ABSTRACT
In the world’s richest biotope, wetlands, and specially floodplains, remain particularly active zones in terms of nutrients, biodiversity, flows spreading, sediment transfer, and human activities. Today, these sensitive zones are facing increasing human pressures and augmentation in frequency and intensity of extreme hydrological events. The impact of such extreme events on floodplain ecology and biogeochemistry is still difficult to assess. In this context, hydrodynamic models are attractive tools for studying water circulation patterns in the floodplain and exchange with mainstream. However, they require relatively high-quality data of topography, land cover, water levels, and water flows to produce realistic results. In large unmonitored regions, such as the low Amazonian basin, remotely sensed data appear as a solution to gather input data in view of hydrodynamics modeling. Parallelly, the recent and ongoing proliferation of free Earth observation (EO) data brings the challenge of integrating the many heterogeneous geospatial datasets in monitoring and modeling, in view of effective information management. In this study, we aimed to simulate one of the largest flood events (2009–2010) ever recorded for a floodplain of the low Amazon basin. We detailed the steps of generating the input, setting, calibrating, and validating. All these steps involve various EO data products: altimetry, airborne digital elevation model, land cover derived from synthetic aperture radar (SAR) imagery, vegetation height map derived from altimetry (Light Detection and Ranging [LIDAR]) and optical imagery, as well as inundation maps derived from SAR and optical images. Precursory work consists of mapping the zone in term of water level and generating topographic data. Setting the model includes defining the modeling zone and boundary conditions. Calibration is performed over roughness coefficient against in situ water level during a hydrological year. Simulation is validated in terms of vertical accuracy, comparing altimetrics and in situ water levels, as well as in terms of horizontal accuracy, comparing simulated flood extent against inundation maps deduced from optical- and SAR images.
