Sediment flows dynamics (erosion, transport and deposition) have been disrupted in South America (SA), a continent with the highest erosion and sediment transport rates globally. However, the magnitude and spatial distribution of the main drivers of changes have been poorly identified and explored. Here, we performed simulations using a hydrological-hydrodynamic-sediment model to comprehensively estimate the spatial and temporal sediment changes and trends in SA from 1984 to 2019. We found that 51% of the main SA rivers experienced significant changes in simulated sediment transport (QST) over this period, with 36% due to Amazon deforestation and river damming and 15% due to precipitation changes. We also estimated a 10% reduction in the average sediment delivery to the oceans. Deforestation was responsible for QST changes above 80% in some Amazon sites, and hydropower expansion led to a greater reduction of sediment flows (as high as 80-100%) in the Tocantins, Uruguay, Upper Paraná, lower São Francisco, Desaguadero, and Negro rivers. In addition, our results suggest that reservoirs built in the Amazon region in the last decade are also affecting sediment transport. Our modeling outputs provide unprecedented information about the status of sediment dynamics in SA, and a means to develop evidence-based strategies and transboundary policies related to continental-wide sediment dynamics and the conservation and restoration of ecosystems.

The accuracy of hydrological model predictions is limited by uncertainties in model structure and parameterization, and observations used for calibration, validation and model forcing. While calibration is usually performed with discharge estimates, the internal model processes might be misrepresented, and the model might be getting the “right results for the wrong reasons”, thus compromising model reliability. An alternative is to calibrate model parameters with remote sensing (RS) observations of the water cycle. Previous studies highlighted the potential of RS-based calibration to improve discharge estimates, focusing less on other variables of the water cycle. In this study, we analyzed in detail the contribution of five RS-based variables (water level (h), flood extent (A), terrestrial water storage (TWS), evapotranspiration (ET) and soil moisture (W)) to calibrate a coupled hydrologic-hydrodynamic model for a large Amazon sub-basin with extensive floodplains. Single-variable calibration experiments with all variables were able to improve discharge KGE from around 6.1% to 52.9% when compared to a priori parameter sets. Water cycle representation was improved with multi-variable calibration: KGE for all variables were improved in the evaluation period. By analyzing different calibration setups, a consistent selection of complementary variables for model calibration resulted in a better performance than incorporating all RS variables into the calibration. By looking at multiple RS observations of the water cycle, inconsistencies in model structure and parameterization were found, which would remain unknown if only discharge observations were considered.

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.

River floodplains and reservoirs interact throughout a basin drainage network, defining a coupled human-water system with multiple feedbacks. Recent modeling developments have aimed to improve the representation of such processes at regional to continental scales. However, most large-scale hydrological models adopt simplified lumped reservoir schemes, where an offline routine is run with inflows estimated by the model, with limited consideration of the complementarity between floodplains and reservoirs on attenuating floods at regional scale. This paper presents a novel approach that fully couples river-floodplain-reservoir hydrodynamic and hydrological models, significantly improving the representation of reservoir dynamics and operation in the river-floodplain-reservoir continuum at large scale and across multiple dam cascades. The model is applied to the Paraná River Basin with explicit simulation of 31 large dams and river hydraulic variables at basin scale. Three types of reservoir bathymetry representation are compared, from lumped to distributed methods, combined with three reservoir operation schemes and varying degrees of input data requirement within two parameterization scenarios (global and regional setups). The operation schemes were more relevant than the reservoir bathymetry representation to estimate downstream flows and water levels. While the data-driven operation scheme, based on linear regressions between observed water levels and dam outflows, provided the best estimates of both active storage and discharges, the more generic operation reasonably estimated discharges and peak attenuation, albeit not as accurately for active storage. The global parameterization of reservoir operation resulted in poorer performance compared to the regional-based one, but it satisfactorily modeled discharge and peak attenuation. Regarding the reservoir bathymetry representation, a basin scale comparison of the lumped and distributed schemes indicated the inability of the former to represent backwater effects. This was further corroborated by validating the longitudinal water level profile of Itaipu dam with ICESat satellite altimetry data. Finally, the model was used to show the complementarity between floodplains and reservoirs on attenuating floods at regional scale. Large scale models should move beyond offline coupling strategies, and include regional-based, data-driven reservoir operation schemes together with a distributed representation of reservoir bathymetry into river-floodplain hydraulic schemes. This will largely improve the estimation of river discharges, water levels and flood storage, and thus the model ability to represent the regional scale river-floodplain-reservoir continuum.

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.

This study intends to integrate heterogeneous remote sensing observations and hydrological modelling into a simple framework to monitor hydrological variables in the poorly gauged Congo River basin (CRB). It focuses on the possibility to retrieve effective channel depths and discharges all over the basin in near real time (NRT). First, this paper discusses the complexity of calibrating and validating a hydrologic–hydrodynamic model (namely the MGB model) in the CRB. Next, it provides a twofold methodology for inferring discharge at newly monitored virtual stations (VSs, crossings of a satellite ground track with a water body). It makes use of remotely sensed datasets together with in-situ data to constrain, calibrate and validate the model, and also to build a dataset of stage/discharge rating curves (RCs) at 709 VSs distributed all over the basin. The model was well calibrated at the four gages with recent data (Nash-Sutcliffe Efficiency, NSE> 0.77). The satisfactory quality of RCs basin-wide (mean NSE between simulated discharge and rated discharge at VSs, NSEmean = 0.67) is an indicator of the overall consistency of discharge simulations even in ungauged upstream sub-basins. This RC dataset provides an unprecedented possibility of NRT monitoring of CRB hydrological state from the current operational satellite altimetry constellation. The discharges estimated at newly monitored locations proved to be consistent with observations. They can be used to increase the temporal sampling of water surface elevation (WSE) monitoring from space with no need for new model runs. The RC located under the fast sampling orbit of the SWOT satellite, to be flown in 2022, will be used to infer daily discharge in major contributors and in the Cuvette Centrale, as soon as data is released.