The spatio-temporal land cover dynamics of a medium‐size floodplain system along the Amazon/Solimões River (Janauacá Lake, 786 km 2) and their hydrological impacts are studied through remote sensing and modeling. Hence, the analysis of 5 satellite-derived land cover maps (1972-2016 period) reveals a decrease in natural environments (from 65% to 35%) to the benefit of anthropic classes (from 17% to 51%) through deforestation vectors (two highways and lake banks). Deforestation is a non-stationary process with significant increase over specific subperiods (1972-1986, and 2005-2016). It occurs in stages with conversions into secondary vegetation then into non-natural environments. 7 land cover scenarios (5 satellite-derived, 1 deforested and 1 forest, used as reference) are used as inputs to run simulations with the same meteorology over the 2006-2018 period. Beside high ( ≥ 24%) and low ( ≤ 7%) interannual variability of runoff-rainfall ratio (RRR) and evapotranspiration (ET), the numerical experiments evidence, on an annual scale, the RRR decreases and the ET increases with deforestation increases. Deforested scenario suggests a convergence: for the RRR, around 0.34 (-87%) and for the ET, around 1146 mm.yr -1 (+6%). At the seasonal scale, the landuse/landcover changes (LUCC) induce positive wet season ET anomaly (<9%) and large negative dry season RRR anomaly (-87%). The highest LUCC-induced disturbances (from -15% to 18%) in the FP mixture are recorded at seasonal scale, during LW and RW and, at interannual scale, during dry and normal HY. The LUCC-induced disturbances patterns of FP mixture mainly concern river and runoff. They are different regarding the hydrological period or HY type. Our experiments suggest the existence of a tipping point between present land cover (2016) and fully deforested cover associated with reversal phenomena and enhancing of seasonal and interannual LUCC-induced disturbance. At last, the model shows the LUCC augment the vulnerability associated with drought periods.

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.