Technologies such as aerial photogrammetry allow production of 3D topographic data including complex environments such as urban areas. Therefore, it is possible to create High Resolution (HR) Digital Elevation Models (DEM) incorporating thin above ground elements influencing overland flow paths. Even though this category of big data has a high level of accuracy, there are still errors in measurements and hypothesis under DEM elaboration. Moreover, operators look for optimizing spatial discretization resolution in order to improve flood models computation time. Errors in measurement, errors in DEM generation, and operator choices for inclusion of this data within 2D hydraulic model, might influence results of flood models simulations. These errors and hypothesis may influence significantly flood modelling results variability. The purpose of this study is to investigate uncertainties related to (i) the own error of high resolution topographic data, and (ii) the modeller choices when including topographic data in hydraulic codes. The aim is to perform a Global Sensitivity Analysis (GSA) which goes through a Monte-Carlo uncertainty propagation, to quantify impact of uncertainties, followed by a Sobol indices computation, to rank influence of identified parameters on result variability. A process using a coupling of an environment for parametric computation (Prom{e}th{e}e) and a code relying on 2D shallow water equations (FullSWOF 2D) has been developed (P-FS tool). The study has been performed over the lower part of the Var river valley using the estimated hydrograph of 1994 flood event. HR topographic data has been made available for the study area, which is 17.5 km 2 , by Nice municipality. Three uncertain parameters were studied: the measurement error (var. E), the level of details of above-ground element representation in DEM (buildings, sidewalks, etc.) (var. S), and the spatial discretization resolution (grid cell size for regular mesh) (var. R). Parameter var. E follows a probability density function, whereas parameters var. S and var. R. are discrete operator choices. Combining these parameters, a database of 2, 000 simulations has been produced using P-FS tool implemented on a high performance computing structure. In our study case, the output of interest is the maximal
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