نبذة مختصرة : Anthropogenic pressures have been having an increasingly significant impact on climate for the last decades, further exacerbating issues related to water resource management, both in terms of quantity and quality. The Amazon basin represents a fantastic case for observing these disturbances caused by human activities. With an area of over six million km², an integrated approach is required to dynamically monitor water and suspended sediment fluxes exchanged between the river channel and its floodplain, by coupling hydrological and sedimentary modelling with remote sensing observations. To address these issues, the physically-based agro-hydroenvironmental model SWAT was adapted to account for the effect of flooding and floodplain dynamics. To begin with, it integrates observations from the SMOS mission, thus offering a more realistic delineation of the floodplains. Previous hydraulic modules present in the rivers, as well as the sediment transport formalisms implemented by default, were redesigned and coupled with a floodplain module developed in order to model a continuity of water and sediment fluxes alongside the alluvial network. To calibrate and to validate the developed model, SWAT-FloodPlain, altimetry data were used and a new methodology was created to specifically validate the simulations in the floodplain. In the river, more than 1100/3800 virtual stations were implemented with an average R² of 0.62 for the Jason-2 and Jason-3 stations and an average R² of 0.75 for the Sentinel-3A stations. Concerning the virtual stations located in floodplains, there are 840 Jason stations with a mean R² of 0.61 and 2570 Sentinel-3A stations with a mean R² of 0.75. As so, surface water storage in the floodplains of the Amazon basin is about 1800 km3 per year, or one third of the average discharge at the basin outlet. The Negro/Branco and Madeira River basins are the main contributors in terms of water storage, with respectively 480 km3 and 312 km3 of water stored each year during the 2000-2018 period. Seasonal flood ...
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