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We analyze the observed relationship between sea surface temperatures (SSTs) over the Atlantic Ocean and the normalized difference vegetation index (NDVI) in the Orinoco and Amazon basins. Monthly correlations between anomalies of NDVI and SSTs are computed for different regions of the Atlantic Ocean. We also use a mixture of observations and reanalysis products to analyze lagged correlations. Our results show that during August–September (i.e., the dry-to-wet transition season), changes in NDVI in the central Amazon and the so-called Arc of Deforestation are associated with precedent changes in the SSTs of the tropical North Atlantic (TNA) and the Caribbean (CABN) during March–June. Anomalous warming of the CABN and TNA generates changes in surface winds and atmospheric moisture transport in the region, decreasing precipitation, with consequent decreases of soil moisture, moisture recycling, and NDVI. An increase in TNA and CABN SSTs during March–June is also associated with an increase of NDVI over the northern Orinoco during June (i.e., the wet season). Unlike in the southern Amazon, precipitation and soil moisture in the Orinoco basin do not exhibit significant changes associated with SSTs. By contrast, atmospheric moisture recycling and transport increase with warmer SSTs in the TNA. Therefore, for the Orinoco, the link between SSTs and NDVI appears to be related not to changes in precipitation but to changes in moisture recycling. However, the causality between these changes needs to be further explored. These findings highlight the contrasting responses of the Amazon and Orinoco basins to Atlantic temperatures and the dominant role of atmospheric moisture transport linking these responses.

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 1000 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. Ministry of Education (MOE) Nanyang Technological University The work was part of the SABERES project financed by the BNPParibas Foundation as part of its “Climate & Biodiversity Initiative” program 2019. A.S.F. was supported by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnologico, ´ Brazil) [grant number 141161/2017-5]. F.P., J.F.F., M.P.B. and F.A. received support from CNES (SWOT-ST project SWOT for SOUTH AMERICA, ID: 6018-4500066497). F.P. and M.P.B. also received support from CNES (SWOT-ST project SWOT Wetlands Hydrology Monitoring). F.P. is supported by the IRD Groupement De Recherche International (GDRI) SCaHyLab. J.M.M. received support from NASA IDS grant NNX17AK49G and the US National Science Foundation (Division of Environmental Biology, grant 1753856). E.P. acknowledges Nanyang Technological University (SUG-NAP EP3/19) and Ministry of Education of Singapore (AcRF Tier1 RT 06/19 and AcRF Tier2 RT 11/ 21). A.F.R. acknowledges the Research Foundation of Sao ˜ Paulo (FAPESP, grant #2019/24049-5). S.W. has been supported by the French AMANECER-MOPGA project funded by ANR and IRD (ref. ANR18-MPGA-0008). M.C. received funding from NASA IDS grant NNX17AK49G. The SWAF dataset development was financed by the CATDS and the SWOT-AVAL programs by CNES.

Projections of climate change are essential to guide sustainable development plans in the tropical Andean countries such as Peru. This study assessed the projections of precipitation and potential evaporation, rain erosive potential, and precipitation concentration in the Mantaro River Basin, in the Peruvian Andes, which is important for agriculture and energy production in Peru. We assumed the Intergovernmental Panel on Climate Change (IPCC) A1B greenhouse gas emission scenario and simulated the global climate change by the HadCM3 global climate model. Due to the steepness of the mountain slopes and the narrowness of the river valley, this study uses the downscaling of the global model simulations by the regional Eta model down to 20-km resolution. The downscaling projections show decrease in the monthly precipitation with respect to the baseline period, especially during the rainy season, between February and April, until the end of the 21st century. Meanwhile, a progressive increase in the monthly evaporation from the baseline period is projected. The Modified Fournier Index (MFI) shows a statistically significant downward trend in the Mantaro River Basin, which suggests a possible reduction in the rain erosive potential. The Precipitation Concentration Index (PCI) shows a statistically significant increasing trend, which indicates increasingly more irregular temporal distribution of precipitation towards the end of the century. The results of this study allow us to conclude that there should be a gradual increase in water deficit and precipitation concentration. Both changes can be negative for agriculture, power generation, and water supply in the Mantaro River Basin in Peru.

Soil degradation by water erosion has been accelerated by human activities. This process is aggravated in the Andes region due to steep slopes, sparse vegetation cover, and sporadic but high intensity rainfall, which together with a shallow soil depth, increases soil erosion risk. The objective of this study was to analyze the soil erosion risk, associated with A1B climate change scenario over the twenty-first century, for the Mantaro River basin (MRB), Peruvian Andes. The temporal analyses revealed maintenance of current soil erosion risk along the twenty-first century in almost all the MRB, whose current risk is either “very severe” or “extremely severe”. At the sub-basin level, for those located in the center and northern MRB, progressive increases were observed in the average erosion rate by the end of this century, increasing the soil erosion risk. In sub-basins under greater influence of the Andes, this risk was classified as “moderate” and remained this way throughout the century, despite the increase in rainfall erosive potential simulated for these. In annual terms, there was a significant trend of decreasing rainfall erosivity and increasing the concentration of rainfall simulated based on A1B climate change scenario. Because the A1B scenario affects rainfall erosivity mainly during the rainy season, this causes a risk to the environmental sustainability and future agricultural activities.

In this paper, we present a methodology to map inland water in tropical areas under dense vegetation at high spatial and temporal resolution using multi-source remote sensing data. A new inundation product (SWAF-HR) is presented. It is characterized by a high spatial resolution (30', 1 km) and high temporal resolution (3 days). The SWAF-HR product is estimated over the Amazon basin for the 2010-2016 period. It is based on a downscaling procedure and the synergistic use of: (1) water surface fraction at coarse spatial resolution from an L-band passive microwave sensor (Soil Moisture and Ocean Salinity - SMOS), (2) Global Surface Water Occurrence from Landsat (GSWO) and (3) the Digital Elevation Model (DEM) Multi-Error-Removed-Improved-Terrain (MERIT) based on the Shuttle Radar Topography Mission (SRTM). Thanks to the high capability of L-band microwave emission to reveal surface water under all-weather conditions and beneath the vegetation, the inundated area extent estimated by the SWAP-HR product is always larger than GSWO estimates obtained by the optical sensor (Landsat). SWAF-HR data is compared to ESA CCI and IGBP land covers, two SAR images and flooded areas over the Purus basin computed by the MGB-IPH model simulation. The results show the coherence of spatial and temporal dynamics of the SWAF-HR data. We show that the flooded area of the Branco River floodplain in Roraima (Brazil) varies from 0.2 x 10(4) to 2.7 x 10(4) km(2) whereas the extent of the Bolivian floodplain (Llanos de Moxos) inundation ranges between 0.8 x 10(4) and 8.1 x 10(4) km(2) during 2010-2016. The flooded area in the Branco floodplain gradually decreased from 2010 to 2015 but in 2016, the flooded area has increased during the rainy season. During 2010-2016, the minimum of the inundated surface extent was reached during 2015-2016 reflecting to a drought event related to ENSO. The most important uncertainties of the DEM are located over tropical areas but this information is essential in the downscaling procedure. Therefore, we investigate the impact of the choice of the DEM for the downscaling procedure. It is found that the choice of the DEM introduces 5% of error in the instantaneous water surface extent estimate but can reach up to 10% in the flood probability estimations over seven years. This new SWAF-HR product will be helpful for the understanding of the water, carbon and biogeochemical cycles of the Amazon.

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 altering the hydrological regime 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 Parana 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.