, water samples for DOC analysis were manually acidified to pH
<2 with HCl and sparged automatically with oxygen (O2) to remove
inorganic carbon before DOC measurement via the high-temperature catalytic
oxidation procedure on a Multi N/C 3100 analyzer. For DIC, water samples were
acidified online using phosphoric acid (H3PO4) with the CO2
analyzed by the non-scattering infrared detector on a Multi N/C 3100 analyzer.
The analytical precision was ±1 % based on repeated measurements of
standards. Inorganic N including ammonium (NH4+-N), nitrate
(NO3--N) and nitrite (NO2--N) was determined
colorimetrically on an AutoAnalyzer-3 (Bran & Luebbe, Germany). Dissolved
organic N (DON) was calculated by subtracting all inorganic N from dissolved
N. Major cations including calcium (Ca2+) and magnesium (Mg2+)
were measured on an inductively coupled plasma mass spectrometer (ICP-MS,
7700X, Agilent, USA) after acidifying to pH < 2.
Lignin phenol analysis
Lignin phenols were analyzed using the alkaline copper oxide (CuO) oxidation
method to trace terrestrially derived OM (Hedges and Ertel, 1982). Dissolved
lignin phenols were concentrated with SPE cartridges containing 5 g of
sorption materials composed of octadecyl carbon moieties (C18)
chemically bonded to a silica support (C18-SPE Mega Bond Elut; Agilent,
USA). Cartridges were pretreated with methanol followed by acidified (pH 2)
Milli-Q water. Filtered (through a 0.7 µm pre-combusted GF/F filter)
river water was acidified to pH 2 using concentrated HCl, amended with pure
methanol to a final concentration of 0.5 % (v/v) to improve SPE efficiency
(Spencer et al., 2010a), thoroughly mixed and passed through the SPE
cartridge with a peristaltic pump at a flow rate of 10 mL min-1
(Louchouarn et al., 2010). The extraction efficiency of the C18 cartridge
for lignin phenols was around 80 %–90 % (Spencer et al., 2010a). All
cartridges were dried and kept in a freezer in the dark until dissolved organic matter (DOM) was eluted
with methanol and dried under nitrogen gas (N2). Dried samples were
mixed with 0.5 g CuO, 50 mg ammonium iron (II) sulfate hexahydrate
[Fe(NH4)2(SO4)2⚫6H2O] and 15 mg glucose
(to avoid excessive oxidation), and soil solutions and leachates were mixed
with the same reagents as well. All samples were mixed with 20 mL of 2 M
N2-purged sodium hydroxide (NaOH) solution in teflon-lined bombs at
170 ∘C for 2.5 h. The lignin oxidation products were spiked with a
recovery standard (ethyl vanillin), acidified to pH < 2 with 12-M
HCl and kept in the dark for 2 h. Oxidation products were extracted with
ethyl acetate three times, spiked with an internal standard (trans-cinnamic
acid) and concentrated under N2 for further analysis. Lignin phenols,
after converting to trimethylsilyl derivatives by reacting with
N,O-bis-(trimethylsilyl)trifluoroacetamide (BSTFA) and pyridine, were
quantified on a gas chromatograph–mass spectrometer (GC-MS; Thermo Fisher
Scientific, USA) using a DB-5MS column (30 m × 0.25 mm inner
diameter; film thickness 0.25 µm) for separation (Dai et al., 2019).
Quantification was achieved by comparing with recovery standards (ethyl
vanillin) to account for compound loss during extraction procedures. In
addition, TSS and soil samples were extracted using the same procedures as
mentioned above without the addition of glucose.
Eight lignin-derived phenols, including vanillyl (V; vanillin,
acetovanillone, vanillic acid), syringyl (S; syringaldehyde, acetosyringone,
syringic acid) and cinnamyl (C; p-coumaric acid, ferulic acid) phenols, are
used to represent the absolute (Σ8; in units of µg L-1) and OC-normalized concentration (Λ8; in units of
mg g-1 OC) of lignin phenols. The acid-to-aldehyde (Ad/Al) ratios of V
and S phenols are used to indicate lignin oxidation (Opsahl and Benner,
1995), which typically increases with elevated degradation (Otto and Simpson,
2006) but may be affected by leaching and sorption processes as well (Hernes
et al., 2007).
Carbon flux estimate from LOADEST
The daily discharge was combined with measured DOC and DIC concentrations at
SLH-4 station to model their fluxes using the USGS load estimator (LOADEST)
program (Runkel et al., 2004; https://water.usgs.gov/software/loadest/, last access: 27 March 2021)
within the LoadRunner software package (URL:
https://environment.yale.edu/loadrunner/, last access: 27 March 2021). The best regression model with
the lowest Akaike information criterion (AIC) and without variables for
long-term change during the calibration period (i.e., models 1, 2, 4 and 6)
within LOADEST was selected to fit the measured DOC (Eq. 1) and DIC
fluxes (Eq. 2) for annual carbon flux estimate (Song et al., 2020; Tank
et al., 2012; Zolkos et al., 2018, 2020):
1ln(DOC flux)=a0+a1lnQ,2ln(DIC flux)=a0+a1lnQ+a2lnQ2+a3Sin(2πdtime)+a4Cos(2πdtime),
where flux is provided in kg d-1, Q is river discharge in cubic meters
per second (m3 s-1) multiplied by a conversion factor of 0.0283, lnQ equals ln(streamflow) minus
center of ln(streamflow), dtime equals decimal time minus center of decimal
time and other parameters (i.e., a0, a1, a2, a3, a4)
are shown in Table S1. LOADEST can provide daily, monthly and annual
fluxes using the adjusted maximum likelihood estimation (AMLE) approach (Song et
al., 2020; Tank et al., 2012).
Statistical analyses
Normal distribution of data and homogeneity of variance were checked for all
variables using the Shapiro–Wilk and Levene tests, respectively. Differences in
river water parameters between the pre-monsoon and monsoon seasons were
tested using the independent-sample t test. Differences in the Ad/Al ratios
between topsoils and subsoils were analyzed by a paired t test. Differences in the
Ad/Al ratios between soil solutions and leachates at SLH-1 station during
thawing events were determined using one-way ANOVA followed by a post hoc
Duncan test. Differences in river water pH were checked by Mann–Whitney U
test due to non-normal distribution of data. Pearson correlation was used to
assess relationships between DIC and cation concentrations and between
riverine carbon concentration and the distance of sampling sites from SLH-0,
where autocorrelation of riverine carbon concentrations among samples was
not significant based on the Durbin–Watson test. Differences and correlations
were considered to be significant at a level of p< 0.05. All
statistical analyses were conducted using SPSS 25 (SPSS, Chicago, USA).
ResultsDischarges and carbon fluxes
The total annual discharge of Shaliu River at SLH-4 station was 2.4 × 108 m3 from 2015 to 2016. Its daily discharge
ranged from 0.25 to 82.40 m3 s-1, following a normal distribution
with a maximum of 82.40 m3 s-1 in the monsoon season (Fig. 2a).
DIC concentration at SLH-4 station showed a large intra-annual variability
from 20.8 to 50.9 mg L-1 with a minimum value in the monsoon season
and had a negative relationship with river discharge (Fig. 2a). Conversely, DOC concentrations were relatively constant and fluctuated around
2.5–3.1 mg L-1 (Fig. 2a). Accordingly, the DIC and DOC fluxes at
SLH-4 station were estimated to be 8.0 and 0.6 Gg yr-1 (1 Gg = 109 g), respectively, both of which were highest in summer, accounting
for 58 %–60 % of the corresponding annual fluxes (Fig. 2b). Moreover,
annual DIC flux was more than 10 times larger than the DOC flux in the
Shaliu River and constituted 90 % of the total dissolved carbon flux.
Discharge, dissolved inorganic carbon (DIC), and dissolved organic
carbon (DOC) concentrations (a) and fluxes (b) exported from Shaliu River at
SLH-4 station from May 2015 to April 2016. The modeled concentrations in
(a) and modeled fluxes in (b) are derived from load estimator (LOADEST).
The inserted columns in panel (b) show the seasonal variations in carbon
fluxes classified as follows: spring (May to June), summer (July to
September), autumn (October to November) and winter (December to the next
April).
River water properties and lignin phenols
River water was slightly alkaline with higher pH values in the pre-monsoon
(8.4 ± 0.04) than monsoon season (7.7 ± 0.2) in the Shaliu River
(p< 0.05; Table 1). DO concentration was also higher in the
pre-monsoon (5.91–6.76 mg L-1) than monsoon season (4.39–5.87 mg L-1; p< 0.05). Consistent with the annual monitoring results,
DIC was the dominant carbon species, accounting for approximately 90 % of
the total carbon, followed by DOC, POC and PIC, respectively (Fig. S2a).
DOC concentration was higher in the monsoon than pre-monsoon season, while
both PIC and POC concentrations were higher in pre-monsoon season (p<0.05; Fig. S2a) due to the higher TSS in the majority of sampling sites
(Table 1). The DOC/POC ratio was lower in the pre-monsoon than monsoon
season (p< 0.05; Table 1). The POC concentration was positively
related to TSS (p< 0.05; Table 1), and DIC concentration had a positive
correlation with cation (Ca2++Mg2+) in both seasons (p<0.05; Fig. S2b). Both dissolved (i.e., DOC and DIC) and
particulate carbon (i.e., POC and PIC) concentrations fluctuated along the
Shaliu River in the monsoon season, while most of them increased
significantly downstream in the pre-monsoon season (p< 0.05; Fig. 3a) except for a marginally significant increasing trend for POC (p=0.07;
Fig. S3).
River water properties along the Shaliu River.
SampleLat.Long.ETpHCond.DOTSSDINDONCarbon contentDOC/DOC/(∘ N)(∘ E)(m a.s.l.)(∘C)(µs cm-1)(mg L-1) (POC) (%)POCDONPre-monsoon season (May 2015) SLH-037.7399.78384610.48.53696.213.2NANA7.801.25NASLH-137.6699.8936788.28.42676.415.2NANA8.291.09NASLH-237.60100.0035538.88.53026.118.6NANA5.541.74NASLH-337.55100.0634857.78.44176.823.4NANA7.481.14NASLH-437.33100.12329311.58.43556.625.8NANA5.471.63NASLH-537.25100.19324312.98.23425.933.4NANA6.980.99NAMean9.9a8.4a342a6.3a21.6aNANA6.92a1.31bNAMonsoon season (August 2015) SLH-037.7399.7838468.87.63395.35.20.80.35.009.3910.9SLH-137.6699.89367810.07.73055.36.21.20.47.264.896.8SLH-237.60100.0035539.37.53135.822.41.50.44.962.437.3SLH-337.55100.0634858.07.53775.99.83.00.55.204.325.2SLH-437.33100.12329317.98.43554.426.81.60.35.631.6310.5Mean10.8a7.7b338a5.3b14.1a1.60.45.61b4.54a8.2
Different lowercase letters indicate significant differences between the
pre-monsoon and monsoon seasons (p< 0.05). Lat., latitude; Long.,
longitude; E, elevation; T, water temperature; Cond., conductivity; DO,
dissolved oxygen; TSS, total suspended solid; DOC, dissolved organic carbon;
POC, particulate organic carbon; DIN, dissolved inorganic nitrogen; DON,
dissolved organic nitrogen; DOC/POC, ratio of DOC to POC; DOC/DON, the
atomic ratio of DOC to DON; a.s.l., above sea level; NA, not analyzed.
Variations in dissolved organic carbon (DOC) in Shaliu River water
(a), absolute concentration of lignin phenols (Σ8) in riverine
dissolved organic matter (DOM; b) and particulate organic matter (POM; c)
during the pre-monsoon and monsoon seasons in 2015. The acid-to-aldehyde
(Ad/Al) ratios of syringyl (S) and vanillyl (V) phenols in the riverine DOM,
POM, soil solutions and leachates (d). The abscissa in panels (a), (b) and
(c) mean the distance of sampling sites from SLH-0. The red lines in panels
(a) and (b) correspond to the linear regression of data (p< 0.05),
and the grey shaded regions in panels (a) and (b) show 95 % confidence
intervals. The inserted box in (d) is the comparison of (Ad/Al)V and
(Ad/Al)S ratios of dissolved lignin phenols between pre-monsoon and
monsoon seasons, respectively, with asterisks indicating significant
differences (independent sample t tests, n=5, p< 0.05). The
solid bar and cross in the inserted box mark the median and mean of each
dataset, respectively. The upper and lower ends of the box denote percentiles 0.25 and
0.75, respectively.
The absolute concentration of dissolved lignin phenols (Σ8)
increased longitudinally from 1.33 µg L-1 at SLH-0 to 4.77 µg L-1 at SLH-5 in the pre-monsoon season, while it peaked at SLH-2 (4.13 µg L-1) and decreased to 1.35 µg L-1 in the monsoon
season (Fig. 3b). The absolute concentration of particulate lignin phenols
showed similar trends with dissolved lignin, which increased downstream from
1.07 to 3.44 µg L-1 in the pre-monsoon season and peaked at SLH-2
in the monsoon season. The (Ad/Al)S ratios showed no significant
difference between dissolved and particulate lignin phenols, while the
(Ad/Al)V ratios of dissolved lignin phenols ranging from 0.89 to 1.96
were significantly higher than that in particulate lignin phenols
(0.46–0.95; p< 0.05; Fig. 3d). Both (Ad/Al)V and
(Ad/Al)S ratios of dissolved lignin phenols, fluctuating along the
Shaliu River, were consistently lower in the pre-monsoon than monsoon season
(p< 0.05; Fig. 3d). After examining lignin phenols in the soils of
the Shaliu basin, we found spatially variable concentrations of lignin
phenols (Fig. 4a) but consistently higher (Ad/Al)V and (Ad/Al)S
ratios in the topsoil than subsoil at all sampling sites (p< 0.05;
Fig. 4b–c).
Concentration of lignin phenols normalized by organic carbon (OC)
(Λ8, a). The acid-to-aldehyde (Ad/Al) ratios of vanillyl (V,
b) and syringyl (S, c) phenols in the soil of the Shaliu River basin. The solid
bar and cross in the inserted boxes in panels (b) and (c) mark the median
and mean of each dataset, respectively. The upper and lower ends of boxes
denote percentiles 0.25 and 0.75, respectively. Asterisks indicate
significant differences between topsoil (0–10 cm) and subsoil (40–60 cm;
paired-sample t tests, n=5, p< 0.05). The legends in panel (a)
also apply to other panels.
Carbon variations during local hydrological events
To reveal the riverine carbon variations induced by soil–river water
transfer in the Shaliu River, we focused on two important hydrological
events. First, we monitored DOC and lignin phenol concentrations in soil
solutions along with the progress of thawing (when soil temperature reached
>0∘C). Topsoil DOC and lignin phenols showed an
increasing (albeit not statistically significant) trend from 19.1 to 22.0 mg L-1 and from 44.9 to 57.6 µg L-1 from 11 May to 17 June at
SLH-1 station, respectively (Fig. 5a–c). Similarly, topsoil DOC increased
from 22.4 to 29.4 mg L-1 from 22 April to 17 June at SLH-3 station
(Fig. 5b). Subsoil-derived DOM was gradually released with thawing,
indicated by the increase in DOC (or lignin phenol) concentration from not
detectable (frozen) on 11 May to 13.3 mg L-1 (lignin phenols = 23.1 µg L-1) on 17 June at SLH-1 station and from not detectable on
22 April to 22.1 mg L-1 on 22 May at SLH-3 station
(Fig. 5a–c). Furthermore, lignin Ad/Al ratios were lower in the leachates
of thawed soils than in the topsoil solution (p< 0.05) but similar
to the subsoil solution at SLH-1 station (Fig. 5d–e), indicating a
significant contribution of deep soil DOM to the leachate. Concurrently,
riverine DOC increased from 2.3 to 3.1 mg L-1 on 11 May to 17 June
and from 1.9 to 3.9 mg L-1 on 22 April to 17 June at SLH-1 and SLH-3
stations (Fig. 5a–b). Second, a typically local precipitation event
lasting for ∼ 1 h at SLH-4 station was monitored to
investigate the influence of precipitation on riverine carbon transport.
With the start of rain, riverine DOC concentration increased from 2.0 to 3.3 mg L-1 within 0.5 h due to flushing and leaching of
terrestrially derived OM and then decreased to 2.1 mg L-1 at the end
of precipitation event due to dilution by rainwater (Table 2).
Simultaneously, POC and PIC concentrations increased from 0.36 to 0.46 mg L-1 and from 0.03 to 0.06 mg L-1 accompanied by an increase in TSS
concentrations from 5.8 to 7.4 mg L-1 (Table 2).
Variations in dissolved organic carbon (DOC) in soil solutions and
river water at SLH-1 (a) and SLH-3 (b) stations. Absolute concentration of
lignin phenols (Σ8) in soil solutions and leachates at SLH-1
station (c), and the variations in the acid-to-aldehyde (Ad/Al) ratios of
syringyl (S) and vanillyl (V) phenols in soil solutions and leachates at
SLH-1 station (d–e) during the thawing period in 2018. Error bars in panels
(a–e) represent the standard error of the mean (n=4). Lowercase letters in panel
(d) indicate different levels of Ad/Al ratio among the leachates, topsoil and
subsoil solutions (one-way ANOVA, n=4, p< 0.05) while p values in
panel (e) indicate the marginal difference of the Ad/Al ratio between topsoil
solutions and leachates.
River water properties at the SLH-4 station of Shaliu River during
a precipitation event in August 2015.
DiscussionRiverine carbon fluxes in the Shaliu River
In contrast to Arctic rivers with a pronounced spring freshet from May to
June, both DOC and DIC fluxes in the Shaliu River were highest from July to
August (accounting for 58 %–60 % of the corresponding annual fluxes; Fig. 2a–b) due to the maximum discharge caused by frequent precipitation events
in the monsoon season (Fig. S1; Zeng et al., 2019). DIC, mainly composed
of bicarbonate and carbonate based on the river water pH values (Table 1;
Liu et al., 2020), was the dominant form of dissolved riverine carbon. Its
proportion in dissolved riverine carbon is generally higher than tropical
(∼ 40 %; Huang et al., 2012) and Arctic rivers (52 %–70 %;
Striegl et al., 2007; Prokushkin et al., 2011; Guo et al., 2012) but similar
to rivers sourced from the Qinghai–Tibetan Plateau including the upper
Yangtze River, Yellow River and their tributaries (Cai et al., 2008; Gao et
al., 2019; Song et al., 2019, 2020). The distinct lithology
(such as limestone and sandstone) within the Shaliu River catchment results
in high carbonate and silicate weathering rate (Xiao et al., 2013), which is
an important source of DIC in river water. The connection of DIC in the
Shaliu River to high chemical weathering rate on the Qinghai–Tibetan Plateau
is further reflected by the high riverine Ca2+ and Mg2+
concentrations (Zhang et al., 2013) and their positive correlations with DIC
(p< 0.05; Fig. S2b). The negative correlation of DIC concentration
with river discharge at SLH-4 station (Fig. 2a) indicates interactive
impacts on hydrology by precipitation and thawing. Specifically, the inputs
of subsurface flow and groundwater containing high weathering products
(including DIC) under base flow conditions (including during thawing) in
this permafrost-affected watershed (Walvoord and Striegl, 2007; Giesler et
al., 2014) result in relatively high DIC concentrations. By contrast,
riverine DIC concentration decreases with discharge in the monsoon season
from 2015 to 2016 likely due to the dilution effects of rainwater, which
normally has a lower DIC concentration relative to stream water (Song et
al., 2019).
DOC was the second most abundant riverine carbon with a lower concentration
in the Shaliu River than most Arctic rivers (Mann et al., 2016; Amon et al.,
2012; Spencer et al., 2008), only accounting for 7 % of total riverine
carbon. However, its concentration is equivalent to the upper Yangtze,
Yellow, Lancang and Yarlung Zangbo rivers (Qu et al., 2017; Ran et al.,
2013; Liu et al., 2021). The low DOC concentration reflects the relatively
low SOC density in the alpine grasslands of the Qinghai–Tibetan Plateau
(9.05 kg C m-2 in 0–100 cm depth; Yang et al., 2008) compared to most
Arctic river basins containing organic-matter-rich peatlands and deposits
(32.2–69.6 kg C m-2; Tarnocai et al., 2009). The DOC/DON ratios in the
Shaliu River were generally lower than the global riverine average (14;
Harrison et al., 2005; Seitzinger et al., 2005), potentially indicating high
biodegradability of riverine DOM (Wiegner et al., 2006), which may
contribute to the low DOC concentration in this river as well. Furthermore,
DOC concentrations showed no consistent relationship with river discharge
throughout the year, while it showed opposite relationships with river
discharge in early versus late pre-monsoon seasons (Fig. S4). Snowmelt in
the early pre-monsoon season (early April) may dilute the base-flow DOC,
resulting in a decreasing trend with discharge. By comparison, thawing of
frozen soils in late pre-monsoon season (late April to June) releases more
frozen carbon and thus results in an increasing trend of DOC with discharge.
Spatial–temporal variation in riverine carbon in the Shaliu River
The concentrations of dissolved and particulate carbon fluctuated along the
Shaliu River in the monsoon season while they increased significantly downstream
in the pre-monsoon season. Similarly, the absolute concentration of dissolved and
particulate lignin phenols peaked at SLH-2 in the monsoon season, but they
showed a downstream increasing trend in the pre-monsoon season. These trends
stand in contrast to the downstream accumulation of riverine carbon in the
high-flow conditions and increasing degradation in the low-flow conditions observed in
other rivers including Zambezi River (Lambert et al., 2016) and a tributary
of Yukon River (Dornblaser and Striegl, 2015), suggesting unique seasonality
in the spatial variations in riverine carbon in the Shaliu River. In
addition, the higher particulate carbon was directly related to the higher
TSS concentrations in the pre-monsoon than monsoon season. Thermal erosion
during thawing is the most important pathway supplying particulates and
weathering products into the river on the Qinghai–Tibetan Plateau (Wang et
al., 2016), which may explain the high particulate concentration in
the pre-monsoon season. In contrast, other than aged DOC sourced from thawed
soils, exudates from plant roots are also an important supply to riverine
DOC. Although we did not measure root exudates, we postulate that the higher
riverine DOC during the monsoon than pre-monsoon season is related to
increased plant growth and exudation in the growing season.
Both (Ad/Al)V and (Ad/Al)S ratios of dissolved lignin phenols were lower in
the pre-monsoon season than monsoon season, consistent with the Arctic
rivers (Amon et al., 2012) but different from the invariant or opposite
patterns between seasons in tropical rivers including Congo and Oubangui
(Spencer et al., 2010b; Bouillon et al., 2012). The higher acid-to-aldehyde
ratio of lignin phenols is commonly attributed to increased photo- or
microbial oxidation of lignin. However, photo- and microbial oxidation may
be partially constrained by the short residence time (indicated by high
discharge in monsoon season) and low temperature in this alpine river
(average water temperature of 10.8 ∘C in the monsoon season; Table 1). Alternatively, the acid-to-aldehyde (Ad/Al) ratios of lignin phenols in
topsoil were higher than those in the subsoil, in contrast to the increase
in Ad/Al ratios with soil depth typically reported in other soils (Otto and
Simpson, 2006). Our previous research also finds higher Ad/Al ratios in topsoil than subsoil in an alpine grassland on the Qinghai–Tibetan Plateau due to
the influence of dominant vegetation (i.e., shallow-rooted K. humilis) having high
Ad/Al ratios in its roots (Jia et al., 2019). Here, the Shaliu River basin
is dominated by shallow-rooted K. humilis as well (Li et al., 2013), likely leading to
the higher Ad/Al ratios in topsoil. Streamflow is supplied by snowmelt water
via surface and near-surface pathways in the early pre-monsoon season when
temperature ranges from -2 to 0 ∘C (Tetzlaff et al., 2015) and
thus contains amounts of carbon from surface soils, while streamflow shifts
from surface meltwater to water stored in subsurface frozen soils (i.e., an
increasing contribution of deep soil water) as air and soil temperature
rises. This transition in stream water sources in the pre-monsoon season suggests
that the contribution of deep soil water to streamflow increases with the
progress of thawing events (Carey and Quinton, 2004; Tetzlaff et al., 2015).
In combination with the different Ad/Al ratios between pre-monsoon and
monsoon seasons, we postulate that topsoil makes a relatively larger
contribution to riverine DOM in the monsoon season likely through increased
surface runoff, resulting in higher acid-to-aldehyde ratios of dissolved
lignin in the river compared to the pre-monsoon season.
Riverine carbon variations influenced by local hydrological events
The unique spatial–temporal variations in riverine carbon mentioned above
are likely related to the high sensitivity of the Shaliu River to local
hydrological processes (Biggs et al., 2017), including sporadic occurrences
of local precipitation in the monsoon season (Wu et al., 2016) and thawing
of frozen soils in the pre-monsoon (spring) season. Specifically, the downstream
increase in riverine carbon in the pre-monsoon season (April–May) was
anticipated to be related to spring thawing releasing DOC previously frozen
in the soil. The observed increase in soil DOC and lignin phenols in topsoil
solutions over time reflects carbon release from previously frozen topsoil
and/or inputs via lateral flow paths during thawing. Combined with the
release of subsoil-derived DOM proved by subsoil DOC availability and lignin
phenol Ad/Al ratios, these results indicate the release of frozen DOM during
thawing events. The riverine DOC shows a high synchronousness with the
increase in soil DOM, partially explaining the downstream increase in DOC
along the Shaliu River in the pre-monsoon season. In particular, as the
upstream Shaliu basin has a slightly higher elevation, soil temperature is
slightly lower in the upstream than downstream in spring
(-1.2–0 ∘C at SLH-1 versus 0–2.9 ∘C at SLH-3 on March 26 to April 20; Fig. 1b), leading to
earlier and stronger thawing of soil in the lower basin. This spatiotemporal
variation in freezing–thawing periods affects riverine carbon transport in
the following two aspects. First, the earlier thawing of frozen soils
downstream (at a lower elevation) in the Shaliu basin enhances soil carbon
release compared with the upstream basin, likely leading to an increasing
carbon concentration along the river continuum (Song et al., 2019; Vonk et
al., 2015). Second, the thawing depth of frozen soils increases with time in
the pre-monsoon season due to increasing temperature, thus causing an
increasing riverine carbon concentration with thawing events (Wang et al.,
2017; Song et al., 2019). Overall, inputs of DOC from thawed soil increase
downstream in spring, leading to the downstream increase in riverine carbon
in this alpine river.
In contrast, sporadic local precipitation events occur frequently during the
monsoon season, leading to fast hydrological variations reflected by
increasing TSS concentrations over time in the 1 h precipitation events
(Beel et al., 2018). The rapid response of riverine DOC and POC to the above
hydrological alterations indicates the high sensitivity of riverine carbon
to short-term precipitation. According to historical precipitation records
(Fig. S1), more than 90 % of the annual precipitation occurs in the monsoon
season from June to September, mainly in the form of short-lasting and weak
precipitation events (intensity < 5 mm; Wu et al., 2016), thus
frequently influencing local soil–stream carbon transfer processes. Along
the Shaliu River, DOC concentrations in the monsoon season showed no regular
downstream variations, likely related to variable local precipitation events
frequently occurring in the Shaliu River basin in the summer (Yao et al.,
2013; Wu et al., 2016).
Conclusions
In conclusion, combining annual flux monitoring and dense spatial sampling
along the Shaliu River in two contrasting seasons, we provide a benchmark
assessment of riverine carbon variations in an alpine catchment on the
Qinghai–Tibetan Plateau. We show that DIC constitutes the majority
(>87 %) of riverine carbon in this alpine river, dominated by
carbon fluxes in the monsoonal summer. In addition, riverine carbon in the
Shaliu River increased downstream in the pre-monsoon season due to
increasing contribution of organic matter derived from thawed soils while
organic matter inputs induced by sporadic precipitation during the monsoon
season led to fluctuating concentrations of riverine carbon in the summer.
These results indicate a higher sensitivity of riverine DIC than DOC
concentration in the alpine river to local hydrological events, and DOC
source appears to change as well. Given projected climate warming on the
Qinghai–Tibetan Plateau (Chen et al., 2013), thawing of permafrost and
alterations of precipitation regimes may significantly influence the alpine
headwater carbon transport, with critical effects on the biogeochemical
cycles of the downstream rivers. On the other hand, the alpine catchments
may be utilized as sentinels for climate-induced changes in the hydrological
pathways and/or biogeochemistry of the small basin. Both aspects deserve
further research attention in the future.
Data availability
All data are available within this paper and in the Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-18-3015-2021-supplement.
Author contributions
XF designed the study. XW performed geochemical and lignin phenol
analyses and analyzed related data. TL wrote the manuscript. XW and TL
contributed equally to this work. All authors contributed to the field
sampling.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the editor Ji-Hyung Park and the two anonymous
referees for the improvement of this paper.
Financial support
This research has been supported by the National Basic Research Program of China (grant no. 2019YFA0607303) and the National Natural Science Foundation of China (grant nos. 42025303, 41973075, and 31988102).
Review statement
This paper was edited by Ji-Hyung Park and reviewed by two anonymous referees.
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