# Sizable carbon emission from the floodplain of Ob River

Ivan V Krickov, Svetlana Serikova, Oleg S Pokrovsky, Sergey N Vorobeyev, Artem G Lim, Matthias B Siewert, Jan Karlsson

► To cite this version:

Ivan V Krickov, Svetlana Serikova, Oleg S Pokrovsky, Sergey N Vorobeyev, Artem G Lim, et al. Sizable carbon emission from the floodplain of Ob River. Ecological Indicators, 2021, 131, pp.108164. 10.1016/j.ecolind.2021.108164. hal-04890222

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# Sizable carbon emission from the floodplain of Ob River

Ivan V. Krickov<sup>a</sup>, Svetlana Serikova<sup>b,c</sup>, Oleg S. Pokrovsky<sup>d,e,*</sup>, Sergey N. Vorobeyev<sup>a</sup>,
Artem G. Lim<sup>a</sup>, Matthias B. Siewert<sup>b</sup>, Jan Karlsson<sup>b</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

- Floodplain
- Greenhouse gases
- Ob river
- Seasonal influences
- Carbon emission

## ABSTRACT

The Ob River floodplain is the second largest floodplain in the world. Despite its vast area, estimates of carbon (C) emissions from the Ob River floodplain are largely absent. Here we present seasonal C emission and water area extent from the main channel and the floodplain along a ~4 km reach in the boreal zone of the Ob River. We found strong seasonality in water area extent of the Ob main channel (~1.8 km²) and floodplain (~3 km²) with water covering 34% of land during flood and subsequently declining to ~16% and ~14% during summer and autumn baseflow, respectively. The C emissions also varied seasonally over the open water period, ranging from -0.1 to 0.6 g C m⁻² d⁻¹ for the Ob main channel and from 0 to 9 g C m⁻² d⁻¹ for the floodplain. The dissolved organic carbon positively affected CO₂ concentrations and fluxes in the floodplain during all seasons, whereas pH and oxygen concentration negatively impacted CO₂ concentrations and fluxes. Some nutrients (ammonia and phosphate) positively correlated with CO₂ and CH₄ concentrations in summer. The total C emission from the study reach (1.8 km² main channel, 3 km² floodplain) during moderate flooding was 236 ± 51 tons C yr⁻¹ (>99% CO₂, <1% CH₄) with the floodplain accounting for ~65%. The contribution of the floodplain to the net river C evasion may be even greater during years of high flooding and in northern regions of the Ob River basin, where floodplain soils are more C-rich and are underlain by permafrost, and in years with more extensive flooding.

## 1. Introduction

Rivers and streams are known to be a major source of carbon (C) to the atmosphere with recent estimates (0.6–1.8 Pg C yr⁻¹, Lauerwald et al., 2015; Raymond et al., 2013) corresponding to ~1/8 to 1/4 of global anthropogenic emissions (Ciais et al., 2013). However, regional estimates of river and stream C emissions vary considerably (Borges et al., 2015; Richey et al., 2002; Striegl et al., 2012) and have large uncertainties, much of which stem from a limited number of direct C flux measurements including from high-latitude regions. Even for high-latitude regions where river and stream C flux measurements do exist (Campeau et al., 2014; Denfeld et al., 2013; Serikova et al., 2018; Striegl et al., 2012; Tranvik et al., 2018; Zolkos et al., 2019), they have been mainly carried out in the main channels, implying potential uncertainties in extrapolation to the regional scales. This is especially true

for large Russian high-latitude rivers that are far less characterized than those of North America and Fennoscandia.

A major unknown contribution to the net river C exchange is the role of vast areas of seasonally connected floodplains. Globally, floodplains are estimated to cover between 0.8 × 10⁶ and 2 × 10⁶ km² (Sutfin et al., 2016). Floodplains are unique environments (Terentieva et al., 2019) as they are linked to the rivers they border and exchange material and solutes with the main river channel (Abril & Borges, 2019; Lininger & Wohl, 2019; Valett et al., 2005; Aufdenkampe et al., 2011). Generally, C emissions from the water surface of rivers are a result of decomposition of terrestrially-derived organic C in the water and sediments, as well as inputs of carbon dioxide (CO₂) and methane (CH₄) from the surrounding soils and groundwater (Hotchkiss et al., 2015; (Rasilo et al., 2017; Duvert et al., 2018). Inundation of floodplains during flooding events is therefore likely to increase C emissions from surface waters due to

<footnote><sup>a</sup> BIO-GEO-CLIM Laboratory, Tomsk State University, Lenina 36, 634 050 Tomsk, Russia</footnote>

<footnote><sup>b</sup> Climate Impacts Research Centre (CIRC), Department of Ecology and Environmental Science, Umeå University, Linnaeus väg 6, 901 87 Umeå, Sweden</footnote>

<footnote><sup>c</sup> Swedish Geotechnical Institute, Olaus Magnus väg 35, 581 93 Linköping, Sweden</footnote>

<footnote><sup>d</sup> GET UMR 5563 CNRS, Geoscience and Environment, University of Toulouse, 14 Avenue Edouard Belin, 31400 Toulouse, France</footnote>

<footnote><sup>e</sup> Institute of Ecological Problems of the North, N. Laverov Federal Center for Integrated Arctic Research, Russian Academy of Sciences, Nab. Severnoi Dviny 23, 163 000 Arkhangelsk, Russia</footnote>

<footnote>* Corresponding author at: GET UMR 5563 CNRS, Geoscience and Environment, University of Toulouse, 14 Avenue Edouard Belin, 31400 Toulouse, France.
E-mail address: oleg.pokrovsky@get.omp.eu (O.S. Pokrovsky).</footnote>
enhanced C release from soils that were already inundated previous to flooding and also due to newly flooded areas where C evasion can take place. For these reasons, a large body of research has compared C dynamics in floodplains/main channel of the Amazon River (Richey et al., 1988; Devol et al., 1990; Bartlett et al., 1990; Waichman, 1996; Bustillo et al., 2011; Rudorff et al., 2011; Scofield et al., 2016; Amaral et al., 2019, 2020; Hastie et al., 2019) and in other riverine systems across the world (Pulliam, 1993; Smith et al., 2000; Sun et al., 2013; Corson-Rikert et al., 2016; Peixoto et al., 2016; Geeraert et al., 2017; Zuijdegeest and Wehrli, 2017; Cunada et al., 2018; DelDuco and Xu, 2019; Gareis and Lesack, 2020; dos Santos et al., 2020; Barbosa et al., 2018, 2020; Bass et al., 2013, 2014).

Difficulties associated with floodplain mapping and logistical challenges of undertaking direct measurements during flood events hinder inclusion of floodplains in large-scale C budget. This is particularly true for high-latitude regions due to their remoteness, despite that floodplains cover ~7 ± 4% of land in these regions (Lininger et al., 2018). Moreover, surface waters of Siberian floodplains have been less characterized than those of tropical regions, North America and Fenno-scandia (see Pokrovsky, 2016). One such high-latitude region where the riverine floodplain extends over a large area is the Ob River basin. The Ob River basin is the Arctic's largest watershed, with an area of ~3 million km², and is the second largest flooded area in the world (Papa et al., 2007; Vorobyev et al., 2015). Usually, the snowmelt in the Ob River basin starts in April in its upper mountainous reaches and the discharge rises at the same time reaching its maximum flow in May-June (Papa et al., 2007; Zakharova et al., 2014). During this period of peak discharge, a large part of the Ob River basin, which is located within the area of Western Siberian Lowlands (WSL, ~1.3 million km²), becomes inundated and is referred to as the world's biggest swamp (Papa et al., 2007). The flooded forests and wetlands of WSL are believed to be a source of C emissions into the atmosphere, though their role in the net river C degassing has not been previously assessed.

This study investigates how flooding influences the rates of C degassing from the floodplain ecosystem of the boreal taiga reach of the Ob River basin and quantifies its importance compared to the main channel. Our primary goal was the flux assessment over full open water season in order to estimate the contribution of the floodplain to the net river C evasion. Furthermore, as a first step towards understanding the driving factors of C emissions from the Ob River floodplain, we also tested the links between main environmental parameters (water temperature, DOC, DIC, nutrients) and CO₂ and CH₄ concentrations and emissions. We anticipate that achieving these objectives should allow better understanding of total riverine C balance in strongly understudied boreal regions of Siberia.

## 2. Study site and methods

### 2.1. Study site

The Ob River is a major river in western Siberia and is the world's seventh longest river (3650 km). It is the largest Arctic River in terms of watershed area (2,990,000 km²) and it delivers 15% of the total freshwater flow to the Arctic Ocean. Its floodplain is 10 to 50 km wide and is characterized by many tributaries and lakes. The study site is located in the boreal zone of the Ob River basin ~165 km northwest from Tomsk, Russia, where Tomsk State University has a research station (Kaibasovo) which is a part of the INTERACT network (57.246 °N, 84.184 °E, Fig. 1). The middle course of the Ob River, located within the boreal taiga biome, drains an area of 260,000 km². The only artificial hydraulic structure on the Ob River, the Novosibirskaya Dam, located 350 km upstream of the study site, has negligible impact (less than few percent) on the Ob River discharge at Kaibasovo (Vorobyev et al., 2019). In fact, the influence of this reservoir on the hydrological regime of Ob is not pronounced downstream of Kozhevnikovo (150 km from the dam). The climate is continental with a mean annual temperature of -0.1 °C and a

mean annual precipitation of 589 mm in 2018 (based on meteorological records from the nearest meteorological station ~20 km away from the site, Station code 29332: https://rp5.ru/). The main channel of the study site has a slow velocity (5–5.6 km h⁻¹ during flood and 2.7–3 km h⁻¹ during baseflow), typical for the Ob River in general, allowing the river to meander and develop multiple river arms. In this part of the river, numerous water bodies [which are flooded during the spring period] form a system of interconnected shallow ephemeral lakes and primary and secondary water channels which extend over 5–10 km from the main water channel (called “flood zone”).

We studied a representative ~3 km² area of the floodplain located on the left bank of the main channel (Fig. 2A). The riverine floodplain extends to the meadow terrace. The central part of the floodplain is mainly covered by grass meadows with many isolated or temporary interconnected lakes. Also, there are many wetlands and lakes with a high level of flooding. In the area of the near-terrace floodplain there are groundwater outputs (Rozhkova-Timina et al., 2016). The soils are predominantly alluvial overlain by a thin (~30 cm) humic layer. The vegetation is represented by trees (Betula, Salix and Populus, etc.), shrubs (Rosa canina, Viburnum, Ribes, etc.), perennial herbs (Veronica longifolia) and grasses (Calamagrostis, Phleum, etc.). Grasses are abundant in the elevated and the most remote part of the floodplain, while trees and shrubs being confined to the banks of lakes and river channels. Further description of the flood plain at the Kaibasovo station is provided in Vorobyev et al. (2015, 2019) and Cazzolla Gatti et al. (2018). Overall, the middle course of the Ob River is mostly representative of the permafrost-free Ob River (80% of the total catchment), as the river drains through boreal forest with bogs and has minimal anthropogenic impact. To some degree, the normal transect of the Ob River and its flooding zone is similar to that of the Mackenzie Delta lake zone, comprising a significant flooding gradient (i.e., Squires et al., 2002; Tank et al., 2009).

### 2.2. Sampling locations

We selected 14 sampling locations (Figs. 1 and 2) covering major landscape features of the floodplain such as main channel, lakes, secondary temporary channels, backwater parts of the flood channel, flooded forest, and flooded grasslands. More information is provided in the Supplementary Table S1. First sampling was performed daily for 10 consecutive days (5th–15th May 2018) over the flood event starting two weeks after the ice-break up (22nd April), Fig. 3. Sampling locations that still contained water were sampled five more times throughout the ice-free season: in 9th–10th June (hereafter referred as “Junearly”), 24th–25th June (“Junlate”), 27th–28th July (“Jullate”), 8th–9th September (“Sepearly”) and 18th–19th October (“Octmiddle”) until the ice settled in 2nd November. The main channel of the river was sampled 1–6 times a week, between 5th of May to 29th of October, at 200–300 m distance from the offshore, and within the vicinity of the floodplain. In addition, spatial variability of CO₂ concentration was studied over 1 to 5 km distance of the main channel and across the river in September.

### 2.3. Water samples

Sampling was carried out from a motorboat (in case of connected lakes) and a rubber boat (in case of isolated water bodies); the latter was delivered to the sampling site via an ARGO 8 × 8 all-terrain vehicle. At each sampling location, we measured pH and specific conductivity (WTW Multi 3320 multiparameter) of the surface water, water depth (Cole-Parmer echo sounder), air temperature and atmospheric pressure (Silva), as well as dissolved oxygen saturation and water temperature (YSI ProODO Handheld Optical Dissolved Oxygen meter) at every 50 cm from the water surface to the sediment surface. Water samples for nutrients and Chl a were filtered on site through pre-combusted (at 550 °C for 4 h) acid-washed glass-fibre filters (0.45 µm, Whatman Arbor Technologies) and were also stored frozen in the dark until analysis. The
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Map of the Ob' River basin with the study site. Position of the Ob River watershed in Eurasia (A), with red contour representing the whole Ob basin and dark blue area depicting the part of the watershed upstream of Kaibasovo station, as it is zoomed in B. Drone photos of the Ob' floodplain (~3 km²) in May, July and October (panels "C", "D", and "E" respectively) with the water extent based on the drone imagery analysis. The floodplain is located inside the black contours, and blue areas represent the seasonal water coverage. Sampling locations are shown as dots and are size- and color-coded based on the mean C flux during these months. Added is also the C flux for the main channel sampled ~2 km downstream (for details see Fig. 2).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** a. Detailed map of the study site together with the main channel of the Ob' River. Note the sampling location of "K1" for the main channel of the Ob' River. Map of the full floodplain (~20 km wide, ~48 km² area including the main channel). Study location (red) with information of C flux (in g C m⁻² d⁻¹) at the sampling stations K1 to K14 (see Table S1 for description). b. Floodplain transect (red) with the studied area (black quadrangle). Note that, although the floodplain is heterogeneous with respect to surface water extent, the chosen 20-km transect encompass all major morphological features of the Ob River middle course as analyzed in Vorobyev et al. (2015).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Multiannual monthly discharge of the Ob River at Kaibasovo (the Russian Hydrological Survey). The red line shows the start of the spring flood sampling (5th–15th May 2018), while the blue area denotes the open water sampling period. The annual discharge pattern of 2018 (blue line) was slightly higher than mean multi-annual monthly discharge (combined for periods 1959–1977 and 2002–2020, orange line).</figcaption></figure>

nutrient concentrations were analyzed using an automated flow injection analyzer (FIA star 5000, FOSS, Denmark) with detection limits of 1 µg L⁻¹ for N-NH₄⁺, 0.5 µg L⁻¹ for N-NO₃⁻, and 0.5 µg L⁻¹ for P-PO₄³⁻. Filtered (<0.45 µm) DOC and DIC were analyzed using a Carbon Total Analyzer (Shimadzu TOC 6000) with an uncertainty below 3% using a methodology developed for DOC-rich boreal waters (Payandi-Rolland et al., 2020b). Ultraviolet absorbency at 254 nm (UV₂₅₄) was measured using a quartz cuvette in a CARY-50 UV–Vis spectrophotometer (Bruker). This allowed calculating specific ultraviolet absorbance (SUVA₂₅₄) of the sampled water, which served as a proxy for aromatic C and organic matter quality in river water following the approaches elaborated for western Siberian waters (i.e., Payandi-Rolland et al., 2020b).

For CH₄ analyses, unfiltered water was sampled in 60-mL Serum bottles and closed without air bubbles using vinyl stoppers and aluminum caps and immediately poisoned by adding 0.2 mL of saturated HgCl₂ via a two-way needle system. In the laboratory, a headspace was created by displacing approximately 40% of water with N₂ (99.999%). Two 0.5-mL 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) were used in calculation of the CH₄ content in the water.

Surface water CO₂ concentrations for the sites located in the floodplain were measured *in situ* by deploying a portable infrared gas analyzer (IRGA, GMT222, Vaisala; accuracy ±1.5% based on the range of the sensor) with various detection ranges (3000, 10,000 and 20,000 ppm) enclosed within a waterproof and gas-permeable membrane. The key to aqueous deployment of the IRGA sensor is the use of a protective expanded polytetrafluoroethylene (PTFE) tube or sleeve that is highly permeable to CO₂ but impermeable to water (Johnson et al., 2009). The material is available for purchase as a flexible tube that fits over the IRGA sensor (Product number 200-07; International Polymer Engineering, Tempe, Arizona, USA). During the sampling, the sensor was left to equilibrate in the water for 10 min before measurements were recorded. The hand-held measurement indicator unit (MI 170, Vaisala; accuracy ±0.2%) was connected to the probe allowing reading of surface water pCO₂ concentrations. If the measured CO₂ concentration was 1.5% outside sensors' range, the probe was replaced with another sensor with higher range. The probes were calibrated in the lab and the calibrated values were corrected following methods described elsewhere (Johnson et al., 2009). Temperature-specific solubility coefficients were used to calculate pCO₂ partial pressure (µatm) in the water (Wanninkhof, 2014).

Measurement of CO₂ concentrations in the main channel was conducted from a boat in the middle of one of the river arms (57.249°N 84.182 °E) ~ 1 km downstream from the point where the main channel splits into two arms (Fig. 2A). This was done because reaching the middle of the main channel (which is ~1 km wide) during the flood with high wind and wave action was too dangerous. The pCO₂ in the main channel were recorded every 2 to 3 days during open water period, including summer and autumn baseflow, using a hand held Vaisala IRGA analyzer. A Campbell logger allowed continuous recording of the CO₂ concentration (ppm), water temperature (°C) and pressure (mbar) over 6–8 h of exposure in the middle of the day, yielding 57 daily-averaged individual pCO₂, water temperature and pressure measurements in total).

In order to assess the spatial variability of CO₂ in the main channel, surface water pCO₂ was measured (using the same IRGA system as above) continuously over several km around the study site from the boat. The probe was placed in a perforated steel pipe ~0.5 m below water surface which allowed water flowing freely around the probe. The boat moved with an average speed of 10 km h⁻¹, but regular stops were
arranged so that the distance and time intervals of the measurement were equally distributed.

## 2.4. Measurement and calculation of CO₂ and CH₄ efflux from the floodplain and main stem to the atmosphere

The CO₂ fluxes were measured by using two floating CO₂ chambers at each of the 14 sampling location of the floodplain. The chambers were small lightweight plastic bins (~30–32 cm in diameter, ~300 g, 10 L) equipped with non-dispersive infrared CO₂ loggers (SenseAir) (Bastviken et al., 2015). The CO₂ concentration inside each chamber was recorded continuously at 10 s interval and we used the first 10 to 30 min of measurements for computing CO₂ fluxes (Serikova et al., 2019). The CO₂ concentrations vs time were fit using a linear regression; in 96% cases R² was higher than 0.71. In 46% of the occasions across all seasons, our CO₂ chambers recorded a decrease in CO₂ accumulation rate. We interpreted these measurements as CO₂ uptake and retained the values if the respective pCO₂ concentration in the water next to the chamber was close to or below atmospheric equilibrium set to 404.2 ppm (Alin et al., 2011; Serikova et al., 2018). We further calculated instantaneous diffusive CH₄ fluxes for each of the chambers using chamber-specific gas transfer velocity (k) and the concentrations of dissolved CH₄ in the water and in air–water equilibrium (atm = 1.8 ppm), following the procedure outlined in Serikova et al. (2018), who used the same setup for measurements of GHG emissions from rivers and lakes of the WSL. Note that this setup does not allow measuring the ebullitive CH₄ fluxes and thus it is possible that the evasion of CH₄ from floodplain lakes in this study is sizably underestimated (i.e., Bastviken et al., 2010; Stanley et al., 2016; Villa et al., 2021).

The CO₂ flux ($f_{CO_2}$), g C m⁻² d⁻¹, in the main channel was calculated from measured CO₂ concentration in the Ob River water using Fick's law (Eq. (1)):

$$f_{CO_2} = K_h K_{TCO_2} (C_{water} - C_{air}) \quad (1)$$

where $K_h$ is the Henry's constant corrected for temperature and pressure (mol L⁻¹ atm⁻¹) which also incorporates pH-dependent chemical enhancement factor, $K_{TCO_2}$ is gas transfer velocity (m d⁻¹) which was calculated at a given temperature and wind speed (as described in Guérin et al., 2007; Wanninkhof, 1992; Cole and Caraco, 1998), $C_{water}$ is the water CO₂ concentration (ppmv), and $C_{air}$ is the CO₂ concentration in the ambient air (ppmv)

$$K_{TCO_2} = K_{600} \left( \frac{600}{\text{Sc}_{CO_2}} \right)^n \quad (2)$$

where n is dimensionless parameter which is equal to 0.5 according to Guérin et al. (2007), because the wind speed during study period did not exceed 3.7 m s⁻¹; $Sc_{CO_2}$ is Schmidt number (m s⁻¹) of CO₂ at a given temperature (Wanninkhof, 1992) calculated according to:

$$Sc_{CO_2} = 1911.1 - 118.11t + 3.4527t^2 - 0.041320t^3 \quad (3)$$

t is temperature (degrees Celsius), and $K_{600}$ is Schmidt number of 600 (m s⁻¹) calculated according to (Cole and Caraco, 1998):

$$K_{600} = 2.07 + 0.215 U_{10}^{1.7}$$

where $U_{10}$ is wind speed (m s⁻¹) normalized over 10 m above the water surface, taken from daily measurements of the Russian Hydrological Survey meteo-station Krivosheino, located 20 km upstream of the study site.

## 2.5. Water areas

To capture water area dynamics of the floodplain over the course of the study period, we used drone imagery (DJI Phantom 3 Advanced equipped with 12.4 M pixel camera). We flew the drone three times (5th

May, 24th June and 18th October) at 500 m height and each time took multiple photos of the ~3 km² study area. These photos were later merged and overlaid using graphical software (Adobe Photoshop) creating three seamless images of the floodplain representing respective study periods (May, July and October). We then georeferenced these images in ArcMap 10.5, imported them to RStudio statistical software R Core Team, 2019 and classified water areas using the “randomForest” algorithm, which is a tree-based classifier (Breiman, 2001) (“random-Forest” package). We used ~130 training polygons drawn individually for each of the respective images. We repeated image classification procedure for each of the images five times and picked the best result based on visual inspection together with our field observations. The image classification was assessed using the out-of-bag (OOB) error estimate, which is the standard for this algorithm. The OOB is derived in a bootstrapping procedure using a third of the classification trees (Breiman, 2001). Each image classification procedure had an uncertainty of ~22% as estimated empirically (OOB). Then, in ArcMap 10.5 we masked out water pixels matching the location of the main channel, and quantified the water area corresponding to the floodplain for each of the images as a product of image resolution (0.4 m) and the number of pixels in the water class.

To estimate water area of the main channel we used freely available Sentinel 2B satellite imagery (10 m resolution) matching the location of our study area (https://eos.com/, S2B_tile_20180715_44VPJ_0, 15th July 2018, 0% cloud cover). We manually drew the polygon extending the location of the ~3 km² study site over the Ob main channel (Fig. 1) to the next shore, and quantified its respective area (excluding pixels covering the next shore). Since the satellite imagery has a coarser resolution than the drone imagery, we assumed our quantified main channel area (~1.8 km²) to be representative of the summer baseflow conditions and estimated the corresponding increase and reduction in water areas over spring flood and autumn baseflow periods respectively, using the percent change (116% increase and 13% decrease) in water area derived from the drone image classification.

## 2.6. Total C emission

We quantified seasonal (spring, summer and autumn) and annual (open water period) C emission from the main channel (~1.8 km²) and the floodplain (~3 km²) and assessed respective uncertainties using Monte Carlo approach (R Core Team, 2019) with 10,000 perturbations. For convenience, we define spring as the period of May and early June, summer as late June, July and August, and autumn as September and October. For the seasonal C emission, we used seasonal water areas and median monthly C fluxes (aggregated across all sampling locations) (Table 1). The uncertainties were assessed by applying 10% (assumed otherwise unaccounted for error sources) and 22% (from image classification) variability in water areas of the main channel and floodplain, respectively, over all seasons (Table 1), and 1 s.d. of the median monthly C fluxes. When quantifying annual C emission from the main channel (~1.8 km²) and the floodplain (~3 km²), we used the same approach with the only difference that we used the mean of seasonal C emission from the Monte Carlo output and multiplied it with the duration of the open water period of 194 days (based on our field observations) with an assumed uncertainty of 10% based on satellite observations of river images at different adjacent dates and comparison of satellite images with topographic maps. This value also incorporates maximal uncertainty on the limnicity of the very flat WSL territory (see Polishchuk et al., 2017). The net uncertainty of total annual C emission from the main channel together with the floodplain was estimated by using standard rules of error propagation (Serikova et al., 2019).

The maximum extent of the floodplain (flooded on average every ~40–50 years) in this region extends over ~20 km (Vorobeyev et al., 2019) and therefore is generally larger than the floodplain area that was flooded in 2018 (~2 km wide, 3 km²) and covered in our study. Given this fact, we also estimated the potential total floodplain C emission in
<caption>**Table 1**</caption>

Mean seasonal surface water chemistry parameters and C flux rates per unit of aquatic area (±s.d.) for the floodplain and the main channel of the Ob River. Dash stands for missing values.

<table><thead><tr><th rowspan="2"></th><th colspan="6">Floodplain</th><th colspan="6">Main channel</th></tr><tr><th>May</th><th>Jun<sub>early</sub></th><th>Jun<sub>late</sub></th><th>Jul<sub>late</sub></th><th>Sep<sub>early</sub></th><th>Oct<sub>mid</sub></th><th>May</th><th>Jun<sub>early</sub></th><th>Jun<sub>late</sub></th><th>Jul<sub>late</sub></th><th>Sep<sub>early</sub></th><th>Oct<sub>mid</sub></th></tr></thead><tbody><tr><td>n</td><td>110</td><td>10</td><td>9</td><td>4</td><td>3</td><td>3</td><td>8</td><td>14</td><td>10</td><td>10</td><td>7</td><td>9</td></tr><tr><td>pCO<sub>2</sub>, [ppmv]</td><td>1552 (2053)</td><td>5938 (4734)</td><td>18,064 (10561)</td><td>16,372 (6348)</td><td>4598 (738)</td><td>2377 (1762)</td><td>884 (107)</td><td>699 (158)</td><td>672 (126)</td><td>437 (149)</td><td>315 (114)</td><td>380 (84)</td></tr><tr><td>n</td><td>109</td><td>10</td><td>9</td><td>4</td><td>3</td><td>3</td><td>7</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>pCH<sub>4</sub>, [ppmv]</td><td>4.7 (5.3)</td><td>7.4 (5.8)</td><td>193.4 (249.4)</td><td>106.3 (118.1)</td><td>8.8 (12.0)</td><td>25.6 (18.0)</td><td>1.6 (0.1)</td><td>2.9</td><td>-</td><td>4.3</td><td>1.0</td><td>15.8</td></tr><tr><td>n</td><td>110</td><td>10</td><td>9</td><td>4</td><td>3</td><td>3</td><td>8</td><td>14</td><td>10</td><td>10</td><td>7</td><td>9</td></tr><tr><td>CO<sub>2</sub> flux, [g C m<sup>-2</sup> d<sup>-1</sup>]</td><td>0.02 (1.78)</td><td>4.15 (2.36)</td><td>7.09 (3.86)</td><td>8.94 (6.37)</td><td>1.95 (1.77)</td><td>1.36 (0.81)</td><td>0.56 (0.17)</td><td>0.35 (0.2)</td><td>0.3 (0.1)</td><td>0.04 (0.2)</td><td>-0.1 (0.1)</td><td>-0.02 (0.12)</td></tr><tr><td>n</td><td>105</td><td>10</td><td>4</td><td>3</td><td>3</td><td>3</td><td>7</td><td>-</td><td>-</td><td>-</td><td>1</td><td>-</td></tr><tr><td>CH<sub>4</sub> flux, [g C m<sup>-2</sup> d<sup>-1</sup>]</td><td>0.01 (0.02)</td><td>0.02 (0.02)</td><td>0.06 (0.03)</td><td>0.09 (0.02)</td><td>0.01 (0.02)</td><td>0.05 (0.01)</td><td>0.01 (0.01)</td><td>-</td><td>-</td><td>-</td><td>0.09</td><td>-</td></tr><tr><td>n</td><td>109</td><td>10</td><td>9</td><td>4</td><td>3</td><td>2</td><td>8</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>DOC, [mg L<sup>-1</sup>]</td><td>6.2 (1.7)</td><td>7.9 (1.8)</td><td>9.2 (3.5)</td><td>8.1 (1.1)</td><td>8.0 (1.6)</td><td>9.3 (2.1)</td><td>4.9 (0.5)</td><td>5.3</td><td>4.2</td><td>3.3</td><td>3.3</td><td>2.5</td></tr><tr><td>n</td><td>110</td><td>10</td><td>9</td><td>4</td><td>3</td><td>2</td><td>8</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>DIC, [mg L<sup>-1</sup>]</td><td>24.6 (2.8)</td><td>24.4 (2.0)</td><td>30.7 (6.2)</td><td>30.9 (8.4)</td><td>31.9 (14.5)</td><td>36.5 (10.9)</td><td>18.6 (1.7)</td><td>22.5</td><td>23.2</td><td>22.8</td><td>23.9</td><td>27.8</td></tr><tr><td>n</td><td>110</td><td>9</td><td>9</td><td>4</td><td>3</td><td>3</td><td>8</td><td>-</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>O<sub>2</sub>, [mg L<sup>-1</sup>]</td><td>14.1 (6.3)</td><td>5.1 (2.9)</td><td>2.3 (1.3)</td><td>4.6 (3.2)</td><td>5.9 (3.8)</td><td>6.2 (4.2)</td><td>22.4 (28.7)</td><td>-</td><td>7.3</td><td>7.5</td><td>11.4</td><td>11.4</td></tr><tr><td>n</td><td>-</td><td>9</td><td>9</td><td>4</td><td>3</td><td>-</td><td>-</td><td>1</td><td>1</td><td>1</td><td>1</td><td>-</td></tr><tr><td>PO<sub>4</sub>, [µg P L<sup>-1</sup>]</td><td>-</td><td>11.7 (13.8)</td><td>20.0 (18.9)</td><td>12.5 (12.5)</td><td>3.8 (2.5)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>NO<sub>3</sub>, [µg N L<sup>-1</sup>]</td><td>-</td><td>2.1 (0.5)</td><td>1.3 (0.5)</td><td>0.8 (0.8)</td><td>1.6 (0.8)</td><td>-</td><td>-</td><td>236.4</td><td>0.8</td><td>0.3</td><td>0.7</td><td>-</td></tr><tr><td>NH<sub>4</sub>, [µg N L<sup>-1</sup>]</td><td>-</td><td>8.5 (3.9)</td><td>31.6 (42.5)</td><td>12.9 (5.9)</td><td>20.1 (22.0)</td><td>-</td><td>-</td><td>8.6</td><td>3.5</td><td>16.7</td><td>3.8</td><td>-</td></tr><tr><td>Chl a, [µg L<sup>-1</sup>]</td><td>-</td><td>5.3 (4.1)</td><td>3.1 (4.8)</td><td>4.2 (4.0)</td><td>8.7 (10.9)</td><td>-</td><td>-</td><td>7.1</td><td>1.3</td><td>15.3</td><td>9.9</td><td>-</td></tr><tr><td>n</td><td>52</td><td>-</td><td>9</td><td>4</td><td>-</td><td>-</td><td>4</td><td>-</td><td>1</td><td>1</td><td>-</td><td>-</td></tr><tr><td>SUVA<sub>254</sub></td><td>2.7 (1.5)</td><td>-</td><td>3.4 (0.6)</td><td>3.4 (0.6)</td><td>-</td><td>-</td><td>2.1 (0.1)</td><td>-</td><td>3.1</td><td>3.2</td><td>-</td><td>-</td></tr><tr><td>n</td><td>110</td><td>10</td><td>9</td><td>4</td><td>3</td><td>3</td><td>8</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>pH</td><td>8.3 (0.7)</td><td>7.3 (0.4)</td><td>7.1 (0.3)</td><td>7.1 (0.5)</td><td>8.0 (0.7)</td><td>7.5 (0.6)</td><td>7.5 (0.1)</td><td>8.2</td><td>8.2</td><td>8.6</td><td>8.9</td><td>8.5</td></tr><tr><td>Conductivity, [µS cm<sup>-1</sup>]</td><td>255 (31.2)</td><td>249 (37.1)</td><td>374 (133)</td><td>658 (715)</td><td>1122 (1057)</td><td>880 (786)</td><td>177 (32)</td><td>214</td><td>224</td><td>210</td><td>220</td><td>250</td></tr><tr><td>n</td><td>110</td><td>10</td><td>9</td><td>4</td><td>3</td><td>3</td><td>8</td><td>14</td><td>10</td><td>10</td><td>7</td><td>9</td></tr><tr><td>Water temperature, [°C]</td><td>8.2 (1.9)</td><td>20.3 (0.5)</td><td>23.2 (2.4)</td><td>17.7 (2.6)</td><td>17.3 (2.1)</td><td>3.5 (1.1)</td><td>5.7 (1.4)</td><td>13.6 (2.0)</td><td>19.7 (1.9)</td><td>20.9 (1.3)</td><td>15.4 (1.6)</td><td>7.4 (3.7)</td></tr></tbody></table>

this region by assuming similar C flux and estimating water area dynamics using the same algorithm across entire floodplain (a typical transect of the Ob River middle course having ~20 km wide, ~43 km² in area excluding the main channel; ~48 km² in area if the main channel is included, for details see Fig. 2B) as in our study site.

## 2.7. Statistics

All statistical analyses and calculations were performed in RStudio statistical software. We used non-parametric Mann-Whitney test to address differences in solute concentrations and C fluxes between the main channel and the floodplain as well as non-parametric Kruskal-Wallis test to test for seasonality in the concentrations of the respective variables. The data on GHG, DOC, DIC and nutrient concentrations and CO2 fluxes were, firstly, checked for normal distribution using the Shapiro-Wilk test. Nonparametric statistics methods were employed even if a part of the data used in statistical processing was normally distributed and another part was distributed abnormally. The values of the median and interquartile range (median ± IQR, Interquartile Range) were used to describe the uncertainty due to a large number of extrema and outliers in the data sets, and abnormally distributed data. For comparison, we also present the average values and standard deviation (mean ± SD). We used linear regression and Pearson pairwise correlation when analyzing the relationship between variables such as water temperature and GHG fluxes. Note that the uncertainty is

expressed as a mean ± 1 s.d. for all variables apart from water extent where uncertainties are expressed as ±10 or ±22%, respectively. All statistical tests used a significance level of 5% (α = 0.05).

## 3. Results

### 3.1. Seasonal water areas and C fluxes

Approximately 1 ± 0.2 km² out of the 3 km² floodplain was covered by water during the period of the flood, i.e. ~34% of land area (Fig. 1, Table 1). Following the flood event, the water extent decreased to ~0.4 ± 0.09 km² (16%) in summer and to ~0.3 ± 0.08 km² (14%) in autumn. The area of the main channel was ~1.8 ± 0.18 km² during the summer period, 2.1 ± 0.21 km² during flood, and 1.5 ± 0.15 km² over autumn period. The area of the entire study site (floodplain + main channel) was ~4.8 km².

Mean DOC and DIC concentrations during the flood were 6.2 ± 1.7 mg L⁻¹ and 24.6 ± 2.8 mg L⁻¹ in the floodplain, and 4.9 ± 0.5 mg L⁻¹ and 18.6 ± 1.6 mg L⁻¹ in the main channel. Concentration of DOC during the open water period increased in the floodplain (from 6.2 ± 1.7 mg L⁻¹ in May to 9.3 ± 2.1 mg L⁻¹ in Oct<sub>middle</sub>; the difference is significant at p < 0.05, Table 1, Fig. 4), whereas it decreased in the main river channel (from 4.9 ± 0.5 mg L⁻¹ in May to 2.5 mg L⁻¹ in Oct<sub>middle</sub>). At the same time, concentration of DIC increased throughout the open water period in the floodplain (from 24.6 ± 2.8 mg L⁻¹ in May to 36.5 ±
<figure><img src="image_7.png" /><figcaption>Fig. 4. The CO₂ concentrations (a), C flux to the atmosphere (b), Dissolved Organic Carbon, DOC (c) and Dissolved Inorganic Carbon, DIC (d) concentrations from May to October for the floodplain (a1, b1, c1, d1) and the main channel (a2, b2, c2, d2) of the Ob River. Points and bars represent mean values and standard deviation, respectively (for *n*, see Table 1). Presentations are for May (5–15.05.2018), early June (9–10.06.2018), late June (24–25.06.2018), late July (27–28.07.2018), September (8–9.09.2018) and middle of October (18–19.10.2018).</figcaption></figure>

10.9 mg L⁻¹ in Octmiddle, H = 20.5, p < 0.05, Table 1, Fig. 4) and in the main channel (from 18.6 ± 1.6 mg L⁻¹ in May to 27.9 mg L⁻¹ in Octmiddle, p < 0.05).

The mean CO₂ concentration of the floodplain water was 1552 ± 2053 ppmv during the flood period, followed by a steady increase during the summer (from 5938 ± 4734 ppmv in Junearly to 16372 ± 6348 ppmv

in Jullate), and a subsequent decline closer to the end of the open water season (2377 ± 1762 ppmv in Octmiddle, Table 1, Fig. 4). Such high standard deviations CO₂ concentrations during the flood reflect occasional uptake of CO₂ from the atmosphere. The mean CO₂ concentration of the main river channel ranged from slight oversaturation in early season (884 ± 107 ppmv during flood, 672 ± 126 ppmv in Junlate, 437 ±
149 ppmv in Jul<sub>late</sub>), and undersaturation in late season (380 ± 84 ppmv in Oct, Table 1, Fig. 4, Fig. S1).

The CO₂ concentration of the main channel are in reasonable agreement with the ones calculated (Pipko et al., 2019) or measured (Karlsson et al., 2021) by two independent ship expeditions on the middle course of the Ob River transect in the end of July 2016 (400–500 ppmv). Moreover, spatial variations of CO₂ concentration in the main channel were relatively minor (within ±20%) over sizable distances upstream and downstream the Kaibasovo station during baseflow (Fig. S2). The floodplain was supersaturated in CH₄, with lower values during flood (4.7 ± 5.3 ppmv) than in autumn (25.6 ± 18.0 ppmv) (the means are statistically different, H = 43.7, *P* < 0.05, Table 1). The main channel had relatively low mean CH₄ concentrations across all seasons (1.6 ± 0.1 ppmv during flood, 15.8 ppmv in autumn).

Average CO₂ fluxes were higher (p < 0.05) in the floodplain (1.1 ± 3.2 g C m⁻² d⁻¹) compared to the main channel (0.2 ± 0.3 g C m⁻² d⁻¹) throughout the open water season, whereas the average CH₄ fluxes were slightly lower for the main channel (0.02 ± 0.02 g C m⁻² d⁻¹) compared to the floodplain (0.03 ± 0.03 g C m⁻² d⁻¹, Table 1). Note that such a high standard deviation reflects sizable number of negative fluxes, i.e., atmospheric CO₂ uptake by the water surface.

The CO₂ fluxes exhibited strong seasonal variability and peaked during the flood period for the main channel (from 0.6 ± 0.2 g C m⁻² d⁻¹ in May to 0.3 ± 0.1 g C m⁻² d⁻¹ in Jul<sub>late</sub>; Table 1). For the floodplain, C flux (CO₂ flux from 0.02 ± 1.8 g C m⁻² d⁻¹ in May to 8.9 ± 6.4 g C m⁻² d⁻¹ in Jul<sub>late</sub>; CH₄ flux from 0.01 ± 0.02 g C m⁻² d⁻¹ in May to 0.09 ± 0.02 g C m⁻² d⁻¹ in Jul<sub>late</sub>, Table 1) gradually declined towards the end of the open water period. The total C flux (the sum of CO₂ and CH₄ flux) was dominated by CO₂ (99%) and on average 2 to 3-times greater from the floodplain (1.1 ± 3.2 g C m⁻² d⁻¹) compared to the main channel (0.4 ± 0.3 g C m⁻² d⁻¹) across all seasons (*P* < 0.05, Table 1, Fig. 5). Also, there were pronounced seasonal variabilities in total C fluxes with a C drawdown during the flood period for the floodplain, and during the autumn baseflow for the main channel (Table 1, Fig. 5).

During the flood period, the differences between fluxes of CO₂, CH₄, concentrations of DOC, DIC and O₂ between three main categories of water bodies (temporary channels, riparian zone and floodplain lakes) were significant by Mann-Whitney test (see relevant results in Fig. S3 and Table S2). During the summer baseflow, only CO₂ flux exhibited significant difference between all three groups, whereas DIC was significantly different temporary channel and riparian zone, as well as between temporary channel and floodplain lakes. The other components did not demonstrate any statistically significant differences between groups of water objects. Finally, during the autumn baseflow, due to strong decrease of the number of remaining water bodies, the tests are not sufficiently robust; there was no significant difference in any of the component between permanent channel and floodplain lakes.

## 3.2. Environmental factors potentially affecting Green House gas (GHG) concentrations and emissions

The K<sub>TCO₂</sub> values calculated from chambers for the floodplain ranged from 1.5 to 5.5 m d⁻¹ (Fig. S4). These values were generally the highest in May but did not show any significant differences between other seasons. Significant differences in the K<sub>TCO₂</sub> were observed between temporary channel/floodplain lakes and temporary channel/riparian zone during spring flood period and between permanent channel/floodplain lakes during summer baseflow.

The value of K<sub>TCO₂</sub> calculated from the wind speed for the main channel ranged from 1.3 to 3.0 m d⁻¹ with a median of 2.2 m d⁻¹. This value is higher than the one used by Raymond and Cole (2001) in Amazon main stem and tributaries (0.7–1.7 m d⁻¹), but lower than for the Kolyma River and its large tributaries (3.9 ± 2.5 m d⁻¹, Denfeld et al., 2013), the tributaries and main stem of the Yukon river basin (4.9–7.6 m d⁻¹, Striegl et al., 2012), the larger rivers in Amazon and Mekong river basins (3.5 ± 2.1 m d⁻¹, Alin et al., 2011) and recent

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Diffusive C fluxes (CO₂ + CH₄) of the Ob River floodplain (A) and the main channel (B). Columns and bars represent mean values and standard deviation, respectively (for *n*, see Table 1). Presentations are for May (5–15.05.2018), early June (9–10.06.2018), late June (24–25.06.2018), late July (27–28.07.2018), September (8–9.09.2018) and middle of October (18–19.10.2018). The diffuse C flux of CH₄ represents <1% of the total C flux shown in the figure. Note different Y scales in (A) and (B).</figcaption></figure>

modelling value for larger rivers globally (3 to 4 m d⁻¹, Raymond et al., 2013).

Given that nutrient, DIC and DOC are known to influence the production of CO₂ in the water column (i.e., Stanley et al., 2016), for all seasons of the floodplain, we tested the correlations between CO₂ concentrations, fluxes and available hydrochemical parameters of the water column including nutrients. The correlation coefficients between most important hydrochemical parameters and GHG concentrations and fluxes for individual seasons and the full period of observation are provided in Table 2. The DOC concentration and water temperature positively affected CO₂ concentration and fluxes across all seasons, whereas pH and O₂ concentration sizably anti-correlated with both pCO₂ and FCO₂. In spring, CO₂ concentrations and emission fluxes positively correlated with DOC and water temperature, and negatively correlated with O₂ concentration and pH. During this period, CH₄ concentrations positively correlated with O₂ and pH and negatively correlated with DOC and water temperature, although these correlations were much weaker pronounced. In summer, only CO₂ concentrations (not fluxes) positively correlated with DOC, PO₄, and NH₄ and negatively correlated with O₂ and pH. During this season, CH₄ concentrations positively correlated with DOC, DIC, PO₄ and NH₄ concentrations and negatively correlated with O₂ and pH. Limited number of sampling points in autumn (when most sites were dried up) did not allow robust
<caption>**Table 2**</caption>

Pearson correlations coefficients (p < 0.05, asterisk) between Green House Gas concentrations (GHG: CO₂, CH₄), their fluxes and the environmental parameters in water samples from the floodplain.

<table><thead><tr><th>Pearson correlations</th><th>DOC mg/L</th><th>DIC mg/L</th><th>O₂ mg/L</th><th>SUVA₂₅₄</th><th>pH</th><th>Conduc-tivity µS/cm</th><th>Water t°C</th><th>Air t°C</th><th>PO₄ µg/L</th><th>NO₃ µg/L</th><th>NH₄ µg/L</th><th>Chl a µg/L</th></tr></thead><tbody><tr><td colspan="13"><strong>Flood</strong></td></tr><tr><td>N</td><td>107</td><td>107</td><td>107</td><td>50</td><td>107</td><td>107</td><td>107</td><td>107</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>pCO₂ ppmv</td><td>0.72*</td><td>0.24</td><td>-0.89*</td><td>0.18</td><td>-0.83*</td><td>0.29*</td><td>0.46*</td><td>0.11</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CH₄ ppmv</td><td>-0.43*</td><td>-0.05</td><td>0.39*</td><td>-0.15</td><td>0.43*</td><td>0.05</td><td>-0.31*</td><td>-0.15</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO₂ g C m⁻² d⁻¹</td><td>0.59*</td><td>-0.01</td><td>-0.80*</td><td>0.12</td><td>-0.83*</td><td>0.10</td><td>0.30*</td><td>0.02</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CH₄ g C m⁻² d⁻¹</td><td>-0.35*</td><td>0.04</td><td>0.41*</td><td>-0.13</td><td>0.11</td><td>-0.25</td><td>0.03</td><td>0.41</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td colspan="13"><strong>Summer baseflow</strong></td></tr><tr><td>N</td><td>22</td><td>22</td><td>22</td><td>13</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td><td>22</td></tr><tr><td>pCO₂ ppmv</td><td>0.56*</td><td>0.28</td><td>-0.50*</td><td>0.45</td><td>-0.67*</td><td>0.03</td><td>-0.20</td><td>-0.32</td><td>0.57*</td><td>-0.06</td><td>0.55*</td><td>0.19</td></tr><tr><td>CH₄ ppmv</td><td>0.80*</td><td>0.56*</td><td>-0.47*</td><td>-0.12</td><td>-0.54*</td><td>-0.04</td><td>0.00</td><td>-0.10</td><td>0.75*</td><td>-0.04</td><td>0.91*</td><td>0.36</td></tr><tr><td>CO₂ g C m⁻² d⁻¹</td><td>-0.19</td><td>0.29</td><td>-0.11</td><td>0.01</td><td>-0.15</td><td>-0.05</td><td>-0.04</td><td>-0.29</td><td>-0.03</td><td>-0.40</td><td>-0.23</td><td>-0.37</td></tr><tr><td>CH₄ g C m⁻² d⁻¹</td><td>0.09</td><td>0.12</td><td>-0.11</td><td>0.55</td><td>-0.41</td><td>0.21</td><td>-0.10</td><td>-0.30</td><td>0.02</td><td>-0.35</td><td>0.33</td><td>-0.12</td></tr><tr><td colspan="13"><strong>Autumn baseflow</strong></td></tr><tr><td>N</td><td>5</td><td>5</td><td>5</td><td>-</td><td>5</td><td>5</td><td>5</td><td>5</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>pCO₂ ppmv</td><td>-0.87</td><td>-0.70</td><td>0.72</td><td>-</td><td>0.23</td><td>0.66</td><td>0.73</td><td>0.65</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CH₄ ppmv</td><td>0.18</td><td>-0.11</td><td>0.25</td><td>-</td><td>-0.85</td><td>0.18</td><td>-0.61</td><td>-0.59</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CO₂ g C m⁻² d⁻¹</td><td>0.75</td><td>0.51</td><td>-0.86</td><td>-</td><td>-0.29</td><td>-0.74</td><td>-0.07</td><td>0.11</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>CH₄ g C m⁻² d⁻¹</td><td>0.73</td><td>0.22</td><td>-0.32</td><td>-</td><td>-0.48</td><td>-0.38</td><td>-0.81</td><td>-0.76</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td colspan="13"><strong>All seasons</strong></td></tr><tr><td>N</td><td>135</td><td>135</td><td>135</td><td>65</td><td>137</td><td>138</td><td>138</td><td>138</td><td>25</td><td>25</td><td>25</td><td>25</td></tr><tr><td>pCO₂ ppmv</td><td>0.61*</td><td>0.34*</td><td>-0.64*</td><td>0.27</td><td>-0.65*</td><td>0.16</td><td>0.65*</td><td>0.40*</td><td>0.60*</td><td>-0.07</td><td>0.51*</td><td>0.05</td></tr><tr><td>CH₄ ppmv</td><td>0.50*</td><td>0.38*</td><td>-0.28</td><td>0.10</td><td>-0.29</td><td>0.06</td><td>0.37*</td><td>0.22</td><td>0.75*</td><td>-0.04</td><td>0.86*</td><td>0.23</td></tr><tr><td>CO₂ g C m⁻² d⁻¹</td><td>0.45*</td><td>0.32*</td><td>-0.72*</td><td>0.21</td><td>-0.71*</td><td>0.11</td><td>0.67*</td><td>0.39*</td><td>0.06</td><td>-0.38</td><td>-0.22</td><td>-0.29</td></tr><tr><td>CH₄ g C m⁻² d⁻¹</td><td>-0.10</td><td>0.13</td><td>0.10</td><td>0.08</td><td>0.07</td><td>0.11</td><td>0.15</td><td>0.11</td><td>0.11</td><td>-0.34</td><td>0.14</td><td>-0.02</td></tr></tbody></table>

assessment of environmental factor control, and none of the hydrochemical parameters was correlated to the GHG concentrations or fluxes. Noteworthy that other possible hydrochemical variables (SUVA₂₅₄, Chl *a*, NO₃⁻, Specific Conductivity) did not exhibit any statistically significant control on GHG concentrations in floodplain waters regardless of seasons.

The pH ranged from 7.1 to 8.3 in the floodplain, and from 7.5 to 8.8 in the main channel (Table 1). About one unit higher pH in the main channel during baseflow period (Table 1) may reflect sizable underground feeding of the Ob River during this period, due to the presence of carbonate-bearing rocks as discussed in our previous work (Vorobeyev et al., 2015). Such a difference in pH may indeed lead to lower CO₂ emissions from the main channel compared to the floodplain. This is further confirmed by a strong negative relationship ($R_{Pearson} = -0.83$, p < 0.01) between water pH and CO₂ concentration and emission in the floodplain during spring (Table 2).

The DOC quality is known to impact CO₂ production in the water column via biodegradation (Payandi-Rolland et al., 2020b). However, there was no link between SUVA₂₅₄ (which is a surrogate of the proportion of easily biodegradable autochthonous organic matter versus less biodegradable allochthonous (humic) DOC) and CO₂ concentration, because Pearson pairwise correlations were not significant during both spring and summer period (Table 2).

### 3.3. Upscaling for areal and annual C emission

The areal C emissions from the main channel and floodplain were drastically different during the flood (1.04 ± 0.04 and -0.71 ± 1.71 t C d⁻¹), summer (0.41 ± 0.04 and 2.60 ± 1.94 t C d⁻¹), and autumn baseflow (-0.18 ± 0.02 and 0.49 ± 0.49 t C d⁻¹) (Fig. 6a, Table 3). The total annual C emission was twice lower in the main channel (82 ± 40 t C yr⁻¹) compared to the floodplain (154 ± 178 t C yr⁻¹) (Fig. 6b, Table 1), resulting in a combined C evasion of 236 ± 51 t C yr⁻¹ for our study site. However, given that the floodplain is generally wider (~20 km wide, ~43 km², for details see Methods and Fig. 2) than the one covered in our study (~2 km wide, 3 km²), the combined C evasion of the full floodplain together with the main channel in wet years could potentially increase to a maximum 2754 ± 1658 t C yr⁻¹ with floodplain

accounting up to 92% in net river C degassing (Fig. 6c, d).

## 4. Discussion

### 4.1. Seasonality of C fluxes and environmental factors controlling C emissions

The range of CO₂ flux from the floodplain (0–9 g C m⁻² d⁻¹) greatly exceeded that from the main channel (-0.1 to 0.56 g C m⁻² d⁻¹, Table 1). The latter was comparable to the one reported for middle reaches of the Ob River (0–1.2 g C m⁻² d⁻¹, average 0.54 g C m⁻² d⁻¹; Pipko et al., 2019) but more than two times lower than emission flux in the non-permafrost zone of the Ob River (1.32 ± 0.14 g C m⁻² d⁻¹, Karlsson et al., 2021). It is possible that both low wind speed during period of measurements in this study (small fetch and sheltered sampling points, Alin et al., 2011) and enhanced primary productivity in the water column in summer 2018 (compared to July 2016 of Pipko et al., 2019) may be responsible for lower CO₂ emission from the main channel at the Kaibasovo station (Fig. 1A).

Both the main channel and the floodplain were sources of C to the atmosphere, but with rather contrasting patterns in C evasion over the course of the open water period. Interestingly, the floodplain showed almost zero C emission with a C drawdown trend in the beginning of the flood at maximum water extent (~34%), whereas the main channel at the same time acted as a net source of C into the atmosphere. During this time the exchange of water between the main channel and the floodplain is likely the greatest (Hamilton et al., 1997; Valett et al., 2005), and could be assumed to result in high C fluxes from the waters of the inundated floodplain. Yet, the lack of such elevated C emissions from the floodplain in our study is likely a result of the combined effect of low inputs of terrestrial organic C and low temperatures during this period of time. Indeed, the water temperature exerts clear positive effect ($r² = 0.44$, p < 0.05) on C emission from the floodplain (Table 2, Fig. S5), suggesting that low water temperatures (8 ± 2 °C) constrain the efficiency of microbial mineralization of DOC during spring flood. Even though the inputs of the terrestrial organic C could be assumed to be greater upon the onset of flood when large areas of the previously dry floodplain soils become rewetted (Hamilton et al., 1997; Terentieva
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Daily C emission for three seasons (Flood, Summer, Autumn) and separately (a, moderate flooding, measured in this study) for the floodplain, 3 km², and the main channel (1.8 km²), (c, potential maximum flooding, extrapolated) for the floodplain, 43 km², and the main channel (4.5 km²); annual C emission (b, moderate flooding) for the floodplain, 3 km², and the main channel (1.8 km²), (d, potential maximum flooding) for the floodplain, 43 km², and the main channel (4.5 km²). Columns and bars represent mean values and standard deviation, respectively. Positive values indicate C emission into the atmosphere; negative values indicate CO₂ uptake from the atmosphere.</figcaption></figure>

<caption>**Table 3**</caption>

Median seasonal C flux (±s.d.) as well as means of seasonal and annual C emission (±s.d.) from the Monte Carlo analysis for the floodplain (~3 km²) and the main channel (~1.8 km²) of the Ob River.

<table><thead><tr><th rowspan="2"></th><th colspan="3">Floodplain</th><th colspan="3">Main channel</th></tr><tr><th>Flood</th><th>Summer</th><th>Autumn</th><th>Flood</th><th>Summer</th><th>Autumn</th></tr></thead><tbody><tr><td>Area, [km²]</td><td>0.95<br>(0.20)</td><td>0.44<br>(0.09)</td><td>0.38<br>(0.08)</td><td>2.10<br>(0.21)</td><td>1.80<br>(0.18)</td><td>1.56<br>(0.15)</td></tr><tr><td>n</td><td>110</td><td>23</td><td>6</td><td>8</td><td>33</td><td>16</td></tr><tr><td>C flux, [g C m<sup>-2</sup> d<sup>-1</sup>]</td><td>-0.79<br>(1.77)</td><td>5.85<br>(4.10)</td><td>1.33<br>(1.28)</td><td>0.37<br>(0.20)</td><td>0.12<br>(0.23)</td><td>-0.12<br>(0.12)</td></tr><tr><td>C flux, [t C d<sup>-1</sup>]</td><td>-0.71<br>(1.71)</td><td>2.60<br>(1.94)</td><td>0.49<br>(0.49)</td><td>0.78<br>(0.04)</td><td>0.21<br>(0.04)</td><td>-0.18<br>(0.02)</td></tr><tr><td>C flux, [t C yr<sup>-1</sup>]</td><td>154<br>(178)</td><td></td><td></td><td>82<br>(40)</td><td></td><td></td></tr></tbody></table>

et al., 2019), this appear to not be the case in the Ob floodplain as indicated by the lower concentrations of DOC and DIC during the flood event compared to summer and autumn baseflow conditions (Table 1, Fig. 4). This suggests a strong dilution effect during the rising limb of hydrograph (Fig. 3) with large inputs of water reaching floodplain from the surrounding landscape (Vorobyev et al., 2015, 2019). Note that this dilution is confirmed by a decreasing DIC concentrations during the spring flood compared the summer baseflow, which was especially pronounced for the main channel (Fig. 4d). Such a decrease is consistent with results of previous monitoring of the Ob River main channel (Vorobyev et al., 2019). At the same time, the shallow flood zone (1.6 ± 1.2 m depth) results in improved light conditions in the water column and at the sediment surface which can enhance CO₂ fixation of submerged vascular plants and algae (Thorp and Delong, 1994).

Following the end of the flood and with the onset of summer baseflow conditions, the C fluxes from the floodplain increase (Table 1, Figs. 4, 5). During this time, the water extent of the floodplain drops and covers now only ~16% of land. Having lost almost half of the water
coverage, the floodplain loses direct connection with the main channel and becomes rather isolated. Also, given the overall low topographic relief of the floodplain (~70–80 m a.s.L.) with vast flat terrace and a gentle slope (Vorobeyev et al., 2019; Rozhkova-Timina et al., 2016), the remaining floodplain water is forced to reside in the landscape having no opportunities for surface drainage, whereas the subsurface drainage is very limited (Vorobeyev et al., 2015). At the same time, higher concentration of DOC and water temperatures (20 ± 4 °C) can result in increased mineralization in flooded soils (van Huissteden et al., 2009), and promote high rates of C fluxes from the water surface into the atmosphere. This is also confirmed by a positive relationship between the temperature and C emissions (Table 2, Fig. S5). We therefore hypothesize that the increasing pCO₂ in the floodplain waters during summer is a result of elevated respiration due to 10–15° higher water temperatures. Another important factor could be DOC leaching from riparian vegetation, as supported by recent mesocosm experiments with grasses from the Ob River floodplain (Bobrova et al., 2020). Generally, an increase in aqueous CO₂ concentration due to plant litter degradation is well known from mesocosm experiments (Shirokova et al., 2021).

During the autumn baseflow, the floodplain water coverage (~14%) was still substantial, but the rates of C evasion were 3 times lower compared to the summer period (Figs. 4a, 5a), despite high DOC and water temperatures (15 ± 4 °C). Potential explanations for low C evasion include low organic matter availability and relatively high primary production. Contrary to the floodplain, the main channel was undersaturated in CO₂ and had strong C drawdown from the atmosphere, and this is presumably a result of a corresponding decrease in DOC concentrations during this period when the primary production exceeded the respiration. The consistently low autumn DOC and CO₂ concentrations in the main channel over this period agree with the previous study of this area (Vorobeyev et al., 2019). Although the mechanistic explanation for observed patterns in atmospheric C exchange of the river system is unclear, these substantial spatial and temporal dynamics need to be accounted for in assessments of overall C balance of the river system.

The concentrations and emissions of methane from the main stem and floodplain of the Ob River were quite low over full open water period. Generally, the rivers draining wetland dominated catchments exhibit elevated methane concentration and fluxes (Stanley et al., 2016). One reason for relatively low methane emissions in our study is that the Ob River is a large system that is well oxygenated and with relatively long water residence time that allow substantial oxidation of methane compared to many other systems. Decreasing CH₄ degassing downstream in subarctic catchments is well known (Zolkos et al., 2019) and generally, both pCO₂ and pCH₄ decline along the river continuum in the boreal zone (Campeau et al., 2014). The diffusive CH₄ emission from the Ob River floodplain lakes (0.01–0.09 g C m⁻² h⁻¹) is comparable with the range reported for other non-permafrost lakes of western Siberia (0.007–0.2 g C m⁻² h⁻¹, with the a diffusive to ebullitive flux ratio ranging between 1 and 2, Sabrekov et al., 2017).

On a broader circumpolar context, the floodplain lakes of the middle course of the Ob River are different from biologically productive and cold thermokarst lakes of the Mackenzie Delta (Squires et al., 2009; Tank et al., 2009). The latter exhibited net C uptake during open-water season. This difference may be caused by more efficient heterotrophic processing of water column and sediments organic matter in Siberian floodplain lakes at typical water temperature of 20 ± 5 °C. Overall, studies of carbon quality control on GreenHouse Gas (GHG) emission (i. e., Cunada et al., 2018), sources of C and factors controlling the primary productivity are necessary for floodplain lakes of the Western Siberian Lowland. This will allow to relate seasonal features of CO₂ and CH₄ emissions to dissolved and particulate organic matter quality of the water column and lake sediments and assess the external inputs of GHG, depending on the connectivity of lakes to the main stem of the river.

Although this study was not designed to reveal the driving factors of CO₂ and CH₄ pattern in the Ob River floodplain, we noticed strong

positive correlation between DOC and CO₂ concentrations ($R_{Pearson}$ = 0.72, p < 0.01 for all seasons, Table 2). Many studies have reported a positive relationship between DOC and pCO₂ (Teodoru et al., 2009; Lapierre and del Giorgio, 2012; Lapierre et al., 2013; Campeau and del Giorgio, 2014), which is traditionally interpreted as DOC being a substrate for the heterotrophic bacterioplankton that produces CO₂ in the water column. In the case of Ob's floodplain, however, the specific ultraviolet absorbance (SUVa₂₅₄), which reflects the relative amount of easily degradable autochthonous OM versus recalcitrant aromatic allochthonous DOC, was not a significant driver of CO₂ concentration and fluxes. It is therefore possible that co-variance of DOC and CO₂ in floodplain waters reflected progressive accumulation of both components in the stagnant water bodies over open water period. This accumulation was mostly related to water temperature rise from May to August, which indeed strongly affected CO₂ concentrations and fluxes (significant positive correlation with $R_{Pearson}$ = 0.65–0.67, p < 0.05). A positive control of ammonia and phosphate on both CO₂ and CH₄ concentration in the summer likely reflects respiration of sediments accompanied by enhanced diffusion of these nutrients from partially anoxic pore waters to the water column. Possible control of anoxic environment at the bottom layers on the GHG emission from the water column is consistent with a negative correlation ($R_{Pearson}$ = −0.47 and −0.50 for CO₂ and CH₄, respectively) of both GHG with oxygen concentration in the water column during this period.

## 4.2. Quantitative importance of the floodplain in relation to the main channel

The floodplain (~2 km wide, 3 km²) and the main channel (~1.8 km²) together covered ~4.8 km² and evaded 236 ± 51 t C yr⁻¹ to the atmosphere, with relatively low contribution of the main channel to the annual C annual emission (35%, Table 3, Fig. 6a, b). The flood was moderate in 2018 (Figs. 1, 3), with the study site covering only ~15% of the entire floodplain that gets flooded once in ~40–50 years (Vorobeyev et al., 2019). Extrapolating our results to the full 20 km width of the floodplain (48 km² including the main channel, for details see Methods), the total C evasion would be 2754 ± 1658 t C yr⁻¹, with a share of floodplain as high as 92% (Fig. 6c, d). Note that the change of gas transfer coefficient (Eq. (1)) from wind-based (median 2.2 m d⁻¹; range 1.3 to 3.0 m d⁻¹) to a fixed value for the Ob River of 4.46 m d⁻¹ (Karlsson et al., 2021) will only slightly decrease the floodplain contribution to overall C fluxes (82% floodplain; 18% main channel). Although this estimate is associated with major uncertainties in both C evasion rates and water coverage, it indicates the potential importance of extreme flood events for net river C emission. Note that, in order to account for both spatial and temporal variation in water coverage, the floodplain extent should preferably be quantified in terms of surface area-days (i.e., Tockner and Stanford, 2002) which was beyond the scope of the present study.

The floodplain extends over large areas of the Ob River basin (mainly due to basin's low topographic relief) and therefore covers substantial parts of the permafrost-affected region of the Ob, implying that the quantitative importance of the floodplain in relation to the main channel likely varies spatially. The inundation of large floodplain areas where floodplain soils are more C-rich and are underlain by permafrost may result in greater C emissions from the inundated land and thus may lead to the overall greater floodplain contribution to the river C degassing. Also, in such areas the seasonal patterns in floodplain C emission may be different to what we found in this study since the inundation of thawing C-rich soils, despite colder climatic conditions, likely promotes rapid C mineralization in the water column and the sediments across all seasons (Payandi-Rolland et al., 2020a). Given that, quantification of the role of floodplain in net river C emission in permafrost-affected areas is needed and especially in areas that currently experiencing permafrost thaw (i.e. for Ob in isolated to discontinuous permafrost zones between 60 and 64 °N, Romanovsky et al., 2010).
The results of this study are based on direct measurements covering both spatial and temporal variability of C fluxes and water area dynamics from the floodplain and the main channel. Yet, our estimate of the role of the floodplain in net river C evasion contains uncertainties related to the challenges associated with C flux measurements across larger floodplain area as well as to the mapping of the water extent across the full floodplain terrace. In particular, in our study we assumed the width of full floodplain of ~20 km (~48 km² area) as well as similar water extent dynamics and C fluxes across this area. However, the landscape heterogeneity is substantial and factors as local topography and hydrology are likely important determinants of which areas of the floodplain receive water, as well as of processes controlling C emissions, potentially resulting also in small-scale heterogeneity in C emissions from water bodies (Crawford et al., 2017) even in such relatively flat terrains as the Ob River floodplain. Future studies should therefore address these research gaps and provide better mechanistic understanding of the controls of C evasion across larger floodplain areas during the short period of flood event.

## 5. Conclusions

Although the floodplains are known to provide the largest share of surface water C emission in lotic systems, Siberian rivers remained strongly understudied. Here we present a detailed data set covering the spring flood and full ice-free season of virtually unknown Ob River floodplain, the second largest in the world after the Amazon River floodplain. We measured both floodplain and main channel diffusive C fluxes (CO₂ + CH₄), and used drone and high-resolution satellite imagery to map the floodplain water area dynamics over the open water season of 2018. The highest C emissions from the middle Ob River floodplain lakes occurred during late June and July, and largely exceeded the emissions from the main stem. Among possible driving factors of CO₂ concentration and emissions from floodplain water surfaces, we invoke dissolved organic carbon and water temperature, whereas the pH and oxygen concentration anti-correlated with both CO₂ concentrations and emissions.

For the full open-water period, the floodplain accounted for 65% of the net river C emission. It is not unlikely that floodplains can have even greater relative importance for C gassing from rivers in permafrost-affected regions and in more extreme flood years when larger areas of land become inundated during longer time. As such, ignoring the floodplain C emission in high latitude regions can lead to underestimation of the total river C emissions. This calls for large-scale floodplain C flux measurements in large and medium size rivers of high latitudes.

## 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.

## Acknowledgement, Samples, and Data

This work was supported by the Swedish Research Council grants (325-2014-6898 and 2016-05275) awarded to J.K., and an RSF grant (18-17-00237_P) awarded to S.N.V, and VULCAR-FATE project of the Belmont Forum CRA Soils and Groundwater Call awarded to O.P. We thank BAF (Biogeochemical Analytical Facility, Umeå University, Sweden) for chemical analysis and Dominic Vachon for comments and suggestions on the manuscript.

The study was carried out using the research equipment of the Unique Research Installation “System of experimental bases located along the latitudinal gradient” TSU.

Authors declare no competing interests. The complete dataset (Dataset S1) for this paper is available at: https://zenodo.org/record/4563905, DOI: 10.5281/zenodo.4563905. A video clip on the Ob

River floodplain and carbon emission is available at https://www.youtube.com/watch?v=LCeG2bjNJrg and its description http://www.aparcharge.com/metadata/youtube/12374d6be67d416987e8996c8ba122a1 (assessed 4.08.2021).

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolind.2021.108164.

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