Article | Open

Divergent biophysical controls of aquatic CO2 and CH4 in the World’s two largest rivers

  • Scientific Reports 5, Article number: 15614 (2015)
  • doi:10.1038/srep15614
  • Download Citation
Received:
Accepted:
Published online:

Abstract

Carbon emissions to the atmosphere from inland waters are globally significant and mainly occur at tropical latitudes. However, processes controlling the intensity of CO2 and CH4 emissions from tropical inland waters remain poorly understood. Here, we report a data-set of concurrent measurements of the partial pressure of CO2 (pCO2) and dissolved CH4 concentrations in the Amazon (n = 136) and the Congo (n = 280) Rivers. The pCO2 values in the Amazon mainstem were significantly higher than in the Congo, contrasting with CH4 concentrations that were higher in the Congo than in the Amazon. Large-scale patterns in pCO2 across different lowland tropical basins can be apprehended with a relatively simple statistical model related to the extent of wetlands within the basin, showing that, in addition to non-flooded vegetation, wetlands also contribute to CO2 in river channels. On the other hand, dynamics of dissolved CH4 in river channels are less straightforward to predict, and are related to the way hydrology modulates the connectivity between wetlands and river channels.

Introduction

There is an increasing recognition of the importance of inland waters (streams, rivers, lakes and reservoirs) in global budgets of CO2 and CH4. According to the most recent estimate, the CO2 emission from inland waters totals 2.1 PgC yr−11 which is equivalent to the ocean or land CO2 sinks2. The global emission of CH4 to the atmosphere from freshwater ecosystems of 103 TgCH4 yr−13 is significant when compared to all other natural (220–350 TgCH4 yr−1) and anthropogenic (330–335 TgCH4 yr−1) CH4 emissions4. Wetlands are among the largest natural CH4 sources to the atmosphere ranging between 175 and 220 TgCH4 yr−14, although pristine freshwater wetlands sequester carbon (C) below ground as organic matter at a rate of ~0.8 PgC yr−15. We adopt, here, the common definition of wetlands as habitats with continuous, seasonal, or periodic standing water or saturated soils6. The total estimated CO2 emission from rivers and streams of 1.8 PgC yr−11 is mostly related to tropical areas that account for 1.4 PgC yr−1 (78%). However, the CO2 data distribution is skewed towards temperate and boreal systems in the Northern Hemisphere, and data in several tropical basins (including the Congo) were derived from interpolation from adjacent basins rather than actual measurements. About 49% of the CH4 emission to the atmosphere from freshwater ecosystems occurs in the tropics, although, there is equally a strong under-representation of tropical inland waters in global estimates, whereby the most recent global synthesis resorted to extrapolating CH4 fluxes from temperate rivers3.

The C emissions from inland waters result from complex interactions between hydrology, biogeochemical processing within the aquatic environment and connectivity with riparian zones and the watershed. The CO2 emissions from inland waters have been traditionally interpreted as mainly resulting from the in-situ degradation of organic C from non-flooded land (that is, terra firme)7,8,9,10,11,12,13,14,15. Yet, other sources of CO2 could also contribute to CO2 emissions from inland waters. In lakes, there is an increasing recognition of the role of hydrological inputs of CO2 (rivers and groundwaters) in sustaining CO2 emissions to the atmosphere16,17,18,19,20. In rivers, the contribution of groundwater inputs of CO2 to riverine CO2 emissions is also recognized as particularly important in headwaters21,22. There is also an increasing recognition of the inputs of C from wetlands in sustaining CO2 and CH4 emissions to the atmosphere from rivers and lakes. Wetlands contribute to CO2 emissions through the respiration from flooded roots of vegetation and by providing labile organic C to sustain bacterial degradation23,24. In the Central Amazon basin, CO2 and CH4 emissions from floodplain lakes23,25 and from river channels24,26 have been attributed to C from wetlands (flooded forest and macrophytes) in addition to non-flooded terrestrial organic C. This was established with a mass balance approach of organic C23,26, high-resolution pCO2 distributions24, and stable-isotope signatures of organic C. In African rivers, spatial patterns of pCO2 and CH4 relate to the distribution of the fraction of wetland in the catchment within a given system (Congo and Zambezi) and across different basins27,28. However, both non-flooded terrestrial biomass and wetlands contribute to CO2 emissions from inland waters and their relative importance remains uncertain and has not yet been quantitatively resolved27,29. This is in part due to the absence of specific molecular tracers for terrestrial organic matter, since numerous plants are common in flooded and non-flooded forests30. On the other hand, stable isotopes allow to trace organic matter from floating macrophytes that frequently have a C4 signature31, while non-flooded C4 grasslands have been found to contribute little to organic matter transported by rivers even in catchments where they occupy extensive areas32. The relative contribution of flooded and non-flooded biomes to riverine CO2 emissions will vary from one basin to another as a function of climate27. It will also vary within a given basin with a dominance of non-flooded terrestrial inputs in headwaters and highlands and an increased contribution of wetlands in lowlands24,27,29,31. In the Amazon basin, wetlands have been conclusively shown to be hotspots of CH4 emission compared to river channels25,33,34.

Here, we compare the CO2 and CH4 distributions in lowland river channels of the two largest rivers in the World and in the tropics, the Amazon and the Congo (Table 1), using a data-set of concurrent pCO2 and CH4 concentration measurements in river channels (Fig. 1, Table 2). We acknowledge that there are several other data-sets of pCO2 and CH4 in Amazonian aquatic systems29 but we focus on direct measurements of pCO2 (not calculated from pH and TA that are highly biased in acid waters35) concurrent with dissolved CH4 measurements (most other studies are based on either one dissolved gas or the other, but not both). The aim of this study is to determine the extent to which the patterns of CO2 and CH4 differ or converge in these two tropical giant water bodies.

Table 1: Main characteristics of the Amazon and Congo basins.
Figure 1: Location of sampling stations in the Amazon and Congo at the scale of the whole basin overlain on the land cover (a,b), and a zoom overlain on the main rivers (d,e).
Figure 1

Maps were generated with ArcGIS using publically available spatial datasets60,61. MS = mainstem. T > 100m = large tributaries. T < 100m = small tributaries.

Table 2: Dates, river stage, spatial coverage and number (n) of paired samples of pCO2 and CH4 collected in the Amazon and Congo rivers.

Results

The pCO2 values spanned two orders of magnitude in the Amazon (70 to 16,880 ppm) and one order of magnitude in the Congo (1090 to 22,900 ppm) (Fig. 2a). The CH4 concentrations spanned four orders of magnitude in the Amazon (11 to 189,100 nmol L−1) and three orders of magnitude in the Congo (22 to 71,430 nmol L−1) (Fig. 2b). Data were aggregated into mainstem (MS), large and small tributaries (T > 100 m and T < 100 m width, respectively11,36). The pCO2 values significantly increased from the mainstem to the small tributaries in the Amazon (Kruskal-Wallis (KW) test, p = 0.0001) and in the Congo (KW test, p < 0.0001). The same pattern was observed for CH4 concentrations in the Amazon (KW test, p < 0.0001) and in the Congo (KW test, p < 0.0001). In the mainstem, large and small tributaries of both Amazon and Congo, the median pCO2 and CH4 (Fig. 2a,b) were distinctly above atmospheric equilibrium of ~390 ppm and ~2 nmol L−1, respectively. The pCO2 in the Amazon mainstem was significantly higher than in the Congo mainstem, but pCO2 values were not significantly different in large and small tributaries (Fig. 2a). The CH4 in the mainstem, large and small tributaries were significantly higher in the Congo than in the Amazon (Fig. 2b). The median CH4 in the Congo was three to four times higher than in the Amazon, for mainstem/small tributaries and large tributaries, respectively. For a given pCO2 value, CH4 concentrations were systematically higher in the Congo than in the Amazon (Fig. 3a–c).

Figure 2: Box and whisker plots of pCO2 (a) and CH4 (b) in the Amazon and Congo.
Figure 2

The box spans the interquartile range (25–75 percentiles), whiskers correspond to 5–95 percentiles, horizontal bar to median, cross to average, and circles to outliers. Differences were tested with a Mann Whitney test at 0.05 confidence interval level, where **** corresponds to p < 0.0001, * to p = 0.0278, and ns to not significant. MS = mainstem. T > 100m = large tributaries. T < 100m = small tributaries.

Figure 3: Log of CH4 concentration as function of pCO2 in the mainstem (MS) (a), the large tributaries (T > 100 m) (b), and small tributaries (T < 100 m) (c) of the Amazon and Congo basins.
Figure 3

Lines correspond to the linear regressions of log transformed CH4 as a function of pCO2.

Discussion

The contribution of wetlands to CO2 emissions in the Amazon, Congo and across tropical rivers

The pattern of higher pCO2 values in streams compared to rivers in the Amazon and the Congo (Fig. 2) is consistent with an analysis of global averages36 and also with the regional studies in part of the Congolese “Cuvette Centrale”37 and in the Oubangui sub-catchment38. Higher CH4 and CO2 concentrations in tributaries than in the mainstem were also reported in the Paraguay River39. The higher pCO2 in the mainstem of the Amazon than in the Congo in their lowland regions could be due to the higher wetland coverage (Table 1), since organic and inorganic C from wetlands has been shown to partly sustain the CO2 emission from the Central Amazon mainstem and floodplains24,26. In order to expand the range of wetland coverage, we included pCO2 data acquired in four other African rivers27 (Fig. 4). In the small and large tributaries and mainstem, pCO2 was positively correlated to wetland coverage across these six tropical rivers, confirming the contribution of wetland C in partly sustaining CO2 emissions from lowland tropical river channels24,26,27. These positive correlations between pCO2 and wetland coverage do not necessarily imply that wetlands are the sole drivers of CO2 in river channels. As previously noted, semi-arid rivers such as the Tana that are virtually devoid of wetlands are CO2 sources to the atmosphere, although less intense than other tropical rivers, implying that non-flooded land also sustains CO2 emissions from river channels27. The relative importance of non-flooded land and wetlands in sustaining riverine CO2 emissions remains uncertain and has not yet been quantitatively resolved29.

Figure 4: Median river-channel pCO2 in mainstem (MS), large tributaries (T > 100 m) and small tributaries (T < 100 m) as function of wetland coverage (fraction of the catchment) in the Amazon (n = 136), Congo (n = 280), Zambezi (n = 153), Betsiboka (n = 21), Rianila (n = 9), and Tana and Athi−Galana−Sabaki (Tana/AGS) (n = 442).
Figure 4

Solid lines indicate linear regressions, and r2 are the corresponding coefficient of determination. Amazon and Congo data are from the present study, other data from Borges et al.27.

Several hypotheses can explain the different behavior of CH4 in the Amazon and Congo river channels

Although in African rivers average CH4 concentrations correlate with wetland coverage27, CH4 concentrations were significantly higher in the Congo than in the Amazon river channels (Fig. 2), despite the fact that the Amazon has a higher wetland coverage (Table 1). Further, the correlations of CH4 and pCO2 are different in the Amazon and Congo river channels (Fig 3). In small streams (T < 100 m), the strong positive relationship between CH4 and pCO2 in both rivers indicates a common origin. It might indicate a stronger contribution of CO2 production from anaerobic organic matter degradation compared to aerobic respiration, and that both CO2 and CH4 production are related to C processing within wetlands. Small streams receive higher contributions from groundwater that are rich in CO221,22. However, data in African rivers show that groundwater had an extremely low CH4 content27,40. While groundwater input certainly contributes to high CO2 in small streams it cannot explain the extremely high CH4 in small streams. Consequently, the strong correlation between pCO2 and CH4 in small streams (Fig. 3b) indicates that groundwater inputs are probably not the major drivers of the high pCO2 values at our sampling sites in lowland regions. In the mainstem, CH4 is only weakly positively correlated to pCO2 in the Congo, while a weak negative relation is observed in the Amazon. This might indicate that in the well mixed and well oxygenated Amazon mainstem, there is a stronger contribution to CO2 production of aerobic respiration fueled by both non-flooded and wetland organic matter41, while CH4 is lost by emission to the atmosphere and bacterial oxidation. In large tributaries (T > 100 m) an intermediary situation is observed in the Amazon, while in the Congo, CH4 and pCO2 remain strongly correlated. These fundamental differences in the dynamics of CH4 in these two rivers can be further examined by invoking several hypotheses.

First, the Congo flooded wetland is in majority flooded forest42 and there are no temporary floodplain lakes but only a handful of relatively large permanent lakes (Mai-Ndombe (2,300 km2), Tumba (765 km2)). In the Central Amazon, on the other hand, flooded forest accounts for 80% of the maximum flooded wetland extent, and the remaining 20% corresponds to temporary and permanent lakes (7% of open water and 13% of floating macrophytes). There are 6,500 floodplain lakes from 52.5°W to 70.5°W along the floodplain fringing the Amazon mainstem plus 2,300 lakes on the major tributaries, totaling a surface area of 10,400 km243. Floodplain lakes are abundant downstream of the confluence of the Negro and Solimões Rivers, while upstream wetland is dominated by flooded forest. Floodplain lakes are characterized by high gas transfer velocity (k) values44,45, that promote the evasion of CH4 to the atmosphere and water oxygenation that will favor bacterial CH4 oxidation. In the Congolese and Amazonian flooded forest, k values should be low due to wind shielding and moderate diurnal water and air temperature variations below the dense canopy, and the release by the flooded plants of hydrophobic organic matter, which might behave as surfactants. This limits CH4 loss by evasion to the atmosphere and by bacterial oxidation (low oxygen levels).

Second, local upland runoff is the main source of the wetland water in the Congo, and not flooding by riverine overflow as in the Amazon46. This unidirectional flow pattern will promote the transport of the CH4 produced in the flooded forest towards the small and large river channels of the Congo, unlike in the Central Amazon where during rising water and high water, the water transport is from the river channels towards the wetlands. It is during rising water and high water that floating macrophytes grow and their biomass peaks47. This corresponds to the period of highest CH4 emissions33, and presumably also highest CH4 production, when the water transfer from wetlands to the river channels is blocked by flooding. The same applies to flooded forest where CH4 emissions were also found to be highest during high water33.

Third, the Congo wetlands are mostly permanently flooded unlike the Amazon floodplains that are seasonally flooded. Permanently flooded wetlands are known to be stronger CH4 emitters and presumably CH4 producers than seasonal flooded wetlands48,49.

Fourth, in the Congo, floating macrophytes (mainly Vossia cuspidata) commonly occur along channel edges and within channels, and form large meadows in streams, rivers and mainstem, in all types of waters (white and black). Floating macrophytes are known to host high CH4 production and emission25,33,34 that will be directly delivered into the Congo river channels. This does not occur in the Amazon where macrophytes are mainly present in floodplain lakes and do not occur in large tributaries and the mainstem due to important depth and strong currents. This is consistent with the higher CH4 concentrations in the Congo than in the Amazon mainstem for pCO2 values >7000 ppm (Fig. 3a). The CH4 released by floating macrophytes in the Amazonian wetland lakes will be lost locally by evasion to the atmosphere and CH4 oxidation (see above), and little dissolved CH4 will be transported to the river channels.

All these differences are related to the smaller water height variations in the Congo mainstem (3–4 m) compared to the Amazon (10–12 m). The Congo basin straddles on the equator, and the dry season on the Northern part of the basin is compensated by the rainy season on the Southern part of the basin, and vice-versa, leading to a regulation of seasonal water height variations50. These different hypotheses need to be tested and verified although this would require a detailed investigation of the hydrology and wetland habitat mapping that are lacking in the Congo where research on aquatic biogeochemistry and ecology was largely abandoned since the early 1960’s compared to the Amazon that has been the subject of continued investigations for more than five decades.

Re-evaluation of CO2 emissions from tropical rivers and streams

The total CO2 emission from river and streams estimated by Raymond et al.1 of 1.8 PgC yr−1 is mostly related to tropical areas that account for 1.4 PgC yr−1 (78%). However, the data coverage in the tropics was lower than for temperate and boreal regions, and data in several basins (including the Congo) were derived from interpolation from adjacent basins rather actual measurements. Furthermore, only one value of pCO2 was used for the whole watershed while pCO2 values increase in lower order streams as shown here (Fig. 2) and across the United States15. For African rivers we have previously shown that the Raymond et al.1 dataset underestimated CO2 fluxes in five basins where new direct pCO2 measurements were recently made27. Although based on a limited number of river basins, we used the regressions in Fig. 4 as a first attempt to re-evaluate CO2 emissions from tropical rivers and streams globally. The river basins shown in Fig. 4 cover a large range of size, climate, and land and wetland cover typical of those encountered in tropical areas. The resulting flux for the tropics is 1.8 ± 0.4 PgC yr−1, i.e. 25% higher than the value originally computed by Raymond et al.1. While additional data will be required to further refine global estimates, this exercise confirms the importance of CO2 emissions from rivers in tropical areas.

In conclusion, the analysis of data in river channels in six tropical rivers including the two largest ones (Amazon and Congo) reported here demonstrates that large-scale patterns in pCO2 across different basins can be apprehended with a relatively simple statistical model related to the extent of wetlands within the basin. Dynamics of dissolved CH4 in river channels are less straightforward to predict, and appear to be related to the way hydrology modulates the connectivity between wetlands and river channels. The differences we have highlighted in CH4 concentration in the river channels of the Amazon and Congo should translate into same differences in CH4 emissions, since in river channels the diffusive CH4 emission is much higher than CH4 ebullition flux in both rivers27,51. This is not the case in wetlands where ebullition represents the majority of the CH4 emission to the atmosphere25,52. In the Amazon basin, overall aquatic CH4 emissions are dominated by wetlands25, while equivalent estimates are unavailable for the Congo basin.

Methods

Study site characteristics

The Amazon and Congo are the first and second largest rivers in the World, respectively, in terms of catchment area and freshwater discharge (Table 1). The Amazon basin is on average ~1 °C warmer and has an annual precipitation about two times higher than in the Congo. This leads to a specific discharge that is also much higher in the Amazon than in the Congo. The higher precipitation can also explain the higher coverage of the basin by evergreen forest (dense and mosaic) in the Amazon (87%) than in the Congo (67%), where conversely savannah (shrubland and grassland) is more abundant (30%), in particular in the northern and southern rims of the catchment (Fig. 1). Consequently, average above ground biomass is higher in the Amazon than in the Congo. The Amazon and Congo basins include the largest tropical wetlands in the World, with annual mean flooded area of 730,000 and 360,000 km2, respectively25,42.

Field data collection

Data were acquired during 5 cruises in the Amazon and 6 cruises in the Congo covering different stages of the annual flood cycle (Table 2). The pCO2 in the Amazon was measured with an equilibrator53 coupled to an infra-red gas analyzer (IRGA), as described in detail by Abril et al.24. The pCO2 in the Congo was measured with both an equilibrator (in the mainstem and largest tributaries) and with a syringe headspace technique (in the mainstem and large and small tributaries) with an IRGA, as described in detail by Borges et al.27. Both approaches were inter-calibrated and compared very well35. Only the data acquired with a syringe headspace technique in the Congo are presented here. Samples for the determination of CH4, were conditioned in 50 ml serum borosilicate vials, poisoned with a saturated solution of HgCl2 (100 μL) and sealed with gas tight butyl stoppers until analysis by gas chromatography (GC)54. The CH4 partial pressure was measured in a 1 mL subsample of the headspace of 20 mL of N2 that was allowed to equilibrate about 12h after initial vigorous shaking. The CH4 concentrations in the Amazon were measured with a flame ionization detector (FID) with a Hewlett Packard 5890A GC calibrated with certified CH4:N2 mixtures (Air Liquide France) of 10 ppm and 200 ppm CH4. The CH4 concentrations in the Congo were measured with a SRI 8610C GC-FID calibrated with certified CH4:CO2:N2O:N2 mixtures (Air Liquide Belgium) of 1, 10, 30 and 509 ppm CH4. The overall precision of measurements was ±2% and ±4% for pCO2 and CH4, respectively. Additional data in the Amazon were digitalized with PlotDigitizer© from the plots of Richey et al.55. Data presented in Richey et al.55 were obtained by headspace technique and GC analysis, from April 1982 to August 1985 during 9 cruises upstream of Manaus, while data reported in the present study were acquired downstream of Manaus.

Computation of tropical river CO2 efflux and error propagation

The air-water CO2 flux (F) was computed according to:

where α is the CO2 solubility coefficient, k is the gas transfer velocity and ΔpCO2 is the pCO2 air-water gradient, whereby a positive value corresponds by convention to an emission of CO2 from the water to the atmosphere.

We used the geographical information system (GIS) of Raymond et al.1. The GIS provides k values, surface areas and width for streams and rivers globally, and the data are structured by stream order into COSCATs (coastal segmentation and its related catchment56). The k values themselves are derived from a parameterization as a function of slope and stream velocity57 included in the GIS. For each of the COSCAT units we derived wetland cover from another GIS, the global database of lakes, reservoirs and wetlands58. Based on the wetland coverage and the equations of the regressions in Fig. 4, we computed the water pCO2 in MS, T > 100m and T < 100m. Since river/stream surface areas in the GIS are structured by stream order it is not possible to distinguish the surface areas corresponding to MS and tributaries. So, the pCO2 of MS and T > 100m computed from the regressions for each COSCAT were averaged, and computations were further carried for T < 100m and for MS and T > 100m lumped together. The F values were then computed from the k values derived from the GIS for streams/rivers narrower and wider than 100 m, a constant water temperature of 25 °C to compute α59 and a constant atmospheric pCO2 of 390 ppm. The F areal values per COSCAT were scaled to the respective stream/river surface area and the data between 30°N and 30°S were summed to provide a total flux value for tropical areas.

An error analysis on the CO2 flux computation and upscaling was carried out by error propagation of the pCO2 computation, the k value estimates, and the estimate of surface areas of river channels to scale the areal fluxes, using a Monte Carlo simulation with 1000 iterations. The uncertainty on the pCO2 computation was derived from the errors on the slope and Y-intercept of the linear regressions in Fig. 4. The uncertainty on k values from the GIS was estimated to be ±10.0% based on the errors on slope and constant of the parameterization57. The river/stream surface areas in the GIS were estimated using two different hydraulic equations, that allow to estimate an uncertainty of ±31.0%.

Statistical analysis

The statistical tests were done with GraphPad Prism® Version 6.05 for Windows.

Original data-set

The timestamped and geo-referenced data-set of pCO2 and CH4 concentrations (Table 2) are available as a supplementary table.

Additional Information

How to cite this article: Borges, A. V. et al. Divergent biophysical controls of aquatic CO2 and CH4 in the World’s two largest rivers. Sci. Rep. 5, 15614; doi: 10.1038/srep15614 (2015).

References

  1. 1.

    et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013).

  2. 2.

    et al. Global carbon budget 2014. Earth Syst. Sci. Data 7, 47–85 (2015).

  3. 3.

    , , , & Freshwater methane emissions offset the continental carbon sink. Science 331, 50–50 (2011).

  4. 4.

    et al. Three decades of global methane sources and sinks. Nat. Geosci. 6, 813–823 (2013).

  5. 5.

    et al. Wetlands, carbon, and climate change. Landscape Ecol. 28, 583–597 (2012).

  6. 6.

    et al. Wetlands of the lowland Amazon basin: Extent, vegetative cover, and dual-season inundated area as mapped with JERS-1 Synthetic Aperture Radar. Wetlands 35, 745–756 (2015).

  7. 7.

    , , & Carbon dioxide supersaturation in the surface waters of lakes. Science 265, 1568–1570 (1994).

  8. 8.

    , , & Linking planktonic biomass and metabolism to net gas fluxes in northern temperate lakes. Ecology 80, 1422–1431 (1999).

  9. 9.

    & Carbon in catchments: connecting terrestrial carbon losses with aquatic metabolism. Mar. Fresh. Res. 52, 101–110 (2001).

  10. 10.

    , & The summer metabolic balance in the epilimnion of southeastern Quebec lakes. Limnol. Oceanogr. 47, 316–321 (2002).

  11. 11.

    , , , & Outgassing from Amazonian rivers and wetlands as a large tropical source of atmospheric CO2. Nature 416, 617–620 (2002).

  12. 12.

    , & Temperature independence of carbon dioxide supersaturation in global lakes. Global Biogeochem. Cycles 19(GB2003), 10.1029/2004GB002264 (2005).

  13. 13.

    et al. Biophysical controls on organic carbon fluxes in fluvial networks. Nature Geosci. 1, 95–100 (2008).

  14. 14.

    et al. Lakes and reservoirs as regulators of carbon cycling and climate. Limnol. Oceanogr. 54, 2298–2314 (2009).

  15. 15.

    & Significant efflux of carbon dioxide from streams and rivers in the United States. Nature Geosci. 4, 839–842 (2011).

  16. 16.

    , , & Regulation of spatial and temporal variability of carbon flux in six hard-water lakes of the northern Great Plains. Limnol. Oceanogr. 54, 2553–2564 (2009).

  17. 17.

    , , , & Hydrologic support of carbon dioxide flux revealed by whole-lake carbon budgets. J. Geophys. Res. 114(G01008), 10.1029/2008JG000783 (2009).

  18. 18.

    , , & Inorganic carbon loading as a primary driver of dissolved carbon dioxide concentrations in the lakes and reservoirs of the contiguous United States. Global Biogeochem. Cycles 27, 10.1002/gbc.20032 (2013).

  19. 19.

    et al. Carbon cycling of Lake Kivu (East Africa): net autotrophy in the epilimnion and emission of CO2 to the atmosphere sustained by geogenic inputs. Plos One 9(10), e109500, 10.1371/journal.pone.0109500 (2014).

  20. 20.

    et al. Carbonate weathering as a driver of CO2 supersaturation in lakes. Nature Geosci. 8, 107–111 (2015).

  21. 21.

    et al. CO2 efflux from Amazonian headwater streams represents a significant fate for deep soil respiration. Geophys. Res. Lett. 35, L17401, 10.1029/2008GL034619 (2008).

  22. 22.

    et al. Sources of and processes controlling CO2 emissions change with the size of streams and rivers. Nature Geosci. 8, 696–699 (2015).

  23. 23.

    & An organic carbon budget for an Amazon floodplain lake. Verh. Internat. Verein. Limnol. 30, 1179–1182 (2009).

  24. 24.

    et al. Amazon River carbon dioxide outgassing fuelled by wetlands. Nature 505, 395–398 (2014).

  25. 25.

    et al. Regionalization of methane emissions in the Amazon Basin with microwave remote sensing. Global Change Biol. 10, 530–544 (2004).

  26. 26.

    , , & High rates of net primary production and turnover of floating grasses on the Amazon floodplain: implications for aquatic respiration and regional CO2 flux. Glob. Change Biol. 14, 369–381 (2008).

  27. 27.

    et al. Globally significant greenhouse-gas emissions from African inland waters, Nature Geosci. 8, 637–642 (2015).

  28. 28.

    et al. Spatial variability and temporal dynamics of greenhouse gas (CO2, CH4, N2O) concentrations and fluxes along the Zambezi River mainstem and major tributaries, Biogeosciences 12, 2431–2453 (2015).

  29. 29.

    in The Large-scale Biosphere Atmosphere Programme in Amazonia (eds , & ) (Springer, 2015).

  30. 30.

    in Transport of carbon in the major World rivers (eds , & ) 267–283 (Part 3. Mitt. Geol. Paläont. Inst. University Hamburg, SCOPE/UNEP Sonderbd. 58, 1985).

  31. 31.

    et al. The compositional evolution of dissolved and particulate organic matter along the lower Amazon River-Óbidos to the ocean. Mar. Chem. 10.1016/j.marchem.2015.06.013.

  32. 32.

    , , , & Disproportionate contribution of riparian inputs to organic carbon pools in freshwater systems, Ecosystems 17, 974–989 (2014).

  33. 33.

    , , & Seasonal dynamics of CH4 emissions from the amazon river floodplain to the troposphere. J. Geophys. Res. 95, 16417–16426 (1990).

  34. 34.

    , , , & Methane flux from the Amazon River flooplain: emissions during rising water. J. Geophys. Res. 95(D10), 16773–16788 (1990).

  35. 35.

    et al. Technical Note: Large overestimation of calculated pCO2 in acidic, organic-rich freshwaters. Biogeosciences 12, 67–78 (2015).

  36. 36.

    et al. Riverine coupling of biogeochemical cycles between land, oceans, and atmosphere. Front. Ecol. Environ. 9, 53–60 (2011).

  37. 37.

    et al. The biogeochemistry of carbon across a gradient of streams and rivers within the Congo Basin. J. Geophys. Res. Biogeosci. 119, 10.1002/2013JG002442 (2014).

  38. 38.

    et al. Contrasting biogeochemical characteristics of right-bank tributaries and a comparison with the mainstem Oubangui River, Central African Republic (Congo River basin). Sci. Rep. 4, 5402, 10.1038/srep05402 (2014).

  39. 39.

    , & Oxygen depletion and carbon-dioxide and methane production in waters of the Pantanal wetland of Brazil. Biogeochemistry 30, 115–141 (1995).

  40. 40.

    et al. River geochemistry, chemical weathering and atmospheric CO2 consumption rates in the Virunga Volcanic Province (East Africa). Geochem. Geophys. Geosyst. 10.1002/2015GC005999 (2015).

  41. 41.

    et al. Degradation of terrestrially derived macromolecules in the Amazon River. Nature Geosci. 6, 530–533 (2013).

  42. 42.

    , , , & Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices. Remote Sens. Environ. 114, 73–86 (2010).

  43. 43.

    & in Biogeochemistry of the Amazon Basin and its Role in a Changing World (eds , & , ). 235–276 (Oxford University Press, 2001).

  44. 44.

    , , , & Seasonal and spatial variability in CO2 emissions from a large floodplain lake in the lower Amazon. J. Geophys. Res. Biogeosci. 116(G04007), 10.1029/2011JG001699 (2011).

  45. 45.

    et al. Thermal enhancement of gas transfer velocity of CO2 in an Amazon floodplain lake revealed by eddy covariance measurements. Geophys. Res. Lett. 40, 1734–1740, 10.1002/grl.50291 (2013).

  46. 46.

    et al. Characterization of terrestrial water dynamics in the Congo Basin using GRACE and satellite radar altimetry. Remote Sens. Environ. 115, 3530–3538 (2011).

  47. 47.

    Investigations on the ecology and production-biology of the “floating meadows” (Paspalo-Echinochloetum) on the middle Amazon, I. The floating vegetation and its ecology. Amazonia 2, 449–495 (1970).

  48. 48.

    & Methane flux from created riparian marshes: relationship to intermittent versus continuous inundation and emergent macrophytes. Ecol. Eng. 28, 224–234 (2006).

  49. 49.

    , & Carbon dioxide and methane emissions from interfluvial wetlands in the upper Negro River basin, Brazil. Biogeochemistry 105, 171–183 (2001).

  50. 50.

    in Large Rivers: Geomorpholgy and Management (ed ) 293–309 (John Wiley & Sons, 2008).

  51. 51.

    et al. Methane emissions from Amazonian Rivers and their contribution to the global methane budget. Global Change Biol. 20, 2829–2840 (2014).

  52. 52.

    et al. Methane emissions from Pantanal, South America, during the low water season: Toward more comprehensive sampling. Environ. Sci. Technol. 44, 5450–5455 (2010).

  53. 53.

    , & A new design of equilibrator to monitor carbon dioxide in highly dynamic and turbid environments. Water Res. 35, 1344–1347 (2001).

  54. 54.

    Determinations of carbon dioxide and methane by dual catalyst flame ionization chromatography and nitrous oxide by electron capture chromatography. J. Chromatogr. Sci. 19, 611–616 (1981).

  55. 55.

    , , , & Biogenic gases and the oxidation and reduction of carbon in Amazon River and floodplain waters. Limnol. Oceanogr. 33, 551–561 (1988).

  56. 56.

    , & Global coastal segmentation and its river catchment contributors: A new look at land-ocean linkage. Global Biogeochem. Cycles 20, GB1S90, 10.1029/2005GB002540 (2006).

  57. 57.

    et al. Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers. Limnol. Oceanogr.: Fluids Environm. 2, 41–53 (2012).

  58. 58.

    & Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).

  59. 59.

    Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Mar. Chem. 2, 203–215 (1974).

  60. 60.

    et al. A land cover map of South America. Global Change Biol. 10, 731–744 (2004).

  61. 61.

    , , & A new land-cover map of Africa for the year 2000. J. Biogeogr. 31, 861–877 (2004).

  62. 62.

    U.S. Geological Survey's Earth Resources Observation and Science Center, HYDRO1K elevation derivative database, (2000) Date of access: 16/09/2014 .

  63. 63.

    , , & Changes in continental freshwater discharge from 1948 to 2004. J. Clim. 22, 2773–2792 (2009).

  64. 64.

    , , , & Very high resolution interpolated climate surfaces for global land areas. Int. J. Clim. 25, 1965–1978 (2005).

  65. 65.

    , , , & A first Map of tropical Africa’s above-ground biomass derived from satellite imagery. Environ. Res. Lett. 3, 045011, 10.1088/1748-9326/3/4/045011 (2008).

Download references

Acknowledgements

This work was funded by the European Research Council (ERC-StG 240002 AFRIVAL), the Fonds National de la Recherche Scientifique (FNRS, TransCongo, 14711103), the Belgian Federal Science Policy (BELSPO, COBAFISH, SD/AR/05A), the Research Foundation Flanders (FWO-Vlaanderen), the Research Council of the KU Leuven, the Fonds Léopold III pour l'Exploration et la Conservation de la Nature, the French National Research Agency (ANR 08-BLAN-0221 CARBAMA), the Brazilian National Council of Research and Development (CNPq Universal 477655/2010-6), and the Hybam Observatory (INSU/IRD). TL is a postdoctoral researcher at the FNRS. AVB is a senior research associate at the FNRS. We are grateful for help in sampling from T Mambo Baba and E Tambwe Lukosha (Université de Kisangani, DRC), for analytical support from S Petrovic (University of Liège), for sharing the COSCAT k values to P Raymond, and to two anonymous reviewers for comments on the previous version of the manuscript.

Author information

Affiliations

  1. Université de Liège, Unité d’Océanographie Chimique, Institut de Physique (B5), B-4000, Belgium

    • Alberto V. Borges
    • , François Darchambeau
    •  & Thibault Lambert
  2. Laboratoire Environnements et Paléoenvironnements Océaniques et Continentaux (EPOC), CNRS, Université Bordeaux 1, Avenue des Facultés, 33405 Talence, France

    • Gwenaël Abril
    •  & Jonathan Deborde
  3. Programa de Geoquímica, Universidade Federal Fluminense, Outeiro São João Batista s/n, 24020015, Niterói, RJ, Brazil

    • Gwenaël Abril
  4. Katholieke Universiteit Leuven, Department of Earth and Environmental Sciences, Celestijnenlaan 200E, B-3001 Leuven, Belgium

    • Cristian R. Teodoru
    •  & Steven Bouillon
  5. Laboratório de Ciências Ambientais, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense – UENF, Av. Alberto Lâmego, Parque Califórnia, CEP 28013602, Campos dos Goytacazes, RJ, Brazil

    • Luciana O. Vidal

Authors

  1. Search for Alberto V. Borges in:

  2. Search for Gwenaël Abril in:

  3. Search for François Darchambeau in:

  4. Search for Cristian R. Teodoru in:

  5. Search for Jonathan Deborde in:

  6. Search for Luciana O. Vidal in:

  7. Search for Thibault Lambert in:

  8. Search for Steven Bouillon in:

Contributions

A.V.B., G.A. and S.B. designed the study; A.V.B., G.A., F.D., C.R.T., J.D., L.O.V., T.L and S.B. collected the field data; A.V.B. analyzed the data and drafted the manuscript that was revised and approved by G.A., F.D., C.R.T., J.D., L.O.V., T.L. and S.B.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Alberto V. Borges.

Supplementary information

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Creative Commons BYThis work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/