Carbon dioxide partial pressures and emissions of the Yarlung Tsangpo River on the Tibetan Plateau

1 Introduction

Rivers are distinct ecosystems, geomorphological agents, sites of biogeochemical transformation, and conduits for material transport across continents and to the oceans (Marcé et al., 2015; Einarsdottir et al., 2017). Furthermore, they are strongly linked with human activities (Aufdenkampe et al., 2011; Liu et al., 2022). Traditionally, rivers have been considered to be passive pipes in the regional and global budgets for carbon (C) and weathering products (i.e., dissolved ions). Recent studies have reported that C emission fluxes to the atmosphere are comparable to the fluvial C fluxes to the ocean (Battin et al., 2009; Teodoru et al., 2015; Lu et al., 2018; Bussmann et al., 2022). Reported values of CO₂ emissions for the fluvial network alone ranged from 0.26 Pg C yr⁻¹ to 1.8 Pg C yr⁻¹ (Richey et al., 2002; Cole et al., 2007; Tranvik et al., 2009; Aufdenkampe et al., 2011; Raymond et al., 2013; Regnier et al., 2013), the largest published estimate (1.8 Pg C yr⁻¹, Raymond et al., 2013) being about twice as large as the lateral export of carbon to the coastal ocean from the global river network (Regnier et al., 2013). Thus, carbon remobilization, particularly through water-air interface exchange from freshwaters, including river systems, represents an important component of the global carbon cycle. A large number of studies on river outgassing have been undertaken in typical rivers, such as the Amazon (Richey et al., 2002; Alin et al., 2011; Sawakuchi et al., 2021), the Mississippi (Dubois et al., 2010), the Yangtze River (Zhao et al., 2013; Liu et al., 2016; Ni et al., 2019), the Yellow River (Ran et al., 2015) and the karst river system (Ni et al., 2020; Ni and Li, 2022). However, there is substantial uncertainty in current understanding about global carbon flux, in part due to a lack of direct measurements of rivers in the high-altitude cryosphere (Zhang et al., 2020).

The Tibetan Plateau is sometimes called the “Third pole” of the earth, has an average elevation of over 4,000 m, and is the cradle for 10 large Asian rivers (Immerzeel and Bierkens, 2012). Compared to other inland rivers, rivers on plateaus have higher altitudes and greater erosion and weathering capacities, but their primary productivities are lower (Ye, 2019; Yu et al., 2021). Owing to the negligible impact of anthropogenic activities, the YT River is typical to reflect the geochemical nature of a plateau river and serves as the water vapour channel Tibet (Qu et al., 2017). These factors may considerably influence the basic characteristics of the river carbon balance on plateaus. Only a few studies have investigated the carbon emissions from the rivers on the Tibetan Plateau. Qu et al. (2017) assessed the spatial distributions of CO₂ concentration and emission fluxes from headstreams on the Tibetan Plateau and found that all the studied streams released CO₂ into the atmosphere. Yan et al. (2018) showed that the littoral zones of lakes on the Tibetan Plateau were supersaturated and acted as sources of greenhouse gases. Research on the alpine riverine dissolved inorganic carbon (DIC) of Nyangqu River found that rock weathering controlled the DIC input into the riverine system and groundwater played a significant role in delivering DIC to the surface water (Ge et al., 2021). However, the degassing effect of plateau rivers might be still unclear. Therefore, it is important to investigate this effect if we are to understand the roles that plateau rivers play in global and regional carbon cycles. Additionally, improved understanding of CO₂ emissions from rivers in this climate-sensitive region is critical for forecasting responses to current and future climates.

In this study, we chose the reaches of the Yarlung Tsangpo (YT) River on the plateau as the study region to 1) assess the spatial and seasonal distributions of CO₂ partial pressure (pCO₂) and CO₂ emissions across the river and 2) demonstrate the “outgassing effect” of rivers on the plateau from the perspective of the carbon budget.

2 Materials and methods

2.1 Study area

The Tibetan Plateau is one of the most sensitive areas to climate change in the world and covers approximately 2.5 × 10⁶ km². The YT River, which originates in the Jiemayangdrung glacier of the Himalayan Mountains, is one of the longest plateau rivers in the world (Huang et al., 2011). It is 2,104 km in length (2,057 km of which is in Tibet) with a total drainage area of 24.048 × 10⁴ km². The upper part of the river, known as the Maquan River, runs for about 161 km to Lizi village and has an elevation of over 4,400 m. The middle reaches bring together many tributaries, such as the Nimu, Lhasa, and Pailong Zangbu Rivers (Jiang et al., 2015; Tong et al., 2016). The Zangmu reservoir was built in the mainstream part of the middle reaches. The climate in this basin is prominently influenced by the India/South Asia monsoon and about 50%–80% of the precipitation occurs in the wet season, which lasts from July to September (Liu et al., 2007).

Geologically, the catchment of the YT River is composed of different, well-exposed strata. In particular, Mesozoic strata, siliceous rocks, harzburgite, dunite, and basalt are distributed along the YT ophiolite zone (Zhu 2012). Generally, the terranes of the catchment mostly belong to the Tethian Sedimentary Series, which is composed of Palaeozoic-Mesozoic carbonate and clastic sedimentary rocks (Galy and France-Lanord, 1999).

Sampling was carried out along the entire stretch of the middle and upper reaches of YT River (upstream of the Yarlung Zangbo Grand Canyon). Three parallel samples were collected simultaneously at one site in December, 2017 (dry season) and July, 2018 (wet season) for parallel measurements. There were 11 sites in the mainstream part of the river and nine sites in its different tributaries and samples were taken in both the wet and dry seasons. In this study, the upper reaches of Nugesha hydrological station (M1-M3 and T1-T3) were taken as the upstream, and the middle reaches were recognized from Nugesha hydrological station to the Zangmu reservoir Dam (M4-M10 and T4-T6). The low reaches were behind the dam (M11 and T7-T9). Detailed information about the sampling points in the mainstream area and the main tributaries is shown in Figure 1 and Supplementary Schedule S1.

FIGURE 1

FIGURE 1. Locations of the sampling sites in the Yarlung Tsangpo River on the Tibetan Plateau.

2.2 Hydrochemecal and laboratory analysis

At each sampling site, water temperature (T), pH, dissolved oxygen (DO), electroconductivity (EC), and total dissolved solid (TDS) were measured in situ using a YSI EXO2 multisensor sonde (Yellow Springs Instruments, Yellow Springs, OH, United States). The DIC was calculated by summing up all the inorganic carbon species (HCO₃⁻, CO₃^2-, and H₂CO₃). The pCO₂ was estimated using the pH-alkalinity-temperature method, whereby the alkalinity was titrated in the field with 0.025 M HCl using a Hach digital titrator (Loveland, CO, United States).

The riverine DIC equilibrium conversion can be described by the following equation:

${\mathrm{C}\mathrm{O}}_2+{\mathrm{H}}_2\mathrm{O}\leftrightarrow {\mathrm{H}}_2{\mathrm{C}\mathrm{O}}_3*\leftrightarrow {\mathrm{H}}^{+}+{\mathrm{H}\mathrm{C}\mathrm{O}}_3^{–}\leftrightarrow 2{\mathrm{H}}^{+}+{\mathrm{C}\mathrm{O}}_3^{2–}(1)$

${\mathrm{K}}_{{\mathrm{C}\mathrm{O}}_2}=\frac{\left[{\mathrm{H}}_2{\mathrm{C}\mathrm{O}}_3^{*}\right]}{{\mathrm{p}}_{{\mathrm{C}\mathrm{O}}_2}}(2)$

${\mathrm{K}}_1=\frac{\left[{\mathrm{H}}^{+}\right]\left[{\mathrm{H}\mathrm{C}\mathrm{O}}_3^{–}\right]}{\left.{\left[\mathrm{H}\right.}_2{\mathrm{C}\mathrm{O}}_3^{*}\right]}(3)$

${\mathrm{K}}_2=\frac{\left[{\mathrm{H}}^{+}\right]\left[{\mathrm{C}\mathrm{O}}_3^{2–}\right]}{\left[\mathrm{H}{\mathrm{C}\mathrm{O}}_3^{–}\right]}(4)$

where H₂CO₃* is the analytical sum of CO₂ (aq) and the true value for H₂CO₃; and K_CO2, K₁, and K₂ are temperature dependent dissociation constants and can be calculated from the empirical expressions according to Clark and Fritz (1997).

The pCO₂ was then calculated using alkalinity, in situ pH, and water temperature:

${p\mathrm{C}\mathrm{O}}_2=\frac{\left[{\mathrm{H}}^{+}\right]\left[{\mathrm{H}\mathrm{C}\mathrm{O}}_3^{–}\right]}{{\mathrm{K}}_1{\mathrm{K}}_{{\mathrm{C}\mathrm{O}}_2}}(5)$

The suspended particulate sediment (SPS) was collected by filtering 2 L of water through Whatman GF/F filters (0.7 μm pore size). The samples were acidified with 2 M HCl to remove the inorganic carbon and then dried at 60°C to complete dryness. The particulate organic carbon (POC) was analyzed using an ECS 4010 CHNSO analyzer (Costech International S.p.A., Milan, Italy). Duplicate samples were taken and the precision was ± 5% for POC.

Water for the dissolved organic carbon (DOC) analysis was first filtered through 0.7 μm Whatman GF/F filters and then 2–3 drops of 2 M HCl were added. The DOC was determined by an Elementar Vario TOC Select Analyzer (Elementar, Langenselbold, Germany) by the high temperature (850°C) combustion method. The triple injections produced a precision of within ± 5%.

The carbon flux (f) was calculated as follows:

$f=\mathrm{C}\times \mathrm{Q}(6)$

where C and Q are the monthly average values for carbon concentration and water discharges from the control sections during the dry and wet seasons, respectively.

2.3 CO₂ flues across the water-air interface

2.3.1 Floating chamber method

The CO₂ emission flux (F_CO2) was measured in situ using a cylindrical floating chamber (FC) method which is an inexpensive and convenient method to measure direct diffusive fluxes at the surface of aquatic ecosystems. The floating chamber had a water surface area of seven dm², a height of 40 cm, and was attached to an ultra-portable gas analyzer (UPGA, LGR company, United States). The chamber mounted on plastic foam that had streamlined ends to limit artificial disruption due to near-surface turbulence. A vent on the top of the chamber equilibrated the pressure inside the chamber with the atmospheric pressure prior to each measurement. At each site, the chamber walls were lowered 2 cm into the water and installed at the same distance (about 1 m) away from the bank. The gas analyzer automatically recorded at 1 s intervals for about 10 min, and two or consecutive measurements were made.

The CO₂ emission flux from the water-air interface (F_CO2, mmol m⁻²d⁻¹) was determined from the slope of a linear regression of the CO₂ concentration in the gas analyzer against time (coefficient of determination r² > 0.90):

$F_{\mathrm{C}\mathrm{O}2}=\frac{{\mathrm{F}}_1\times {\mathrm{F}}_2\times \mathrm{V}}{\mathrm{A}}\times \frac{{\mathrm{d}}_{\mathrm{c}}}{{\mathrm{d}}_{\mathrm{t}}}(7)$

where dc/dt is the slope of the CO₂ connection in the chamber headspace versus time (ppm s⁻¹), F₁ is the conversion factor from ppm to mg m⁻³, F₂ is the conversion factor from seconds to days, and V/A is the height of the floating chamber.

2.3.2 Thin boundary layer method

The CO₂ flux across the water–air interface was also calculated using the thin-boundary layer method based on Fick’s law (UNESCO/IHAGHG, 2010).

${\mathrm{F}}_{\mathrm{C}\mathrm{O}2}=\mathrm{K}\times \left({\mathrm{C}}_{\mathrm{w}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{r}}-{\mathrm{C}}_{\mathrm{e}\mathrm{q}-\mathrm{a}\mathrm{i}\mathrm{r}}\right)(8)$

Where F_CO2 is the CO₂ flux from water to air (mmol.m⁻².d⁻¹), K is the gas transfer velocity (cm.h⁻¹). C_water is the gas concentration in surface water (mmol L⁻¹), and C_eq-air is the gas concentration in surface water that is in equilibrium with the atmospheric concentration (mmol L⁻¹). We calculated K using the temperature-dependent Schmidt number (S_CT) for fresh water (Raymond et al., 2012):

$\mathrm{K}={\mathrm{K}}_{600}\times {\left({\mathrm{S}}_{\mathrm{C}\mathrm{T}}/600\right)}^{-0.5}(9)$

with

${\mathrm{S}}_{\mathrm{C}\mathrm{T}}=1911.1-118.11\mathrm{T}+3.4527{\mathrm{T}}^2-0.04132{\mathrm{T}}^3(10)$

Where T is the in situ water temperature (^oC), and K₆₀₀ is the K for CO₂ at 20°C in freshwater. K values depend on river size (Alin et al., 2011) and can be 60% greater in rivers with channels <100 m wide than in rivers with channels > 100 m wide. Different equations are available for large and small rivers. Because the YT River’s width is usually greater than 100 m, we estimated K₆₀₀ using the large rivers formula according to the research of Alin et al. (2011).

${\mathrm{K}}_{600}=4.46+\left(7.11\times {\mathrm{U}}_{10}\right)(11)$

${\mathrm{U}}_{10}=1.22\times {\mathrm{U}}_1(12)$

Where U₁₀ (m.s⁻¹) is the wind speed at a height of 10 m above the water surface and U₁ (m.s⁻¹) is the wind speed over the water body obtained from field monitoring using the wind speed meteorological meter (Kestrel4500, United States).

2.4 Data analysis

The Pearson correlation analysis and Kruskal–Wallis (K-W) test of environment variables were carried out with Statistical Program for Social Sciences 22 (SPSS22, IBM Corporation, Amon, NY, United States) to test the relationship and significant difference in the seasons.

3 Results

3.1 Comparison of floating chamber and TBL methods

As shown in Figures 2, 3, the CO₂ fluxes obtained by the TBL in the wet and dry season had greater variation range and higher values in than those obtained by the FC. From the qualitative perspective, about 80% of the results of the two methods in the CO₂ sink/source were generally consistent, and five CO₂ fluxes showed inconsistencies in the CO₂ sink/source which were mainly located in the downstream. The results obtained by the two methods showed relatively good positive correlation (R² = 0.63, p < 0.01), indicating that the two methods could reflect the general trend of the CO₂ fluxes in the monitoring of YT River Basin. The differences between two monitoring results were due to different monitoring principles and error sources which were still not very clear about its main causing environmental factors (Zhang et al., 2019). Since FC method measured the CO₂ flux across the water-air surface directly, we use CO₂ fluxes estimated from the FC method for further discussion in this study. However, due to the influence of instantaneous meteorological processes (water temperature difference, wind speed, etc.), CO₂ fluxes had diurnal changes. In this study, the sampling was conducted at each sampling point in the daytime (mainly from 10:00 to 15:00), so it only represented the CO₂ exchange in the daytime of the YT River.

FIGURE 2

FIGURE 2. The correlation analysis of FC method and TBL method.

FIGURE 3

FIGURE 3. Discrete-degree diagrams of the FC method and TBL method (the middle line, lower edge and upper edge, dots in or outside the boxes refer to mean values, 25th and 75th, 5th and 95th, and <5th and >95th percentiles of all data, respectively).

3.2 Physicochemical characteristics

The physicochemical parameters of the YT River are listed in Table 1 and Figure 4. The T, pH, DO showed significant seasonal differences (p < 0.01). The wind speed in rainy season ranged from 0.7 to 2.6 m s⁻¹, with an average of 1.7 ± 1.1 m s⁻¹. A slight high wind speed was observed in dry season, the average was 1.8 ± 1.2 m s⁻¹. Similar K₆₀₀ averages (19.84 and 20.47 cm h⁻¹, respectively) were observed across the seasons. As expected, T decreased from 16.63 ± 3.26°C in the wet season to 5.59 ± 2.46°C in the dry season. On the contrary, the DO concentration increased from 6.17 ± 0.57 mg L⁻¹ in the wet season to 7.79 ± 0.43 mg L⁻¹. The DO saturations were relatively high (90.6%–110.2%). The pH variation was relatively stable, ranging from 7.95 to 8.98 with a mean value of 8.22 in the wet season and from 8.14 to 8.70 with a mean value of 8.42 in the dry season, indicating the YT River water system was weakly alkaline. This was due to the strongly alkaline and weakly acidic nature of the carbonate rock geology on the plateau (Galy and France-Lanord, 1999). The TDS values increased from 189.34 ± 85.32 mg L⁻¹ in the wet season to 213.64 ± 49.37 mg L⁻¹ in the dry season. The values showed no significance difference for the seasonal pattern. Spatially, TDS decreased along the river, especially in the wet season (p < 0.01). The water vapor channel was transported upward from the downstream area, and thus the precipitation increased from the lower reaches to the upper reaches of the river (Jiang 2020). Therefore, dilution caused by precipitation might be one of the main reasons for the spatial differences of TDS in the wet season.

TABLE 1

TABLE 1. Statistics of physicochemical parameters.

FIGURE 4

FIGURE 4. Box diagram of pH, DO, TDS, DIC, DOC, POC, pCO₂, and F_CO2 in different reaches of YT River (the middle line, lower edge and upper edge, dots in or outside the boxes refer to mean values, 25th and 75th, 5th and 95th, and <5th and >95th percentiles of all data, respectively).

3.3 Spatial and temporal variability of carbon species and pCO₂

The results of Kruskal–Wallis (K-W) test demonstrated that POC and pCO₂ had seasonal variation (p < 0.05), while DIC and DOC had no significant seasonal variations. The DIC varied from 0.23 to 3.69 mmol L⁻¹ and 0.75–2.60 mmol L⁻¹ in the wet season and dry season with the mean values of 1.75 mmol L⁻¹ and 1.81 mmol L⁻¹, respectively. The spatial variation of DIC was similar to the variations in TDS. The DOC concentration varied from 0.07 to 0.31 mmol L⁻¹ with a mean value of 0.17 mmol L⁻¹ in the wet season, whereas, it varied from 0.05 to 0.35 mmol L⁻¹ with an average value of 0.13 mmol L⁻¹ in the dry season. The POC content, ranged from 0.02 to 0.99 mmol L⁻¹ with a mean value of 0.29 mmol L⁻¹ in the wet season, which was much higher than that in the dry season with a mean value of 0.09 mmol L⁻¹ (ranging from 0.02 to 0.30 mmol L⁻¹). The seasonal difference in POC was greater than that for DIC and DOC, which indicated that the POC was more significantly affected by the flush effect. The melting of glaciers and permafrost into the basin meant that more terrigenous organic matter entered into the river, resulting in significant increases in suspended solids and POC concentrations in the river during wet season. The spatial variation in DIC, DOC and POC contents showed a gradually decreasing trend in the YT River during the wet season (p < 0.01), but was relatively stable in the dry season, which might be caused by the increase in precipitation from the upstream part of the river to the downstream part in the wet season.

The pCO₂ exhibited spatial and seasonal variations in the YT River (p < 0.05), ranging from 597.12 ± 292.63 μatm in the wet season to 368.72 ± 123.50 μatm in the dry season. The result was lower than the research of Qu et al. (2017) (median value of 864 μatm with headspace balance method) and comparably to the result of Ye (2019) (mean value of 751 μatm). Our results showed about 75% of samplings were supersaturated in CO₂ than the atmospheric CO₂ (about 400 μatm). The highest pCO₂ was measured in the headwater (M1) while the lowest values were found in the lower reaches of the river (M11). In general, a decreasing trend was identified for pCO₂ along the mainstream part of the river, especially in the wet season.

4 Discussion

4.1 Controls on pCO₂

The riverine pCO₂ is closely related to both internal carbon dynamics and external biogeochemical processes. 1) The regional geological background and chemical weathering intensity determine the thermodynamic equilibrium system for carbonate in the river, such as DIC concentration, pH, etc., (Neal et al., 1998; Li et al., 2013). 2) Soil CO₂ influxes and the aquatic respiration of organic carbon increases pCO₂ and decreases pH. In contrast, photosynthesis will decrease pCO₂ levels but increase water pH (Hagedorn and Cartwright, 2010). 3) The above processes changed the pCO₂ of the river body and they contributed to degassing or absorption phenomena (Ni et al., 2019).

Seasonal temperature and precipitation influenced the aquatic pCO₂, as it was the dominant effect on soil respiration and carbon transportation (Hope et al., 2004) The pCO₂ value was significantly higher in the wet season than in the dry season, which was attributed to the enhancement of soil respiration by warm temperature (16.63 ± 3.26°C). More precipitation will flush the soil with relatively a high CO₂ content into the river water (Hope et al., 2004; Li et al., 2013), which was also confirmed by the higher POC content in the wet season (Figure 4F). Besides, the concentrated rainfall also promoted rapid kinetics of hydrological cycle (Jung et al., 2012; Zhang et al., 2017; Zhong, 2017), which resulted in low pCO₂ in the downstream part of YT River in wet season (Figure 4G). Similar observations were also found in the upper Yangtze in the rainy season (Li et al., 2012). In aquatic ecosystem, the degree of photosynthesis and respiration in the sub-aquatic community will also influence pCO₂ content. The organic carbon could be biodegraded into CO₂ by respiration of microbes (Guillemette et al., 2013). This was corresponded with the positive Pearson correlation coefficients between DOC and pCO₂ (Figure 5). However, the average DOC concentration in the YT River (0.15 mmol L⁻¹) was relatively low compared to other large rivers across the globe (0.479 mmol L⁻¹) (Meybeck, 1982) and it only accounted for about 8.6% of DIC. The internal primary production was a non-essential driver for pCO₂ due to low primary productivity on the plateau (Ye, 2019).

FIGURE 5

FIGURE 5. Pearson correlation coefficient of F_CO2 and environmental factors (*Correlation is significant atp < 0.05 level; **Correlation is significant at p < 0.01 level).

Moreover, the local geological background and water carbonate balance also showed multiple mechanisms influencing pCO₂ (Li et al., 2013). Dissolution of carbonate minerals in the YT River basin resulted in high DIC concentrations (1.82 ± 0.69 mmol L⁻¹), and thus potentially contributed to the pCO₂ levels. Meanwhile, there are three species of DIC, i.e., CO₂^* (dissolved CO₂ and H₂CO₃), HCO₃⁻, and CO₃^2–, and their relative activities in water are largely functions of pH (Liu et al., 2010). At a relatively high pH (8.30 ± 0.22), CO₂^*dissociates into the ionized species HCO₃⁻and CO₃^2–, and by converting CO₂^* into non-volatile forms, this “carbonate buffering” may reduce pCO₂ concentrations (Cai et al., 2008).

4.2 CO₂ emission fluxes from the Yarlung Tsangpo river

Differences in F_CO2 was also found in YT River between seasons (p < 0.05), with average values varying from 8.44 ± 6.94 mmol m⁻² d⁻¹ in wet season to 3.62 ± 6.32 mmol m⁻² d⁻¹ in dry season (Table 1 and Figure 4H). Generally, the river acted as “weak carbon source” for atmospheric CO₂. The spatial average showed that the largest F_CO2 occurred at the upstream of T2 (25.36 mmol m⁻² d⁻¹) in wet season and negative flux occurred at the downstream part in dry season which appeared as a CO₂ sink, which corresponded with the lower pCO₂ in the downstream.

To further understand the degassing CO₂ flux in the YT River, it was compared to other large rivers in different climate zones in Table 2. Our result showed that the CO₂ emission flux of the YT River was the lowest among other rivers across the globe. For instance, it was much smaller than that of the tropical Amazon River, the temperate Mississippi River, and the subtropical Yangtze and Lancang Rivers. It was relatively close to the Eastmain Quebec River in the boreal zone. According to Aufdenkampe et al. (2011), the median CO₂ efflux of large tropical rivers is about 2.2 times greater than that of large temperate rivers and 6.5 times greater than that of large rivers in the cold zone, which suggested that temperature had an important influence on CO₂ effluxes. Figure 5 shows the Pearson correlation coefficient of F_CO2 and environmental factors. F_CO2 was apparently positively dominated by the riverine pCO₂ (R = 0.77, p < 0.01) and negatively correlated with pH (R = −0.46, p < 0.01). Obviously, the low F_CO2 resulted from the low pCO₂ value which was due to the low primary productivity and “carbonate buffering” of the alkaline water body (high pH) in the plateau river.

TABLE 2

TABLE 2. A comparison of the CO₂ effluxes produced by large rivers across the globe.

4.3 Regional carbon flux and worldwide comparation

With large river systems, assessing carbon emission flux is essential when attempting to understand the fate of carbon entering the drainage network and its significance in the global carbon cycle. We took hydrological station Nuxia (M11) as the outflow section of the plateau. It was used to calculate the flux of carbon that was transported from the plateau (altitude > 2000 m) to the downstream plain.

The carbon outgassing load to the atmosphere was calculated as the product between surface area and measured average CO₂ flux, and the surface areas of the rivers were estimated from the Landsat images. The average surface plateau areas of the YT River during the wet and dry seasons in 2010 and 2015 were 806.70 km² and 493.11 km², respectively. The average CO₂ emission values in the wet and dry season were 19.92 × 10⁷ mol month⁻¹ and 5.35 × 10⁷ mol month⁻¹. The annual CO₂ release flux was about 1.52 × 10⁹ mol a⁻¹ based on the mean values for the two seasons. The total annual carbon transport recorded at Nuxia was 93.86 × 10⁹ mol a⁻¹, which consisted of 67.77 × 10⁹ mol a⁻¹ DIC, 12.87 × 10⁹ mol a⁻¹ DOC, and 13.22 × 10⁹ mol a⁻¹ POC.

It can be seen that the CO₂ degassing effect was relatively weak and only accounted for 2.2% of the annual DIC flux in the river according to the FC method and about 10.6% of the DIC flux according to the TBL method (Table 3). These values were much lower than global average and for rivers such as the Yangtze, Mekong, and Mississippi. For instance, the CO₂ emission flux to the atmosphere was 80% and 50% of the DIC for the Yangtze River and the global average value for rivers, respectively. The YT River probably acted as a “pipeline” to transport the nutrient to downstream. Generally, we concluded that plateau rivers were characterized by relatively low degassing fluxes and low carbon transformation intensities and that these were related to weak biological effects and carbonate buffer activities in the carbonate background on the plateau.

TABLE 3

TABLE 3. CO₂ evasion fluxes and carbon transport by several large rivers.

It should be noted that while our estimates of carbon losses from the YT River were a significant portion of the carbon budget for the Tibetan Plateau, they may be potentially large uncertainties due to 1) the lack of long-term measurements in the control hydrology section; 2) limiting number of repeated samplings at the same sites and taking repeated samples at a same site over a representative period of time; and 3) the use of different measurement methods for CO₂ emission flux. Our data get from the FC methods were measured at the sites which was about 1 m away from the bank at where the flow velocity might be relatively lower. It meant that CO₂ emission flux obtained by FC methods might be lower compared to the actual CO₂ emission flux in or near the middle part of river. However, our results reflected the basic characteristics of the “weak outgassing effect and a high carbon transport flux” phenomenon shown by the YT River on the Tibetan Plateau. The considerable global relevance of the Tibetan Plateau and its importance to water supply and climate change meant that these topics deserve attention in future research studies.

5 Conclusion

1) The YT River on the plateau was a weak carbon source for the atmosphere. Aquatic pCO₂ displayed obvious seasonal variabilities across the river basin, averaging about 597.12 μatm and 368.72 μatm in the wet and dry seasons, respectively. The influx of soil CO₂, due to watershed erosion, and the dilution effect may explain the seasonal and spatial variations in pCO₂.

2) The pCO₂ values for the YT River were much lower than those for other larger rivers in different climate zones. Unlike most rivers and lakes, which are supersaturated with CO₂, biological aerobic respiration was not the main driver of CO₂ production in the YT River due to its lower primary productivity (low DOC content and weak microbial activity). The regional geological background and its internal carbon dynamics (the carbonate equilibrium system) mainly determined the basic aquatic pCO₂ of the YT River.

3) According to the static float method, the CO₂ emission fluxes for the YT River were 8.44 mmol m⁻² d⁻¹ and 3.62 mmol m⁻² d⁻¹ in the wet and dry seasons, respectively. It was estimated that about 1.51 × 10⁹ C mol a⁻¹ was emitted as CO₂ to the atmosphere and about 93.86 × 10⁹ mol a⁻¹ carbon was discharged from the plateau to the lower reaches of the river. The carbon mass balance suggested that CO₂ emissions by the YT River to the atmosphere represented only 2.2 and 10.6% of the DIC budget. This suggested that rivers on the Tibetan Plateau exhibited the characteristics of “high transport flux and weak outgassing” which was a different pattern to that of other large rivers in the world. Therefore, the carbon cycles of rivers on the Tibetan Plateau should be the focus of future studies to improve global carbon budget and regional environmental predictions.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Funding

This study was supported by National Natural Science Foundation of China (No. U1802241, 92047204, U2040211), the National key research and development project (2021YFC3201002, 2022YFC3205000), the Project of China ThreeGorges Corporation (No.201903144) and the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research (SKL2020TS07). The authors declare that this study received funding from China Three Gorges Corporation. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

Acknowledgments

We would like to thank all the contributors and the Frontiers staff who have made this research topic possible.

Conflict of interest

Author XW was employed by China Three Gorges Corporation.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2022.1036725/full#supplementary-material

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