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Abstract Floodplain lakes are widespread and ecologically important throughout tropical river systems, however data are rare that describe how temporal variations in hydrological, meteorological and optical conditions moderate stratification and mixing in these shallow lakes. Using time series measurements of meteorology and water‐column temperatures from 17 several day campaigns spanning two hydrological years in a representative Amazon floodplain lake, we calculated surface energy fluxes and thermal stratification, and applied and evaluated a 3‐dimensional hydrodynamic model. The model successfully simulated diel cycles in thermal structure characterized by buoyancy frequency, depth of the actively mixing layer, and other terms associated with the surface energy budget. Diurnal heating with strong stratification and nocturnal mixing were common; despite considerable heat loss at night, the strong stratification during the day meant that mixing only infrequently extended to the bottom at night. Simulations indicated that the diurnal thermocline up and downwelled creating lake‐wide differences in near‐surface temperatures and mixing depths. Infrequent full mixing creates conditions conducive to anoxia in these shallow lakes given their warm temperatures. 

Abstract The CO₂flux () from lakes to the atmosphere is a large component of the global carbon cycle and depends on the air–water CO₂concentration gradient (ΔCO₂) and the gas transfer velocity (k). Both ΔCO₂andkcan vary on multiple timescales and understanding their contributions to is important for explaining variability in fluxes and developing optimal sampling designs. We measured and ΔCO₂and derivedkfor one full ice‐free period in 18 lakes using floating chambers and estimated the contributions of ΔCO₂andkto variability. Generally,kcontributed more than ΔCO₂to short‐term (1–9 d) variability. With increased temporal period, the contribution ofkto variability decreased, and in some lakes resulted in ΔCO₂contributing more thankto variability over the full ice‐free period. Increased contribution of ΔCO₂to variability over time occurred across all lakes but was most apparent in large‐volume southern‐boreal lakes and in deeper (> 2 m) parts of lakes, whereaskwas linked to variability in shallow waters. Accordingly, knowing the variability of bothkand ΔCO₂over time and space is needed for accurate modeling of from these variables. We conclude that priority in assessments should be given to direct measurements of at multiple sites when possible, or otherwise from spatially distributed measurements of ΔCO₂combined withk‐models that incorporate spatial variability of lake thermal structure and meteorology. 

Abstract Floodplains lakes are abundant in the Amazon basin and are important methane sources to the atmosphere. Existing biogeochemical models require modifications and inclusion of hydrodynamic processes operative in shallow, warm waters to be applied to these aquatic ecosystems. We modified a 1‐dimensional process‐based, lake biogeochemical model and combined a 3‐dimensional hydrodynamic model to suit Amazon floodplains. We evaluated the combined model's performance simulating methane concentrations and fluxes and several related processes in the open lake and an embayment of a well‐studied Amazon lake. Parameters for calibration were selected through sensitivity tests using a machine learning‐based algorithm, classification, and regression trees. Comparison between simulated and measured fluxes indicate generally good agreement in seasonal patterns and magnitudes. Comparisons of near‐surface concentrations varied with no clear patterns. Simulations of methane concentrations at near‐surface and near‐bottom, and diffusive emissions are most sensitive to carbon mineralization rate, Q₁₀factors for methanogenesis and oxidation, and methane oxidation potential. Modeled rates of planktonic photosynthesis were generally lower than measurements, though simulated planktonic respiration was often similar to measurements. Simulated rates of methane oxidation were considerably lower, with a few exceptions, than measurements of methane oxidation in oxic water of the lake. Improvements of results of the linked hydrodynamic‐biogeochemical model will result from inclusion of advective transport, use of parameter values appropriate for tropical waters, especially for methane oxidation and photosynthesis, and addition of changes in hydrostatic pressure to model of ebullition. 

Abstract 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 by in-situ and 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. 

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.