# Floodplain carbon dioxide emissions strongly exceed those of the main river stem: A case study of the Ob River, western Siberia

Sergey N. Vorobyev, Larisa G. Kolesnichenko, Yuri Kolesnichenko, Anatoly S. Prokushkin, Arina V. Lugovaya-Dolmatova, Jan Karlsson, Oleg S. Pokrovsky

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## To cite this version:

Sergey N. Vorobyev, Larisa G. Kolesnichenko, Yuri Kolesnichenko, Anatoly S. Prokushkin, Arina V. Lugovaya-Dolmatova, et al.. Floodplain carbon dioxide emissions strongly exceed those of the main river stem: A case study of the Ob River, western Siberia. Journal of Hydrology, 2024, 638, pp.131468. 10.1016/j.jhydrol.2024.131468. insu-04831468

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Research papers

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# Floodplain carbon dioxide emissions strongly exceed those of the main river stem: A case study of the Ob River, western Siberia

Sergey N. Vorobyev<sup>a</sup>, Larisa G. Kolesnichenko<sup>a</sup>, Yuri Kolesnichenko<sup>a</sup>, Anatoly S. Prokushkin<sup>b</sup>, Arina V. Lugovaya-Dolmatova<sup>a</sup>, Jan Karlsson<sup>c</sup>, Oleg S. Pokrovsky<sup>d,e,*</sup>

## ARTICLE INFO

**Keywords:**

Flood

Discharge

Modeling

GIS

Water coverage

CO₂

## ABSTRACT

The importance of floodplains in carbon (C) evasion from the lotic systems is especially important in continental plains of low runoff such as the organic-rich Western Siberian Lowland (WSL). To quantify the relative importance of the floodplain compared to main stem CO₂ emissions, we monitored a large region of the Ob River's middle course (permafrost-free zone) over 3 months from spring to summer. We calculated seasonal water coverage using remote sensing, GIS and hydrologically-based approaches and measured CO₂ emissions using floating chambers. There was a strongly pronounced seasonality in the water area's extent of the floodplain with water covering > 40 % of land during the ~ 30 days of the most intensive spring flood (May – June) and subsequently declining to ≤ 10 % during summer (July-August). Maximal CO₂ emissions were recorded in most shallow water bodies of the floodplain, notably in temporary flooded fens and birch forests. The CO₂ emissions during the study period ranged from 0.2 ± 0.2 to 0.9 ± 0.2 g C m⁻² d⁻¹ for the floodplain and 0.03 ± 0.34 g C m⁻² d⁻¹ for the Ob's main channel.

CO₂ emissions from the floodplain were ~ 163 ± 20 t C per km for the river's main stem during the 95 day study period. The partial contributions of temporary flooded zones, main stem, and permanent lakes / secondary channels to total emissions (1820 km² area) were 70, 16, and 14 %, respectively. Over spring and summer seasons, contributions from flooded zones ranged from 43 to 99 % of total CO₂ emissions from water surfaces of the Ob River's middle course. Extrapolation of obtained results to the entire territory of the Ob River floodplain indicates that not accounting for the floodplain emissions may sizably—up to an order of magnitude—underestimate the CO₂ emissions from riverine systems in Western Siberia during open water period. Future work on the Ob River floodplain in the permafrost-bearing zone should be prioritized and would allow adequate upscaling of C emission from this environmentally important territory.

## 1. Introduction

Warming climate intensifies hydrological cycles, resulting in extreme precipitation, early snow melt and frequent floods (Brutsaert and Parlange, 1998; Barnett et al., 2005; Huntington, 2006; Min et al., 2011). Such climate change is expected to result in an increase in the magnitude, frequency and duration of flood events (Catford et al., 2013; Blöschl et al., 2019; Gudmundsson et al., 2021). All these factors are especially important in riverine system of lowland plains with low runoff

and an excessive precipitation over evaporation regime. Large coverage of such regions by a river network implies important C emissions from lotic systems (Wallin et al., 2013), comparable to the terrestrial (land) uptake (Karlsson et al., 2021). The riparian (floodplain) zones of boreal rivers and streams are the important sites of hydrological C cycle control (Ledesma et al., 2018), given that the lateral transfer of CO₂ from the terrestrial to the aquatic system could largely be originated in the riparian zones (Leith et al., 2015). Floodplains are linked to the rivers they border and exchange material and solutes with the main river channel

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

<footnote><sup>b</sup> V.N. Sukachev Institute of Forest SB RAS, Akademgorodok 50/28, 660036 Krasnoyarsk, Russia</footnote>

<footnote><sup>c</sup> Climate Impacts Research Centre (CIRC), Department of Ecology and Environmental Science, Umeå University, Linnaeus väg 6, 901 87 Umeå, 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>
(Abril and Borges, 2019; Lininger and Wohl, 2019; Valett et al., 2005; Bolpagni et al., 2019). Generally, C emissions from the water surface of rivers result from the decomposition of terrestrially-derived organic C in the water and sediments, as well as inputs of carbon dioxide ($CO_2$) and methane ($CH_4$), from the surrounding soils and groundwater (Duvert et al., 2018; Hotchkiss et al., 2015; Rasilo et al., 2017).

Riverine floodplains are a major feature of river basins globally (i.e., Tockner and Stanford, 2002; Sutfin et al., 2016), strongly contributing to $CO_2$ emissions in tropical and temperate riverine systems (Richey et al., 2002; Almeida et al., 2017; Ruiz-Halpern et al., 2015; Abril and Borges, 2019; Zuijdgeest and Wehrli, 2017; Sun et al., 2013; dos Santos Pinto et al., 2020; Bass et al., 2013). Yet, in boreal and subarctic zones, river and stream C flux measurements (Campeau et al., 2014; Denfeld et al., 2013; Serikova et al., 2018; Striegl et al., 2012; Tranvik et al., 2018; Zolkos et al., 2019) have mainly been carried out on the main (permanent) channels and oxbow lakes, ignoring the temporarily flooded parts of floodplain zones. This implies potential uncertainties in extrapolation to the regional scales, especially in high-latitude regions where floodplains cover ~7 ± 4% of land (Lininger et al., 2018). This is especially true for western Siberia, which accommodates the Ob River, the Arctic's largest watershed, with an area of ~3 million km²; it also represents the second largest flooded area in the world (Papa et al., 2007; Vorobeyev et al., 2015). Recently, we demonstrated that $CO_2$ emissions from water surfaces of a small site (3–4 km² of the main stem and adjacent floodplain) of the Ob River greatly (by a factor of 2) exceeded the emissions from the main stem (Krickov et al., 2021). Main novelties of the present work include: 1) three orders of magnitude larger surface area of the floodplain, which is not achievable by simple extrapolation of previously studied 3 km² site given the potential spatial variability of $CO_2$ emissions from various types of flooded landscapes within the floodplain; 2) GIS- and ground-based typification of floodplain aquatic landscapes and identification of spatial hot spots of $CO_2$ emissions; 3) assessment of diel pattern of $CO_2$ emissions from water surfaces, and 4) quantitative hydrological model of water coverage and residence time at the floodplain. Specifically, here we selected a relatively large (~1800 km²) site to quantify the relative share of floodplain versus main stem for the annual $CO_2$ emissions from the water. For this, we estimated the $CO_2$ emission and water coverage of different types of water bodies

across the floodplain in order to (1) calculate the water coverage of a large test site of the floodplain for the flood period and summer baseflow (end of April – end of July), (2) estimate $CO_2$ emissions across the various floodplain landscapes, and (3) provide the relative contribution of the main stem and various water bodies of the floodplain (i.e., flooded meadows, shrubs and forests, permanent floodplain lakes and channels/streams) to total riverine $CO_2$ emission. Achieving these objectives should allow i) unprecedented seasonal and spatial coverage of $CO_2$ patterns within the largest Eurasian floodplain zone; and ii) creating a background of quantifying riverine C emissions in other lotic waters of the boreal and subarctic regions dominated by taiga forests and peatlands.

## 2. Materials and methods

### 2.1. Study site

The permafrost-free (middle course) part of the Ob River catchment is located within the boreal taiga biome (Fig. 1). The floodplain forms a system of interconnected shallow ephemeral and permanent lakes and primary and secondary water channels which extend over 5–10 km from the main water channel (see example in Fig. S1 of the Supplement). For this study, we selected a large (1800 km²) sector of the main stem and floodplain, which included both left and right bank of the Ob River in the boreal zone of the Ob’ River close to the research station (Kaibasovo) which is a part of the INTERACT network (57.246 °N, 84.184 °E, Fig. 1). More detailed description of the floodplain at the Kaibasovo station is provided in Vorobeyev et al., (2015,2019) and Krickov et al. (2021). The Ob River at Kaibasovo drains 260,000 km² area. The study site includes a variety of aquatic and terrestrial floodplain landscapes on both banks of the Ob River. It is thus representative for the middle course of the Ob basin (1810 km from the Novosibirskaya Dam to the confluence with Irtysh, with an overall floodplain area of 28,690 km²) although cannot serve as an analogue of northern, permafrost-affected part of the river. The climate is semi-continental with a mean annual temperature of –0.1 °C and a mean annual precipitation of 589 mm in 2018. The width of the floodplain in this part of the Ob River is between 10 and 30 km (Vorobeyev et al., 2015). The flow velocity of the main channel is

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Map of the Ob’ River basin with the study site. Position of the Ob River watershed in Eurasia (A), with pink contour representing the whole Ob basin and dark red area depicting the part of the watershed upstream of Kaibasovo station. Satellite photos of the Ob’ floodplain around Kaibasovo station in May (spring flood, B) and August/September (baseflow, C). D, daily hydrograph of the Ob River at Kaibasovo.</figcaption></figure>
<table><caption>**Table 1**
Sampling sites (1800 km² of the floodplain), water coverage, CO₂ concentration and fluxes.</caption><thead><tr><th>Sampling site, number of measurements (n)</th><th>water area, km²</th><th>Flooding area km² per km³ main stem</th><th>pCO₂, ppmv</th><th>FCO₂, g C-CO₂ m⁻² d⁻¹</th><th>Average days of flooding, 2020-2021</th><th>Flux per km of main stem, during period of flooding, t C/km³ main stem</th><th>Contribution (%) to overall CO₂ emission from the water surfaces during 95 days, 29.04 – 31.07</th><th>Range of contribution (%) to annual C emission</th></tr></thead><tbody><tr><td>Floodplain sand, n = 4</td><td>4.7</td><td>0.04</td><td>890</td><td>-0.024</td><td>95 ± 2</td><td>-0.09</td><td>-0.06</td><td>-</td></tr><tr><td>Flooded fen, n = 4</td><td>252</td><td>1.94</td><td>10260 ± 8000</td><td>0.9 ± 0.2</td><td>39 ± 7</td><td>68.1</td><td>41</td><td>35 – 45</td></tr><tr><td>Meadow, n = 20</td><td>121</td><td>0.93</td><td>3308 ± 2000</td><td>0.3 ± 0.3</td><td>29.5 ± 7.5</td><td>8.23</td><td>5.0</td><td>0 – 9</td></tr><tr><td>Shrubs, n = 10</td><td>106</td><td>0.81</td><td>1840 ± 700</td><td>0.4 ± 0.4</td><td>28 ± 8</td><td>9.07</td><td>7.0</td><td>0 – 14</td></tr><tr><td>Forest (willow), including flooded birch forest, n = 11</td><td>91</td><td>0.70</td><td>4330 ± 3600</td><td>0.8 ± 0.7</td><td>29 ± 7</td><td>16.2</td><td>9.8</td><td>1.3 – 17</td></tr><tr><td>Swampy meadow, n = 18</td><td>163</td><td>1.27</td><td>3831 ± 2342</td><td>0.3 ± 0.2</td><td>35 ± 8</td><td>11.8</td><td>8.1</td><td>3 – 15</td></tr><tr><td>Swampy shrubs, n = 7</td><td>13.5</td><td>0.10</td><td>9053 ± 6387</td><td>0.5 ± 0.2</td><td>30.5 ± 7.5</td><td>1.53</td><td>0.9</td><td>0.5 – 1.3</td></tr><tr><td>Permanent non-connected (oxbow) lakes, n = 16</td><td>36.6</td><td>0.30</td><td>3080 ± 1740</td><td>0.4 ± 0.2</td><td>95</td><td>11.4</td><td>6.9</td><td>3.6 – 10</td></tr><tr><td>Other water bodies (Connected lakes and channels), n = 6</td><td>60.7</td><td>0.61</td><td>2370 ± 1600</td><td>0.2 ± 0.2</td><td>95</td><td>11.6</td><td>7.0</td><td>0 – 13</td></tr><tr><td>Ob River main stem</td><td>119</td><td>0.96</td><td>855 ± 35</td><td>0.6 ± 0.2*<br>0.3 ± 0.1**<br>0.0 ± 0.2***</td><td>30*<br>30**<br>35***</td><td>26 ± 16</td><td>16</td><td>7 – 37</td></tr></tbody></table>

Footnote: * May; ** June; *** July to October and the rest of the year. Based on Krickov et al. (2021).

between 5–5.6 km h⁻¹ during the flood and 2.7–3 km h⁻¹ during the baseflow, which is typical for the Ob River in general (Zemtsov et al., 2023). The typification of the floodplain water bodies include permanent channels and lakes, and temporary flooded forests, fens, shrublands and grasslands (meadows).

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.) and grasses (Calamagrostis, Phleum, Veronica longifolia, 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.

## 2.2. Sampling locations and strategy

The sampling strategy combined a regional survey of interconnected lakes, channels and flooded land (forests, shrubs and grassland) from a motor boat (where the boat could pass during the flood) and regular monitoring of specific water objects (main stem, permanent lakes and channels), via ground access, using an off-road tracked vehicle. The CO₂ measurements and floating chamber deployment were always performed in the open water, at least two meters from the shore (for large water objects), or in the middle of small < 10 m² water bodies. Altogether, we visited 70 flooded locations during flood period (May to July) as listed in Table 1. We also sampled 20 permanent floodplain lakes and channels, essentially during spring flood. The main stem was sampled regularly, typically once a week during the spring flood and once a month during the baseflow.

Based on GIS assessment of the studied part of the floodplain (see section 2.3 below), 9 main aquatic landscape units were identified and visited from 4 to 9 times, from the end of April to the end of July. These included: coastal sand at the Ob River shore; flooded fen, meadow, and shrubs; flooded willow forest within the proximity (100–300 m) of the main stem; flooded birch forest, up to 2–3 km distance from the Ob’s shore; temporary flooded swampy meadow and shrubs; permanent non-connected (oxbow) lakes, and lakes and channels, permanently connected to the river.

## 2.3. Hydrological parameters of the Ob River main stem and floodplain coverage by water

The discharges of the Ob River at Kaibasovo station were retrieved from daily water level measurements at the station (using a limnimeter) and converted to the discharges using the data of adjacent (20 km upstream) gauged Nikolskoe station (Russian Hydrological Service), where both daily discharges and water levels are available. For the verification of the hydrological balance of elevated parts of the floodplain or the watershed divides, we used analogous modeling of river Bolshoi Tatosh (S<sub>watershed</sub> = 1140 km²), which is a left tributary of the Ob River that flows through developed floodplain some 60 km downstream the Kai-basovo study site. The river hydrographs were modelled using HBV Light program package (https://www.geo.uzh.ch/en/units/h2k/Services/HBV-Model.html) as described elsewhere (Bergström, 1992, 1995; Seibert and Vis, 2012). For this gauged river, we obtained model parameters allowing to calculate the daily runoff based on water temperature and precipitation from average data of adjacent meteo-stations. The data for reproducing the water discharge at key sites were taken from automatic information system of State monitoring of water objects [AIS GMVO, 2020]. The daily air temperature and precipitation were taken from Russian Hydrometeocenter (URL:http://aisori.meteo.ru/ClimateR; Bulygina et al., 2020). The calibration of model parameters demonstrated that the HBV model adequately reproduces the seasonal dynamics of runoff (quality criteria of 0.81). There was a good agreement between mean monthly discharges of this river (1–2.3 %, see Fig. S1 of the Supplement).

Hydrological model of the water mass balance of the floodplain allowed to calculate the areal coverage of the test site for each day of the
year. It was based on the amount of water delivered by the Ob River main stem. The area of flooding of different landscapes of the floodplain and the water residence time at the flooded zones were calculated based on the amount of water from the main stem, atmospheric precipitation and the micro-topography of the study site. The daily temperature and precipitation were obtained from GMS Molchanovo of Russian Hydro-meteorological Service (Russian Hydrometeocenter (URL:https://aisori. meteo.ru/ClimateR; Bulygina et al., 2020), which is located 40 km north of the Kaibasovo Station.

The flooded area for the required period was calculated using two models. The first model was based on multi-temporal satellite images. Layers from the Landsat 8 - level2 and Sentinel-2 L1C satellites (from May to August), which are available on the US Geological Survey portal (https://Earthexplorer.usgs.gov/), were compiled and multi-temporal Normalized Difference Water Index (NDWI) images were obtained. The flood area was recorded for characteristic levels at the boundary gauging stations. Subsequently, interpolation was carried out between the NDWI layers, averaged values were obtained (with a hydrograph level step of about 10 cm), and the flooded areas and the volume of water on the floodplain were calculated. For this, we used a relationship NDWI = (Green - NIR) / (Green + NIR) based on Near-Infrared Channel (NIR) in order to obtain the NDWI images, corresponding to flooding area of each segment of the floodplain. The images were referred to the water level at the gauging stations at each date when the image was available. The resulting model was ground-calibrated on a small test site of 40 km² area in the vicinity of the Kaibasovo Station. The second model was based on water discharge hydrographs and built in the HEC-RAS 6.3.1 program. For this, JAXA images were taken as a basis, on which a digital elevation model was compiled. The model was used to delineate flooded areas above the maximum level recorded in the NDWI imagery. After verifying the obtained data, we used both models to carry out calculations over the entire study area (1800 km²) of the Ob River floodplain. Specifically, we employed compilation and contouring of satellite images, obtained at different water level as recorded at two gauging stations encompassing the studied floodplain transect (Molchanovo and Nikolskoe). We used linear interpolation between two areas of flooding at two different dates for which the images were available, in order to calculate the flooding area during several intermediate dates based on daily water levels.

The duration of flooding of each specific landscape unit was calculated based on the satellite images ALOS (https://earth.esa.int/eoga teway/missions/almos) taken during different day of the year and ordered according to the Ob River water level at the Nikolskoe gauging station. Further, for the model construction, we used the system «Hydrologic Engineering Centers River Analysis System» (HEC-RAS – https://www.hec.usace.army.mil/software/hec-ras). The value of the duration and area of flooding of different landscapes, which were manually digitalized, was calculated according to the Eqn. (1):

$$ \tau = \sum \frac{S_1}{S} * \tau_1 \qquad (1) $$

where τ is the duration of landscape flooding; τ₁ is the time of landscape unit flooding; S₁ is surface area of landscape unit flooding, and S is total area of the flooding.

## 2.4. CO₂ 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 (3 000, 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. If the measured pCO₂ 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; Serikova et al., 2018, 2019). Temperature-specific solubility coefficients were used to calculate CO₂ concentrations in the water (Wanninkhof, 2014).

The CO₂ fluxes (FCO₂) were measured by using two floating (free-drifting) CO₂ chambers at 90 sampling locations of the floodplain, which included both temporary flooded zones and permanent water bodies. 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) (Bastviken et al., 2015). The CO₂ concentration inside each chamber was recorded continuously at 10 s interval and we used the first 20 min of measurements for computing CO₂ fluxes via calculating CO₂ accumulation rate inside each chamber by 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), thus discarding less than 10 % out of ~ 200 chamber measurements. Also, in < 10 % of the occasions across all seasons our CO₂ chambers recorded a decrease in CO₂ accumulation rate, which were interpreted as CO₂ uptake. Based on measured FCO₂ and CO₂ concentration in the water and in the air, we calculated the chamber-specific gas transfer coefficient (Kₜ value, or CO₂ transfer velocity). Further details of CO₂ flux measurements are provided elsewhere (Krickov et al., 2021, 2023; Lim et al., 2022).

On seven selected water bodies in spring (flooded fen, 3 floodplain lakes, permanent channel, flooded birch swamp and flooded forest), and four water bodies in summer (flooded fen, 2 lakes and a permanent channel), we measured day/night dynamics of CO₂ flux in water. We upscaled the results to the total period of observations, taken into account the duration of night and the partial contribution of each i-type of water body to overall water coverage of the floodplain following the equation (2):

$$ F_{\text{total}} = \sum_{i} (F_{\text{day}} \times D_{\text{day}} + F_{\text{night}} \times D_{\text{night}}) \times W_{i} \qquad (2) $$

where F_total is total CO₂ flux, i is the specific water body of the floodplain, F_day and F_night are day and night time fluxes from the i-th water body; D_day and D_night are duration of day and night for each seasons and W_i is relative coverage of the floodplain by the i-th specific water body.

The CO₂ emission fluxes obtained over the selected test site (1800 km²) in spring and early summer were then extrapolated to the full length of the Ob River in the non-permafrost zone (1810 km, 28690 km² floodplain area).

## 2.5. Hydrochemical analyses

At each sampling location, we measured pH and specific conductivity (WTW Multi 3320 multiparameter) of the surface water, water depth (Cole-Parmer), air temperature and atmospheric pressure (Silva), as well as dissolved oxygen saturation and water temperature (YSI ProODO Handheld Optical Dissolved Oxygen meter). 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 bottle from 20-30 cm depth in the middle of the river 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 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. The
spectral slope (S) of each absorbance spectra was determined by
applying log linear fits across the wavelengths 275–295 nm (S₂₇₅–₂₉₅)
and 350–400 nm (S₃₅₀–₄₀₀), and the spectral slope ratio (S<sub>R</sub>) calculated
as the ratio between the two (Helms et al., 2008). The SUVA₂₅₄, S<sub>R</sub>, E2:
E4 (absorbance at 254 and 436 nm) ratio, E2:E3 (absorbance at 250 and
365 nm) ratio, have been shown as proxies used to approximate for
carbon aromaticity, molecular weight, and DOM source (e.g., Weishaar
et al., 2003; Helms et al., 2008). The Picarro analyzer (L2140-i, hosted at
Krasnoyarsk Forest Institute) was used to measure stable water isotope
composition (δ¹⁸O, δ²H, d-excess) of collected samples.

2.6. *Statistics*

All statistical analyses and calculations were performed in RStudio
statistical software. We used non-parametric Mann-Whitney test to
assess differences in solute concentrations and C fluxes between
different aquatic landscapes of the floodplain as well as non-parametric
Kruskal-Wallis test to test for seasonality in the concentrations and
fluxes. We used linear regression when analyzing the relationship be-
tween 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 coverage where uncertainties are expressed as ± %,
respectively. Comparisons of CO₂ concentration and flux in the main
stem and flood zone during period of observation were conducted using
a non-parametric Mann Whitney test at a significance level of 0.05. For
comparison of unpaired data, a non-parametric H-criterion Kruskal-

<figure><img src="image_6.png" /><figcaption>Fig. 2. Duration of flooding of the key study site and relevant CO₂ fluxes (g C-CO₂ m⁻² d⁻¹) during spring flood period.</figcaption></figure>
Wallis test was used to reveal the differences between different sampling sites. The Pearson rank order correlation coefficient ($p < 0.05$) was used to determine the relationship between $pCO_2$, $FCO_2$ and main hydrochemical parameters of the water column. All statistical tests used a significance level of 5 % ($\alpha = 0.05$).

# 3. Results

## 3.1. Spatial and temporal variations of CO₂ concentrations and emission fluxes in the floodplain

The majority of the floodplain territory is covered by water during 20–40 days per year (Fig. 2). There was a sizable contrast in water area extension on the floodplain between the flood and baseflow periods (Fig. S2). In some of the larger (3 to 10 km²) low-relief sites, the water persisted just a few days during the flood. These sites, however, were not associated with particularly high $CO_2$ concentrations and emissions (Fig. 2; Fig. S3). Most of the data on $CO_2$ concentrations and chamber-based emissions were obtained during the spring flood period (end of April to end of June) as illustrated in Fig. 2. All primary data acquired in this study are available on the Mendeley Data portal (Vorobyev et al., 2023), whereas the main parameters of the $CO_2$ pattern, averaged for main aquatic landscape and seasons, are listed in Table 1.

Diurnal variations of $CO_2$ concentrations and emissions were tested for several typical aquatic systems: permanent lake, flooded meadow, fen and permanent channel (Fig. 3). For all tested permanent water bodies (channels, lakes and main stem), the variations in $CO_2$ emissions between day and night were insignificant ($p > 0.05$) or not systematic (Fig. 4 A). In flooded water regions, the difference between day and night fluxes was pronounced for the flooded fen, permanent oxbow lake 1 and lake Krivoe in early spring (21–28 April), when emissions during the day exceeded the night emissions by a factor of 2 to 5 (Table 2). In early summer (13–14 June), the night emissions in shallow fen, oxbow and permanent lakes and secondary channel lakes were 1.3 to 3 times higher than the day time ones, with the highest contrast observed for permanent lake and channel (Fig. 4 B–E; Table 2). Based on these results, we assessed the global effects of diel variation in $CO_2$ emissions on the total C flux using Eqn. (2). Specifically, in summer (June), the emissions were a factor of 1.5 higher during the night compared to the day, but short duration of the night (6–7 h) required only minor + 12 to + 15 % corrections of overall fluxes. In contrast, during spring (May–June), when the night duration was even shorter, the night-time fluxes were a factor of 1.5 – 2 lower than the day fluxes thus leading to overall corrections of less than –10 to –20 %. Due to the opposite direction of diel

effects in summer and spring, global corrections never exceeded 10 % which was within any reasonable uncertainty of flux quantification. We also noted that the diel effects were most pronounced in permanent floodplain lake, which provided less than 5 % of water coverage of the floodplain. Moreover, the representability of several sites where the diel variations were assessed remains an issue of concern. Overall, given the rather short night length period from May through June for this latitude, these results allow for consideration of the concentration and flux measurements performed during the day (described below) as most conservative and representative of the entire 24 h of observations. Thus, these observations can be used for mass balance calculations across the entirety of open water seasons.

The flooded fen exhibited the highest $CO_2$ concentrations and evasions (10260 ± 8000 µatm and 0.9 ± 0.2 g C-$CO_2$ m⁻² d⁻¹, respectively), followed by the flooded forest (4300 ± 3500 µatm and 0.8 ± 0.7 g C-$CO_2$ m⁻² d⁻¹, respectively) and swampy shrubs (9050 ± 6300 µatm and 0.5 ± 0.2 g C-$CO_2$ m⁻² d⁻¹, respectively). The gas transfer velocities calculated from measured $FCO_2$ flux and $pCO_2$ in the water column ranged from 1.0 to 3.3 m/d (mean $K_T$ = 2.0 ± 0.4 m/d). Such relatively low values are consistent with gas tracer measurements in macrophyte dominated shallow wetlands (Ho et al., 2018).

In May, concentrations of dissolved $CO_2$ were much higher in the permanent (and temporary) channels of the floodplain compared to the main stem, as shown on a detailed map of the study site (Fig. S3). There was no clear relationship between concentration or emission in the permanent or temporal water bodies and the distance between this water body and the main stem. The degree of connectivity of floodplain lakes, via permanent channels to the main stem, also did not impact the concentrations and emission values.

Water depth in the temporary flooded zones did not exhibit any correlation with the $CO_2$ concentrations and emissions. There were significant differences in $CO_2$ concentration, fluxes and some basic hydrochemical parameters (pH, $O_2$ concentration, optical ratio) across various types of floodplain landscape as revealed by non-parametric Kruskal-Wallis ANOVA by Ranks (Table S1). Maximal $CO_2$ concentrations and emissions were observed in flooded fens, swampy shrubs and flooded birch forests (Fig. S4). Further, we found the highest DIC and DOC concentrations and OM aromaticity occur in a near-terrace part of the floodplain, whereas minimal $pCO_2$ was observed in a part of the floodplain adjacent the main stem (Fig. S5; Table S2). Significant differences in main hydrochemical parameters were observed between the left and right floodplain banks (Table S3); highest pH, S.C., DIC concentrations and water isotopic parameters ($\delta^{18}O$, $\delta^{2}H$) typical of floodplain water bodies were associated with the left bank of the Ob River (Fig. S6).

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Map of the water bodies at the floodplain in which the day/night dynamics of $CO_2$ flux has been studied. 1 – flooded fen (N 57.24463, E 84.19819); 2 – oxbow lake 1 (N 57.24294, E 84.19505); 3 – permanent channel Ishkhatkta (N57.23687, E84.19564); 4 – permanent lake Krivoe (N 57.24047, E 84.22235); 5 – flooded birch swamp (N 57.2287, E 84.25792); 6 – flooded willow forest (N 57.23266, E 84.24904); 7 – oxbow lake 2 (N 57.24326, E 84.20692).</figcaption></figure>
## 3.2. Testing the link between CO₂ concentrations/fluxes and hydrochemical parameters of the water column

Considering the full data set from May through July, CO₂ concentration exhibited significant ($p < 0.05$) positive correlations with water temperature, DOC and SUVA₂₅₄ and negative correlations with pH and O₂ concentration (Table 3). Note the majority of the data used for these correlations were acquired during the highest water coverage in May, therefore allowing testing spatial rather than temporal variability. The CO₂ flux exhibited a positive tendency with SUVA₂₅₄ and a negative tendency with pH and [O₂], although these correlations were not significant. During diel cycle in early spring (end of April), water temperature exerted a strong positive impact on CO₂ in flooded fen, one oxbow lake and a permanent channel (Fig. S7 A-D). At the same time, flooded birch fen and shrubs did not demonstrate sizable impact of water temperature on CO₂ (Fig. S7 E, F). In summer, the relationship of CO₂ with water temperature in the two studied water bodies on a diel

scale was negative in the fen, permanent lake and channel (Fig. S7 H, J, K), given 30 to 50 % higher nightly emissions during summer (Table 2). Not that the high variation of water temperatures (8–10 °C) between day and night can be linked to the extremely shallow (<10–20 cm) depth of studied water bodies.

A PCA treatment of the data (Table S4) demonstrated a rather poor explanation for the pattern of CO₂ and basic hydrochemistry of the water column (15.5 % F1, 5.6 % F2). The 1st factor negatively acted on DOC, DIC, water temperature and SUVA₂₅₄ whereas the 2nd factor was positively linked to pH and specific conductivity. No factor was related to CO₂ concentration and flux.

## 3.3. Hydrological mass balance estimation

Evaporation from the water surfaces during the period of May 1st – August 31 was equal to 411 mm. The water level did however decrease, but only by 18 cm, due to precipitation during the same period. In this

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Day/night dynamics of CO₂ flux in water bodies of the floodplain (sites # 1–7, shown in Fig. 3) during spring period (21–28 April), panel a. Panels **b** to **e**: Day/night dynamics of CO₂ flux in water bodies of the floodplain during spring (26 April) and summer (13 June) period in the: (b) flooded fen # 1; (c) oxbow lake # 2; (d) permanent lake Krivoe # 4; and (e) permanent channel Ishkhatkta #3.</figcaption></figure>
<caption>**Table 2**</caption>

Day/night dynamics of CO₂ flux in water bodies of the floodplain. Mean values with s.d. and results of Mann-Whitney Test. Significant differences between day and night values are labelled by asterisk.

<table><thead><tr><th>Object</th><th>Mean, day</th><th>Mean night</th><th>Rank Sumday</th><th>Rank Sumnight</th><th>U</th><th>Zadjusted</th><th>p-value</th></tr></thead><tbody><tr><td><strong>Spring (April 21–28)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Flooded fen</td><td>0.31 ± 0.06</td><td>0.12 ± 0.08</td><td>489</td><td>177</td><td>86</td><td>2.1*</td><td>0.04*</td></tr><tr><td>Oxbow Lake 1</td><td>0.25 ± 0.03</td><td>0.12 ± 0.04</td><td>71</td><td>20</td><td>5</td><td>2.1*</td><td>0.03*</td></tr><tr><td>Channel Ishtakhta</td><td>0.46 ± 0.03</td><td>0.33 ± 0.05</td><td>68</td><td>23</td><td>8</td><td>1.7</td><td>0.09</td></tr><tr><td>Lake Krivoe</td><td>0.61 ± 0.09</td><td>0.09 ± 0.10</td><td>74</td><td>17</td><td>2</td><td>2.6*</td><td>0.01*</td></tr><tr><td>Birch swamp</td><td>0.23 ± 0.03</td><td>0.18 ± 0.04</td><td>64</td><td>27</td><td>12</td><td>1.1</td><td>0.3</td></tr><tr><td>Flooded shurbs</td><td>0.17 ± 0.03</td><td>0.16 ± 0.04</td><td>55</td><td>36</td><td>19</td><td>-0.07</td><td>0.9</td></tr><tr><td>Oxbow Lake 2</td><td>0.29 ± 0.02</td><td>0.31 ± 0.03</td><td>50</td><td>41</td><td>14</td><td>-0.8</td><td>0.4</td></tr><tr><td><strong>Summer (June 13–14)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Flooded fen</td><td>1.03 ± 0.15</td><td>1.32 ± 0.27</td><td>44</td><td>61</td><td>8.00</td><td>-2.0*</td><td>0.05*</td></tr><tr><td>Oxbow Lake 1</td><td>0.73 ± 0.32</td><td>1.16 ± 0.06</td><td>44</td><td>47</td><td>2.0</td><td>-2.4*</td><td>0.02*</td></tr><tr><td>Lake Krivoe</td><td>0.76 ± 0.31</td><td>1.64 ± 0.17</td><td>45</td><td>46</td><td>0.0</td><td>-2.4*</td><td>0.01*</td></tr><tr><td>Channel Ishtakhta</td><td>0.35 ± 0.26</td><td>0.90 ± 0.19</td><td>44</td><td>47</td><td>2.0</td><td>-2.4*</td><td>0.02*</td></tr></tbody></table>

simulation, we ignored soil infiltration but explicitly accounted for lateral input and output considering the connectivity of the water bodies controlled by relief. A detailed hydrological balance of the study site is presented in Table S5 of the Supplement. As summarized in Table 4, during the 2 to 3 weeks of highest water level, more than 40 % of the total study area is covered by water with 55 % water coverage occurring for only a few days. These results allowed for quantification of water coverage for each aquatic landscape of the floodplain over the open water period (May-October). For this quantification, we summed up daily coverage of each typified flooded landscape and water body (Table 1). The flooded fens, forests and meadows provided the largest contribution to total water coverage of the Ob River floodplain.

### 3.4. CO₂ flux variation in the main aquatic landscapes of the floodplain

The data on daily water coverage of the floodplain allowed for calculation of C emission for each specific landscape of the floodplain, including the main stem and permanent water bodies (channels and lakes). For this, we multiplied the measured or interpolated daily CO₂ flux by the surface area of each type of aquatic systems (daily water coverage by this landscape per linear km of the main stem) over the period of observation (95 days, 29 April-31 July). For these calculations, we assumed, within < 10 % uncertainty, a constant flux between the day and night. This assumption is consistent with observations from May-June (see Fig. 3) and given that there were no sizable diurnal (day/night) variations in CO₂ emissions from the flooded zone that occurred during spring when the night period remained quite short (<6h).

The flooded fen and birch forest were by far dominating contributors to the overall emissions from the aquatic surfaces of the main stem and floodplain for the 1820 km² study site (Table 1). Given that the selected area encompassing the Kaibasovo INTERACT station is sufficiently large and representative of the entire permafrost-free floodplain area (28,690 km²), between the Novosibirskaya Dam and the confluence with Irtysh

<caption>**Table 4**</caption>

Area of water coverage of the key study site at different water level (in percentage of total floodplain).

<table><thead><tr><th>N°</th><th>Water level in the Ob River</th><th>area, %</th><th>Number of day per year</th><th>Season</th></tr></thead><tbody><tr><td>1</td><td>0–300</td><td>7.1</td><td>365</td><td></td></tr><tr><td>2</td><td>300–400</td><td>8.2</td><td>318</td><td></td></tr><tr><td>3</td><td>400–500</td><td>9.7</td><td>177</td><td>May- August</td></tr><tr><td>4</td><td>500–600</td><td>10.5</td><td>111</td><td>May-July</td></tr><tr><td>5</td><td>600–700</td><td>18.8</td><td>84</td><td>May-July</td></tr><tr><td>6</td><td>700–800</td><td>20.2</td><td>59</td><td>May-June</td></tr><tr><td>7</td><td>800–900</td><td>21.5</td><td>41</td><td>May-June</td></tr><tr><td>8</td><td>900–1000</td><td>47.1</td><td>21</td><td>May</td></tr><tr><td>9</td><td>>1000</td><td>55.5</td><td>2</td><td>May</td></tr></tbody></table>

(Vorobeyev et al., 2015, 2019; Cazzola Gatti et al., 2018; Pokrovsky, 2016), obtained results can be extrapolated for the full length of permafrost-free Ob River. Taking into account the average emissions from the Ob River main stem (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 June and 0 ± 0.2 g C m⁻² d⁻¹ in July-October and the rest of the year, Krickov et al., 2021) and considering that the main stem accounts for, on average, 13 ± 3 % of the total water-covered area in the floodplain, we calculate that typical underestimation of actual emission from the river is a factor of 3 to 12 in the case if one ignores floodplain water surfaces of permanent [lakes and channels] and temporary flooded [grasslands and forests] areas. This upscale is valid for the Ob River middle course, which is located in the permafrost-free region of boreal taiga. At the present time, we cannot provide the estimation of relative role of the floodplain water vs main stem for the entire Ob River basin, because this would require separate consideration of its largest southern tributary (Irtysh, 1,65 million km² watershed area)) and the low reaches of the Ob River, located within the permafrost zone.

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

Spearman Rank Order Correlations between main physical and hydrochemical parameters of the water bodies and CO₂ pattern (concentration and fluxes). Marked * correlations are significant at p < 0.05.

<table><thead><tr><th></th><th>Depth</th><th>pCO<sub>2</sub></th><th>FCO<sub>2</sub></th><th>T<sub>water</sub></th><th>pH</th><th>O<sub>2</sub></th><th>E.C.</th><th>T<sub>air</sub></th><th>DIC</th><th>DOC</th><th>SUVA<sub>254</sub></th></tr></thead><tbody><tr><td>Depth</td><td>1.00</td><td>-0.46*</td><td>-0.04</td><td>-0.10</td><td>0.30*</td><td>0.40*</td><td>0.04</td><td>0.04</td><td>0.10</td><td>-0.30*</td><td>-0.19</td></tr><tr><td>pCO<sub>2</sub></td><td>-0.46*</td><td>1.00</td><td>0.43*</td><td>0.41*</td><td>-0.70*</td><td>-0.85*</td><td>0.07</td><td>0.29*</td><td>0.05</td><td>0.51*</td><td>0.54*</td></tr><tr><td>FCO<sub>2</sub></td><td>-0.04</td><td>0.43*</td><td>1.00</td><td>0.13</td><td>-0.43*</td><td>-0.38*</td><td>0.02</td><td>0.17</td><td>0.07</td><td>0.11</td><td>0.35*</td></tr><tr><td>T<sub>water</sub></td><td>-0.10</td><td>0.41*</td><td>0.13</td><td>1.00</td><td>-0.26*</td><td>-0.18</td><td>0.25*</td><td>0.86*</td><td>0.31*</td><td>0.48*</td><td>0.14</td></tr><tr><td>pH</td><td>0.30*</td><td>-0.70*</td><td>-0.43*</td><td>-0.26*</td><td>1.00</td><td>0.57*</td><td>0.32*</td><td>-0.08</td><td>0.34*</td><td>-0.12</td><td>-0.48*</td></tr><tr><td>O<sub>2</sub></td><td>0.40*</td><td>-0.85*</td><td>-0.38*</td><td>-0.18</td><td>0.57*</td><td>1.00</td><td>-0.10</td><td>-0.18</td><td>-0.07</td><td>-0.36*</td><td>-0.44*</td></tr><tr><td>E.C.</td><td>0.04</td><td>0.07</td><td>0.02</td><td>0.25*</td><td>0.32*</td><td>-0.10</td><td>1.00</td><td>0.26*</td><td>0.90*</td><td>0.56*</td><td>0.02</td></tr><tr><td>T<sub>air</sub></td><td>0.04</td><td>0.29</td><td>0.17</td><td>0.86*</td><td>-0.08</td><td>-0.18</td><td>0.26*</td><td>1.00</td><td>0.31*</td><td>0.28*</td><td>-0.03</td></tr><tr><td>DIC</td><td>0.10</td><td>0.05</td><td>0.07</td><td>0.31*</td><td>0.34*</td><td>-0.07</td><td>0.90*</td><td>0.31*</td><td>1.00</td><td>0.54*</td><td>-0.06</td></tr><tr><td>DOC</td><td>-0.30*</td><td>0.51*</td><td>0.11</td><td>0.48*</td><td>-0.12</td><td>-0.36*</td><td>0.56*</td><td>0.28*</td><td>0.54*</td><td>1.00</td><td>0.21</td></tr><tr><td>SUVA<sub>254</sub></td><td>-0.19</td><td>0.54*</td><td>0.35*</td><td>0.14</td><td>-0.48*</td><td>-0.44*</td><td>0.02</td><td>-0.03</td><td>-0.06</td><td>0.21</td><td>1.00</td></tr></tbody></table>
# 4. Discussion

## 4.1. Strong spatial and seasonal heterogeneity of CO₂ concentration and flux

Compared to recent pioneering work on a small test site of the Ob River floodplain (Krickov et al., 2021), the present study extends the areal coverage of floodplain water bodies by three orders of magnitude, assesses diurnal variations in CO₂ emission fluxes, identifies hot spots of CO₂ emissions from specific landscape units, and proposes a hydrological model of floodplain water coverage, supported by GIS and remote sensing observations.

The majority of sampled water bodies and flooded landscapes acted as CO₂ sources to the atmosphere, given the concentrations of CO₂ were oversaturated with respect to atmospheric equilibrium. In general agreement with a previous assessment for a small (3 km²) site of the floodplain (Krickov et al., 2021), permanent water bodies (lakes and channels/rivers) exhibited quite low C emission or even a CO₂ uptake in the beginning of the flood (May), at the maximum water extent (Fig. 2, Fig. S3). This contrasts with behavior of the main channel in early spring, which emitted CO₂ to the atmosphere (Krickov et al., 2021). A likely cause for low or negative CO₂ emission in the floodplain is intensive algal growth on decaying grass during early spring and in cold waters. Thus, a sizable amount of nutrients leached from grass and ground vegetation could trigger extensive development of periphyton such as diatoms which represent a sizable CO₂ sink. An illustration of the massive floodplain grass coverage by diatoms is provided in the Supplement (Fig S8). One additional reason for low CO₂ in the flood zone during this period could be that shallow (<0.5–1 m) water bodies with low riverine particulate suspended matter allowed optimal light conditions in the water column and at the sediment surface which can enhance CO₂ fixation via submersed vascular plants and algae (Thorp and Delong, 1994). In contrast, elevated CO₂ in the main channel could be linked to i) low primary production due to depth, strong currents and limited light penetration in murky water; ii) high particulate organic carbon (POC) and organic-rich sediment processing, and iii) CO₂ input from organic-rich streams draining the bog zones of the terraces.

In the Ob River floodplain, we noted a lack of systematic diel variations in CO₂ concentrations and fluxes in oxbow lakes and stagnant temporary water pools regarding of seasons (Fig. 3). Specifically, we examined diurnal variations at seven sites (Fig. 4 A) during spring flood (two oxbow lakes, permanent channel, permanent lake, flooded fen, birch swamp, and willow forest) in the end of April, at the beginning of massive flood (Fig. 4 B-E). Four contrasted sites – flooded fen, oxbow lake, permanent lake and secondary channel – were studied both in the end of April and middle of June (Fig. 4 B-E). In the end of April, only permanent lake and a secondary channel demonstrated sizable, by a factor of 2 to 3, excess of FCO₂ during the day compared to that during the night (Fig. 4 A). In June, permanent water bodies (lakes and channel) showed much higher contrast in FCO₂ between day and night compared to temporary flooded fen. Recent observations on boreal lakes demonstrated that the maximum of emissions during day time were linked to day-night differences in wind speed with occasional upwelling leading to night time peaks (Rudberg et al., 2021). These factors are less important in shallow temporal water bodies and for the sheltered floodplain zone of the Ob River because daily variations of wind speed during our measurements were not significant and upwelling could not be pronounced due to shallow depth and lack of thermal stratification. In contrast, in tropical floodplain settings, a peak of diel emissions of CO₂ occurred during the night time, reflecting a metabolic balance between photosynthetic C uptake during the day and aerobic respiration with CO₂ production overnight (i.e., Bartlett et al., 1990; Belger et al., 2011; Bass et al., 2013), as it is consistent with observations on temperate and boreal rivers across the world (Gomez-Gener et al., 2021; Attermeyer et al., 2021; Battin et al., 2023). Such a diel variations in FCO₂, with the maximum during the night, linked to the metabolism of

the water column and sediments, were also reported for shallow thermokarst lakes of permafrost peatlands during summer baseflow (Shirokova et al., 2020). Presumably, these short-term production/respiration processes were not pronounced in shallow, temporary water bodies of the floodplain, due to their highly non-stationary condition and insufficient water residence time (several days to weeks) to develop a stable plankton/periphyton ecosystem. Only permanent, deep floodplain lakes and secondary channel with developed ecosystems demonstrated night time maximum of CO₂ emissions (Fig. 4 D, E), due to water column and aquatic plants photosynthesis/respiration cycle. Overall, regardless of mechanisms governing diel FCO₂ variations in permanent water bodies of the floodplain in June, rather short (<6h) night duration during this season of the year does not require explicit provision for such daily variations in global CO₂ flux.

A positive correlation between the water temperature and CO₂ flux (Fig. S7) suggests the importance of heterotrophic degradation of DOM and POM in CO₂ emissions during early spring period, when the water temperatures were rather similar in 7 studied sites, and ranged from 0 to 6 °C (Fig. S7 A – G). Indeed, in the course of the season, the water temperature in stagnant water bodies gradually raises (Krickov et al., 2021). Elevated (15 ± 5 °C) water temperature is known to increase the decomposition of flooded grasses and plant litter (van Huissteden et al., 2009), which is accompanied by CO₂ flux from the water surface into the atmosphere. Among the two main OC reservoirs in the floodplain and riparian systems, standing riparian biomass and terrestrial OC (i.e., litter, humus, and soil organic carbon, plant litter and in particular grasses such as reeds) could provide the largest contribution to local CO₂ rise in shallow sites of the floodplain. Indeed, elevated CO₂ concentrations observed in the present study in flooded fens (10,000–18,000 ppmv) are comparable with high CO₂ (>20,000 ppmv) reported in waterlogged-flooded soils of other regions, especially with *Phragmites australtes* (Greenway et al., 2006). Such reeds are highly abundant in fens of the Ob River floodplain. It should be stated that the redox regime was also a controlling factor given the negative trends between pCO₂ and FCO₂ and O₂ saturation, and was most likely linked to coupled aquatic photosynthesis and respiration which was supported by a strong negative correlation between pCO₂ and pH (Table 3). This is consistent with other observations on lowland riverine systems such as those in Europe where CO₂ emission rates were nearly one order of magnitude greater than in the lentic and hypoxic oxbow and quarry lakes (Bolpagni et al., 2019).

Finally, the role of groundwater discharge on dissolved CO₂ pattern within the floodplain, similar to what is known for riverbeds in subarctic settings (i.e., Biehler et al., 2023) and generally across the world (Duvert et al., 2018, 2019), could not be explicitly tested in the present study (i. e., using the ²²Rn approach similar to Burnett et al., 2010) but is likely to be non-negligible (Biehler et al., 2020). We found significant differences in DIC, Electric Conductivity and pH between the left and right banks of the Ob River. The highest values were observed within the left bank part of the floodplain where water isotopic data also suggested highest impact of groundwater (Fig. S6, Table S3). This is consistent with major and trace element results from permanent water bodies within the floodplain during summer baseflow (Vorobyev et al., 2015). The pCO₂ was also significantly higher in the near terrace part of the floodplain (Fig. S5, Table S2) where the influence of original deep groundwater is most pronounced (Vorobyev et al., 2019).

## 4.2. Contribution of permanent and temporary water bodies to C emission from the floodplain

Unprecedented spatial and temporal coverage of contrasting permanent water bodies and temporary flooded land surfaces, representative of western Siberian floodplains, allowed adequate assessment for a large part of the non-permafrost Ob River catchment. Detailed GIS-based mapping of contrasting vegetation types helped in identification of hot spots of CO₂ emission (dependent on the stage of spring flood). The
flooded birch forests, shrubs and especially fens, under shallow water coverage and with sufficiently high water temperature are by far the largest contributors to overall CO₂ emissions in the floodplain, and their role persists for at least 3 weeks at the end of the spring flood.

As expected, the landscape based heterogeneity of floodplain C emissions was substantial and was chiefly governed by local topography and ground vegetation type. Such a small-scale heterogeneity in C emissions from water bodies, which is also known for other territories (Crawford et al., 2017), can be pronounced at the level of hundreds of thousands km² in the extremely flat terrains of the Ob River's floodplain. Water residence time, macrophyte and phototrophic periphyton life cycle and redox regime at the sediment (or flooded grass) – water column interface are the most likely factors controlling direction and magnitude of OC processing and CO₂ production. This is reflected in the positive correlations of pCO₂ with DOC, SUVA₂₅₄ and water temperature. In this regard, repetitive flooding of the same sites, due to abrupt hydrological changes, can create local hot spots of C emissions as it is known that terrestrial plant litter accumulated onto re-wetted river bed can undergo rapid microbial processing and produce short-term CO₂ emissions (Datry et al., 2018). Here we demonstrate the utmost importance of monitoring temporary flooded zones of the floodplain, given that their role in overall CO₂ emission from the water surfaces greatly exceeds that of the main river stem and permanent water bodies. In order to account for temporal flooding variation at larger spatial scales of Siberian rivers, one has first to distinguish permafrost-free zone such as that investigated in this study, which involves the entire Ob's middle course south of its confluence with Irtysh, and the Irtysh River. Given extremely flat and homogeneous relief of the floodplains in western Siberia (Pokrovsky, 2016), it is the total water volume and flood duration rather than local morphological heterogeneities that will determine the magnitude of C emission from large fluvial systems. For the permafrost-affected northern Siberian river floodplains, no data of sufficiently large spatial scale are available, and this should constitute the priority of further research.

Furthermore, future studies should address other, human-impacted (agricultural, cropland) river floodplains in western Siberian that are not covered in this study. This is because it is known that agricultural land use exhibits an overwhelming control on greenhouse gas concentrations in rivers (Bodmer et al., 2016). The role of flooded croplands (and in general agricultural lands) of the floodplain in C emission may turn out to be of primary importance for some agriculturally impacted western Siberian rivers such as the Chulym River, which is the largest eastern tributary of the Ob River, and the Irtysh River, which is its largest southern tributary. Emissions from the temporary dried part of the floodplain, although highly important in overall C cycle in inland waters (i.e., Marcé et al., 2019; Koschorreck et al., 2022, 2023), could also provide sizable uncertainty on the fluxes. Currently, these sites represent unknown hot spots for emissions, at least for the first days after water removal from flooded grassland and forest. In this regard, further studies on “after flood events” in the floodplains of the Ob River are needed; a combination of ground measurements and satellite, lidar and/or drone mapping of water surfaces with high spatial and temporal resolution are needed. Overall, because of strongly pronounced spatial variability in CO₂ pattern, different types of aquatic systems should be included in global emission estimates. Furthermore, it remains to be verified to which degree the obtained results can be applied to the northern, permafrost-bearing part of the Ob River basin as well as other similar rivers in continental lowlands. These rivers demonstrate a high content of organic carbon in soil and much faster (due to permafrost impermeable layer) transport of water from the catchment to the main stem which may produce particular seasonal and spatial features of C emissions/uptake that are different from those obtained in this study.

## 5. Conclusions

Based on the seasonal and spatial monitoring of a large key site of the

Ob River floodplain (about 1800 km²), combined with hydrological modeling and satellite imagery, we calculated water area extent of the floodplain with daily resolution that allowed quantification of the water residence time in various parts of the terrestrial and aquatic landscapes. We found a peak of CO₂ emissions from the middle to end of the spring flood, which lasted on average 4–5 weeks when temporary water coverage of the floodplain exceeded (by a factor of 4) the main river stem water area.

We identified hot spots of CO₂ emissions on the floodplain were dependent on landscape type and dominant vegetation. Maximal CO₂ emissions were from most shallow water bodies of the floodplain, in particular from flooded birch forest, fens, and grasslands. For this key site, during open water season (May-October), the floodplain represented > 80 % of total (floodplain + main stem) CO₂ emissions as compared to the contribution of permanent water bodies (oxbow lakes, channels/streams) which did not exceed 20 %. The extrapolation of obtained results to the full length of the Ob River non-permafrost zone (1810 km, 28,690 km² floodplain area) demonstrates that ignoring temporary zones of flooding within the river floodplain leads to sizable (by a factor of 3 to 10) underestimation of real fluvial CO₂ evasion, which is usually considered only from the permanent water channels and the main stem. Future research on floodplain greenhouse gas emissions on such rivers should focus on thorough mapping of specific landscapes within the floodplain, notably those including hot spots of CO₂ emissions such as flooded birch forests, fens and meadows.

## CRediT authorship contribution statement

**Sergey N. Vorobyev:** Writing – original draft, Data curation, Conceptualization. **Larisa G. Kolesnichenko:** Formal analysis, Data curation. **Yuri Kolesnichenko:** Visualization, Software, Methodology. **Anatoly S. Prokushkin:** Methodology, Investigation. **Arina V. Lugovaya-Dolmatova:** Investigation, Data curation. **Jan Karlsson:** Writing – review & editing, Methodology, Formal analysis. **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.

## Data availability

Mendeley Data, V1, https://data.mendeley.com/datasets/hr9gxsdfsf/1

## Acknowledgments

The study was carried out on the state order of the Ministry of Science and Higher Education, project № FSWM-2023-0005 (S.N.V.), and grant RSF 23-16-00218 (L.K., Y.K., A.V.L-D.). The study was carried out using equipment of the Unique Research Installation “System of experimental bases located along the latitudinal gradient” TSU. OP is grateful to partial support from TSU program “Priority 2030” and PEACE of PEPR FairCarboN ANR-22-PEXF-0011. Chris Benker is thanked for English editing. We are grateful to two anonymous reviewers and Associate Editor Nandita Basu for their very insightful and constructive comments. Authors declare no competing interests.

**Data Availability:** The complete dataset (Dataset S1) for this paper is available at: https://data.mendeley.com/datasets/hr9gxsdfsf/1.

**Author Contributions:** Conceptualization, S.N.V., J.K., O.S.P.; methodology, L.G.K., A.S.P., J.K.; software, Y.K.; validation, S.N.V., Y. K.; formal analysis, A.B., L.K.; investigation, S.N.V., A.V.L-D., V.A.N., resources, S.N.V., O.P.; data curation, L.G.K., Y.K.; writing—original draft preparation, S.N.V., O.S.P.; writing—review and editing, J.K., O.S.
P. All authors have read and agreed to the published version.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jhydrol.2024.131468.

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