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[1] This paper presents the first application and validation of a 2D hydrodynamic model of the Amazon at a large spatial scale. The simulation results suggest that a significantly higher proportion of total flow is routed through the floodplain than previously thought. We use the hydrodynamic model LISFLOOD-FP with topographic data from the Shuttle Radar Topography Mission to predict floodplain inundation for a 240 Â 125 km section of the central Amazon floodplain in Brazil and compare our results to satellite-derived estimates of inundation extent, existing gauged data and satellite altimetry. We find that model accuracy is good at high water (72% spatial fit; 0.99 m root mean square error in water stage heights), while accuracy drops at low water (23%; 3.17 m) due to incomplete drainage of the floodplain resulting from errors in topographic data and omission of floodplain hydrologic processes from this initial model. Citation: Wilson, M.,

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[1] The proposed Surface Water and Ocean Topography (SWOT) mission would provide measurements of water surface elevation (WSE) for characterization of storage change and discharge. River channel bathymetry is a significant source of uncertainty in estimating discharge from WSE measurements, however. In this paper, we demonstrate an ensemble-based data assimilation (DA) methodology for estimating bathymetric depth and slope from WSE measurements and the LISFLOOD-FP hydrodynamic model. We performed two proof-of-concept experiments using synthetically generated SWOT measurements. The experiments demonstrated that bathymetric depth and slope can be estimated to within 3.0 microradians or 50 cm, respectively, using SWOT WSE measurements, within the context of our DA and modeling framework. We found that channel bathymetry estimation accuracy is relatively insensitive to SWOT measurement error, because uncertainty in LISFLOOD-FP inputs (such as channel roughness and upstream boundary conditions) is likely to be of greater magnitude than measurement error.

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