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.

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.

Suspended sediments (SS) contribute to the maintenance of several ecosystems. However, intense soil erosion can lead to environmental, social, and economic impacts. South America (SA) has very high erosion and sediment transport rates. Here we present a detailed description of the spatio-temporal dynamics of natural SS flows in SA using the continental sediment model MGB-SED AS. We evaluate the model with daily in-situ data from 595 stations, information from regional studies and a global model. The model performance analysis showed that, in general, there was a better agreement between simulated and observed data than with the information found in regional studies and of the global model. The use of the hydrodynamic propagation method has allowed a better representation of sediment flows in rivers and floodplains. Based in the calibrated model results, SA delivers 1.00×109 t/year of SS to the oceans, in which the Amazon (4.36×108 t/year), Orinoco (1.37×108 t/year), La Plata (1.11×108 t/year) and Magdalena (3.26×107) rivers are the main suppliers. The floodplains play an essential role, retaining about 12% (2.40×108 t/year) of the SS loads reaching the rivers. In this study, datasets related to SS flows in SA were generated and can be used to support other large-scale researches or policymakers and stakeholders for adequate management of continental land use.

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.