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# The Great Vasyugan Mire of western Siberia: Hydrochemistry and greenhouse gas dynamics of peatland pools of the world's largest mire

Sergey Vorobyev, Yuri Kolesnichenko, Ivan Krickov, Alexander Berezin,
Sergey Kirpotin, Oleg Pokrovsky

► To cite this version:

Sergey Vorobyev, Yuri Kolesnichenko, Ivan Krickov, Alexander Berezin, Sergey Kirpotin, et al. The Great Vasyugan Mire of western Siberia: Hydrochemistry and greenhouse gas dynamics of peatland pools of the world's largest mire. Ecological Indicators, 2025, 170, pp.113054. 10.1016/j.ecolind.2024.113054. hal-04943948

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Submitted on 14 Feb 2025

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# The Great Vasyugan Mire of western Siberia: Hydrochemistry and greenhouse gas dynamics of peatland pools of the world's largest mire

Sergey N. Vorobyev<sup>a</sup>, Yuri Kolesnichenko<sup>a</sup>, Ivan V. Krickov<sup>a</sup>, Alexander E. Berezin<sup>a</sup>, Sergey N. Kirpotin<sup>a</sup>, Oleg S. Pokrovsky<sup>b,* ID</sup>

## ARTICLE INFO

Keywords:

Wetland

Lake

Carbon

Nutrient

Metal

Emission

## ABSTRACT

Siberian peatlands act as the primary regulator for carbon (C) exchange between land and atmosphere; however, the aquatic component of this ecosystem, peatland pools, remains virtually unknown with respect to biogeochemical parameters and C fluxes. Here we report results from a 'snapshot' (end of spring period) study investigating hydrochemical parameters and greenhouse gas (CO₂ and CH₄) concentrations and emissions from shallow peatland pools of the Great Vasyugan Mire (GVM) in western Siberia. The pools were highly oligotrophic, acidic, humic, iron-rich, poor in chloride and sulfate, and emitted 0.15 g C-CO₂ m⁻² d⁻¹ as measured by floating chambers. There was significant dependence between the pool area and various hydrochemical parameters across wide range of pool sizes (from 700 to 1,380,000 m²). Concentrations of CO₂, CH₄, dissolved organic carbon (DOC), some organic nutrients and lithogenic trace elements increased with decreasing pool size, likely reflecting organic matter delivery from peat and shallow subsurface waters. In contrast, with increasing pool size, there was a decrease in DOC aromaticity and an increase in pH and concentration of labile elements (anions, alkaline and alkaline-earth metals). This was consistent with an increase in autochthonous production and degree of connection to groundwater reservoirs. Annual emissions of C from peatland pools and lakes of the GVM are comparable to peat growth rate in the territory and exceed the lateral export of DOC by rivers. Future studies should address the seasonal aspects of C biogeochemistry, including the ice cover period.

## 1. Introduction

Peatlands serve an important environmental role in the global carbon (C) cycle because of their disproportionally high contribution to C storage in the form of peat soils formed over past millennia (Page et al., 2022; Xu et al., 2018; Dean et al., 2023). Given that the magnitude of peatland C storage is comparable to that of the atmosphere (875 Pg C; Friedlingstein et al., 2022), possible destabilization of peatlands accompanying C release is an issue of critical global concern (Lupascu et al., 2020). Indeed, dissolved C is exported by peatlands to both lakes and streams (Dean et al., 2020), whereas greenhouse gases (CO₂ and CH₄) are evaded into the atmosphere (Evans et al., 2021). Furthermore, peatlands exchange C with the atmosphere via pools or ponds, which are typically small (< 1 ha) and shallow (≤ 1 m depth) standing water bodies located within the area of mires (Arsenault et al., 2018, 2019, 2022; Hassan et al., 2023). The interest of studying peatland pools is two-fold; on one hand, these water bodies act as hot spots for terrestrial

C transformation and CO₂ and CH₄ emissions to the atmosphere (Downing, 2010; Holgerson & Raymond, 2016; Peacock et al., 2021; Rosentreter et al., 2021; Turner et al., 2016; Bussmann et al., 2021; Kurek et al., 2022) but on the other hand, they serve as sentinels for changes in peatland C cycling due to their high sensitivity to both climate and land use change (Arsenault et al., 2022, 2023; Hassan et al., 2023). Furthermore, there is a growing interest to using peat in various technological applications targeted towards increase of soil fertility and greenhouse gas control (Durmanov et al., 2023; Mohamad et al., 2023a, b).

Among the various peatland regions of the world, the large (2,500,000 km²) Western Siberian Lowland (WSL) is one of the major controllers in the global carbon (C) cycle and climate change. By virtue of its huge C stock, essentially in the form of peat (Botch et al., 1995; Sheng et al., 2004), the WSL contributes between 15 and 22 % of the world's terrestrial carbon (Eswaran et al., 1995). Peat deposits of the Western Siberia Lowland (WSL) range between 1 and 7 m in thickness

<footnote><sup>a</sup> BIO-GEO-CLIM Laboratory, Tomsk State University, 36 Lenina av., Tomsk 634004, Russia</footnote>

<footnote><sup>b</sup> GET UMR 5563 CNRS, University of Toulouse, 14 Avenue Edouard Belin, 31400 Toulouse, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: oleg.pokrovsky@get.omp.eu (O.S. Pokrovsky).</footnote>
and have been extensively studied over past 50 years, from the perspective of their C stock, productivity, and paleo-reconstructions (Peregon et al., 2009; Halicki and Kirpotin, 2018; Feurdean et al., 2019).

The reason these peatlands are of the utmost importance to the global C cycle, as compared to other C-rich northern soils such as yedoma, is that the WSL exhibits sizable water coverage (Smith et al., 2007). Locally water coverage ranges between 40 and 70 % of the watershed (Zakharova et al., 2014). Further, unlike frozen yedoma, peat in western Siberia is in active exchange with atmosphere and surface waters, thereby contributing to both C emission from lakes and river surfaces (Karlsson et al., 2021) as well as lateral fluvial export to oceans (Frey and Smith, 2005; Frey et al., 2007a, b; Pokrovsky et al., 2020). As such, thermokarst (thaw) lakes of permafrost peatlands in northern part of western Siberia (1,050,000 km²) were inventoried in great detail (Polishchuk et al., 2017, 2018) from a hydrochemical (Manasypov et al., 2014, 2015, 2020; Pokrovsky et al., 2011, 2014), hydrobiological (Pokrovsky et al., 2022; Noskov et al., 2024), water column GHG pattern (Serikova et al., 2019; Shirokova et al., 2013) and sediment C storage/production perspective (Manasypov et al., 2022, 2024). In contrast, these factors for lakes located in peatlands of the southern, permafrost-free zone, of the WSL (1–1.5 million km²) remain virtually unknown. To our knowledge, there are only a few studies of the Ob River floodplain lakes—which is outside the context of peatlands (Sabrekov et al., 2017; Krickov et al., 2021; Cazzola Gatti et al., 2018; Vorobyev et al., 2015, 2024). Typical peatland lakes and pools located above these floodplains, at the river terraces and watershed divides, have never been investigated.

Among the most iconic peatlands of Siberia, the Great Vasyugan Mire (GVM) holds ca. 800,000 small pools and lakes (Kirpotin et al., 2021). The GVM is the world's largest mire system which was formed about 10,000 years ago as a result of the coalescence of many small regional mires (Kirpotin et al., 2021). According to the only available GIS-based evaluation, for the total area of the GVM of 55,000 km², lakes larger than 4 ha cover 395 km², therefore providing less than 1 % of overall limnicity (Berezin et al., 2014). At the same time, the total surface of mire landscapes, where peatland pools constitute between 20 and 30 % of the bog surface, is 1,006,575 ha, which means that peatland pools of the GVM occupy more than 2000 km², thereby providing 3.6 % of the overall limnicity (Berezin et al., 2014). Although terrestrial systems of the GVM were extensively studied on the basis of primary productivity (Koronatova, 2022; Koronatova and Kosykh, 2022), peat accumulation rate (Borren et al., 2004; Veretennikova, 2015; Pastukhov et al., 2021), paleo-reconstruction (Feurdean et al., 2019) and GHG exchange (Dyukarev et al., 2021; Golovatskaya and Dyukarev, 2009, 2011, 2012; Inisheva and Golovchenko, 2022; Mikhailchuk et al., 2022; Terentieva et al., 2016), the information on aquatic ecosystems is highly limited and includes occasional studies of streams, soil and groundwaters (Eckstein et al., 2015; Kharanzhevskaya et al., 2020a, b, 2023; Savichev et al., 2020) and some experiments on dissolved organic matter biodegradation along the hydrological continuum (Raudina et al., 2022). These studies demonstrated huge variability in solute concentrations across various types of aquatic landscapes (fen, transitional mire and raised bog), notably in terms of redox conditions, nutrient and dissolved Fe content (Kharanzhevskaya et al., 2020a). In a recent study of shallow soil and peat waters in the GVM across seasons, it was shown that mean concentrations of major ions and nutrients during the ice-free period can be adequately represented by sampling during May-June (Kharanzhevskaya et al., 2020b). Extensive seasonal monitoring of thermokarst lakes and thaw ponds in the permafrost-affected regions of western Siberia—focusing on their greenhouse gas (GHG) regimes (both concentrations and emissions)—was conducted in a previous study by our group (Serikova et al., 2019). The study found that, within the southern part of the permafrost-affected zone, there were no substantial differences in CO₂ and CH₄ concentrations and emissions between spring, summer, and autumn, or that any seasonal variations were much smaller than the spatial (within-lake) differences. Another study from

northern western Siberia (Manasypov et al., 2015) reported nearly constant DOC concentrations in large permafrost peatland lakes—13.2 ± 6.6 mg L⁻¹ in spring, 12.9 ± 2.8 mg L⁻¹ in summer, and 16.8 ± 6.2 mg L⁻¹ in autumn. At the same time, it is known that dissolved organic matter (DOM) composition of lakes in high latitudes is highly seasonal, with ice exclusion concentrating microbial-processed DOM in the winter water columns, thus influencing DOM cycling the following summer (Kurek et al., 2022). Overall, we do not anticipate large seasonal fluctuations in DOC and GHG concentrations or emissions—beyond a factor of 1.5 to 2, which is comparable to spatial variability—across the open-water seasons (spring, summer, and autumn). However, none of the former studies addressed the hydrochemistry and C exchange fluxes of peatland pools of the permafrost-free zone. As a result, the working hypotheses of this study will be based on what is known from thermokarst lakes and thaw ponds located in the permafrost peatland of similar bog/fen context and other peatland pools across the world. Similar to previous studies on hydrochemistry of peatland pools in the UK, Canada and Patagonia (Pelletier et al., 2014, 2015; Arsenault et al., 2022, 2023; Hassan et al., 2023), we expected peatland pools of the GVM will exhibit weak within-region variability in solute composition, a high concentration of DOC and high UV absorbance, low nutrients and sizable GHG concentrations and emissions.

As a first working hypothesis, we suggested a straightforward control of pool size (surface area) on DOC, nutrient concentration and CO₂ emissions, with maximal values of these parameters observed in smallest pools. The smallest pools have the highest ratio of circumference to water volume and are therefore subject to highest impact from surrounding histosols and shallow soil waters which are capable of delivering solutes and GHG to the pool water (i.e., Shirokova et al., 2013; Manasypov et al., 2015, 2020; Zabelina et al., 2021). Second, we hypothesized that DOC of the water column will be a major governing factor for CO₂ emissions, given that mineralization of allochthonous DOC is known to provide CO₂ flux, notably in DOC-rich aquatic settings (Turner et al., 2016; Vachon et al., 2016). Third, we hypothesized that strongly pronounced diel variations in CO₂ emissions from peatland pools are linked to photosynthesis, photo- and bio-mineralization processes, respiration and sediment mixing (Bass et al., 2013; Belger et al., 2011; Rudberg et al., 2021; Shirokova et al., 2020; Sø et al., 2024). To evaluate these hypotheses, we chose 21 peatland pools from the northeastern part of the GVM. The pools were located within typical raised bog context, representative for the ridge - lake complexes of boreal mires, in which we assessed, via a snap-shot sampling, the hydrochemistry of the water column and CO₂ emissions.

## 2. Methods

### 2.1. Study area and sampling design

The present study was carried out within a peatland pool complex in the northern part of the GVM (Novosibirsk region), within the Ob and Irtysh Rivers watershed. This area is a ridge-lake complex where ponds and lakes that occupy the central flat and drainless parts of the mires are 1 to 2 m deep (Inisheva et al., 2011). The majority of lakes are free of macrophytes. The ridge-lake mire landscape is represented by a pine-dwarf shrub-sphagnum vegetation community that is typical for western Siberia (Fig. 1).

The tree layer is represented by pine, with a projected coverage of 40 % (crown density 0.4, height up to 2 m, and diameter up to 1.5 cm). The grass-shrub layer includes *Ledum palustre* 30 %, *Chamaedaphne calyculata* 60 %, *Andromeda polifolia* 5 %, *Vaccinium uliginosum* 10 %, *Eriophorum vaginatum* 5 % and singularly *Rubus chamaemorus*. *Sphagnum* mosses stand out (95–100 %), of which 80 % are *Sphagnum fuscum*, *Sphagnum magellanicum* and *Sphagnum angustifolium* are also present. The average peat thickness is 4–5 m and the groundwater table position in May ranges from –2 to –5 cm from the surface (Mikhailchuk et al., 2022, 2023). The peat deposits in the study area of the GVM are
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** A: Location of the study site of the Great Vasyugan Mire in Northern Eurasia (A), where the water bodies were inventoried via remote sensing; B, C, D: Lakes and pools sampled in this study. The numbers in panels C and D represent the sample sites as indicated in Supplementary Table S1.</figcaption></figure>
represented by a mixed type. In the upper part and up to a depth of 120 cm, the peat is composed of Sphagnum debris, with a 5–15 % degree of decay. Down the profile, it changes to oligotrophic grassy-moss peat, and then to transitional woody-grass peat with up to a 15 % degree of decomposition; this increases in the lower part up to 20 %. At a depth of 150–285 cm, the peat is composed of eutrophic grassy, woody-grass and grass-moss debris with a 20–40 % degree of decomposition.

The climate of the study area is continental with long cold winters and short hot summers (MAAT of 0.5 °C; MAP of 497 mm, 30 % of which falls as snow). The mean multi-annual (1936–2022) air temperature for May is 10.0 °C with a mean precipitation of 44 mm. Snow cover is formed in early November and persists until mid-April. During this period, a seasonally frozen layer ranging from 0.3 to 0.9 cm thickness is formed (Dyukarev et al., 2019). In 2022, the snow pack thickness was close to the long-term average, with a maximum snow depth of 74 cm recorded in mid-February and a peat bog freezing depth of 13 cm. Snowmelt began in early April 2022, and the thawing of the peat deposit began in mid-April. Full thawing of the peat deposit occurred at the end of April.

Central portions of the GVM, like the one chosen in this study, are virtually inaccessible via ground transportation even when including all-terrain tracked vehicles. The reason for this is the extremely water-logged and, at the same time, densely forested border zone of the mire (1 to 3 km wide). In May 2022, we used helicopter transportation (MI-8, useful cargo of 2 tons) from the nearest town of Bakchar, (223 km to the site) to build a basecamp at the border of the largest lake, Schuchie Lake (2 km diameter), where we performed a snapshot study of the ridge-lake complex over 7 days. To travel between the lakes, an all-terrain Argo Avenger 8 × 8 tracked vehicle brought by helicopter was used.

We selected 21 pools of drastically different surface area, all located within a 700 km² test site. Via numerous ground observations and remote sensing inventory, we made sure that the selected part of the GVM is representative for typical ridge-lake bog complexes of this mire (Fig. 1 D). This and other ridge-lake complexes of the GVM are characterized by relatively uniform peat thickness, a consistent lithological background, and a highly similar pine–dwarf shrub–sphagnum vegetation community. Therefore, our findings can be generalized to similar peatland pools in mires of the boreal, non-permafrost zones of western Siberia and northeastern Europe. The peatland pools are shallow (depths ranging from 1 to 2 m) and lack any connection to a hydrological network via an inlet or outlet. The shapes were generally round-oval, but some were elongated, with smooth coastal area and no islands. The watershed for each pool cannot be quantified unambiguously but can be approximated as not more than twice the area of the pool itself.

## 2.2. CO₂ and CH₄ measurements

Surface water pCO₂ was measured in situ by deploying a hand-held infrared gas analyzer (IRGA, GMT222, Vaisala; accuracy ± 1.5 %) with various detection ranges (3000, 10,000 and 20,000 ppm) enclosed within a waterproof and gas-permeable membrane. During the sampling, the probe was placed into the water column for ~10 min and the hand-held measurement indicator unit (MI 170, Vaisala; accuracy ± 0.2 %) was connected to the probe allowing reading of surface water pCO₂ concentrations. The probes were calibrated in the lab and the measured values were corrected following methods described elsewhere (Johnson et al., 2009; Serikova et al., 2018, 2019). Temperature-specific solubility coefficients were used to calculate CO₂ concentrations in the water (Wanninkhof, 2014). All in-situ pCO₂ measurements were performed at the same period of the day, between 11 am and 16 pm.

CO₂ fluxes (FCO₂) were measured using two floating chambers at the 20 sampling locations of peatland pools. The chambers were constructed from small lightweight plastic bins (~30 cm in diameter, ~300 g, 10 L) equipped with non-dispersive infrared CO₂ loggers (SenseAir). Atmospheric pressure was routinely recorded by sensors and factored into the flux and GHG concentration calculations. The infrared CO₂ loggers

(Sensairs) were calibrated in the laboratory using N₂–CO₂ mixtures prior to the sampling campaign. Before and after each chamber deployment, we regularly measured pCO₂ in the air to verify proper logger operation. The CO₂ concentration inside the chamber was recorded continuously over 10 s intervals and we used the first 30 min of measurements for computing CO₂ fluxes via calculating CO₂ accumulation rate inside each chamber via linear regression, based on the slope of CO₂ accumulation in the chamber over time (i.e., Serikova et al., 2018). We set the threshold for the slope of CO₂ change over time at R² ≥ 0.8 for consistency with former studies in this region (Serikova et al., 2018, 2019; Krickov et al., 2021). Chamber CO₂ measurements on all lakes were performed in the early afternoon, between 1 pm and 4 pm. Based on measured FCO₂ and CO₂ concentrations in the water and in the air, we calculated the chamber-specific gas transfer coefficient (K_T value, or CO₂ transfer velocity). Further details of CO₂ flux measurements are provided elsewhere (Krickov et al., 2021, 2023; Lim et al., 2022; Vorobyev et al., 2024). Diel cycle of CO₂ concentrations and emissions together with main hydrochemical parameters of the water column (T<sub>water</sub>, pH, Electrical Conductivity and O₂ concentration) were assessed for the Schuchie Lake (surface area of 1.35 km²) over 34-hour time span. However, we did not measure diel photosynthesis, respiration, and sediment mixing in this lake.

For CH₄ analyses, unfiltered water was sampled in 30-mL Serum bottles which were closed without air bubbles using vinyl stoppers and aluminum caps and immediately preserved by adding 2 mL of saturated KCl via a two-way needle system. In the laboratory, a headspace was created by displacing approx. 30 % of water with N₂ (99.99 %). Two replicates of the equilibrated headspace were analyzed for their concentrations of CH₄, using a Bruker GC-456 gas chromatograph (GC) equipped with flame ionization and thermal conductivity detectors. After every 10 samples, a calibration of the detectors was performed using Air Liquid gas standards (i.e. 145 ppmv). Duplicate injection of the samples showed that results were reproducible within ± 5 %. The specific gas solubility for CH₄ (Yamamoto et al., 1976) was used in the calculation for total CH₄ content in the vials and then recalculated to µmol L⁻¹ of the initial waters. Diffusive CH₄ fluxes were calculated using chamber-specific transfer coefficients obtained from CO₂ flux measurements, concentrations of dissolved CH₄ in the water and air-water equilibrium yielded a pCH₄ concentration of 1.8 ppm, following standard procedures (Serikova et al., 2019).

## 2.3. Hydrochemical analyses

At each sampling location, we measured the pH and specific conductivity (WTW Multi 3320) of the surface water, water depth, air temperature and atmospheric pressure (Silva), as well as dissolved oxygen saturation and water temperature (YSI ProODO Handheld Optical Dissolved Oxygen meter). Similar to the CO₂ concentration and flux measurements, hydrochemical parameters were measured in all lakes during the early afternoon, between 12 pm and 4 pm.

The dissolved oxygen (CellOx 325; accuracy of ± 5 %), specific conductivity (TetraCon 325; ±1.5 %), and water temperature (±0.2 °C) were measured in-situ at 20 cm depth using a WTW 3320 Multimeter. The water was sampled in pre-cleaned polypropylene bottles from 20-30 cm depth in the central part of the pool and immediately filtered through disposable single-use sterile Sartorius filter units (0.45 µm pore size). The first 50 mL of filtrate was discarded. The DOC and Dissolved Inorganic Carbon (DIC) were determined by a Shimadzu TOC-VSCN Analyzer (Kyoto, Japan) with an uncertainty of 3 % and a detection limit of 0.1 mg/L. Blanks of Milli-Q water that passed through the filters demonstrated negligible release of DOC from the filter material. Data from UV-visible absorbance measurements were used to calculate various indicators of DOC quality. For calculating SUVA₂₅₄ (absorbance at 254 nm normalized for DOC concentration, L mg⁻¹ m⁻¹) we measured ultraviolet absorbance at 254 nm (UV₂₅₄) using a 10-mm quartz cuvette on a Bruker CARY-50 UV–VIS spectrophotometer. SUVA₂₅₄ is
recognized as a useful proxy for carbon aromaticity and DOM source (e. g., Weishaar et al., 2003; Helms et al., 2008). Total bacterial cell concentration (TBC) was measured via flow cytometry after sample fixation in glutaraldehyde.

Carboxylic acids and main inorganic anions (F⁻, Br⁻, Cl⁻, SO₄²⁻, NO₃⁻, NO₂ and PO₄³⁻) were measured by high-performance ion chromatography (Dionex Ics-5000 +) with an uncertainty of 2 %. We used an in-house standard for organic acid analysis and two ISO standards (ISO 10304-1 and ISO 10304-2) for inorganic anion analysis. The certified reference materials Ion-915 and Ion-964 were used to verify the accuracy of analyses; the reference material measured concentrations were within 10 % of certified values. Quantification limits were equal to 0.05 mg L⁻¹ for Cl⁻, 0.001 mg L⁻¹ for F⁻, Br⁻, PO₄³⁻, 0.01 mg L⁻¹ for NO₃⁻, SO₄²⁻ and 0.002 mg L⁻¹ for acetate, formate, succinate, and oxalate. Other organic acids – quinate, lactate, propionate, butyrate, galacturonate, glutarate, malate, tartrate, maleate, fumarate, phthalate and citrate – were present in concentrations comparable with detection limits and thus could not be quantified.

Major cations (Ca, Mg, Na and K), Si, P, and ~ 40 trace elements were determined using an ICP-MS Agilent ce 7500 with In and Re as internal standards and 3 external standards (1, 10 and 100 µg L⁻¹). For all major and most trace elements analyzed by ICP MS, concentrations in experimental control reactors were below analytical detection limits, determined as three times signal intensity of a 2 % HNO₃ blank. SLRS-6 (Riverine Water Reference Material for Trace Metals certified by the National Research Council of Canada) was used to check accuracy and reproducibility of analyses (Yeghicheyan et al., 2019).

## 2.4. Remote sensing assessment of pool number and area

Satellite imagery from Landsat-8 Operational Land Imager (30 m medium resolution, MR) [available at USGS Global Visualization Viewer: https://glovis.usgs.gov] together with high (2 m) resolution (HR) Kanopus-V scenes [available at Roscosmos website: https://eng.nt.somz.ru/] were used to map lake distribution over the full territory of the GVM test site (Fig. 1 B). We used medium-resolution (MR) Landsat-8 images collected from end of July through beginning of August 2021–2023. High-resolution Kanopus-V images were collected during summer 2022.

Lake identification was performed using the Automated Water Extraction Index (AWEI) as follows. First, an AWEI was calculated for each image. Next, through visual inspection, a suitable threshold value of the index was determined to distinguish water bodies from other land features. Using this threshold, the resulting raster image was reclassified so that it contained only water bodies. Finally, all reclassified raster data were merged into a single mosaic, producing a unified raster image covering the entire study area. Overlapping areas in adjacent images were removed, and the resulting mosaic included all water features in the study area, including rivers and streams. Lotic waters were then eliminated using a river mask derived from OpenStreetMap data (https://www.openstreetmap.org/). Typical uncertainties in lake area measurements from high-resolution images range from 2.5 to 3.0 %, as determined from multiple test sites across Northern Eurasia (Kupriyanov et al., 2018). Landsat-8 images were processed using the Fmask algorithm, following Polishchuk et al. (2016, 2017). The uncertainty in measuring lake surface area using Landsat-8 data is approximately 3.5 % for lakes larger than 10⁵ m² and about 7 % for lakes larger than 2 × 10⁴ m² (Bryksina and Polishchuk, 2013; Kupriyanov et al., 2018).

Ultra-small pools (100–1000 m²) were assessed manually from Yandex and Google Earth images. This free-ice period corresponds to the minimal coverage of the territory by lakes and minimal seasonal variation of lake water levels. Combination of images from multiple years was necessary because a single year observation could not provide full coverage of the territory. All images were analyzed using ArcToolBox interface of ArcPro software.

Pool sized weighted CO₂ emissions from the studied test site (F<sub>total</sub>)

were calculated taking into account the partial contribution of pools of each size range following the grid size approach:

$$F_{\text{total}} = F(0-10^3) \times S(0-10^3) + F(10^3-10^4) \times S(10^3-10^4) + F(10^4-10^5) \times S(10^4-10^5) + F(10^5-10^6) \times S(10^5-10^6) + F(>10^6) \times S(>10^6).$$

where F(0-10³) represents mean emission from the pools smaller than 1000 m², and S(0-10³) represents the relative contribution of these pools to overall water area. A similar grid for partial contribution was calculated for other sizes of the pools (10³-10⁴, 10⁴-10⁵, 10⁵-10⁶, and > 10⁶ m²).

## 2.5. Statistics

We used linear regression when analyzing the relationship between variables such as the logarithm of pool surface area and main hydrochemical parameters including GHG fluxes. The Pearson Product Moment (r) correlation coefficient (p < 0.05) was used to determine the relationship between pCO₂, FCO₂ and main hydrochemical parameters of the water column. The data were tested for assumptions of normality and heterogeneity of variances. All statistical tests used a significance level of 5 % (α = 0.05). The hydrochemical data were also treated via multiparametric statistical approaches, which included a Principal Component Analysis using a variance estimation method; this allowed for testing the effect of various environmental factors (pool size, water temperature and bacterial number) and minor and major components of the pool water column on dissolved and gaseous C pattern including CO₂ emissions. Hierarchical Cluster Analysis (HCA) helped to distinguish groups of inter-connected components, including both major and trace elements, nutrients and dissolved gases. Statistical treatment was performed using STATISTICA-7 (https://www.statsoft.com), whereas the correlation plots were constructed using Excel of MS Office.

# 3. Results

## 3.1. Peatland pool size distribution and total water area in the GVM

The medium resolution images of the Landsat-8 allowed resolving pools and lakes with a minimal size of 625 m², corresponding to a 25 x 25 m pixel necessary to distinguish lakes from the surrounding landscape. The total water area at the 716 km² land area test site was equal to 156.6 km² (22 % limnicity; Table 1). We estimate a typical uncertainty on lake determination at 10 %, which is consistent with previous studies on similar peatland lakes located in the permafrost region (Bryksina and Polishchuk, 2013; Polishchuk et al., 2018).

The total number of pools and lakes (> 100 m²) was equal to 14,778. The largest number of pools were smaller than 1000 m² and these pools provided the main contribution (44 %) to the total pool number (Table 1). However, the area of small pools only constituted 1.2 % of total water area. Mid-sized lakes (10,000 – 100,000 m²), that accounted

<caption>Table 1</caption>

GIS-based inventory of peatland pools and bog lakes of the test site (716 km² land, 157 km² water) of the Great Vasyugan Mire sampled in the present study. The majority of lakes are smaller than 73,000 m², and the median lake area is 4124 m². Pool number and area distributions for pools smaller than 1000 m² are shown in Fig. S1 of the Supplement.

<table><thead><tr><th>Lake size range, m²</th><th>Lake number</th><th>% of total lake number</th><th>Area, m²</th><th>% of total water area</th></tr></thead><tbody><tr><td>0–1000</td><td>6461</td><td>44</td><td>1,874,264</td><td>1.2</td></tr><tr><td>1000–3000</td><td>2477</td><td>17</td><td>4,667,986</td><td>3.0</td></tr><tr><td>3000–10,000</td><td>3039</td><td>21</td><td>17,502,135</td><td>11.2</td></tr><tr><td>10,000–30,000</td><td>1809</td><td>12</td><td>30,835,421</td><td>19.7</td></tr><tr><td>30,000–100,000</td><td>772</td><td>5</td><td>39,457,400</td><td>25.2</td></tr><tr><td>100,000–300,000</td><td>184</td><td>1.2</td><td>28,602,607</td><td>18.3</td></tr><tr><td>300,000–10⁶</td><td>28</td><td>0.2</td><td>14,324,640</td><td>9.1</td></tr><tr><td>> 10⁶</td><td>8</td><td>0.1</td><td>19,370,170</td><td>12.4</td></tr><tr><td>Total</td><td>14,778</td><td>100</td><td>156,634,622</td><td>100</td></tr></tbody></table>
for 17 % of lake numbers, provided the majority (45 %) of all lake areas. Finally, larger lakes (100,000 – 300,000 m²) constituted only 1.2 % of number of all lakes but sizably contributed (18.3 %) to total water coverage. The smallest pools (< 200 m²) constituted half the number of all pools smaller than 1000 m² and accounted for 28 % of their water area (Fig. S1).

## 3.2. Spatial pattern of carbon dioxide, organic and inorganic solutes across peatland pools of different sizes

Primary data on sampled pools and lakes physical parameters and water column components are listed in Table S1. Across a wide range of pool sizes (from 700 to 1,380,000 m²), there was significant (p < 0.05) correlation between pool area and various hydrochemical parameters (Table S2) which allowed for distinguishing three main solute groups. Group 1 demonstrated increased concentrations of CO₂, CH₄, DOC, some organic nutrients (formate, oxalate), metal micronutrients (Fe, Ni and

Zn), toxicants (Be, Cr, Cd and Bi) and low solubility lithogenic trace elements (Al, Ti, Ga, Y, Zr, REEs and Th) with decreased pool size. This is illustrated by linear relationships between element concentration and logarithm of pool surface area ($S_{area}$, m²) with $R^2$ ranging from 0.12 to 0.41 (p < 0.05). Increases were maximal in smallest pools sampled in this study (Fig. 2 A-F). It should also be noted that there was a strong ($r_{Pearson} = -0.73$) decrease in SUVA₂₅₄ with increasing pool size ($R^2 = 0.57$; Fig. S2). Chamber-measured emissions of CO₂ also exhibited an increasing trend towards the pools with the lowest surface area (Fig. 2 B), however data scatter did not allow confirming this statistically ($r_{Pearson} = -0.30$). The calculated CO₂ gas transfer coefficient did not vary among pools of different size, being equal to 2.68 ± 0.24 d m⁻¹, which is comparable with estimations for other Siberian regions (Krickov et al., 2021, 2023; Serikova et al., 2019; Vorobeyev et al., 2022, 2024). Group 2 included some nutrients not linked to organic matter (ammonia, P), alkaline (Li, Na and Rb) and alkaline-earth (Mg, Ca, Sr and Ba) metals, major (SO₄ and Cl) and some trace anions/neutral

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Examples of the first group of components exhibiting the highest concentrations in the smallest peatland pools: pCO₂ (A), CO₂ flux (B), CH₄ (C), DOC (D), Al (E), and Fe (F). Lines represent linear fits to the data (p < 0.05), with the corresponding parameters and regression coefficients ($R^2$) indicated on each panel. $S_{area}$ is pool surface area, m². Pearson correlation coefficients are provided in Table S2.</figcaption></figure>
molecules (B and Mo), as well as total bacterial number, pH and O₂ whose concentrations increased with increased pool size demonstrating weak relationships (R² = 0.18 to 0.77; 0.4 < r<sub>Pearson</sub> < 0.7). These increases were maximal in the largest lakes, as illustrated for several examples in Fig. 3 (A-F) and Fig. S2 B. Finally, group 3 included major (Si, NO₃ and K) and micro (V, Mn, Co, Cu and Se) nutrients, potential toxicants (As, Sb, Tl and Pb) and some trace elements (Cs, W and U). These solutes did not exhibit any significant dependency on pool size and demonstrated rather strong scattering in their concentrations as a function of S<sub>area</sub>.

### 3.3. Correlations and multi-parametric statistics of pool solutes

A pairwise correlation matrix (Table S2) of all major and trace components of pool waters, including water temperature, pH, optical parameters of DOM and total bacterial number demonstrated strong

positive (r<sub>Pearson</sub> > 0.70) linear relationships (R² = 0.51 to 0.77; p < 0.05) of DOC with organic ligands, lithogenic elements (Al, Be, Ti, V, Fe, Cr, Fe, Ga, Y, Zr, Nb, REE and Th) and some trace metals (Co, Ni, Zn, Cd, Pb, Bi and Cs) as illustrated in Fig. 4. CH₄ concentration negatively correlated with concentrations of Na, P, K and Ba (Fig. S3 A-D, as illustrated by linear regressions with 0.17 ≤ R² ≤ 0.47, p < 0.05). Neither CO₂ concentration nor the CO₂ flux exhibited strong correlation with other hydrochemical parameters.

Further treatment of the data via multi-parametric statistics (PCA) demonstrated the presence of two main factors that were capable explaining 25 and 15 % of the overall data variability (Fig. S4). The 1st factor exhibited high loading (eigenvalue > 0.75) on DOC, oxalate, succinate, and lithogenic trace elements (Be, Al, Fe, Ga, Y, Zr, REE, Th), as well as heavy metals/toxicants (V, Cr, Ni, Cd, Bi, Sb, As and Se). The 2nd factor positively acted on labile cations and anions (F, Cl, B, Li, Na, Mg, P, K, Ca, Rb, Sr and Ba) with an eigenvalue > 0.77. The hierarchical

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Examples of the second group of components whose parameters increased with an increase in pool surface area (S<sub>area</sub>, m²): pH (A), total bacterial count (B), SO₄ (C), P<sub>tot</sub> (D), Ca (E), and Mo (F). Lines represent linear fits to the data (p < 0.05), with the corresponding parameters and regression coefficients (R²) indicated on each panel. Pearson correlation coefficients are provided in Table S2.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Correlations between DOC and insoluble, low-mobility trace metals (A, Al; B, Ti; C, Cr; D, Fe; E, Y; F, La; G, Pb and H, Th) in peatland pools. Lines represent linear fits to the data (p < 0.05), with the corresponding parameters and regression coefficients (R²) indicated on each panel. Pearson correlation coefficients are provided in Table S2.</figcaption></figure>

cluster analysis yielded two significant links between two main groups of components at distances < 0.1 (Fig. S5). The first cluster included DOC, Fe, lithogenic elements such as low mobility trivalent and tetra-valent hydrolysates, as well as some heavy metals and toxicants (Cr, Ni, Zn, Pb, Cd, Bi, Be, Se and As). The second cluster grouped lake area and concentrations of alkalis and alkaline-earth metals, anions (B, Cl, F and

SO₄) and bacterial number (TBC). Concentrations of CO₂, CH₄ and their fluxes did not reveal any significant linkage to these two clusters.

### 3.4. *Diel cycle of CO₂ and some solutes in large peatland pools*

Diurnal cycles of CO₂ concentration, chamber-based fluxes and basic
hydrochemical parameters were followed in the surface horizon of the largest lake, Schuchie Lake, during stable anticyclonic weather (Fig. 5). There was no sizable change in C concentration and fluxes during the night (10 pm – 5 am) compared to day over 36 h of continuous measurements. Variations in water temperature did not exceed 1 °C, and those in pH were less than 0.05 units. Dissolved oxygen concentration did not exhibit any diurnal pattern and concentrations remained between 8.5 and 8.8 mg L⁻¹ for the entire observation period. This result is important for upscaling the fluxes obtained in this snap-shot study, although we acknowledge that further observations during the longer night period (summer and early autumn) for GVM peatland pools are necessary.

# 4. Discussion

## 4.1. Pool size control on GHG, dissolved C, and nutrients

An increase in concentrations of DOC, GHG and nutrients in the smallest pools compared to mid-sized and large pools confirmed the 1st hypothesis of this study. Such an increase likely reflects DOC and organic acids release from peat and shallow subsurface waters. An increasing trend of DOC concentration with a decrease in water body surface area is quite common in peatland pools of other geographic regions including Eastern Canada, Chilean Patagonia and the United Kingdom (ca. Arsenault et al., 2022, 2023). Much less is known about pool area controls on GHG fluxes, and major and trace element concentrations. Mesocosm-scale experiments performed with surface and deep peat from various boreal wetlands demonstrated that peat can be an important source of DOC, nutrients and major ions to the surrounding lake water (Shirokova et al., 2021, Manasypov et al., 2017). Furthermore, ground vegetation—notably mosses—is capable of delivering a sizable amount of organic matter, nutrients and trace metals to the surrounding aquatic systems via leaching of the biomass by snow/rain

melt water (Shirokova et al., 2017a, b; Kuzmina et al., 2024). Therefore, the observed increase in DOC, GHG and some nutrients with a decrease in pool size suggests that the primary source of these components in the pools is surface layers of peat and ground vegetation, as it is known for WSL rivers (Starr et al., 2024). The delivery of leaching products to the pool from surrounding terrestrial systems may occur via shallow subsurface and surface flow at the lake border (Raudina et al., 2018). As a result, higher concentrations of the GHG, DOC and organic nutrients is observed in pools with the highest perimeter to volume ratio, a feature which is well established in thermokarst lakes of permafrost-affected peatlands (Shirokova et al., 2013, 2021; Zabelina et al., 2021).

At the same time, lack of correlation between DOC and pCO₂ or FCO₂ dismissed the 2nd hypothesis of this study on DOC mineralization of the water column as an important governing factor for CO₂ production. It is likely that the essentially refractory character of allochthonous, peat-originated DOM precluded its efficient mineralization in the water column as confirmed by experimental studies in surface waters of peatlands (Shirokova et al., 2019; Raudina et al., 2022). Similarly, lack of a diel pattern in pH, O₂, pCO₂ and FCO₂ in a large lake indicated negligible aquatic metabolism and weak DOM mineralization in the humic (dystrophic) peat waters of the GVM. This contrasted with the 3rd hypothesis which was based on studies in low DOC, essentially oligotrophic or productive eutrophic waters. In contrast, an increase in pH and bacterial number with pool area likely reflected some ongoing autochthonous production processes (phytoplankton and periphyton activity), as was also consistent with a decrease in SUVA₂₅₄ (r<sub>Pearson</sub> = -0.73; Fig. S2 A) and an increase in O₂ concentration (r<sub>Pearson</sub> = 0.76) in the largest pools (Fig. S2 B). A concomitant increase in TBC (r<sub>Pearson</sub> = 0.81) could also signify an enhancement of biological processes of C turnover due to both phytoplankton (including pico-cyanobacteria) bloom and heterotrophic bacterial processing of autochthonous organic matter. These processes, however, could not sizably affect the diel CO₂ pattern and relevant C emissions.

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Diurnal variations of pCO₂ and FCO₂ (A), water temperature, O₂, Electrical Conductivity (E.C.) and pH (B) in the Lake Schuchie. Note different X scale for A and B panel. Shaded blue area represents night time.</figcaption></figure>
## 4.2. Spatial pattern of trace elements

To explain the spatial pattern of trace elements, such as metals micro-nutrients (Fe, Ni and Zn), some toxicants (Be, Cd and Bi) and low mobility lithogenic elements (Al, Ti, Cr, Ga, Y, Zr, REEs and Th) whose concentrations also increased in the smallest pools, two processes can be invoked. First, some of these elements, such as divalent metals (Ni, Zn and Cd), can be controlled by atmospheric deposition on the surface of mosses and lichens via accumulating in the form of snowpack over winter (Shevchenko et al., 2017; Krickov et al., 2022). In spring, these metals are delivered to the water ponds via surface runoff, and hence, the smallest pools of high perimeter/volume ratio are subjected to the strongest impact of the surrounding vegetation leaching. Second, in the smallest peatland pools, high acidity and DOC concentrations help to overcome transport limitation problems of these generally low mobility insoluble trace elements, notably trivalent and tetravalent hydrolysates. Most likely, delivery of these elements to the pools occurs via organic and organo-mineral (Fe and Al) colloids, as has been established in permafrost peatlands of western Siberia (Pokrovsky et al., 2016b). Therefore, a special study of trace element colloidal forms in peatland pools of the GVM is needed to further address the transport/speciation control on the spatial distribution of lithogenic trace elements in peatland pools.

In contrast with organic solutes and DOM-associated trace metals, the concentrations of 2nd group of water column components sizably increased with an increase in pool size, being maximal in the largest lake (Schuchie Lake). These are typically labile, highly soluble elements (alkalis and alkaline-earth metals, Cl, SO₄, B and Mo) whose concentrations increased with Sarea. Most likely, this spatial pattern reflects increasing connection of larger lakes with subsurface and ground water flow. Shallow groundwater is enriched in labile elements due to its interaction with mineral horizons (> 3–5 m depth) that are underlying thick peat deposits. This was first established via hydrological studies of major solutes in the WSL rivers (Frey et al., 2007b; Frey and McClelland, 2009) and later confirmed by extensive seasonally and spatially resolved campaigns on trace elements (Pokrovsky et al., 2016a). In both rivers and lakes, the concentration of labile trace elements increases during baseflow season and thus they act as good markers for surface water connectivity with groundwater reservoirs (Pokrovsky et al., 2020; Manasypov et al., 2015, 2022). Unfortunately, in this permafrost-free zone, we could not employ a common approach for tracing the deep groundwater influx via taliks, as it is possible in lakes of the continuous permafrost zone (e.g., Hinkel et al., 2016). To the best of our knowledge, there is no information on bog lakes connectivity to local groundwater in the GVM and a special, seasonally-resolved study (i.e., using Rn tracers) is needed.

## 4.3. Role of peatland pools in regional C emission

Carbon dioxide emission fluxes from GVM peatland pools measured in this study (median 0.14; range 0–0.7 g C m⁻² d⁻¹) are comparable to values reported for other peatland pools in Arctic and subarctic settings of Northern Eurasia, including ponds of the Indigirka Lowland (0.36 ± 0.25 g C m⁻² d⁻¹, Dean et al., 2020); thermokarst lakes of the Yamal-Gydanskaya Tundra (0.4–0.5 g C m⁻² d⁻¹; Repo et al., 2007; Serikova et al., 2019) and Bolshezemelskaya Tundra (0.2–1.8 g C m⁻² d⁻¹; Zabelina et al., 2021). Note that the range of CO₂ emissions from peatland pools across the world is from 0.06 to 1.2 g C m⁻² d⁻¹ (Waddington and Roulet, 2000; McEnroe et al., 2009; Repo et al., 2007; Arsenault et al., 2023).

Pool size-weighted CO₂ emission amounted to 0.15 g C m⁻² d⁻¹, which is similar to the mean and median values of measured fluxes (0.18 and 0.14 g C m⁻² d⁻¹, respectively). Note that there are relatively low emissions (0.01–0.06 g C m⁻² d⁻¹) from largest lakes (> 10⁵ m²), although these lakes provided a sizable contribution (40 %) to the total water coverage. Taking into account the dependences between GHG

fluxes and pool size (section 3.2), we calculated the partial contribution of pools for each size range into global C emission. Five main grid sizes (< 1000 m², 10³–10⁴ m², 10⁴–10⁵ m², 10⁵–10⁶ m², and > 10⁶ m²) provided 2, 18.8, 68.3, 10.4 and 0.6 %, respectively, of the total CO₂ emissions from the test site. Therefore, despite the highest measured concentrations of both CO₂ and CH₄ and CO₂ emissions (Fig. 2 A-C), small pools (< 1000 m²) contributed only 2 % to the overall CO₂ and CH₄ emission flux. This result contrasts the generally accepted paradigm that small water bodies (ponds and lakes) are important hot spots for GHG emission and C storage (Holgerson and Raymond, 2016). Despite the sizable concentrations of DOC, CO₂, CH₄ and GHG emissions, the total surface area of such small water bodies is simply not sufficient to provide an equivalently important contribution to overall fluxes. The main reason for this is that the Pareto law, used in previous model-based studies of ultra-small ponds (i.e., Holgerson and Raymond, 2016), is not warranted for permafrost-free wetlands of western Siberia. The non-applicability of the Pareto law has been also demonstrated for thermokarst lakes in the permafrost-affected part of the WSL (1,050,000 km²; Polishchuk et al., 2017, 2018) and northeastern European tundra peatlands (200,000 km²; Zabelina et al., 2021). The present study corroborates this important conclusion for the permafrost-free zone of the largest peatland in the world. We further speculate that a similar distribution pattern of dissolved and gaseous C parameters is expected in other peatland pool complexes developed in mires of western Siberia and European parts of Russia.

Note that CO₂ emissions from water surfaces in the studied territory of the GVM (0.15 g C m⁻² d⁻¹) are lower than typical values for terrestrial primary productivity of different landscapes in this territory (230 g C m⁻² y⁻¹ or 0.63 g C m⁻² d⁻¹; Golovatskaya and Dyukarev, 2009). Upscaling of obtained results allow a first-order assessment of the role of peatland pools in the C balance of the Great Vasyugan Mire (55,000 km²). With their 2000 km² water surface area (Berezin et al., 2014), peatland pools and lakes of the GVM emit 0.054 Mt C-CO₂ (< 10 % CH₄), which is lower than the range of annual carbon uptake in this territory (3–10 Mt of CO₂ (0.8–2.7 Mt C) (Inisheva and Golovchenko, 2022; Kirpotin et al., 2021). However, the mean peat growth rate (net terrestrial C sequestration) over past 2000 years in the southern part of western Siberia ranges from 0.05 to 0.12 g C m⁻² d⁻¹ (Beilman et al., 2009) which is comparable with mean emissions from peatland pools assessed in the present study. Furthermore, during 6 month open-water period (May to October), lacustrine CO₂ emissions from the entire GVM territory amounted to 0.98 t C km²/land period⁻¹ which is higher than the riverine lateral downstream DOC export (yield) at these latitudes from the WSL permafrost-free zone (0.64 t C km²/land period⁻¹; Krickov et al., 2024). In contrast, over the same 6 month period, calculated CO₂ emissions from lakes of the GVM are two times lower than those from rivers of this region (2.4 t C-CO₂ km²/land period⁻¹, Krickov et al., 2024). Widely, in the permafrost-free zone of the WSL, emissions from rivers and lakes are roughly comparable (Karlsson et al., 2021). We therefore suspect that the real annual emissions from GVM peatland pools can be significantly higher than 0.054 Mt C. Indeed, the C emissions from water surfaces assessed in this study are highly conservative and likely represent a lower threshold of true values. First, the limnicity value for the entire GVM assessed in the sole available estimation of Berezin et al. (2014), 3.5 %, is 6 times lower than the one quantified in this study for a relatively small test site of 716 km² (21.9 %). Second, the water coverage of the wetlands is much higher during one or two months of spring period compared to summer baseflow (end of July–September) used for quantifying pool number and area of the test site in this study and in previous global estimations of GVM limnicity (Berezin et al., 2014). Third, emissions from lakes during summer and autumn seasons can be higher than those in spring; however, the only available data originate from floodplain lakes of the region (Krickov et al., 2021), which could be drastically different from peatland pools. In Arctic lakes of the Lena River Delta, methane concentration in winter was 40 times higher than in summer (Bussmann et al., 2021). Pelletier et al. (2014)
calculated an annual C release from pools of 103 $g C m^{-2} yr^{-1}$ of which 15 % was released during the spring ice melt. However, based on seasonally resolved studies of lakes in the southern, permafrost-free regions of the WSL (Serikova et al., 2019), we do not expect significant seasonal fluctuations in GHG emissions during the open-water seasons (spring, summer, and autumn). Any seasonal variations are unlikely to exceed the spatial variability observed among individual pools.

Further assessment of C cycle in the GVM requires $CO_2$ and $CH_4$ evasion from the terrestrial part of the GVM, including temporarily flooded zones due to groundwater level rising (e.g., Mikhalchuk et al., 2022, 2023) and streams (Krickov et al., 2023). Another important unknown factor of $CO_2$ balance is the C accumulation in pool sediments. The latter was shown to be non-negligible (25–30 %) compared to C evasion from thermokarst lake surfaces, in the northern part of the WSL peatlands (Manasypov et al., 2022). To our knowledge, C accumulation rates in sediments of peatland pools and lakes located within the permafrost-free zone of the WSL are not available. Note that this study did not aim to fully constrain the entire carbon (C) balance—including terrestrial uptake and emissions, C storage in sediments, lateral export by rivers, and C emissions from water surfaces—across the Great Vasyugan Mire (55,000 $km^2$) or even within the selected test site (700 $km^2$). However, based on findings from WSL river catchments (Karlsson et al., 2021; Vorobyev et al., 2022; Krickov et al., 2024), carbon emissions from inland water surfaces may surpass downstream (lateral) carbon export, a factor that should be considered in regional carbon budgets.

## 5. Conclusions

Carbon (DOC, $CO_2$ and $CH_4$) concentrations and GHG emission patterns along with organic and inorganic nutrients and trace elements were assessed in peatland pools of the Great Vasyugan Mire of western Siberia, the largest peatland in the world. We found a clear increasing trend of GHG concentration and emission fluxes, DOC, some macro- and micronutrients and lithogenic trace element concentrations with decreasing pool size. The highest C parameters, nutrients and trace element concentrations were recorded in small pools (≤ 5000 $m^2$), likely due to maximal terrestrial input of peat and ground vegetation leaching byproducts and shallow subsurface waters, achieved at the highest ratio of circumference to water volume. Aqueous concentrations of labile anions, alkaline and alkaline earth metals increased with increasing water body size, reflecting possible groundwater connectivity of largest lakes.

There were no sizable variations in $CO_2$ concentration and emission patterns over diel cycles in the large peatland lake, which allowed to extrapolate obtained fluxes over the entire open water season. Global estimation of water body size distributions and surface coverages in the test site of the Great Vasyugan Mire (716 $km^2$ land, 126 $km^2$ water) demonstrated that large pools (> 10,000 $m^2$) provide by far the largest contribution (> 85 %) to the overall water coverage and about 80 % of the total $CO_2$ emissions. As a result, despite much lower DOC, GHG concentrations and GHG fluxes compared to very small (< 1000 $m^2$) pools, which are dominant in number (44 % of all water bodies), the large pools contribute the major portion of dissolved C and GHG emissions. At the scale of the entire Great Vasyugan Mire (55,000 $km^2$), with their 2000 $km^2$ water surface area, and over a 6 month open water period, peatland pools and lakes emit 0.054 Mt $C-CO_2$ (< 10 % $CH_4$). This is comparable to annual peat growth rate in the mire. Even the most conservative estimation of overall $CO_2$ emissions from water surfaces of the Great Vasyugan Mire suggests that they are higher than lateral DOC export by rivers of this region. However, seasonally-resolved observations of C emissions from WSL pools and measurements of C accumulation rates in pool sediments are required to assess the annual C balance in terms of export, storage and emission.

## CRediT authorship contribution statement

**Sergey N. Vorobyev:** Writing – original draft, Validation, Methodology, Funding acquisition, Formal analysis, Conceptualization. **Yuri Kolesnichenko:** Software, Methodology, Data curation. **Ivan V. Krickov:** Methodology, Investigation. **Alexander E. Berezin:** Validation, Methodology. **Sergey N. Kirpotin:** Writing – review & editing, Supervision, Project administration, Funding acquisition, Data curation. **Oleg S. Pokrovsky:** Writing – original draft, Supervision.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

Main support for this work was from the RSF project 24-17-00300 "Role of lakes in carbon balance of the boreal zone, western Siberia". OP was partially supported by TSU Programme Priority-2030 and project PEACE of PEPR FairCarbON ANR-22-PEXF-0011. We are grateful to three anonymous reviews for critical comments and constructive remarks.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolind.2024.113054.

## Data availability

Primary hydrochemical data of sampled sites and GIS-based pool size and number inventory are available from the Mendeley Database repository: Pokrovsky, O., Vorobyev, S., Kolesnichenko, Y., Krickov, I., 2024. "Hydrochemical and hydrological parameters of the peatland pools in the Great Vasyugan Mire", Mendeley Data, V1, https://data.mendeley.com/datasets/72rfhyc9t9/1.

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