Challenges Regionalizing Methane Emissions Using Aquatic Environments in the Amazon Basin as Examples

1 Introduction

Emissions of methane from inland aquatic ecosystems are large and highly variable (Saunois et al., 2020; Rosentreter et al., 2021). Hence, estimating regional and global emissions is important and challenging. Bottom-up extrapolations often lack sufficient measurements for robust estimates. Simulation models of fluxes and aircraft or satellite analyses of atmospheric concentrations and emissions have other limitations and uncertainties (Ma et al., 2021). Tropical wetlands, in particular, are large, natural sources of methane and their interannual variations in area contribute to varying atmospheric concentrations (Nisbet et al., 2016; Pandey et al., 2017). As the climate warms, the role of tropical wetlands is likely to be enhanced (Zhang et al., 2017). With aquatic ecosystems extending over about 20% of its area (Junk et al., 2011), the Amazon basin represents a major proportion of tropical methane emissions. Hence, we use the Amazon basin as a data-rich, tropical region, and take advantage of its extensive and varied aquatic environments to illustrate and evaluate regionalization approaches, data requirements and results. The challenges considered for the Amazon basin are general to regionalization efforts elsewhere, and lessons learned can be applied to other regions, such as the warming arctic (Wik et al., 2016) or African wetlands (Lunt et al., 2021).

Aquatic ecosystems contribute to large fluxes of carbon in the Amazon basin. High rates of primary production, respiration and methanogenesis lead to fluxes of carbon dioxide and methane to the atmosphere from rivers, lakes, floodplains and other wetlands (Richey et al., 2002; Melack et al., 2004; Melack et al., 2009; Forsberg et al., 2017; Pangala et al., 2017). Though remote sensing of inundation (e.g., Hamilton et al., 2002; Parrens et al., 2019; Prigent et al., 2020) and aquatic habitats (Hess et al., 2003, 2015), inundation modeling (e.g., Coe et al., 2007; Paiva et al., 2013), and measurements of gas concentrations and fluxes in rivers, reservoirs, lakes and wetlands are available (Melack, 2016; Barbosa et al., 2020), considerable uncertainty and information gaps remain. Key challenges to regionalization of methane fluxes in the Amazon basin are the large seasonal variation in inundated areas and habitats, the wide variety of aquatic ecosystems throughout the Amazon basin, and the variability in methane fluxes in time and space. Further issues stem from the various types of methods used and difficulties estimating ebullitive fluxes.

Regionalization can be done at several scales from that of floodplain lakes and wetland types to large regions to the whole Amazon basin. Habitat-specific fluxes can be combined with estimates of habitat areas and their seasonal variations. Mechanistic models can provide an alternative way to estimate regional fluxes. Results from aircraft and satellite measurements of gas concentrations combined with atmospheric transport models can offer integrated regional estimates.

To examine challenges regionalizing methane emissions in the Amazon basin we first consider the hydrological variability and the variety of aquatic ecosystems and their spatial extent. Next the adequacy of sampling and of field methods plus atmospheric measurements, as applied to the Amazon basin, are discussed, followed by a summary of published fluxes. Then, understanding of relevant biogeochemical and physical processes in Amazon aquatic environments and their incorporation into mechanistic and statistical models are examined. Prior and current regional estimates using bottom-up and top-down approaches are reviewed and critiqued. Lastly, recommendations for further study are made.

2 Hydrological Variability and Inundation Estimates

The hydrological Amazon basin extends over ∼6 million km² with major rivers including the Solimões, Madeira, Negro and Japurá joining to form the mainstem Amazon River with annual discharge up to about 20% of global fluvial inputs to oceans. Recent reviews and analyses offer valuable perspectives on hydrological conditions in the Amazon basin of relevance to regionalization of methane fluxes. Fassoni-Andrade et al. (2021) provide a comprehensive review of the water cycle, associated hydrological processes and relevant remote sensing advances in the Amazon basin with its high rates of precipitation, extensive floodplains, diverse tropical forests, complex topography, and large variations in freshwater storage and discharge. Melack and Coe (2021) focus on hydrological aspects of Amazon floodplains in relation to ecological processes. Fleischmann et al. (2021) present an intercomparison of 29 inundation datasets for the Amazon basin derived from remote sensing-based products, hydrological models and multi-source products, and illustrate the variety and divergences among the datasets currently available (Figures 1, 2).

FIGURE 1

FIGURE 1. Maximum and minimum inundated areas for Amazon basin <500 m asl based on different remote sensing and analysis techniques. GIEMS: Prigent et al. (2020), GIEMS-D15: Fluet-Chouinard et al. (2015), Chapman: Chapman et al. (2015), SWAF: Parrens et al. (2019), Rosenqvist: Rosenqvist et al. (2020), Hess: Hess et al. (2015).

FIGURE 2

FIGURE 2. Hydrological Amazon basin with inundated areas shown for basin <500 m asl based on different remote sensing and analysis techniques. Each 1 km pixel shows the number of basin-scale inundation datasets (total of 14 products were assessed) that agree that the pixel is floodable, according to Fleischmann et al. (2021). Rivers and wetlands mentioned in the text: Solimões—S, Madeira—Md, Negro—N, Japurá—Jp, Juruá—Ju, Purus—P, Amazonas—A, Tapajós—T, Xingu—X, Ji-Paraná—JiP, Uatumã—U, Llanos de Moxos - Mo, Pacaya-Samiria - PS, Negro interfluvial wetlands - Nif, Roraima—R, Amazon delta wetlands—Ad.

Variations in rainfall and large changes in river stage and discharge combined with backwater effects and flood propagation result in seasonal and interannual variations in extent of inundation of thousands of lakes, floodplains and other wetlands (Meade et al., 1991; Espinoza et al., 2009; Paiva et al., 2013). Annual amplitude variation in river water levels can be as high as 15 m (Fassoni-Andrade et al., 2021). Particularly high or low rainfall is linked to ENSO events and strong warming of surface waters in the tropical North Atlantic (Marengo and Espinoza 2016). Moreover, the hydrology of the Amazon is not stationary, and positive trends in maximum river water levels across the central basin are evident (Gloor et al., 2013; Barichivich et al., 2018), with several record-breaking floods in the last decade registered in cities such as Manaus (Espinoza et al., 2021). The hydrology of floodplains and other wetlands combines inputs from local catchments with regional-scale fluxes and is characterized by variations in the amplitude, duration, frequency, and predictability of inundation and a seasonally flooded ecotone, called the aquatic-terrestrial transition zone, that often contains woody and herbaceous vegetation (Melack and Coe 2021).

Inundation extent can be simulated with process-based models, and models have been applied at the scale of specific lakes (Ji et al., 2019), floodplain reaches (Wilson et al., 2007; Rudorff et al., 2014 a,b; Pinel et al., 2019) and the whole basin (e.g., Coe et al., 2007; Yamazaki et al., 2011; Miguez-Macho and Fan 2012; Paiva et al., 2013). Apers et al. (2022) used literature-based parameters for natural peatlands to develop and integrate a tropical peatland hydrology module into a global land surface model; global meteorological reanalysis data were used as inputs. In the Amazon basin both lowland and Andean peatland hydrology were simulated; both need further validation.

Temporal (from static to monthly intervals, up to a few decades) and spatial (at resolutions from 12.5 m to 25 km) changes in water level and inundation can be detected with remote sensing techniques, as summarized in Fleischmann et al. (2021). Long-term, maximum inundated area for the basin <500 m asl is estimated as ∼600000 ± ∼82,000 km² using synthetic aperture radar (SAR)-based products, though subregional products suggest a basin-wide underestimation of ∼10%. Minimum inundation extent using SAR-based products is estimated as 139,300 ± 127,800 km². Differences among products arise from differing characteristics of sensors, periods of acquisitions, spatial resolution, and data processing algorithms. Especially large uncertainties exist for interfluvial wetlands (Llanos de Moxos, Pacaya-Samiria, campinas and campinaranas in the Negro basin, Roraima), where inundation tends to be shallower and more variable in time than along riverine floodplains.

While quite useful, remote sensing and modeling results do have limitations. Gauges of river stage are widely spaced, and floodplains are ungauged with a few exceptions; satellite-borne altimeters have wide spacing along tracks, though work fairly well for rivers. Gravity anomaly sensors based on the GRACE missions (Tapley et al., 2004) have been used to monitor changes in floodplain water storage at the basin scale (Alsdorf et al., 2010). For monitoring inundation dynamics, passive microwave has coarse spatial resolution, and SAR data have limited temporal or spatial coverage, though new sensors offer repeated regional coverage. Forthcoming missions, such as the Surface Water and Ocean Topography (SWOT) and the NASA-ISRO Synthetic Aperture Radar (NISAR) missions, will provide useful data to monitor the extent and water levels of Amazonian wetlands. Topographic and bathymetric data at high vertical resolution are fundamental to understand the dynamics of floodplain water storage, but they are rare, though improved digital elevation models are now becoming available (see https://eop-cfi.esa.int/index.php/docs-and-mission-data/dem for list; including the Copernicus global 30 m product; O’Loughlin et al., 2016; Nardi et al., 2019; Fassoni-Andrade et al., 2020).

3 Aquatic Systems and Their Spatial Extent in Amazon Basin

Aquatic ecosystems in the basin range from small streams to large rivers fringed by floodplains, lakes and seasonally flooded forests and savannas. Topographic, climatic, and landscape features range from Andean highlands in the west to lowlands across the central and eastern basin. Based on information available for climate, hydrology, water, sediments and plants, Junk et al. (2011) classified 14 major types of natural wetlands in the lowland Amazon. The amplitude, duration, frequency and predictability of inundation are key criteria in their classification, though these hydrological aspects have insufficient spatial and temporal data for many parts of the basin. Furthermore, a functional classification of these aquatic ecosystems in terms of their relevance to methane biogeochemistry and flux, as encouraged by Sahagian and Melack (1998), is lacking. Hence, our first challenge to regionalizing methane emissions concerns the appropriate characterization of the varied systems, their spatial extent and temporal variability.

Based on available measurements of methane emission and areal extent, seven types of aquatic systems will be considered: streams and rivers, lakes, seasonally flooded forests, seasonally flooded savannas and other interfluvial wetlands, herbaceous plants on riverine floodplains, peatlands, and hydroelectric reservoirs. Aquatic habitats with insufficient information about methane fluxes include coastal freshwater wetlands, such as the wetlands on Marajós Island in the Amazon delta, agricultural ponds (Macedo et al., 2013), road-blocked streams (Leitão et al., 2018), tank bromeliads and cultivated rice. More broadly, retarded drainage can lead to saturated soils without standing water throughout the basin with unknown extent or duration. Estimates of areal extent, judged as the most reliable with caveats, are provided. General issues are the regional boundaries represented by different datasets, as well as inherent limitations of the methods and their validation.

Given the Amazon basin’s immense scale, variability and difficult access, remote sensing approaches are essential to characterize the aquatic systems. Aquatic habitats range in dimension from headwater streams (<1 m across) to ponds and large lakes to floodplains tens of kilometers wide to wetlands covering tens of thousands of km², adding further challenges. Passive and active microwave, laser, visible and near-infrared and gravity anomaly detection systems have been applied in the Amazon basin (Melack 2004; Fassoni-Andrade et al., 2021). SAR techniques are of particular utility because inundation under vegetation and relevant types of aquatic vegetation can be detected, and data can be acquired during day or night and under clouds (e.g., Kasischke et al., 1997; Silva et al., 2015). Melack and Hess (2010) and Hess et al. (2015) used the methodology described in Hess et al. (2003) to determine floodable area, inundated area, and areal extent of major habitats permanently or periodically inundated in the lowland Amazon basin based on mosaics of SAR data obtained during low and high river stages in 1995 and 1996. Open water and herbaceous and woody plants within floodable regions were distinguished at a spatial resolution of about 100 m. High-resolution airborne videography and laser altimetry were used to validate the classifications (Hess et al., 2002). Other remotely sensed products that characterize aquatic habitats are available for specific locations (e.g., Silva et al., 2010; Renó et al., 2011; Hawes et al., 2012; Arnesen et al., 2013; Ferreira-Ferreira et al., 2015).

3.1 Rivers and Streams

Allen and Pavelsky (2018) used Landsat and river stage data to determine river widths at mean annual discharge and judged their estimates accurate for widths wider than 90 m. Since the HydroBASINS dataset used for the river network is derived from a single flow direction algorithm, it cannot represent braided channels, such as occur in some reaches of Amazonian rivers. Their river channel, surface area estimate within the Amazon basin is ∼58,000 km². Mouthbays of rivers, such as the Tapajós, Xingu, Tefé and Coari, were considered lakes, and the analysis did not include the whole delta. Combining their river channel area with estimates for the delta (9,500 km², Castello et al., 2013 plus L.L. Hess, personal communication) and mouthbays of the Tapajós and Xingu (3,800 km², Sawakuchi et al., 2014) results in a river channel area of 77,500 km². An analysis for drainage areas from 1 to 431,000 km² and channels >2 m in width used hydraulic geometry and the drainage network to estimate the Amazon basin (excluding the delta and mouthbays) to have a combined area of about 60,000 km² (Beighley and Gummadi 2011). Given that this procedure included smaller rivers than Allen and Pavelsky’s analysis and used independent data, the two estimates are quite similar. As another example, Rasera et al. (2008) developed empirical relationships between drainage area and channel widths combined with river lengths derived from a digital river network to determine the area of streams and rivers in the Ji-Paraná River basin. Assuming their relationship applied to the whole lowland Amazon basin, an area of 23,000 km² would result for rivers from third to sixth order, similar to that estimated by Beighley and Gummadi (2011) for rivers in that size range. These estimates do not include headwater streams (zero or first orders) that require high resolution topography quite difficult to obtain under forest canopies or labor-intensive surveying. Riparian corridors, often with saturated soils, periodically flooded (Chambers et al., 2004), are also not included.

Large Amazon rivers are typically considered “white waters” with near-neutral pH and relatively high suspended sediment and nutrient concentrations (e.g., Amazon, Madeira, Purus, Juruá and Japurá), “black waters” with low pH, nutrients and suspended sediments and high dissolved organic carbon (e.g., Negro, Uatumã), and “clear waters” with low to neutral pH and low nutrients, suspended sediments and dissolved organic carbon (e.g., Tapajós and Xingu) (Mayorga and Aufdenkampe 2002; Junk et al., 2011). These three types of river water were classified for 6th to 11th order rivers based on field observations and visual inspection of optical images by Venticinque et al. (2016). Smaller rivers and streams are likely to vary considerably in chemical composition, but regional characterization is lacking.

3.2 Lakes

If calculated as the difference between SAR analysis of open water areas at ∼100 m resolution for a period with near-maximum inundation in the central lowland Amazon basin (Hess et al., 2015) minus the river channel areas for rivers wider than 90 m (Allen and Pavelsky 2018), an area of ∼20,000 km² results. Since the river channel areas were derived for the whole basin and the SAR analysis applies only to the basin <500 asl, the lake area is probably under estimated. The HydroLAKES database (Messager et al., 2016) subsetted for the Amazon basin has a lake area of ∼23,000 km². This data set for lakes >0.1 km² used several data sources, including the SRTM waterbody data (Slater et al., 2006), and underwent manual removal of river and wetland polygons and other corrections.

Using side-looking airborne radar imagery acquired mostly during periods of low to moderate inundation by Projecto RadamBrasil (e.g., Departmento Nacional da Produção Mineral), Sippel et al. (1992) measured lake areas on the mainstem floodplain from 51^o to 70^o W and along the lower 400 km of the Japurá, Purus, Negro and Madeira rivers totaling ∼11,800 km².

Radar mosaics and maps generated from these images, both at 1:250,000 scale, allowed areas ≥∼0.05 km² to be estimated. Hamilton et al. (1992) found that the areas of lakes on the Amazon floodplains appear to follow the Pareto distribution, with censorship and truncation on both ends, which indicates that the lakes are statistically self-similar and that descriptive statistics for the lakes will vary with the scale of observation.

Seasonal and interannual variations in inundation lead to a range of lake areas and associated depths that are not represented by the available regional estimates. Results for well-studied lakes provide an indication of variations: Calado, 2–8 km² (Lesack and Melack 1995), Janauacá, 23–390 km² (Pinel et al., 2015), Curuai, 850–2,250 km² (Rudorff et al., 2014a); these values include some flooded vegetation at high stages. Limnological and ecological conditions in Amazon floodplain lakes are reviewed in Melack and Forsberg (2001), Melack et al. (2009), Melack et al. (2021), and Junk (1997).

3.3 Seasonally Flooded Forests

The SAR-based analysis used by Hess et al. (2015) is well validated for detection of inundated forests. By combining the proportion of woody vegetation (forest, woodland and shrubs) compared to total inundated areas at the time of the high (70%) and low (62%) water levels analyzed by Hess et al. with the maximum (631,000 km²) and minimum (53,000 km²) inundated areas from Parrens et al. (2019), maximum (442,000 km²) and minimum (∼33,000 km²) flooded forest areas can be estimated. Seasonally flooded forests vary considerably in species diversity and composition, biomass and productivity depending on fertility of the sediments, duration of inundation and ecological factors not fully understood (Junk et al., 2010). Most flooded forests are associated with white-water rivers, and called várzea forests. Those along black-water rivers are called igapó forests, cover up to ∼84,000 km², and those near clear-water rivers that include várzea and igapó forests, are estimated to cover up to ∼50,000 km², based on the maximum flooded forest area calculated above and proportional areas from Melack and Hess (2010).

3.4 Seasonally Flooded Savannas and Other Interfluvial Wetlands

Seasonally inundated savannas with a variety of herbaceous plants and palms and other trees occur in Roraima (Brazil) and Llanos de Moxos (Bolivia) (Melack and Hess 2010; Junk et al., 2011). Interannual maxima, minima and mean inundation over an 8-year period in these two regions are provided by Hamilton et al. (2002): Roraima (16,500, 250 and 3,000 km²) and Llanos de Moxos (83,000, 6,100 and 34,000 km²). The Rupununi savannas, near Roraima are similar, and may reach a maximum area of ∼15,000 km² (Junk et al., 2011).

Other interfluvial wetlands occur in several regions within the Amazon basin and are not well studied (Junk et al., 2011). In the middle Negro basin ∼30,000 km² of wetlands with sedges, other aquatic plants, patches of palm, mainly Mauritia flexuosa, and open water occur; Frappart et al. (2005) estimated a similar inundated area for this region. A time series of SAR data was used to determine inundation duration, extent and vegetation in the Cuini (minimum, 784 km²; maximum, 964 km²), and Itu (minimum, 550 km²; maximum, 762 km²) wetlands, both located near the middle Negro River (Melack et al., 2009; Belger et al., 2011). Fleischmann et al. (2020) compared several inundation datasets for the Negro interfluvial wetlands associated with campinas and campinaranas (Rossetti et al., 2017), with maximum inundation extent reaching up to 20,000 km². The muted variation in area of these wetlands is likely owed to the limited hydrological connection to the rivers. Between the Purus and Madeira rivers wetland patches a few hectares to ∼150 km², totaling approximately 5,000 km² occur (Junk et al., 2011).

3.5 Herbaceous Plants on Riverine Floodplains

Herbaceous plants are abundant throughout the white-water floodplains of the Amazon basin. These plants grow profusely on sediments during low water and as rooted emergent and as free-floating plants, as inundation and water depths increase seasonally (Junk and Piedade 1997). Common C4 grasses are Echinochloa polystachya, Paspalum repens and Paspalum fasciculatum; other plants include the C3 grass, Hymenachne amplexicaulis, rice (Oryza perennis), and the genera Eichhornia, Ludwigia, Neptunia, Salvinia, and Pistia (Engle et al., 2008; Silva et al., 2013). SAR and other remotely sensed imagery can detect these plants, though their seasonal succession, growth and decline require time-series data. Also, narrow bands of floating plants fringing lakes and rivers and intermingled with woodlands can limit their accurate measurement. For the lowland basin, the combination of the proportion of inundated area represented by herbaceous vegetation in white-water river catchments and areas inundated permit an estimated area during high water of approximately 25,000 km² (Melack and Hess 2010; Hess et al., 2015). As an example of areal variations, in floodplain lakes totaling ∼9,400 km² along the eastern Amazon River, herbaceous plant coverage ranged from 770 to 2,900 km², based an analysis of Radarsat and MODIS images over 2 years (Silva et al., 2013). Herbaceous plants are rarely abundant in black-water river systems, and tend to be only moderately common in clear-water rivers (Junk et al., 2011).

Insufficient information is available to determine basin-wide variations and differences.

3.6 Peatlands

Among the floodplain and other wetlands delineated in sections 3.3–3.5, some are likely to be peatlands, though the classification of organic-rich soils in the region as peats is not standardized, and sampling is insufficient to determine their extent. Gumbricht et al. (2017) defined peat as a soil having at least 50% organic matter in the upper 0.3 m, while others have used different criteria, for example to identify minerotrophic and ombrotrophic sites (Lähteenoja et al., 2009). Among the possible peatlands based on the analysis by Gumbricht et al. (2017), the Pacaya-Samiria wetlands in the Peruvian lowlands, composed of seasonally flooded forests, palm swamps and peatlands, have had their inundated area and peat fairly well characterized (Lähteenoja et al., 2012; Jensen et al., 2018). By combing multi-sensor remote sensing with forest censuses and cores of peat thickness Draper et al. (2014) estimated that peatlands cover 35,600 ± 2,130 km² in the Pastaza-Marañon foreland basin (located in the Pacaya-Samiria region). A portion of this area is included within wetland areas described earlier, though during the period of the high water represented in Hess et al. (2015) fluvial flooding had receded in the Peruvian lowlands. Evidence for peat accumulation is also provided by sampling in parts of the Negro and Madre de Dios river basins, and suggested by model results (summarized in Gumbricht et al., 2017). However, the hybrid expert system used by Gumbricht et al. to estimate the regional distribution of wetlands and peatlands is not consistent with other estimates of inundation (Fleischmann et al., 2021), perhaps because of overestimation of soil moisture by the topographic index used or large rainfall in 2011, the year used. Their designation of floodplains and other wetlands as peatlands requires considerably more evidence, though recent studies are detecting peats scattered through the basin (e.g., Winton et al., 2021)

3.7 Hydroelectric Reservoirs

Hydroelectric reservoirs currently in the Amazon basin cover approximately 4,575 km² (Almeida et al., 2019; Kemenes et al., 2007 for Balbina). In Bolivia (50 km²), Ecuador (35 km²) and Peru (103 km²) almost all are above 1,000 m asl while in Brazil all are <500 m asl. The area of reservoirs (∼2,600 km²) estimated by Messager et al. (2016) is too low. Plans for more hydroelectric systems, especially in the Andes, could considerably expand their area (Almeida et al., 2019) and have substantial ecological consequences (Forsberg et al., 2017).

4 Methane Measurement Methods

4.1 Sampling Issues

The majority of the studies in Amazonian aquatic environments, albeit sampled infrequently and usually in open waters of lakes or rivers, represent considerable effort given the immense area and remoteness of the basin. Few studies covered a complete hydrological cycle, and rarely included diel variations or multiple years. Related limnological, hydrological and meteorological measurements were seldom done concurrently, limiting the ability to evaluate the role of ecological factors, thermal structure and other processes on CH₄ fluxes and concentrations. Regions or habitats with few or no data include the Llanos de Moxos, coastal freshwater wetlands, riparian zones along streams, small reservoirs associated with agriculture, cultivated rice in Roraima and elsewhere, and habitats above 500 m. The recent findings of significant fluxes from trees, especially when inundated, included measurements in several types of forests (Pangala et al., 2017) and seasonal variations (Gauci et al., 2021), and point to the need for considerably more data on fluxes from trees given the large diversity of trees. Among hydroelectric reservoirs, measurements are available for a few (summarized in Melack et al., 2004; Guerin et al., 2006; Barros et al., 2011; Kemenes et al., 2016), but only Balbina reservoir has data collected from multiple reservoir and downstream stations as well as measurements of fluxes associated with turbines over a year (Kemenes et al., 2007, 2011). Abril et al. (2005) provide comparable, long-term data for Petit Saut reservoir in French Guyana.

4.2 Field Methods

4.2.1 Diffusive CH₄ Fluxes

Floating chambers of various designs have been used to measure diffusive fluxes based on deployments of often less than 30 min on regional surveys and at specific locations (e.g., Barlett et al., 1988; Devol et al., 1990; Kemenes et al., 2007; Barbosa et al., 2016, 2020). As CH₄ accumulated in the chambers, sequential samples are removed or the gas is circulated from the chamber to a portable instrument measuring CH₄ continuously; analytical methods are described below. An alternative approach is to calculate the diffusive flux (F) based on measurements of concentration of methane in the water and estimation of gas exchange velocity (k) using the following equation:

$F = k (C_w – C_{eq})$

where C_w is the observed dissolved CH₄ concentration and C_eq is the CH₄ concentration in equilibrium with the atmosphere. To estimate k, MacIntyre et al. (2019) and MacIntyre et al. (2021) provide theoretical and empirical evidence for the validity of the surface renewal model, though most studies in Amazon lakes used wind-based equations (Engle and Melack 2000; Barbosa et al., 2016), which are likely to underestimate fluxes by a factor of two or more. For large rivers, equations for k often incorporate current velocity and/or wind speed (Alin et al., 2011), while equations applied to streams and small rivers use hydraulic parameters, such as slope and velocity (Raymond et al., 2012). In small rivers, the dominant driver for near-surface turbulence driving gas exchange has thresholds which vary with current speed and wind speed (Guseva et al., 2021). Using data from montane streams, Ulseth et al. (2019) found that turbulent diffusion is important in low-energy streams and that entrainment of air bubbles in high-energy streams enhanced gas exchange.

4.2.2 Fluxes via Woody and Herbaceous Plants

Pangala et al. (2017) and Gauci et al. (2021) attached chambers to tree trunks to estimate methane fluxes from the trees at several locations during a 1-month period of rising water and in seasonal deployments at several locations, respectively. Given the considerable variations in fluxes from the trees, large diversity of trees, and difficulty of extrapolating to whole trees and then forests, many more measurements of both fluxes and forest characteristics are needed to characterize fluxes from flooded and upland trees.

Although plant-mediated transport is known to occur via herbaceous plants (e.g., Villa et al., 2020), it has not been consistently observed, albeit seldom sampled, in Amazon lakes or wetlands. Wassmann et al. (1992) did not detect different fluxes between chambers with or without mats of Paspalum repens, a dominant floating grass with few roots reaching the sediments. Among the few measurements by Bartlett et al. (1988), floating mats of Eichornia or Paspalum had no or slight enhancement compared to open water, while rooted Victoria regia growing in shallow water did emit more CH₄ than open water, as would be expected based on other studies of water lilies (Dacey 1981). In Eichornia stands in the southern Amazon and Pantanal, Oliveira Junior et al. (2020) found that diffusive CH₄ emissions were much higher when the plants were rooted, but that emissions from free-floating plant mats were lower than those from nearby open water. In interfluvial wetlands with shallow water in the Negro basin, Belger et al. (2011) reported higher fluxes in chambers covering emergent, rooted plants at one seasonally inundated site but not at another, permanently flooded site.

4.2.3 Ebullitive CH₄ Flux

Most estimates of ebullitive CH₄ fluxes in Amazon lakes have been made using floating chambers during a short deployment. Devol et al. (1988), Devol et al. (1990) and Sawakuchi et al. (2014) used floating chambers with discrete sampling of gas and estimated ebullition indirectly by subtracting diffusive fluxes, calculated from an estimated gas transfer velocity and CH₄ concentration gradient, from combined ebullitive and diffusive fluxes. Alternatively, chambers can be coupled to a portable gas analyzer to obtain high-frequency measurements, and CH₄-enriched bubbles detected from abrupt increases in gas concentration (Crill et al., 1988; Wassmann et al., 1992; Barbosa et al., 2021). Submerged inverted funnels (bubble traps) deployed for hours to days integrate episodic fluxes. Acoustic methods to detect bubbles in the water column and estimate ebullition (DelSontro et al., 2011; Linkhorst et al., 2020) have seldom been used in the Amazon. Barbosa et al. (2021) made the most complete set of measurements to date in both vegetated habitats and open water over 2 years using floating chambers with high frequency measurements and bubble traps, and during falling water, a hydroacoustic echo sounder was employed to detect bubbles.

The variability of ebullition introduces considerable uncertainty in estimates of ebullitive and total CH₄ flux, and the three approaches have different problems. Floating chambers cover a small area and are typically deployed for less than 30 min; very small bubbles cannot be detected. Bubble traps also cover small areas, and bubble volumes of less than ∼1 ml are difficult to measure. Hydroacoustic surveys capture bubbles at high spatial resolution but for only a short time interval, although moored acoustic transponders could be deployed. All three approaches are likely to underestimate ebullition.

4.2.4 CH₄ Concentrations

Until recently, almost all samples from the atmosphere, bubble traps, floating chambers and dissolved in water were assayed in a gas chromatograph equipped with a flame ionization detector. Gas samples for analyses of dissolved CH₄ were obtained using a headspace technique by vigorous shaking of water and air in the sampling syringe (Hamilton et al., 1995). A customized system used during the 1980s employed a gas filter correlation technique (Sebacher and Harriss 1982) with the infrared detector inside the floating chamber to generate a continous record of concentration changes. Portable, off-axis integrated cavity output spectrometers (e.g., products of Los Gatos Research, Inc. and Picarro, Inc.) are now available to measure gas samples directly from floating chambers, from equilibrators receiving water pumped from rivers or lakes, or after headspace extraction in samples. However, the equilibration time between water and gas in equilibrators varies among designs and concentrations.

4.2.5 Environmental Variables

Factors relevant to interpretation of methane fluxes include meteorological variables, stratification and mixing, dissolved oxygen, nutrient and other solute concentrations, current velocities and water depths, underwater light attenuation, chlorophyll and dissolved and particulate carbon concentrations, and sediment characteristics. Time-series of vertical profiles of temperature and dissolved oxygen measured with moored sensors are especially important given the strong diel variations typical of these tropical locations. Seldom do such measurements accompany those of fluxes; exceptions include Barbosa et al. (2020).

4.3 Atmospheric Measurements

4.3.1 Eddy Covariance and Tower-Based Measurements

Eddy covariance is now widely used to measure methane fluxes in wetlands (Dalmagro et al., 2019; Delwiche et al., 2021) and lakes (e.g., Schubert et al., 2012; Podgrajsek et al., 2016), and has been used in short campaigns to measure CO₂ fluxes in an Amazon lake (Polsenaere et al., 2013) and reservoir (Souza do Vale et al., 2016). Two studies provide methane fluxes over upland forests using tower-based measurements. Carmo et al. (2006) used a profiling system based on mixing ratio measurements at three sites in the Amazon. Querino et al. (2011) provides one of the few eddy covariance estimates over upland forest at a site north of Manaus. Both studies focused on within canopy gradients and the uppermost inlet was not more than 10 m above the canopy. A recent study using a profiling system of atmospheric mixing ratio measurements at the Amazon Tall Tower Observatory (ATTO) suggest a nighttime source of methane from flooded forests along the Uatumã River (Botía et al., 2020a). An eddy covariance flux tower is now measuring CO₂ and CH₄ fluxes in a natural palm peatland near Iquitos, Peru (Griffis et al., 2020).

4.3.2 Airborne Sampling and Analysis

Airborne sampling of methane and other gases, followed by assays of concentrations, and in some cases, isotopic composition, in the lower atmosphere, when combined with atmospheric transport models, have provided integrated calculations of methane fluxes over subregions of the Amazon basin (Miller et al., 2007; Beck et al., 2012; Basso et al., 2016; Wilson et al., 2016; Basso et al., 2021; Wilson et al., 2021). For example, Basso et al. (2021) reported seasonal and annual CH₄ fluxes based on measurements of atmospheric CH₄ in vertical profiles from ∼300 m to 4.4 km collected about twice per month from 2010 through 2018 at sites located in the southeastern, northeastern, southwest-central and northwest-central Brazilian Amazon basin. The fluxes, estimated with a column budgeting technique, include all sources and sinks within the area traversed by air masses flowing from the Atlantic coast to each site, representing regional scales of ∼10⁵–10⁶ km². For each of the sites’ quarter-yearly resolved air-mass trajectory, density weighted regions of influence were calculated (Cassol et al., 2020), as illustrated for site TAB in the northwest-central Amazon basin (Figure 3). These weighted trajectories show that areas closer to the flights have greater influence than more distant regions, and that the air mass trajectories vary by season. Hence, attributing the fluxes to sources requires incorporating these spatial and temporal differences.

FIGURE 3

FIGURE 3. Weighted mean quarterly regions of influence for 2010 through 2018 for the TAB site (5.9°S, 70.0°W). Adapted from Cassol et al. (2020) and Basso et al. (2021), where methods are explained.

Results reported by Basso et al. (2021) indicate that wetlands are likely the major source of methane, at least for seasons and sites with extensive inundated areas. Other sources include fires, anthropogenic emissions from enteric fermentation by cattle (Crippa et al., 2019) and urban areas, termites (van Asperen et al., 2021), and, perhaps, emissions associated with canopies of upland forests (Carmo et al., 2006; Martinson et al., 2010). Carbon monoxide measured concomitantly with CH₄ was used to estimate CH₄ emissions from biomass burning. Uptake by soils also occurs (Keller et al., 2005). Though each of these upland fluxes have uncertainties and regionalization challenges, we are not examining these issues here.

4.3.3 Satellite Sensors

Satellite retrievals of atmospheric concentrations of CH₄ are now available from several sensing systems (e.g., SCIAMACHY, Frankenbert et al., 2011; GOSAT, Webb et al., 2016; Parker et al., 2020; TROPOMI, Yu et al., 2021; AIRS onboard the NASA/AQUA satellite, Ribeiro et al., 2016). For example, Webb et al. (2016) found good agreement in the seasonal patterns from airborne vertical profiles extrapolated through the atmosphere and remote sensing data from GOSAT. When combined with inverse modeling and subsetted, these analyses permit estimates of methane emissions in the Amazon basin (e.g., Frankenberg et al., 2008; Tunnicliffe et al., 2020). Evaluations of these approaches are provided by several recent studies (e.g., Parker et al., 2018).

5 Methane Flux Measurements

In parallel with section 3 on areal extent, available estimates of methane emissions are summarized for seven types of aquatic systems: streams and rivers, lakes, seasonally flooded forests, seasonally flooded savannas and other interfluvial wetlands, herbaceous plants on riverine floodplains, peatlands, and hydroelectric reservoirs. A final section identifies major gaps. Fluxes judged as representative of each habitat and that span seasonal variations are selected, if possible. Values are usually expressed in mass of CH₄, not molar units, and converted to daily rates by simple multiplication, as needed.

Most of the published data have expressed averaged values as arithmetic mean fluxes, and these are used here. However, since diffusive and especially episodic fluxes are not normally distributed, arithmetic means are biased toward higher values, and standard deviations often suggest unrealistic negative fluxes. As discussed by Rosentreter et al. (2021) and others, expressing results as medians with interquartile ranges offers an alternative that represents the spread. Geometric means, while statistically sensible for these skewed data, are seldom used (e.g., Barbosa et al., 2020). Applying an approach, such as Monte Carlo uncertainty analysis, would be appropriate, except very few of the published datasets include the individual measurements required to do such analysis. For example, Melack et al. (2004) were able to calculate means and Monte Carlo-based uncertainties for habitat-specific methane emissions with individual measurements reported in Devol et al. (1990).

5.1 Rivers and Streams

Methane emissions based on floating chambers deployed in the Amazon River and major tributaries during low and high water, in most cases, reported by Sawakuchi et al. (2014) ranged from ∼0.2 to 297 mg CH₄ m⁻² d⁻¹ with averages (as mg CH₄ m⁻² d⁻¹): Amazon (21.6), Solimões (5), Negro (8.6), Madeira (0.6), Tapajós (38), Xingu (96), Preto (1.4) and Para (5). Barbosa et al. (2016) measured diffusive CH₄ fluxes using floating chambers and estimated fluxes based on the concentration of the gas in the water and calculated gas exchange coefficients along a 700-km transect including four stations in the Negro River and 21 of its tributaries at low and high water plus six stations on a 1100-km transect of the Solimões-Amazon River and one on the Maderia River occupied four times. Fluxes ranged from ∼0.2 mg CH₄ m⁻² d⁻¹ in the Solimões (during early falling water) to ∼3,900 mg CH₄ m⁻² d⁻¹ in the Jutaí River during low falling water with averages (as mg CH₄ m⁻² d⁻¹): Amazon and Solimões (18), Negro River and tributaries (54), Madeira (6.4), Purus (6.4), Juruá (5), Japurá (9.6). While ebullition may occur in rivers, as suggested by Sawakuchi et al. (2014), the indirect method used may be compromised by the wide ranges of gas exchange velocities expected in turbulent rivers.

Measurements in tributaries were made near their confluence with the Negro, Amazon or Solimões, and no upper reaches of rivers were sampled.

Additional data for the Solimões and Amazon rivers include those by Richey et al. (1988), who combined measurements of dissolved CH₄ concentrations in the main stem with estimates of air–water gas exchange to estimate a diffusive evasion of 3.2 mg CH₄ m⁻² d⁻¹, which is similar to 2.7 mg CH₄ m⁻² d⁻¹determined by Bartlett et al. (1990), but lower than measurements by Sawakuchi et al. (2014) and Barbosa et al. (2016). In the Uatumã River, downstream of Balbina dam, emission was 2,200 mg CH₄ m⁻² d⁻¹ (Kemenes et al., 2007), a value far above other rivers, with the exception of the Jutaí River. Interactions between the fringing floodplains and river channels are not well understood, though a gradient of increasing methane toward the margins of the Solimões River suggests inputs from the floodplains (Richey et al., 1988; Bartlett et al., 1990; Devol et al., 1994).

In a perennial first-order stream in the upper Xingu catchment, average fluxes based on floating chambers deployed monthly for a year were 108 ± 25 mg CH₄ m⁻² h⁻¹ or 2,600 ± 600 mg CH₄ m⁻² d⁻¹ (Neu et al., 2011). These very high fluxes could reflect input of groundwater high in CH₄, and may have over-estimated emissions because stationary chambers in streams can increase turbulent exchange. No other fluxes from streams are available. For comparison, average fluxes of 18 mg CH₄ m⁻² d⁻¹ summarized in Stanley et al. (2016) for tropical and subtropical streams are much lower. Clearly, many more measurements are needed.

5.2 Lakes

Several studies have focused on open waters of lakes, though few included a complete year, adequately measured diffusive and ebullitive fluxes and rarely sampled diel variations. Over 2 years, nearly monthly measurements in an embayment and open water area of Lake Janauacá, located in várzea of the central basin, CH₄ fluxes were made with floating chambers connected to an off-axis integrated cavity output spectrometer and inverted funnels to capture bubbles (Barbosa et al., 2020; Barbosa et al., 2021). Diffusive fluxes, with measurements over diel periods combined for both sites, averaged ∼27 mg CH₄ m⁻² d⁻¹, and when combined with ebullition averaged 85 mg CH₄ m⁻² d⁻¹; diel variations were observed often with higher values during the day.

In várzea Lake Camaleão on Marchantaria Island in the Solimões River, Wassmann et al. (1992) deployed floating chambers connected to an automated system assaying CH₄ concentrations during a range of water levels, each with at least 3 days of sequential measurements, allowing bubble detection, and reported an average flux of 29 mg CH₄ m⁻² d⁻¹. Diel variations were not observed. As part of regular sampling over 18 months in two lakes on the floodplain of the Negro River and six on the floodplain of the Solimões River (Forsberg et al., 2017), Devol et al. (1990) reported average fluxes of 44 mg CH₄ m⁻² d⁻¹. Average diffusive fluxes during high water in várzea Lake Calado were 8.3 mg CH₄ m⁻² d⁻¹ (Crill et al., 1988), during rising water averaged 6.6 mg CH₄ m⁻² d⁻¹, increased up to 220 mg CH₄ m⁻² d⁻¹ during passage of a rare cold front, while during falling water averaged 54 mg CH₄ m⁻² d⁻¹; total flux averaged 163 mg CH₄ m⁻² d⁻¹ during falling water (Engle and Melack 2000).

As part of a transect along the Solimões and Amazon rivers, Barbosa et al. (2016) reported an overall average of 59 mg CH₄ m⁻² d⁻¹, ranging below detection to 298 mg CH₄ m⁻² d⁻¹ for 10 lakes, including white waters and black waters, sampled during four hydrological phases. Sawakuchi et al.’s (2014) few measurements of total fluxes from the eastern várzea Lake Curuai averaged 18 mg CH₄ m⁻² d⁻¹.

5.3 Seasonally Flooded Forests

Methane fluxes within seasonally flooded forests occur from water surfaces, from the trunks of trees and from exposed soils during low water periods. In the flooded forests fringing Lake Janauacá, Barbosa et al. (2020) recorded an average of 110 mg CH₄ m⁻² d⁻¹, based on floating chamber and bubble trap measurements. Diffusive CH₄ fluxes within flooded forest measured by Wassmann et al. (1992) ranged from 1 to 12 mg CH₄ m⁻² d⁻¹, and ebullition averaged 69 mg CH₄ m⁻² d⁻¹. Fluxes at four water levels with floating chambers by Gauci et al. (2021) ranged as follows (expressed as mg CH₄ m⁻² d⁻¹) for plots along the Solimões (43–55 except for a high water value of 450), Negro (12–19) and Tapajós (36–55) rivers. Based on measurements with floating chambers in inundated igapó forests along the Jaú River Rosenqvist et al. (2002) calculated a mean annual emission of methane 30 mg CH₄ m⁻² d⁻¹.

Fluxes through trees in seasonally flooded forests can be high and quite variable when expressed per unit area of emitting surface. Based on sampling transects in twelve 0.4 ha forested plots along the Negro, Amazon, Madeira and Tapajós rivers during a period of rising water, Pangala et al. (2017) reported fluxes for mature and young trees per unit area of stem surface from 0.33 to 337 mg CH₄ m⁻² h⁻¹ and 0.39–581 mg CH₄ m⁻² h⁻¹, respectively. Similar measurements at four water levels in plots along the Solimões, Negro and Tapajós rivers by Gauci et al. (2021) ranged as follows (expressed as mg CH₄ m⁻² h⁻¹ per unit area of stem surface): Solimões (0.013–78.9), Negro (0.005–50.5) and Tapajós (-0.004–69.7). Soil CH₄ fluxes when the water table was below the surface in these plots were low and often negative, ranging from uptake of ∼1 mg CH₄ m⁻² d⁻¹ to evasion of ∼1 mg CH₄ m⁻² d⁻¹ (Gauci et al., 2021).

5.4 Seasonally Flooded Savannas and Other Interfluvial Wetlands

The Pantanal wetland can serve as a surrogate for the savanna wetlands in the Llanos de Moxos (Bolivia), which lack measurements of methane fluxes. Regular vertical profiles of methane and other gases obtained by aircraft from March 2017 to September 2019 in the Pantanal were combined with a planetary boundary layer budgeting technique to calculate a regional CH₄ flux of 50 mg CH₄ m⁻² d⁻¹ averaged over 1 year, assuming a planetary boundary layer-free troposphere exchange time of 3 days (Gloor et al., 2021). This flux integrates emissions from lakes, rivers and wetlands plus enteric fermentation by cattle and fires. Floating chambers deployed at five sites over a year in a Pantanal floodplain near the Miranda River yielded an average flux of 142 mg CH₄ m⁻² d⁻¹ (Marani and Alvala 2007).

Mean emission of monthly measurements with floating chambers in numerous wetlands near Boa Vista (Roraima) of about 14 mg CH₄ m⁻² d⁻¹ seem rather low compared to other similar Amazonian habitats (Jati 2014), perhaps because ebullition was not captured. Emissions from cultivated rice in Roraima are not available. In the mid-Negro basin, Belger et al. (2011) measured methane uptake on unflooded lands, evasion from flooded areas as diffusive and ebullitive fluxes with chambers and funnels, and as transport through rooted plants. Emission from wetland areas averaged 77 mg CH₄ m⁻² d⁻¹.

5.5 Herbaceous Plants on Riverine Floodplains

As water levels rise on riverine floodplains, herbaceous plants form floating mats. Several studies have deployed floating chambers to measure methane fluxes from the water surface or, rarely, from plants on the surface. In mats of floating herbaceous plants, Barbosa et al. (2020) measured an average diffusive CH₄ flux of 53 mg CH₄ m⁻² d⁻¹ and estimated an average ebullition of 97 mg CH₄ m⁻² d⁻¹, totaling 150 mg CH₄ m⁻² d⁻¹. Diffusive fluxes within similar mats reported by Bartlett et al. (1988; 1990) were similar, averaging 42 and 44 mg CH₄ m⁻² d⁻¹, respectively. Wassmann et al. (1992) reported diffusive fluxes from ∼2 to 28 mg CH₄ m⁻² d⁻¹, average ebullition of 23 mg CH₄ m⁻² d⁻¹ and did not detect plant-mediated transport via floating mats of Paspalum repens.

5.6 Peatlands

Fluxes in palm-dominated peatlands and nearby habitats in the western Amazon basin are variable and can be high. Eddy covariance measurements in a natural palm (Mauritia flexuosa) peatland near Iquitos (Peru) over a 2-year period averaged 22 g C m⁻² y⁻¹ (= ∼80 mg CH₄ m⁻² d⁻¹) (Griffis et al., 2020). Teh et al. (2017) measured fluxes in the Pastaza–Marañón basin in forests, a Mauritia flexuosa-dominated palm swamp, and a mixed palm swamp during wet and dry seasons in four campaigns. Among all data, diffusive CH₄ emissions averaged ∼48 ± 4 mg CH₄ m⁻² d⁻¹; fluxes in the M. flexuosa palm swamp averaged ∼49 ± 5 mg CH₄ m⁻² d⁻¹ (assuming their notation, CH₄–C, means the mass of C in the CH₄). As noted by the authors, their estimates of ebullition from short deployments of static chambers are probably not representative. In the same region, del Aguila-Pasquel (2017) sampled 8 times over 8 months spanning wet and dry seasons in a palm swamp and reported a mean flux of 73 ± 5.4 mg CH₄ m⁻² d⁻¹ (n = 129). In peatlands in the Madre de Dios River basin (Peru) Winton et al. (2017) found that open canopy Cyperacea-dominated areas emitted 4.7 ± 0.9 mg CH₄ m⁻² h⁻¹ (=113 ± 22 mg CH₄ m⁻² d⁻¹), and Mauritia flexuosa palm-dominated areas emitted 14.0 ± 2.4 mg CH₄ m⁻² h⁻¹ (= 336 ± 58 mg CH₄ m⁻² d⁻¹) during a short period in 1 month. That CH₄ can be emitted through M. flexuosa trunks suggests that emissions from palm-dominated peatlands based on soil flux chambers underestimate fluxes (van Haren et al., 2021). CH₄ fluxes were significantly correlated to pneumatophore density in M. flexuosa stands (van Lent et al., 2019).

5.7 Hydroelectric Reservoirs

Several hydroelectric reservoirs have measurements available while most, usually smaller ones, do not. In Balbina reservoir, measurements of diffusive and ebullitive fluxes from multiple stations within the reservoir (average 63 mg CH₄ m⁻² d⁻¹), degassing at the turbines and downstream were made over a year, when combined produce an annual CH₄ emission of 97 Gg for the whole system, excluding methane oxidation in the river (Kemenes et al., 2007). Additional measurements at Samuel and Curua-Una reservoirs indicated the significance of degassing at the turbines and downstream (Kemenes et al., 2016). Based on measurements using drifting chambers during four periods in the first 2 years after filling of the Belo Monte hydroelectric system, Bertassoli et al. (2021) reported averages of 104 and 283 mg CH₄ m⁻² d⁻¹, and whole systems annual totals of 20 and 50 Gg CH₄. About half the flux was attributed to ebullition, and degassing in turbines was deemed minor.

Paranaíba et al. (2021) combined measurements of diffusive methane fluxes and calculations of gas exchange velocities with floating chambers at several sites and concentrations of methane sampled and analyzed on continuous transects through the Curuá-Una reservoir. During the rainy season with rising water levels, they reported average fluxes of 9.6 mg CH₄ m⁻² d⁻¹ (range from 1.4 to 112) and during the drier season with falling water levels average fluxes of 14.4 mg CH₄ m⁻² d⁻¹ (range from 1.3 to 69). As part of a study of organic carbon burial in Curuá-Una reservoir, Quandra et al. (2021) found methane dissolved in pore water to be above saturation in about one quarter of their measurements, indicating a potential for ebullition. Porewater concentrations were similar during periods with rising and falling water, varied spatially, but were not related to C:N ratios or organic carbon burial rates.

Emission from other Brazilian reservoirs based on overall average diffusive and ebullitive emissions from the surfaces of ten reservoirs within southern portions of the basin, as summarized in Deemer et al. (2016) is ∼80 mg CH₄ m⁻²d⁻¹; this value does not include degassing through turbines or below the dam. Estimating the emissions from the reservoirs in Bolivia, Ecuador and Peru is more difficult because no measurements exist and temperatures will be less at higher elevations and the watersheds differ from conditions in Brazil; a value of half that from Deemer et al. (2016) is suggested. The extent that the reservoir emissions represent net emissions, i.e., emissions additional to those associated with the undammed rivers, are uncertain, with estimates at Belo Monte (Bertassoli et al., 2021) and Balbina (Kemenes et al., 2011); upland forest soils, before being inundated, are likely to be sinks for methane.

5.8 Major Gaps

The seasonally flooded ecotone, called the aquatic-terrestrial transition zone, is a varying mixture of bare soil and cover by herbaceous and woody plants exposed during periods of low water. Areas occupied by the aquatic-terrestrial transition zone are especially large in regions with shallow seasonal flooding, such as in savannas. Few measurements of methane flux or related processes are available for these periods in the Amazon (Pangala et al., 2017; Gauci et al., 2021); those of Smith et al. (2000) for the Orinoco floodplain are relevant. Microbial activity in response to desiccation (Conrad et al., 2014) and CH₄ oxidation in exposed sediments on Amazon floodplains (Koschorreck 2000) have also been examined.

Sediments exposed as reservoir levels decline are analogous to the aquatic-terrestrial transition zone. Experimental measurements of methane emissions from sediment cores from a tropical Brazilian reservoir exposed to drying and rewetting indicated enhanced emissions from sediments with overlying water removed and when rewetted compared to sediments that had overlying water (Kosten et al., 2018). A comparative study for several types of aquatic systems including ones in tropical climates by Paranaíba et al. (2022) found emissions from portions of inland waters exposed to the atmosphere as water levels decline were consistently higher than emissions in nearby uphill soils. Statistical analyses revealed that methane emissions were negatively related to organic matter content and positively related to moisture of the sediments.

Riparian zones along streams often have soils with high water content but without standing water and occur throughout the Amazon basin. Topographic features are illustrated in Nobre et al. (2011). These environments are likely sources of methane emission. For example, soils in upland forests can release CH₄, depending on soil moisture levels (Sihi et al., 2021).

6 Biogeochemical and Physical Processes

Interpretation and modeling of methane fluxes requires understanding of the key processes; hence we discuss aspects of these processes with a focus on conditions relevant to the Amazon basin. Methane emissions reflect differences between CH₄ production by methanogens and consumption by methanotrophs, and physical processes. Environmental factors that influence biological rates include water temperature, dissolved oxygen, trophic status and substrate availability. Wind, diel variations in thermal structure and physical processes such as convective and shear-driven mixing alter gas distributions and transfer velocities. CH₄ can reach the atmosphere by three pathways: via diffusive fluxes at the air-water interface, via bubbles that form in the sediment, rise through the water column and are emitted to the atmosphere (ebullition), and through the vascular systems of herbaceous and woody aquatic plants. Ebullitive fluxes depend on bubble formation and hydrostatic pressure over the sediment, while diffusive fluxes depend on concentration gradients and turbulence. Here, we examine understanding of relevant biogeochemical and physical processes in Amazon aquatic environments.

6.1 Biogeochemical Processes

The biogeochemical and microbial processes involved in the production and consumption of methane are known though uncertainties remain (e.g., Segers 1998; Borrel et al., 2011; Bridgham et al., 2013; Schlesinger and Bernhardt 2013). However, the quantitative importance of these processes varies among habitats and regions, and needs further study in aquatic environments within the Amazon basin. The availability to methanogens of the varied organic compounds present in Amazonian waters has not been characterized, though studies of the use of these substances for metabolism, in general, are available (e.g., Waichman 1996; Mayorga et al., 2005; Amaral et al., 2013; Ward et al., 2013; Vihermaa et al., 2014). Using samples from sediment cores obtained in an Amazon reservoir and two other tropical reservoirs in Brazil and incubated over about 2 years, Isidorova et al. (2019) found that rates of CH₄ production had a strong statistical relation to sediment total nitrogen content and age of the sediment. Given the variations of sediment characteristics in Amazon floodplains and reservoirs (Hedges et al., 1986; Martinelli et al., 2003; Smith et al., 2003; Guyot et al., 2007; Moreira-Turcq et al., 2013; Cardoso et al., 2014; Sobrinho et al., 2016), further studies will likely reveal different methane production rates associated with these variations.

CH₄ oxidation has been measured in a tropical reservoir (Guérin and Abril 2007) and inferred to occur in floodplain lakes (Crill et al., 1988; Engle and Melack 2000) or exposed sediments (Koschorreck 2000). Based on stable isotopic mass balances of CH₄, Sawakuchi et al. (2016) estimated substantial rates of CH₄ oxidation in large Amazonian rivers, and found that genetic markers for methane oxidizing bacteria were positively correlated with CH₄ oxidation. Using incubations and measurements of δ¹³C-CH₄ in a várzea lake, Barbosa et al. (2018) found that a large fraction of dissolved CH₄ was oxidized with volumetric CH₄ oxidation rates ranging from ∼1 to 175 mg CH₄ m⁻³ d⁻¹. Heavier values of δ¹³C-CH₄ in surface waters when compared to bottom waters and sediment bubbles corroborate these high rates. They also found that CH₄ oxidation had a positive relation with CH₄ concentration and the presence of dissolved oxygen.

The microbial assembles in floodplain lakes (e.g., Melo et al., 2019) including methanogens and methanotrophs have received limited investigation in the Amazon (e.g., Finn et al., 2020; Bento et al., 2021; Gontijo et al., 2021). Conrad et al. (2010, 2011) and Ji et al. (2016) examined microbial communities and measured rates of methanogenesis in sediments by incubating sediment slurries from different floodplain lakes; CH₄ production was found to be mainly hydrogenotrophic based on isotopic fractionation. However, the congruence of laboratory incubations of sediment slurries with CH₄ production in intact sediments is unclear. More study is needed relating microbial activity to environmental conditions, such as the examination of responses to desiccation by Conrad et al. (2014). Anaerobic oxidation of methane and microbial methane production in oxygenated water also need attention, as indicated by studies elsewhere (e.g., Caldwell et al., 2008; Grossart et al., 2011; Roland et al., 2016). For example, Gabriel et al. (2020) used slurries of flooded Amazon forest soils to demonstrate the potential for anaerobic methane oxidation by Fe(III) reduction.

6.2 Physical Processes

In the warm waters of the Amazon basin, high latent heat fluxes lead to convective mixing while diurnal heating under strong insolation leads to periods of stable stratification (e.g., Augusto-Silva et al., 2019). Exchange of methane between surficial water and overlying atmosphere depends on the concentration gradient between air and water and on physical processes at the interface, usually parameterized as a gas transfer velocity (k). Gas transfer velocities are influenced by atmospheric stability and, in water, are altered by currents, wind and convection, as well as rain, temperature and organic surficial films. Melack (2016) summarized estimates of k available for rivers and lakes in the Amazon basin. Results reported by Ulseth et al. (2019) indicate quite large k values can occur in high-energy montane streams due to bubble entrainment. Recent studies by MacIntyre et al. (2019) and MacIntyre et al. (2021) have used near-surface measurements of dissipation rates of turbulent kinetic energy and hydrodynamic theory to calculate k under low wind conditions in open water and within flooded forests.

Though gas transfer velocity can be parameterized based on wind speed (Wanninkhof 1992; Cole and Caraco 1998), the dependence of k on the dissipation rate of turbulent kinetic energy (ϵ) has theoretical and empirical evidence. The surface renewal model, k = c₁ (ϵν)^1/4) Sc⁻ⁿ has an explicit dependence on dissipation rates, where dissipation rates have units of m² s⁻³, ν is kinematic viscosity (m s⁻²), Sc is the Schmidt number, n is −1/2 or −2/3, and c₁ is an empirical coefficient. Dissipation rates depend on the shear just below the air-water interface and are augmented if cooling or heating are appreciable relative to shear (MacIntyre et al., 2021). This model has been applied to Amazon flooded forests (MacIntyre et al., 2019) and reservoirs (MacIntyre et al., 2021) and confirmed with indirect estimates of k using results of experiments with floating chambers (Amaral et al., 2020).

Stratification within tropical lakes and reservoirs can be appreciable as a result of intense heating when winds are light (e.g., Augusto-Silva et al., 2019). In that case, near-surface values of dissipation rate can be higher than observed under cooling. With stratification retarding the downward mixing of heat, much of the turbulence produced by wind is dissipated. Values of k computed using the surface renewal model during heating averaged 10 cm h⁻¹ but reached 18 cm h⁻¹ for winds up to 4 m s⁻¹, were independent of wind speed, and increased with heating (MacIntyre et al., 2021). Hence, the fluxes estimated from wind-based models for many of the lakes in the Amazon basin are likely underestimated.

CH₄ ebullition, a major mechanism for evasion to the atmosphere, is regulated by the production and accumulation of CH₄ in sediments and processes leading to the release of bubbles from the sediments. Release of bubbles can be influenced by variations in hydrostatic pressure caused by a drop in atmospheric pressure or drop in water level. Other factors include currents, waves, shear-stress at the sediment-water interface and possibly sediment disturbances by benthic organisms (Barbosa et al., 2021).

Vertical and horizontal water movements connect littoral, pelagic and benthic regions of lakes and wetlands (MacIntyre and Melack 1995) and are likely to influence CH₄ concentrations and fluxes. One-dimensional (e.g., DYRESM, Yeates and Imberger 2003) and three-dimensional (e.g., AEM3D, Hodges and Dallimore 2019) hydrodynamic models include processes induced by surface heat fluxes, wind, inflows and outflows. One-dimensional models characterize a lake as a single water column in the vertical and are computationally efficient permitting long-term and regional applications. Three-dimensional models divide a lake into grids in three directions, resolve spatial variability of thermal structure, internal waves, horizontal exchanges and calculate dissipation rates of turbulent kinetic energy, a key term in the surface renewal model of gas exchange. An example of an application of a three-dimensional model to a floodplain lake illustrates spatial differences in diel cycles of stratification and mixing (Figures 4A–D), and circulations driven by wind-induced basin-scale internal waves when the near-surface water is stratified (Figures 4E,F). Three-dimensional hydrodynamic modeling has demonstrated that diel differences in water temperature between floating plant mats and open water as well as basin-scale motions can cause lateral exchanges linking vegetated habitats to open water (Amaral et al., 2021). Higher CH₄ concentrations in herbaceous plant mats than in open water suggest that vegetated habitats can be sources of CH₄ to other regions (Bartlett et al., 1988; Barbosa et al., 2020).

FIGURE 4

FIGURE 4. Three-dimensional modeling of thermal structure in Lake Janauacá. (A) The net heat flux and wind speed at site one on map in panel d; (B,C) AEM3D simulated temperatures of water column at sites 1,2 over 5 days; (D) bathymetry of Lake Janauacá, white dots indicate sites 1,2 shown in panels b and c, and white dashed line marks orientation of transect C1; (E) AEM3D simulated near-surface temperatures at the time marked by the vertical blue dashed line in panel a (25 August 2016, 14:29:30 h); (F) AEM3D simulated temperatures and horizontal velocities along transect C1 at the time marked by the vertical blue dashed line in panel a (25 August 2016, 14:29:30 h). Numbered black lines in panels b, c, e and f are the isotherms at 1°C intervals. Application of AEM3D to Lake Janauacá is described in Amaral et al. (2021).

7 Modeling Methane Emissions

7.1 Wetland Models

In a summary of results from the Wetland and Wetland CH₄ Inter-comparison of Models Project (WETCHIMP), Melton et al. (2013) stated that the models disagreed in their simulations of wetland areal extent and methane emissions, in both space and time, and had parameter and structural uncertainty, and noted that lakes and rivers were not included. Moreover, mechanistic models of methane production and evasion appropriate for tropical floodplains are not available (Riley et al., 2011), though relevant conceptual models have been proposed (Cao et al., 1996; Potter 1997; Potter et al., 2014). While several models have potentially useful components or formulations (e.g., Walter and Heimann 2000; Tang et al., 2010; Bloom et al., 2012; Wania et al., 2013; Ringeval et al., 2014; Tan et al., 2015; Lu et al., 2016), no spatially explicit model exists that incorporates the inundation dynamics, ecological characteristics and limnological conditions of tropical floodplains and wetlands. Of special importance is inclusion of plant functional groups common in these habitats combined with appropriate algorithms to estimate their productivity, as these plants supply most of the organic carbon subsequently released as methane (Melack and Engle 2009; Melack et al., 2009). Algorithms are required that simulate stratification and mixing of the water column with concomitant influence on the extent of anoxia and on air-water gas exchange.

Current regional biogeochemical models of methane emissions from wetlands calculate grid-averaged methane fluxes based on soil temperature and carbon availability or heterotrophic respiration. Examples of these models include the Joint United Kingdom Land Environment Simulator (JULES) (Clark et al., 2011; McNorton et al., 2016), the Lund-Potsdam-Jena model (LPJ-WSL; Sitch et al., 2003; Zhang et al., 2016), a CH₄ biogeochemistry model based on the Integrated Biosphere Simulator (TRIPLEX-GHG; Zhu et al., 2014), the LPX-Bern model (Ringeval et al., 2014), a revision of TEM-MDM (Liu et al., 2020) and WetCHARTs (Bloom et al., 2017), JPL-WHyMe (Wania et al., 2010), the Dynamic Land Ecosystem Model (DLEM, Zhang et al., 2017), and CLM4Me (Riley et al., 2011; Meng et al., 2012). A methane model for Amazon peatlands is under development (Yuan et al., 2020). One problem with these models is their use of molecular diffusion through soil layers that is not appropriate for the turbulence-based gas transfer needed for conditions with surface inundation. Inundation is usually simulated as overland hillslope flows and an approximated terrain model, and does not represent the large seasonal inundation variations and hydrologic fluxes present in the Amazon basin. Climatic inputs are typically obtained from reanalysis products with insufficient temporal resolutions to force diel processes. The plant functional groups included do not represent the aquatic plants common in the Amazon basin. Furthermore, these models do not explicitly include biogeochemical processes, such as methanogenesis and methane oxidation, or physical processes, such as mixing through the water, lateral exchanges, ebullition, or air-water exchange via turbulent mixing. Also, they do not simulate dissolved oxygen concentrations and thus the impact of dissolved oxygen on the methane oxidation or prescribe bulk dissolved oxygen concentrations to the grids (Wania et al., 2010; Riley et al., 2011).

Results from an atmospheric transport model and ATTO mixing ratios (Botia et al., 2020) were compared to six versions of the extended ensemble available in WetCHARTs v1.3.1 (Bloom et al., 2017) to account for variability in wetland extent and the temperature-dependent CH₄ respiration fraction (Q₁₀) (Figure 5). All WetCHARTs’ simulations used precipitation from ERA5 (C3S, 2017) to drive the temporal variability of methane emissions and the CARDAMOM model for heterotrophic respiration, but the wetland spatial extent was varied. The simulations ending in three used the Global Lakes and Water Database (Lehner and Döll 2004) and those ending in four used the sum of all wetland and freshwater land cover types in GLOBCOVER (Bontemps et al., 2011). Each pair of simulations had a different temperature dependence of the CH₄ respiration fraction (Q₁₀), according to the second digit of each simulation (Q₁₀ = 1,2,3). The variability in the WetCHARTs simulations indicates that when using a different wetland extent but the same temperature dependence of the CH₄ respiration fraction (Q₁₀), the effect on the integrated CH₄ signal can be about 10 ppb (i.e., lines 913 and 914). This effect seems to be larger at lower Q₁₀ values. When using a different Q₁₀ and the same wetland extent (e.g., line 914 with 924 or 934), the differences are larger when comparing the Q₁₀ = 1 with the Q₁₀ = 2 simulations than between the Q₁₀ = 2 and Q₁₀ = 3 simulations. Furthermore, these comparisons suggest that WetCHARTs fluxes for the ATTO footprint are too high, and that the seasonality of WetCHART-derived mixing ratios at ATTO are 1 month out of phase when compared to the ATTO observations. When accounting for transport errors (not shown here), the simulated wetland signal at ATTO is still higher than the observations (Botia et al., 2020).

FIGURE 5

FIGURE 5. Seasonal cycle of the regional signal (as measurements - background concentration) for simulated and observed mixing ratios of CH₄ at ATTO, expressed as parts per billion (ppb). The observed mixing ratios at ATTO (measurement height 79 m) are shown as a black line and the error bars represents ±1 sigma (standard deviation). This methodology was applied for CO₂ at ATTO by Botía et al. (2021). The background concentrations are from the inversion available via the Copernicus Atmosphere Monitoring Service (CAMS) (Segers and Houweling 2020). For the simulated and observed mixing ratios, only daytime values (13:00–17:00 h local time) were used to ensure well-mixed conditions in the planetary boundary layer. WetCHARTs v1.3.1 is used for the simulated contribution of wetlands to the integrated CH₄ signal at ATTO. The set of WetCHARTs ensemble members show different temperature dependence factors for the CH₄ respiration fraction (Q₁₀) and different wetland extents.

7.2 Lake Models

Potter et al. (2014) built a model for Amazon floodplain lakes based on the supply of organic carbon, as a key factor determining methane production. The LAKE model (Stepanenko et al., 2016) includes most major processes operative in lakes, but the methane module has only been tested on a small boreal lake. Zimmermann et al. (2021) used measurements and a one-dimensional physical model of a small temperate lake to examine how seasonal or more frequent thermocline deepening influenced methane emission and consumption by methanotrophs. Though conceptually relevant, this model would require revision because diel cycles of stratification and mixing are common in shallow Amazon lakes.

The Arctic Lake Biogeochemical Model (ALBM) is a one-dimensional process-based, biogeochemical model that simulates the thermal and methane dynamics of lakes (Tan et al., 2015). The model has been applied to arctic and boreal lakes on seasonal time scales (Tan et al., 2015; Guo et al., 2020), but the thermal module and several other aspects need revisions for conditions in shallow, warm waters. In section 6.2 we discuss alternative models of the relevant physical processes. By combining sensitivity analyses and calibration and validation with appropriate data, the models have been shown to perform reasonably well for specific lakes. However, to apply these models on a regional scale is more difficult as success depends on the calibration and validation sites being representative of the region, and on the availability of data for lakes in the region. An application of ALBM to the Amazon basin produced reasonable results for open water of lakes that further modifications are likely to improve (Figure 6). The parameters used for the Amazon basin were calibrated with data from two well-studied floodplain lakes (Janauacá and Calado) with relevant references cited above.

FIGURE 6

FIGURE 6. Mean annual methane fluxes for only lakes, expressed as mmoles of CH₄ m⁻² y⁻¹, weighted averaged by lake area within 0.5° × 0.5° grid cells for 2004 through 2006 as modeled by ALBM (Tan et al., 2015; Guo et al., 2020). Both the Amazon and Tocantins basins are shown.

Several biogeochemical and physical processes need to be incorporated or improved in mechanistic methane models for floodplain lakes. New formulations of gas transfer velocity and inclusion of lateral exchanges and diel stratification and mixing, as described in section 6, are recommended. Developing parameterizations for fluxes through trees and herbaceous plants, oxic methane production, anaerobic methane oxidation and supply of carbon by autotrophic growth and hydrological inputs are challenging but important. To improve modeling of ebullition, inclusion of changes in hydrostatic pressure and from disturbances of the sediments may help.

7.3 Statistical Models and Analyses

Statistical models offer another approach for estimating lake and reservoir emissions, albeit with limitations related to data availability

DelSontro et al. (2016) analyzed the relationship between emission rates and the predictor variables of lake size, chlorophyll a, total phosphorus and total nitrogen using simple and multiple linear regression for lake and reservoirs distributed around the world. Though tropical reservoirs were included, Amazon floodplain lakes are not represented in this analysis. Among the variables evaluated only chlorophyll a had a positive effect on total methane emission. For comparison, a statistical model applied to Lake Janauacá found variables related to CH₄ production (temperature, dissolved organic carbon) and consumption (dissolved nitrogen, oxygenated water column), as important to dissolved CH₄ concentrations (Barbosa et al., 2020).

Using eddy covariance data from wetland sites largely in the north temperate zone, Knox et al. (2021) applied several statistical techniques to examine the importance of a variety of physical and biological predictors of the timing and magnitude of CH₄ fluxes. While quite informative with regard to the wetlands included, the only site in tropical South America is in the Pantanal, a seasonal savanna wetland. Though the Pantanal shares similarities to the Moxos wetlands, the majority of floodplain and other wetlands in the Amazon basin are not represented in the analyses.

While it is well known that all models have biases from limitations and assumptions within the models and as a result of the data used in calibration and validation, given the logistic and scientific challenges with measurements in the vast and complex Amazon basin, models of all types can surely contribute to understanding and projections of methane emissions.

8 Previous and Current Estimates of Regional Emissions for Amazon Basin

8.1 Bottom-Up Approaches

Estimates of methane fluxes at different spatial scales illustrate varied approaches and limitations. Early estimates of regional methane fluxes made for the mainstem Solimões-Amazon floodplain and the whole basin were limited by lack of data on areas of inundation and aquatic habitats and few measurements of fluxes (e.g., Bartlett et al., 1988; Devol et al., 1990). The first estimates that incorporated remote sensing of inundation and habitat areas were done by Melack et al. (2004) for the mainstem Solimões-Amazon floodplain and a 1.77 million km² area in the central basin (Hess et al., 2003). They combined habitat-specific methane fluxes with seasonal changes in the area of aquatic habitats, derived from active and passive microwave remote sensing, and applied Monte Carlo error propagation to establish uncertainties. By assuming similar fluxes by habitat and adding approximations for particular regions (e.g., Moxos), an extrapolation to whole lowland basin was made. For the central basin quadrant, annual emission was estimated as 9 ± 1.7 Tg CH₄, and if extrapolated to the basin below 500 m asl, resulted in approximately 29 Tg CH₄ y⁻¹. The largest omission from these estimates is evasion from seasonally flooded forests. The measurements of emissions for trees, extrapolated to the Amazon basin, by Pangala et al. (2017; 15.1 ± 1.8 to 21.2 ± 2.5 Tg CH₄ y⁻¹) and Gauci et al. (2021; 12.7 to 21.1 Tg CH₄ y⁻¹ for inundated trees plus 2.2 to 3.6 Tg CH₄ y⁻¹ for riparian zones with water tables below the surface) substantially increase regional fluxes. However, considerable uncertainty in these estimates stems from the lack of adequate data on the abundance and emitting surface areas of the trees and the high variability in fluxes per unit area of trees.

As part of the Science Panel for the Amazon (SPA) report, released at COP26 (Gatti L. et al., 2021; Mahli et al., 2021), J. Melack, L. Basso and S. Pangala provided bottom-up estimates of methane emission from aquatic environments in the combined hydrological Amazon and Tocantins basins, and for the Amazon forest biome. A total area of aquatic habitats for these two basins of 970,500 km² was used to generate their bottom-up estimate of approximately 51 Tg CH₄ y⁻¹. However, this estimate does not include seasonal and interannual variations or a rigorous uncertainty analysis. A second estimate of methane emissions was done for the Amazon biogeographic province that encompasses tropical forests in much of Amapá, French Guiana, Guyana, Suriname, and southern Venezuela and eastern Colombia that drains into the Orinoco River (Albert et al., 2021). This estimate had added uncertainty because of the lack of data for most of the northeastern areas outside of the hydrological Amazon basin.

Improvements needed in bottom-up estimates are inclusion of seasonal variations in flooding and aquatic habitats, as noted in sections 2,3, and more measurements of all fluxes, summarized in section 4. An example of doing so is provided by Barbosa et al. (2021) for a floodplain lake with fringing inundated forests and herbaceous plants. They combined seasonal variations in ebullitive, diffusive and tree-mediated fluxes with variations in water depths and areal coverage of aquatic habitats, i.e., weighting fluxes by seasonal variations in habitat areas and habitat-specific fluxes. This approach was especially important for ebullition for which the fraction of total emission from water surfaces varied from 1% (rising and high water) to 89% (low water) in open water of the lake, and from 73% (rising water) to 93% (falling water) in flooded forests.

An example of the challenges and compromises in regionalization is offered for a reach of the mainstem Solimões-Amazon rivers and associated floodplains (Table 1). The values in Table 1 provide a sense of the relative importance of different habitats as sources of methane to the atmosphere based on areal fluxes and habitat areas for low and high water conditions. However, the fluxes should be viewed with caution because of the uncertainties associated with the data and calculations. As summarized in section 5, several studies provide measurements of methane fluxes from water surfaces in river channels and floodplain habitats. Those selected here were judged representative and offering the most complete data. To fully use data from all studies is not possible because actual datasets are not available in most publications. The fluxes from trees are especially difficult to extrapolate because of the large variance in per tree fluxes and the very limited measurements of the surface areas of trees. Fluxes from floodplain soils during low water periods are not included.

TABLE 1

TABLE 1. Methane emission for reach of the mainstem Solimões-Amazon rivers and floodplains from 73.57^o W, 4.5^o S (confluence of Marañón and Ucayali rivers) to 51.4^o W, 0.46^o S (Gurupá Island). Areas of aquatic habitats from Melack and Hess (2010) and of rivers from Allen and Pavelsky (2018). Mg = 10⁶ g.

In an analysis for the reach of the mainstem Solimões-Amazon floodplain from 54^o to 70^o W, annual fluxes of 1.7 ± 0.4 Tg CH₄ were calculated with 67% of the uncertainty attributed to habitat-specific fluxes and 33% owed to uncertainties in areal estimates of inundation and vegetative cover (Melack et al., 2004). To do so, the methane emission rate per unit area for the mainstem Solimões-Amazon floodplain, representing a mixture of habitats, and using a mean annual flooded area of 42,700 km² (determined as monthly means from Sippel et al. (1998) summed over 12 months and divided by 12) was estimated as ∼40 Mg CH₄ km⁻² y⁻¹.

8.2 Top-Down Estimates With Aircraft Sampling

Several aircraft campaigns provide methane flux estimates for different regions and periods. Beck et al. (2012) estimated fluxes for the lowland Amazon during November as 36 ± 12 mg CH₄ m⁻² d⁻¹ and during May as 43 ± 19 mg CH₄ m⁻² d⁻¹. Miller et al. (2007) collected vertical profiles of methane over 4 years at sites near Santarém and Manaus and calculated average emissions of ∼27 mg CH₄ m⁻² d⁻¹. Wilson et al. (2016) applied the TOMCAT model using aircraft vertical profiles and estimated methane emissions of 36.5 to 41.1 Tg CH₄ y⁻¹in 2010 and 31.6 to 38.8 Tg CH₄ y⁻¹ in 2011 for an area of 5.8 × 10⁶ km⁻²; non-combustion emissions represented 92–98% of total emissions. Pangala et al. (2017) estimated emissions of 42.7 ± 5.6 Tg CH₄ y⁻¹ for an area of 6.77 × 10⁶ km² based on vertical profiles from 2010 to 2013; 10% of emission was attributed to biomass burning.

The most comprehensive record is provided by Basso et al. (2021), who used lower-troposphere vertical profiles of methane concentration from four sites in the Brazilian Amazon basin between 2010 and 2018 and a column budget technique to calculate emissions of 46.2 ± 10.3 Tg CH₄ y⁻¹ with no temporal trend. This finding of no temporal trend during a period with exceptionally high and low river stages and varying inundation areas suggests further examination of how methane fluxes could have varied through these years.

Basso et al. (2021) used the methodology of Gatti L. V. et al. (2021) to estimate fluxes for three subregions and combined these as a weighted flux scaled to an area of 7.25 million km², a region that includes parts of Venezuela, and all of Suriname, French Guyana and Guiana plus coastal watersheds in Brazil and upper Tocantins basin. The large region to the west of their sampling locations near Tefé and Tabatinga was not sampled and has some environments not represented by the regions to the east, such as the Peruvian peatlands and Andean highlands. Based on concurrent measurements of carbon monoxide, 17% of the sources were attributed biomass burning and 83% mainly to wetlands. Causes for seasonality in wetland fluxes and for the larger signals in the northeast are not fully understood, indicating that more studies are needed.

8.3 Top-Down Estimates Using Satellite Data

Based on SCIAMACHY data, Bergamaschi et al. (2009) calculated total Amazon emissions of 47.5 to 53.0 Tg CH₄ y⁻¹ in 2004 for an area of 8.6 × 10⁶ km². Based on an inversion model, Fraser et al. (2014) estimated an emission of 59.0 ± 3.1 Tg CH₄ y⁻¹ from tropical South America (approximately ∼9.7 × 10⁶ km²) in 2010. Tunnicliffe et al. (2020) using inverse modelling estimates and GOSAT measurements reported mean emissions for the Brazilian Amazon wetlands (and not clearly defined) lower than other estimates (9.2 ± 1.8 Tg CH₄ y⁻¹). Wilson et al. (2021) using GOSAT data and an inverse model estimated total emissions from natural, agricultural and biomass burning sources of 38.2 ± 5.3 (between 2010 and 2013) and 45.6 ± 5.2 Tg CH₄ y⁻¹ (between 2014 and 2017) for the Amazon basin (approximately ∼6.0 × 10⁶ km²). Given the different regions represented and the mixture of sources, it is difficult to compare these estimates with each other or with the aircraft or bottom-up values.

8.4 Model Results

Methane emissions for the Amazon basin, extracted from the WetCharts ensemble of wetland models (Bloom et al. (2017) at monthly resolution for 2009 and 2010 by Basso et al. (2021), are 39.4 ± 10.3 Tg CH₄ y⁻¹, and results are similar to emissions derived from aircraft samples except for the northeastern portion of the basin. Gedney et al. (2019) provided estimates of methane flux for the Amazon basin using the JULES model that varied from ∼24 to ∼58 Tg CH₄ y⁻¹, a rather wide range that stems from problems with inundation estimates and parameterization of methane fluxes. As part of an application of ALBM to global lakes (Guo et al., 2020), an estimate for only lakes in the Amazon and Tocantins basins, based a static lake area from Messager et al. (2016) and calibration with data from lakes Janauacá and Calado, yielded estimates of ∼0.7 Tg CH₄ y⁻¹ and ∼0.1 Tg CH₄ y⁻¹, respectively. This estimate is similar to the estimate of ∼0.7 Tg CH₄ y⁻¹ in Mahli et al. (2021) for the same regions based on extrapolation of bottom-up measurements of fluxes and similar lakes areas.

9 Further Studies and Recommendations

Given that an important source of uncertainty in the global methane budget is attributed to emissions from wetlands and other inland waters (Saunois et al., 2020), further improvements in measurements and the understanding of large sources of methane emissions from aquatic ecosystems in the Amazon basin and similar systems elsewhere are clearly a priority. Our review has identified several key ways to do so: 1) Application of remote sensing techniques to better characterize the spatial extent and temporal variations in inundation and aquatic habitats. 2) Increased sampling frequency and duration of methane fluxes and related environmental conditions in all aquatic systems using a combination of approaches including ground-based, aircraft and satellite measurements. 3) Conducting experimental studies to improve understanding of biogeochemical and physical processes. In particular, more studies of the complex roles of microbial assembles and their response to environmental conditions, and of methane emissions in relation to qualities of organic matter and biogeochemical conditions in sediments would improve predictive capabilities. 4) Further development of models appropriate for hydrological and ecological conditions throughout the Amazon basin.

Current hydrological models provide estimates of variations in inundation, and remote sensing products include inundated areas, though the longest time-series underestimate areas in some habitats and high-resolution products are temporally sparse. Mapping inundated lands during low water season poses special challenges in the Amazon basin (Fleischmann et al., 2021). Biogeochemical wetland models do not incorporate key processes and need to use better inundation estimates. For example, Fleischmann et al. (2021) judged that the WAD2M product used in several global methane models does not represent well the inundation dynamics of various wetland complexes in the Amazon basin. Large uncertainties stem from the paucity of measurements of methane fluxes throughout the Amazon basin. Particularly large gaps exist for the Llanos de Moxos, peatlands throughout the Amazon basin, coastal systems, interfluvial wetlands and habitats above 500 m. Streams and medium-sized rivers have very few data. Measurements during low water periods in exposed areas with herbaceous plants and floodable forests are rare. Measurements above and below the dams are available for few hydroelectric projects. Diel, seasonal and interannual variations are very seldom documented. To evaluate potential impacts of increased temperatures and atmospheric CO₂ concentrations on different plants and other organisms would benefit from a combination of experiments and modeling. With the increased availability of in situ sensors and automatic recording systems, improved temporal coverage is possible, though logistic challenges remain. The results from an observing system, as suggested by Bloom et al. (2016) for the Amazon basin, would provide valuable data.

As illustrated for specific lakes or subregions, spatially and temporally representative measurements combined with remote sensing-based inundation and habitat estimates can produce seasonally-weighted fluxes (e.g., Rosenqvist et al., 2002; Barbosa et al., 2021). Mechanistically correct and properly calibrated models provide a complementary approach. Models of autotrophic productivity as a source of the organic carbon that fuels methanogenesis and influences other conditions need to use functional traits appropriate to the ecology of the Amazon. Expanded use of hydrological models linked to carbon supply from uplands to aquatic systems (e.g., Lauerwald et al., 2017; Hastie et al., 2019) is also recommended.

Recent results can be used to develop robust regional estimates of methane fluxes from aquatic environments in the Amazon basin, and these approaches can be applied elsewhere. Multiyear measurements of methane concentrations obtained from aircraft (e.g., Basso et al., 2021) and towers, such as the ATTO facility (e.g., Botía et al., 2020), when combined with atmospheric transport models, provide spatially integrated methane concentrations in the regions of influence. However, attributing sources of the methane requires examination of the inundated area and methane fluxes represented by the gridded regions of influence. A critical aspect is applying uncertainty analyses that incorporate natural variability and evaluate systematic biases of the measurements.

Indeed, incorporating all these approaches to an area as vast and diverse as the Amazon basin is a daunting challenge. Though serendipity and national and international initiatives will continue to influence research opportunities, we suggest a few examples. Promising planned remote sensing missions are SWOT and NISAR (mentioned in section 2). The SWOT mission will simultaneously measure surface water elevation, slope and extent and provide the basis for calculations of river discharge and monitoring of lake water levels. NISAR will complement other SAR missions by providing global 12-days coverage of L-band radar, which penetrates forest canopies to detect inundation under seasonally flooded forests. Cost-effective sampling designs depend on institutional infrastructure, interested and trained personnel, funding and international coordination. Hence, many studies were concentrated in the vicinity of research institutes or universities or were conducted as surveys along major rivers. Less accessible or remote regions or habitats, such as the Llanos de Moxos, other interfluvial wetlands and Andean slopes, are likely to be significant sources of methane and will require extra effort and expense. In contrast, streams and medium-sized rivers are surprisingly under-sampled given their proximity to established research centers. Applications of advanced methods in microbial ecology and experimental manipulations, including characterization of methanogenic and methanotrophic assembles and their temporal variability in abundance and activity, would strengthen modeling efforts and predictive understanding. Creative individuals will continue to develop new ideas and methods and venture into unexplored areas.

Author Contributions

The ideas represented in our review evolved from discussions among all co-authors over several years. JM developed and wrote the manuscript and all co-authors contributed with results and revisions.

Funding

National Aeronautics and Space Administration (NASA grant NNX17AK49G) and the US National Science Foundation (NSF DEB grant 1753856) to JM and SM supported data analysis and writing by JM, SM, and WZ. Sao Paulo Research Foundation (FAPESP grants 2018/14006-4 and 2019/23654-2) supported data analysis by LB. Bundesministerium für Bildung und Forschung (grants 01LB1001A and 01LK1602A) and the International Max Planck Research School for Global Biogeochemical Cycles (IMPRS-gBGC) supported data analysis and writing by SB.

Acknowledgments

SB acknowledges the ATTO team running the site, and Jost Lavric and David Walter for data handling and processing, and thanks Anthony A. Bloom for providing version 1.3.1 of WetCHARTs for the STILT simulations.

Conflict of Interest

The 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.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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