Topography is critical information for water resources management in lakes, and remote sensing provides a unique opportunity to estimate it in ungauged regions. We introduce here a new method which estimates near shore topography of water bodies based on a flood frequency map and time series of water levels by assuming the equivalence between flood frequency and water level exceedance probability at a given area. Test cases are performed for two lakes and 12 hydropower reservoirs in Brazil using the proposed Flood2Topo app. This new application generates the bottom level pixel by pixel and a level-area-active storage relationships directly from the topography map, without the need to fit functions. Flood extent estimates from the Landsat based JRC Global Surface Water (GSW) dataset, current state-of-the-art, were used to run Flood2Topo, together with water levels from satellite altimetry and in-situ gauges. Results show bottom level root mean square deviation (RMSD) values of 18.5 cm and 146 cm for Lake Poopó (Bolivia) and Lake Curuai (Amazon basin), respectively. For reservoir active storage, RMSD normalized values ranged from 2% to 11.09% for 11 reservoirs (average NRMSD of 6.39 %). The method can be applied to any area seasonally flooded, for instance, it is applicable in 35.8 % (86%) of the global water surface area mapped by occurrence map from GSW dataset, when considering the number of pixels with occurrence between 0 and 95% (99%) over 35 years. This is a promising tool for obtaining data for hydrodynamic simulations and monitoring of ungauged water bodies.

Water fluxes in the Amazon River floodplain affect hydrodynamic and ecological processes from local to global scales. Nevertheless, these fluxes remain poorly understood due to difficult access and limited data. In this study, we characterize the hydrodynamics of eight floodplain units of the central Amazon River (40’000 km2) using the 2D hydraulic model HEC-RAS. High resolution modeling improved the representation of river and floodplain discharge, water surface elevation (77 cm accuracy) and flood extent (~80% - high water period, ~52% -low water period). We have learned 13 lessons about river and floodplain hydrodynamics from the modeling. The most remarkable lessons are that the floodplain is organized in units of about 80 km with upstream inflow and downstream outflow. These gross flows are much larger than the net flows with values of up to 20% of the Amazon River discharge and a residence time around 6 days during floods (several months during low water period). Water extent does not a have strong interannual variability during floods as the volume stored in the floodplain, possibly due to topographic constrains. Significant flood extent and volume hysteresis, as well as active flow and storage zones on the floodplain, highlight the complexity of floodplain hydrodynamics. Extreme floods strongly impact the onset and duration of the flood of up to 2 months and, consequently, on the period of high connectivity with the river. These findings are important for understanding carbon and sediment fluxes, and the effects of climate change on water fluxes and riparian communities.

As the largest river basin on Earth, the Amazon is of major importance to the world’s climate and water resources. Over the past decades, advances in satellite-based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin-scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of “rainfall hotspots” in the Andes-Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology-oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space-time resolution, this review aims to guide future research agenda towards an integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure.

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 derived from remote sensing-based products, hydrological models and multi-source products. Spatial resolutions range from 12.5 m to 25 km, and temporal resolution from static to monthly intervals, covering up to a few decades. Overall, 26% of the lowland Amazon basin is estimated as subject to inundation by at least one product. The long-term maximum inundated area across the entire basin (lowland areas with elevation < 500 m) is estimated at 599,700 ± 81,800 km² if considering only higher quality SAR-based products and 490,300 ± 204,800 km² if considering 18 basin-scale datasets. However, even the highest resolution SAR-based product underestimates the local maximum values, as estimated by subregional products, suggesting a basin-wide underestimation of ~10%. The minimum inundation extent shows greater disagreements among products than the maximum extent: 139,300 ± 127,800 km² for SAR-based products and 112,392 ± 79,300 km² for the overall average. 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 mainstem rivers, 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 inter-comparison 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.