Given altimetry measurements, the identification capability of time varying inflow dischargeQin(t)and13the Strickler coefficientK(defined as a power-law inhthe water depth) of the 1D river Saint-Venant model is investi-14gated. Various altimetry satellite missions provide water level elevation measurements of wide rivers, in particular the15future Surface Water and Ocean Topography (SWOT) mission. An original and synthetic reading of all the available16information (data, wave propagation and the Manning-Strickler’s law residual) are represented on the so-callediden-17tifiability map.The latter provides in the space-time plane a comprehensive overview of the inverse problem features.18Inferences based on Variational Data Assimilation (VDA) are investigated at the limit of the data-model inversion19capability : relatively short river portions, relatively infrequent observations, that is inverse problems presenting a20lowidentifiability index. The inflow dischargeQin(t)is infered simultaneously with the varying coefficientK(h). The21bed level is either given or infered from a lower complexity model. The experiments and analysis are conducted for22different scenarios (SWOT-like or multi-sensors-like). The scenarios differ by the observation frequency and by the23identifiability index. Sensitivity analyses with respect to the observation errors and to the first guess values demon-24strate the robustness of the VDA inferences. Finally this study aiming at fusing relatively sparse altimetric data and25the 1D Saint-Venant river flow model highlights the spatiotemporal resolution lower limit, also the great potential in26terms of discharge inference including for a single river reach.