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Extensive floodplains throughout the Amazon basin support important ecosystem services and influence global water and carbon cycles. A recent change in the hydroclimatic regime of the region, with increased rainfall in the northern portions of the basin, has produced record-breaking high water levels on the Amazon River mainstem. Yet, the implications for the magnitude and duration of floodplain inundation across the basin remain unknown. Here we leverage state-of-the-art hydrological models, supported byin-situand remote sensing observations, to show that the maximum annual inundation extent along the central Amazon increased by 26% since 1980. We further reveal increased flood duration and greater connectivity among open water areas in multiple Amazon floodplain regions. These changes in the hydrological regime of the world’s largest river system have major implications for ecology and biogeochemistry, and require rapid adaptation by vulnerable populations living along Amazonian rivers.

 

Freshwater ecosystem contributions to the global methane budget remains the most uncertain among natural sources. With warming and accompanying carbon release from thawed permafrost and thermokarst lake expansion, the increase of methane emissions could be large. However, the impact and relative importance of various factors related to warming remain uncertain. Based on diverse lake characteristics incorporated in modeling and observational data, we calibrate and verify a lake biogeochemistry model. The model is then applied to estimate global lake methane emissions and examine the impacts of temperature increase for the first and the last decades of the 21st century under different climate scenarios. We find that current emissions are 24.0 ± 8.4 Tg CH₄ yr⁻¹from lakes larger than 0.1 km², accounting for 11% of the global total natural source as estimated based on atmospheric inversion. Future projections under the RCP8.5 scenario suggest a 58%–86% growth in emissions from lakes. Our model sensitivity analysis indicates that additional carbon substrates from thawing permafrost may enhance methane production under warming in the Arctic. Warming enhanced methane oxidation in lake water can be an effective sink to reduce the net release from global lakes.

 

Tropical floodplains are an important source of methane (CH₄) to the atmosphere, and ebullitive fluxes are likely to be important. We report direct measurements of CH₄ebullition in common habitats on the Amazon floodplain over two years based on floating chambers that allowed detection of bubbles, and submerged bubble traps. Ebullition was highly variable in space and time. Of the 840 floating chamber measurements (equivalent to 8,690 min of 10‐min deployments), 22% captured bubbles. Ebullitive CH₄fluxes, measured using bubble traps deployed for a total of approximately 230 days, ranged from 0 to 109 mmol CH₄m⁻² d⁻¹, with a mean of 4.4 mmol CH₄m⁻² d⁻¹. During falling water, a hydroacoustic echosounder detected bubbles in 24% of the 70‐m segments over 34 km. Ebullitive flux increased as the water level fell faster during falling water periods. In flooded forests, highest ebullitive fluxes occurred during falling water, while in open water and herbaceous plant habitats, higher ebullitive fluxes were measured during low water periods. The contribution of diffusive plus ebullitive CH₄flux represented by ebullition varied from 1% (high and rising water in open water of the lake) to 93% (falling water in flooded forests) based on bubble traps. Combining ebullitive and diffusive fluxes among habitats in relation to variations in water depth and areal coverage of aquatic habitats provides the basis for improved floodplain‐wide estimates of CH₄evasion.

 

Seasonally flooded forests along tropical rivers cover extensive areas, yet the processes driving air‐water exchanges of radiatively active gases are uncertain. To quantify the controls on gas transfer velocities, we combined measurements of water‐column temperature, meteorology in the forest and adjacent open water, turbulence with an acoustic Doppler velocimeter, gas concentrations, and fluxes with floating chambers. Under cooling, measured turbulence, quantified as the rate of dissipation of turbulent kinetic energy (ε), was similar to buoyancy flux computed from the surface energy budget, indicating convection dominated turbulence production. Under heating, turbulence was suppressed unless winds in the adjacent open water exceeded 1 m/s. Gas transfer velocities obtained from chamber measurements ranged from 1 to 5 cm/hr and were similar to or slightly less than predicted using a turbulence‐based surface renewal model computed with measuredεandεpredicted from wind and cooling.

 

Computational advances reveal opportunities for more sustainable hydropower development in large transboundary river basins. 

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. Based on available measurements of methane emission and areal extent, seven types of aquatic systems are considered: streams and rivers, lakes, seasonally flooded forests, seasonally flooded savannas and other interfluvial wetlands, herbaceous plants on riverine floodplains, peatlands, and hydroelectric reservoirs. We evaluate the adequacy of sampling and of field methods plus atmospheric measurements, as applied to the Amazon basin, summarize published fluxes and regional estimates using bottom-up and top-down approaches, and discuss current understanding of biogeochemical and physical processes in Amazon aquatic environments and their incorporation into mechanistic and statistical models. Recommendations for further study in the Amazon basin and elsewhere include application of new remote sensing techniques, increased sampling frequency and duration, experimental studies to improve understanding of biogeochemical and physical processes, and development of models appropriate for hydrological and ecological conditions. 

The Amazon River basin harbors some of the world’s largest wetland complexes, which are of major importance for biodiversity, the water cycle and climate, and human activities. Accurate estimates of inundation extent and its variations across spatial and temporal scales are therefore fundamental to understand and manage the basin’s resources. More than fifty inundation estimates have been generated for this region, yet major differences exist among the datasets, and a comprehensive assessment of them is lacking. Here we present an intercomparison of 29 inundation datasets for the Amazon basin, based on remote sensing only, hydrological modeling, or multi-source datasets, with 18 covering the lowland Amazon basin (elevation < 500 m, which includes most Amazon wetlands), and 11 covering individual wetland complexes (subregional datasets). Spatial resolutions range from 12.5 m to 25 km, and temporal resolution from static to monthly, spanning up to a few decades. Overall, 31% of the lowland basin is estimated as subject to inundation by at least one dataset. The long-term maximum inundated area across the lowland basin is estimated at 599,700 ± 81,800 km² if considering the three higher quality SAR-based datasets, and 490,300 ± 204,800 km² if considering all 18 datasets. However, even the highest resolution SAR-based dataset underestimates the maximum values for individual wetland complexes, suggesting a basin-scale underestimation of ~10%. The minimum inundation extent shows greater disagreements among datasets than the maximum extent: 139,300 ± 127,800 km² for SAR-based ones and 112,392 ± 79,300 km² for all datasets. Discrepancies arise from differences among sensors, time periods, dates of acquisition, spatial resolution, and data processing algorithms. The median total area subject to inundation in medium to large river floodplains (drainage area > 1,000 km²) is 323,700 km². The highest spatial agreement is observed for floodplains dominated by open water such as along the lower Amazon River, whereas intermediate agreement is found along major vegetated floodplains fringing larger rivers (e.g., Amazon mainstem floodplain). Especially large disagreements exist among estimates for interfluvial wetlands (Llanos de Moxos, Pacaya-Samiria, Negro, Roraima), where inundation tends to be shallower and more variable in time. Our data intercomparison helps identify the current major knowledge gaps regarding inundation mapping in the Amazon and their implications for multiple applications. In the context of forthcoming hydrology-oriented satellite missions, we make recommendations for future developments of inundation estimates in the Amazon and present a WebGIS application (https://amazon-inundation.herokuapp.com/) we developed to provide user-friendly visualization and data acquisition of current Amazon inundation datasets. 

Measurements of turbulence, as rate of dissipation of turbulent kinetic energy (ε), adjacent to the air-water interface are rare but essential for understanding of gas transfer velocities (k) used to compute fluxes of greenhouse gases. Variability in ε is expected over diel cycles of stratification and mixing. Monin-Obukhov similarity theory (MOST) predicts an enhancement in ε during heating (buoyancy flux, β+) relative to that for shear (u*w 3/κz where u*w is water friction velocity, κ is von Karman constant, z is depth). To verify and expand predictions, we quantified ε in the upper 0.25 m and below from profiles of temperature-gradient microstructure in combination with time series meteorology and temperature in a tropical reservoir for winds <4 m s−1. Maximum likelihood estimates of near-surface ε during heating were independent of wind speed and high, ∼5 × 10−6 m2 s−3, up to three orders of magnitude higher than predictions from u*w 3/κz, increased with heating, and were ∼10 times higher than during cooling. k, estimated using near-surface ε, was ∼10 cm hr−1, validated with k obtained from chamber measurements, and 2–5 times higher than computed from wind-based models. The flux Richardson number (Rf) varied from ∼0.4 to ∼0.001 with a median value of 0.04 in the upper 0.25 m, less than the critical value of 0.2. We extend MOST by incorporating the variability in Rf when scaling the influence of β+ relative to u*w 3/κz in estimates of ε, and by extension, k, obtained from time series meteorological and temperature data.