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.

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.

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.